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  • 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.

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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.


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.


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

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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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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
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Table 1

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.


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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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 ).





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.



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.

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Lay perceptions of diabetes mellitus and prevention costs and benefits among adults undiagnosed with the condition in Singapore: a qualitative study

  • Jumana Hashim 1 ,
  • Helen Elizabeth Smith 2 ,
  • E Shyong Tai 3 &
  • Huso Yi 1  

BMC Public Health volume  22 , Article number:  1582 ( 2022 ) Cite this article

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Therapeutic lifestyle changes can reduce individual risk of type 2 diabetes (T2D) by up to 58%. In Singapore, rates of preventive practices were low, despite a high level of knowledge and awareness of T2D risk and prevention. The study explored the context of the discrepancy between knowledge and practices in T2D prevention among adults undiagnosed with the condition.

In-depth interviews with 41 adults explored lay beliefs of T2D and the sources of these perceptions, subjective interpretation of how T2D may impact lives, and perceived costs and benefits of practising preventative behaviours. Purposive sampling was used to maximise the variability of participants in demographic characteristics. Thematic analysis was conducted to identify themes related to the domains of inquiry.

Participants’ risk perceptions were influenced by familial, social, and cultural contexts of the representation and management of T2D conditions. The adverse effects of T2D were often narrated in food culture. The cost of adopting a healthy diet was perceived at a high cost of life pleasure derived from food consumption and social interactions. Inconveniences, loss of social functions, dependency and distress were the themes related to T2D management. Participants’ motivation to preventive practices, such as exercise and weight loss, were influenced by short-term observable benefits.


T2D risk communication needs to be addressed in emotionally impactful and interpersonally salient ways to increase the urgency to adopt preventative behaviours. Shifting perceived benefits from long-term disease prevention to short-term observable wellbeing could reduce the response cost of healthy eating.

Peer Review reports


Globally, 1 in 11 adults live with diabetes, and 10% of health expenditure is spent on treating individuals with diabetes [ 1 ]. Complications from type 2 diabetes (T2D) like renal, ocular, cardiovascular disease, and lower extremity amputations can lead to premature death and loss of productivity among the working-age population. In Singapore, the prevalence of T2D is projected to be 15% overall and about 40% of those over 60 years in 2050 [ 2 ]. The economic burden is expected to increase to SG$ 2.5 billion by 2050 [ 3 ]. However, about 80% of T2D incidences can be prevented by reducing modifiable risk factors with lifestyle changes [ 4 ]. Preventive behaviours, including weight management, physical activity, and healthy eating can reduce one’s risk for diabetes by up to 58% [ 5 , 6 , 7 ].

Since the Ministry of Health in Singapore declared a “War on Diabetes” in 2016, the Health Promotion Board (HPB) launched a myriad of prevention efforts like the ‘National Steps Challenge’ to increase physical activity and ‘National Diabetes Reference Materials’ to disseminate information on risk management [ 8 ]. While these efforts may have increased awareness and knowledge, practice of preventative behaviours is still suboptimal. Nationally, 81% had adequate functional health literacy regarding diabetes [ 9 ]. However, the 2020 national population health survey found an increase in obesity prevalence across all age groups compared to 2017 [ 10 ]. A household survey in 2019 found that 86% and 89% of participants agreed that healthy eating and exercise respectively, can control the risk of diabetes. However, only 37% ate the recommended 5 servings of fruits and vegetables, and 28% met HPB’s physical activity recommendation of 150 min/week [ 11 ]. To reach higher engagement in T2D prevention, the discrepancy between knowledge and practice needs to be addressed.

In addition to knowledge, contextual and individual barriers can influence behaviour. While accessibility challenges due to logistical or financial reasons and psychological barriers due to limited perceived benefit or threat were identified to influence T2D prevention across 12 studies globally, the expressed reasons were diverse, complex, and context-specific [ 12 ]. The influence of culture and social environment on the likelihood of undertaking preventative measures highlights the importance of understanding subjective experiences and values. Lay beliefs are the subjective and informal ways individuals contextualise their actions, affecting motivation to adopt healthy and preventative behaviours [ 13 ]. Understanding lay beliefs and risk perceptions in context would help explain why knowledge does not translate into practice and identify intervention focal points to increase the effectiveness of current T2D prevention efforts. In this study, we qualitatively explored the lay beliefs and risk perceptions of T2D, and attitudes towards preventive behaviours among adults undiagnosed with the condition.

Participants and procedure

The study population was adults aged between 30 and 60 years without a diagnosis of T2D. Our sampling strategy ensured the diversity of the ethnic groups as T2D was more prevalent among people with Malay and Indian background than Chinese background [ 2 ]. A study invitation was posted on bulletin boards in primary care clinics and circulated through email and social media like Facebook. During the screening of eligibility, we collected age, ethnicity, education level, and housing to ensure our recruited participant sample represented the Singapore population by demographic. We had an overwhelming interest from Chinese participants and a few Malay and Indian participants. To address this, we encouraged recruited participants to circulate the poster to their Malay and Indian friends. Interviews were conducted on a video conference call or in-person and in English, Mandarin, Malay, or Tamil. We employed several approaches to ensure the consistency of data collection in Chinese, Malay, and Tamil. We made an interview protocol describing the details of interview data collection: preparation, informed consent, introduction, a topic guide with probing questions (see supplementary file ), debriefing, and rules of translation and transcription. Researchers who conducted the interviews in Mandarin, Malay, and Tamil were appropriately briefed by the first author (JH), who conducted majority of the other interviews in English. JH has training in behavioural science and qualitative research. Additionally, 2 of the 3 of these researchers were current PhD candidates with focus on qualitative research. The third researcher went through an extensive training and several practice rounds with JH before they independently conducted the interviews.

Interviews lasted from 40 to 70 min. Participants were provided with a voucher of SG$30 for compensation for their time. With 41 participants, our interviews had reached theoretical and thematic saturation. Theoretical and thematic saturation was determined using a hybrid and an iterative process using three criteria, as discussed by JH and the last author (HY), who is an expert in qualitative research and health psychology: (1) holistic understanding of each emergent theme to illustrate them appropriately, (2) three consecutive transcripts with no new themes found and (3) sufficiently diverse range of perspective, when our study sample relatively represented the Singapore population [ 14 , 15 , 16 ]. The study was approved by the university research ethics committee. All methods were performed in accordance with the ethics committee’s guidelines and regulations and the Declaration of Helsinki.

Interview guide

We sought to identify the social and cultural attributes that participants considered when thinking about T2D and how they may contribute in weighing the ‘cost’ and ‘benefit’ of engaging in T2D prevention. An interview topic guide was developed based on the literature review on knowledge, attitudes, and practice of T2D prevention with a focus on three major domains: (1) lay beliefs of T2D and the sources of these perceptions; (2) subjective interpretation of how individuals thought T2D might impact their lives; and (3) perceived costs and benefits of practising preventative behaviours. The interview topic guide is provided as supplementary material.

Interviews were audio-recorded, translated for non-English interviews, transcribed verbatim, and entered in NVivo 12. Non-English interviews were read by JH and HY after transcription to ensure there was consistency in data collection across the different researchers before entered into the software. The six-phase reflexive thematic approach was used for analysis [ 17 ]. JH and HY read the transcripts while interviewing to familiarise the data and revise probes if necessary. After the first 10 interviews, JH and HY worked independently before discussing the preliminary codes and generating initial themes. The initial code scheme was applied to the remaining interviews using a constant comparison method to refine and finalise the codebook. This also allowed us to assess theoretical and thematic saturation. We addressed reflexivity throughout the phase of data collection and interpretation to check any influence of preconceptions and ensure that significant findings were not left out or misinterpreted. The interviewers had regular meetings for debriefing and checking whether personal attributes, qualifications, experiences, and values affect interviewing and leading interview questions. The second author (HES) is an expert in primary care and community engagement; the third author (TES) is an endocrinologist and an expert in diabetes prevention, especially in Singapore. All had regular meetings where the first author presented preliminary data analysis, and all discussed and finalised data interpretation. To ensure the credibility of the analysis, a subset of the participants ( n  = 8) was invited to a workshop to discuss the findings as a form of member-checking. They were selected based on the following criteria: (1) participants had consented to re-contact during informed consent, and (2) participants were available for a focus group discussion, which allowed the participants to freely talk among themselves as we took overall notes. This was not part of the data collection; therefore, we did not record or transcribe their discussions. No major change was made as the consensus was positive and in agreement with the themes we identified. We reviewed their comments after member checking to iterate and strengthen our themes and the narrative to weave them together.

Table 1 shows the demographic characteristics of 41 participants. There were 24 females and 17 males, with 16 participants in their 30 s, 14 in their 40 s, and 11 in their 50 s. Ethnic distribution followed 61%, 15%, and 20% for Chinese, Malay, and Indian, respectively.

Table 2 presents a hierarchal thematic scheme of the novel findings. We identified 5 main themes, each with 3 sub-themes: (i) perceptions of diabetes, (ii) sources of perceptions, (iii) relational identity between food and T2D, (iv) perceived losses from healthy eating in T2D, and (v) perceived gains from physical activity in T2D. Even though the findings are categorized by the domains of inquiry, all the sub-themes are interrelated and create the narrative of the given context.

Perceptions of diabetes

All the participants were aware of diabetes with a good understanding of its risk factors, like obesity, family history, dietary habits, and sedentary lifestyle. Commonly cited symptoms included increased thirst, frequent urination, changes in weight, and “sweet pee which attracted ants.” A few responded there would be no visual symptoms until a blood test has been taken. Participants also had a good understanding of the disease progression. Apart from the cost of treatment, the initial stages of the disease management were perceived as “inconvenient” due to the daily medications and diet considerations.

If I had it, I had to take medications regularly and properly. I had to bring medications with me. It’s inconvenient. If I were to be in a social setting, I’d be like, “Oh, I’m sorry, I can’t eat this or drink that” or like “I need to take my medication.” Then, people would look at me weirdly. I’d be like, “Should I explain or not?” (30s, F, Chinese)

Later stages of T2D were perceived as “disastrous to quality life” due to the complications arising from T2D. Many participants were concerned that they may become a burden and be unable to care for others. Complications of T2D were associated with disabilities that could cause “(loss of) ability to work, (and) ability to live independently.”

There is a risk of complications like having kidney problems, amputations, or maybe even blindness, or losing your sensitivity, your extremities. These are the complications that someone with diabetes will have to anticipate. But if I develop complications that result in me developing blindness or limb amputation, that one will be quite disastrous to the quality of my life. (30s, M, Chinese)

However, most participants expressed that the development of these complications would be far away, and the progression from the initial stage to complications would be slow. They believed such a slower progression of diabetes compared to other diseases meant that it was not as life-threatening and that diabetic patients have an opportunity to control and manage diabetes with medication and lifestyle adjustment.

You may have diabetes, but it may not happen like a one-shot. For diabetes, first, you have medication to manage it. You have time for treatment. You still can control in a way. You can try to minimise potential injuries. It will not get fatal as compared to heart disease where it strikes up, the recovery time and saving the person is very acute (30s, M, Chinese)

Sources of perceptions

Participants said they actively seek “expert” knowledge only after specific triggers like health screening results or hearing about T2D diagnosis from their social circles. Some participants found the amount of information and use of jargon overwhelming, and the information on actionable steps sometimes contradicting.

I usually inquire into a condition when somebody I know is diagnosed with the condition. It usually takes a few searches to understand because there are many sources, which tend to be overly clinical in their jargon, which is not very helpful and only targeted to medical professionals. Usually, the contradictions are not in the diagnosis but understanding if it is major or minor, or if any meaningful action should be taken. (50s, M, Chinese)

Hence, many lay perceptions were influenced by the media portrayal of diabetic patients. Participants recollected that the characters with diabetes in the media were often in the later stages with limb amputations, which were somewhat disturbing. However, diabetes was rarely reported as a cause of death, even if it was an underlying health condition.

To me, diabetes is a bit far away. We hear about stroke and heart attacks when the media reports that ‘somebody collapsed while jogging’. Whereas, when somebody dies from diabetes, we don’t usually read it in the papers. You might die of heart attack with a pre-existing condition of diabetes. But people just report your heart attack. Diabetes tends to be at the back of everybody’s mind. It exists, but the media doesn’t put it in the spotlight that often. (30s, M, Chinese)

For participants who had family, relatives, or friends with T2D, their perceptions of the disease – cause, risk, and consequence of diabetes – were influenced by what they observed and heard from the patients. In particular, participants who had parents and relatives with late stage-associated conditions, their descriptions about the impact of T2D on life were specific and vivid.

She suddenly started to bleed very badly after just gently scratching a black spot, but she didn’t feel any pain. She passed out at home because of the excess bleeding. We had to call the ambulance, and she had to go for another operation for her leg. When you have diabetes, it will take longer for the wounds to be healed, so it took her a long time to heal. This is a real problem. (40s, F, Indian)

Relational identity between food and T2D

Common factors influencing perceived risk among participants were poor health screening results, obesity, positive family history, and unhealthy practices, especially around dietary choices. Many participants perceived that having too much sugar was the main cause of diabetes, which translated to reduced perceived susceptibility of T2D among those who did not have many sugary foods.

I think my risk is very low. I am someone who is not into sugar - I don’t drink bubble tea, I don't have a lot of sweets, biscuits or cakes or chocolates. I don’t have that kind of craving. (40s, F, Chinese)

Several participants said when friends and families speak about diabetes, it is usually candidly referring to having too many sweet food items. However, the colloquial reference to sweet foods and sugar as the cause of diabetes did not reduce the consumption of these foods.

When you have a gathering, you look at the amount of food and sugar. Then, you casually say like ‘this is going to get me diabetes.’ But it’s a form of a joke than anything serious. (40s, M, Malay)

Participants were asked to share how they thought their lives could be impacted if they were to be diagnosed with T2D. A common perceived loss was related to the restriction of diet to manage T2D.

If I had diabetes, I would have to have a more restrictive lifestyle. I would not be able to eat as much of the food that I enjoy – snacking, eating ice cream and things like this. I myself have sweet tooth. For me having to be a bit more restrictive would be quite a downer (30s, M, Chinese)

Many participants shared that the diet restriction was particularly impactful in Singapore as the local food culture is important in shaping the Singaporean identity. With the variety of food, there were expectations of having a certain level of culinary experience during social gatherings.

Given that we are Singaporeans, we love to eat. It is difficult to maintain a healthy lifestyle or a healthy diet. Our culture is about eating – we have a fusion of food and all kinds of foods from all around the world. Even if healthier, people do not want to meet friends over a fruit platter. They will meet for a Korean barbeque. So, from a cultural perspective, it’s very hard to disconnect from food. (30, M, Chinese)

Participants defined good food as tasty and cheap and shared that people are willing to travel significant distances in search of good food. Singaporeans take pride in finding food that has the best value for money, and this pursuit is often a topic of conversation among friends and family.

I think it is difficult for people to control their diet. Singaporeans like to travel around to find food to eat. They might be living in [a neighbourhood in the east], but they do not mind travelling to [a neighbourhood in the west]. They want the best food that they can get for the three dollars fifty cents. They will talk to each other about where to go and what to eat. They enjoy eating so much and want total value for money in getting the best bang for their buck. (40s, M, Indian)

Participants pointed out the convenience, ease of access, and budget-friendly options; ‘hawker centres’ located within every public housing estate providing diverse local cuisines quickly and cheaply. Furthermore, in recent years’ options of delivery service and the availability of all types of cuisine, one can access cheaper and more delicious food any time, from the comfort of home.

When you are craving something, or you want to eat something, usually I must travel all the way there. But now everything is a lot easier to eat something, and it will come to your doorstep. Even if I am tired or it is late at night, and I feel like having ice cream, there is [food delivery platform]. So, there are a lot more opportunities to indulge in these kinds of things. (30s, F, Chinese)

Conversely, many participants pointed out that healthy food options are more expensive and can take a long time to prepare.

In Singapore, the faster and cheaper options are unhealthy. So, if you want to prepare healthy food, and you have working hours, you need to make a lot of sacrifices – like wake up early or prepare it the night before. And the ingredients for healthy meals are not cheap. (40s, F, Malay)

Perceived losses from healthy eating in T2D prevention

When speaking of restrictive diet and healthy eating, participants alluded to a ‘loss’ in their lifestyle due to reduced enjoyment and impaired social interactions associated with food. Social relationships and celebrations are centred on food, and declining food or refusing to eat could be interpreted as an insult to the host. This was mentioned by participants across all the ethnic groups.

You’re stopping me from eating my favourite food, you know? I rather die. What makes it really hard is that any form of Chinese celebration has got to do with food. The bigger the celebration, the more food we have. It’s like, if you don’t eat, you’re extremely rude – it’s insulting not to eat something that is placed before you. (50s, M, Chinese)

Many participants also shared that eating provided a source of enjoyment and that some participants turned to food when they were upset or stressed. While some participants shared that they exercise to de-stress, some participants shared that they eat to de-stress. A participant mentioned the endorphins released when exercising, while another said the same but when indulging in delicious food. While there was awareness for the need to mitigate the effects of unhealthy eating, it came in the form of compromising other meals instead of giving up the pleasure derived from unhealthy foods.

The only thing that I’m doing now to control my eating is trying not to have breakfast in the morning. I will just try to have lunch and dinner, but it is usually not controlled. I should stop eating less fried food. But I don’t think I can give up fried chicken that easily. It’s just really too good to give up. (30, M, Chinese)

Similarly, some participants expressed that they justify their eating habits by having ‘earned their calories’ after exercising and consider their indulgence as a reward. The influence of social media culture was also reported, where people post pictures of the aesthetics of the setting and the food. Participants shared that social identity is associated with food and enjoying life, and rarely with healthy eating in the context.

People eat to survive. But for me, I live to eat because I love to eat. So, if I’m not happy, I need to eat to be happy. I love food. To continue eating unhealthy food, I compensate for it by doing more exercise. So, I had the calories burned to eat. If I don’t exercise and eat, I’ll get fat or something like that. But if I exercise and eat, it can balance out, right? Nowadays, people post their food on social media. Wow, they’re so yummy! But, if you burn a fish at home, you wouldn’t post on social media. You will only post nice and presentable ones. (40s, F, Chinese)

Perceived gains from physical activity in T2D prevention

Demanding work environments and familial responsibilities created multiple competing priorities even though exercise is desirable. These responsibilities often lead to sacrificing sleep, poor eating habits, and exercise time to meet these expectations. Participants shared that Singapore’s competitive work environment creates high-stress situations. There is an expectation to constantly improve skills and qualifications to ensure job security.

Stress is one contributing factor. People tend to eat more and badly when they have stress. People want to have job security. Now there is digital disruption, so you can become invalid, which is quite scary. So, we need to upgrade ourselves. I have attended many courses, and I will attend more, so there is no time to exercise sometimes even though I want to. (40s, M, Others)

Participants, especially mothers, shared that time for themselves when they could exercise is seen as a “luxury” or “culturally challenging”. A Muslim woman shared how she felt different and watched when running with a hijab, a head covering worn by many Muslim women. Internalised expectations of clothing worn during exercise contrasted with wearing a hijab, creating potential psychological barriers.

I’m making a conscious effort, but it is really tough for me to find a time with kids, work, and everything. Most of the days, as a working mom, I seldom get time for myself to do what I want. You feel good about yourself with those endorphins. It is very good to mentally detox by getting away from home and kids. But, when I ran in the park, I used to feel a bit shy and embarrassed because I was wearing hijab, and then I felt like everyone was looking at me (30s, F, Indian)

Despite these challenges, there is interest to engage as several participants shared their rewarding experiences from physical activity. When talking about physical activity, participants alluded to a ‘gain’, citing how they feel ‘lighter’ and ‘good’ after exercising. Participants shared that initial adoption of physical activity was often in response to an external cue, including a worrying health screening result or a recent loss of life. Accountability through exercise programmes or friends or incentives were cited as facilitators of engaging in physical activity. However, the reasons to sustain behaviour were to ensure they could maintain their physical appearance, retain independence and physical mobility to continue doing the things they enjoy, or continue experiencing the immediate benefits and enjoyment of certain exercises.

“I don’t want to be obese or unhealthy. I don’t want to inject myself all the time or spend my hard-earned money on doctors or medications. So, I exercise. Then, I’ll feel lighter. I’ll feel good, fit. I’ll be happy, and I can do a lot of things. Through all these exercises, my muscle won’t be so stiff. I can do a lot of things together with my children. I can cook for them and continue to work. Then I can go travel if money permits.” (50s. F. Chinese)

Many participants also reported that observing self-improvement by tracking progress acted as positive feedback for their self-efficacy, and in return, motivated them to exercise further or longer. Participants who exercise regularly also pointed out that exercise is a more individual activity, and therefore it is not affected like healthy eating by its social context.

One day you cycle down a road, you see some things and buildings. Then the next time, you motivate yourself to cycle further. It’s with running also – in my mind, I will motivate myself to jog slowly. And then now I can run to this place, to that place, and then further. Then slowly, I can run back. It motivates me. So every time, you look for a new goal to achieve. I can go somewhere further, you know? (40s, F, Chinese)

The study findings highlight the importance of understanding the social and cultural contexts of T2D risk in the development of effective interventions among adults who have elevated risk yet do not engage in prevention behaviours. The findings explained the dissonance between knowledge and practice of T2D behaviours. In the study, dietary change was generally perceived negatively due to the hedonistic approach to food and its strong association with Singapore food culture and social interactions. Further, access to healthy food required more effort to prepare and costs more than unhealthy options. Visible impacts from healthy eating, like weight loss, can be small and slow, creating limited observable short-term benefits. Time needed for physical activity can also be overshadowed by competing priorities of work and familial commitments. However, exercise was perceived to have short-term gains related to wellness and physical performance. These gains contribute to the positive feedback loop and enforce self-efficacy of behaviour, making one more confident and motivated to practice. Hence, the differential view of loss and gain associated with healthy eating and exercise could influence the varied sustainability of the respective behaviours.

Our findings align with a local study that showed high awareness of diabetes and perceived efficacy of preventative behaviours and shed some light on why actual uptake of these behaviours may be low despite the high knowledge [ 11 ]. Perceived severity of diabetes comes from the downstream complications of the later stages of T2D, which seem to be distant for those without T2D. With an incomplete understanding of diabetes, the lack of sugar consumption creates a lower perceived susceptibility to T2D. The perception of T2D developing slowly and being influenced by personal experiences was also reported in a similar study in the US [ 18 ]. Temporal discounting of the future reduces the benefits of preventative behaviours especially given the short-term costs [ 19 ], and optimism bias can also lower the perceived risk of diabetes [ 20 ]. These give rise to lower levels of motivation for behaviour change and the lack of urgency to act now.

Economic utility theory, which suggests that people will only change their behaviour if the perceived benefits outweigh the perceived costs, could explain why some people might not see adapting healthy eating now to prevent T2D in the future as a worthwhile investment [ 21 ]. Perceived costs of healthy eating overlap with the perceived losses of developing diabetes; they are both associated with an increased cost of time and money. Diabetes prevention and management both require dietary changes related to impaired enjoyment and social interactions. Pleasure derived from food was so important that individuals looked to various compromises and justifications to continue eating their favourite foods. Conversely, perceived benefit is weak due to the limited observable short-term benefits, creating present bias [ 22 ]. Hence, when assessing the costs and benefits of healthy eating, there might be hesitance to pay these ‘costs’ now instead of waiting until the risk is higher, or even until diagnosed with T2D.

Physical activity, on the other hand, has important short-term benefits that create a positive feedback loop. The importance of positive feedback in diabetes prevention and management through short-term gains was deemed particularly important as altering blood glucose can be discouraging due to a lack of observable results [ 23 ]. Though physical activity shares short-term costs like time and effort with dietary change, it is less subjected to some of the barriers like impaired social interactions and pleasure. Physical activity is instead seen to facilitate social interactions and pleasure. Other facilitators include incentives and tracking progress, which aligns with the successes of the National Steps Challenge (NSC) [ 24 ] but can be challenging to leverage for healthy eating. Lack of objectivity in food measurement resists an incentive-based framework, but HPB has launched the ‘Eat, Drink, Shop Healthy Challenge’ that is currently attempting this. However, like NSC, it is likely to fall victim to compliance and sustainability issues.

Translating our findings using the constructs of Protection Motivation Theory (PMT) can inform strategies to address the dissonance between knowledge and practice. PMT suggests that threat and coping appraisals influence the intention of behaviour following an emotion-evoking stimulus [ 25 , 26 ]. Our findings indicate that both the threat appraisal and coping appraisal were sub-optimal among adults without T2D in Singapore. Using an appropriate risk communication tool to return health screening results to elicit an emotional and salient response is an opportunistic time to elevate this threat appraisal. Health care professionals should be vigilant in appropriately framing the consequences of living with diabetes. In efforts to encourage active diabetes management, it was communicated that diabetes does not prevent a fulfilling life, which may have reduced the perceived severity of T2D. An elevated risk appraisal is associated with an increased intention for behaviour change due to heightened emotional response and perceived severity [ 27 ]. To increase coping appraisal, it is important to address barriers and create positive associations to preventative behaviour. Reducing perceived response cost and increasing perceived benefits through observed performance is critical for sustainable behaviour change [ 28 ].

A key systematic-level barrier cited was the accessibility of healthier food options due to increased cost and inconvenience. The implications are likely inequitable among the different socio-economic groups of the population. Health outcome inequities due to the cost of healthy eating, an example of social determinants of health, have been demonstrated globally [ 29 , 30 , 31 ]. Structural interventions are necessary to address health inequity, such as direct health promotion at the point of sale (e.g., labelling policies on menu boards and food packaging) and food supply interventions to support the “Healthier Hawker Program” [ 32 ]. Understanding the unique barriers of the different ethnic groups could also address the differentiated risk profiles. The tension between internalised expectations of exercise and culturally accepted practices among Muslim women has also been demonstrated among their communities in the UK [ 33 ]. Further, in-depth exploration of challenges specific to Singaporean-Malay women has uncovered similar findings to ours [ 34 ]. Even though Malays and Indians are disproportionately affected by T2D [ 2 , 35 ], there are no population-tailored interventions to address this health disparity. For example, providing accessible exercise spaces for Muslim women, who are usually Malay or Indian, can promote privacy and inclusivity.

When creating positive associations, short-term benefits need not be health-focused. Highlighting benefits associated with well-being can influence motivations for uptake of T2D prevention [ 36 , 37 ]. Moving to non-health but valued aspects of well-being can create salient perceived benefits and leverage present bias. This is especially beneficial among those who perceive to have little to no risk of diabetes and do not perceive the need for change [ 23 ]. For example, the Asian tendency to put familial and work responsibility above self-care is reflected in the time-management barrier. However, successfully fulfilling these responsibilities contribute to their quality of life. Hence, shifting narratives on how healthy eating and physical activity can facilitate one’s career growth and in taking care of their family not only honour what is important but can also make the perceived benefits larger than perceived costs.

Shifting narratives is not easy. Given the influence on the perception of T2D, media platforms can be an important channel to consider for such dissemination. It is important to tailor the language appropriately to avoid overwhelming amounts of medical jargon. Dramatic television series have demonstrated impact in showcasing lived experiences of diseases which can shape perceptions and attitudes [ 38 ]. However, with the younger population, leveraging culturally popular individuals may be more effective. Social media ‘influencers’ have increased uptake of healthy behaviours by associating exercise and dieting with health and happiness [ 39 ]. These channels can be particularly useful in demonstrating how healthier choices can be enjoyed and integrated into social aspects to reduce the perceived costs of dietary change.


We did not collect data from other stakeholders, such as healthcare providers, to understand their perspectives. Primary care physicians could provide insights on their conversations with at-risk patients to gain insight on common challenges and success stories. This could have been particularly beneficial as it addressed potential response bias. Since health-seeking individuals are more likely to participate in health research, we might not have captured the experiences of a particularly important group of population. Our recruitment strategy was reliant on social media and bulletin boards in primary care clinics which might have left out perspectives of individuals who did not engage with either of those platforms. Providing a voucher as compensation for their time could have added an incentive to share the study details with their families and peers making our study prone to selection bias. We attempted to minimise this by recruiting participants across all socio-economic groups. While all the interviewers grew up in the region and were familiar with the local jargon and speaking the same language as the participants provided a level of comfort, they were all female. Having male interviewers might have made some participants more comfortable sharing certain topics.

Lay beliefs and perceptions of T2D risk are contextual, shaped by social representations of the disease conditions and cultural practices relating to T2D prevention. T2D was perceived as a disease that slowly progressed and caused inconvenience and disability but did not lead to death. Motivation to practice healthy eating was suboptimal. Participants believed that it would have the same ‘costs’ as the perceived loss of pleasure from enjoying food in social interactions, narrated as the essence of the local culture and belonged identity. Cue to initiate behaviour needs to be emotionally driven in collective contexts while sustaining behaviour is through individual positive feedback from observed short-term benefits. Future T2D prevention interventions need to emphasise the roles of lay beliefs and perceptions of the disease in practices for adults who are knowledgeable but undetermined for prevention.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.


Type 2 Diabetes

Health Promotion Board

National Steps Challenge

Protection Motivation Theory

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The authors thank the participants in this research for their time. We thank Ho Cheng En, Tan Sok Teng, and Aysha Farwin who conducted in-depth interviews in Chinese, Tamil, and Malay.

This work was supported by National Medical Research Council, under the Open Fund Large Collaborative Grant. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Council.

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Family Medicine and Primary Care, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore

Helen Elizabeth Smith

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JH and HY conceived the design for the current research study and the preliminary coding. JH conducted the majority of the data collection. All authors were involved in data analysis. JH wrote the first draft of the manuscript under the supervision of HY, who later revised and made the final draft. All authors commented on the final draft and approved the version of the manuscript to be published.

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Hashim, J., Smith, H.E., Tai, E.S. et al. Lay perceptions of diabetes mellitus and prevention costs and benefits among adults undiagnosed with the condition in Singapore: a qualitative study. BMC Public Health 22 , 1582 (2022). https://doi.org/10.1186/s12889-022-14020-z

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DOI : https://doi.org/10.1186/s12889-022-14020-z

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  • Risk perception
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  • Type 2 diabetes
  • Qualitative study

BMC Public Health

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type 2 diabetes research singapore

Multiple Biomarkers Improved Prediction for the Risk of Type 2 Diabetes Mellitus in Singapore Chinese Men and Women


  • 1 Health Services and Systems Research, Duke-NUS Medical School, Singapore. [email protected].
  • 2 Health Services and Systems Research, Duke-NUS Medical School, Singapore.
  • 3 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore.
  • 4 UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA.
  • 5 Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
  • 6 Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • PMID: 31769241
  • PMCID: PMC7188981
  • DOI: 10.4093/dmj.2019.0020

Background: Multiple biomarkers have performed well in predicting type 2 diabetes mellitus (T2DM) risk in Western populations. However, evidence is scarce among Asian populations.

Methods: Plasma triglyceride-to-high density lipoprotein (TG-to-HDL) ratio, alanine transaminase (ALT), high-sensitivity C-reactive protein (hs-CRP), ferritin, adiponectin, fetuin-A, and retinol-binding protein 4 were measured in 485 T2DM cases and 485 age-and-sex matched controls nested within the prospective Singapore Chinese Health Study cohort. Participants were free of T2DM at blood collection (1999 to 2004), and T2DM cases were identified at the subsequent follow-up interviews (2006 to 2010). A weighted biomarker score was created based on the strengths of associations between these biomarkers and T2DM risks. The predictive utility of the biomarker score was assessed by the area under receiver operating characteristics curve (AUC).

Results: The biomarker score that comprised of four biomarkers (TG-to-HDL ratio, ALT, ferritin, and adiponectin) was positively associated with T2DM risk ( P trend <0.001). Compared to the lowest quartile of the score, the odds ratio was 12.0 (95% confidence interval [CI], 5.43 to 26.6) for those in the highest quartile. Adding the biomarker score to a base model that included smoking, history of hypertension, body mass index, and levels of random glucose and insulin improved AUC significantly from 0.81 (95% CI, 0.78 to 0.83) to 0.83 (95% CI, 0.81 to 0.86; P =0.002). When substituting the random glucose levels with glycosylated hemoglobin in the base model, adding the biomarker score improved AUC from 0.85 (95% CI, 0.83 to 0.88) to 0.86 (95% CI, 0.84 to 0.89; P =0.032).

Conclusion: A composite score of blood biomarkers improved T2DM risk prediction among Chinese.

Keywords: Biomarkers; Case-control studies; Diabetes mellitus, type 2; Epidemiology; Prognosis.

Copyright © 2020 Korean Diabetes Association.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Adiponectin / blood
  • Alanine Transaminase / blood
  • Asian People / ethnology*
  • Biomarkers / blood*
  • C-Reactive Protein / metabolism
  • Case-Control Studies
  • Diabetes Mellitus, Type 2 / blood*
  • Diabetes Mellitus, Type 2 / epidemiology
  • Diabetes Mellitus, Type 2 / metabolism
  • Ferritins / blood
  • Lipoproteins, HDL / blood
  • Middle Aged
  • Predictive Value of Tests
  • Prospective Studies
  • Retinol-Binding Proteins, Plasma / metabolism
  • Risk Factors
  • Singapore / ethnology
  • Triglycerides / blood
  • alpha-2-HS-Glycoprotein / metabolism
  • Adiponectin
  • Lipoproteins, HDL
  • RBP4 protein, human
  • Retinol-Binding Proteins, Plasma
  • Triglycerides
  • alpha-2-HS-Glycoprotein
  • C-Reactive Protein
  • Alanine Transaminase

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  • UM1 CA182876/NH/NIH HHS/United States
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type 2 diabetes research singapore

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.






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Type 2 Diabetes Mellitus

Type 2 diabetes mellitus - what it is.

Diabetes is a condition characterized by high glucose levels. Our body produces a hormone called insulin which enables glucose to enter our cells. If our bodies do not produce enough insulin, or if our cells are not able to respond well enough to insulin, glucose cannot enter our cells and instead accumulates in the bloodstream.

diabetes conditions & treatments

In type 2 diabetes (T2DM), the cells are not able to respond well enough to the insulin in the body. The cells require more insulin than usual in order to absorb glucose from the bloodstream. The body is not able to produce enough insulin to cope with these increased needs, causing glucose levels to rise.

Type 2 Diabetes Mellitus - Symptoms

Early symptoms of diabetes may not be obvious, or there may not be any symptoms at all.Some possible symptoms of high blood glucose levels are listed below:

  • Excessive thirst and urination
  • Weight loss
  • Blurred vision
  • Slow-healing sores or frequent infections

Diabetes can also give rise to complications if it is not well controlled. These may include blindness, kidney failure , nerve damage, ulcers and amputations, heart attacks or strokes. With good control of diabetes, these complications can be prevented.

Type 2 Diabetes Mellitus - How to prevent?

You can take steps to prevent type 2 diabetes, especially if you have risk factors or have prediabetes.

Lifestyle changes which can help to reduce the risk of type 2 diabetes include:

  • Achieving a healthy body weight For people who are overweight or obese, aim to gradually achieve modest weight loss (5-10% of body weight)
  • Aim to achieve a healthy body mass index (BMI) of less than 23kg/m2
  • Increasing physical activity Moderate-intensity physical activity (150 minutes per week): brisk walking, slow cycling, dancing, housework / gardening, walking the dog OR
  • Vigorous-intensity physical activity (75 minutes per week): running, fast cycling, competitive sports
  • Maintaining a healthy and balanced diet
  • Stop smoking

Type 2 Diabetes Mellitus - Causes and Risk Factors

There are 7 main risk factors for type 2 diabetes:

  • Being overweight or obese
  • Having a sedentary lifestyle (doing very little physical activity)
  • A family history of type 2 diabetes in a parent or sibling
  • High blood pressure (above 140/90mmHg)
  • Abnormal cholesterol levels
  • Gestational diabetes , or delivering a baby weighing more than 4kg previously
  • Polycystic ovarian syndrome

Type 2 Diabetes Mellitus - Diagnosis

Blood tests can be done to diagnose diabetes. These include:

Random blood glucose

  • This is a blood glucose sample that is taken without fasting
  • A random blood glucose 11.1mmol/L or greater is suggestive of diabetes

Fasting blood glucose

  • This is a blood glucose sample taken after an overnight fast
  • Diabetes is diagnosed with the fasting blood glucose is 7.0mmol/L or greater

Oral glucose tolerance test (OGTT)

  • A fasting blood glucose level will be taken, after which you will drink a standard amount of sugary drink (75g)
  • Diabetes is diagnosed when the fasting blood glucose is 7.0mmol/L or greater, or a glucose reading 2 hours after the sugary drink is 11.1mmol/L or greater

People with symptoms of high blood glucose will only need one test to diagnose diabetes. People who do not have symptoms of high blood glucose will need to be tested on 2 separate occasions to diagnose diabetes.

Type 2 Diabetes Mellitus - Treatments

Some people with type 2 diabetes can achieve their target blood glucose levels with diet and exercise alone, but many also need diabetes medications.

Most medications for type 2 diabetes are oral medications. Some come as injections, including insulin.

People with type 2 diabetes are often treated with oral medications. Some people with type 2 diabetes need insulin therapy. In the past, insulin therapy was used as a last resort, but today it is often prescribed sooner because of its benefits.

The classes of oral medications used to treat type 2 diabetes include: metformin, sulphonylureas, SGLT2 inhibitors, DPP-4 inhibitors, alpha-glucosidase inhibitors, and thiazolidinediones (TZD). The types of injections used to treat type 2 diabetes include GLP-1 receptor agonists, and insulin.

Besides taking medications aimed at achieving target blood glucose levels, it is also important to maintain a healthy blood pressure and blood cholesterol. Medications may be required to do this.

People with diabetes should undergo a yearly eye and foot screening. This will allow eye and foot problems to be detected and treated early.

Type 2 Diabetes Mellitus - Preparing for surgery

Type 2 diabetes mellitus - post-surgery care, type 2 diabetes mellitus - other information.

  • Article contributed by Endocrinology , Singapore General Hospital


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type 2 diabetes research singapore

Impact of Type 2 Diabetes and Glycated Hemoglobin Levels Within the Recommended Target Range on Mortality in Older Adults With Cognitive Impairment Receiving Care at a Memory Clinic: NCGG-STORIES

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Taiki Sugimoto , Takashi Sakurai , Kazuaki Uchida , Yujiro Kuroda , Haruhiko Tokuda , Takuya Omura , Taiji Noguchi , Ayane Komatsu , Takeshi Nakagawa , Kosuke Fujita , Nanae Matsumoto , Rei Ono , Paul K. Crane , Tami Saito; Impact of Type 2 Diabetes and Glycated Hemoglobin Levels Within the Recommended Target Range on Mortality in Older Adults With Cognitive Impairment Receiving Care at a Memory Clinic: NCGG-STORIES. Diabetes Care 2024; dc232324. https://doi.org/10.2337/dc23-2324

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To determine the impact of type 2 diabetes and glycated hemoglobin (HbA 1c ) levels within the recommended target range according to the Japan Diabetes Society/Japan Geriatrics Society Joint Committee on mortality in older adults with cognitive impairment.

This retrospective cohort study included 1,528 and 468 patients aged ≥65 years without and with type 2 diabetes, respectively, who were visiting a memory clinic. The 468 patients with type 2 diabetes were divided into three groups (within, above, and below the target range) based on their HbA 1c levels, cognitive function, ability to perform activities of daily living, and medications associated with a high risk of hypoglycemia. The impact of diabetes and HbA 1c levels on mortality was evaluated using Cox proportional hazards models.

Over a median follow-up period of 3.8 years, 353 patients (17.7%) died. Compared with individuals without type 2 diabetes, HbA 1c levels above (hazard ratio [HR] 1.70, 95% CI 1.08–2.69) and below (HR 2.15, 95% CI 1.33–3.48) the target range were associated with a higher risk of death; however, HbA 1c levels within the target range were not (HR 1.02, 95% CI 0.77–1.36).

HbA 1c levels above and below the target range were associated with a higher risk of mortality, whereas patients with HbA 1c levels within the target range did not exhibit a higher risk of mortality than individuals without type 2 diabetes. These results provide empirical support for the current target ranges among older adults with cognitive impairment.

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This article contains supplementary material online at https://doi.org/10.2337/figshare.25267102 .

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UK study identifies ideal weight for adults with type 2 diabetes to minimize risk of dying from cardiovascular disease

by European Association for the Study of Obesity


New research being presented at this year's European Congress on Obesity (ECO) in Venice, Italy (12–15 May), identifies the optimum body weight range for adults with type 2 diabetes to minimize their risk of dying from any cardiovascular disease, including heart failure, heart disease, stroke, and chronic kidney disease.

The findings, based on health data from the UK Biobank, indicate that for adults aged 65 years or younger, maintaining a body mass index (BMI) within the normal range of 23–25 kg/m² was associated with the lowest risk of dying from cardiovascular disease. But for those over 65 years old, being moderately overweight with a BMI of 26–28 kg/m², had the lowest risk.

Maintaining a healthy weight is crucial for reducing the risk of cardiovascular diseases, particularly for people with type 2 diabetes who are predisposed to cardiovascular disease and death. However, it's not clear whether the optimal BMI range for people with type 2 diabetes varies by age.

To plug these knowledge gaps , researchers explored the age differences in the association between BMI and risk of cardiovascular death in 22,874 UK Biobank participants with a previous diagnosis of type 2 diabetes at the time they enrolled between 2006 and 2010. Patients with prior cardiovascular diseases were not excluded.

The average age of all the participants was 59 years, and around 59% were women. Their cardiovascular health was tracked, using linked health records, for nearly 13 years during which time 891 participants died from cardiovascular diseases.

Researchers analyzed data in two age groups—the elderly (over 65 years) and the middle-aged (age 65 years or younger)—and assessed the relationship between variables such as BMI, waist circumference, and waist-to-height ratio and the risk of cardiovascular death.

The optimal BMI cut-off point was also calculated in different age groups and the findings were adjusted for traditional cardiometabolic risk factors and other factors associated with adverse cardiovascular outcomes including age, sex, smoking history, alcohol consumption, level of physical exercise, and history of cardiovascular diseases.

The analyses found that in the middle-aged group, having a BMI in the overweight range (25 kg/m² to 29.9 kg/m²) was associated with a 13% greater risk of dying from cardiovascular disease than those with a BMI in the normal range (less than 25.0 kg/m²).

However, in the elderly group, having a BMI in the overweight range (25 kg/m² to 29.9 kg/m²) was associated with an 18% lower risk of dying compared to having a BMI in the normal range (less than 25.0 kg/m²).

The relationship between BMI and cardiovascular death risk exhibited a U-shaped pattern, even after stratification by age, so the optimal BMI cut-off point was different in the elderly and middle-aged groups.

For the middle-aged group, the optimal BMI cut-off was 24 kg/m², whereas for the elderly group, it was 27 kg/m². Consequently, personalized treatment plans can be developed in clinical settings by tailoring recommendations to different age groups.

The researchers also found a positive relationship between both waist circumference and waist-to-height ratio and the risk of cardiovascular death. As waist circumference increased, the risk of cardiovascular death also showed a corresponding rise.

When the study population was divided into older and middle-aged categories, this upward trend remained consistent. Similar patterns were observed for the waist-to-height ratio. However, no significant BMI cut-off point was identified.

"Importantly, we demonstrate that optimal BMI for people with type 2 diabetes varies by age, independent of traditional cardiometabolic risk factors," says lead author Dr. Shaoyong Xu from Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China.

"Our findings suggest that for older individuals who are moderately overweight but not obese, maintaining rather than losing weight may be a more practical way of reducing their risk of dying from cardiovascular disease."

He adds, "Our findings also indicate that adiposity may offer some protection against fatal diseases to some extent. The possible biological mechanisms that explain this 'obesity survival paradox' in elderly people may be associated with a lower rate of bone mass loss, which reduces the effects of fall and trauma episodes, and greater nutritional reserves to accommodate periods of acute stress."

The authors say that in the future, measures of central obesity, such as waist circumference , would be used to further refine the risk.

This is an observational study , and as such, can't establish cause. And the researchers acknowledge various limitations to their findings, including small numbers of cardiovascular deaths and no information on type of cardiovascular disease or specific treatments.

They also note that most of the UK Biobank study participants are white, so the findings might not apply to people of other ethnic backgrounds. Also, the nature of the cohort study may create potential classification errors that could partially affect the conclusions, because anthropometric measurements were only assessed at the start of the study, and body weight may change during the follow-up period.

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March 26, 2024

Weight-loss surgery yields long-term benefits for type 2 diabetes

At a glance.

  • Bariatric surgery helped people with type 2 diabetes better control their blood glucose years later compared to medical and lifestyle interventions.
  • The findings support the use of weight-reduction surgery for treating type 2 diabetes in people with obesity.

Doctor talking with an overweight patient in a wheelchair.

Diabetes affects more than 38 million people nationwide. It occurs when levels of blood sugar, or glucose, are too high. Over time, excess blood glucose can lead to serious health problems, such as heart disease, stroke, nerve damage, and eye disease.

Some people with type 2 diabetes—the most common type—keep blood glucose in check by making lifestyle changes, including diet and exercise. Medications can also help to control blood glucose. Clinical trials over the past few decades have found that bariatric surgery, or weight-control surgery, can also help control type 2 diabetes. But it had been unclear which of these interventions might have better long-term outcomes.

To learn more, NIH-supported researchers at four institutions drew on data collected from four previous clinical trials conducted between May 2007 and August 2013. These trials were single-center studies comparing the effectiveness of bariatric surgeries to medical and lifestyle interventions. The surgeries included sleeve gastrectomy, Roux-en-Y gastric bypass, and adjustable gastric banding. The medical and lifestyle interventions included nutrition counseling, self-monitoring of glucose, and medication to treat diabetes. By pooling data from the four clinical trials, the researchers had a larger, more diverse data set to analyze. Follow-up data was collected 7 to 12 years after the start of the original trials, through July 2022.

In total, 262 study participants agreed to long-term follow-up. All were between ages 18 and 65. Each had overweight or obesity, as measured by body mass index (BMI). Nearly 70% of participants were women, 31% were Black, and 67% were white. More than half (166) were randomized to receive bariatric surgery. The remaining 96 received diabetes medications plus lifestyle interventions known to be effective for weight loss. Results appeared in the Journal of the American Medical Association on February 27, 2024.

The researchers found that, seven years after the original intervention, 54% of those in the surgery group had an A1c measurement less than 7%. A1c is a blood test that measures a person’s average blood sugar levels over the previous two or three months. In contrast, only 27% of those in the medical/lifestyle group had similar A1c values.

In addition, 18% of those in the surgery group no longer had signs or symptoms of diabetes by year seven, compared to 6% in the medical/lifestyle group. The surgery group also had an average weight loss of 20%, compared to 8% in the other group. The differences between groups remained significant at 12 years.

No differences in major side effects were detected. The surgery group did have a higher number of fractures, anemia, low iron, and gastrointestinal events. These might have been due to greater weight loss and associated nutritional deficiencies. Sleeve gastrectomy and Roux-en-Y gastric bypass were both better than adjustable gastric banding at reducing A1c levels.

The surgeries appeared to be beneficial even among those with lower BMI scores, between 27 and 34 at study enrollment. That BMI range includes overweight and low-range obesity. Such people had typically been excluded from receiving bariatric surgery for diabetes. But this finding aligns with other recent data that support the use of surgery for some people with a BMI less than 35.

“These results show that people with overweight or obesity and type 2 diabetes can make long-term improvements in their health and change the trajectory of their diabetes through surgery,” says Dr. Jean Lawrence of NIH’s National Institute of Diabetes and Digestive and Kidney Diseases.

Related Links

  • Intermittent Fasting for Weight Loss in People With Type 2 Diabetes
  • Popular Diabetes Drugs Compared in Large Trial
  • Diabetes Control Worsened Over the Past Decade
  • Weight-loss (Metabolic & Bariatric) Surgery
  • Type 2 Diabetes
  • Calculate Your Body Mass Index

References:  Long-Term Outcomes of Medical Management vs Bariatric Surgery in Type 2 Diabetes. Courcoulas AP, Patti ME, Hu B, Arterburn DE, Simonson DC, Gourash WF, Jakicic JM, Vernon AH, Beck GJ, Schauer PR, Kashyap SR, Aminian A, Cummings DE, Kirwan JP. JAMA . 2024 Feb 27;331(8):654-664. doi: 10.1001/jama.2024.0318. PMID: 38411644.

Funding:  NIH’s National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).

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Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore

Thao p phan.

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

Leontine Alkema

2 Department of Statistics and Applied Probability, Faculty of Science, National University of Singapore, Singapore

E Shyong Tai

3 Division of Endocrinology, National University Hospital and National University Health System, Singapore

4 Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore

Kristin H X Tan

Wei-yen lim.

5 Ministry of Health, Singapore

Yik Ying Teo

6 Life Sciences Institute, National University of Singapore, Singapore

Ching-Yu Cheng

7 Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore

8 Singapore Eye Research Institute, Singapore

9 Center for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore

Tien Yin Wong

Kee seng chia, alex r cook.

10 Program in Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore

11 Yale-NUS College, Singapore

Associated Data

Singapore is a microcosm of Asia as a whole, and its rapidly ageing, increasingly sedentary population heralds the chronic health problems other Asian countries are starting to face and will likely face in the decades ahead. Forecasting the changing burden of chronic diseases such as type 2 diabetes in Singapore is vital to plan the resources needed and motivate preventive efforts.

This paper describes an individual-level simulation model that uses evidence synthesis from multiple data streams—national statistics, national health surveys, and four cohort studies, and known risk factors—aging, obesity, ethnicity, and genetics—to forecast the prevalence of type 2 diabetes in Singapore. This comprises submodels for mortality, fertility, migration, body mass index trajectories, genetics, and workforce participation, parameterized using Markov chain Monte Carlo methods, and permits forecasts by ethnicity and employment status.

We forecast that the obesity prevalence will quadruple from 4.3% in 1990 to 15.9% in 2050, while the prevalence of type 2 diabetes (diagnosed and undiagnosed) among Singapore adults aged 18–69 will double from 7.3% in 1990 to 15% in 2050, that ethnic Indians and Malays will bear a disproportionate burden compared with the Chinese majority, and that the number of patients with diabetes in the workforce will grow markedly.


If the recent rise in obesity prevalence continues, the lifetime risk of type 2 diabetes in Singapore will be one in two by 2050 with concomitant implications for greater healthcare expenditure, productivity losses, and the targeting of health promotion programmes.

Key messages

  • Asians in general, and Singaporeans in particular, are increasingly at risk of diseases such as type 2 diabetes that are associated with modern, high calorie, sedentary lifestyles.
  • Forecasts of the prevalence of diseases such as type 2 diabetes require models that quantify and predict the changing dynamics of the drivers of the epidemic, including population age structure and evolving obesity levels.
  • Using evidence synthesis, Bayesian inference, and individual-based modeling, we have developed forecasts of type 2 diabetes prevalence and incidence to 2050 in different segments of the population of Singapore, with ethnic minorities bearing a disproportionate burden, a marked rise in the incidence of type 2 diabetes in the workforce, and a rise in the lifetime risk to one in two.

––Yogi Berra


Type 2 diabetes mellitus (T2DM) looms large over Asia. Asians, especially South Asians, are predisposed toward T2DM to a greater extent than ethnic Europeans. 1 2 At even greater risk are ethnic Asians living in Europe or the Americas, where this predisposition is accentuated by the adoption of modern, urban lifestyles rich in processed, energy-dense foods and reduced physical activity. Examples abound: rates of T2DM are 1.3–4.8 times higher among American Asians and Pacific Islanders than in Europeans living in the Americas, 3–6 2–3 times higher among American Japanese than in Japanese living in Japan, 7–9 and 1.9–6 times higher among South Asians versus Europeans living in Europe. 10 11 India (51 million) and China (43 million) already have more people with type 2 diabetes than the USA (27 million), 12 but as lifestyles and diets in rapidly developing Asia become increasingly urbanized, it therefore must be expected that the burden of T2DM will continue to grow in the most populous continent.

Singapore is a microcosm of Asia. Three broad ethnicities, corresponding to the three major population centers in Asia, are represented in the city-state: East Asians, in the Chinese majority, South East Asians, via the Malay, and South Asians of mostly Indian and Sri Lankan descent. Over the past few decades, these groups have been exposed to significant changes in lifestyle, diet, and other environmental influences that are typical of a high-income society, changes that are reflected in the doubling of the prevalence of T2DM from 5% in the 1980s 13 to 11% in 2010. 14 Rapidly ageing, increasingly sedentary, Singapore presages the problems other Asian countries will face in the decades ahead.

Since T2DM is one of many competing public health issues that will accompany the ageing of Singapore, as in Asia, it is vital to be able to forecast the future burden of T2DM to facilitate rational planning of public health campaigns. To predict involves positing a model that encapsulates epidemiological and medical subject-area expertise on the main drivers of T2DM at the individual and population levels. Rigorous subsequent parameterization of the model ensures its relevance to the population to which it is applied. The degree of complexity of the model depends on the objective of the analysis and the data available: neither too simplistic, lest it fail in extrapolation to scenarios it was not validated for; nor more complex than is needed to meet those objectives. Methods used in other settings to forecast the evolving burden of chronic diseases include microsimulation models positing assumptions on future obesity and physical activity trends, 15 extrapolating linear regressions of the prevalence of overweight and adjusting geographic distribution using deprivation indices, 16 forecasting the changing demography of a country with or without increasing incidence, 17–20 and modeling body mass index (BMI) and its impact on development of T2DM and related complications. 21 22

One of the most challenging issues in developing a model for a future public health phenomenon is that the health of a population is never in a stable equilibrium. Although the observed rise in T2DM prevalence from 8.6% (95% CI 7.7% to 9.6%) in 1992 to 11.3% (95% CI 10.3% to 12.3%) in 2010 in Singapore can be attributed to ageing, as the age-specific prevalence has remained relatively static since the 1990s, 14 it would be misleading to forecast the future prevalence of T2DM by applying the historic age-specific prevalence of T2DM to a projected age distribution at some future time point—for the age-specific prevalence of obesity , and overweight, another important risk factor of T2DM, 23 have risen substantially in most demographic segments over this time period. 24 This rise foreshadows an increase in the age-specific prevalence of T2DM, as the increasingly obese young of today become the increasingly diabetic old of tomorrow. Predictions must hence incorporate ageing and secular trends in obesity, reflecting changes in diet and physical activity, as otherwise they may severely understate the future burden. Furthermore, evidence of a genetic contribution to T2DM from familial aggregation (the risk of T2DM increases twofold to fivefold for individuals having a family history, 25 26 while heritability of T2DM—the proportion of phenotypic variance attributed to genetic factors—has been estimated at approximately 26% in a Danish population-based twin study 27 ) suggests that the effect of genetics should also be incorporated.

This paper describes a demographic, epidemiological model of Singapore and its use in forecasting the total prevalence of T2DM (diagnosed and undiagnosed) to 2050. The model is an individual-based model which represents each resident in the city-state, past (from 1990) and future (to 2050), thereby facilitating the incorporation of obesity trends, both secular and over an individual's life span. The model incorporates demographic processes including the mass migration Singapore has experienced over the past two decades, submodels for the evolution of each individual's yearly BMI and genetic risk of T2DM, and a T2DM onset submodel, and data from national statistics, nationally representative cross-sectional surveys, longitudinal studies, molecular epidemiological cohort studies, and the literature, analyzed using Bayesian statistical methods.

The model contains submodels as depicted in figure 1 and summarized below. Mathematical details are provided in the online supplementary methods.

An external file that holds a picture, illustration, etc.
Object name is bmjdrc2013000012f01.jpg

Overview of model structure. Boxes represent submodels; arrows indicate direction of information flow between submodels. BMI, body mass index; T2DM, type 2 diabetes mellitus.

Demographic model

The model is incremented in units of 1 year and tracks the resident population of Singapore from 1990 to 2050. Individuals die according to mortality rates that vary by age, year of birth, gender, and for the three main ethnicities of Singapore—Chinese, Indian, Malay—and a fourth category aggregating others (mostly of mixed ethnicity; other South East Asians; and Europeans 28 ).

The mortality rate is parameterized as a smooth spline function stratified by gender with proportional hazards for other effects, including T2DM status.

Fertility rates differ for each age, year of birth, and ethnicity, with ethnicity assumed to be inherited maternally. The fertility rate is modeled as a Gaussian function with parameters that are functions of demographic factors.

Migration (outward and, especially, inward: the population grew from 3 million in 1990 to 5.1 million in 2010) is represented by a baseline migrant age profile curve, a spline curve stratified by ethnicity and gender, with a random effect applied to each year to reflect the economic situation and government policy changes. The parameters of these models are estimated from national statistics released by the Singapore Department of Statistics, in particular the 1990, 2000, and 2010 censuses of population, the annual yearbook of statistics, which conveyed information on the size, age structure, gender, and ethnic composition over time, and life tables by gender and fertility and mortality rates. These rates and statistics (except the censuses and life tables) had for the most part a resolution only to 5-year age bands—coarser for older ages—and no information on migration. Crude birth rates and death rates by ethnicity during the period 1990–2010 were obtained from the Report on the Registration of Births and Deaths.

A three-state Markov model describes how the resident population moves between work, unemployment, and out of the workforce, conservatively assuming no correlation between T2DM and workforce participation, and no changes in retirement ages. In this, weekly transition probabilities between states vary by age, calendar year, and gender, and are estimated from the annual Report on the Labour Force in Singapore, which provides data on the resident population by 5-year age groups, gender, and workforce status, and unemployed resident population by 10-year age groups and duration of unemployment (in weeks), and the General Household Survey in 1995 and 2005, which provide data by 5-year age groups, gender, and workforce status. We obtained estimates of weekly age-dependent transition probabilities via Markov chain Monte Carlo (MCMC) methods. 29 30

BMI model and data

We developed an individual, hierarchical model of BMI trajectories over an adult life span, stratified by gender and ethnicity. In this, an individual's BMI over time is described by Gaussian fluctuations around a sequence of connected lines, with joints at age 35, 55, and 75. Each individual has a different starting BMI (at age 18) and three BMI gradients, which are assumed to come from a multivariate normal distribution whose hyperparametric mean and covariance are common to all individuals of that gender and ethnic group. These hyperparameters are estimated using (1) longitudinal data from the Singapore Prospective Study programme (SP2), which contains BMI measurements and T2DM status at two of three time points (the 1992 or 1998 National Health Survey (NHS) and a follow-up visit around 2005), and (2) aggregate data from the 2004 and 2010 NHSs on the proportions of four BMI categories (underweight, normal weight, overweight, and obese) within age bands (18–29, 30–39, 40–49, 50–59, 60–69) and gender/ethnicity groups.

Genetic risk factor model and data

From a combined list of 44 single-nucleotide polymorphisms (SNPs) previously reported to be associated with T2DM in an Asian population, 31–33 association analysis between the SNPs and T2DM was performed using additive logistic regression on SNPTEST software 34 in three cohort studies—the Singapore Chinese Eye Study (SCES, with 302 people with type 2 diabetes and 1089 without), the Singapore Malay Eye Study (SiMES, with 794 patients with type 2 diabetes and 1420 non-diabetes), and the Singapore Indian Eye Study (SINDI, with 977 people with type 2 diabetes and 1169 without). Fourteen SNPs were collectively selected for the p value threshold of 0.05 in at least one cohort study representing one major ethnic group in Singapore (see online supplementary table S1). To account for heterogeneous genetic risks within ethnic groups, the joint distribution of 14 SNPs associated with T2DM in each of the three main ethnicities of Singapore was determined from the corresponding cohort study. Assuming representativeness of the cohorts and no gender bias in the distributions of the associated risk alleles, the frequencies of all 16 384 allele combinations of 14 SNPs were determined within these groups. A point estimate of the odds ratio (OR) for each SNP from a meta-analysis 35 was combined with these frequencies to determine the distribution of ORs for T2DM for each ethnicity. As the distribution of ORs conditional on ethnicity was approximately log normal, we derived its mean and SD by weighing log ORs with associated allele frequencies. To prevent double counting the effect of ethnicity on T2DM incidence and genetic risks, we standardized the ORs such that the weighted mean OR within each ethnic group was 1 (see online supplementary figure S1). In the simulation model, for people belonging to the three main ethnicities, individuals’ genetic risks, which were modeled to be conditionally independent of their BMI trajectories given ethnicity, were selected randomly from the appropriate distribution of ORs. For people belonging to other ethnic groups, the distribution of ORs of the Chinese majority was used.

Type 2 diabetes onset model and data

Using the same longitudinal data as in the BMI model, we generated a single putative BMI trajectory that matches the observed data for each individual using importance sampling. This was then used together with age, gender, and ethnic group within a logistic model for T2DM incidence. The cumulative probability of developing T2DM between the two observation times was derived by summation and used to generate the likelihood function, which permitted estimation using MCMC. In the simulation model, the probability of progressing from a non-diabetic state to T2DM was calculated annually conditional on the individual's demographics and BMI and genetic risk, with the effects assumed to operate multiplicatively in the ORs.

All participants provided written informed consent.

Sensitivity analysis

We also developed a model in which BMI and genetics were excluded as risk factors and the risk of getting T2DM was a function of age, ethnicity, and gender only. This model is described in the online supplementary methods.

All statistical analyses were performed in R V.3.0.0 36 or JAGS V.3.1.0 37 38 using customized scripts that took around 24 h to run on a desktop for each model and demographic group. All graphics were created using the grid package. 39 Simulations were run in C++ with individuals represented as objects, linked to their mothers, with attached static and dynamic variables. The simulation was initialized using the demographic structure described in the 1990 census, with individuals added to the population when their mothers gave birth or when they immigrated to Singapore. Multiple runs using different random number seeds and parameters, drawn from the posterior distribution to account for parametric uncertainty, were used to build up a Monte Carlo sample, with each simulated population queried to output characteristics, such as the number of people with type 2 diabetes within any age range at any time. The C++ code was compiled using the GCC compiler, and runs, covering the time horizon 1990–2050 and around 6.25 million individuals, took an average of 3 min for one whole run.

Incidence of type 2 diabetes

Incidence rates were estimated and projected from the fitted model by extracting new, potentially undiagnosed cases of T2DM among various demographic segments. Crude incidence rates, past and future, are tabulated in table 1 by gender and ethnicity. Incidence rates are expected to double over the period 1990–2050 for all the demographic groupings considered. Among the Chinese, the incidence is expected to rise from 5 (95% prediction interval 4–5) per 1000 woman-years to 9 (7–10), or 6 (5–6) per 1000 man-years to 12 (10–13), over these six decades. For Malays, the rise is steeper (7 (6–8) to 14 (13–16) among women or to 17 (15–18) in men), and for Indians, steeper still, with an annual incidence of 17 (16–19) to 19 (17–21) per 1000-person years by 2050, from 8 (7–10) to 10 (9–12) in the 1990s.

Table 1

Modeled and forecast crude type 2 diabetes incidence rates, per 1000 person-years

Numbers in parentheses are 95% prediction intervals.

Prevalence of type 2 diabetes

The total prevalence of T2DM (diagnosed and undiagnosed) among Singapore adults (age 18–69) is projected to rise from 7.3% (6.8–8%) in 1990 to 15% (13.8–16.2%) in 2050 ( figure 3 B). Modeled past and projected future age-specific prevalence rates are depicted in figure 2 . The prevalence was generally markedly higher in Indians and Malays than Chinese Singaporeans, with Malays and Indians having a risk profile roughly the same as a Chinese 10 years their senior. Although prevalence among female Chinese sexagenarians is projected to stay relatively constant, rates are expected to grow substantially in other groups: by 2050, we expect 35% (31–39%) of Chinese men aged 60–69 having T2DM, and around half of the Malays and Indians of that age group ( figure 2 ). A moderate risk in prevalence among young adults is forecast (see online supplementary table S2).

An external file that holds a picture, illustration, etc.
Object name is bmjdrc2013000012f02.jpg

Age-specific, gender-specific, and ethnicity-specific prevalence estimates and forecasts of (diagnosed and undiagnosed) type 2 diabetes. Model forecasts are presented as bars with 95% prediction intervals. Data are indicated by dots with 95% empirical CIs.

An external file that holds a picture, illustration, etc.
Object name is bmjdrc2013000012f03.jpg

Obesity and type 2 diabetes forecasts. Top: forecast prevalence of obesity and overweight in adults (A), forecast prevalence of type 2 diabetes among working age adults (B) and number of patients with type 2 diabetes in the workforce (C). Means and 95% prediction intervals are plotted. For prevalence, point estimates from the National Health Surveys are overlaid. Bottom (D–G): modeled age pyramids with patients with type 2 diabetes and diabetic workers overlaid. Red and blue bars indicate women and men, respectively; black bars indicate patients with type 2 diabetes (not in the workforce) of both genders; and green bars indicate working diabetics. The + symbol indicates data from the censuses of 2000 and 2010.

Age and overweight

The projected rise in the total prevalence of T2DM in Singapore is driven by two factors: the modeled ageing and fattening of the population. The age pyramid ( figure 3 D–G) is predicted to become increasingly top heavy, with the proportion of the population under age 20 falling from 25.2% (2010) to 15.9% (2050), and the proportion over the age of 60 soaring from 13.3% to 31.9% over the same time frame. The effect of this rise in the prevalence of the main risk factor (advanced years) is compounded by a dramatic rise in obesity and overweight levels. The fraction of the population that is obese is predicted to quadruple from 4.3% in 1990 to 15.9% by 2050, while those overweight are projected to expand in number from 24.6% in 1990 to 38.6% by 2050 ( figure 3 A). This projected increase in BMI at the population level can be attributed to all subgroups (see online supplementary figure S3–S7). The forecast rise in obesity levels is most stark for Malays and Indians (hitting 40% among Malay women aged over 40), but the large Chinese majority is also expected to see a rise in obesity levels of around 10 percentage points (see online supplementary table S3).

The confluence of these factors will, if the projections hold true, lead to a rise in the number of those in the workforce living with T2DM, a proxy for the impact of T2DM on productivity and corporate health insurance plans, from 97 600 (89 800–106 100) in 1990 to 321 600 (293 000–353 700) by 2050 ( figure 3 C). The type 2 diabetic population is predicted to increase from 358 500 (333 900–386 100) in 2010 to 673 200 (624 700–727 400) in 2030 and to 909 300 (839 700–986 900) in 2050.

Model validation

Demographic structure.

The model is seeded with the 1990 census. It reproduces the 2010 census very accurately ( figure 3 E), save for a slight underprediction of the number of women aged 25–40, which we attribute to migration.

BMI trajectories

The distribution of each pair of BMI observations for the SP2 participants agrees well with the posterior predictive distribution of trajectories within each ethnic group and gender demographic segment (see online supplementary figure S2).

The modeled overall proportion of patients with type 2 diabetes closely corresponds to results of the NHSs, except for the outlying 2004 survey ( figure 3 B). It is not known why the 2004 NHS is so discrepant from the other NHSs. Prevalence of T2DM within age, gender, and ethnic groups is similar between the model and data ( figure 2 ), though the small sample sizes on stratification lead to unstable empirical estimates with broad uncertainty intervals, so the concordance is not perfect.

We also developed a simpler model for T2DM that does not take into account BMI changes and genetic effects, with population ageing being the main factor contributing to the increase in prevalence of T2DM in this model. Consequently, projected T2DM prevalence among Singaporean adults aged 18–69 by 2050 for the simpler model is 11.8% (11.2–12.6%), lower than in the full model (15%) even though the overall historic prevalence of the two models is quite close to each other (7.1% for the reduced model and 7.3% for the full model in 1990; see online supplementary figure S9). The reduced model for T2DM assumes that the effect of age on T2DM risk is constant over time. As a result, the lifetime risk for Singaporean adults aged 18–69 for this model does not change much over time, from 38.9% (36.3–41.9%) in 1990 to 39.2% (36.9–42.5%) in 2050. In the full model, lifetime risk for T2DM for Singaporean adults aged 18–69 is projected to rise from 34.5% (31.9–38.2%) in 1990 to 43.8% (40.8–47.5%) in 2050 as the increasing BMI trend is accounted for. An interesting observation is the gender difference in projected lifetime risk of T2DM in the two models (see online supplementary table S4). For the reduced model, women would have a marginally higher lifetime risk than men (39.9% vs 38.4% in 2050). In the full model, however, women are forecast to have a lower lifetime risk than men (lifetime risks of T2DM by 2050 are 37% in women and 51% in men). This is due to Chinese women, the largest group of women in Singapore, not experiencing the same rise in overweight as did men and other women, so that simple forecasts based on current age prevalence would substantially underestimate future prevalence of T2DM in all groups other than Chinese women.

Modeling provides a way to explore what-if scenarios quickly and cost effectively. In this paper, we use modeling to answer the question: If the recent rise in obesity levels in Singapore were maintained, what would the effect on the prevalence of T2DM be one generation from now? The answer is worrying: a rise in the overall prevalence from 1 in 13 to around 1 in 6 working age adults, a lifetime risk of around 1 in 2, and an increasing burden of T2DM in the workplace. T2DM has been estimated to reduce a worker's productivity by around a third in the USA, 40 due to disability, premature mortality, early retirement, and absenteeism, in that order, 41 while in Canada, those with T2DM were found to be between 150% and thrice as likely not to be in the labor force, and to have an income approximately 25% lower than non-diabetics. 42 Employers in Singapore will have to decide whether they should take responsibility for preventive action, such as screening or weight loss programmes, to mitigate future losses.

Singapore is an ideal test bed for public health research in Asia. Not only does its Chinese, Indian, and Malay population make it a miniature of Asia as a whole, but other countries in Asia are likely to look increasingly like Singapore, as they become increasingly developed, urbanized, sedentary, and aged. The current prevalence of T2DM in populations comparable genetically and culturally, but at an earlier stage of development, is markedly lower (in mainland Chinese sexagenarians 19% 43 vs 25% in Singapore Chinese, in elderly Malays in Malaysia 21% 44 vs 37%), boding ill for the future elsewhere in Asia.

All modeling studies make some degree of simplifying assumptions. In this study, the risk of developing T2DM is determined by demographic factors, a secular trend, genetics, and current BMI, as a proxy for overweight and general ill health. The model averages over other factors that have a role include epigenetics, 45 physical activity, 46 diet, 47 family history, 48 socioeconomic status, and pregnancy. 49 The formulation as an individual-level model allows observed variability between individuals to be characterized, along with risk factors that vary dynamically over lifetimes. The genetic risk model includes 14 SNPs that are significantly associated with T2DM in the Singapore population and assumes that these have an effect independent of BMI, as none of the 14 SNPs has been reported to be associated with overweight/obesity in the Singapore setting. 50 Future work should verify this assumption and might incorporate the effect of genetic factors on BMI, for which twin studies indicate that estimated BMI heritability is 47–90%. 51 The primary data source on weight that was available to us was aggregate statistics from the NHS, which on stratification led to small demographic segments with substantial sampling variability. The model we used pools information between age groupings and from cohort studies, which we believe yields more reliable estimates. Even more reliable estimates of change might result from mining medical records, which would permit relaxation of the distributional assumptions used herein. 52 The data used to parameterize the models of BMI and T2DM were from cohort studies based on nationally representative samples of adults, overcoming the common difficulty in generalizing from cohort studies to the general population, though this means that BMI trajectories in childhood and adolescence were not modeled. With data on childhood obesity, the prevalence of which has risen globally in recent decades, 53 we would have been able to capture any recent changes in this critical age period. Future work should address this data paucity. The model assumes no interaction between overweight/T2DM and workforce participation except interactions mediated by demographics, and research is needed in the Singapore setting to elucidate whether any additional interactions are present. The T2DM model does not incorporate prediabetes, an intermediate state of T2DM when blood glucose level shows abnormalities—impaired fasting glucose or impaired glucose tolerance—but does not exceed the threshold determining T2DM. Introducing a prediabetic state into the T2DM model would stratify the non-diabetic population into low-risk and high-risk groups, enhancing the capability of the model for possible intervention evaluation. This would, however, require additional information on reversion rates from a prediabetic state to a normal state or on progression rates from a prediabetic state to T2DM in the Singapore context.

The long-term goal of this modeling project is to bring together three models: the present one, which projects the prevalence of T2DM in different subpopulations, a model of outcomes —from more complications from macrovascular diseases (eg, cardiovascular disease) and microvascular diseases (eg, kidney, nerve, and eye diseases), to healthcare expenditure and workplace absenteeism—and a model of interventions , such as healthy eating or active lifestyle programmes. Taken together, these would allow the effectiveness, and cost effectiveness, of different health promotion interventions to be assessed in silico to enhance the evidence base of public health decision making by determining how much of a reduction to levels of overweight and obesity would be needed to substantially reduce the burden of T2DM, and how much can realistically be achieved by health promotion campaigns.

Supplementary Material

Contributors: TPP developed and programmed the model, performed statistical analysis, and wrote the paper. LA developed the model, provided demographic expertise, and wrote the paper. EST provided endocrinological expertise and wrote the paper. KHXT performed statistical analysis and wrote the paper. QY performed statistical analysis and wrote the paper. WYL provided epidemiological expertise and wrote the paper. YYT, CYC, XW, and TYW contributed to genetic analysis and wrote the paper. KSC conceived the study, provided public health expertise, and wrote the paper. ARC conceived the study, supervised the study, developed the model, and wrote the paper. He is also the guarantor of this paper.

Funding: This work was supported by the Population Health Metrics and Analytics project, funded by the Ministry of Health, Singapore. ARC is also grateful for funding from the NUS Initiative to Improve Health in Asia. Grant number NIHA-2011-1-004.

Competing interests: None.

Ethics approval: The SP2, SCES, SINDI, and SiMES studies were approved by the Singapore Health Services Centralised Institutional Review Board.

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

Data sharing statement: No additional data are available.


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