data analysis and presentation of findings

To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.

If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves):

This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States.

Crosstabulation

When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.

Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.

If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this:

A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree.

Correlation

When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.

Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.

If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this:

Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong.

To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.

The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.

If we were to discuss the results of this regression in a quantitative paper, the results might look like this:

Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]

• Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper.
• Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper.
• Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis.
• Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4.
• Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Cochrane Training

Chapter 12: synthesizing and presenting findings using other methods.

Joanne E McKenzie, Sue E Brennan

Key Points:

• Meta-analysis of effect estimates has many advantages, but other synthesis methods may need to be considered in the circumstance where there is incompletely reported data in the primary studies.
• Alternative synthesis methods differ in the completeness of the data they require, the hypotheses they address, and the conclusions and recommendations that can be drawn from their findings.
• These methods provide more limited information for healthcare decision making than meta-analysis, but may be superior to a narrative description where some results are privileged above others without appropriate justification.
• Tabulation and visual display of the results should always be presented alongside any synthesis, and are especially important for transparent reporting in reviews without meta-analysis.
• Alternative synthesis and visual display methods should be planned and specified in the protocol. When writing the review, details of the synthesis methods should be described.
• Synthesis methods that involve vote counting based on statistical significance have serious limitations and are unacceptable.

Cite this chapter as: McKenzie JE, Brennan SE. Chapter 12: Synthesizing and presenting findings using other methods. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook .

12.1 Why a meta-analysis of effect estimates may not be possible

Meta-analysis of effect estimates has many potential advantages (see Chapter 10 and Chapter 11 ). However, there are circumstances where it may not be possible to undertake a meta-analysis and other statistical synthesis methods may be considered (McKenzie and Brennan 2014).

Some common reasons why it may not be possible to undertake a meta-analysis are outlined in Table 12.1.a . Legitimate reasons include limited evidence; incompletely reported outcome/effect estimates, or different effect measures used across studies; and bias in the evidence. Other commonly cited reasons for not using meta-analysis are because of too much clinical or methodological diversity, or statistical heterogeneity (Achana et al 2014). However, meta-analysis methods should be considered in these circumstances, as they may provide important insights if undertaken and interpreted appropriately.

Table 12.1.a Scenarios that may preclude meta-analysis, with possible solutions

12.2 Statistical synthesis when meta-analysis of effect estimates is not possible

A range of statistical synthesis methods are available, and these may be divided into three categories based on their preferability ( Table 12.2.a ). Preferable methods are the meta-analysis methods outlined in Chapter 10 and Chapter 11 , and are not discussed in detail here. This chapter focuses on methods that might be considered when a meta-analysis of effect estimates is not possible due to incompletely reported data in the primary studies. These methods divide into those that are ‘acceptable’ and ‘unacceptable’. The ‘acceptable’ methods differ in the data they require, the hypotheses they address, limitations around their use, and the conclusions and recommendations that can be drawn (see Section 12.2.1 ). The ‘unacceptable’ methods in common use are described (see Section 12.2.2 ), along with the reasons for why they are problematic.

Compared with meta-analysis methods, the ‘acceptable’ synthesis methods provide more limited information for healthcare decision making. However, these ‘acceptable’ methods may be superior to a narrative that describes results study by study, which comes with the risk that some studies or findings are privileged above others without appropriate justification. Further, in reviews with little or no synthesis, readers are left to make sense of the research themselves, which may result in the use of seemingly simple yet problematic synthesis methods such as vote counting based on statistical significance (see Section 12.2.2.1 ).

All methods first involve calculation of a ‘standardized metric’, followed by application of a synthesis method. In applying any of the following synthesis methods, it is important that only one outcome per study (or other independent unit, for example one comparison from a trial with multiple intervention groups) contributes to the synthesis. Chapter 9 outlines approaches for selecting an outcome when multiple have been measured. Similar to meta-analysis, sensitivity analyses can be undertaken to examine if the findings of the synthesis are robust to potentially influential decisions (see Chapter 10, Section 10.14 and Section 12.4 for examples).

Authors should report the specific methods used in lieu of meta-analysis (including approaches used for presentation and visual display), rather than stating that they have conducted a ‘narrative synthesis’ or ‘narrative summary’ without elaboration. The limitations of the chosen methods must be described, and conclusions worded with appropriate caution. The aim of reporting this detail is to make the synthesis process more transparent and reproducible, and help ensure use of appropriate methods and interpretation.

Table 12.2.a Summary of preferable and acceptable synthesis methods

12.2.1 Acceptable synthesis methods

12.2.1.1 summarizing effect estimates.

Description of method Summarizing effect estimates might be considered in the circumstance where estimates of intervention effect are available (or can be calculated), but the variances of the effects are not reported or are incorrect (and cannot be calculated from other statistics, or reasonably imputed) (Grimshaw et al 2003). Incorrect calculation of variances arises more commonly in non-standard study designs that involve clustering or matching ( Chapter 23 ). While missing variances may limit the possibility of meta-analysis, the (standardized) effects can be summarized using descriptive statistics such as the median, interquartile range, and the range. Calculating these statistics addresses the question ‘What is the range and distribution of observed effects?’

Reporting of methods and results The statistics that will be used to summarize the effects (e.g. median, interquartile range) should be reported. Box-and-whisker or bubble plots will complement reporting of the summary statistics by providing a visual display of the distribution of observed effects (Section 12.3.3 ). Tabulation of the available effect estimates will provide transparency for readers by linking the effects to the studies (Section 12.3.1 ). Limitations of the method should be acknowledged ( Table 12.2.a ).

12.2.1.2 Combining P values

Description of method Combining P values can be considered in the circumstance where there is no, or minimal, information reported beyond P values and the direction of effect; the types of outcomes and statistical tests differ across the studies; or results from non-parametric tests are reported (Borenstein et al 2009). Combining P values addresses the question ‘Is there evidence that there is an effect in at least one study?’ There are several methods available (Loughin 2004), with the method proposed by Fisher outlined here (Becker 1994).

Fisher’s method combines the P values from statistical tests across k studies using the formula:

One-sided P values are used, since these contain information about the direction of effect. However, these P values must reflect the same directional hypothesis (e.g. all testing if intervention A is more effective than intervention B). This is analogous to standardizing the direction of effects before undertaking a meta-analysis. Two-sided P values, which do not contain information about the direction, must first be converted to one-sided P values. If the effect is consistent with the directional hypothesis (e.g. intervention A is beneficial compared with B), then the one-sided P value is calculated as

In studies that do not report an exact P value but report a conventional level of significance (e.g. P<0.05), a conservative option is to use the threshold (e.g. 0.05). The P values must have been computed from statistical tests that appropriately account for the features of the design, such as clustering or matching, otherwise they will likely be incorrect.

Reporting of methods and results There are several methods for combining P values (Loughin 2004), so the chosen method should be reported, along with details of sensitivity analyses that examine if the results are sensitive to the choice of method. The results from the test should be reported alongside any available effect estimates (either individual results or meta-analysis results of a subset of studies) using text, tabulation and appropriate visual displays (Section 12.3 ). The albatross plot is likely to complement the analysis (Section 12.3.4 ). Limitations of the method should be acknowledged ( Table 12.2.a ).

12.2.1.3 Vote counting based on the direction of effect

Description of method Vote counting based on the direction of effect might be considered in the circumstance where the direction of effect is reported (with no further information), or there is no consistent effect measure or data reported across studies. The essence of vote counting is to compare the number of effects showing benefit to the number of effects showing harm for a particular outcome. However, there is wide variation in the implementation of the method due to differences in how ‘benefit’ and ‘harm’ are defined. Rules based on subjective decisions or statistical significance are problematic and should be avoided (see Section 12.2.2 ).

To undertake vote counting properly, each effect estimate is first categorized as showing benefit or harm based on the observed direction of effect alone, thereby creating a standardized binary metric. A count of the number of effects showing benefit is then compared with the number showing harm. Neither statistical significance nor the size of the effect are considered in the categorization. A sign test can be used to answer the question ‘is there any evidence of an effect?’ If there is no effect, the study effects will be distributed evenly around the null hypothesis of no difference. This is equivalent to testing if the true proportion of effects favouring the intervention (or comparator) is equal to 0.5 (Bushman and Wang 2009) (see Section 12.4.2.3 for guidance on implementing the sign test). An estimate of the proportion of effects favouring the intervention can be calculated ( p = u / n , where u = number of effects favouring the intervention, and n = number of studies) along with a confidence interval (e.g. using the Wilson or Jeffreys interval methods (Brown et al 2001)). Unless there are many studies contributing effects to the analysis, there will be large uncertainty in this estimated proportion.

Reporting of methods and results The vote counting method should be reported in the ‘Data synthesis’ section of the review. Failure to recognize vote counting as a synthesis method has led to it being applied informally (and perhaps unintentionally) to summarize results (e.g. through the use of wording such as ‘3 of 10 studies showed improvement in the outcome with intervention compared to control’; ‘most studies found’; ‘the majority of studies’; ‘few studies’ etc). In such instances, the method is rarely reported, and it may not be possible to determine whether an unacceptable (invalid) rule has been used to define benefit and harm (Section 12.2.2 ). The results from vote counting should be reported alongside any available effect estimates (either individual results or meta-analysis results of a subset of studies) using text, tabulation and appropriate visual displays (Section 12.3 ). The number of studies contributing to a synthesis based on vote counting may be larger than a meta-analysis, because only minimal statistical information (i.e. direction of effect) is required from each study to vote count. Vote counting results are used to derive the harvest and effect direction plots, although often using unacceptable methods of vote counting (see Section 12.3.5 ). Limitations of the method should be acknowledged ( Table 12.2.a ).

12.2.2 Unacceptable synthesis methods

12.2.2.1 vote counting based on statistical significance.

Conventional forms of vote counting use rules based on statistical significance and direction to categorize effects. For example, effects may be categorized into three groups: those that favour the intervention and are statistically significant (based on some predefined P value), those that favour the comparator and are statistically significant, and those that are statistically non-significant (Hedges and Vevea 1998). In a simpler formulation, effects may be categorized into two groups: those that favour the intervention and are statistically significant, and all others (Friedman 2001). Regardless of the specific formulation, when based on statistical significance, all have serious limitations and can lead to the wrong conclusion.

The conventional vote counting method fails because underpowered studies that do not rule out clinically important effects are counted as not showing benefit. Suppose, for example, the effect sizes estimated in two studies were identical. However, only one of the studies was adequately powered, and the effect in this study was statistically significant. Only this one effect (of the two identical effects) would be counted as showing ‘benefit’. Paradoxically, Hedges and Vevea showed that as the number of studies increases, the power of conventional vote counting tends to zero, except with large studies and at least moderate intervention effects (Hedges and Vevea 1998). Further, conventional vote counting suffers the same disadvantages as vote counting based on direction of effect, namely, that it does not provide information on the magnitude of effects and does not account for differences in the relative sizes of the studies.

12.2.2.2 Vote counting based on subjective rules

Subjective rules, involving a combination of direction, statistical significance and magnitude of effect, are sometimes used to categorize effects. For example, in a review examining the effectiveness of interventions for teaching quality improvement to clinicians, the authors categorized results as ‘beneficial effects’, ‘no effects’ or ‘detrimental effects’ (Boonyasai et al 2007). Categorization was based on direction of effect and statistical significance (using a predefined P value of 0.05) when available. If statistical significance was not reported, effects greater than 10% were categorized as ‘beneficial’ or ‘detrimental’, depending on their direction. These subjective rules often vary in the elements, cut-offs and algorithms used to categorize effects, and while detailed descriptions of the rules may provide a veneer of legitimacy, such rules have poor performance validity (Ioannidis et al 2008).

A further problem occurs when the rules are not described in sufficient detail for the results to be reproduced (e.g. ter Wee et al 2012, Thornicroft et al 2016). This lack of transparency does not allow determination of whether an acceptable or unacceptable vote counting method has been used (Valentine et al 2010).

12.3 Visual display and presentation of the data

Visual display and presentation of data is especially important for transparent reporting in reviews without meta-analysis, and should be considered irrespective of whether synthesis is undertaken (see Table 12.2.a for a summary of plots associated with each synthesis method). Tables and plots structure information to show patterns in the data and convey detailed information more efficiently than text. This aids interpretation and helps readers assess the veracity of the review findings.

12.3.1 Structured tabulation of results across studies

Ordering studies alphabetically by study ID is the simplest approach to tabulation; however, more information can be conveyed when studies are grouped in subpanels or ordered by a characteristic important for interpreting findings. The grouping of studies in tables should generally follow the structure of the synthesis presented in the text, which should closely reflect the review questions. This grouping should help readers identify the data on which findings are based and verify the review authors’ interpretation.

If the purpose of the table is comparative, grouping studies by any of following characteristics might be informative:

• comparisons considered in the review, or outcome domains (according to the structure of the synthesis);
• study characteristics that may reveal patterns in the data, for example potential effect modifiers including population subgroups, settings or intervention components.

If the purpose of the table is complete and transparent reporting of data, then ordering the studies to increase the prominence of the most relevant and trustworthy evidence should be considered. Possibilities include:

• certainty of the evidence (synthesized result or individual studies if no synthesis);
• risk of bias, study size or study design characteristics; and
• characteristics that determine how directly a study addresses the review question, for example relevance and validity of the outcome measures.

One disadvantage of grouping by study characteristics is that it can be harder to locate specific studies than when tables are ordered by study ID alone, for example when cross-referencing between the text and tables. Ordering by study ID within categories may partly address this.

The value of standardizing intervention and outcome labels is discussed in Chapter 3, Section 3.2.2 and Section 3.2.4 ), while the importance and methods for standardizing effect estimates is described in Chapter 6 . These practices can aid readers’ interpretation of tabulated data, especially when the purpose of a table is comparative.

12.3.2 Forest plots

Forest plots and methods for preparing them are described elsewhere ( Chapter 10, Section 10.2 ). Some mention is warranted here of their importance for displaying study results when meta-analysis is not undertaken (i.e. without the summary diamond). Forest plots can aid interpretation of individual study results and convey overall patterns in the data, especially when studies are ordered by a characteristic important for interpreting results (e.g. dose and effect size, sample size). Similarly, grouping studies in subpanels based on characteristics thought to modify effects, such as population subgroups, variants of an intervention, or risk of bias, may help explore and explain differences across studies (Schriger et al 2010). These approaches to ordering provide important techniques for informally exploring heterogeneity in reviews without meta-analysis, and should be considered in preference to alphabetical ordering by study ID alone (Schriger et al 2010).

12.3.3 Box-and-whisker plots and bubble plots

Box-and-whisker plots (see Figure 12.4.a , Panel A) provide a visual display of the distribution of effect estimates (Section 12.2.1.1 ). The plot conventionally depicts five values. The upper and lower limits (or ‘hinges’) of the box, represent the 75th and 25th percentiles, respectively. The line within the box represents the 50th percentile (median), and the whiskers represent the extreme values (McGill et al 1978). Multiple box plots can be juxtaposed, providing a visual comparison of the distributions of effect estimates (Schriger et al 2006). For example, in a review examining the effects of audit and feedback on professional practice, the format of the feedback (verbal, written, both verbal and written) was hypothesized to be an effect modifier (Ivers et al 2012). Box-and-whisker plots of the risk differences were presented separately by the format of feedback, to allow visual comparison of the impact of format on the distribution of effects. When presenting multiple box-and-whisker plots, the width of the box can be varied to indicate the number of studies contributing to each. The plot’s common usage facilitates rapid and correct interpretation by readers (Schriger et al 2010). The individual studies contributing to the plot are not identified (as in a forest plot), however, and the plot is not appropriate when there are few studies (Schriger et al 2006).

A bubble plot (see Figure 12.4.a , Panel B) can also be used to provide a visual display of the distribution of effects, and is more suited than the box-and-whisker plot when there are few studies (Schriger et al 2006). The plot is a scatter plot that can display multiple dimensions through the location, size and colour of the bubbles. In a review examining the effects of educational outreach visits on professional practice, a bubble plot was used to examine visually whether the distribution of effects was modified by the targeted behaviour (O’Brien et al 2007). Each bubble represented the effect size (y-axis) and whether the study targeted a prescribing or other behaviour (x-axis). The size of the bubbles reflected the number of study participants. However, different formulations of the bubble plot can display other characteristics of the data (e.g. precision, risk-of-bias assessments).

12.3.4 Albatross plot

The albatross plot (see Figure 12.4.a , Panel C) allows approximate examination of the underlying intervention effect sizes where there is minimal reporting of results within studies (Harrison et al 2017). The plot only requires a two-sided P value, sample size and direction of effect (or equivalently, a one-sided P value and a sample size) for each result. The plot is a scatter plot of the study sample sizes against two-sided P values, where the results are separated by the direction of effect. Superimposed on the plot are ‘effect size contours’ (inspiring the plot’s name). These contours are specific to the type of data (e.g. continuous, binary) and statistical methods used to calculate the P values. The contours allow interpretation of the approximate effect sizes of the studies, which would otherwise not be possible due to the limited reporting of the results. Characteristics of studies (e.g. type of study design) can be identified using different colours or symbols, allowing informal comparison of subgroups.

The plot is likely to be more inclusive of the available studies than meta-analysis, because of its minimal data requirements. However, the plot should complement the results from a statistical synthesis, ideally a meta-analysis of available effects.

12.3.5 Harvest and effect direction plots

Harvest plots (see Figure 12.4.a , Panel D) provide a visual extension of vote counting results (Ogilvie et al 2008). In the plot, studies based on the categorization of their effects (e.g. ‘beneficial effects’, ‘no effects’ or ‘detrimental effects’) are grouped together. Each study is represented by a bar positioned according to its categorization. The bars can be ‘visually weighted’ (by height or width) and annotated to highlight study and outcome characteristics (e.g. risk-of-bias domains, proximal or distal outcomes, study design, sample size) (Ogilvie et al 2008, Crowther et al 2011). Annotation can also be used to identify the studies. A series of plots may be combined in a matrix that displays, for example, the vote counting results from different interventions or outcome domains.

The methods papers describing harvest plots have employed vote counting based on statistical significance (Ogilvie et al 2008, Crowther et al 2011). For the reasons outlined in Section 12.2.2.1 , this can be misleading. However, an acceptable approach would be to display the results based on direction of effect.

The effect direction plot is similar in concept to the harvest plot in the sense that both display information on the direction of effects (Thomson and Thomas 2013). In the first version of the effect direction plot, the direction of effects for each outcome within a single study are displayed, while the second version displays the direction of the effects for outcome domains across studies . In this second version, an algorithm is first applied to ‘synthesize’ the directions of effect for all outcomes within a domain (e.g. outcomes ‘sleep disturbed by wheeze’, ‘wheeze limits speech’, ‘wheeze during exercise’ in the outcome domain ‘respiratory’). This algorithm is based on the proportion of effects that are in a consistent direction and statistical significance. Arrows are used to indicate the reported direction of effect (for either outcomes or outcome domains). Features such as statistical significance, study design and sample size are denoted using size and colour. While this version of the plot conveys a large amount of information, it requires further development before its use can be recommended since the algorithm underlying the plot is likely to have poor performance validity.

12.4 Worked example

The example that follows uses four scenarios to illustrate methods for presentation and synthesis when meta-analysis is not possible. The first scenario contrasts a common approach to tabulation with alternative presentations that may enhance the transparency of reporting and interpretation of findings. Subsequent scenarios show the application of the synthesis approaches outlined in preceding sections of the chapter. Box 12.4.a summarizes the review comparisons and outcomes, and decisions taken by the review authors in planning their synthesis. While the example is loosely based on an actual review, the review description, scenarios and data are fabricated for illustration.

Box 12.4.a The review

12.4.1 Scenario 1: structured reporting of effects

We first address a scenario in which review authors have decided that the tools used to measure satisfaction measured concepts that were too dissimilar across studies for synthesis to be appropriate. Setting aside three of the 15 studies that reported on the birth partner’s satisfaction with care, a structured summary of effects is sought of the remaining 12 studies. To keep the example table short, only one outcome is shown per study for each of the measurement periods (antenatal, intrapartum or postpartum).

Table 12.4.a depicts a common yet suboptimal approach to presenting results. Note two features.

• Studies are ordered by study ID, rather than grouped by characteristics that might enhance interpretation (e.g. risk of bias, study size, validity of the measures, certainty of the evidence (GRADE)).
• Data reported are as extracted from each study; effect estimates were not calculated by the review authors and, where reported, were not standardized across studies (although data were available to do both).

Table 12.4.b shows an improved presentation of the same results. In line with best practice, here effect estimates have been calculated by the review authors for all outcomes, and a common metric computed to aid interpretation (in this case an odds ratio; see Chapter 6 for guidance on conversion of statistics to the desired format). Redundant information has been removed (‘statistical test’ and ‘P value’ columns). The studies have been re-ordered, first to group outcomes by period of care (intrapartum outcomes are shown here), and then by risk of bias. This re-ordering serves two purposes. Grouping by period of care aligns with the plan to consider outcomes for each period separately and ensures the table structure matches the order in which results are described in the text. Re-ordering by risk of bias increases the prominence of studies at lowest risk of bias, focusing attention on the results that should most influence conclusions. Had the review authors determined that a synthesis would be informative, then ordering to facilitate comparison across studies would be appropriate; for example, ordering by the type of satisfaction outcome (as pre-defined in the protocol, starting with global measures of satisfaction), or the comparisons made in the studies.

The results may also be presented in a forest plot, as shown in Figure 12.4.b . In both the table and figure, studies are grouped by risk of bias to focus attention on the most trustworthy evidence. The pattern of effects across studies is immediately apparent in Figure 12.4.b and can be described efficiently without having to interpret each estimate (e.g. difference between studies at low and high risk of bias emerge), although these results should be interpreted with caution in the absence of a formal test for subgroup differences (see Chapter 10, Section 10.11 ). Only outcomes measured during the intrapartum period are displayed, although outcomes from other periods could be added, maximizing the information conveyed.

An example description of the results from Scenario 1 is provided in Box 12.4.b . It shows that describing results study by study becomes unwieldy with more than a few studies, highlighting the importance of tables and plots. It also brings into focus the risk of presenting results without any synthesis, since it seems likely that the reader will try to make sense of the results by drawing inferences across studies. Since a synthesis was considered inappropriate, GRADE was applied to individual studies and then used to prioritize the reporting of results, focusing attention on the most relevant and trustworthy evidence. An alternative might be to report results at low risk of bias, an approach analogous to limiting a meta-analysis to studies at low risk of bias. Where possible, these and other approaches to prioritizing (or ordering) results from individual studies in text and tables should be pre-specified at the protocol stage.

Table 12.4.a Scenario 1: table ordered by study ID, data as reported by study authors

* All scales operate in the same direction; higher scores indicate greater satisfaction. CI = confidence interval; MD = mean difference; OR = odds ratio; POR = proportional odds ratio; RD = risk difference; RR = risk ratio.

Table 12.4.b Scenario 1: intrapartum outcome table ordered by risk of bias, standardized effect estimates calculated for all studies

* Outcomes operate in the same direction. A higher score, or an event, indicates greater satisfaction. ** Mean difference calculated for studies reporting continuous outcomes. † For binary outcomes, odds ratios were calculated from the reported summary statistics or were directly extracted from the study. For continuous outcomes, standardized mean differences were calculated and converted to odds ratios (see Chapter 6 ). CI = confidence interval; POR = proportional odds ratio.

Figure 12.4.b Forest plot depicting standardized effect estimates (odds ratios) for satisfaction

Box 12.4.b How to describe the results from this structured summary

12.4.2 Overview of scenarios 2–4: synthesis approaches

We now address three scenarios in which review authors have decided that the outcomes reported in the 15 studies all broadly reflect satisfaction with care. While the measures were quite diverse, a synthesis is sought to help decision makers understand whether women and their birth partners were generally more satisfied with the care received in midwife-led continuity models compared with other models. The three scenarios differ according to the data available (see Table 12.4.c ), with each reflecting progressively less complete reporting of the effect estimates. The data available determine the synthesis method that can be applied.

• Scenario 2: effect estimates available without measures of precision (illustrating synthesis of summary statistics).
• Scenario 3: P values available (illustrating synthesis of P values).
• Scenario 4: directions of effect available (illustrating synthesis using vote-counting based on direction of effect).

For studies that reported multiple satisfaction outcomes, one result is selected for synthesis using the decision rules in Box 12.4.a (point 2).

Table 12.4.c Scenarios 2, 3 and 4: available data for the selected outcome from each study

* All scales operate in the same direction. Higher scores indicate greater satisfaction. ** For a particular scenario, the ‘available data’ column indicates the data that were directly reported, or were calculated from the reported statistics, in terms of: effect estimate, direction of effect, confidence interval, precise P value, or statement regarding statistical significance (either statistically significant, or not). CI = confidence interval; direction = direction of effect reported or can be calculated; MD = mean difference; NS = not statistically significant; OR = odds ratio; RD = risk difference; RoB = risk of bias; RR = risk ratio; sig. = statistically significant; SMD = standardized mean difference; Stand. = standardized.

12.4.2.1 Scenario 2: summarizing effect estimates

In Scenario 2, effect estimates are available for all outcomes. However, for most studies, a measure of variance is not reported, or cannot be calculated from the available data. We illustrate how the effect estimates may be summarized using descriptive statistics. In this scenario, it is possible to calculate odds ratios for all studies. For the continuous outcomes, this involves first calculating a standardized mean difference, and then converting this to an odds ratio ( Chapter 10, Section 10.6 ). The median odds ratio is 1.32 with an interquartile range of 1.02 to 1.53 (15 studies). Box-and-whisker plots may be used to display these results and examine informally whether the distribution of effects differs by the overall risk-of-bias assessment ( Figure 12.4.a , Panel A). However, because there are relatively few effects, a reasonable alternative would be to present bubble plots ( Figure 12.4.a , Panel B).

An example description of the results from the synthesis is provided in Box 12.4.c .

Box 12.4.c How to describe the results from this synthesis

12.4.2.2 Scenario 3: combining P values

In Scenario 3, there is minimal reporting of the data, and the type of data and statistical methods and tests vary. However, 11 of the 15 studies provide a precise P value and direction of effect, and a further two report a P value less than a threshold (<0.001) and direction. We use this scenario to illustrate a synthesis of P values. Since the reported P values are two-sided ( Table 12.4.c , column 6), they must first be converted to one-sided P values, which incorporate the direction of effect ( Table 12.4.c , column 7).

Fisher’s method for combining P values involved calculating the following statistic:

The combination of P values suggests there is strong evidence of benefit of midwife-led models of care in at least one study (P < 0.001 from a Chi 2 test, 13 studies). Restricting this analysis to those studies judged to be at an overall low risk of bias (sensitivity analysis), there is no longer evidence to reject the null hypothesis of no benefit of midwife-led model of care in any studies (P = 0.314, 3 studies). For the five studies reporting continuous satisfaction outcomes, sufficient data (precise P value, direction, total sample size) are reported to construct an albatross plot ( Figure 12.4.a , Panel C). The location of the points relative to the standardized mean difference contours indicate that the likely effects of the intervention in these studies are small.

An example description of the results from the synthesis is provided in Box 12.4.d .

Box 12.4.d How to describe the results from this synthesis

12.4.2.3 Scenario 4: vote counting based on direction of effect

In Scenario 4, there is minimal reporting of the data, and the type of effect measure (when used) varies across the studies (e.g. mean difference, proportional odds ratio). Of the 15 results, only five report data suitable for meta-analysis (effect estimate and measure of precision; Table 12.4.c , column 8), and no studies reported precise P values. We use this scenario to illustrate vote counting based on direction of effect. For each study, the effect is categorized as beneficial or harmful based on the direction of effect (indicated as a binary metric; Table 12.4.c , column 9).

Of the 15 studies, we exclude three because they do not provide information on the direction of effect, leaving 12 studies to contribute to the synthesis. Of these 12, 10 effects favour midwife-led models of care (83%). The probability of observing this result if midwife-led models of care are truly ineffective is 0.039 (from a binomial probability test, or equivalently, the sign test). The 95% confidence interval for the percentage of effects favouring midwife-led care is wide (55% to 95%).

The binomial test can be implemented using standard computer spreadsheet or statistical packages. For example, the two-sided P value from the binomial probability test presented can be obtained from Microsoft Excel by typing =2*BINOM.DIST(2, 12, 0.5, TRUE) into any cell in the spreadsheet. The syntax requires the smaller of the ‘number of effects favouring the intervention’ or ‘the number of effects favouring the control’ (here, the smaller of these counts is 2), the number of effects (here 12), and the null value (true proportion of effects favouring the intervention = 0.5). In Stata, the bitest command could be used (e.g. bitesti 12 10 0.5 ).

A harvest plot can be used to display the results ( Figure 12.4.a , Panel D), with characteristics of the studies represented using different heights and shading. A sensitivity analysis might be considered, restricting the analysis to those studies judged to be at an overall low risk of bias. However, only four studies were judged to be at a low risk of bias (of which, three favoured midwife-led models of care), precluding reasonable interpretation of the count.

An example description of the results from the synthesis is provided in Box 12.4.e .

Box 12.4.e How to describe the results from this synthesis

Figure 12.4.a Possible graphical displays of different types of data. (A) Box-and-whisker plots of odds ratios for all outcomes and separately by overall risk of bias. (B) Bubble plot of odds ratios for all outcomes and separately by the model of care. The colours of the bubbles represent the overall risk of bias judgement (green = low risk of bias; yellow = some concerns; red = high risk of bias). (C) Albatross plot of the study sample size against P values (for the five continuous outcomes in Table 12.4.c , column 6). The effect contours represent standardized mean differences. (D) Harvest plot (height depicts overall risk of bias judgement (tall = low risk of bias; medium = some concerns; short = high risk of bias), shading depicts model of care (light grey = caseload; dark grey = team), alphabet characters represent the studies)

12.5 Chapter information

Authors: Joanne E McKenzie, Sue E Brennan

Acknowledgements: Sections of this chapter build on chapter 9 of version 5.1 of the Handbook , with editors Jonathan J Deeks, Julian PT Higgins and Douglas G Altman.

We are grateful to the following for commenting helpfully on earlier drafts: Miranda Cumpston, Jamie Hartmann-Boyce, Tianjing Li, Rebecca Ryan and Hilary Thomson.

Funding: JEM is supported by an Australian National Health and Medical Research Council (NHMRC) Career Development Fellowship (1143429). SEB’s position is supported by the NHMRC Cochrane Collaboration Funding Program.

12.6 References

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Crowther M, Avenell A, MacLennan G, Mowatt G. A further use for the Harvest plot: a novel method for the presentation of data synthesis. Research Synthesis Methods 2011; 2 : 79–83.

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Grimshaw J, McAuley LM, Bero LA, Grilli R, Oxman AD, Ramsay C, Vale L, Zwarenstein M. Systematic reviews of the effectiveness of quality improvement strategies and programmes. Quality and Safety in Health Care 2003; 12 : 298–303.

Harrison S, Jones HE, Martin RM, Lewis SJ, Higgins JPT. The albatross plot: a novel graphical tool for presenting results of diversely reported studies in a systematic review. Research Synthesis Methods 2017; 8 : 281–289.

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McKenzie JE, Brennan SE. Complex reviews: methods and considerations for summarising and synthesising results in systematic reviews with complexity. Report to the Australian National Health and Medical Research Council. 2014.

O’Brien MA, Rogers S, Jamtvedt G, Oxman AD, Odgaard-Jensen J, Kristoffersen DT, Forsetlund L, Bainbridge D, Freemantle N, Davis DA, Haynes RB, Harvey EL. Educational outreach visits: effects on professional practice and health care outcomes. Cochrane Database of Systematic Reviews 2007; 4 : CD000409.

Ogilvie D, Fayter D, Petticrew M, Sowden A, Thomas S, Whitehead M, Worthy G. The harvest plot: a method for synthesising evidence about the differential effects of interventions. BMC Medical Research Methodology 2008; 8 : 8.

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Schriger DL, Sinha R, Schroter S, Liu PY, Altman DG. From submission to publication: a retrospective review of the tables and figures in a cohort of randomized controlled trials submitted to the British Medical Journal. Annals of Emergency Medicine 2006; 48 : 750–756, 756 e751–721.

Schriger DL, Altman DG, Vetter JA, Heafner T, Moher D. Forest plots in reports of systematic reviews: a cross-sectional study reviewing current practice. International Journal of Epidemiology 2010; 39 : 421–429.

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Valentine JC, Pigott TD, Rothstein HR. How many studies do you need?: a primer on statistical power for meta-analysis. Journal of Educational and Behavioral Statistics 2010; 35 : 215–247.

For permission to re-use material from the Handbook (either academic or commercial), please see here for full details.

Analysing and Interpreting Data in Your Dissertation: Making Sense of Your Findings

Introduction    

Understanding Data Analysis     

Preparing Your Data for Analysis      

Quantitative Data Analysis Techniques     

Qualitative Data Analysis Techniques     

Interpreting Your Findings     

Presenting Your Data    

Common Challenges and How to Overcome Them    

Conclusion      

Additional Resources    

Introduction

as the bridge between the raw data you collect and the conclusions you draw. This stage of your research process is vital because it transforms data into meaningful insights, allowing you to address your research questions and hypotheses comprehensively. Proper analysis and interpretation not only validate your findings but also enhance the overall quality and credibility of your dissertation.

Effective data analysis involves using appropriate statistical or qualitative techniques to examine your data systematically. Interpretation goes a step further, making sense of the results and explaining their implications in the context of your study. Together, these processes ensure that your research contributions are clear, well-founded, and significant.

This article aims to provide a comprehensive guide for analysing and interpreting data in your dissertation. It will cover essential topics such as preparing your data, applying quantitative and qualitative analysis techniques, and effectively presenting and interpreting your findings. By following this guide, you will gain tools and knowledge needed to make sense of your data, ultimately enhancing the impact and credibility of your dissertation.

Understanding Data Analysis

Definition and scope of data analysis in the context of a dissertation.

Data analysis in a dissertation involves systematically applying statistical or logical techniques to describe and evaluate data. This process transforms raw data into meaningful information, enabling researchers to draw conclusions and support their hypotheses. In a dissertation, data analysis is crucial as it directly influences the validity and reliability of your findings. The scope of data analysis includes data collection, data cleaning, statistical analysis, and interpretation of results. It encompasses both quantitative and qualitative methods, depending on the nature of the research question and the type of data collected.

Differences Between Quantitative and Qualitative Data Analysis

Quantitative data analysis involves numerical data and statistical methods to test hypotheses and identify patterns. Common techniques include descriptive statistics, inferential statistics, and various forms of regression analysis. Quantitative analysis aims to quantify variables and generalize results from a sample to a larger population. On the other hand, qualitative data analysis focuses on non-numerical data such as interviews, observations, and text. It involves identifying themes, patterns, and narratives to provide deeper insights into the research problem. Techniques include thematic analysis, content analysis, and discourse analysis. While quantitative analysis seeks to measure and predict, qualitative analysis aims to understand and interpret complex phenomena.

Importance of Choosing the Right Analysis Methods for Your Research Questions and Data Types

Choosing the right analysis methods is crucial for accurately answering your research questions and ensuring the validity of your findings. The selected methods should align with your research objectives, the nature of your data, and the overall research design. For quantitative research, statistical techniques must match the level of measurement and the distribution of your data. For qualitative research, the chosen methods should facilitate an in-depth understanding of the data. Incorrect analysis methods can lead to invalid conclusions, misinterpretation of data, and ultimately, a flawed dissertation. Therefore, a thorough understanding of both quantitative and qualitative analysis techniques is essential for any researcher.

Preparing Your Data for Analysis

Steps to clean and organize your data.

Before analysing your data, it is essential to clean and organize it to ensure accuracy and reliability. Data cleaning involves identifying and correcting errors, such as duplicates, missing values, and inconsistencies. Start by reviewing your dataset for any obvious mistakes or anomalies. Next, handle missing data by deciding whether to delete, replace, or impute missing values based on the extent and nature of the missing data. Organize your data by categorizing variables, ensuring consistent naming conventions, and creating a clear structure for your dataset.

Handling Missing Data and Outliers

Missing data and outliers can significantly impact the results of your analysis. For missing data, several strategies can be employed, such as deletion (removing incomplete cases), mean imputation (replacing missing values with the mean), or more advanced techniques like multiple imputation. The choice of method depends on the proportion and pattern of missing data. Outliers, which are extreme values that deviate from other observations, should be carefully examined. Determine whether outliers are errors or genuine observations. If they are errors, correct or remove them. If they are legitimate, consider their potential impact on your analysis and decide whether to include or exclude them.

Data Coding and Categorization for Qualitative Data

In qualitative research, data coding is a critical step that involves categorizing and labelling data to identify themes and patterns. Start by familiarizing yourself with the data through repeated readings. Next, create codes that represent key concepts and assign these codes to relevant data segments. Group similar codes into categories and identify overarching themes. This process helps in organizing qualitative data in a way that facilitates in-depth analysis and interpretation.

Tools and Software for Data Preparation and Organization

Several tools and software can assist in data preparation and organization:

SPSS: Ideal for statistical analysis and data management in quantitative research.

NVivo: Suitable for qualitative data analysis, providing tools for coding, categorization, and theme identification.

Excel: Useful for basic data cleaning, organization, and preliminary analysis.

R: An open-source software for advanced statistical analysis and data manipulation.

Python: Widely used for data cleaning, analysis, and visualization, especially with libraries like Pandas and NumPy.

Quantitative Data Analysis Techniques

Overview of common quantitative analysis methods.

Quantitative data analysis involves the application of statistical methods to test hypotheses and uncover patterns in numerical data. Common techniques include descriptive statistics, which summarize data, and inferential statistics, which allow researchers to draw conclusions and make predictions based on sample data.

Descriptive Statistics (Mean, Median, Mode, Standard Deviation)

Descriptive statistics provide a basic summary of the data. The mean (average) indicates the central tendency of the data, while the median (middle value) and mode (most frequent value) offer alternative measures of central tendency. The standard deviation measures the spread or variability of the data, indicating how much individual data points differ from the mean.

Inferential Statistics (Regression Analysis, ANOVA, t-tests)

Inferential statistics enable researchers to make inferences about a population based on sample data. Common methods include:

Regression Analysis: Examines the relationship between dependent and independent variables, predicting the impact of changes in the latter on the former.

ANOVA (Analysis of Variance): Compares the means of three or more groups to determine if there are significant differences among them.

t-tests: Compare the means of two groups to see if they are significantly different from each other.

How to Choose the Appropriate Statistical Tests for Your Data

Selecting the right statistical test depends on the nature of your research question, the type of data, and the research design. Consider the level of measurement (nominal, ordinal, interval, or ratio) and the distribution of your data. Use parametric tests (like t-tests and ANOVA) for normally distributed data with equal variances, and non-parametric tests (like Mann-Whitney U and Kruskal-Wallis) for data that do not meet these assumptions.

Step-by-Step Guide to Performing Quantitative Analysis

Define Your Hypotheses: Clearly state the null and alternative hypotheses.

Select Your Statistical Test: Choose the test that matches your data and research question.

Prepare Your Data: Ensure your data is clean and properly formatted.

Perform the Analysis: Use statistical software to conduct the analysis.

Interpret the Results: Evaluate the statistical significance and practical implications of your findings.

Using Software Tools Like SPSS, R, or Python

Software tools simplify the process of quantitative analysis:

SPSS: Offers a user-friendly interface for performing a wide range of statistical tests.

R: Provides powerful statistical packages and customization options for advanced analysis.

Python: Features libraries like Pandas and SciPy for data manipulation and statistical analysis.

Qualitative Data Analysis Techniques

Overview of common qualitative analysis methods.

Qualitative data analysis involves examining non-numerical data to identify patterns, themes, and meanings. Common methods include thematic analysis, content analysis, and discourse analysis.

Thematic Analysis

Thematic analysis is a method for identifying, analyzing, and reporting patterns (themes) within data. It involves coding the data, searching for themes, reviewing and defining these themes, and reporting the findings.

Content Analysis

Content analysis quantifies and analyzes the presence, meanings, and relationships of certain words, themes, or concepts within qualitative data. It can be used to interpret text data by systematically categorizing content.

Discourse Analysis

Discourse analysis examines how language is used in texts and contexts, exploring how language constructs meaning and how power, knowledge, and social relations are communicated.

How to Code and Categorize Qualitative Data

Initial Familiarization: Read through your data to get a sense of the content.

Generate Initial Codes: Identify and label key features of the data that are relevant to your research questions.

Search for Themes: Group codes into potential themes.

Review Themes: Refine themes by checking them against the data.

Define and Name Themes: Clearly define what each theme represents and name them accordingly.

Write Up: Summarize the findings and illustrate them with quotes from the data.

Step-by-Step Guide to Performing Qualitative Analysis

Prepare Your Data: Transcribe interviews, organize field notes, or collect relevant documents.

Familiarize Yourself with the Data: Read and re-read the data to immerse yourself in it.

Generate Codes: Systematically code interesting features of the data.

Identify Themes: Collate codes into potential themes and gather all data relevant to each theme.

Review Themes: Refine themes to ensure they accurately represent the data.

Define Themes: Define the specifics of each theme and how it relates to your research questions.

Write Up: Present the analysis in a coherent and compelling narrative.

Using Software Tools Like NVivo or ATLAS.ti

NVivo: Facilitates qualitative data analysis by allowing researchers to organize, code, and visualize data.

ATLAS.ti: Offers tools for qualitative data management and analysis, helping to uncover complex phenomena through a systematic approach.

Interpreting Your Findings

The difference between data analysis and data interpretation.

Data analysis involves processing data to uncover patterns and insights, while data interpretation involves making sense of these patterns and understanding their implications in the context of your research questions and hypotheses. Interpretation connects the numerical or thematic results of your analysis with broader theoretical and practical implications.

Strategies for Interpreting Quantitative Findings

Statistical Significance: Assess whether your findings are statistically significant using p-values and confidence intervals.

Effect Size: Evaluate the practical significance of your results by examining effect sizes.

Contextualize Findings: Relate your statistical findings to your research questions and theoretical framework.

Visualize Data: Use graphs and charts to illustrate your findings clearly.

Making Sense of Statistical Significance and Confidence Intervals

Statistical Significance: Indicates whether an observed effect is likely due to chance. A p-value below a predetermined threshold (e.g., 0.05) suggests significance.

Confidence Intervals: Provide a range within which the true population parameter is likely to fall, offering insight into the precision of your estimate.

Connecting Results to Research Questions and Hypotheses

Interpret your results in the context of your original research questions and hypotheses. Discuss whether your findings support or refute your hypotheses and how they contribute to the existing body of knowledge.

Strategies for Interpreting Qualitative Findings

Identify Patterns and Themes: Look for recurring themes and patterns in the data.

Contextualize Findings: Relate themes to your research questions and theoretical framework.

Use Exemplary Quotes: Support your interpretations with direct quotes from your data.

Reflect on the Research Process: Consider how your data collection and analysis processes might have influenced your findings.

Identifying Patterns and Themes

Systematically review your coded data to identify consistent patterns and themes. Use these patterns to build a narrative that addresses your research questions.

Drawing Meaningful Insights and Conclusions from Qualitative Data

Interpret qualitative findings by relating them to your research questions and theoretical framework. Draw conclusions that provide a deeper understanding of the research problem and suggest implications for practice, policy, or further research.

Presenting Your Data

Best practices for presenting data in your dissertation.

Effective data presentation is crucial for communicating your findings clearly and convincingly. Use tables, charts, and narratives to present your data in an accessible and engaging manner.

Creating Clear and Informative Tables and Charts

Choose the Right Type: Select tables and charts that best represent your data (e.g., bar charts for categorical data, line graphs for trends over time).

Label Clearly: Ensure all tables and charts have clear titles, labels, and legends.

Simplify: Avoid clutter and focus on presenting key information.

Writing Up Your Findings in a Coherent and Structured Manner

Organize your findings logically, following a structure that aligns with your research questions and hypotheses. Use headings and subheadings to guide readers through your analysis and interpretation.

How to Integrate Data Presentation with Interpretation

Link your data presentation directly to your interpretation. Use visual aids to illustrate key points and enhance the narrative flow.

Linking Visual Data Representations with Your Narrative

Ensure that tables, charts, and graphs are integrated into the text and discussed in detail. Explain what each visual representation shows and how it relates to your research questions.

Tips for Making Your Data Presentation Accessible and Engaging

Consistency: Use consistent formatting for tables and charts.

Clarity: Avoid technical jargon and explain complex concepts in simple terms.

Engagement: Use visual aids and narratives to keep your readers engaged.

By following these guidelines, you can ensure that your data analysis, interpretation, and presentation are thorough, accurate, and compelling, ultimately enhancing the overall quality and impact of your dissertation.

Common Challenges and How to Overcome Them

Data analysis and interpretation in a dissertation come with several challenges. Common pitfalls include misinterpreting statistical results, where researchers may draw incorrect conclusions from p-values or overlook the importance of effect sizes. Overlooking important themes in qualitative data is another frequent issue, often due to inadequate coding or failure to recognize subtle patterns.

To avoid these challenges, it's crucial to follow a few key practices:

1.     Understand Statistical Results: Ensure you have a solid grasp of statistical concepts and methods. Use resources such as textbooks, online courses, or statistical consultants to improve your understanding. Pay attention to both statistical significance and practical significance.

2.     Thorough Qualitative Analysis: Spend ample time coding qualitative data and revisit the data multiple times to identify emerging themes. Use software tools like NVivo to organize and analyze the data systematically.

3.     Seek Feedback: Regularly seek feedback from advisors, peers, or experts in your field. They can provide fresh perspectives and identify potential issues you might have missed.

4.     Validation Techniques: Employ validation techniques such as triangulation, which involves using multiple data sources or methods to cross-verify findings. This enhances the reliability and validity of your results.

By being mindful of these common challenges and proactively seeking solutions, you can significantly improve the quality and credibility of your dissertation's data analysis and interpretation.

Data analysis and interpretation are critical stages in your dissertation that transform raw data into meaningful insights, directly impacting the quality and credibility of your research. This guide has provided a comprehensive overview of the steps and techniques necessary for effectively analysing and interpreting your data.

Understanding the scope of data analysis, including the differences between quantitative and qualitative methods, is fundamental. Choosing the appropriate analysis methods that align with your research questions and data types ensures accurate and valid conclusions. Preparing your data through thorough cleaning and organization is the first step toward reliable analysis, whether dealing with missing data, outliers, or coding qualitative data.

For quantitative data, techniques such as descriptive and inferential statistics help summarize and make inferences about your data, while qualitative methods like thematic and content analysis offer deep insights into non-numerical data. Using the right software tools, such as SPSS, NVivo, R, and Python, can significantly streamline and enhance your analysis process.

Interpreting your findings involves connecting your analysis to your research questions and hypotheses, making sense of statistical significance, and drawing meaningful conclusions from qualitative data. Effective presentation of your data, through clear tables, charts, and well-structured narratives, ensures that your findings are communicated clearly and compellingly.

Common challenges in data analysis and interpretation, such as misinterpreting statistical results or overlooking themes in qualitative data, can be mitigated by seeking feedback, understanding statistical concepts, and using validation techniques like triangulation.

By following these best practices and utilizing the tools and techniques discussed, you can enhance the rigor and impact of your dissertation, making a significant contribution to your field of study. Remember, the thorough and thoughtful analysis and interpretation of your data are what ultimately make your research findings credible and valuable.

Additional Resources

To further enhance your understanding and skills in writing a dissertation methodology, consider exploring the following resources:

Books and Guides:

"Research Design: Qualitative, Quantitative, and Mixed Methods Approaches" by John W. Creswell and J. David Creswell : This book provides a comprehensive overview of various research design methodologies and their applications.

"Data Analysis Using Regression and Multilevel/Hierarchical Models" by Andrew Gelman and Jennifer Hill : A detailed guide to advanced statistical techniques, particularly useful for quantitative researchers.

"Qualitative Data Analysis: Practical Strategies" by Patricia Bazeley : Offers practical approaches and strategies for analysing qualitative data effectively.

"SPSS for Dummies" by Keith McCormick, Jesus Salcedo, and Aaron Poh : A beginner-friendly guide that simplifies the complexities of SPSS, making statistical analysis accessible to all.

"Best Practices in Data Cleaning: How to Clean Your Data to Improve Accuracy" by Ronald D. Fricker Jr. and Mark A. Reardon: This article provides practical tips for data cleaning, a crucial step in the analysis process.

"Qualitative Data Analysis: A Practical Example" by Sarah E. Gibson: An article that walks through a real-life example of qualitative data analysis, providing insights into the process.

"The Importance of Effect Sizes in Reporting Statistical Results: Essential Details for the Researcher" by Lisa F. Smith and Thomas F. E. Smith: This article highlights the significance of effect sizes in interpreting statistical results.

Lined and Blank Notebooks: Available for purchase from Amazon , we offer a selection of lined and blank notebooks designed for students to capture all dissertation-related thoughts and research in one centralized place, ensuring that you can easily access and review your work as the project evolves.

The lined notebooks provide a structured format for detailed notetaking and organizing research questions systematically

The blank notebooks offer a free-form space ideal for sketching out ideas, diagrams, and unstructured notes.

By utilizing these resources, you can deepen your understanding of secondary research methods, enhance your research skills, and ensure your dissertation is well-supported by comprehensive and credible secondary research.

As an Amazon Associate, I may earn from qualifying purchases.

Writing Your Dissertation Hypothesis: A Comprehensive Guide for Students

Secondary research for your dissertation: a research guide.

Data Analysis, Interpretation, and Presentation Techniques: A Guide to Making Sense of Your Research Data

by Prince Kumar

Last updated: 27 February 2023

Table of Contents

Data analysis, interpretation, and presentation are crucial aspects of conducting high-quality research. Data analysis involves processing and analyzing the data to derive meaningful insights, while data interpretation involves making sense of the insights and drawing conclusions. Data presentation involves presenting the data in a clear and concise way to communicate the research findings. In this article, we will discuss the techniques for data analysis, interpretation, and presentation.

1. Data Analysis Techniques

Data analysis techniques involve processing and analyzing the data to derive meaningful insights. The choice of data analysis technique depends on the research question and objectives. Some common data analysis techniques are:

a. Descriptive Statistics

Descriptive statistics involves summarizing and describing the data using measures such as mean, median, and standard deviation.

b. Inferential Statistics

Inferential statistics involves making inferences about the population based on the sample data. This technique involves hypothesis testing, confidence intervals, and regression analysis.

c. Content Analysis

Content analysis involves analyzing the text, images, or videos to identify patterns and themes.

d. Data Mining

Data mining involves using statistical and machine learning techniques to analyze large datasets and identify patterns.

2. Data Interpretation Techniques

Data interpretation involves making sense of the insights derived from the data analysis. The choice of data interpretation technique depends on the research question and objectives. Some common data interpretation techniques are:

a. Data Visualization

Data visualization involves presenting the data in a visual format, such as charts, graphs, or tables, to communicate the insights effectively.

b. Storytelling

Storytelling involves presenting the data in a narrative format, such as a story, to make the insights more relatable and memorable.

c. Comparative Analysis

Comparative analysis involves comparing the research findings with the existing literature or benchmarks to draw conclusions.

3. Data Presentation Techniques

Data presentation involves presenting the data in a clear and concise way to communicate the research findings. The choice of data presentation technique depends on the research question and objectives. Some common data presentation techniques are:

a. Tables and Graphs

Tables and graphs are effective data presentation techniques for presenting numerical data.

b. Infographics

Infographics are effective data presentation techniques for presenting complex data in a visual and easy-to-understand format.

c. Data Storytelling

Data storytelling involves presenting the data in a narrative format to communicate the research findings effectively.

In conclusion, data analysis, interpretation, and presentation are crucial aspects of conducting high-quality research. By using the appropriate data analysis, interpretation, and presentation techniques, researchers can derive meaningful insights, make sense of the insights, and communicate the research findings effectively. By conducting high-quality data analysis, interpretation, and presentation in research, researchers can provide valuable insights into the research question and objectives.

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Syllabus – Research Methodology

01 Introduction To Research Methodology

• Meaning and objectives of Research
• Types of Research
• Research Approaches
• Significance of Research
• Research methods vs Methodology
• Research Process
• Criteria of Good Research
• Problems faced by Researchers
• Techniques Involved in defining a problem

02 Research Design

• Meaning and Need for Research Design
• Features and important concepts relating to research design
• Different Research design
• Important Experimental Designs

03 Sample Design

• Introduction to Sample design
• Censure and sample survey
• Implications of Sample design
• Steps in sampling design
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• Published: 05 July 2024

A retrospective analysis of the clinical profile and factors associated with mortality and poor hospital outcomes in adult Guillain–Barre syndrome patients

• Zinabu Derso Tewedaj 1 ,
• Dawit Kebede Huluka 2 ,
• Yabets Tesfaye Kebede 3 ,
• Abel Tezera Abebe 3 ,
• Meksud Shemsu Hussen 4 ,
• Bekri Delil Mohammed 4 &
• Leja Hamza Juhar 5  

Scientific Reports volume  14 , Article number:  15520 ( 2024 ) Cite this article

Metrics details

• Demyelinating diseases
• Diseases of the nervous system

Guillain–Barré syndrome (GBS) is an acute autoimmune polyneuropathy with substantial geographic variations in demography, antecedent events, clinical manifestations, electrophysiological sub-types, diagnostic findings, treatment modalities, and prognostic indicators. However, there is limited contemporary data on GBS patient profiles and prognostic factors from low-resource settings like Ethiopia. The objective of this study is to investigate the clinical profile, factors associated with mortality, and hospital outcomes among GBS patients admitted to Tikur Anbessa Specialized Hospital (TASH) in Addis Ababa, Ethiopia. A retrospective cross-sectional study was conducted among 60 GBS patients admitted to TASH from January 2018 to December 2022. Data on demographics, clinical features, treatments, complications, and outcomes were extracted from medical records. Bivariate and multivariate logistic regression analyses identified factors associated with mortality and poor hospital outcomes. The cohort had a mean age of 28.5 years, with 76.7% aged 14–34 years. Males comprised 61.7% of cases. Ascending paralysis (76.7%) was the predominant presentation. Absent or reduced reflexes were seen in 91.7% of patients. The most common antecedent event was gastroenteritis (26.7%), followed by upper respiratory tract infection (URTI) (15%) and vaccination (11.7%). The mean interval from symptom onset to hospital presentation was 8.77 days, and the peak symptom severity was 4.47 days. The axonal variant (75.5%) was the most common subtype, followed by the demyelinating variant (24.5%). Intravenous immunoglobulin was administered to 41.7% of patients. Respiratory failure requiring invasive mechanical ventilator (MV) support occurred in 26.7% of cases. The mortality rate was 10%, with mechanical ventilation being the only factor significantly associated with mortality (95% CI 2.067–184.858; P < 0.010). At discharge, 55% had a good outcome, and 45% had a poor outcome, according to the Hughes Functional Disability Scale (HFDS). Mechanical ventilation (AOR 0.024, 95% CI 0.001–0.607) and a GBS disability score > 3 (AOR 0.106, 95% CI 0.024–0.467) were factors significantly associated with poor hospital outcomes. GBS in this cohort primarily affected individuals of young age, commonly preceded by gastroenteritis and characterized by a high frequency of the axonal variant. Mechanical ventilation was found to be significantly linked to mortality. Alongside mechanical ventilation requirements, severe disability upon presentation emerged as a crucial determinant of poor outcomes upon discharge, underscoring the importance of early identification of high-risk patients and prompt interventions.

Introduction

Guillain–Barré syndrome (GBS) is an acute polyradiculoneuropathy characterized by immune-mediated damage to the peripheral nervous system, leading to varying degrees of motor dysfunction, sensory impairment, and autonomic instability 1 . It represents the most common cause of acute flaccid paralysis globally, exerting a substantial burden on healthcare systems due to the intensity of care required during the acute phase and the long-term rehabilitation requirements 2 , 3 . Epidemiological data from North America and Europe indicate an annual incidence of GBS ranging from 0.8 to 1.9 cases per 100,000 person-years 4 .

GBS exhibits notable variations in incidence, demographic distribution, preceding events, clinical manifestations, electrophysiological subtypes, diagnostic approaches, therapeutic interventions, and prognostic outcomes across different geographical regions 5 , 6 , 7 , 8 . These variations can be attributed to multifaceted factors. Firstly, regional differences in the prevalence and strains of infectious agents such as cytomegalovirus (CMV), Epstein-Barr virus (EBV), and Campylobacter contribute to regional discrepancies in GBS incidence rates 9 . Moreover, variations in hygiene practices across regions affect exposure to these pathogens, potentially influencing GBS development 10 . Dietary habits and nutrient deficiencies also affect disease progression 11 . Environmental factors unique to specific regions also serve as potential triggers for the onset of GBS 12 . Furthermore, genetic variations among populations influence susceptibility to GBS and disease severity 12 , 13 . In regions with limited access to advanced diagnostic tools, underdiagnosis or misdiagnosis of GBS subtypes may occur, impacting reported incidence rates 10 . Moreover, slight differences in diagnostic criteria and disease reporting practices across regions further complicate the accurate assessment of GBS burden 5 , 10 . The absence of affordable and effective treatments significantly worsens outcomes in low- and middle-income countries. Furthermore, socioeconomic factors such as poverty, inadequate infrastructure, and healthcare disparities further compound the difficulties in accessing timely and appropriate care 10 .

Most comprehensive studies investigating GBS patient profiles and outcomes originate from high-income regions, particularly North America and Europe. Consequently, there exists a need for more contemporary data on GBS from low- and middle-income countries, including Africa, with limited representation from Ethiopia, thereby impeding a comprehensive understanding of geographical variations in the disease. Moreover, existing studies from the region need to be updated, to accurately depict the current GBS landscape in Ethiopia.

This study aims to address this gap in the literature by thoroughly investigating the clinical profile and factors associated with mortality and hospital outcomes among patients diagnosed with GBS admitted to Tikur Anbessa Specialized Hospital (TASH), Ethiopia. By elucidating the contemporary epidemiological, clinical, and prognostic features of GBS in the Ethiopian context, this research endeavors to provide invaluable insights into managing and treating the condition within the local healthcare setting.

Methodology

Study design and setting.

A retrospective cross-sectional chart review study was conducted at TASH, focusing on patients admitted to the medical intensive care unit (MICU) and medical ward who were diagnosed with GBS during the period from January 1, 2018, to December 30, 2022. The inclusion criteria encompassed patients aged 14 years and older whose clinical records provided comprehensive information. Excluded from the study were individuals with missing and incomplete medical documentation. Data encompassing clinical and paraclinical variables, inclusive of sociodemographic factors, primary presenting symptoms, symptom and in-hospital stay duration, antecedent events, complications, utilized treatment modalities, mechanical ventilation requirement, and investigation outcomes such as lumbar puncture cytochemistry and nerve conduction studies, were obtained.

Patients were stratified based on GBS diagnostic certainty as per Brighton’s criteria 14 , alongside their functional status at hospital admission, assessed utilizing the Hughes Functional Disability Scale (HFDS), also known as the GBS disability score 15 , 16 (see Supplementary Table S1 ). The classification of patients' nerve conduction studies into electrophysiological variants of GBS relied on Rajabally's electrophysiological criteria following a single nerve conduction study 17 .

Operational definitions

In our study, dysautonomia is defined by the presence of blood pressure fluctuations (hypertension or hypotension), occurrences of postural hypotension (a drop of 20 mmHg in systolic blood pressure or 10 mmHg in diastolic blood pressure within 5 min of rising from a supine or seated position), and manifestations of cardiac dysrhythmias (tachycardia or bradycardia) attributable solely to autonomic nervous system dysfunction 18 , 19 . Assessment of the need for mechanical ventilator support encompassed evaluations of respiratory rate, single breath count, incapacity to lift the head, and oxygen saturation levels. A poor outcome was identified by the inability to ambulate independently, denoted by a GBS disability score of 3 or higher upon hospital discharge 20 .

Ethical approval

The present research received ethical clearance from the Institution of Health Research Ethics Review Committee of Tikur Anbessa Specialized Hospital, Internal Medicine Department. The study was conducted in strict accordance with the relevant guidelines and regulations set forth by the committee. Informed consent was waived by the Institutional Health Research Ethics Review Committee of TASH due to the retrospective nature of the study, following established protocols.

Statistical analysis

We utilized SPSS version 26 for data analysis. Before analysis, data completeness was ensured. Socio-demographic characteristics were presented in tabular format, detailing both numbers and percentages. A bivariate analysis was conducted to identify independent variables at a significance level of 5%, which were subsequently incorporated into the multivariate binary logistic regression analysis. In the multivariate logistic regression, a 95% confidence interval was calculated for the adjusted odds ratio (AOR), with variables exhibiting a p-value ≤ 0.05 considered statistically associated with poor hospital outcomes among GBS patients.

Ethics approval and consent to participate

Ethical clearance for the study was obtained from the Institution of Health Research Ethics Review Committee of Tikur Anbessa Specialized Hospital, Internal Medicine Department. Officials at various levels within the study area were duly informed through official letters issued by the Internal Medicine Department. Throughout the study, strict measures were implemented to uphold the confidentiality of collected information, and the privacy of participants was meticulously maintained, ensuring compliance with ethical standards and safeguarding the rights of all involved individuals. Informed consent was waived due to the retrospective nature of the study by the Institutional Health Research Ethics Review Committee of TASH.

During the study period spanning from January 2018 to December 2022, a total of 60 GBS patient charts were thoroughly reviewed and included in the analysis for the study (see Fig.  1 ).

Flow chart showing the number of identified and excluded medical records of patients.

Sociodemographic and clinical profile of patients

The study exhibited a mean age of 28.5 ± 12.5 years, ranging from 14 to 70 years. The male-to-female ratio was calculated as 1.61, with males comprising 37 individuals (61.7%). Analysis of the age distribution revealed that most cases, comprising 46 (76.7%), fell within the age bracket of 14–34 years (see Table 1 ).

Ascending weakness emerged as the predominant presenting symptom among GBS patients, accounting for 46 (76.7%) cases. Bulbar nerve involvement (cranial nerves IX and X) resulting in dysphagia was observed in 11 patients (18.3%), while cranial nerve VII involvement causing facial palsy was noted in 6 patients (10%). Details are provided in Table 1 .

The primary antecedent event identified in this study was gastroenteritis, observed in 16 (26.7%) cases. Post-vaccination GBS was seen in 7 (11.7%) cases. Of the 7 vaccination instances, 6 pertained to anti-rabies vaccines and 1 to the COVID-19 vaccine. Notably, COVID-19 infection preceded the onset of GBS in 1 patient. Conversely, 27 (45%) patients exhibited no antecedent infection. 16 (26.7%) patients required mechanical ventilation (see Table 1 ). Additionally, six patients presented with comorbid illnesses, including 4 cases of hypertension (HTN), 1 case of dilated cardiomyopathy (DCMP), and 1 case of chronic myeloid leukemia (CML).

The mean interval from the onset of symptoms to presentation at the hospital was 8.77 (± 7.25) days, ranging from 1 to 40 days. Additionally, the mean duration from the initial symptom to peak symptomatology was 4.47 (± 4.78) days, ranging from 1 to 21 days. Hospitalization durations varied widely, ranging from 2 to 180 days, with a mean stay of 26.08 (± 31.08) days. Among the 16 patients who required mechanical ventilation (MV) support, the mean duration of MV support was 25.50 (± 18.79) days, ranging from 8 to 82 days.

Diagnosis, laboratory, and nerve conduction profile of patients

Regarding the laboratory tests, lumbar puncture was conducted on 47 patients, revealing albuminocytological dissociation in 39 cases (82.9%). Nerve conduction studies were performed on 45 individuals. The predominant GBS variant observed in this study was the axonal variant, present in 34 out of 45 cases (75.5%), followed by the demyelinating variant in 11 out of 45 cases (24.5%). Among the axonal variant cases, 28 cases (82.3%) were classified as acute motor axonal neuropathy (AMAN), while 6 cases (17.7%) were classified as acute motor and sensory axonal neuropathy (AMSAN). None of the 33 patients who underwent serological testing for HIV yielded reactive results (see Table 2 ).

The diagnostic certainty of patients in this study is depicted in Fig.  2 . A Brighton score of 2 was the most common score, observed in half of the patients, totaling 30 cases (50%). Similarly, nearly half of the patients had a Brighton score of 1, comprising 26 cases (43.3%).

Brighton criteria level of diagnostic certainty of diagnosis of GBS in TASH, Addis Ababa, Ethiopia, Jan 2018–Dec 2022 (n = 60).

Treatment and outcomes of patients

Intravenous immunoglobulin (IVIg) treatment was administered to 25 patients, accounting for 41.7% of the cohort. Additionally, one patient received steroids for a severe hospital-acquired infection, while none of the patients underwent plasmapheresis. Notably, specific treatment was not provided to 34 patients (56.7%), with only supportive care being administered.

Upon bivariate logistic regression analysis, IVIg treatment did not demonstrate an association with either death (p = 0.22) or hospital outcome (p = 0.90). Furthermore, a Mann–Whitney U test revealed that the length of hospital stays for patients receiving IVIg (mean rank = 34.9 days) was not significantly different from those not receiving IVIg (mean rank = 27.3 days), with a p-value of 0.096).

Despite a shorter duration of mechanical ventilation support observed in patients who received IVIg (mean = 20.6 days, SD = 11.7 days) compared to those who did not (mean = 30.3 days, SD = 23.7 days), this difference was not statistically significant according to t-test analysis (t(16) = − 1.041, p = 0.316).

The hospital mortality rate among patients diagnosed with GBS in this study was determined to be 10%, with 6 out of 60 patients succumbing to their condition. The causes of death were attributed to sudden cardiac arrest in 3 patients, respiratory arrest in 2 patients, and uncontrolled urosepsis in 1 patient. Notably, the requirement for mechanical ventilation support was significantly associated with death on bivariate analysis (5 out of 6 cases; 95% CI 2.067–184.858; p < 0.010).

Common complications observed in this study included infections in 19 cases (31.7%), comprising catheter-associated urinary tract infections (CA-UTI) in 12 cases, hospital-acquired pneumonia (HAP) in 10 cases, COVID-19 infection in 1 case, and thrombophlebitis in 1 case. Autonomic dysfunction was noted in 17 cases (28.3%), while bed sores were observed in 4 cases (6.7%). Additionally, tracheoesophageal fistula (TEF) occurred in 3 cases (5%), and pneumothorax was documented in 2 cases (3.3%).

Among the total of 60 patients admitted in this study, 33 patients (55%) had a good outcome at discharge, while 27 patients (45%) experienced a poor outcome, as indicated by a high Hughes score.

Factors associated with poor hospital outcome (high Hughes score) at discharge

In bivariate binary logistic regression analysis conducted at a 95% level of significance (p < 0.05), several factors were identified as significantly associated with poor hospital outcomes. These factors included respiratory failure at presentation, the requirement for MV support, autonomic dysfunction, infection, and a GBS functional disability score > 3 at admission, as delineated in Table 3 .

However, upon conducting multivariable binary logistic regression analysis, only the need for MV support and a GBS functional disability score > 3 at admission were found to be significantly associated with a poor hospital outcome at discharge (p < 0.05).

Limitations

The limitation of our study is its retrospective nature, relying on chart reviews, which are contingent upon the accuracy and completeness of documentation. Additionally, the relatively small sample size represents another limitation, diminishing the statistical power of the findings and impeding their generalizability to broader patient populations.

Sociodemographic and clinical profiles of patients

GBS affects all age groups, with prevalence generally increasing with age 21 , 22 . While common in children, it is less frequent than in adults 23 . Notably, studies show a bimodal distribution of the disease 22 , 24 . The first peak occurs between ages 15 and 34, a trend corroborated by our study. The second peak occurs after age 50. Some studies reported mean ages of 30 and 29.3 years 25 , 26 . Conversely, others have documented comparatively older mean ages, ranging from 40.69 to 52.6 years 22 , 27 , 28 , 29 , 30 . The age-related variations in GBS may stem from immune system changes 31 , declining nerve repair mechanisms 32 , and varied exposure to infectious agents 9 .

GBS is more prevalent in males than females, with ratios ranging from 1.1:1 to 1.7:1 23 , 33 . Interestingly, while girls and adolescent females are more likely to develop GBS, this trend reverses in older age groups 34 . The higher prevalence in males may be due to sex differences in immune response, but factors like sex hormones, genetics, and environmental influences also play significant roles, warranting further investigation 33 .

In our study, the predominant GBS presentation was ascending paralysis, consistent with other studies 35 , 36 , 37 . The mean interval from symptom onset to hospital presentation in Ethiopia improved from 11.2 days two decades ago to 8.77 days in our study, likely due to better awareness and healthcare access 38 , 39 . IVIg use increased to 41.7% from 6.2%, indicating improved treatment 38 . However, the mean hospital stay remains longer than in Thailand (14.2 days) and the Netherlands (17 days), reflecting ongoing healthcare challenges in Ethiopia 29 , 40 .

Albuminocytological dissociation (ACD), a hallmark diagnostic feature of GBS with reported incidences ranging from 44 to 81%, was observed in 82.9% of participants in our study 14 , 41 , 42 . This high prevalence may be due to delayed healthcare presentation, lumbar puncture procedures conducted later in disease progression, and the absence of localized variants in our cohort 43 , 44 .

In our study, the predominant variant of GBS was axonal, accounting for 75.5%. This aligns with findings from studies in northern China, India, and Mexico 26 , 45 , 46 . However, it contrasts with studies in southern China, the Balkans, Wuhan-China, Thailand, and Canada, where acute inflammatory demyelinating polyneuropathy (AIDP) is more common. 14 , 29 , 47 , 48 . The difference may be attributed to a higher prevalence of preceding gastroenteritis and a younger age distribution in our cohort, factors often associated with axonal variants.

Factors associated with mortality and poor hospital outcomes

In our study, the observed mortality rate of 10% in GBS patients aligns with the reported range (1–18%) and is higher among those requiring mechanical ventilation (12–20%) 49 . Mortality was primarily associated with the need for MV, reflecting the severity of nerve involvement and risks such as ventilator-associated pneumonia (VAP) and ventilator-induced lung injury (VILI) 50 , 51 . These complications underscore the challenges and increased mortality risks associated with MV in GBS. Additionally, a significant subset (45%) experienced poor outcomes at discharge, characterized by a GBS disability score > 3 at discharge. Factors significantly associated with a poor hospital outcome (p < 0.05) include the requirement for MV support and a GBS disability score > 3 at admission. A GBS disability score > 3 at admission can exacerbate complications like pneumonia and deep vein thrombosis (DVT) 52 , 53 . Early mobilization and proactive management strategies are crucial to mitigate these risks and improve patient recovery and outcomes.

In conclusion, this retrospective cross-sectional study provides valuable insights into the contemporary clinical profile and factors influencing the outcomes and mortality of GBS patients in Ethiopia. The study addresses a notable gap in the literature by examining this neurological condition within the context of a low-resource setting. Key findings revealed a predominance of the axonal variant of GBS, with the majority of patients presenting with ascending paralysis. Mechanical ventilation requirements and a GBS disability score > 3 at admission emerged as significant risk factors associated with poor hospital outcomes. Moreover, the need for mechanical ventilation was identified as a predictor of mortality risk. While the observed overall mortality rate aligned with global estimates, a substantial proportion of discharged patients exhibited residual functional disability. These findings underscore the complexities of managing GBS and highlight the need for early identification of high-risk patients, prompt initiation of appropriate treatments, and the implementation of comprehensive rehabilitation strategies tailored to the local healthcare environment. By elucidating the challenges and prognostic factors in the Ethiopian context, this study provides a foundation for developing targeted interventions and optimizing resource allocation to improve care delivery and mitigate the burden of GBS in similar resource-constrained settings.

Data availability

The data supporting the findings of this study will be available from the corresponding author upon reasonable request.

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Department of Internal Medicine, College of Health Science, Mekelle University, Mekelle, Ethiopia

Zinabu Derso Tewedaj

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Dawit Kebede Huluka

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Z.D.T. contributed to the study’s conception, design, initial drafting, and data analysis. Y.T.K. and B.D.M. contributed to manuscript revision, data analysis, and final intellectual content assembly. D.K.H. and L.H.J. guided the design, initial drafting, and data analysis phases. A.T.A. and M.S.H. contributed to data acquisition and proofreading.

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Understanding the challenges of identifying, supporting, and signposting patients with alcohol use disorder in secondary care hospitals, post COVID-19: a qualitative analysis from the North East and North Cumbria, England

• Katherine Jackson 1 ,
• Rosie Baker 2 ,
• Amy O’Donnell 1 ,
• Iain Loughran 3 ,
• William Hartrey 4 &
• Sarah Hulse 5  

BMC Health Services Research volume  24 , Article number:  772 ( 2024 ) Cite this article

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Alcohol-related mortality and morbidity increased during the COVID-19 pandemic in England, with people from lower-socioeconomic groups disproportionately affected. The North East and North Cumbria (NENC) region has high levels of deprivation and the highest rates of alcohol-related harm in England. Consequently, there is an urgent need for the implementation of evidence-based preventative approaches such as identifying people at risk of alcohol harm and providing them with appropriate support. Non-alcohol specialist secondary care clinicians could play a key role in delivering these interventions, but current implementation remains limited. In this study we aimed to explore current practices and challenges around identifying, supporting, and signposting patients with Alcohol Use Disorder (AUD) in secondary care hospitals in the NENC through the accounts of staff in the post COVID-19 context.

Semi-structured qualitative interviews were conducted with 30 non-alcohol specialist staff (10 doctors, 20 nurses) in eight secondary care hospitals across the NENC between June and October 2021. Data were analysed inductively and deductively to identify key codes and themes, with Normalisation Process Theory (NPT) then used to structure the findings.

Findings were grouped using the NPT domains ‘implementation contexts’ and ‘implementation mechanisms’. The following implementation contexts were identified as key factors limiting the implementation of alcohol prevention work: poverty which has been exacerbated by COVID-19 and the prioritisation of acute presentations (negotiating capacity); structural stigma (strategic intentions); and relational stigma (reframing organisational logics). Implementation mechanisms identified as barriers were: workforce knowledge and skills (cognitive participation); the perception that other departments and roles were better placed to deliver this preventative work than their own (collective action); and the perceived futility and negative feedback cycle (reflexive monitoring).

Conclusions

COVID-19, has generated additional challenges to identifying, supporting, and signposting patients with AUD in secondary care hospitals in the NENC. Our interpretation suggests that implementation contexts, in particular structural stigma and growing economic disparity, are the greatest barriers to implementation of evidence-based care in this area. Thus, while some implementation mechanisms can be addressed at a local policy and practice level via improved training and support, system-wide action is needed to enable sustained delivery of preventative alcohol work in these settings.

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Alcohol is now the leading risk factor for ill-health, early mortality, and disability amongst working age adults (aged 15 to 49) in England, and the fifth leading risk factor for ill-health across all age groups [ 1 ]. Evidence also shows significant socioeconomic inequalities in alcohol-related harm [ 2 ]. Over half of the one million hospital admissions relating to alcohol in England each year occur in the lowest three socioeconomic deciles [ 3 ] and rates of alcohol-related deaths increase with decreasing socioeconomic status [ 4 ]. In 2020 people under 75 years living in the most deprived areas in England had a 4.8 times greater likelihood of premature mortality from alcohol-related liver disease than those living in the most affluent areas [ 5 ].

Although globally, there is mixed evidence about the impact of the COVID-19 pandemic and associated social and economic restrictions on alcohol consumption [ 6 ], some studies suggest that people who were already drinking alcohol heavily increased their intake during this period [ 7 , 8 ]. Latest data for England show that the total number of deaths from conditions that were wholly attributed to alcohol rose by 20% in a single year in 2020, the largest increase on record [ 9 ]. In England, and elsewhere, it has been argued that COVID-19 should be regarded as a syndemic rather than a pandemic, as it has interacted with, and most adversely affected those in the most deprived social groups who were already experiencing the greatest inequalities [ 10 ]. In the case of alcohol use, COVID-19 may have interacted with and exacerbated the social conditions associated with alcohol use such as poverty, and loneliness and isolation [ 11 , 12 ]. Moreover, with evidence that alcohol-related harms will continue to increase, there is concern this will further widen health inequalities for those communities and regions who are likely to be most affected [ 8 , 13 ]. Thus, there is an urgent need for the implementation of evidence-based preventative strategies to reduce alcohol harm and associated inequalities, as part of a wider system level approach that includes primary, secondary and specialist care settings [ 8 ]. From here we use the term Alcohol Use Disorder (AUD), to refer to a spectrum of alcohol use from harmful to dependent alcohol use [ 14 ].

In secondary care hospitals, the UK government prioritised the implementation of Alcohol Care Teams (ACTs) in England in the National Health Service (NHS) Long Term Plan with the aim of improving care and reducing alcohol-related harms [ 15 ]. ACTs are clinician-led, multidisciplinary teams designed to support provision of integrated alcohol treatment pathways across primary, secondary and community care, and have been shown to reduce alcohol harms through reductions in avoidable bed days; readmissions; Accident and Emergency Department (AED) attendances; and ambulance call outs [ 16 ]. However, the non-specialist secondary care workforce also has an essential role in identifying and managing people at risk, using evidence-based approaches such as screening patients for excessive alcohol use and the provision brief advice [ 17 ]. Given that people may not always present primarily with alcohol-related concerns, routine screening provides an important opportunity to identify people at an earlier stage in their drinking and thereby prevent escalation of alcohol-related problems. Current NHS clinical guidance [ 18 ] requires that non-specialist healthcare staff ‘should be competent to identify harmful drinking (high-risk drinking) and alcohol dependence’ (p46). This includes having the skills to assess the need for an intervention or to provide an appropriate referral.

Despite this guidance however, evidence from prior to the pandemic suggests a range of barriers exist in the delivery and widespread implementation of alcohol prevention work by non-specialist secondary care staff. These include time pressures, limited knowledge and awareness of AUD, and a lack of training, skills, and financial support [ 19 , 20 , 21 , 22 ]. Many studies also highlight that the delivery of preventative support for AUD in secondary care is hampered by wider social cultural challenges such as the stigma of heavy alcohol use and widespread belief that problematic alcohol use is a personal responsibility and represents moral failing, leading to an emphasis on individuals to manage their own care [ 22 ]. Additionally, as AUD frequently co-occurs with other physical and mental health conditions [ 23 ], non-specialist healthcare staff can find themselves ill-equipped to provide the best standard of care for these patients who have multiple and complex needs [ 24 ]. Moreover, in England, as in other health systems, the impact of COVID-19 has created additional pressures and challenges for the whole NHS, including secondary hospitals. There are more people visiting AED than before the pandemic, with longer waiting lists for treatment and fewer hospital beds [ 25 ]. There is also record dissatisfaction amongst the workforce, with more doctors now stating they want to leave the NHS than before the pandemic [ 26 ].

Given the clear need for preventive work to reduce inequalities in alcohol-related harm and the current challenges within secondary care in a post-COVID-19 context, there is value in exploring the views of secondary care staff about supporting patients with AUD since the pandemic. Moreover, the low levels of delivery of preventative support for AUD across different sites suggest there is merit in using implementation science theory [ 27 ] to support improved explanation and understanding of this situation [ 27 , 28 ]. Normalisation Process Theory [ 29 ] has been used extensively in studies conducted in other health settings to understand and evaluate past and future implementation efforts e.g. [ 28 , 30 , 31 , 48 , 33 ], including in relation to alcohol screening and brief intervention in England and Australia [ 30 , 31 ]. NPT is a sociological implementation theory that identifies three domains as shaping the implementation of a new intervention or practice: contexts; mechanisms; and outcomes. Contexts refer to the ‘events in systems unfolding over time within and between settings in which implementation work is done.’ [ 34 ]; mechanisms are factors that ‘motivate and shape the work that people do when they participate in implementation processes’ [ 34 ]; outcomes refer to what changes occur when interventions are implemented. NPT is a conceptual tool and can be used at different stages of the research process [ 29 ]. In this study NPT has been used retrospectively during the analysis stage.

The aim of the present study is to use NPT to elucidate possible explanations for why the preventative practice of identifying, supporting, and referring patients with AUD to appropriate support is not consistently taking place in secondary care in the NENC in the post COVID-19 context. We also aim to make recommendations for areas that should be targeted by policy and practice initiatives.

Study setting

We conducted a qualitative study with health care professionals working in eight secondary care hospitals in the eight NHS Trusts in the North East and North Cumbria (NENC) region of England. The NENC experiences significant health inequalities [ 35 ], including health inequalities in alcohol-related harm. In 2021, the region had the highest reported alcohol specific and alcohol related mortality and the most alcohol related and alcohol specific admissions in England [ 36 ].

The data collection was carried out between June and October 2021. At this time, most COVID-19 restrictions had just been lifted in the NENC [ 37 ] but the impacts of COVID-19 on patients, staff and health care delivery were still ongoing.

As such, the study was planned to contribute to a baseline understanding of support for AUD in secondary care in the NENC conducted as part of a wider regional alcohol health needs assessment (2022) which would inform and direct strategic action and resource allocation in secondary care to improve alcohol-related outcomes post-COVID-19. The Principal Investigator (PI) for the study was the alcohol lead for the NENC Integrated Care System (SH), and the wider study team included representation from Primary Care, Secondary Care, Public Health, and Academia.

We used the method of qualitative semi-structured interviews to enable us to focus on issues that we wanted to explore, as well as allowing the participants flexibility to discuss the issues that were important to them [ 38 ]. We adopted a critical realist approach to the interpretation of data which purports that data can be taken as evidence for ‘real phenomena and processes’, but also recognises that the knowledge generated through qualitative research is situated and partial [ 39 ].

As part of a wider ambition to build research capacity in the study region, a novel aspect of the study design is that six junior doctors from the Gastroenterology Research and Audit through North Trainees, were trained in qualitative interview skills by a qualitative methodologist from the NIHR Applied Research Collaboration (ARC) North East and North Cumbria (NENC) and supported by members of the study team to recruit staff and carry out the interviews with secondary care clinicians.

Participants

We used a form of stratified purposive sampling [ 40 ] as the recruitment of healthcare professionals was structured to provide insights across all the NHS Trusts in the study region, a range of clinical specialities, and a range of points across the clinical pathway, with both medical and nursing staff. As such, professionals working in AED, Medical specialties, Psychiatric Liaison (PL), Gastroenterology or Surgical specialties were eligible to participate. Junior doctor interviewers or the PI contacted potential participants either by email or face-to-face and explained the purpose of the study. People who expressed an interest were then provided with the study participant information sheet and consent form. The sampling was deemed complete when the quota of participants was met for each trust.

Data collection involved semi-structured interviews based on a topic guide. The topic guide was developed by the study team and was informed by the National Institute for Clinical Excellence – Quality Standard 11 [ 41 ], which contains guidance about identifying and supporting adults and young people who may have an AUD and caring for people with alcohol-related health problems (see Additional file 1 ).

All interviews were conducted via Microsoft Teams, lasted an average of 33 min, were audio recorded and transcribed by professional transcriptionists before being fully anonymised by KJ and IL.

Data analysis involved three stages:

Stage 1: Generating descriptive codes from each area of the data set

In the first stage of analysis, once all transcripts were available, in order to generate insights that could contribute to the baseline understanding of the current situation with regards to support for AUD in secondary care, one researcher (IL) used a method of thematic analysis [ 42 ] and drew on deductive and inductive reasoning to identify descriptive codes against each focus question area of the interview topic guide. This researcher read and re-read the full data set, allowing them to identify descriptive codes across staff accounts.

Stage 2: Generating descriptive and interpretive codes and themes from across the full data set

Following this, to generate insights which went beyond the question areas of the topic guide a second researcher (KJ) familiarised themselves with the data. In contrast to Stage 1, they were less restricted by the original topic guide and through a process of constant comparison began to identify both descriptive and interpretive broad thematic topic areas and codes, across the different areas of the interviews. After the first half of the interview transcripts were coded by the researcher in this way, the broad thematic topic areas were discussed with the wider study team in two meetings. In these meetings the broad topic areas and associated coding framework were refined. This refined framework was applied to future transcripts, with flexibility to add further codes as the analysis progressed. At the end of this process, a decision was made by the team to focus the interpretation for this paper on current practices around identifying, supporting, and signposting patients with AUD in secondary care hospitals because it was felt that this focus could make a meaningful contribution to the existing literature in a post-pandemic context.

Stage 3: Applying Normalisation Process Theory retrospectively to data to generate the final interpretation

To ensure the usefulness of the findings of the current analysis to support the design and delivery of future policy and practice to reduce inequalities in alcohol related harm, academic members of the team suggested using an appropriate implementation theory, namely NPT, to guide our interpretation and understanding of data from this point in the analysis [ 34 ]. NPT had not been used in the study to this point and has been used retrospectively as a sensitising, and partial structuring, device, as seen in previous comparable research e.g. [ 28 , 43 ].

[ 29 , 34 ]. First, when applying NPT, we returned to the codes identified at Stage 2 to identify those that related to the practice of identifying, supporting, and signposting patients with AUD to explore how they may fit alongside the domains of NPT. At this point it was evident that most of the codes related to how implementation contexts and mechanisms were felt to adversely affect provision of support for patients with AUD. In contrast, we found negligible data related to the third NPT domain of outcomes (i.e. what changes occur when interventions are implemented). It was therefore agreed that applying the context and mechanisms domains could be valuable to show how contexts and mechanisms limit the implementation of the phenomena of interest. For transparency however, data not included at this stage is indicated in Additional file 2 .

Next, we separated the codes generated in Stage 2 into overarching thematic areas, these were then labelled as either contexts or mechanisms. For example, poverty and austerity were labelled as contexts, and workforce skills and knowledge were labelled as mechanisms. Details of each stage of the analysis and where the codes generated at Stage 2 of the analysis were mapped, against the NPT context and mechanism domains are shown in Additional file 2 .

Following this we endeavoured to align the thematic topic areas in each NPT domain into its associated constructs. It should be noted that our initial researcher-generated thematic areas aligned easily with three of the four NPT mechanism constructs. Conversely, as the NPT context constructs are a new addition to NPT theory, there were few practical examples of how these should be operationalised meaning it took more interpretive work to understand how our data mapped to these constructs. Through reflective discussions as a team, however, we identified that the researcher-generated themes aligned with three of the four context constructs. Table  1 below summarises the implementation context and mechanism constructs and identifies where our data do and do not map to these constructs. COVID-19 provides an overarching context to the study however as the timing of the interviews meant it penetrated almost all the data.

In keeping with the critical realist approach which recognises the situatedness of knowledge, we see researcher positionality as important to consider in the interpretation of qualitative data. Research can never be value free but, it is necessary to be explicit about where positionality might have affected the interactions [ 45 ]. The junior doctor interviewers and the PI who collected the data had experience of clinical work on the topic of the research. Indeed, the transcripts indicated that there were times when the interviewers aligned themselves or discussed their own experiences in the interviews. Some of the junior doctor interviewers recorded reflexive notes about the interviews, these were used during Stages 1 and 2 of the analysis to support interpretation, but have not been used as data. The researcher who conducted Stage 1 of the analysis has a professional background in healthcare but no direct experience of the topic area. The researcher who led the rest of the analysis has experience of carrying out research about AUD, but no clinical experience of working with people experiencing AUD. Other members of the project team have direct experience of working in hospital settings with patients experiencing AUD. Agreement amongst this heterogeneous research team about the final interpretation gives us confidence that it is grounded in the data. Moreover, this agreement amongst the research team about the final interpretation, and the congruence of findings with the existing literature on the topic of the research prior to COVID-19, gives us confidence that the insider researchers did not compromise the quality of the original empirical data.

In total, 30 staff in the study region were interviewed across the eight NHS Trusts, including 20 nurses and 10 doctors (see Table  2 ) based in five departments: AED; PL; Medical; Surgical; and Gastroenterology ( n  = 6 each). Information related to participant gender and ethnicity are not available and we have not analysed the data with these as a focus. The absence of this data also helps to preserve the anonymity of participants because the geographical region of the study is named.

Overall, participants’ accounts suggested that they were not consistently trying to identify AUD or assessing the need for intervention in the patients they worked with. Where any identification of AUD did take place, this appeared to often be through informal questioning rather than utilising formal, validated screening questionnaires. The following response was typical:

We’ll just ask about units a week. I know that there is a screening tool, there is a chart of some sort and it’s a physical thing that I think the alcohol and drugs nurses use on medications. So we don’t use that on a regular basis. As of now, there’s still a paper–based documenting system, but we don’t use that necessarily. (Participant 14 – Doctor, Trust 4, AED)

Conversely, some staff working in PL teams suggested they more commonly tried to identify AUD. Although again, validated screening questionnaires appeared to be used inconsistently:

Substance misuse is always an integral part of the assessment that we do. . We do have specific packs that we are trained to carry out our assessments to. I think in practice, we often don’t follow those verbatim and we will just do a free form assessment and substances are always part of that… .: “Do you consider that’s an issue for you, is it something that you want help with?” We’re always having those conversations. (Participant 8 – Nurse, Trust 2, PL)

Many staff’s accounts suggested they did not consistently signpost patients with identified AUD to a service that could provide an assessment of need or provide further care. Using NPT to frame our interpretation, in the next section we aim to highlight current practice around these phenomena and identify areas that appeared to be key barriers to implementation.

Implementation contexts

The successful implementation of interventions requires supportive implementation environments both within and outside the settings in which they are delivered. Our data highlighted several key aspects of the implementation context/s that are barriers to the widespread implementation of asking about, supporting, and signposting patients with AUD in secondary care in the study region. As the data collection was conducted very soon after COVID-19 restrictions ended, COVID-19 was an overarching context of the staffs’ accounts.

Widespread poverty, austerity, and the prioritisation of acute conditions – negotiating capacity

Negotiating capacity refers to how contexts shape the extent to which interventions can fit into existing ways of working [ 34 ]. Through the participants’ accounts we identified two aspects of context which appear to limit negotiating capacity: widespread poverty and austerity within the study region; and the focus of secondary care hospitals on the acute and presenting health needs of patients.

Most staff accounts suggested they perceived AUD to be common in the communities their hospitals covered and the patients they saw. Many staff linked the prevalence of AUD in the region to the high rates of poverty. To illustrate, Participant 23 commented that the basic provision for patients with AUD in the hospital, was in stark contrast to the apparent need in the community:

The demographic for around here, people are poor, they do drink, people do smoke,. . people take drugs a lot around here and the help, there isn’t [anything for them] it’s absolutely crazy. (Participant 23 - Nurse, Trust 6, Surgical)

While the need to support patients with AUD was perceived to have been high prior to the COVID-19 pandemic, many staff noted that they had seen a rise in patients presenting with or showing signs of AUD following the pandemic, with some suggesting that they felt that the presentations of alcohol-related morbidity and mortality were likely to increase in the future:

Our numbers [of patients with AUD] have gone up by 100% in five years. . So it’s not going anywhere, and I predict that at the beginning of next year we’re going to see huge influence on alcoholic dependence. Because we’ve already seen people who are having fits, first fits, people who were drinking prior to COVID or probably drinking too much, at high risk, not necessarily dependent and then, furloughed, have begun to drink every day and developed alcohol dependence. (Participant 25 - Nurse, Trust 7, Gastroenterology)

A small number of participants mentioned that because of the observed high levels of AUD in the study region it was harder to decide how to prioritise who to ask about alcohol. They indicated that they were unlikely to ask patients about alcohol if they were drinking at what they saw as lower levels, as they perceived most people were drinking a lot. For example, Participant 7 said:

If they were a binge drinker or they drank more than was recommended, it’s kind of like, where do you take that? How do I talk to my patients about that? Thinking about where we live, our demographic of the type of patients that we see, it’s very common that patients would drink more alcohol than the recommended. So, I guess that is the challenge of how you would approach that to the patient, without coming across like you were being judgmental or self-righteous when you’re trying to give them this advice. And actually asking them; ‘do you even see it as a problem?’ A lot of patients that you would speak to you wouldn’t even say that that is a problem. (Participant 7 - Nurse, Trust 2, Surgical)

Thus, these accounts indicated that the normalisation and prevalence of heavy drinking in some communities actively constrained the extent to which staff could integrate asking about and supporting patients with alcohol use into their day to day work .

Conversely, and illustrating how contexts can be barriers to implementation in one setting but facilitate it in others [ 44 ], some staff working in PL described how they had recently begun doing more systematic screening for AUD because it was recognised as being so prevalent in the patients they saw.

[Previously] unless alcohol was kind of front and centre and was an issue that was discussed from the get-go, it wasn’t always something that was really looked into in great detail as part of our assessments. Whereas now that we do the AUDIT, there’s an AUDIT-C tool with all patients. (Participant 4 – Nurse, Trust 1, PL)

Nonetheless, staff accounts more commonly focused on the need to tackle severe alcohol harm rather than preventative work. In-keeping with other research studies and clinical knowledge, the participants’ suggested that a key reason that patients aren’t routinely being asked about AUD in secondary care is because staff need to prioritise the presenting acute condition/s. Something which is colloquially termed ‘the rule of rescue’. Thus, any identification of AUD, where it did happen, was primarily focused on managing patients whose alcohol use was already affecting, or had the potential to affect, the treatment of their acute physical or mental illness. Participants almost always linked this to the pressurised setting and the restricted time they had to work with patients, as further limiting their capacity to address a patient’s drinking. This context is illustrated in the following quotes:

‘I’m asking [about alcohol] because it effects how I care for that patient and not necessarily about educating them’ (Participant 15 – Doctor, Trust 4, Medical). . .I think asking about the preventative problems, and screening for problems, is something that we just don’t do. If someone comes in and they’re alcohol dependent, realistically the thing you think about most is, right well we need to make sure that we’ve got the right things for if they withdraw, you don’t think, oh well shall we see if there’s anything we can do and to be fair, you don’t really have the time, I don’t think. (Participant 6 - Doctor, Trust 2, AED)

Overall, time and the focus on acute conditions, were commonly cited by staff as key contextual factors, that limited their negotiating capacity to ask patients about alcohol and to provide follow-up support.

Stigma at a structural level – strategic intentions

Strategic intentions refers to how contexts shape the formulation and planning of interventions. Many staff accounts suggested that they perceived there was little visible commitment to the prevention of AUD within their NHS trust or at a national NHS level. Many staff suggested they had seen no communications about providing preventative support to patients with AUD from their trust:

There’s nothing to my knowledge, Trust–wide, of how we help this cohort of patients. There doesn’t seem to be anything written in stone, on the help that we provide. (Participant 21 – Nurse, Trust 6, AED)

Others emphasised that although they had seen some communications about alcohol from their trust, these were limited. Some participants’ accounts indicated a sense of frustration that alcohol was not being prioritised by the NHS and moreover that any care offered to patients with AUD was voluntary rather than a designated part of their core work. For example, in one trust it was noted that the role of the Alcohol Lead was not formalised:

At the moment it’s almost voluntary and there’s always something else that comes along that’s more immediate, more important or seems that way. People aren’t taking the longer view that if we don’t address this problem now then the tsunami of liver disease will just continue. (Participant 10 - Doctor, Trust 3, Gastroenterology)

Relational stigma – reframing organisational logic

Reframing organisational logic refers to the extent to which social structural and social cognitive resources shape the implementation environment [ 34 ]. The stigma which was evident at a structural level was also directly perceived to impact the care of patients with AUD at a relational level. Many staff mentioned that the identification of AUD and subsequent signposting for patients who drink heavily are obstructed because some staff perceive that heavy alcohol use is a personal failing and individual problem. Indeed, judgement or stigma was explicitly proposed by participants as one of the key reasons that AUD prevention and treatment interventions were not implemented, or attempts weren’t made to help people with AUD:

People find them incredibly frustrating and [like] they’re not real patients or people who need [help]. (Participant 4 - Nurse, Trust 4, PL)

This judgement was also seen to be compounded by austerity and the increased demands on health and social care post COVID-19, meaning those who were more challenging or difficult to help were often the easiest group to not manage.

Relational stigma appeared evident in the reluctance of some staff to speak to patients about alcohol. For example, a few participants expressed concern about how patients would respond if they were to ask them about their alcohol use because heavy alcohol consumption can sometimes be perceived by patients and wider society as a personal failing or as evidence of a lack of control:

It’s quite a personal conversation to have with somebody and you’ve got a small thin curtain between every single patient and having those conversations when everybody hears the conversation that you have in the bay, so I think that sometimes contributes to it. (Participant 24 – Nurse, Trust 7, Medicine)

Moreover, the effects of stigma seemed evident in the extent to which staff perceived people would be honest about or disclose their heavy drinking and the extent to which would subsequently make adaptions to investigate further. Some staff said that they did not have the time to build rapport with patients to generate a context where they perceived patients might be more likely to be truthful about their drinking:

It comes down to them being honest. If they say that they don’t drink a lot then we wouldn’t give any advice. (Participant 26 – Nurse, Trust 7, Surgical)

The data also suggests that the extent to which staff appeared willing to identify or support patients with AUD is related to them not seeing it as relevant to the presenting problem which relates to the prioritisation of acute conditions and the negotiating capacity.

Implementation mechanisms

Alongside contexts, we identified a number of mechanisms that appeared to be barriers to implementation across our participants’ accounts.

Workforce knowledge and skills – cognitive participation

All participants’ accounts suggested that there was no mandatory training within trusts to support staff to deliver alcohol prevention work. While participants acknowledged there was indeed very little mandatory training about most conditions, many staff suggested they had not been trained post-University in how to have conversations with patients about alcohol, to assess need, or how to refer and signpost on:

. . we’ve got team days where we go through mandatory training and do little courses and do all our training, but there’s nothing about alcohol on there whereas it might be quite useful because we do get a lot of patients with alcohol issues so that would be beneficial. . we’ve had no training or updates on what’s out there in the community. (Participant 9 – Nurse, Trust 2, Medical)

In a small number of trusts, some staff with a specific remit around alcohol stated they were in the process of developing training about identification within their teams and appeared optimistic about the spread and impact of this.

Where staff did ask about alcohol, a barrier to referring people with AUD to appropriate services was their limited awareness of relevant services within the community. Indeed, a few participants conveyed the sentiment of Participant 11 who described their perception of asking about alcohol in their hospital as a ‘ tick box exercise rather than purposeful tool .’ (Nurse, Trust 3, Medical). Only a small number of participants seemed very knowledgeable about local community services; like Participant 9 above, most staff accounts suggested a lack of awareness of relevant organisations they could refer patients to. Some staff indicated that knowledge of appropriate services was made more challenging because of the frequent change in service provision and cuts and short-term commissioning of relevant voluntary and community sector services:

It is a bit vague at the moment as to exactly what they are going to do with the provider changing over. . when the Covid stuff started, they stopped coming in and just did electronic stuff. But I think they’ve started coming in again. But I don’t quite know what hours they are planning to come in, with the new changeover of people. (Participant 1 – Doctor, Trust 1, Gastroenterology)

In a context of frequent service changeovers and decommissioning, widespread poverty and austerity, and limited awareness of appropriate local services, there appeared to be a heavy reliance on referrals to primary care by staff, even when they didn’t know what primary care would offer patients. This is illustrated by this quote from Participant 15:

Sometimes if people ask me, or if I’ve found that they’ve got like deranged liver functions, I’ll often just sort of say to them, if it fits with an alcohol picture, I would say: “It does look like your alcohol use is affecting your liver, it might be something you think about cutting down,” but at that point I’m not always sure where to refer them to, so I usually end up saying you can get support from your GP. Yes. (Participant 15 – Doctor, Trust 4, Medical)

Role legitimacy – collective action

When asked directly in the interviews about whether they felt that managing AUD was their responsibility most participants stated that it was. However, their wider accounts indicated that many participants and their colleagues relied heavily on calling on staff in other departments to manage patients with AUD who they saw as better placed to address these patients’ needs. In particular, the participants commonly suggested that alcohol nurses or other staff in gastroenterology were most able to help:

In our trust, I’m not sure if it’s the same as any others, when we do the nurse’s admission, we ask how many units they’ve had and if they score over ten then they automatically get pinged to the alcohol nurses who will come and see them. Or we refer them and call the alcohol nurses here. . (Participant 28 – Nurse, Trust 8, AED)

Staff in the site where an ACT had recently been set-up suggested that the introduction of this service had significantly improved the care that they could offer people with visible presentations of AUD and provided a clearer route for signposting. However, the reliance on this service also served to illustrate the limited support prior to this in these sites and the significant care gap at other sites who did not have this provision. Moreover, the accounts of a few participants suggested that due to the high level of need for alcohol dependent support, the ACTs appeared to have little capacity to do preventative work:

The alcohol care team nurses are building up good relationships with some of our more frequent members that are coming on ward. And then they’re able to get permission off them to do more like referrals to [community alcohol service], discussions about tapering down or alcohol reduction therapy, discussions about cognitive behavioural therapies, discussions with housing officers and things, discussions with safeguarding. . having said that, like I say they are getting an abundance of referrals daily now and I think unfortunately it’s ended up a lot bigger than they were expecting, a bit of a mammoth task. (Participant 2 – Nurse, Trust 1, Medical)

In contrast to staff in other departments, as mentioned above, staff from PL teams suggested that identifying patients’ patterns of alcohol use, usually through formalised screening, had relatively recently become part of their core work. Nonetheless, the focus was still on management of AUD rather than prevention, as most indicated that the implementation of this was in response to the prevalence of heavy drinking in the patients they saw. Here the mechanism of collective action appears to be shaped by the context of poverty and austerity.

Perceived futility and negative feedback cycle – reflexive monitoring

Participants’ accounts indicated that they had little information about the outcomes of the people that they saw with AUD. Some staff mentioned that the only time they saw patients again, whether or not they delivered an intervention, was when they re-attended. The following response was typical:

We put them on file with the GP letter, and we don’t know what happens after that. (Participant 26 – Nurse, Trust 7, Surgical)

In the context of this perceived futility, staff appeared to find it difficult to have hope for patients when they experienced only negative reinforcement. Compounding this it was also evident that the recording of information about alcohol use and any advice or signposting were limited in most departments. Although some PL services and some trusts seemed to be trying to record screening more systematically at the time of the research, it was still not mandatory and was not always prioritised as the following quote illustrates:

[We] have the AUDIT -C put on e-records, and that provided some challenges as well. . there’s a lot of things that are recorded, you get a lot of alerts, we know that. . staff just tap off them, if they’re not mandatory, So, it was about trying to sell it is an important message. (Participant 25 - Nurse, Trust 7, Gastroenterology)

Here again we see the link between contexts and mechanisms whereby the lack of systematic recording of patients’ alcohol use is likely to be influenced by the context of structural stigma and its impact on strategic intentions.

This paper reports the findings of a collaborative study between practitioners, policy makers, and academics which aimed to explore the challenges to the delivery of identification, support, and subsequent signposting for AUD in the secondary care settings in the NENC region post- COVID-19. Our findings broadly concur with what was already known about the challenges of implementing identification and support for AUD in secondary care hospitals prior to the COVID-19 pandemic. For example, the persistent contextual challenge of time pressures, and the lack of key enabling mechanisms, such as having a workforce with the skills and knowledge to confidently ask about alcohol and signpost patients appropriately [ 22 ]. However, our findings extend existing evidence by highlighting some additional barriers to alcohol prevention work in secondary care in the post-COVID-19 context. Moreover, the use of theory, specifically NPT domains, enables us to illuminate the interplay of context and mechanisms which make implementation of AUD care especially difficult in this setting.

A key contribution of this study to the extant literature is that it provides empirical evidence of how COVID-19 has served to amplify the challenges already experienced by secondary care staff trying to delivery preventative alcohol work in hospital settings. Many staff indicated that the sheer scale of people presenting with possible AUD since COVID-19, meant they did not have the time to ask people or to prioritise asking people about alcohol. Where people were identified as experiencing AUD, provision of effective signposting and support for patients was adversely affected by lack of staff awareness about relevant care providers and lack of capacity in local services due to the impact of austerity and cuts to public services. Two trusts in the study region had ACTs in place at the time of the interviews, as part of the wider NHS commitment to reduction alcohol harm in England [ 16 ]. This appeared to have increased the capacity of the non-specialist workforce at these two sites to refer patients identified as experiencing AUD onto appropriate specialist support. However, a tentative, but notable, finding of this study was that while ACTs were making a difference in these trusts for those with existing alcohol dependence, they were limited in their capacity to deliver more preventative work around AUD (initially part of their remit) due to the high level of need amongst the dependent patient population. This warrants further exploration, with further insights potentially to come via the wider programme of work around ACTs that is currently ongoing in England [ 46 ]. Overall, the study provides empirical evidence that the implementation of the preventative practices to support a reduction in AUD may be particularly difficult in areas of deprivation such as the NENC meaning that inequalities are likely to be widening with other more affluent regions.

Stigma, the process of marking certain groups as being somehow contagious or of less value than others [ 47 ], is internationally recognised as a significant constraining factor to the delivery of compassionate and appropriate healthcare for patients with AUD and other substance use in secondary care and other health and social care settings [ 47 , 48 ]. In this study we chose to approach stigma as a structural and relational concept, seeing relational stigma as developing from structural stigma [ 49 ]. The role of structural stigma for limiting the implementation of identifying, supporting, and signposting patients with AUD was striking, as our data highlighted that the prevention of heavy alcohol use does not appear to be a visible priority within individual trusts, and arguably the wider NHS. Limited resources were perceived available for this area of care, and little visible commitment to support patients with AUD despite the scale of the problem. Stigma was also evident at a relational level in our participants accounts of the interactions between staff and patients, notably staff’s reluctance to ask about alcohol use and their perception that patients did not want to disclose their AUD. However, it should be noted that many of the staff who took part in the study suggested that they did not perceive patients in this way yet continued to struggle to provide alcohol prevention care. Thus, this relational stigma is likely an important, but only partial explanation for limited care provision. Nonetheless, our findings suggest that structural stigma is one of the main barriers to the identification of alcohol use and care in secondary care settings in the NENC. This echoes the damning findings of the ‘Remeasuring the Units’ report, also published since the pandemic, that argued that stigma contributes to the missed opportunities in secondary care for patients who ultimately die from alcohol-related liver disease [ 5 ].

This study was conducted primarily as a vehicle to understand and bring about change in workforce practice around the prevention of alcohol harm in NENC secondary care services. It was an integral component of a broader Health Care Needs Assessment (2022) on alcohol undertaken in response to increasing levels of alcohol harm in this region of the UK, which led to recommendations over four overarching themes: service delivery; workforce; data; and leadership from the healthcare system. The results of the study have directly shaped the regional strategy for the reduction of alcohol harm, a key element of which is the integrated alcohol workforce strategy for the NENC which aims to better support the NHS workforce to prevent alcohol harm through: increased awareness of the Chief Medical Officer alcohol guidance; improved pathways to community-based alcohol treatment and recovery support; workforce training and development; and support for staff to address their own drinking. The evidence highlighting the importance of stigma have additionally led to a strategic drive for senior leaders to acknowledge the impact alcohol has on their organisation and the communities they serve, and to take action to work in partnership to reduce this. There is also cross-system support to tackle relational stigma, initially though a co-ordinated multi-agency media campaign.

Overall, our interpretation has signalled areas of policy and practice which can be targeted to try to increase the uptake of these preventive strategies in the secondary care settings. However, ultimately the findings illustrate that the challenge for implementation of these evidence based preventative measures is not just upskilling the workforce or increasing resources. It also indicates that we need to address the complex interplay of contextual factors and implementation mechanisms which have been compounded by the pandemic and contribute to reinforcing and increasing existing inequalities. The works contributes to calls for a multi-layered response to reducing alcohol harm and wider cultural change for how alcohol use and substance use is perceived.

Study strengths and limitations

A strength of the study is that it was undertaken in an area experiencing some of the greatest inequalities from the COVID-19 pandemic. This allowed us to see the challenges to delivering preventative work in these contexts, which might be similar in other regions. A further strength is that mapping the empirical data onto an evidence-based implementation theory, which has been widely use in different settings, enabled us to focus on the aspects of the implementation, that are likely to be important across other settings too. Framing the interpretation using the NPT domains has helped us to emphasise how contexts and mechanisms interact to make the implementation at this particular time and place difficult. A key limitation of the study is that as it was based in one region of England, we cannot know for sure if these insights are transferrable beyond this context.

Secondary care hospitals are an important setting for the delivery of preventative care for AUD, due to the frequency with which AUD co-occurs with other physical and mental health conditions. Prior to the pandemic there was evidence that non-specialist healthcare staff can find caring for patients with alcohol-related presentations difficult, meaning that identifying, supporting, and that signposting patients was happening inconsistently. In this study, we highlight the additional challenges facing secondary care staff due to post-pandemic pressures and the significant rise in alcohol-related harm in some regions such as the NENC. Thus, whilst the mechanisms for implementing alcohol prevention work in secondary care need attention, our findings suggest that the greatest barrier is contextual, including widespread structural stigma.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Normalisation Process Theory

Alcohol Care Teams

North East and North Cumbria

Alcohol Use Disorder

Accident and Emergency Department

Psychiatric Liaison Teams

Alcohol Use Disorders Identification Test

Alcohol Use Disorders Identification Test Consumption

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Acknowledgements

In addition to co-authors WH and RB we are grateful to the four junior doctors Jamie Catlow, Rebecca Dunn, Sarah Manning and Satyasheel Ramful from the Gastroenterology Research and Audit through North Trainees who collected data for the study. We are grateful to Dr Matthew Breckons the qualitative methodologist who co-trained (with AOD and KJ) the junior doctors in qualitative interview skills. We are especially grateful to the thirty staff who gave up their time to participate in the research.

The project was funded by the North East and North Cumbria Integrated Care System Prevention Programme.

AO is Deputy Theme Lead – Prevention, Early Intervention and Behaviour Change within the NIHR Applied Research Collaboration (ARC) North East and North Cumbria (NENC) (NIHR200173). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. AO and KJ are also part-funded by a NIHR Advanced Fellowship (ADEPT: Alcohol use disorder and DEpression Prevention and Treatment, Grant: NIHR300616). The NIHR have not had any role in the design, implementation, analysis, write-up and/or dissemination of this research.

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SH and RB designed the study; SH, RB and WH were involved in the data collection; IL and KJ analysed and interpreted the data with support from AOD, SH, RB and WH; KJ drafted the manuscript with support from SH, AOD, RB, IL and WH. All authors approved the submitted version.

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Jackson, K., Baker, R., O’Donnell, A. et al. Understanding the challenges of identifying, supporting, and signposting patients with alcohol use disorder in secondary care hospitals, post COVID-19: a qualitative analysis from the North East and North Cumbria, England. BMC Health Serv Res 24 , 772 (2024). https://doi.org/10.1186/s12913-024-11232-4

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Macrophage-derived CD36 + exosome subpopulations as novel biomarkers of Candida albicans infection

• Sun, Yuanyuan
• Hao, Ruiying
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