Adjustment for survey non-participation using record linkage and multiple imputation: A validity assessment exercise using the Health 2000 survey

Aims: It is becoming increasingly possible to obtain additional information about health survey participants, though not usually non-participants, via record linkage. We aimed to assess the validity of an assumption underpinning a method developed to mitigate non-participation bias. We use a survey in Finland where it is possible to link both participants and non-participants to administrative registers. Survey-derived alcohol consumption is used as the exemplar outcome. Methods: Data on participants (85.5%) and true non-participants of the Finnish Health 2000 survey (invited survey sample N=7167 aged 30-79 years) and a contemporaneous register-based population sample (N=496,079) were individually linked to alcohol-related hospitalisation and death records. Applying the methodology to create synthetic observations on non-participants, we created 'inferred samples' (participants and inferred non-participants). Relative differences (RDs) between the inferred sample and the invited survey sample were estimated overall and by education. Five per cent limits were used to define acceptable RDs. Results: Average weekly consumption estimates for men were 129 g and 131 g of alcohol in inferred and invited survey samples, respectively (RD -1.6%; 95% confidence interval (CI) -2.2 to -0.04%) and 35 g for women in both samples (RD -1.1%; 95% CI -2.4 to -0.8%). Estimates for men with secondary levels of education had the greatest RD (-2.4%; 95% CI -3.7 to -1.1%). Conclusions: The sufficiently small RDs between inferred and invited survey samples support the assumption validity and use of our methodology for adjusting for non-participation. However, the presence of some significant differences means caution is required.Peer reviewe

Alcohol-related Outcomes and All-cause Mortality in the Health 2000 Survey by Participation Status and Compared with the Finnish Population

Background: In the context of declining levels of participation, understanding differences between participants and non-participants in health surveys is increasingly important for reliable measurement of health-related behaviors and their social differentials. This study compared participants and non-participants of the Finnish Health 2000 survey, and participants and a representative sample of the target population, in terms of alcohol-related harms (hospitalizations and deaths) and all-cause mortality. Methods: We individually linked 6,127 survey participants and 1,040 non-participants, aged 30-79, and a register-based population sample (n = 496,079) to 12 years of subsequent administrative hospital discharge and mortality data. We estimated age-standardized rates and rate ratios for each outcome for non-participants and the population sample relative to participants with and without sampling weights by sex and educational attainment. Results: Harms and mortality were higher in non-participants, relative to participants for both men (rate ratios = 1.5 [95% confidence interval = 1.2, 1.9] for harms; 1.6 [1.3, 2.0] for mortality) and women (2.7 [1.6, 4.4] harms; 1.7 [1.4, 2.0] mortality). Non-participation bias in harms estimates in women increased with education and in all-cause mortality overall. Age-adjusted comparisons between the population sample and sampling weighted participants were inconclusive for differences by sex; however, there were some large differences by educational attainment level. Conclusions: Rates of harms and mortality in non-participants exceed those in participants. Weighted participants' rates reflected those in the population well by age and sex, but insufficiently by educational attainment. Despite relatively high participation levels (85%), social differentiating factors and levels of harm and mortality were underestimated in the participants.Peer reviewe

Socio-economic inequalities in rates of amenable mortality in Scotland: Analyses of the fundamental causes using the Scottish Longitudinal Study, 1991-2010

Socio‐economic inequalities in amenable mortality rates are increasing across Europe, which is an affront to universal healthcare systems where the numbers of, and inequalities in, amenable deaths should be minimal and declining over time. However, the fundamental causes theory proposes that inequalities in health will be largest across preventable causes, where unequally distributed resources can be used to gain an advantage. Information on individual‐level inequalities that may better reflect the fundamental causes remains limited. We used the Scottish Longitudinal Study, with follow‐up to 2010 to examine trends in amenable mortality by a range of socio‐economic position measures. Large inequalities were found for all measures of socio‐economic position and were lowest for educational attainment, higher for social class and highest for social connection. To reduce inequalities, amenable mortality needs to be interpreted both as an indicator of healthcare quality and as a reflection of the unequal distribution of socio‐economic resources

Age, sex, and socioeconomic differences in multimorbidity measured in four ways:UK primary care cross-sectional analysis

Background: Multimorbidity poses major challenges to healthcare systems worldwide. Definitions with cut-offs in excess of ≥2 long-term conditions (LTCs) might better capture populations with complexity but are not standardised. Aim: To examine variation in prevalence using different definitions of multimorbidity. Design and setting: Cross-sectional study of 1 168 620 people in England. Method: Comparison of multimorbidity (MM) prevalence using four definitions: MM2+ (≥2 LTCs), MM3+ (≥3 LTCs), MM3+ from 3+ (≥3 LTCs from ≥3 International Classification of Diseases, 10th revision chapters), and mental–physical MM (≥2 LTCs where ≥1 mental health LTC and ≥1 physical health LTC are recorded). Logistic regression was used to examine patient characteristics associated with multimorbidity under all four definitions. Results: MM2+ was most common (40.4%) followed by MM3+ (27.5%), MM3+ from 3+ (22.6%), and mental–physical MM (18.9%). MM2+, MM3+, and MM3+ from 3+ were strongly associated with oldest age (adjusted odds ratio [aOR] 58.09, 95% confidence interval [CI] = 56.13 to 60.14; aOR 77.69, 95% CI = 75.33 to 80.12; and aOR 102.06, 95% CI = 98.61 to 105.65; respectively), but mental–physical MM was much less strongly associated (aOR 4.32, 95% CI = 4.21 to 4.43). People in the most deprived decile had equivalent rates of multimorbidity at a younger age than those in the least deprived decile. This was most marked in mental–physical MM at 40–45 years younger, followed by MM2+ at 15–20 years younger, and MM3+ and MM3+ from 3+ at 10–15 years younger. Females had higher prevalence of multimorbidity under all definitions, which was most marked for mental–physical MM. Conclusion: Estimated prevalence of multimorbidity depends on the definition used, and associations with age, sex, and socioeconomic position vary between definitions. Applicable multimorbidity research requires consistency of definitions across studies

A Digital Tool for Clinical Evidence-Driven Guideline Development:Studying Properties of Trial Eligible and Ineligible Populations

Background: Clinical guideline development preferentially relies on evidence from randomised controlled trials (RCTs). RCTs are the gold-standard method to evaluate the efficacy of treatments with the highest internal validity but limited external validity, in the sense that their findings may not always be applicable to, or generalisable to clinical populations/population characteristics. The external validity of RCTs for the clinical population is constrained by the lack of tailored epidemiological data analysis designed for this purpose due to data governance, consistency of disease/condition definitions, re-duplicated effort in analysis code, etc.Objective: To develop a digital tool that characterises the overall population and differences between clinical trial eligible and ineligible populations from the clinical populations of a disease/condition regarding demography (in terms of groupings for e.g., age, sex, ethnicity), comorbidity, co-prescription, hospitalisation and mortality. Currently, the process is complex, onerous and time consuming whereas a real-time tool may be used to rapidly inform a guideline developer’s judgement about the applicability of evidence.Methods: The National Institute for Health and Care Excellence (NICE) – in particular the gout guideline development group - and the Scottish Intercollegiate Guidelines Network (SIGN) guideline developers were consulted to gather their requirements and evidential data needs when developing guidelines. An R shiny tool was designed and developed using electronic primary healthcare data linked with hospitalisation and mortality data built upon an optimised data architecture. Disclosure control mechanisms were built into the tool to ensure data confidentiality.Results: The tool supports 128 chronic health conditions as index conditions and 161 conditions as comorbidities (33 in addition to the 128 index conditions). It enables two types of analyses via the graphic interface: overall population and stratified by user-defined eligibility criteria. The analyses produce overview statistical tables (on e.g. age, gender) of the index condition population and, within the overview groupings, produce details on e.g. electronic Frailty Index (eFI), comorbidities, co-prescriptions. The disclosure control mechanism is integral to the tool limiting tabular counts to meet local governance needs. An exemplar result for gout as an index condition is presented. Guideline developers from NICE and SIGN provided positive feedback on the tool.Conclusions: Using the digital tool can potentially improve evidence-driven guideline development through the availability of real-world data in real time

A Digital Tool for Clinical Evidence-Driven Guideline Development:Studying Properties of Trial Eligible and Ineligible Populations

Background: Clinical guideline development preferentially relies on evidence from randomised controlled trials (RCTs). RCTs are the gold-standard method to evaluate the efficacy of treatments with the highest internal validity but limited external validity, in the sense that their findings may not always be applicable to, or generalisable to clinical populations/population characteristics. The external validity of RCTs for the clinical population is constrained by the lack of tailored epidemiological data analysis designed for this purpose due to data governance, consistency of disease/condition definitions, re-duplicated effort in analysis code, etc.Objective: To develop a digital tool that characterises the overall population and differences between clinical trial eligible and ineligible populations from the clinical populations of a disease/condition regarding demography (in terms of groupings for e.g., age, sex, ethnicity), comorbidity, co-prescription, hospitalisation and mortality. Currently, the process is complex, onerous and time consuming whereas a real-time tool may be used to rapidly inform a guideline developer’s judgement about the applicability of evidence.Methods: The National Institute for Health and Care Excellence (NICE) – in particular the gout guideline development group - and the Scottish Intercollegiate Guidelines Network (SIGN) guideline developers were consulted to gather their requirements and evidential data needs when developing guidelines. An R shiny tool was designed and developed using electronic primary healthcare data linked with hospitalisation and mortality data built upon an optimised data architecture. Disclosure control mechanisms were built into the tool to ensure data confidentiality.Results: The tool supports 128 chronic health conditions as index conditions and 161 conditions as comorbidities (33 in addition to the 128 index conditions). It enables two types of analyses via the graphic interface: overall population and stratified by user-defined eligibility criteria. The analyses produce overview statistical tables (on e.g. age, gender) of the index condition population and, within the overview groupings, produce details on e.g. electronic Frailty Index (eFI), comorbidities, co-prescriptions. The disclosure control mechanism is integral to the tool limiting tabular counts to meet local governance needs. An exemplar result for gout as an index condition is presented. Guideline developers from NICE and SIGN provided positive feedback on the tool.Conclusions: Using the digital tool can potentially improve evidence-driven guideline development through the availability of real-world data in real time