Understanding multimorbidity trajectories in Scotland using sequence analysis.

Understanding how multiple conditions develop over time is of growing interest, but there is currently limited methodological development on the topic, especially in understanding how multimorbidity (the co-existence of at least two chronic conditions) develops longitudinally and in which order diseases occur. We aim to describe how a longitudinal method, sequence analysis, can be used to understand the sequencing of common chronic diseases that lead to multimorbidity and the socio-demographic factors and health outcomes associated with typical disease trajectories. We use the Scottish Longitudinal Study (SLS) linking the Scottish census 2001 to disease registries, hospitalisation and mortality records. SLS participants aged 40-74 years at baseline were followed over a 10-year period (2001-2011) for the onset of three commonly occurring diseases: diabetes, cardiovascular disease (CVD), and cancer. We focused on participants who transitioned to at least two of these conditions over the follow-up period (N = 6300). We use sequence analysis with optimal matching and hierarchical cluster analysis to understand the process of disease sequencing and to distinguish typical multimorbidity trajectories. Socio-demographic differences between specific disease trajectories were evaluated using multinomial logistic regression. Poisson and Cox regressions were used to assess differences in hospitalisation and mortality outcomes between typical trajectories. Individuals who transitioned to multimorbidity over 10 years were more likely to be older and living in more deprived areas than the rest of the population. We found seven typical trajectories: later fast transition to multimorbidity, CVD start with slow transition to multimorbidity, cancer start with slow transition to multimorbidity, diabetes start with slow transition to multimorbidity, fast transition to both diabetes and CVD, fast transition to multimorbidity and death, fast transition to both cancer and CVD. Those who quickly transitioned to multimorbidity and death were the most vulnerable, typically older, less educated, and more likely to live in more deprived areas. They also experienced higher number of hospitalisations and overnight stays while still alive. Sequence analysis can strengthen our understanding of typical disease trajectories when considering a few key diseases. This may have implications for more active clinical review of patients beginning quick transition trajectories.

Complex differences in infection rates between ethnic groups in Scotland: a retrospective, national census-linked cohort study of 1.65 million cases.

BACKGROUND: Ethnicity can influence susceptibility to infection, as COVID-19 has shown. Few countries have systematically investigated ethnic variations in infection. METHODS: We linked the Scotland 2001 Census, including ethnic group, to national databases of hospitalizations/deaths and serological diagnoses of bloodborne viruses for 2001-2013. We calculated age-adjusted rate ratios (RRs) in 12 ethnic groups for all infections combined, 15 infection categories, and human immunodeficiency virus (HIV), hepatitis B (HBV) and hepatitis C (HCV) viruses. RESULTS: We analysed over 1.65 million infection-related hospitalisations/deaths. Compared with White Scottish, RRs for all infections combined were 0.8 or lower for Other White British, Other White and Chinese males and females, and 1.2-1.4 for Pakistani and African males and females. Adjustment for socioeconomic status or birthplace had little effect. RRs for specific infection categories followed similar patterns with striking exceptions. For HIV, RRs were 136 in African females and 14 in males; for HBV, 125 in Chinese females and 59 in males, 55 in African females and 24 in males; and for HCV, 2.3-3.1 in Pakistanis and Africans. CONCLUSIONS: Ethnic differences were found in overall rates and many infection categories, suggesting multiple causative pathways. We recommend census linkage as a powerful method for studying the disproportionate impact of COVID-19.

Ethnic variations in falls and road traffic injuries resulting in hospitalisation or death in Scotland: the Scottish Health and Ethnicity Linkage Study.

OBJECTIVES: To investigate ethnic differences in falls and road traffic injuries (RTIs) in Scotland. STUDY DESIGN: A retrospective cohort of 4.62 million people, linking the Scottish Census 2001, with self-reported ethnicity, to hospitalisation and death records for 2001-2013. METHODS: We selected cases with International Classification of Diseases-10 diagnostic codes for falls and RTIs. Using Poisson regression, age-adjusted risk ratios (RRs, multiplied by 100 as percentages) and 95% confidence intervals (CIs) were calculated by sex for 10 ethnic groups with the White Scottish as reference. We further adjusted for country of birth and socio-economic status (SES). RESULTS: During about 49 million person-years, there were 275,995 hospitalisations or deaths from fall-related injuries and 43,875 from RTIs. Compared with the White Scottish, RRs for falls were higher in most White and Mixed groups, e.g., White Irish males (RR: 131; 95% CI: 122-140) and Mixed females (126; 112-143), but lower in Pakistani males (72; 64-81) and females (72; 63-82) and African females (79; 63-99). For RTIs, RRs were higher in other White British males (161; 147-176) and females (156; 138-176) and other White males (119; 104-137) and females (143; 121-169) and lower in Pakistani females (74; 57-98). The ethnic variations differed by road user type, with few cases among non-White motorcyclists and non-White female cyclists. The RRs were minimally altered by adjustment for country of birth or SES. CONCLUSION: We found important ethnic variations in injuries owing to falls and RTIs, with generally lower risks in non-White groups. Culturally related differences in behaviour offer the most plausible explanation, including variations in alcohol use. The findings do not point to the need for new interventions in Scotland at present. However, as the ethnic mix of each country is unique, other countries could benefit from similar data linkage-based research.

The development and validation of a multivariable prognostic model to predict foot ulceration in diabetes using a systematic review and individual patient data meta-analyses.

AIMS: Diabetes guidelines recommend screening for the risk of foot ulceration but vary substantially in the underlying evidence base. Our purpose was to derive and validate a prognostic model of independent risk factors for foot ulceration in diabetes using all available individual patient data from cohort studies conducted worldwide. METHODS: We conducted a systematic review and meta-analysis of individual patient data from 10 cohort studies of risk factors in the prediction of foot ulceration in diabetes. Predictors were selected for plausibility, availability and low heterogeneity. Logistic regression produced adjusted odds ratios (ORs) for foot ulceration by ulceration history, monofilament insensitivity, any absent pedal pulse, age, sex and diabetes duration. RESULTS: The 10 studies contained data from 16 385 participants. A history of foot ulceration produced the largest OR [6.59 (95% CI 2.49 to 17.45)], insensitivity to a 10 g monofilament [3.18 (95% CI 2.65 to 3.82)] and any absent pedal pulse [1.97 (95% CI 1.62 to 2.39)] were consistently, independently predictive. Combining three predictors produced sensitivities between 90.0% (95% CI 69.9% to 97.2%) and 95.3% (95% CI 84.5% to 98.7%); the corresponding specificities were between 12.1% (95% CI 8.2% to 17.3%) and 63.9% (95% CI 61.1% to 66.6%). CONCLUSIONS: This prognostic model of only three risk factors, a history of foot ulceration, an inability to feel a 10 g monofilament and the absence of any pedal pulse, compares favourably with more complex approaches to foot risk assessment recommended in clinical diabetes guidelines.

Differences in all-cause hospitalisation by ethnic group: a data linkage cohort study of 4.62 million people in Scotland, 2001-2013.

OBJECTIVES: Immigration into Europe has raised contrasting concerns about increased pressure on health services and equitable provision of health care to immigrants or ethnic minorities. Our objective was to find out if there were important differences in hospital use between the main ethnic groups in Scotland. STUDY DESIGN: A census-based data linkage cohort study. METHODS: We anonymously linked Scotland's Census 2001 records for 4.62 million people, including their ethnic group, to National Health Service general hospitalisation records for 2001-2013. We used Poisson regression to calculate hospitalisation rate ratios (RRs) in 14 ethnic groups, presented as percentages of the White Scottish reference group (RR = 100), for males and females separately. We adjusted for age and socio-economic status and compared those born in the United Kingdom or the Republic of Ireland (UK/RoI) with elsewhere. We calculated mean lengths of hospital stay. RESULTS: 9.79 million hospital admissions were analysed. Compared with the White Scottish, unadjusted RRs for both males and females in most groups were about 50-90, e.g. Chinese males 49 (95% confidence interval [CI] = 45-53) and Indian females 76 (95% CI 71-81). The exceptions were White Irish, males 120 (95% CI 117-124) and females 115 (95% CI 112-119) and Caribbean females, 103 (95% CI 85-126). Adjusting for age increased the RRs for most groups towards or above the reference. Socio-economic status had little effect. In many groups, those born outside the UK/RoI had lower admission rates. Unadjusted mean lengths of stay were substantially lower in most ethnic minorities. CONCLUSIONS: Use of hospital beds in Scotland by most ethnic minorities was lower than by the White Scottish majority, largely explained by their younger average age. Other countries should use similar methods to assess their own experience.

Associations between weight change and biomarkers of cardiometabolic risk in South Asians: secondary analyses of the PODOSA trial.

BACKGROUND/OBJECTIVES: The association of weight changes with cardiometabolic biomarkers in South Asians has been sparsely studied. SUBJECTS/METHODS: We measured cardiometabolic biomarkers at baseline and after 3 years in the Prevention of Diabetes and Obesity in South Asians Trial. We investigated the effect of a lifestyle intervention on biomarkers in the randomized groups. In addition, treating the population as a single cohort, we estimated the association between change in weight and change in biomarkers. RESULTS: Complete data were available at baseline and after 3 years in 151 participants. At 3 years, there was an adjusted mean reduction of 1·44 kg (95% confidence interval (95% CI): 0.18-2.71) in weight and 1.59 cm (95% CI: 0.08-3.09) in waist circumference in the intervention arm as compared with the control arm. There was no clear evidence of difference between the intervention and control arms in change of mean value of any biomarker. As a single cohort, every 1 kg weight reduction during follow-up was associated with a reduction in triglycerides (-1.3%, P=0.048), alanine aminotransferase (-2.5%, P=0.032), gamma-glutamyl transferase (-2.2%, P=0.040), leptin (-6.5%, P<0.0001), insulin (-3.7%, P=0.0005), fasting glucose (-0.8%, P=0.0071), 2-h glucose (-2.3%, P=0.0002) and Homeostatic Model Assessment of insulin resistance (HOMA-IR: -4.5%, P=0.0002). There was no evidence of associations with other lipid measures, tissue plasminogen activator, markers of inflammation or blood pressure. CONCLUSIONS: We demonstrate that modest weight decrease in SAs is associated with improvements in markers of total and ectopic fat as well as insulin resistance and glycaemia in South Asians at risk of diabetes. Future trials with more intensive weight change are needed to extend these findings.