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• Volume 14, Issue 6
• Integrated patient-centred care for type 2 diabetes in Singapore Primary Care Networks: a mixed-methods study
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• http://orcid.org/0000-0003-1536-3050 Lay Hoon Goh 1 ,
• Chiew Jiat Rosalind Siah 2 ,
• Anna Szücs 1 ,
• E Shyong Tai 1 ,
• Jose M Valderas 1 ,
• Doris Young 1
• 1 Medicine , National University of Singapore Yong Loo Lin School of Medicine , Singapore
• 2 National University of Singapore Yong Loo Lin School of Medicine , Singapore
• Correspondence to Dr Lay Hoon Goh; mdcglh{at}nus.edu.sg

Objective Patients with type 2 diabetes require patient-centred care as guided by the Chronic Care Model (CCM). Many diabetes patients in Singapore are managed by the Primary Care Networks (PCNs) which organised healthcare professionals (HCPs) comprising general practitioners, nurses and care coordinators into teams to provide diabetes care. Little is known about how the PCNs deliver care to people with type 2 diabetes. This study evaluated the consistency of diabetes care delivery in the PCNs with the CCM.

Design This was a mixed-method study. The Assessment of Chronic Illness Care (ACIC version 3.5) survey was self-administered by the HCPs in the quantitative study (ACIC scores range 0–11, the latter indicating care delivery most consistent with CCM). Descriptive statistics were obtained, and linear mixed-effects regression model was used to test for association between independent variables and ACIC total scores. The qualitative study comprised semi-structured focus group discussions and used thematic analysis.

Setting The study was conducted on virtual platforms involving the PCNs.

Participants 179 HCPs for quantitative study and 65 HCPs for qualitative study.

Results Integrated analysis of quantitative and qualitative results found that there was support for diabetes care consistent with the CCM in the PCNs. The mean ACIC total score was 5.62 (SD 1.93). The mean element scores ranged from 6.69 (SD 2.18) (Health System Organisation) to 4.91 (SD 2.37) (Community Linkages). The qualitative themes described how the PCNs provided much needed diabetes services, their characteristics such as continuity of care, patient-centred care; collaborating with community partners, financial aspects of care, enablers for and challenges in performing care, and areas for enhancement.

Conclusion This mixed-methods study informs that diabetes care delivery in the Singapore PCNs is consistent with the CCM. Future research should consider using independent observers in the quantitative study and collecting objective data such as patient outcomes.

• DIABETES & ENDOCRINOLOGY
• Primary Care
• QUALITATIVE RESEARCH
• Surveys and Questionnaires

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information. Data available as online supplemental tables.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2024-083992

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STRENGTHS AND LIMITATIONS OF THIS STUDY

Cross-sectional design limits the ability to evaluate causality in the associations between Primary Care Networks (PCNs) healthcare professionals’ (HCPs’) characteristics and perceived integrated care in the quantitative study data.

Convenience sampling of the HCPs may limit the generalisability of the findings but recruitment across the PCNs ensured fair representation of the entire population.

First mixed-method study in Singapore to investigate diabetes care delivery across all PCNs using the Chronic Care Model, a relevant and validated integrated patient-centred care delivery model.

The mixed-methods findings give a more comprehensive understanding of the study topic that can inform enhancements to the care delivery in the PCNs.

Consistent with worldwide trends, Singapore is rapidly ageing with one in four Singaporeans becoming 65 years and older in 2030. 1 Likewise, the prevalence of diabetes is projected to double by 2050, reaching 15%. 2 The most common chronic conditions seen in the Singapore primary care are type 2 diabetes, hypertension and hyperlipidaemia. 3 Primary care in Singapore comprises public-funded polyclinics and private general practitioner (GP) clinics. Polyclinics are primary healthcare centres that provide a wide range of government subsidised medical and laboratory services to Singapore citizens. 4 In 2014, polyclinics saw 40% of all chronic visits in primary care. 5 People with chronic conditions are over-crowding the polyclinics leading to longer waiting time and over-straining of the subsidised resources. 6–8 Contrastingly, there are 1800 private GP clinics in Singapore. 4 Majority of GP clinics are single provider or small group practices with two to three doctors; the remaining are medium or large corporate groups with more doctors. 5 Most GP clinics are situated within the community and accessible on foot. The clinics have in-house dispensaries while laboratory and imaging needs are provided by external vendors. Additionally, GP clinics are staffed by administrative clinic assistants who are not clinically trained; the clinics do not have nurses or other allied health support. In 2014, only 29% of GP clinics fully adopted electronic medical records for their patients’ clinical information. 5 Despite the large numbers of private GP clinics, chronic care comprised only 20% of their workload. 5 Thus, the private GPs remained a resource to help manage chronic diseases in Singapore. Patients who see GPs paid the full amount directly for medical consultations, investigations and medications for their chronic conditions without receiving subsidies from the government, unlike patients in the polyclinics. 9

In 2018, the Primary Care Networks (PCNs) were established by organising GPs into teams with nurses and care coordinators to provide integrated and coordinated care to people with chronic conditions. 10 The nurses provide ancillary and support services for people with chronic conditions such as diabetic retinal photography, diabetic foot screening and health education, while the care coordinators established the Chronic Disease Registry to track care processes and patient outcomes and assist with care coordination. 10 To encourage patients with chronic diseases to see the PCNs for care, patients can use Community Health Assist Scheme, a means-tested subsidy to reduce out-of-pocket payments in the PCNs. 11 Additionally, the Singapore Ministry of Health and other government agencies provide PCNs with manpower and administration support. 12 13

In total, 10 PCNs were formed based on three organisational types 10 : (1) GP-led PCN, formed and coordinated by single provider GPs, (2) group PCN, led by two GP corporate groups and (3) cluster PCN, formed between GPs and three Regional Health System clusters. Each PCN has a headquarters comprising a clinical leader who oversees the development and clinical governance and an administrative leader who manages funding and resources in the PCNs. Majority of GP clinics in PCNs are single GP provider clinics including those clinics in the group and cluster PCNs. The GP clinics from the group and cluster type PCNs may have received more administrative and IT support from their headquarters than those from the GP-led PCNs. Nevertheless, funding for PCNs and access to subsidised services for patients from the Ministry of Health is the same for all PCNs.

Although support is provided for diabetes care in the PCNs, their effectiveness of care integration has not been evaluated and may be suboptimal, as it was not explicitly based on any evidence-based framework upon its creation. Contrastingly, the polyclinics have redesigned their chronic care delivery processes using the Chronic Care Model (CCM), 14 which is an effective framework with six healthcare elements that influence chronic care delivery. 15–17 To date, evidence is lacking about whether diabetes care delivery in the PCNs applies the CCM.

Previous PCN studies obtained perspectives of GPs, 18 PCN representatives 19 and type 2 diabetes patients 20 on care delivery for chronic conditions. To capture differing perspectives of the healthcare professionals (HCPs) on diabetes care delivery in the PCNs, 21 this study involved the GPs, nurses and care coordinators across all 10 PCNs. Additionally, we used a mixed-methods design to integrate findings from qualitative and quantitative studies and derive a more comprehensive understanding of the diabetes care delivery. 22 Thus, the study aims to evaluate the consistency of diabetes care delivery in the PCNs in relation to the CCM. The research questions are: In relation to the CCM, (1) what is the consistency of support for diabetes care delivery for the PCN HCPs? and (2) what are the HCPs’ perspectives on the role of the PCNs in diabetes care delivery?

Design and sample

We used a cross-sectional convergent mixed-methods approach to evaluate: (1) diabetes care support in the PCNs using the Assessment of Chronic Illness Care (ACIC) version 3.5 as perceived by the HCPs and (2) HCPs’ perspectives through focus group discussions. The leaders from the 10 PCNs invited their GPs, nurses and care coordinators to participate in the quantitative and qualitative studies using their routine email correspondence. The numbers of each HCP type in each PCN were not known. However, the HCPs in the study were recruited from all 10 PCNs to show fair representation from each PCN ( online supplemental table 1 ). All participants gave written consent before participating in the studies. Participants in the quantitative study indicated their written consent using the electronic consent-taking mechanism on the online survey while participants in the qualitative study gave written consent using a soft copy consent form. The studies were conducted between January 2020 and February 2022. All HCPs were reimbursed SGD20 for their participation in each study.

Supplemental material

Across all PCNs, HCPs (GPs, nurses and care coordinators) were recruited by email to participate in the quantitative study using an anonymous online survey. There were 1030 PCN HCPs in 2021, comprising 889 GPs, 18 nurses and 123 care coordinators (source: Singapore Ministry of Health, December 2021). With a margin of error of 0.25, 95% CI and a population variance of 2.89, 23–25 the sample size for the quantitative study was calculated to be 152. The response rate from the HCPs for the quantitative study was 14.7% (131 out of 889) for GPs, 100% (18 out of 18) for nurses and 24.4% (30 out of 123) for care coordinators. For the qualitative study, the HCPs were purposefully recruited based on their job type (GP, nurse or care coordinator), age, gender and their PCN type (GP-led, group or cluster).

Patient and public involvement

Data collection, quantitative study.

The ACIC version 3.5 with 34 items was used to rate what best described the support for diabetes care in the PCNs 26 ( online supplemental table 2 ). The ACIC version 3.5 provides subscale scores corresponding to six CCM elements with the seventh element evaluating integration of CCM components. The HCPs chose from a 0–11 scale, with 0–2 indicating ‘little support for chronic illness care’, 3–5 indicating ‘basic support’, 6–8 indicating ‘good support’ and 9–11 indicating ‘full support’. Item means for each subscale were obtained by the average of the item scores within the subscale. The ACIC total score was derived by summing the average scores of each subscale and dividing by seven. The ACIC version 3.5 was validated by content and face validation by a panel of seven experts comprising five primary care doctors, a nurse and a care coordinator. The validation resulted in minor adaptations such as changing the phrasing and examples of the items ( online supplemental table 3 ). The Cronbach’s alpha for the total score in the adapted ACIC was 0.95 in the study sample. The following HCPs’ characteristics were collected in this study: (1) age, gender, ethnicity, years of education, HCP role (sociodemographics), (2) duration of working in the PCN, number of hours spent per week in the clinic and number of patients with type 2 diabetes (practice characteristics) and (3) PCN type.

Qualitative study

Two authors LHG, a family physician and CJRS, a nurse, conducted the focus group discussions of three to seven HCPs using a semi-structured interview guide, structured following the CCM ( online supplemental material 4 ). The focus groups lasted about an hour and were audiotaped and transcribed verbatim. The interviews were stopped on reaching data saturation when no new information was generated from the qualitative findings. 27

Data analysis

Quantitative analysis.

We calculated descriptive statistics on the quantitative data using the SPSS (V.28, IBM, Armonk, NY, USA). Continuous data were presented as mean and SD or as median, IQR and range. Frequency and percentage were used to describe categorical variables. The ACIC total score was presented in continuous values (mean and SD). Two-tailed tests were conducted, with a predetermined alpha level of 0.05 for statistical significance. Bivariate analyses were conducted to test associations between ACIC total scores and HCP-related and practice-related characteristics, and between PCN types and HCP-related and practice-related characteristics. This analysis used Pearson or Spearman’s correlation for continuous variables, χ 2 test for categorical variables and t-tests, one-way analysis of variance and Kruskal-Wallis tests for associations between continuous and categorical variables. Variables significant in the bivariate analyses (age, ethnicity, HCP type and numbers of diabetes patients) with p<0.05 ( online supplemental tables 5 and 6 ) were entered in a linear mixed-effects regression model, while PCN type was considered as random effect. Education level was strongly correlated with HCP type and was omitted from the model. Missing data were excluded from the analysis using complete case analysis.

Qualitative analysis

Qualitative interviews were transcribed verbatim. Each transcript was independently coded by two researchers (LHG and CJRS) who identified and organised the codes into codebooks. Coding for the transcripts followed the codebooks. The thematic analysis approach by Braun et al was used in this study 27 that consisted of data familiarisation, identifying codes and themes, coding data and organising codes and themes. Field notes were used to capture additional notes of non-verbal communication that occurred during the interviews and the interviewers’ impressions of the interviews. The study used grounded theory techniques involving open-ended questions, line-by-line coding, iterative coding and constant comparison of codes throughout the analysis process. 28 Codes with similar meanings were collapsed under subthemes. Through this iterative process, emergent themes were developed to arrive at the final themes. Team discussions were held regularly to agree on the final list of codes, subthemes and themes. A preliminary analysis of the qualitative findings was performed after 12 participants to assess for saturation. 29 Saturation was assessed to be achieved during analysis when no new information was obtained from the findings. 27 Participants’ quotes were selected to illustrate themes and subthemes. Codes were analysed using NVivo V.R1 (2020) software.

Integrated analysis

Quantitative and qualitative results were analysed and interpreted separately before integration using a joint comparison table. 22 Themes or subthemes that described the same or common meaning or concept as the subscale were compared by putting them on the same rows of the table. For example, quantitative and qualitative results describing leadership in the PCNs were placed in the same row. Based on interpretation of the quantitative and qualitative results (ie, ‘integrated analysis’), each row was then summarised into an overarching idea (ie, a ‘key concept’) that answered the research question. The integrated analysis was classified as confirming if the quantitative and qualitative results converged or agreed with each other, 22 disconfirming if the quantitative and qualitative results diverged or contradicted each other, and expanded if the quantitative and qualitative results enhanced or provided a deeper understanding of each other. Additionally, the key concepts derived from the integrated analysis were guided by the CCM. 26 Thus, these key concepts tracked closely to the CCM but were not identical. This alignment reflected the dual interest in being able to: (1) map to the CCM as it was the overarching theoretical framing of the study and (2) highlight the strengths and areas for enhancement regarding diabetes care delivery in the PCNs based on the CCM.

Here is an example of how the integrated analysis was performed: The quantitative ACIC items 1–5 were compared with the qualitative theme 5 ‘enablers provided for performing PCN care’ in the same row in the joint table ( online supplemental table 7 ). This was because both quantitative and qualitative results contained common concepts of leadership and policies that facilitated diabetes care delivery in the PCNs. ACIC items 1–5 contained scores indicating good support for diabetes care, while three subthemes from theme 5 described good support for diabetes care. Since both quantitative and qualitative results agreed with each other, the integration was categorised as confirming. Contrastingly, ACIC item 6 ‘benefits’ was compared with theme 4 ‘financial aspects of PCN care’ and theme 6 ‘challenges faced in performing PCN care’ in the same row in the joint table. This was because both quantitative and qualitative results referred to the common concept of financial and organisational processes in performing diabetes care in the PCNs. Whereas ACIC item 6 had a score indicating basic to good support for diabetes care, four subthemes under theme 4 and theme 6 described financial and organisational obstacles faced in performing diabetes care in the PCNs. Since both quantitative and qualitative results diverged, the integration was categorised as disconfirming. During the integrated analysis, common concepts (leadership, policies and processes for diabetes care delivery) were thus identified in both quantitative and qualitative data. These concepts are also components within the CCM element of ‘Organisation of Healthcare Delivery System’. 26 Hence, the key concept ‘Organisation of Healthcare Delivery System has good support’ was derived. Besides tracking closely to the CCM as the theoretical framework of the study, the key concept also highlighted the PCNs’ strength in this aspect of diabetes care delivery.

Quantitative results

Overall, 179 HCPs (17.4% of 1030 PCN HCPs) comprising 131 GPs, 18 nurses and 30 care coordinators, participated in the quantitative study ( table 1 ). Their mean age was 45.20 years (SD 11.02, range 23–76). They have worked for a mean of 2.89 years (SD 1.15) in the PCNs and each managed about 50 patients (IQR 20–100) ( table 2 ). There was missing data from three variables from 19 HCPs, comprising 1.2% of all data: (1) duration of working (from five GPs), (2) number of working hours per week (from two GPs) and (3) number of diabetes patients (from eight GPs and four nurses).

• View inline

Healthcare professionals’ characteristics across Primary Care Networks types

Practice characteristics and Assessment of Chronic Illness Care (ACIC) scores across Primary Care Networks (PCNs) types

ACIC in the PCNs

The mean ACIC total score for the PCNs was 5.62 out of 11 (SD 1.93). The mean elements’ scores ranged from 4.91 (SD 2.37) (Community Linkages) to 6.69 (SD 2.18) (Organisation of Healthcare Delivery System) ( table 2 , online supplemental table 8 ). Results showed that being a care coordinator as compared with a GP and managing more diabetes patients were associated with higher ACIC total scores ( table 3 ).

Linear mixed-effects regression model testing associations with Assessment of Chronic Illness Care total scores for healthcare professionals

Qualitative results

A total of 65 HCPs comprising 38 GPs, 12 nurses and 15 care coordinators were interviewed. There were 30 males and 35 females with a median age of 44 years (IQR 33.5–52.0, range 23–61). We identified seven themes about the HCPs’ perspectives about diabetes care delivery in the PCNs in relation to the CCM ( table 4 ): (1) PCNs provided much needed diabetes services, (2) PCN characteristics in diabetes care delivery (comprising continuity of care, convenient access, team-based care, patient-centred care, goal setting, patients empowered for self-care and building rapport with patients), (3) collaborating with community partners, (4) financial aspects of PCN care, (5) enablers provided for performing PCN care, (6) challenges faced in performing PCN care and (7) aspects of care for enhancement.

Healthcare professionals’ themes and subthemes about diabetes care

PCNs provided much needed diabetes services

The HCPs agreed that PCNs provided useful diabetes services comprising diabetic retinal photography, diabetic foot screening and nurse counselling or education for their patients: ‘She’s a nurse educator, she does counselling, and I find it very useful. When you’re looking at the patients’ parameters, they get better when they follow her advice’ (GP-3).

PCN characteristics in diabetes care delivery

Team-based care previously absent in the single-handed PCN clinics, was perceived by the GPs as important in improving chronic care. ‘My patient came down for foot screening. The nurse picked up that she has a foot infection. She took a picture and messaged me. I prescribed the medication and reviewed the patient soon’ (GP-13).

Collaborating with community partners

The GPs suggested that they tapped into the government subsidised medications that the polyclinics were receiving: ‘Our diabetes medications are not cheap, and they (the patients) get such a huge subsidy from polyclinic. My suggestion would be that they see me and the polyclinics send them the medications at polyclinic price’ (GP-28).

Financial aspects of PCN care

Although there were Community Health Assist Scheme subsidies available at the PCNs to make care affordable for patients, there were concerns that the subsidies were insufficient, and that the subsidy amount should be increased: ‘I think giving patients that choice and empowerment so increasing their Community Health Assist Scheme subsidies … Inflation, cost of living is a big issue, and there’s never enough to go around’ (GP-22).

Enablers provided for performing PCN care

The HCPs perceived that PCNs provided integrated patient-centred care to their patients that was structured and considered patients’ individual needs and preferences: ‘For patients, their benefit is that the GPs spend more time giving the consultation and explain about the conditions and the management plan. We (the PCNs) provide all the services in under one roof for foot screening and eye screening instead of referring patient to specialist clinics or polyclinics’ (Coordinator-1).

Participants mentioned the team camaraderie, support and friendships that arose from joining a PCN: ‘There’s a sense of camaraderie. There’s a bridge between the solo doctors and the larger primary care group such as the polyclinics. Besides diabetes, the larger picture is that we have support and resource that I think would have not been possible if it’s just GPs alone’ (GP-27).

Challenges faced in performing PCN care

The HCPs felt that fragmented care occurred in the PCNs when patients moved between the PCNs and the polyclinics:

‘They (the patients) will come in … certain medicines they collect from us (the PCN clinics), certain medicines they collect from the polyclinic. We do their tests, we’ll discuss with them their medical conditions, we tweak the management, or we write a memo to the (polyclinic) doctor. But that’s not an official shared care’ (GP-33).

Nurses did not have adequate access to the patients’ medical records which impeded their effectiveness in tailoring their advice to patients during counselling:

‘When I cannot assess the clinic’s system, it’s based on a lot of my being a detective, my observation, and a detailed assessment before I can work in partnership with the patients. If I can have this information, then I’ll be able to provide customised education to the patient more confidently’ (Nurse-3).

Aspects of care for enhancement

The HCPs advocated for the greater use of technology in facilitating diabetes care in the PCNs: ‘For improving PCN is to harness technology using tele-support, tele-collaborations. Singapore is land scarce, right? Can technology overcome it with tele-team care? Then we don’t need the physical primary space’ (GP-9).

Some GPs proposed that the PCNs should have GPs with special interests to increase their scope of work within the PCNs instead of referring patients to the hospital specialists: ‘We’re (the doctors) not good at everything. If you have several GPs working together, they may have a GP with a special interest in a certain area, and they can do an internal referral’ (GP-28).

Integrated analysis results

The ACIC elements’ scores were integrated with the themes and subthemes using a joint comparison table, resulting in eight key concepts for diabetes care delivery in the PCNs ( table 5 ): (1) CCM-consistent diabetes care delivery has basic support, (2) Organisation of Healthcare Delivery System has good support, (3) Community Linkages has basic support, (4) Self-Management Support has basic support, (5) Decision Support has basic support, (6) Delivery System Design has basic support, (7) Clinical Information Systems has basic support and (8) Integration of Care has basic support. Overall, the qualitative findings supported the CCM-consistent diabetes care delivery in the PCNs, with 19 confirming subthemes, 15 disconfirming subthemes, two expanded subthemes and one subtheme that was not integrated with the quantitative findings ( online supplemental table 7 ). Support provided to PCNs to do their work (subtheme 5.6) was both confirming and disconfirming for the key concept of Decision Support receiving basic support in the PCNs. Among the CCM elements, Organisation of Healthcare Delivery System, Self-Management Support, Decision Support, Delivery System Design and Clinical Information Systems were more supported than Community Linkages and Integration of Care.

Healthcare professionals’ joint comparison table showing integrated analysis of quantitative and qualitative results

The PCNs received support to provide CCM-consistent patient-centred diabetes care which differed according to the ACIC elements. The HCPs perceived that Organisation of Healthcare Delivery System, Self-Management Support, Decision Support, Delivery System Design and Clinical Information Systems in the PCNs were more supported than Community Linkages and Integration of Care. 30 31

CCM-consistent diabetes care delivery

Our study found that there was basic support for CCM-consistent diabetes care delivery in the PCNs. Although the PCNs attempted to deliver integrated under-one-roof diabetes care for patients, there was still fragmentation. For example, coordinating care with patients who moved between polyclinics and the PCN clinics and allowing access to patients’ medical information for the nurses, could be better integrated.

Organisation of Healthcare Delivery System

Our finding of good support in the PCNs was congruent with other studies. 32 33 Support from PCN leaders for high-quality chronic disease management, quality improvement in diabetes care and incentives encouraged the HCPs to provide high quality diabetes care. Contrastingly, the HCPs described how the financial gradient between PCNs and polyclinics influenced how patients perceived that polyclinics were more affordable than GP clinics despite financial enablers such as the Community Health Assist Scheme.

Community Linkages

This element received the least support for diabetes care in the PCNs, contrasting with literature. 33 34 Although there were community providers in Singapore, the HCPs did not refer their patients to them due to challenges such as matching patients’ needs to the resources, the lack of clinical follow-up with the community providers and familiarity of referring patients to the polyclinics.

Self-Management Support

Effective Self-Management Support improved clinical indicators, health-related quality of life, self-efficacy, disease knowledge 35 and reduced healthcare utilisation. 36 The HCPs integrated patient-centred Self-Management Support in the PCNs. They used structured education to empower and support patients in embedding self-care in their lives. This education was coupled with follow-up, provision of self-help materials to improve their disease or treatment knowledge, help with psychological coping, and increasing their responsibility in medication adherence and making lifestyle choices.

Decision Support

The HCPs received good support for the use of evidence-based guidelines and protocols, training and administrative support for their work. However, the diabetes guidelines were not embedded within the clinic management system, thus limiting its effectiveness in providing clinical decision support for the HCPs. 37 Additionally, support from specialists in diabetes care in the PCNs was uncommon. To ensure a successful integration between the specialists and PCNs in providing diabetes care, joint planning, integrated information communication technology, shared clinical priorities, incentives and continuing professional development should be present. 38

Delivery System Design

There was evidence of continuity of care, convenient access, team-based care and provision of ancillary services in the PCNs. 18 39 The HCPs demonstrated willingness to collaborate to deliver high-quality care, aligning with literature that suggested that strong networks and increased communication between providers facilitated CCM implementation. 40 The HCPs also opined that the nurses should play a greater role in the PCNs, a view congruent with studies advocating that nurses should be integrated into GP practices. 41 Hence, the PCNs should address the nurses’ scope of practice, funding and training to expand their role. Additionally, the HCPs called for increase access to subsidised allied health services such as dietitian, podiatry and physiotherapy. The allied health professionals could assist the PCNs in the assessment and treatment of diabetes patients 42 and diabetes complications such as leg ulcers. 43

Furthermore, the HCPs advocated an increase in technology use in diabetes care to enhance patients’ self-management and adherence and mitigate the lack of clinic space for patient care. While telehealth interventions have been shown to improve diabetes health outcomes, clinical monitoring and management, 44 supporting patients’ self-management efforts, 45 and barriers such as lower socioeconomic status, limited language proficiency and access to technology, for example, internet, should be addressed before implementation. 46 Finally, there were mixed views from the PCNs GPs about having GPs with special interests in the PCNs through learning extra skills to manage chronic conditions beyond routine GP care. Potential advantages could be increased access to specialist investigations, increased job satisfaction and improved access for patients, 47 while disadvantages include fragmentation of care and de-skilling of GPs who did not have special interests. A 2019 systematic review called for greater workforce clarity and regulation of GPs with special interests. 48

Clinical Information Systems

The HCPs used shared patients’ electronic medical records to follow-up on patients’ treatment plans. The collaborative use of the Clinical Information Systems can improve patients’ health outcomes by enhancing feedback to providers and improving their responses, for example, medication adjustment to clinical data 15 and improving guideline adherence. 49 Additionally, the PCNs conducted quality improvement sessions for their teams to improve their performance in diabetes care using data from the medical records or the Chronic Disease Registry. 50 However, there were barriers to the care delivery such as the lack of access to medical records for the nurses to manage patients with complex needs and the Chronic Disease Registry not linked to guidelines or reminders.

Integration of Care

After joining the PCNs, the GP clinics were adapting their processes and workflows to do things differently. Within the PCN clinics, there were different stakeholders involved in the work processes. Many nurses and care coordinators were not employees of the GP clinics. Their work processes were determined by the PCN Headquarters and less likely to align to the processes determined by the GPs. For example, the nurses used health education materials that were different from what the GPs used. Another example was the challenge involved in the integration of the clinic management system to ensure continuity, coordination and follow-up of patients within the clinic, with community partners and with the national electronic health records. Hence, integration between different processes, workflows and CCM elements within the PCNs needed more engagement from key stakeholders in obtaining an understanding what was required, and the resources needed for integrating care. 51

Team camaraderie, trust and relationship built through joining the PCNs was unexpected but welcoming to the HCPs. Trust among the PCN HCPs indicated their willingness to collaborate to deliver care, have informal peer sharing and learning, exchange of patients’ information through electronic medical records, sharing of professional knowledge and balancing out members’ differences in skills and contributions to the networks. 52 The PCNs should continue to build on this core strength of team camaraderie to facilitate collaborations within and without their PCNs to enhance diabetes care delivery for their patients.

Associations of ACIC total score with HCPs’ characteristics

In this study, the care coordinators as compared with GPs gave higher ACIC scores. The care coordinators have less clinical interaction with patients and might not have accurately evaluated the practice characteristics related to the CCM elements, as compared with the GPs. However, it was important to involve a mixture of clinicians and non-clinician HCPs to ensure a more balanced perspective on the care delivery in the PCNs. 25 Additionally, managing more diabetes patients in the PCNs and thus having more frequent interactions with the clinics processes was associated with the perception of more CCM-consistent care.

Study limitations include the following: first, the observational cross-sectional design did not make possible to assess causality in the observed associations between participant and clinic features, and perceived quality of care in the quantitative study data. Additionally, the data was obtained by participants’ self-reporting that could have recall bias and social desirability bias. Due to limitation of resources, neither objective measures nor external validation of the reported data were incorporated in this study. Second, convenience sampling was used for HCP recruitment, raising concerns about selection bias, thus affecting the generalisability of the findings. However, the HCPs in the study were recruited across all PCNs to ensure fair representation. Thus, the results obtained from this sample might accurately reflect the characteristics and behaviours of the entire population. Third, there was no information about the non-PCN GP clinics, and other non-participating HCPs to compare their characteristics with our participants. However, the inputs from HCPs across the 10 PCNs were sought in a rigorous way that might mitigate these limitations. Additionally, we used the CCM, a validated and relevant care model to evaluate the consistency of diabetes care delivery and to give recommendations for practical enhancements of the PCNs. Finally, the mixed-methods integrated the study findings to derive meaningful recommendations to enhance the care delivery in the PCNs. Future research should consider using independent observers in completing the quantitative component instead of the HCPs. Additionally, external validation using objective data such as resource allocation, patient outcomes or adherence to clinical guidelines within the PCNs can be performed. This approach will provide a more objective and comprehensive assessment of the support for diabetes care delivery.

This mixed-methods study found that there was support for diabetes care delivery consistent with the CCM in the Singapore PCNs. The PCNs HCPs perceived support for the elements of Organisation of Healthcare Delivery System, Self-Management Support, Decision Support, Delivery System Design and Clinical Information Systems for diabetes care delivery. However, Community Linkages and Integration of Care required enhancement.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by National University of Singapore Institutional Review Board, Reference Code LS-19-298. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The authors would like to thank all participating healthcare professionals from the Singapore Primary Care Networks.

• National Population and Talent Division, Strategy Group, Prime Minister’s Office, Singapore Department of Statistics, Ministry of Home Affairs, Immigration & Checkpoints Authority
• Tai ES , et al
• Ministry of Health Singapore
• Health Information Division, Ministry of Health, Singapore
• Low YS , et al
• Sundram M ,
• Legido-Quigley H
• Tikkanen R ,
• Mossialos E , et al
• Agency for Integrated Care Singapore
• Sankari U , et al
• Stellefson M ,
• Dipnarine K ,
• Tam WWS , et al
• American Diabetes Association Professional Practice Committee
• Surendran S ,
• Tam CH , et al
• Matchar DB , et al
• Siah CJR , et al
• van Staalduinen DJ ,
• van den Bekerom PEA ,
• Groeneveld SM , et al
• Creswell JW ,
• Select Statistical Services Limited
• Scheaffer RL ,
• Mendenhall W ,
• Ott RL , et al
• Parchman ML ,
• Culler SD , et al
• Bonomi AE ,
• Wagner EH ,
• Glasgow RE , et al
• Hayfield N , et al
• Thorogood N
• Boulanger L ,
• Smalarz A , et al
• Palmer RF , et al
• Lanham HJ ,
• Rodriguez-Blazquez C ,
• João Forjaz M ,
• Gimeno-Miguel A , et al
• Morgan JM ,
• Mensa-Wilmot Y ,
• Bowen S-A , et al
• Dineen-Griffin S ,
• Garcia-Cardenas V ,
• Williams K , et al
• Allegrante JP ,
• Peterson JC
• Gorham G , et al
• Nicholson C ,
• Jackson C ,
• Jimenez G , et al
• Van Bogaert P ,
• Bastiaens H , et al
• Dorning H ,
• Enderby P ,
• Harrop D , et al
• Santos DS ,
• Batistelli CRS ,
• Lara MMDS , et al
• Ashrafzadeh S ,
• Agarwal S ,
• Simmonds I ,
• Manski-Nankervis JA
• Yellamaty V ,
• Crossland L , et al
• Campanella P ,
• Marone C , et al
• Prueksaritanond S , et al
• Mitterlechner M

Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

Contributors LHG, EST and DY conceptualised the study design. LHG applied for ethics approval for the study, recruited the study participants, collected the data, managed the survey data and transcribed the interviews. LHG and CJRS coded the transcripts, derived the themes and interpreted the qualitative data. LHG and AS analysed and interpreted the quantitative data and discussed with JMV. LHG wrote the draft manuscript. All authors reviewed and approved the final manuscript. LHG is the guarantor of the overall contents of this study.

Funding National Medical Research Council Singapore and Ministry of Health under Research Training Fellowship (MOH-FLWSHP19nov-0003/MOH-000436-00).

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Open Access

Peer-reviewed

Research Article

Direct Medical Cost of Type 2 Diabetes in Singapore

Affiliation Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore

* E-mail: [email protected] (JYL); [email protected] (MPT)

Affiliations Information Management, Central Regional Health Office, National Healthcare Group, Singapore, Singapore, Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore

Affiliation School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan

• Charmaine Shuyu Ng, 
• Matthias Paul Han Sim Toh, 
• Yu Ko, 
• Joyce Yu-Chia Lee

• Published: March 27, 2015
• https://doi.org/10.1371/journal.pone.0122795
• Reader Comments

Due to the chronic nature of diabetes along with their complications, they have been recognised as a major health issue, which results in significant economic burden. This study aims to estimate the direct medical cost associated with type 2 diabetes mellitus (T2DM) in Singapore in 2010 and to examine both the relationship between demographic and clinical state variables with the total estimated expenditure. The National Healthcare Group (NHG) Chronic Disease Management System (CDMS) database was used to identify patients with T2DM in the year 2010. DM-attributable costs estimated included hospitalisations, accident and emergency (A&E) room visits, outpatient physician visits, medications, laboratory tests and allied health services. All charges and unit costs were provided by the NHG. A total of 500 patients with DM were identified for the analyses. The mean annual direct medical cost was found to be $2,034, of which 61% was accounted for by inpatient services, 35% by outpatient services, and 4% by A&E services. Independent determinants of total costs were DM treatments such as the use of insulin only (p<0.001) and the combination of both oral medications and insulin (p=0.047) as well as having complications such as cerebrovascular disease (p<0.001), cardiovascular disease (p=0.002), peripheral vascular disease (p=0.001), and nephropathy (p=0.041). In this study, the cost of DM treatments and DM-related complications were found to be strong determinants of costs. This finding suggests an imperative need to address the economic burden associated with diabetes with urgency and to reorganise resources required to improve healthcare costs.

Citation: Shuyu Ng C, Toh MPHS, Ko Y, Yu-Chia Lee J (2015) Direct Medical Cost of Type 2 Diabetes in Singapore. PLoS ONE 10(3): e0122795. https://doi.org/10.1371/journal.pone.0122795

Academic Editor: Ulla Kou Griffiths, London School of Hygiene and Tropical Medicine, UNITED KINGDOM

Received: October 23, 2014; Accepted: February 23, 2015; Published: March 27, 2015

Copyright: © 2015 Shuyu Ng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper.

Funding: This work was supported by a MOH Health Services Research Competitive Research Grant (HSRG/0027/2012). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Globally, the total number of people with diabetes mellitus (DM) is projected to rise from 171 million in 2000 to 366 million in 2030 [ 1 ]. There is a growing epidemic of diabetes mellitus, type 2 in particular, in the Asia-Pacific region [ 2 , 3 ]. According to current estimates, the DM population in this region is the largest in the world, with approximately 47.3 million, which is 46% of the global burden of this disease [ 4 ]. In Singapore, as in many developed countries, DM is a growing public health problem. The prevalence of DM has risen to 12.3% in 2013, from 8.2% in 2004 and 9% in 1998 [ 5 – 7 ], surpassing other Asian countries such as Hong Kong (9.5%), Japan (7.2%) and Taiwan (5.7%) [ 8 ]. Moreover, DM is the tenth leading cause of death in Singapore, accounting for 1.7% of total deaths in 2011 [ 9 ].

Diabetes is a chronic medical condition associated with numerous complications that makes it a substantial economic burden incurred by individuals, healthcare systems and society as a whole [ 10 ]. In 2007, the global health expenditure to treat and prevent DM and its complications was estimated to be at least US$232 billion [ 8 ]. Depending on available treatments and local prevalence, the direct costs of DM consume from 2.5% to 15.0% of annual healthcare budgets [ 11 ].

Despite the large number of people with DM, the financial burden in Singapore attributed to DM has not been investigated. Because type 2 diabetes mellitus (T2DM) accounts for approximately 90% of DM cases and its prevalence increases with ageing, understanding the patterns of resource use and cost associated with T2DM is becoming increasingly important for policymakers and budget planners. Therefore, this study aims to identify the total direct medical cost of T2DM in Singapore and to examine the relationship between direct medical costs and individual demographic characteristics, DM treatments (exercise or diet, taking oral medications only, taking insulin only and taking both insulin and oral medications), disease control, complications and comorbidities.

Study design

This study adopted a prevalence-based ‘epidemiological’ approach, employing a bottom-up methodology to estimate different cost components. The prevalence approach can yield more precise estimates because it ascertains the current economic burden of a disease rather than projected ones [ 12 , 13 ]. The perspective for this study was that of the healthcare system (i.e., National Healthcare Group (NHG) institutions). This study was approved by the National Healthcare Group Domain Specific Review Board (NHG-DSRB).

Data source

This was a cross-sectional study of T2DM patients who had received care in any of the NHG institutions in 2010. The NHG is public funded and provides inpatient and ambulatory care (primary care, specialist outpatient and 24-hour emergency) services through a network of 3 acute hospitals, 1 national center, 9 primary care clinics and 3 specialty institutes serving the population in the central and western parts of Singapore. The 9 primary care clinics, also known as polyclinics, had a service load of 3.7 million attendances in 2010, which accounted for 60% of all public sector primary care attendances [ 14 ]. Data was drawn from the NHG Chronic Disease Management System (CDMS), which serves as an operational disease registry within the NHG. The CDMS was commissioned in 2007 to enhance the delivery of care for patients with DM and to facilitate greater efficiency in outcome measurement. It links key clinical data of patients with DM across the NHG healthcare cluster, including records of visits to physicians, nurses, and allied health professionals, as well as medication and laboratory test records [ 15 ]. In addition, it also includes registration and financial cost data related to the care of chronic diseases.

Patient selection

Patients with T2DM were identified using the International Classification of Diseases Ninth Revision (ICD-9-CM) with diagnostic code of 250 as primary or secondary diagnosis, or using pharmacy medication records or laboratory data in the CDMS. Diabetes complications and comorbidities were also identified using ICD-9-CM codes, while only DM-related medications and laboratory data were based on inpatient and outpatient encounters at the hospital or outpatient clinics that were registered with the CDMS. Systematic sampling was conducted for 98,592 identified DM patients (i.e., every 197 th patient was selected). Informed consent was not obtained from the patients as the data was de-identified prior to analysis.

This study included patients who satisfied at least one of the following three criteria: (1) assigned ICD-9-CM code of 250; (2) attended treatment for DM for 1 year in any NHG institution; or (3) prescribed any anti-diabetic medication. Patients with type 1 DM and women with gestational diabetes were excluded.

Laboratory-derived measures related to DM

Measures for DM-related physical examinations were included and categorised as follow: (1) body mass index (BMI) (kg/m 2 ): <18.50 = underweight; 18.50–24.99 = normal; >25.00 = overweight and obese [ 16 ], (2) glycated haemoglobin (HbA1c) (%): ≤7.0 = good disease control; 7.1–8.0 = sub-optimal disease control; >8.0 = poor disease control, (3) low-density lipoprotein cholesterol (LDL-c) (mmol/L): <2.6 = optimal; 2.6–4.0 = near optimal; >4.0 = high, (4) urine albumin-to-creatinine ratio (UACR) (albumin/24h): <30mg = normal; 30-299mg = microalbuminuria; >300mg = macroalbuminuria [ 17 , 18 ].

Estimation of costs

Direct DM-related costs were classified by the type of service, including inpatient hospitalisation, accident and emergency (A&E) and ambulatory outpatient care (physician visits, allied health visits, laboratory tests and medications). Allied health visits include foot screening, eye screening, dietary services and health education. The total medical costs were estimated by the total before-subsidy charges, which is the total medical bill before any deduction for government subsidies or insurance claims. All costs reported were in Singapore currency (S$) for year 2010 prices.

The cost of inpatient care and A&E services were estimated by the total charge based on the length of stay and resources used. Any A&E visits that resulted in hospitalisation were included as inpatient care costs. Unit costs used in the estimation of physician visits, which included visits to primary care clinics (polyclinics) and specialist outpatient clinics (hospitals), were equal to the standardised rate for physician visits at all NHG primary care clinics and hospitals. Therefore, costs were estimated by multiplying the number of physician visits by the unit cost of a visit. Unit costs for allied health visits, laboratory tests and medications were estimated via the same method as physician visits. The cost for drugs other than anti-diabetic medications was not included. Unit costs for all services rendered were provided by the NHG and are in Singapore dollars. Direct non-medical costs, such as transportation expenses and indirect costs were not included.

Statistical methods

Healthcare cost data are often positively skewed because a relatively small proportion of patients incur extremely high costs [ 19 , 20 ]. Such problems were dealt with by logarithmic transformation of the cost data [ 21 ]. Descriptive statistics (frequency, percentage, mean, median, standard deviation and 90 th percentile) were used for demographic information and expenditures. To identify the factors affecting total costs, a multiple linear regression model was developed to evaluate the relationship of both demographic and clinical state variables (HbA1c, DM treatments, complications and comorbidities) to the total calculated expenditure. All statistical analyses were performed using SPSS version 21.0 (SPSS Inc., Chicago, IL, USA).

Patient characteristics

A total of 98,592 patients in the NHG CDMS (2010) were identified as patients with DM. After applying the selection criteria and a systematic sampling, 500 patients were included in the analyses. The socio-demographic profile of the patients is shown in Table 1 . The patients were equally distributed between the two genders (55.4% female). The mean (±SD) age was 69.0 ± 9.4 years, and most study patients were Chinese (77.6%) and non-smokers (89.8%). Although a greater proportion of patients was overweight (42.6%), most had good disease control (44.6%), optimal LDL-c (43.2%) and normal UACR (41.2%). Of the 69.2% of DM patients who were on anti-diabetic medications, the majority used oral medications (57.2%), while only 3% were treated with insulin and the remaining 9% used both insulin and oral medications. Nephropathy (57.2%) and cardiovascular conditions (34.2%) were common DM complications among the cohort. The distributions of subgroups were similar between patients with at least one inpatient visit and those without any inpatient visit.

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https://doi.org/10.1371/journal.pone.0122795.t001

Annual costs of diabetes

The mean annual direct cost was S$2,034.6 (US$1.0 = S$1.3 as of 31 December 2010) [ 22 ], of which S$1,237.2 accounted for by inpatient services, S$84.2 by A&E services and S$713.2 by outpatient services ( Table 2 ). Of the total healthcare expenditure, the main cost driver was inpatient costs (60.8%), while A&E services (4.1%) were only a small portion of the total costs. The major source of costs for outpatient services was physician visits, which accounted for 22.6% of the total healthcare expenditure and 64.0% of total outpatient expenditure ( Fig. 1 ).

https://doi.org/10.1371/journal.pone.0122795.t002

https://doi.org/10.1371/journal.pone.0122795.g001

Patients with at least one inpatient admission had higher mean total costs (S$8,787.8) than those who had no inpatient admission (S$690.5), with the bulk of costs resulting from inpatient services (S$7,453.3). Conversely, patients with no inpatient visits had a substantially higher proportion of overall outpatient costs.

Factors affecting the total costs

Using multiple linear regression with log transformation, the total cost of DM was significantly associated with DM treatments (taking insulin only or both oral medications and insulin) and DM-related complications (cerebrovascular, cardiovascular, and peripheral vascular diseases and nephropathy). This model explained 23.0% of the variance in costs ( Table 3 ). Age, gender, race, smoking status, disease control, taking only oral medication, having retinopathy and comorbidities were not independently associated with cost. The combination of oral medications and insulin resulted in an average increment in annual total cost (17.5%, p = 0.047), while the use of only insulin led to a higher increment (53.2%, p<0.001) when compared with patients who were only on dietary control and healthy lifestyle advice alone. Taking the absence of complications as reference, the cost of DM was higher when complications were present except in the case of retinopathy.

https://doi.org/10.1371/journal.pone.0122795.t003

This prevalence-based cost-of-illness study involved a large captive population with T2DM in Singapore. The analysis was based on cost and administrative data retrieved from the NHG disease registry for 2010. This is the first study to provide estimates of costs associated with diabetes care in Singapore.

The cost per patient estimate in this present study was S$2,034.6 (US$1,575.6), and this appears to be higher than the costs reported in other Asian countries. A study in India reported an estimate of US$525.5 per patient [ 23 ], while a study in China reported costs of US$1,501.7 per patient [ 11 ] for the management of DM. However, the costs reported in these studies were presented without accounting for inflation or difference between currency. Notably, hospital costs reported in the American and European continents were much higher than those obtained in this study [ 24 – 26 ]. Despite the cost differences, inpatient costs still remained the main cost driver of the total estimated expenditure, which was also noted in the earlier DM COI studies [ 25 , 27 – 29 ]. Although the length of stay (LOS) was not reported in this study, the high cost of inpatient services were often strongly correlated to LOS [ 30 , 31 ], with higher LOS resulting in higher costs. This suggested that attempts to expedite services or reduce unnecessary utilisation of diagnostic tests to reduce LOS may be worthwhile in reducing overall costs.

In terms of outpatients costs, physician services contributed to the bulk of the total expenditure in our study, and this was understandable since the growth in the number of physicians and specialists have increased over the years to meet with higher patient demands [ 32 ]. In addition, the introduction of new medical technologies and prescription drugs have also shown significant association with physician cost growth because consumers generally require physician visits to obtain diagnostic tests and prescriptions [ 32 ]. Because physicians are central to the healthcare system, efforts to contain physician spending reverberate through all healthcare services, especially with DM being a chronic condition requiring continuous follow-ups.

Our results from the regression analyses have generally confirmed what might have been expected based on the epidemiologic evidence in the literature [ 11 , 20 , 33 – 35 ], that microvascular and macrovascular complications tend to increase the cost of care. On the contrary, comorbidities such as hypertension and dyslipidaemia did not have an association with overall cost. This result is surprising since cost-effectiveness and medication adherence studies [ 36 – 39 ] have reported that achieving therapeutic clinical parameters would lead to an increase in cost of care albeit increasing the quality-adjusted life years (QALY). A possible explanation could be that hypertension and dyslipidaemia may have been controlled or at a steady state that did not require treatment, resulting in no costs incurred.

In our study, patients with sub-optimal and poor disease control had lower overall costs. This may be due to underutilisation of healthcare services compared to those with good disease control. The importance of managing DM to prevent or delay complications requires effort [ 40 ] and good control of DM results in long-term cost savings due to fewer complications [ 41 ]. Furthermore, The use of insulin only or both insulin and oral antidiabetic medications were found to be associated with higher costs. Consistent with other studies, the most expensive component of total outpatient costs after physician costs were medications [ 24 , 25 , 29 , 42 ]. This rise in cost indicated a growth in the consumption of prescription medications, which may be due to increase adherence to medications. Evidence has shown that better adherence results in better healthcare outcomes and reduces the need for physician visits [ 43 , 44 ], and lead to a net decrease in overall healthcare cost.

As a prevalence-based cost-of-illness study, the strength of this study was that all DM cases were included from a specified year, regardless of whether or not they were diagnosed before or during that year. This breadth allows for analysis of patients at various stages of the illness, since different severities of DM may be associated with different costs. However, there were several limitations in this study. First, data was drawn from a healthcare database, hence relied on the accuracy and completeness of the records. The NHG CDMS has, however, been used in several studies and is recognised for providing well-validated and comprehensive data [ 14 , 45 ]. Second, patients with undiagnosed diabetes as well as indirect/intangible costs and out-of-pocket expenses were not included, which may contribute to an underestimation of the true cost of diabetes. Lastly, the study population was relatively small and limited to the public healthcare sector in Singapore. Future studies may consider these shortcomings to further assess different aspects of diabetes costs.

This study provided a comprehensive cost analysis of expenditures incurred in the treatment of DM in Singapore. The results indicated that both medications and DM complications were strong determinants of costs. With projected increase in diabetes prevalence coupled with obesity and growing need for medical treatment in Singapore, diabetes will continue to be a heavy burden on health budgets. Therefore, evidence on the economic burden related to diabetes-related complication and its drives are indispensable for a health-system reform that seeks to minimise the long-term economic burden of this growing epidemic.

Author Contributions

Conceived and designed the experiments: CSN YK MPT. Performed the experiments: CSN. Analyzed the data: CSN YK JYL MPT. Contributed reagents/materials/analysis tools: CSN. Designed the study: CSN YK MPT. Performed the analysis and prepared the manuscript: CSN. Provided data analysis advice and revision of the final manuscript: YK JYL MPT. Read and approved the manuscript: CSN YK JYL MPT.

• 1. World Health Organisation, International Diabetes Federation. Diabetes action now: An initiative of the World Health Organisation and the International Diabetes Federation. WHO, 2004.
• View Article
• PubMed/NCBI
• Google Scholar
• 5. Health Promotion Board. Information Paper On Diabetes In Singapore. 2011 [cited 2014 April 23]; Available from: http://www.nrdo.gov.sg/uploadedFiles/NRDO/Publications/%28INP-11-7%29 20111103 Diabetes Information Paper 2011.pdf.
• 6. International Diabetes Federation. Diabetes Atlas. Brussels, Belgium: International Diabetes Federation, 2013.
• 7. Ministry of Health Singapore. Disease burden. 2014 [cited 2014 December 09]; Available from: http://www.moh.gov.sg/content/moh_web/home/statistics/Health_Facts_Singapore/Disease_Burden.html .
• 8. International Diabetes Federation. Diabetes Atlas Brussels, Belgium: International Diabetes Federation, 2006.
• 12. World Health Organisation. WHO Guide to Identifying the Economic Consequences of Disease and Injury. Geneva, Switzerland: WHO, 2009.
• 16. World Health Organisation. BMI classification—global database on body mass index. 2006 [cited 2014 April 4]; Available from: http://apps.who.int/bmi/index.jsp?introPage=intro_3.html .
• 17. Ministry of Health Singapore. Clinical practice guidelines—diabetes mellitus. Singapore. 2006 [cited 2014 June 05]; Available from: http://www.moh.gov.sg/content/dam/moh_web/HPP/Doctors/cpg_medical/withdrawn/cpg_Diabetes Mellitus-Jun 2006.pdf.
• 18. National Healthcare Group Polyclinic. Diabetes mellitus. In: Leong H, Lee C, editors. Singapore. 2010.
• 21. Johnson RA, Wichern DW. Applied multivariate statistical analysis. 3rd ed. Englewood Cliffs, NJ: Prentice-Hall; 1992.
• 22. OANDA Corporation. Currency converter. New York, NY: OANDA Corporation [cited 2014 March 31]; Available from: http://www.oanda.com/currency/converter/ .
• 40. International Diabetes Federation. IDF—Global diabetes plan 2011–2021. Brussels, Belgium: International Diabetes Federation, 2010.

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Additional concerning figures…

• In 2010, 1 in 9 Singapore residents aged 18 to 69 years were affected by diabetes
• Indians and Malays consistently had higher prevalence of diabetes compared to Chinese across the years
• An estimated 430,000 (or 14% of) Singaporeans aged 18-19 years are also diagnosed with pre-diabetes
• 1 in 3 individuals with diabetes do not know they have the condition
• Among those diagnosed with diabetes/aware of their disease, 1 in 3 have poor control of their condition, which increases the risk for serious complications
• Diabetes was the 4 th and 8 th most common condition of polyclinic attendances and hospitalization respectively in 2014
• Life of years lost due to mortality and ill-health related to diabetes was the 4 th largest among all diseases in 2010

Diabetes is not a stand-alone issue…

Diabetes can cause complications in many parts of the body causing issues such as kidney failure, leg amputation, nerve damage, heart attack, stroke, vision loss and severe disabilities. Not only that, but Diabetes can also bring about substantial economic loss to people and their families, and cause an economic loss to health systems and national economies as a result of direct medical costs and loss of work and wages. The cost burden from diabetes, including medical expenses and productivity loss, was expected to rise from beyond $940 million in 2014 to $1.8 billion in 2050.

Singapore’s ageing population

As in many countries, Singapore’s population is ageing, and the proportion of individuals aged 60 and above is expected to rise from 13.3% in 2010 to 31.9% in 2050, making it a super-aged country. At a population level, the rapidly ageing population and low mortality rates will increase the proportion of people living with diabetes.

Although diabetes is not fatal in the short term, undiagnosed diabetes or poorly controlled diabetes can eventually lead to disabilities and diseases, compromising the quality of life of individuals and their caregivers. It is important that you manage, prevent or detect as soon as you can.

At Diabetes Singapore, we are here to provide you with these services! Click here to find out more about what we offer.

References:

https://health-policy-systems.biomedcentral.com/articles/10.1186/s12961-021-00678-1

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjf0Ze45t7xAhVHgUsFHTkDBdEQFjADegQIAhAD&url=https%3A%2F%2Fwww.nrdo.gov.sg%2Fdocs%2Flibrariesprovider3%2Fdefault-document-library%2Fdiabetes-info-paper-v6.pdf%3Fsfvrsn%3D0&usg=AOvVaw0TpqBqb7Mj32-qWpvF53MV

https://drc.bmj.com/content/8/1/e000928

https://www.biotechconnection-sg.org/scientific-and-technological-advancements-in-diabetes-management/

• Open access
• Published: 08 February 2021

War on Diabetes in Singapore: a policy analysis

• Lai Meng Ow Yong   ORCID: orcid.org/0000-0002-4035-5848 1 &
• Ling Wan Pearline Koe 1  

Health Research Policy and Systems volume  19 , Article number:  15 ( 2021 ) Cite this article

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In April 2016, the Singapore Ministry of Health (MOH) declared War on Diabetes (WoD) to rally a whole-of-nation effort to reduce diabetes burden in the population. This study aimed to explore how this policy has been positioned to bring about changes to address the growing prevalence of diabetes, and to analyse the policy response and the associated challenges involved.

This qualitative study, using Walt and Gilson's policy triangle framework, comprised analysis of 171 organizational documents on the WoD, including government press releases, organizational archives, YouTube videos, newspaper reports and opinion editorials. It also involved interviews with 31 policy actors, who were policy elites and societal policy actors.

Findings showed that the WoD policy generated a sense of unity and purpose across most policy actors. Policy actors were cognisant of the thrusts of the policy and have begun to make shifts to align their interests with the government policy. Addressing those with diabetes directly is essential to understanding their needs. Being clear on who the intended targets are and articulating how the policy seeks to support the identified groups will be imperative. Issues of fake news, unclear messaging and lack of regulation of uncertified health providers were other identified problem areas. High innovation, production and marketing costs were major concerns among food and beverage enterprises.

While there was greater public awareness of the need to combat diabetes, continuing dialogues with the various clusters of policy actors on the above issues will be necessary. Addressing the various segments of the policy actors and their challenges in response to the WoD would be critical.

Peer Review reports

Diabetes is a condition that affects more than 400 million adults globally, and this number is expected to increase to above 640 million, which equates to one in ten adults, by 2040 [ 1 ]. The global prevalence of diabetes among adults over 18 years of age rose from 4.7% in 1980 to 8.5% in 2014 [ 2 ]. It was estimated to be the seventh leading cause of death in 2016, where 1.6 million deaths were attributed to the condition [ 2 ]. In Singapore, over 400,000 Singaporeans live with the disease. The lifetime risk of developing diabetes is one in three among Singaporeans, and the number of those with diabetes is projected to surpass one million by 2050 [ 1 ]. An estimated 430,000 (or 14% of) Singaporeans aged 18 to 19 years are also diagnosed with pre-diabetes, where their normal blood sugar levels are higher than normal but not high enough to be diagnosed as diabetes [ 3 ].

In response to this, on 13 April 2016, the Singapore Health Minister declared War on Diabetes (WoD), citing the psychosocial burden on individuals and families and economic reasons for the thrusts of this policy [ 4 ]. This fight against diabetes is not new, as Singapore has previously explored measures to combat the rising prevalence of diabetes. For example, the annual National Healthy Lifestyle Campaign, introduced in 1992, aims to raise awareness of how Singaporeans can eat healthier foods and incorporate physical activity into their lives; the campaign concomitantly addresses other concerns such as smoking and mental well-being [ 5 ]. Unlike this campaign, the WoD policy specifically addresses the concerns of diabetes and is positioned to encourage a whole-of-society effort to reduce the burden of diabetes in the population and to keep people healthy as they age [ 1 , 3 ].

Diabetes poses a significant public health concern. It can lead to complications in many parts of the body, including kidney failure, leg amputation, nerve damage, heart attack, stroke, vision loss and severe disabilities [ 6 , 7 , 8 ]. It can also bring about substantial economic loss to people and their families and to health systems and national economies as a result of direct medical costs and loss of work and wages [ 8 ]. The World Health Organization (WHO) [ 8 ], in their 2016 Global Report on Diabetes, calls for a whole-of-government and whole-of-society approach, where all sectors are to systematically consider the health impact of policies in trade, agriculture, finance, transport, education and urban planning. It states that effective approaches, including policies and practices across whole populations and within specific settings, will be needed to contribute to good health for everyone.

This means adopting a life-course perspective and multisectoral and population-based approaches to reduce the prevalence of modifiable diabetes risk factors—such as overweight, obesity, physical inactivity and unhealthy diet—in the general population. It also means addressing the commercial determinants of health, involving multinational or transnational corporations, who are major drivers of noncommunicable disease epidemics, including diabetes, as their strategies and approaches used to promote products and choices could be detrimental to health [ 9 , 10 , 11 , 12 ].

Since the introduction of the WoD policy, there have been no studies exploring how the policy has been positioned to bring about changes and what the policy actors’ perceived challenges are. Not very much is known about the political, economic, infrastructural and ideational constructivist context in facilitating or hindering the policy at the national and subnational levels [ 13 ]. This study thus aims to contribute to addressing this knowledge gap by using the policy triangle framework, articulated by Walt and Gilson [ 14 ], to analyse the WoD policy response. The policy triangle framework has been widely applied to a variety of health policy concerns, including health sector reforms and public health, and in many countries [ 15 , 16 ]. It focuses on the content of the policy, the actors involved in the policy change, the processes in developing and implementing change, and the context within which the policy is developed [ 14 ]. The framework is built on the understanding that policy is a product of and constructed through political and social processes [ 15 ]. This study will identify the contextual factors that shaped the WoD policy, the actors involved, the content of the policy and organizational provisions, and analyse the strategies and policy processes. Results drawn from this study will be used to inform change agents, such as the relevant government authorities, and will contribute to the body of knowledge on diabetes policy, thereby enhancing the links between science and policy, based on the model of strategic science [ 17 ].

This study adopted a qualitative approach as the primary method to address the research questions. Qualitative approaches, as opposed to the natural scientific models used in quantitative research, are interpretive and offer an inductive view of the relationship between theory and research [ 18 , 19 ]. This study comprised interviews with 31 relevant policy actors and members of the general public and the analysis of 171 organizational documents on WoD, including government press releases, organizational archives, YouTube videos, newspaper reports and opinion editorials.

Participants

We conducted purposive sampling of prospective respondents from five distinct clusters of policy actors, including government officials, healthcare providers, food and beverage (F&B) manufacturers/producers/retailers (small and medium enterprises, or SMEs, to multinational corporations, or MNCs), professional associations, academic institutions/think tanks, and the general public (see Table 1 ). Non-general public respondents were senior officials within their agencies (for example, president, chief executive officer, general manager, director, deputy director, associate professor) and were actors in or close observers of the WoD policy.

This approach is consistent with the policy triangle analysis framework, where it considers the political institutions and public bureaucracies in policy-making to be important aspects of the analysis. The framework also acknowledges and considers the influence of non-state actors, such as the private sector, the civil society organizations and the public [ 14 , 15 ]. This is consistent and aligned with WHO’s assertion that non-state actors, such as food producers and manufacturers, healthcare providers and people with diabetes, should be considered collectively in the multicomponent intervention in addressing diabetes [ 8 ]. The inclusion of the general public is also relevant because they are driven mostly by their cultural beliefs or personal experiences, which are often the most difficult to identify in terms of their policy goals; their views will therefore be relevant in this policy analysis [ 20 ].

All respondents who fulfilled the criteria were invited via letter or email to participate in a semi-structured interview. The interviews were conducted face-to-face in English. Three sets of topic guides comprising semi-structured questions were used for the interviews. They were designed specifically for (a) government officials; (b) healthcare providers, service providers (businesses, food manufacturers, and so on), and professional associations and academic institutions/think tanks; and (c) the general public (with and without diabetes, and caregivers of people with diabetes). The topic guides and interview questions were developed based on the policy triangle framework, articulated by Walt and Gilson [ 14 ]. The themes of the topic guides explored participants’ understanding of the following:

The WoD in terms of its policy goals, impetus, aims and problem definition. Includes who the policy addresses and what the concerns are (context)

Who the primary players in the policy are (actors)

The instruments that have been used and parameters that have been put in place, following the introduction of the policy in support of this endeavour (content)

The key challenges and areas needing to be addressed to better manage the issue of diabetes in Singapore (processes).

As policy and organizational documents constitute the socio-materiality of the policy itself, they were sampled for relevance [ 21 ]. All relevant documents within the period 1 January 2016 to 31 December 2019 were reviewed. The documents were obtained directly from the respondents if they were not accessible in the public domain. Documentary analysis was conducted in tandem with face-to-face interviews with the policy actors.

Data analyses

Data analysis consisted of thematic analysis and analysis of documents, including organizational annual reports, meeting minutes, government press releases (such as government statements; Committee of Supply Speech; speeches for conferences, opening ceremonies, and visits and events by ministers), YouTube videos, newspaper reports and opinion editorials. Thematic analysis was used to analyse data derived from the interviews and documents. The data were read for familiarization and then again in an iterative manner to identify emerging themes. Key categories of codes were analysed and grouped based on the predetermined codes and themes articulated by Walt and Gilson, including context, actors, content and processes [ 14 ]. Thereafter, the data derived from both the interviews and documentary analyses were triangulated to enhance the trustworthiness, reliability and validity of the findings [ 22 , 23 , 24 ].

Based on Walt and Gilson’s policy analysis triangle framework, we present the findings below.

All respondents in this study stated that the reasons for the development and introduction of the WoD policy were numerous. They include the rising prevalence of diabetes, an ageing population, an extended life expectancy, increasing comorbidities of diabetes and rising healthcare costs. In addition, the respondents attributed the introduction of the policy to an increasing economic burden of diabetes on the working population and the associated potential adverse impact on society. These factors together created the moral impetus for the government to introduce the policy to nudge its people into living a healthy lifestyle, respondents stated.

The causes of diabetes were many. Respondents pointed to a complex interaction of economic, social, cultural, individual, national and environmental factors, leading to the formulation of the policy [ 25 , 26 ]. For example, they highlighted that access to unhealthy food (exacerbated by food delivery service, technology and ready-to-eat meals), affluence of society, expansion of eating-out places, and roles of the F&B industry (manufacturers and retailers) led to the growing diabetes situation in Singapore. This was seen to be made worse by Singaporeans’ obesogenic lifestyle, characterized by work stress, poor sleep patterns and poor overall eating and living habits. The low health screening uptake and lack of prevention measures at the individual level were other reasons. Genetics, invincibility syndrome, culture, family and personal choice, health literacy, and prevailing treatment models of diabetes were seen to have exacerbated the diabetes situation.

The actors in the WoD comprised policy elites within the government and societal actors, including the F&B business community (SMEs and MNCs), professional associations, healthcare providers, academic think tanks, civil society and the general public. This policy-led implementation, which is inherently cross-sectoral, saw the Diabetes Prevention and Care Taskforce, set up by the Ministry of Health (MOH), facilitating and coordinating the involvement of the various policy actors. Policy actors such as the F&B business community were quick to acknowledge their corporate and social roles to fellow citizens, and promptly moved to align their business and corporate goals with the policy. Respondent 11, who was from a large MNC fast-food chain, stated:

[A]s cliché as it sounds, it is really a social responsibility on the business part to really care for the customers’ well-being.

The role of the civil society was seen in the involvement of professional associations and voluntary welfare organizations to promote healthier eating and living in the community. Funds were directed to academic and healthcare institutions to encourage and foster diabetes-related research to inform policy and practice. Healthcare institutions were seen to expand their ability to offer better diabetes treatment with increased drug subsidies. Schools, workplaces and organizations implemented policies promoting healthier eating on their premises. The general public were engaged through programmes and schemes, although their level of receptivity and engagement towards the policy varied.

In operationalizing the policy, a total of 171 WoD-related organizational documents were analysed. The government, in working with the various policy actors and through public forums and engagements, delivered a slew of measures at different time points following the declaration of the policy. The policy core of WoD, highlighted in the documents, centred primarily on increasing the population’s level of physical activity, improving the quality and quantity of dietary intake, increasing early screening uptake and improving intervention to better control diabetes and its associated complications [ 27 ].

Notably, in the first 2 years of the policy launch, the government actively used words, images and symbols to form winning coalitions with different policy actors, such as the F&B industry and people with diabetes and their caregivers, and through various languages, including dialects and vernacular languages, to address older adults in the public. The modes of the images included posters, health screening booths and media programmes. Some common symbols and schemes, such as the Healthier Choice Symbol (HCS), Healthier Ingredient Development Scheme (HIDS), Healthier Dining Innovation (HDI), Healthier Dining Grant (HDG) and National Steps Challenges™, targeted consumers, F&B enterprises and the general public.

As part of its overall strategy, the government collaborated with the primary care networks (PCNs) to provide more supportive services for people with diabetes [ 1 ]. It subsidized basic screening tests for the public to encourage early detection and treatment. It also put in place systems to foster healthier lifestyles, promote good health by employers in the workplace, and facilitate adjustment of lifestyle habits and better decision-making by individuals [ 28 , 29 ]. Nonstandard drugs in the treatment of diabetes were subsidized, which helped open up options for primary care physicians to offer newer treatments at lower rates to the general public. According to respondent 5, a physician, older generations of drugs were found to have “potential side-effects and less of non-glucose reducing properties”, whereas “newer drugs have heart failure protection, cardiovascular protection”. This could only benefit patients with diabetes.

The health ministry also partnered with the F&B industry to support major beverage companies and companies undertaking innovation to lower sugar content in their products, by fostering a supportive regulatory environment to encourage innovation and experimentation [ 30 , 31 ]. This is illustrated in the 2017 industry pact, where seven beverage companies pledged to reduce the sugar level in their beverages to 12% or less by 2020 [ 32 ]. This incremental decrease signalled the government’s recognition that innovation and (re)formulation of F&B products would need time, and that immediate introduction of any measures or regulation may backfire. Consumers’ taste acceptance of newer and healthier products would also need time to develop. The MOH further supported and enabled the industry to use Singapore as a regional headquarters and launch pad through which to access other Asian markets to sell their healthier products, to provide the economic conditions for the business community to thrive.

Legal parameters were also explored. A public consultation was carried out from 4 December 2018 to 25 January 2019, where a wide range of stakeholders were engaged for their inputs on introducing mandatory front-of-pack nutrient summary labelling, advertising regulations for the least healthy sugar-sweetened beverages (SSBs), excise duty on manufacturers and importers, and banning of higher-sugar prepackaged SSBs [ 33 ]. The proposed measures, which were scheduled to be rolled out later in 2020, came nearly three years after the declaration of the WoD, as the government set the stage to create an environment for its people to lead a healthier lifestyle. In November 2019, the MOH went on to introduce the Patient Empowerment for Self-Care Framework, which constituted the first tranche of materials for people with diabetes to more directly effect change in the lives of those with the condition [ 34 ].

Several critical factors enabled or constrained the context in the implementation of the WoD. The following discusses the support for and resistance to the WoD policy, and the potential resources that are further needed for its implementation.

Why war? Why diabetes?

While the WoD served as a useful “policy frame to galvanize government action, and whole-of-society action and attention”, as stated by a government official (P13), there were considerable competing views among non-policy elites. Many non-policy elite actors, for example, questioned the rationale of the WoD. A member of the general public with diabetes (P19) stated: “I am not sure what the logic is behind using diabetes as the condition, because diabetes is so innocent!” Some respondents, such as P12, a diabetes nurse educator, opined that waging a War on Diabetes was unnecessary, and it might risk perpetuating stigma among those with diabetes. She explained that some of her diabetes patients were upset with the policy and were relatively more withdrawn and “shut off” since its introduction due to their perceived stigma. One of her patients told her, “Then I am not going to tell people I have got diabetes,” because people will relate diabetes to medical complications, she said. Others, including P20, a member of the general public, suggested waging a war against sedentary lifestyle or promoting healthier living might be more appropriate.

Policy actors, particularly professional dieticians and the general public, were unclear whether looking solely at individual nutrients, such as sugar, which was seen to be the primary focus of the WoD, was the best approach to stem diabetes. Respondent 18, a representative from the national nutrition and dietetics association, said: “So I think in a sense we cannot look at individual nutrients; we need to look at diet as a whole. This probably has got to be a very consistent message to the public!” Along the same lines, respondents opined that the policy had focused too heavily on packaged SSBs, rather than on freshly cooked or prepared food. Respondent 3, an MNC F&B manufacturer, highlighted: “The beverage may not be the biggest culprit. In fact, the biggest culprit is food.”

Who is the policy for?

Many respondents were unclear of the intended target of the policy. For example, a respondent (P20), a member of the public, reported: “I am not sure who they are targeting, I always thought it is the general public from all age groups.” Another respondent [ 19 ], a medical social worker who works with diabetes patients, said: “It is more for the general public, not for those who already have diabetes.” Respondent 29, who has type 1 diabetes, explained: “Type 1 (diabetics) will switch off because it’s like it is too late for them, they already have diabetes.” This sentiment was echoed by respondents with type 2 diabetes and their caregivers, who highlighted that WoD should more directly address their immediate concerns, which would include helping them with their immediate treatment costs and costs of consumables and related devices. For type 1 diabetes, the causal factors were also unclear and it would not be possible to wage war against type 1 diabetes, stated respondent 29. Some respondents observed, and as a government official acknowledged (P13), that pre-diabetic programmes, whilst carefully designed to reduce diabetes incidence, were more accessible to retirees who were available to attend the programmes during workdays, rather than the “supposed” at-risk and younger diabetic groups, who may hold full-time jobs. Others, such as general public respondents P15 and P17, who were both aver 60 years of age, felt that any programme following the policy is good, as it signals a step forward in the fight against diabetes.

Messaging quality: unclear images, fake news and diet fads

The barrage of messages pertaining to diabetes was found to be at best overwhelming, at worse conflicting and confusing. Messages such as “white rice is bad” and “too much meat will increase diabetes risk” were confusing to the general public respondents. A respondent (P10), an academic, explained: “Everything you [can't eat] eat also cannot. That’s the flip side of pushing things too hard.” The HCS, which had made significant inroads in encouraging healthier F&B consumption, was found to be unclear in its representation. For example, respondent P10 explained: “If we take drinks with the Healthier Choice Symbol (beverages with lower sugar levels), does it mean drinking five bottles of it will be fine?” Rather than emphasizing a particular nutrient such as sugar, some respondents suggested focusing on individual needs, which might be more appropriate. Fake news and popular commercial “diet fads”, such as the ketogenic and Atkins diets, and intermittent fasting were other concerns reported by respondents. Academic and dietician respondents asserted that consistent advice was lacking, and relevant authorities needed to actively clarify unclear images and fake news, and provide consistent messaging on “diet fads” to the public.

With the proliferation of technology, some professionals and general public respondents highlighted the need to regulate healthcare services provided via online apps and virtual coaching programmes. Respondent 18, a dietician, explained that nutrition coaches on these platforms may not have the necessary qualifications and training, and could in fact, do more harm than good to service users or patients. She asserted that necessary regulation of online healthcare services is crucial to mitigate any potential risks of unregulated online healthcare services.

High innovation, production and marketing costs

High innovation, production and marketing costs in the (re)formulation of F&B products were major challenges for the F&B industry respondents. Respondents in this sector explained that taste acceptance for newer and healthier F&B products may not come immediately. F&B retailers, driven by profits, may not be quick to support the sale of healthier products, as the demand for them may not be there at the start. A general manager of an MNC F&B (P3), which produces aerated drinks among other F&B products, highlighted that government support to assist them in engaging in research and development (R&D), marketing, and diversifying and (re)formulating their products would be important and useful. They reported seeing double-digit negative profit margins since the introduction of the policy, and proposed a collaboration that would be beneficial, not just for their corporation, but also for the government and the general public:

We can actually kind of co-create product that we know that is good. Maybe there are certain health concerns, and can do this. Or it could be even at the launch, they [government] could endorse it, or they [government] could give us some promotional funds—how can we jointly, I mean with the help and the support, we can fund it.

Healthier F&B products must also have reach beyond the local market to offset the R&D costs of F&B manufacturers. F&B manufacturer/producer respondents explained that it would mean having to harmonize accreditation of healthier products across countries in order for it to make business sense for them, particularly for a country with a relatively small domestic market like Singapore. To this end, F&B respondents suggested government-to-government and business-to-business collaborations, expressed in forms of shared policies and practices, to give F&B manufacturers the legitimacy to market their (re)formulated healthier products worldwide.

Smaller F&B manufacturers and outlets, such as SMEs, reported acute cash flow issues and were less able to engage in innovation to (re)formulate healthier products. They had to contend with issues such as rising utility costs, rental footprints, high labour costs and limited physical space for stock-keeping units (SKUs) to offer healthier F&B options to their customers. Many respondents questioned the sustainability of rewards, vouchers and subsidies programmes that encourage healthier cooking, eating and living: “Once you finish, then what? I will go back to my own same old way of cooking. I think it’s about sustainability that we need to consider as well before we start on something” (respondent 12, a diabetes nurse educator).

In contrast, F&B retailers, such as larger supermarkets, were least hit by this policy. They were better resourced and better able to offer wider-ranging F&B products with both high and low/no sugar content to their consumers. Larger food establishments, such as restaurants, similarly reported no impact on their profit margins. They were better resourced and were able to offer a wider variety of F&B choices, whether healthier or otherwise, using better-quality and sometimes more expensive ingredients, to meet the needs of consumers who were more willing and able to pay higher prices in these establishments.

This study has explored how the WoD policy has been positioned to bring about changes in its population and the challenges that have arisen as a result. The findings showed that the WoD has generated, to varying extent, a sense of unity and purpose across most policy actors. Policy actors were cognisant of the thrusts of the policy and were quick to make shifts to align their interests with the policy. Legal parameters and economic conditions were debated at public consultations and would be set in place over time. Different policy actors were engaged at various time points. The findings also showed that most respondents demonstrated comprehension and acceptance of the arguments of the policy, and were able to appreciate the implications of diabetes for individuals, institutions and society.

Words, images and symbols were used to strategically shape the policy to produce “winning coalitions” with the policy actors. However, findings showed that there were competing perspectives or views across the policy actors. For example, some non-policy elites wondered whether a war should be waged against diabetes. Specifying diabetes as the target in the WoD could be seen as labelling or blaming those with diabetes and perpetuating stigma via the causal mechanism or action–consequences typology [ 35 ]. This causal mechanism has been observed elsewhere and among those with poorer diabetes control or advanced diabetic complications [ 36 , 37 ]. Sontag [ 38 ] cautions that describing disease in terms of siege and war or in the form of “militarized rhetoric” could backfire and may have unintended consequences. There is a need to foster and encourage a positive view towards prevention and treatment of diabetes.

Respondents with diabetes generally did not feel engaged by the policy. Many of them felt that the policy was directed at some “other groups”, but not them. Those with type 1 diabetes, for example, were unsure of who or what the war was being waged against, as the causal factors for type 1 diabetes are unclear. Those with type 2 diabetes reported that the policy should more directly address their underlying concerns regarding treatment costs. Being clear on who the intended targets are and articulating how the policy seeks to help them is important, as it will have implications for the end beneficiaries (winners) and target groups (or losers) [ 39 , 40 ]. It may also influence the distribution of costs and benefits, as it determines who gets what, when and how, and would have direct implications for practice and implementation [ 39 , 40 , 41 ]. Concerns over quality of messaging, information fatigue, diet fads and fake news, and the varying interpretations of the symbols (such as HCS) will need to be addressed.

Mitigating the high innovation, production and marketing costs for policy actors in the F&B industry would be crucial. Larger F&B businesses, including manufacturers, producers, retailers and F&B outlets, which were better resourced and better able to innovate and offer diverse and finer products, reported fewer issues in delivering on the policy. Smaller F&B enterprises—which generally have fewer resources—faced acute cash flow issues related to the necessary innovation and (re)formulation of healthier F&B products. Concerns over sustainability, linkages to marketing agencies, and physical space and costs highlighted the varying interests, paradigms, operational concerns and decision-making processes within the F&B business community and their associated implementation challenges, which will need to be addressed.

It will be crucial to continue to explore the concerns of the F&B industry and to support them in ways specific to their challenges. The individual F&B enterprises may differ in their challenges, depending on where they are situated in the larger business ecosystem and environment. They are also influenced by the nature and range of F&B products they produce or offer, their operational size, and their physical capacity and resources. As many of these business enterprises were quick to acknowledge their corporate and social roles to fellow citizens at the start, it would be imperative that they be supported in this endeavour as the challenges they face are real. Rather than describing their relationship with the government or policy-makers in adversarial terms, and masking them as “conflicts of interest”, it will be important, and perhaps more meaningful, to address their operational challenges head-on, and help them problem-solve to facilitate the implementation of the policy.

Additionally, the role of harmonizing accreditation for healthier products across countries will be critical for the F&B manufacturers, considering the relatively small domestic market in Singapore, to encourage them to engage in R&D for healthier products. A political commitment demonstrated as shared policies by governments to foster innovation and strengthen international partnerships to tackle diabetes and develop healthier F&B products will be crucial [ 42 ]. This could be achieved through epistemic communities, policy transfer and policy translation, and collaboration and coordination at the global level.

The role of the F&B enterprises is paramount, and the above discussion has highlighted the importance of making the commercial determinants of health visible. Rather than obscuring the commercial sector responsibility for and contributions to population harms, this study underscores the need to work with these partners to find meaningful ways to work together and ensure policy coherence in tackling the issue of diabetes [ 43 ]. Importantly, it also suggests how it may be possible, and in fact necessary, to make certain  that the commercial determinants are consistent with the public interests to positively influence population health. This may mean shifting away from the dominant emphasis in research and policy on clinical management and behavioural change, and towards prevention based on societal and behavioural change [ 44 , 45 ]. The findings suggest that diabetes should be conceptualized beyond individual-level risk factors, and be reframed as the product of a complex system, in part shaped by the F&B industry [ 46 ]. Addressing the various segments of the policy actors and their challenges in response to the WoD is critical. A continued gathering of constant feedback from the various policy actors and exploring ways to support them in this agenda will also be important [ 47 ].

Study strengths and limitations

Current frameworks looking at diabetes prevention and management generally examine the wider determinants of population health, and the commercial or private sector often does not appear to be prominently included [ 43 , 48 ]. This study explicitly considers their roles and explores how they could be better supported in this WoD to mediate the negative impacts on health arising from their commercial activities. The findings gathered may add to the body of knowledge surrounding commercial determinants of health, where it is still a growing field [ 12 ]. The study’s inclusion of those with diabetes, their caregivers and the general public also means that their myriad views are considered and added to the diverse insights into this policy.

All studies have limitations. As with any qualitative research study, the findings cannot be generalized due to its inductive nature. The respective voice of the various policy actors from the five different clusters cannot be generalized, as they each constitute a small number of respondents. Potential respondents who viewed the WoD negatively or were not informed about the policy might not have participated in this study, and their views and experience would not have been reflected. A deep dive to explore the role of social determinants of health on diabetes in the context of the WoD would be useful.

This study has shown that the WoD policy has generated a general sense of unity and purpose across most policy actors. It has also illustrated the highly complex environment in “doing” policy analysis [ 49 ]. The findings showed that the WoD policy needs to segment and engage the clusters of policy actors separately, and to explore their concerns and listen to their voices. In this instance, addressing those with diabetes directly will be critical to understanding their needs, and being clear on who the intended targets are and articulating how the policy seeks to support them is imperative. Issues of fake news, unclear messaging and lack of regulation of uncertified online health providers need to be addressed. High innovation, production and marketing costs should be looked into in greater detail with the F&B enterprises. The policy also needs to be situated at the global stage and environment, to nurture the economic conditions necessary for the F&B industry (manufacturers and innovators in particular) to engage in innovation and venture into (re)formulation of healthier F&B products. Diabetes is a global issue, and efforts to foster and enhance collaboration and coordination across countries on diabetes prevention and management policy is essential and crucial.

Availability of data and materials

Data can be obtained from the corresponding author on reasonable request.

Abbreviations

Centralised Institutional Review Board

Food and beverage

Healthier Choice Symbol

Healthier Ingredient Development Scheme

Multinational corporations

Ministry of Health

Primary care network

Singapore Health Services

Stock-keeping units

Small and medium enterprises

Sugar-sweetened beverages

War on Diabetes

MOH. Diabetes: The war continues. 2017. https://www.moh.gov.sg/content/moh_web/home/pressRoom/pressRoomItemRelease/2017/diabetes--the-war-continues.html . Accessed 31 May 2018.

WHO. Diabetes Geneva, Switzerland: World Health Organisation; 2020. https://www.who.int/news-room/fact-sheets/detail/diabetes . Accessed 26 Aug 2018.

MOH. Ministry of Health studying measures to better support persons with pre-diabetes. 2017. https://www.moh.gov.sg/content/moh_web/home/pressRoom/pressRoomItemRelease/2017/ministry-of-health-studying-measures-to-better-support-persons-w.html . Accessed 31 May 2018.

MOH. Speech by Minister for Health, Mr Gan Kim Yong, at the MOH Committee of Supply Debate 2016 Singapore: Ministry of Health, Singapore; 2016. https://www.moh.gov.sg/news-highlights/details/speech-by-minister-for-health-mr-gan-kim-yong-at-the-moh-committee-of-supply-debate-2016 . Accessed 14 Apr 2020.

HPB. Health Lifestyle Festival SG to reach over 80,000 Singaporeans in push to shape healthier lifestyles Singapore: Health Promotion Board; 2018. https://www.hpb.gov.sg/article/healthy-lifestyle-festival-sg-to-reach-over-80-000-singaporeans-in-push-to-shape-healthier-lifestyles . Accessed 12 Aug 2018.

Schirr-Bonnans S, Costa N, Derumeaux-Burel H, Bos J, Lepage B, Garnault V, et al. Cost of diabetic eye, renal and foot complications: a methodological review. Eur J Health Econ. 2017;18(3):293–312.

Article   Google Scholar  

Stetson B, Minges KE, Richardson CR. New directions for diabetes prevention and management in behavioural medicine. J Behav Med. 2017;40:127–44.

WHO. Global report on diabetes. 2016.

Moodie R, Stuckler D, Monteiro C, Sheron N, Neal B, Thamarangsi T, et al. Profits and pandemics: prevention of harmful effects of tobacco, alcohol, and ultra-processed food and drink industries. Lancet. 2013;381(9867):670–9.

Kickbusch I, Allen L, Franz C. The commercial determinants of health. Lancet Glob Health. 2016;4(12):e895–6.

de Lacy-Vawdon C, Livingstone C. Defining the commercial determinants of health: a systematic review. BMC Public Health. 2020;20(1022):1–16.

Google Scholar  

Mialon M. An overview of the commercial determinants of health. Glob Health. 2020;16(74):1–7.

Gómez EJ. Contextualising obesity and diabetes policy: Exploring a nested statistical and constructivist approach at the cross-national and subnational government level in the United States and Brazil. Int J Health Policy Manag. 2017;6(11):639–48.

Walt G, Gilson L. Reforming the health sector in developing countries: the central role of policy analysis. Health Policy Plan. 1994;9(4):353–70.

Article   CAS   Google Scholar  

Gilson L, Raphaely N. The terrain of health policy analysis in low and middle income countries: a review of published literature 1994–2007. Health Policy Plan. 2008;23(5):294–307.

Shiroya V, Neuhann F, Muller O, Deckert A. Challenges in policy reforms for non-communicable diseases: the case of diabetes in Kenya. Glob Health Action. 2019;12(1):1–13.

Brownell KD, Roberto CA. Strategic science with policy impact. Lancet. 2015;2015(385):2445–6.

Bryman A. Social research methods. 4th ed. New York: Oxford University Press, Inc.; 2012.

Bryman A. Social research methods. 5th ed. Oxford: Oxford University Press; 2016.

Crow D, Jones M. Narratives as tools for influencing policy change. Policy Politics. 2018;46(2):217–34.

Prince R. Policy transfer, consultants and the geographies of governance. Prog Hum Geogr. 2012;36(2):188–203.

Atkinson P, Coffey A. Analysing documentary realities. In: Silverman D, editor. Qualitative research. 3rd ed. London: SAGE Publication Ltd.; 2011. p. 77–92.

Creswell JW. Research design: qualitative, quantitative, and mixed methods approaches. USA: SAGE Publications, Inc.; 2014.

Mukanu MM, Zulu JM, Mweemba C, Mutale W. Responding to non-communicable diseases in Zambia: a policy analysis. BMC Health Res Policy Syst. 2017;15(34):1–9.

Buse K, Mays N, Walt G. Making health policy: understanding public health. 2nd ed. New York: Open University Press; 2012.

May P, Hynes G, McCallion P, Payne S, Larkin P, McCarron M. Policy analysis: palliative care in Ireland. Health Policy. 2014;2014(115):68–74.

MOH. Speech by Mr Chee Hong Tat, Senior Minister of State for Health, at the 2nd Global Summit on Circulatory Health, 12 July 2017 Singapore: Ministry of Health; 2017. https://www.moh.gov.sg/news-highlights/details/speech-by-mr-chee-hong-tat-senior-minister-of-state-for-health-at-the-2nd-global-summit-on-circulatory-health-12-july-2017 . Accessed 28 Apr 2020.

MOH. Speech by Mr Gan Kim Yong, Minister for Health, at the Ministry of Health Committee of Supply Debate 2019, on Wednesday 6 March 2019 Singapore: Ministry of Health, Singapore; 2019. https://www.moh.gov.sg/news-highlights/details/peech-by-mr-gan-kim-yong-minister-for-health-at-the-ministry-of-health-committee-of-supply-debate-2019 . Accessed 27 Mar 2020.

MOH. Governments strengthen resolve to take comprehensive, coordinated and innovative measures to tackle diabetes (involving 18 countries) Singapore: Ministry of Health, Singapore; 2018. https://www.moh.gov.sg/news-highlights/details/governments-strengthen-resolve-to-take-comprehensive-coordinated-and-innovative-measures-to-tackle-diabetes . Accessed 27 Mar 2020.

MOH. Speech by Mr Chee Hong Tat, Minister ofState for Health, at The Food Vision Asia 2017, 26 April 2017 Singapore: Ministry of Health; 2017. https://www.moh.gov.sg/news-highlights/details/speech-by-mr-chee-hong-tat-minister-of-state-for-health-at-the-food-vision-asia-2017-26-april-2017 . Accessed 28 Apr 2020.

MOH. Diabetes: The war continues Singapore: Ministry of Health; 2017. https://www.moh.gov.sg/news-highlights/details/diabetes-the-war-continues . Accessed 28 Apr 2020.

TST. Seven drink companies pledge to reduce sugar in beverages to 12% or less by 2020 Singapore: The Straits Times; 2017. https://ifonlysingaporeans.blogspot.com/2017/08/national-day-rally-2017.html . Accessed 28 Apr 2020.

MOH. Public consultation on measures to reduce sugar intake from pre-packaged sugar-sweetened beverages Singapore: Ministry of Health, Singapore; 2019. https://www.moh.gov.sg/news-highlights/details/public-consultation-on-measures-to-reduce-sugar-intake-from-pre-packaged-sugar-sweetened-beverages . Accessed 27 Mar 2020.

MOH. Speech by Mr Edwin Tong, Senior Minister of State, Ministry of Law & Ministry of Health, at the World Diabetes Day 2019, 17 November 2019 at Ng Teng Fong General Hospital Singapore: Ministry of Health, Singapore; 2019. https://www.moh.gov.sg/news-highlights/details/speech-by-mr-edwin-tong-senior-minister-of-state-ministry-of-law-ministry-of-health-at-the-world-diabetes-day-2019-17-november-2019-at-ng-teng-fong-general-hospital . Accessed 27 Mar 2020.

Shanahan EA, Adams SM, Jones MD, McBeth MK. The blame game: narrative persuasiveness of the intentional causal mechanism. In: Jones MD, Shanahan EA, McBeth MK, editors. The science of stories: applications of the narrative policy framework in public policy analysis. New York: Palgrave MacMillan; 2014.

Lee SM, Lim LC, Koh D. Stigma among workers attending a hospital specialist diabetes clinic. Occup Med. 2015;65:67–71.

Ng CJ, Lai PS, Lee YK, Azmi SA, Teo CH. Barriers and facilitators to starting insulin in patients with type 2 diabetes: a systematic review. Int J Clin Pract. 2015;69(10):1050–70.

Sontag S. Illness as metaphor. Toronto: McGraw-Hill Ryerson Ltd.; 1978.

Hill M, Varone F. The public policy process. 7th ed. Abingdon: Routledge; 2017.

McBeth MK, Shanahan EA, Arnell RJ, Hathaway PL. The intersection of narrative policy analysis and policy change theory. Policy Stud J. 2007;35(1):87–108.

Lasswell H. Politics: who gets what, when, and how. Gloucester: Peter Smith Publishers; 1936. p. 1990.

MOH. Innovation, social mobilisation and better collaboration critical in the global fight against diabetes Singapore: Ministry of Health; 2018. https://www.moh.gov.sg/news-highlights/details/innovation-social-mobilisation-and-better-collaboration-critical-in-the-global-fight-against-diabetes . Accessed 28 Apr 2020.

Maani N, Collin J, Friel S, Gilmore AB, McCambridge J, Robertson L, et al. Bringing the commercial determinats of health out of the shadows: a review of how the commercial determinats are represented in conceptual frameworks. Eur J Pub Health. 2020;30(4):660–4.

Lee K, Freudenberg N. Addressing the commercial determinants of health begins with clearer definition and measurement. Glob Health Promot. 2020;27(2):1–5.

Ndebele P, Shaikh H, Paichadze N, Bari I, Michaels D, Burgoa CS, et al. Commercial determinants of health: an ethical exploration. Int J Public Health. 2020;2020(65):1123–32.

Knai C, Petticrew M, Mays N, Capewell S, Cassidy R, Cummins S, et al. Systems thinking as a framework for analysing commercial determinants of health. Milbank Q. 2018;96(3):472–98.

Giusti A, Maggini M, Colaceci S. The burden of chronic diseases across Europe: what policies and programmes to address diabetes? A SWOT analysis. BMC Health Res Policy Syst. 2020;18(12):1–7.

Ireland R, Bunn C, Reith G, Philpott M, Capewell S, Boyland E, et al. Commercial determinants of health: advertising of alcohol and unhealthy foods during sporting events. Bull World Health Org. 2019;2019(97):290–5.

Walt G, Shiffman F, Schneider H, Murray SF, Brugha R, Gilson L. “Doing” health policy analysis: methodological and conceptual reflections and challenges. Health Policy Plan. 2008;23(5):308–17.

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Acknowledgements

We would like to acknowledge Dr. Carol Soon, Institute of Policy Studies, Lee Kuan Yew School of Public Policy, National University of Singapore, for her initial advice and guidance in this research. We are also appreciative of the sharing by our respondents in this research study.

This research was funded by the National Medical Research Council Health Services Research—New Investigator Grant (NMRC HSR-NIG) awarded to LM. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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LM is the principal investigator of the study. LM conceived the study design, and conducted the data collection and analysis with PK. All authors read and approved the final manuscript.

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Ow Yong, L., Koe, L.W.P. War on Diabetes in Singapore: a policy analysis. Health Res Policy Sys 19 , 15 (2021). https://doi.org/10.1186/s12961-021-00678-1

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Potential Racial Bias Found in Type 2 Diabetes Risk Prediction Models

New research shows that type 2 diabetes prediction models may contain biases that contribute to over- or under-estimations of risk based on a patient’s race..

• Shania Kennedy, Assistant Editor

Artificial intelligence (AI) algorithms used to screen for and predict type 2 diabetes may be racially biased, which could perpetuate health disparities, according to a study published last week in PLOS Global Public Health .

Risk prediction models for type 2 diabetes have shown promise in bolstering early detection and clinical decision-making, but the researchers pointed out that these models can bias the decision-making process if risk is miscalibrated across patient populations.

Concerns about model miscalibration and bias are especially pertinent as efforts to address health equity and racial disparities in healthcare have grown in recent years.

In this study, the researchers assessed three AI models to determine whether they demonstrate racial bias between non-Hispanic Black and non-Hispanic white populations: the Prediabetes Risk Test (PRT), a screening algorithm for prediabetes and type 2 diabetes, and two prognostic models, the Framingham Offspring Risk Score and the ARIC Model.

The research team utilized data from 9,987 adults without a prior diagnosis of diabetes and with available fasting blood samples from the National Health and Nutrition Examination Survey (NHANES). Patient information was then sampled in two-year batches starting with 1999 and ending with 2010.

From there, the researchers calculated year- and race-specific average predicted risk of type 2 diabetes, and compared these with observed risk across racial groups taken from the US Diabetes Surveillance System.

Doing so revealed that all three algorithms were miscalibrated in terms of race across all survey years evaluated.

The research team found that the Framingham Offspring Risk Score underestimated type 2 diabetes risk for non-Hispanic Black patients, but overestimated risk for their white counterparts.

The ARIC Model and PRT overestimated risk for both groups, but to a greater extent for white patients.

The overestimation of type 2 diabetes risk in non-Hispanic white populations could result in these patients being overdiagnosed or overtreated, in addition to being prioritized for preventive interventions, the researchers noted. This phenomenon may also lead to non-Hispanic Black patients being underprioritized and undertreated.

“Biased prediction models may prioritize individuals of certain racial groups for preventive action at different rates or at different stages in their disease progression. Such unequal predictions would exacerbate the systemic health care inequalities we are currently seeing, which stem from socioeconomic inequalities, differential health literacy and access to health care, and various forms of discrimination between majority and minority populations,” the researchers explained.

Research like this highlights that while data analytics and AI approaches may help find gaps in chronic disease management and care , racial disparities are still a major obstacle to achieving health equity for diabetes patients.

A 2021 study of city-level data revealed significant disparities in diabetes mortality rates across the United States.

The analysis sourced data from the 30 largest cities in the US and demonstrated that mortality rates were higher for Black individuals than for white individuals. Disparities were also found to be up to four times larger in some cities compared to others, with Washington, DC experiencing the highest rates of diabetes mortality inequities.

• Artificial Intelligence Approach Helps Identify Type 2 Diabetes Risk
• City-Level Data Shows Stark Racial Disparities in Diabetes Deaths
• Population Health Management Strategies to Reduce Health Disparities

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2024 rank prizes awarded in london for research into type 2 diabetes and retinal imaging.

The Rank Prize for Nutrition has been awarded to Professor Roy Taylor and Professor Mike Lean

The Rank Prize for Optoelectronics has been awarded to four pioneers of retinal imaging technology

LONDON , July 2, 2024 /PRNewswire/ -- The 2024 Rank Prizes were awarded last night at a ceremony in central London . Dame Sally Davies , the UK Special Envoy on Antimicrobial Resistance, was Guest of Honour.

Rank Prize for Nutrition

Professor Roy Taylor and Professor Mike Lean were the winners of the 2024 Rank Prize for Nutrition. Their research has furthered understanding of how type 2 diabetes develops, and has shown for the first time that remission from type 2 diabetes is possible for some by following a low-energy weight management programme. Their work is transforming services for people newly diagnosed with type 2 diabetes by giving them the support to manage their health and reverse the effects of this serious condition.

Professor John C. Mathers , Chair of Rank Prize's Nutrition Committee, explained that: "The ground-breaking research by Professors Taylor and Lean has shown that a diagnosis of type 2 diabetes is not a life sentence. Their demonstration that type 2 diabetes can be put into remission by sustained weight loss will empower millions of people globally to change their eating behaviour and to improve their health."

On receiving the award, Professor Lean commented that: "Success in research, making a difference for our patients, is gratifying, and for all this to be recognised by the Rank Prize is immensely rewarding." Professor Taylor added: "I am delighted to receive this recognition on behalf of the physicists, doctors, nurses, dietitians and others who have provided fantastic team input over many years of this research."

Rank Prize for Optoelectronics

The 2024 Rank Prize for Optoelectronics was awarded to four internationally leading scientists for the development of instruments that use adaptive optics technologies to capture high-resolution images of the living human retina. Their pioneering research has generated new fundamental insights into the structure and function of the human eye in both health and disease as well as new clinical interventions to remedy sight loss from common disorders. The winning scientists are:

Dr Junzhong Liang

Professor Donald T. Miller

Professor Austin Roorda

Professor David R. Williams

Professor Donal Bradley , Chair of Rank Prize's Optoelectronics Committee, noted that: "The Prize recognizes a seminal contribution to imaging within the eye that opens new opportunities to understand this complex optical instrument and to improve eyesight through precise interventions. The winners are to be commended both on their highly insightful contributions to vision science and their subsequent development of applications."

Professor David R. Williams responded: "Inventions and discoveries are almost always made by teams and this certainly was the case in this instance. I am so proud to be sharing this award with my former teammates, each of whom was not only critical to the initial development of ophthalmic adaptive optics but also continues to lead its evolution so successfully."

About the Rank Prize

Established by Lord J. Arthur Rank , a British industrialist and philanthropist, the Rank Prizes are awarded biennially in the fields of nutrition and optoelectronics. Previous winners include Arthur Ashkin and Shuji Nakamaru , who have gone on to win the Nobel Prize. Find out more at www.rankprize.org .

Photo: https://mma.prnewswire.com/media/2452618/2024_Rank_Prize_Winners.jpg

View original content to download multimedia: https://www.prnewswire.co.uk/news-releases/2024-rank-prizes-awarded-in-london-for-research-into-type-2-diabetes-and-retinal-imaging-302188051.html

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Differences in type 2 diabetes risk between East, South, and Southeast Asians living in Singapore: the multi-ethnic cohort

Jowy yi hoong seah.

1 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore

Xueling Sim

Chin meng khoo.

2 Division of Endocrinology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore

E Shyong Tai

3 Duke-NUS Medical School, Singapore

Rob M van Dam

4 Departments of Exercise and Nutrition Sciences and Epidemiology, The George Washington University, Washington, District of Columbia, USA

Associated Data

Data are available on reasonable request. Data can be requested following standard procedures ( https://blog.nus.edu.sg/sphs/ ).

Introduction

Prospective data on differences in type two diabetes (T2D) risk between Asian ethnic groups are sparse. We, therefore, compared T2D risk for East (Chinese), South (Indian), and Southeast (Malay) Asians and examined biological factors that may contribute to ethnic differences.

Research design and methods

We included 7427 adults of Chinese, Malay, and Indian origin participating in the Singapore multi-ethnic cohort. Information on sociodemographic, lifestyle, and biological risk factors (body mass index (BMI), waist circumference, blood lipids, blood pressure, C reactive protein, adiponectin, and homeostasis model assessment for insulin resistance and beta-cell function) were collected using standardized interviews and physical examinations. T2D cases were based on physician diagnoses, a national medical registry, fasting plasma glucose, or glycated hemoglobin A1c. We used multivariable logistic association and mediation analyses.

During an average follow-up of 7.2 years (SD 2.2 years), we documented 595 cases of incident diabetes. Ethnic Malays (OR 2.08, 95% CI 1.69 to 2.56) and Indians (OR 2.22, 95% CI 1.80 to 2.74) had an approximately twofold higher risk of T2D compared with ethnic Chinese. Higher BMI explained the higher risk for Malay compared with Chinese ethnicity. Higher BMI, waist circumference, inflammation, and insulin resistance, and lower beta-cell function and high-density lipoprotein-cholesterol significantly contributed to the higher T2D risk for Indian compared with Chinese ethnicity. However, part of the higher T2D risk associated with Indian ethnicity remained unexplained. Despite their lower diabetes risk, Chinese participants had the lowest adiponectin levels.

Conclusions

• Different Asian ethnic groups have unique biological risk factor profiles related to T2D development that may warrant targeted approaches for prevention and treatment.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• In cross-sectional studies, the prevalence of type 2 diabetes (T2D) and related metabolic risk factors differed substantially between South, East, and Southeast Asians residing in the same country.

WHAT THIS STUDY ADDS

• The incidence of T2D was substantially higher in South (Indians) and Southeast (Malay) Asians than in East Asians (Chinese) residing in Singapore.
• Greater adiposity explained the higher risk for Malays compared with Chinese ethnicity.
• Unfavorable adiposity, abdominal fat distribution, systemic inflammation, high-density lipoprotein-cholesterol, insulin resistance, and beta-cell function contributed to the higher T2D risk for Indian compared with Chinese ethnicity.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Asia carries >60% of the global burden of diabetes mellitus, with China (89 million) and India (66 million) being the countries with the largest number of people with diabetes in the world. 1 2 It is widely recognized that Asians are more susceptible to developing type 2 diabetes (T2D) than people of European descent, which may be attributed to genetic, epigenetic, and other environmental differences. 3 Chinese (East Asians), Malays (Southeast Asians), and Indians (South Asians) are genetically distinct and represent major Asian ethnic groups. 4 The prevalence of T2D has been reported to be higher in South and Southeast Asians compared with East Asians in the USA 5 and Singapore. 6 Several reasons may explain the differences in susceptibility to T2D between these Asian ethnic groups. Results from previous studies indicate that ethnic Indians are more abdominally obese and insulin resistant than ethnic Chinese individuals. 7–11 Differences in adiponectin levels 8 12 13 and inflammation 12 13 between Asian ethnic groups have also been reported.

There have been no prospective studies on differences in T2D risk and possible explanations for differences in T2D risk between Asian ethnic groups. In the Singapore multi-ethnic cohort (MEC), we assessed differences in T2D risk between ethnic Chinese, Malays, and Indians, considering socioeconomic status (SES), lifestyle factors, and body mass index (BMI). We also estimated the contributions of adiposity, dyslipidemia, hypertension, inflammation, insulin resistance, and beta-cell function to ethnic differences in T2D risk using mediation analyses.

Study population

Our study participants were from the Singapore MEC, a population-based cohort with baseline assessments conducted from 2004 to 2010. A detailed description of the methodology of this cohort has been published. 14 Briefly, the MEC was formed by inviting participants from several previous population-based studies conducted in Singapore, with oversampling of ethnic minority groups. The data collection at baseline and follow-up consisted of home interviews and physical examinations at a study site. At baseline, we interviewed 14 465 male and female participants aged 21 years and above, and 11 085 participated in the health examination.

Using standardized questionnaires, trained interviewers collected information on sociodemographic characteristics, lifestyle, and medical history. Information on ethnicity was based on the participant’s National Registration Identity Card, the identity document used in Singapore. We assessed education level based on participants’ highest education attained using six answer categories aligned with the local education system. Based on this information, we created a variable for education level with the categories ‘primary or less’ (‘no formal qualifications/lower primary’ and ‘primary’), ‘lower secondary’ (‘O’/‘N’ level), ‘higher secondary’ (‘Institute of Technical Education/Nitec (NTC)’ and ‘A’ level/Polytechnic/Diploma’), and ‘college’ (‘University’). We assessed monthly household income using five response categories from <$2000 to >$10 000 (Singapore dollar). We assessed cigarette smoking using standardized questions about current and past smoking and physical activity using a locally validated questionnaire on the type, frequency, and duration of activities. 15 We expressed leisure time physical activity in metabolic equivalent of task hours per week (MET-hrs/wk) and divided the participants into three groups: ‘low’ (0 MET-hrs/wk), ‘intermediate’ (>0 to <12 MET-hrs/wk), and ‘high’ (≥12 MET-hrs/wk) leisure time physical activity. These cutoffs were chosen to divide the participants into approximate thirds.

We subsequently invited participants to a physical examination consisting of anthropometric measurements and collecting 8–12 hours fasting blood samples. We measured participants’ height without shoes on a portable stadiometer (SECA 200 series, Germany) in the Frankfurt Plane position. Weight was measured using SECA digital scales (SECA digital scales (SECA 700 series, Germany). BMI was computed by taking the weight (kg) divided by the square of a participant’s height (m 2 ). Using a stretch-resistant tape, we measured waist circumference at the midpoint between the last rib and iliac crest. We took two readings of systolic and diastolic blood pressure measurements using an automated digital monitor (Dinamap Carescape V100, General Electric) after the participants rested for 5 min. A third reading was taken if the difference between the first two readings was >10 mm Hg (for systolic blood pressure) or 5 mm Hg (for diastolic blood pressure). Average values of blood pressure readings were used in subsequent analyses.

Plasma glucose, insulin, adiponectin, C reactive protein (CRP), triglycerides, HDL-cholesterol, and glycated hemoglobin A1c (HbA1c) were measured using enzymatic, immunoassays, or spectrophotometric methods on the same day as the blood collection. 14 The intra-assay coefficient of variation (CV) ranged from 0.6% to 4.0%, and the inter-assay CV ranged from 2.3% to 4.5%. 14 We calculated the homeostasis model assessment of insulin resistance (HOMA-IR) by multiplying fasting insulin (mIU/L) with fasting glucose (mmol/L) and dividing by 22.5, and the homeostasis model assessment of beta-cell function (HOMA-B) by the formula (20×insulin in mIU/mL)/(glucose in mmol/L−3.5). 16

After an average of 7.2 years (SD 2.2 years), follow-up interviews and fasting blood samples were collected. At baseline and follow-up, participants were considered to have diabetes if they had a self-reported physician diagnosis (“Has a physician ever told you that you have diabetes?”), had diabetes according to a linked national medical registry, or had fasting plasma glucose (≥7.0 mmol/L) or HbA1c (≥6.5 %) levels above American Diabetes Association cutoffs. 17

At follow-up, 4022 participants were uncontactable, 374 participants were ineligible, and 4011 participants declined. Through linkage to a national medical registry, 9380 participants were followed up for incident T2D. We excluded participants with heart disease (n=302), stroke (n=102), cancer (n=89), or diabetes (n=1642) at baseline. In addition, participants who were not of Chinese, Malay, or Indian ethnicity (n=50) or had missing baseline data on age or sex (n=121) were excluded. Participants may have been excluded for one or more reasons. Finally, data from 7426 participants were available for our analysis.

Statistical analysis

We examined differences in characteristics according to ethnicity using analysis of variance (for continuous variables) and χ 2 tests (for categorical variables). We also assessed differences in means for biological risk factors adjusted for age, sex, education level, monthly household income, cigarette smoking, physical activity, and BMI using analysis of covariance. BMI-adjusted waist circumference was calculated by regressing waist circumference on BMI and using the residuals in subsequent analyses. In this residual method, BMI-adjusted waist is uncorrelated with BMI and therefore reflects an abdominal body fat distribution independent of overall body fatness. 18

In logistic regression models, we assessed associations between ethnicity and sociodemographic, anthropometric, lifestyle, and biological factors and T2D risk. The baseline model included age and sex as covariates, and an adjusted model additionally included SES (education and income level), lifestyle (smoking and physical activity), and BMI (kg/m 2 ). The likelihood ratio test was used to assess the goodness-of-fit of the logistic regression models and the significance of associations for risk factors. In mediation analyses, we calculated direct and indirect effects using the Stata command medeff . 19 The indirect effects, direct effects, proportion mediated, and 95% CIs were generated from 1000 Monte Carlo draws for quasi-Bayesian approximation. 19 For all analyses in which HOMA-B was the exposure of interest, we further adjusted for HOMA-IR as beta-cell function should be interpreted in the context of insulin sensitivity. 20

All statistical analyses were conducted using Stata Software V.14 (StataCorp, College Station, Texas, USA), and all two-sided p values <0.05 were considered statistically significant.

The mean age of the study population was 43.6 (SD 12.5) years. Participants were of Chinese (n=3662, 49.3%), Malay (n=1945; 26.2%), and Indian (n=1819; 24.5%) ethnicity. The baseline characteristics of the participants by ethnicity are shown in table 1 . Chinese tended to have higher education and income levels, be more physically active, have a lower BMI, and were less likely to be current smokers than Malays or Indians. Fasting plasma glucose and HbA1c were highest in Indians, intermediate in Malays, and lowest in Chinese.

Baseline characteristics of the study population by ethnicity

*P values are based on F-tests from analysis of variance for continuous variables and χ 2 tests for categorical variables.

†Mean±SD (all such values).

‡Primary school or less.

§Lower than 2000 Singapore dollars per month.

¶No leisure time for moderate-to-vigorous physical activity.

BMI, body mass index; HbA1c, glycated hemoglobin A1c; HDL, high-density lipoprotein; HOMA-B and HOMA-IR, homeostasis model assessment of beta-cell function and insulin resistance, respectively.

We examined ethnic differences in biological T2D risk factors in basic (age-adjusted and sex-adjusted) and multivariable (age, sex, SES, lifestyle, and BMI-adjusted) models ( table 2 ). Of the three ethnic groups, Indians had the highest insulin resistance, waist circumference, and systemic inflammation (CRP) and the lowest HDL-cholesterol levels. However, they also had the lowest blood pressure. Chinese had the highest HDL-cholesterol levels but the lowest adiponectin levels. These differences were observed in both the basic and the multivariable model. Malays had the highest adiponectin levels and, after multivariable adjustment, the lowest waist circumference and insulin resistance. In the basic model, HOMA-B was lowest in Chinese, but after multivariable adjustment, Indians had the lowest beta-cell function. No significant ethnic differences in fasting triglycerides remained after multivariable adjustment.

Ethnic differences in biological risk factors for diabetes after age and sex or multivariable (age, sex, socioeconomic status, lifestyle factors and BMI)* adjustment

Values are adjusted means (95% CIs).

*Adjusted for age, sex, education level, household income, cigarette smoking, physical activity, and BMI.

†P values are based on F-tests from analysis of covariance.

‡HOMA-B analyses were additionally adjusted for HOMA-IR.

BMI, body mass index; HDL, high-density lipoprotein; HOMA-B and HOMA-IR, homeostasis model assessment of beta-cell function and insulin resistance, respectively.

During an average follow-up of 7.2 years (SD 2.2 years), we documented 595 cases (cumulative incidence 8.0%) of incident diabetes, including 216 (5.9%) in Chinese, 193 (9.9%) in Malays, and 186 (10.2%) in Indians. In the age-adjusted and sex-adjusted logistic regression model, Malays (OR 2.08, 95% CI 1.69 to 2.56) and Indians (OR 2.22, 95% CI 1.80 to 2.74) had an approximately twofold higher risk of developing T2D than Chinese ( table 3 ). There was no significant difference in T2D risk when we compared Indians with Malays (OR 1.06, 95% CI 0.86 to 1.32). After further adjustment for SES, lifestyle factors, and BMI in the multivariable model, Indian (OR 1.51, 95% CI 1.16 to 1.96) but not Malay (OR 1.17, 95% CI 0.89 to 1.54) ethnicity remained significantly associated with a higher T2D risk compared with Chinese ethnicity. Higher education levels were associated with a lower T2D risk in the basic model, but this association was weaker and non-significant after adjustment for lifestyle factors and BMI. Cigarette smoking (OR 1.12; 95% CI 0.85 to 1.48 for current vs never) and physical activity (OR 0.87; 95% CI 0.71 to 1.08 for high vs low) were not significantly associated with T2D risk in the basic model. In contrast, higher BMI, waist circumference, systolic blood pressure, HOMA-IR, CRP levels, and triglyceride levels, and lower adiponectin and HDL-cholesterol levels were significantly associated with a higher risk of T2D in both models. HOMA-B was only significantly associated with a lower T2D risk in the multivariable model. When we adjusted for all biological risk factors simultaneously, the risk of T2D remained higher in Indians (OR 1.52, 95% CI 1.13 to 2.04) compared with Chinese participants.

ORs* of type 2 diabetes for ethnicity, socioeconomic, and biological risk factors

*Estimates are ORs (95% CIs) per SD increase for the continuous variables.

†Further adjusted for education level (four categories), household income (four categories), cigarette smoking, physical activity, and BMI. For HOMA-B, models 1 and 2 also included HOMA-IR.

BMI, body mass index; HDL, high-density lipoprotein; HOMA-B and HOMA-IR, homeostasis model assessment of beta-cell function and insulin resistance, respectively; Ref., reference; SGD, Singapore dollar.

We subsequently examined factors significantly associated with T2D in the multivariable model in mediation analysis. Specifically, we estimated the proportion of the excess T2D risk in Indians and Malays compared with Chinese that these risk factors may explain. A large part of the excess T2D risk in Malays and Indians compared with Chinese appeared to be mediated by BMI (Malays: 68.8%; Indians: 47.9%) and HDL-cholesterol levels (Malays: 33.6%; Indians: 51.9%) ( table 4 ). For Indians compared with Chinese, higher insulin resistance (23.8%), CRP (13.1%), and waist circumference (10.4%) were also estimated to mediate part of the higher T2D risk in Indians. In contrast, we observed a negative mediation effect for adiponectin for the T2D risk in Indians (−21.3%) and Malays (−37.4%) compared with Chinese participants. These negative mediation estimates suggest that higher adiponectin levels in Indians and Malays resulted in a smaller excess T2D risk compared with Chinese.

Estimates for the direct and indirect effects, and percentage mediated by different risk factors for associations between ethnicity and T2D incidence*

*The ‘indirect effect’ estimates the average mediation effect of risk factors on the association between ethnicity and T2D. The ‘direct effect’ estimates the association between ethnicity and T2D risk unaccounted for by the risk factor; ‘% mediated’ reflects the size of the indirect effect relative to the total effect. Lifestyle factors were not included in the mediation analyses because these were not associated with T2D risk in multivariable logistic regression models.

†For HOMA-B, we adjusted for HOMA-IR.

BMI, body mass index; HDL, high-density lipoprotein; HOMA-B and HOMA-IR, homeostasis model assessment of beta-cell function and insulin resistance, respectively; T2D, type 2 diabetes.

We examined ethnic differences in T2D risk and possible mediation by biological risk factors in a prospective cohort including participants of East (Chinese), South (Indian), and Southeast (Malay) ancestry. Malay and Indian ethnicity was associated with an approximately twofold higher T2D risk compared with Chinese ethnicity. The higher T2D risk in Malays than in Chinese was primarily mediated by a higher BMI. The higher T2D risk in Indians than in Chinese was partly explained by a higher BMI, waist circumference, HOMA-IR, and CRP level, and a lower index for beta-cell function and HDL-cholesterol level. Despite lower diabetes risk, ethnic Chinese had the lowest adiponectin levels among the three ethnic groups. To our knowledge, this is the first study that evaluated potential mediators of differences in T2D risk in major Asian ethnic groups using a prospective design. Previous studies have either been cross-sectional 6 or focused only on ethnic differences in T2D risk factors rather than the incidence of T2D. 7–13 21–25

Major contributors to the higher T2D risk in Indians than Chinese appeared to be the greater general and abdominal adiposity in Indians in our study. Previous studies have demonstrated that Indians have more body fat and less lean mass than Chinese 7 and Europeans 26–28 for the same BMI. Low lean mass in Indians is already apparent at birth and persists for several generations after migration, possibly resulting from genetic or epigenetic mechanisms. 29 Indians may also have larger adipocytes than Europeans, 28 contributing to insulin resistance. 30

Consistent with our study, other authors also reported higher insulin resistance in ethnic Indians than Chinese or Malays 8–13 21 25 and Europeans, 9 22 23 26 28 31 even after adjustments for BMI or waist circumference. 21–23 In a study conducted in Singapore where body fat and insulin sensitivity were directly measured, Indians were also more insulin resistant than Chinese, independent of adiposity. 24 However, this was not observed in two other studies conducted in Canada and Singapore when matched for body fat. 9 11 In a study that examined the skeletal muscle transcriptome in Chinese, Malays, and Indians, the expression of SNRK and AMPKα2 genes involved in glucose uptake was lowest in Indians. 25 In a UK study, higher truncal obesity and insulin resistance accounted for the twofold higher diabetes risk in Indian women compared with Europeans, but the excess risk in men remained unexplained. 31

In addition to higher adiposity and insulin resistance, our findings suggest that a higher degree of inflammation, lower beta-cell function, and lower HDL-cholesterol contributed to the higher T2D risk in Indians than in Chinese. Inflammation may promote diabetes development by inducing insulin resistance and pancreatic beta-cell death, 32 and higher CRP levels have been consistently associated with a higher risk of T2D. 33 In previous studies, Indians also had higher CRP levels than Chinese, Malays, and Europeans. 12 34 35 Adjustments for adiposity did not explain the association between Indian ethnicity and CRP in our study and previous studies. 12 34 For a given BMI and insulin resistance, Indians in our study also had the lowest beta-cell function. 20 21 In a previous analysis of Asian men from Singapore, the differences in beta-cell function were not statistically significant between Chinese, Malay, or Indians, 21 but the authors did not adjust for measures of adiposity. In our study, HDL-cholesterol concentrations were lower in Indians than in Chinese. Lower levels of HDL-cholesterol have been associated with higher T2D risk in epidemiological studies, although this may reflect bidirectional effects. 36 In previous studies, Indians also had lower HDL-cholesterol levels than Europeans. 26 Taken together, these findings indicate that both higher insulin resistance and lower insulin secretion capacity may underpin the higher T2D risk in Indians, with body composition, dyslipidemia, and inflammation contributing to these conditions.

In our study, a higher BMI largely explained the higher T2D risk in Malays than in Chinese. In unadjusted analyses, several diabetes risk factors were elevated in Malays compared with Chinese, but adjustment for BMI explained these ethnic differences. Furthermore, adiponectin levels and BMI-adjusted waist circumference were lower in Malays compared with Chinese, suggesting that Malays have a more favorable abdominal fat distribution and adipocyte function than Chinese for a given BMI. Indeed, results from our mediation analysis suggest that the excess T2D risk in Malays, compared with Chinese, may have been larger if they would not have more favorable adiponectin levels and fat distribution.

Higher adiponectin levels have been consistently associated with a lower T2D risk. 37 Adiponectin activates AMP kinase, promotes skeletal muscle glucose uptake and oxidation, reduces hepatic glucose production and fatty acid synthesis, and promotes fatty acid oxidation. In addition, adiponectin might protect against beta-cell death. 38 Although adiponectin is secreted primarily by white adipose tissue, adiponectin levels are paradoxically inversely correlated with obesity. 39 Although ethnic Chinese had a lower T2D risk than the other ethnic groups, they had lower adiponectin levels. This paradox may reflect more favourable levels for other risk factors in Chinese participants (eg, lower adiposity), compensating for the lower adiponectin levels. Consistently, Chinese participants also had lower adiponectin levels than Malays and Indians, 8 12 or Europeans 8 28 in previous studies, even after adjusting for waist circumference. 8 In one cohort study, Indians had lower adiponectin levels than Chinese, 13 but the authors did not adjust for adiposity. Chinese may thus have a lower capacity to store fat optimally without metabolic perturbations, 13 38 which is a concern given the rising prevalence of obesity in China. 1 2

A strength of our study was the prospective population-based MEC of ethnic Chinese, Malays, and Indians. Because all participants resided in Singapore, which has government policies that prevent ethnic segregation, ethnic differences are unlikely to be due to differences in living environments. 40 We also acknowledge several limitations. First, we used BMI and waist circumference as measures of adiposity rather than direct measurements of different fat depots. Still, BMI and waist circumference capture most of the variance in visceral abdominal fat and subcutaneous fat. 41 Similarly, we used the HOMA method for estimating insulin sensitivity and beta-cell function instead of the ‘gold standard’ clamp techniques. However, HOMA-IR and HOMA-B provide a reasonably good estimate of insulin resistance and beta-cell function, respectively. 20 42 43 Given the observational nature of our study, we cannot establish causality or rule out the possibility of residual confounding arising from imperfectly or unmeasured confounders. Finally, our assessment of T2D risk factors was not complete and other factors may contribute to unexplained ethnic differences in T2D risk. Given the diversity within populations (eg, North vs South Indians and Chinese) and possible influence of living environments, caution is needed in generalizing to all ethnic Chinese, Indians, and Malays worldwide. However, our findings are consistent with Asian ethnic differences observed in cross-sectional studies in other countries such as the USA. 5

We documented marked ethnic differences in T2D risk in Malays and Indians compared with Chinese residing in the same geographical setting independent of SES. Our results suggest that the higher T2D risk in Malays compared with Chinese can largely be explained by the greater general adiposity in Malays. In contrast, higher adiposity could not fully explain the higher T2D risk in Indians than in Chinese. The levels of several T2D risk factors were worse in Indians than in Chinese, including markers of general adiposity, abdominal fat, insulin resistance, beta-cell function, inflammation, and dyslipidemia. Furthermore, a substantial proportion of the difference in T2D risk between Indians and Chinese remained unexplained, warranting further research into underlying biological mechanisms. Our results suggest that interventions preventing excess adiposity have the potential to substantially reduce ethnic disparities in T2D risk between Chinese, Malays, and Indians. However, our results also highlight that different Asian ethnic groups have unique biological risk factor profiles related to T2D that may warrant targeted approaches for prevention and treatment.

Acknowledgments

The authors thank all investigators, staff members, and study participants for contributing to the MEC. The authors also thank Dr Paul Zimmet for his comments on the manuscript.

Contributors: JYJS and RMvD conceived the study and planned the data analysis. JYJS analyzed the data and drafted the manuscript. XS, EST, and RMvD supervised the data collection. All authors contributed to the interpretation of the results and revision of the manuscript and approved the final version of the manuscript. RMvD is the guarantor.

Funding: This work was supported by grants from the National Medical Research Council (MOH-000271-00, 0838/2004, 1111/2007, and MOH-000271-00), Biomedical Research Council (grant 03/1/27/18/216), and National Research Foundation (through the Biomedical Research Council, grants 05/1/21/19/425 and 11/1/21/19/678) of the Republic of Singapore.

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.

Ethics approval

Ethics approval was obtained from the National University of Singapore Institutional Review Board (NUS-IRB-20220327-N). Informed consent was obtained from all participants.

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Few with type 2 diabetes receive guideline-recommended chronic kidney disease screening

by Elana Gotkine

Fewer than one-quarter of patients with type 2 diabetes (T2D) receive recommended chronic kidney disease (CKD) screening, according to a study published online June 26 in JAMA Network Open .

Daniel Edmonston, M.D., from the Duke University School of Medicine in Durham, North Carolina, and colleagues conducted a retrospective cohort study to examine risk factors for nonconcordance with guideline-recommended CKD screening and treatment in patients with T2D. Adults with an outpatient clinician visit linked to T2D diagnosis between Jan 1, 2015, and Dec 31, 2020, were included; concordance with CKD screening guidelines was assessed in 316,234 adults.

The researchers found that 24.9, 56.5, and 18.6% of participants received creatinine and urinary albumin-to-creatinine ratio screening, one screening measurement, and neither measurement, respectively. There was an association observed for Hispanic ethnicity with lack of screening (relative risk, 1.16).

Lower risk of nonconcordance was seen for heart failure , peripheral artery disease, and hypertension. In 4,215 patients with CKD and albuminuria, 78.0, 4.6, and 21.0% received an angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, sodium-glucose cotransporter 2 inhibitor, or neither therapy, respectively.

Associations were seen for peripheral artery disease and lower estimated glomerular filtration rate with lack of CKD treatment; however, diuretic or statin prescription and hypertension were associated with treatment.

"These limitations in CKD screening and treatment identify areas of focus for implementation strategies to improve concordance with guideline-recommended screening and therapies for CKD," the authors write.

Several authors disclosed ties to biopharmaceutical companies, including Boehringer Ingelheim and Eli Lilly. The study was funded by the Boehringer Ingelheim & Lilly Diabetes Alliance.

© 2024 HealthDay . All rights reserved.

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EATING PATTERNS FOR PEOPLE WITH TYPE 2 DIABETES

Due to the fact that they have the most profound effect on blood glucose levels, carbohydrates (e.g., starches and sugars) have been the primary nutrient focus of diabetes prevention and management for millennia. Over the past 100 years, medical nutrition therapy for the management of diabetes has swung from severe carbohydrate restriction prior to the identification and commercial availability of insulins in the 1920’s, to liberalisation in the 1950s using carbohydrate exchanges as a tool for moderating intakes, to the recommendation of higher (≥45% of energy) carbohydrate diets in the 1970’s, and to the most recent recommendation of no set amount/percent of carbohydrate – instead taking into account personal and cultural preferences.

Indeed, the American Diabetes Association’s 2024 Standards of Care in Diabetes recommends a variety of eating patterns for the prevention and management of diabetes, including Mediterranean-style, vegetarian, vegan, low-fat, low-calorie and very low-calorie, low-carbohydrate, and Dietary Approaches to Stop Hypertension (DASH). It recommends that people follow individualized meal plans that keep nutrient quality, total calories, and metabolic goals (e.g., blood glucose, cholesterol, triglycerides, etc…) in mind, as data do not support a specific macronutrient pattern. Furthermore, they recommend food-based dietary patterns that emphasize key nutrition principles like the regular consumption of non-starchy vegetables, whole fruits, legumes, whole grains, nuts/seeds, and lower-fat dairy products and minimize consumption of processed meats, sugar-sweetened beverages and refined grains/starches.

While they may not be as well known as they should be, these guidelines have existed in various forms for several decades now. Perhaps due to the very social nature of foods, meals and eating in general, food fashion and even tribalism often tries to convince people that there’s only one eating pattern for preventing and managing diabetes. This is a great shame, because one size most definitely does not fit all.

Low and very low carbohydrate diets are currently very fashionable (again) in many parts of the world, including Australia, the United Kingdom and United States of America (USA). Many proponents/tribalists suggest that most health professionals still predominantly recommend low fat diets – even though they went out of fashion at least 20 years ago.

Luckily, a new survey of Registered Dietitians (RDs), and other primary care health professionals who provide nutrition counselling, provides insight into what dietary patterns are currently being recommended to patients with type 2 diabetes. RDs affiliated with an academic health system or in primary care practices in midwestern USA were invited to complete an on-line survey asking them to select the eating pattern(s) that they commonly recommend or avoid for patients with type 2 diabetes and why. Survey respondents recommended a broad range of eating patterns including:

• low-carbohydrate (77.8% of participants)
• Mediterranean-style (52.8%)
• energy-modified/energy-restricted (36.1%)
• low fat (23.6%)
• DASH (20.8%)
• vegetarian (16.7%)
• vegan (11.1%).

On the other hand, survey respondents most commonly recommended avoiding very low-carbohydrate/ketogenic (51.0%) and very low-energy (49.0%) eating patterns, with most concerned about the eating patterns being too restrictive (93.0%) and difficult to sustain over time (82.5%).

There’s no need to follow the latest food fashion. A variety of eating patterns can be enjoyed by people with diabetes and those at risk. Discuss the options with your Accredited/Registered Dietitian and find the one that’s best for you for long-term health and wellbeing.

Read more :

• American Diabetes Association Professional Practice Committee; 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes Standards of Care in Diabetes—2024 . Diabetes Care 1 January 2024
• Khosrovaneh and colleagues. Nutrition counsellors’ recommended eating patterns for individuals with type 2 diabetes in the USA . BMJ Nutrition, Prevention & Health, 2024.

Dr Alan Barclay , PhD, is a consultant dietitian and chef with a particular interest in carbohydrates and diabetes. He is author of Reversing Diabetes (Murdoch Books), and co-author of 40 scientific publications, The Good Carbs Cookbook (Murdoch Books), Managing Type 2 Diabetes (Hachette Australia) and The Ultimate Guide to Sugars and Sweeteners (The Experiment Publishing).

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Predicting and preventing heart failure in type 2 diabetes

Affiliations.

• 1 Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
• 2 Division of Cardiology, Duke University School of Medicine, Durham, NC, USA.
• 3 Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, USA.
• 4 Mount Sinai Heart, Icahn School of Medicine at Mount Sinai Health System, New York, NY, USA.
• 5 Division of Cardiac Surgery, St Michael's Hospital, University of Toronto, Toronto, ON, Canada. Electronic address: [email protected].
• PMID: 37385290
• DOI: 10.1016/S2213-8587(23)00128-6

The burden of heart failure among people with type 2 diabetes is increasing globally. People with comorbid type 2 diabetes and heart failure often have worse outcomes than those with only one of these conditions-eg, higher hospitalisation and mortality rates. Therefore, it is essential to implement optimal heart failure prevention strategies for people with type 2 diabetes. A detailed understanding of the pathophysiology underlying the occurrence of heart failure in type 2 diabetes can aid clinicians in identifying relevant risk factors and lead to early interventions that can help prevent heart failure. In this Review, we discuss the pathophysiology and risk factors of heart failure in type 2 diabetes. We also review the risk assessment tools for predicting heart failure incidence in people with type 2 diabetes as well as the data from clinical trials that have assessed the efficacy of lifestyle and pharmacological interventions. Finally, we discuss the potential challenges in implementing new management approaches and offer pragmatic recommendations to help overcome these challenges.

Copyright © 2023 Elsevier Ltd. All rights reserved.

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Conflict of interest statement

Declaration of interests AP has received research support from the National Institute on Aging (GEMSSTAR grant: 1R03AG067960-01) and the National Institute on Minority Health and Disparities (R01MD017529); grant funding from Applied Therapeutics and Gilead Sciences; has received honoraria outside of the present study as an advisor or consultant for Tricog Health, Eli Lilly, Rivus, Cytokinetics, and Roche Diagnostics; has received non-financial support from Pfizer and Merck; and is also a consultant for Palomarin with stocks compensation. KVP has served as a consultant to Novo Nordisk. DLB sits on the advisory board of Angiowave, Bayer, Boehringer Ingelheim, Cardax, CellProthera, Cereno Scientific, Elsevier Practice Update Cardiology, High Enroll, Janssen, Level Ex, McKinsey, Medscape Cardiology, Merck, MyoKardia, NirvaMed, Novo Nordisk, PhaseBio, PLx Pharma, Regado Biosciences, and Stasys; and on the board of directors of Angiowave (with stock options), Boston VA Research Institute, Bristol Myers Squibb (with stock), DRS.LINQ (with stock options), High Enroll (with stock), Society of Cardiovascular Patient Care, and TobeSoft. DLB was the inaugural chair of the American Heart Association Quality Oversight Committee. DLB is a consultant for Broadview Ventures and Hims; and sits on data monitoring committees for Acesion Pharma, Assistance Publique-Hôpitaux de Paris, Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute) for the PORTICO trial funded by St Jude Medical (now Abbott), Boston Scientific (chair for PEITHO trial), Cleveland Clinic (including for the ExCEED trial, funded by Edwards), Contego Medical (chair for PERFORMANCE 2), Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine for the ENVISAGE trial funded by Daiichi Sankyo and for the ABILITY-DM trial funded by Concept Medical, Novartis, Population Health Research Institute, and Rutgers University for the National Institutes of Health-funded MINT Trial. DLB reports honoraria from the American College of Cardiology as Senior Associate Editor of Clinical Trials and News and chair of American College of Cardiology Accreditation Oversight Committee; Arnold and Porter law firm for work related to Sanofi–Bristol Myers Squibb clopidogrel litigation; Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute) for the RE-DUAL PCI clinical trial steering committee funded by Boehringer Ingelheim and AEGIS-II executive committee funded by CSL Behring; Belvoir Publications as Editor in Chief of Harvard Heart Letter; Canadian Medical and Surgical Knowledge Translation Research Group for clinical trial steering committees; Cowen and Company; Duke Clinical Research Institute for clinical trial steering committees, including for the PRONOUNCE trial, funded by Ferring Pharmaceuticals; HMP Global as Editor in Chief of the Journal of Invasive Cardiology; Journal of the American College of Cardiology as Guest Editor and Associate Editor; K2P as co-chair of interdisciplinary curriculum; Level Ex; Medtelligence–ReachMD for continuing medical education steering committees; MJH Life Sciences; Oakstone CME as Course Director of the Comprehensive Review of Interventional Cardiology; Piper Sandler; Population Health Research Institute for the COMPASS operations committee, publications committee, steering committee, and USA national co-leader, funded by Bayer; Slack Publications as Chief Medical Editor of Cardiology Intervention Today; Society of Cardiovascular Patient Care as secretary and treasurer; WebMD for continuing medical education steering committees; Wiley for steering committee. DLB reports non-financial relationships as Deputy Editor of Clinical Cardiology; as chair of the National Cardiovascular Data Registry–Acute Coronary Intervention and Outcomes Network Registry steering committee; as chair of the Veterans Affairs Cardiovascular Assessment, Reporting, and Tracking Research and publications committee. DLB reports a patent for sotagliflozin (named on a patent for sotagliflozin assigned to Brigham and Women's Hospital who assigned to Lexicon; however, neither DLB or Brigham and Women's Hospital receive any income from this patent). DLB reports research funding for Abbott, Acesion Pharma, Afimmune, Aker Biomarine, Amarin, Amgen, AstraZeneca, Bayer, Beren, Boehringer Ingelheim, Boston Scientific, Bristol Myers Squibb, Cardax, CellProthera, Cereno Scientific, Chiesi, CinCor, Cleerly, CSL Behring, Eisai, Ethicon, Faraday Pharmaceuticals, Ferring Pharmaceuticals, Forest Laboratories, Fractyl, Garmin, HLS Therapeutics, Idorsia, Ironwood, Ischemix, Janssen, Javelin, Lexicon, Eli Lilly, Medtronic, Merck, Moderna, MyoKardia, NirvaMed, Novartis, Novo Nordisk, Owkin, Pfizer, PhaseBio, PLx Pharma, Recardio, Regeneron, Reid Hoffman Foundation, Roche, Sanofi, Stasys, Synaptic, The Medicines Company, Youngene, and 89Bio. DLB reports royalties from Elsevier as Editor of Braunwald's Heart Disease; as site co-investigator for Abbott, Biotronik, Boston Scientific, CSI, Endotronix, St Jude Medical (now Abbott), Philips, SpectraWAVE, Svelte, and Vascular Solutions; as trustee for the American College of Cardiology; and for unfunded research of FlowCo and Takeda. SV holds a Tier 1 Canada Research Chair in Cardiovascular Surgery; and reports receiving research grants or speaking honoraria from Amarin, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Canadian Medical, and Surgical Knowledge Translation Research Group, Eli Lilly, HLS Therapeutics, Janssen, Novartis, Novo Nordisk, Pfizer, PhaseBio, S & L Solutions Event Management, and Sanofi. SV is the President of the Canadian Medical and Surgical Knowledge Translation Research Group, a federally incorporated not-for-profit physician organisation. MSK declares no competing interests.

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Strengthening VCOM’s Partnership with Auburn University through Obesity and Diabetes Research

Obesity is a growing health crisis affecting nearly 20% of the global adult population. It is associated with increased severity of infections, reduced responses to vaccines and increased risk for autoimmune disease. Further, it is a factor in major metabolic disorders such as type 2 diabetes, nonalcoholic fatty liver disease and cardiovascular disease. Despite the widespread nature of obesity, however, the mechanisms underlying obesity-associated immune system dysfunction are poorly understood.

To address this problem, Joseph W. Brewer, PhD, a professor of immunology at VCOM-Auburn, is collaborating with Robert L. Judd, PhD, a professor at the Auburn University College of Veterinary Medicine and an expert in adipocytes (fat cells). Adipocytes store fat as an energy source for the body and help regulate metabolic processes. They also affect cell types in other tissues, so Brewer and Judd reasoned that adipocytes are well-equipped to communicate with the immune system.

With intramural funding from the VCOM Center for One Health Research, the team developed a system to investigate the hypothesis that adipocytes influence the behavior of B lymphocytes, a key cell type in the immune system that produces antibodies that fight infection but can also cause disease. VCOM-Auburn students participate in the work, helping generate critical knowledge about adipocyte-B lymphocyte interactions.

In their initial studies, the researchers investigated interactions between adipocytes and B lymphocytes. Preliminary findings suggest that adipocytes can indeed modulate B lymphocyte function, potentially leading to altered immune responses in obese individuals.

In the long term, Brewer and Judd plan to extend their work into studies of adipocytes and B lymphocytes from mouse models of diet-induced obesity and aging, as well as from healthy and obese human subjects. This comprehensive approach will make it possible to understand more fully the relationships between obesity and immune system functions. The goal is to identify novel therapeutic targets to mitigate the adverse effects of obesity on immune health.

Brewer, Judd and many other VCOM-Auburn faculty members participate in the Boshell Research Program, which aims to enhance opportunities for diabetes research at Auburn University by facilitating cross-disciplinary scientific discussion, supporting the study of new ideas, fostering the development of investigators new to the field of diabetes and expanding the overall base of diabetes investigation at the university. The vision of the Boshell Research Program is to improve the lives of people with diabetes through the world-class investigation into the prevention, cure and management of the disease and its complications.

That vision nicely complements VCOM-Auburn’s mission to cultivate community-focused physicians who will improve the lives of rural and medically underserved populations and advance research to enhance human health. The collaborative efforts of Brewer and Judd are a key part of the meaningful relationship between VCOM and Auburn University.

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Groundbreaking study shows why drinking from plastic bottles may increase your risk of type 2 diabetes

• BPA is an industrial chemical that scientists have linked to hormone disruption and diabetes risk.
• Plastic water bottles and food containers can leach BPA into what you eat and drink. 
• A new study found it can be risky at levels previously considered safe by government agencies. 

Scientists have long suspected that industrial chemicals used in plastic water bottles can disrupt human hormones .

But, to date, evidence has been observational, meaning it shows an association between plastics exposure and certain diseases, but can't prove a causal effect.

Now, a groundbreaking new study shows direct evidence that bisphenol A — or, BPA, a chemical used to package food and drink — can reduce sensitivity to insulin, a hormone that helps regulate blood sugar.

An impaired ability to respond to insulin, known as insulin resistance , can mean chronically high blood sugar levels and a much higher risk of type 2 diabetes.

The researchers, who presented their findings at the 2024 Scientific Sessions of the American Diabetes Association , said this study shows the EPA may need to reconsider the safe limits for exposure to BPA in plastic bottles, food containers, and other containers.

Even so-called safe levels of BPA may cause health issues

Researchers from California Polytechnic State University studied 40 healthy adults who were randomly assigned to receive either a placebo or a small dose of BPA daily.

After four days, the participants who were given BPA were less responsive to insulin, while the placebo group did not experience any change.

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The dose of BPA that participants received, 50 micrograms per kilogram of body weight per day, is an amount currently classified as safe by the EPA .

"These results suggest that maybe the US EPA safe dose should be reconsidered and that healthcare providers could suggest these changes to patients," Todd Hagobian, senior author of the new study and professor at California Polytechnic State University, said in a press release.

The FDA considers BPA to be safe at low levels occurring in food containers, up to 5 mg per kg body weight per day, or 100 times the amount the new study found to be risky. Some researchers argue the FDA guidelines are outdated .

Other regulatory agencies around the world have taken a tougher stance on the chemical — the European Commission proposed to ban BPA in products that come into contact with food or beverages by the end of 2024.

Environmental contaminants can be a major threat to human health

The concern about BPA is part of a broader alarm being raised about our everyday exposure to substances that may be harmful to our health.

Other recent research has found microplastics , particles so tiny they can infiltrate human cells, may potentially wreak havoc with our health. They've been found everywhere, from human lungs to reproductive organs .

Understanding how the substances we encounter every day may affect our health long-term could help us make better decisions about how to reduce the risk of chronic illnesses like type 2 diabetes.

"Given that diabetes is a leading cause of death in the US, it is crucial to understand even the smallest factors that contribute to the disease," Hagobian said in the press release. " We were surprised to see that reducing BPA exposure, such as using stainless steel or glass bottles and BPA-free cans, may lower diabetes risk."

June 24, 2024 — An earlier version of this article misstated the difference between what the EPA considers safe BPA exposure and what the study found to be safe. It is 100 times higher, not 1000.

Watch: Every difference between US and UK Coca-Cola

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