Journal of Epidemiology and Community Health最新文献

Background: Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) is a leading quantitative approach for intersectionality-informed health research, but most applications analyse binary or cross-sectional outcomes, ignoring event timing. We applied a multilevel survival (shared frailty) model within the MAIHDA framework to examine intersectional disparities in time-to-diagnosis of hypertension.

Methods: Using 2019 Korean Community Health Survey data (n=228 632), we defined intersectional strata by sex, education, income and residential area. Three survival specifications were implemented: accelerated failure time (AFT), parametric proportional hazards (PHs) and semi-parametric Cox PH models, each with stratum-level random intercepts (shared frailty terms). Between-stratum variance was summarised with the variance partition coefficient (VPC) where estimable and proportional change in variance quantified fixed-effect contributions. Stratum-specific random effects were compared across model types to assess ranking stability.

Results: Between-stratum variance was small overall (AFT VPC: 1.8%), but several strata deviated markedly from the grand mean. Strata with low education and low income were diagnosed earlier than average, while high-education, low-income strata were diagnosed later. Geographic context modified these effects. Time-to-diagnosis patterns often diverged from prevalence patterns. Across models, random effect estimates and ranks were highly correlated (Spearman's ρ>0.97), though some middle-ranked strata shifted by up to six positions.

Conclusions: Applying a multilevel survival (shared frailty) model within MAIHDA enables examination of when disparities emerge, not just whether they exist. This approach retains MAIHDA's interpretability while leveraging time-to-event data, offering advantages in settings with incomplete follow-up or irregular observation windows.

Background: Many studies have detected a negative relationship between income inequality and general measures of health. However, data limitations have prevented a full understanding of whose health is impacted and in what ways.

Methods: In this study, we combined area-level census data with individual-level health claims data to estimate the cross-sectional association between county-level income inequality and healthcare utilisation across a range of member characteristics.

Results: We found that a 1 SD increase in the Gini coefficient was associated with about 5% higher medical and pharmacy costs and a 0.2 percentage-point increase in the probability of a hospital visit within the year. Income inequality was associated with higher medical costs primarily among adults with commercial insurance, more emergency department visits among children and Medicaid members, and more hospital visits among older adults, including Medicare members. By examining diagnoses attached to claims, we found that income inequality was associated with detrimental impacts on mental health, as indicated by higher spending for anxiety and depression and more emergency department visits for substance-use disorders.

Conclusions: Income inequality was associated with worse health across a wide range of members by age, income and insurance type, and can be considered as a risk factor by policymakers and health systems.

Background: During preconception counselling, pregnant women who smoke are advised to quit smoking. While the adverse effects of paternal smoking on pregnancy and perinatal outcomes have been increasingly recognised, the health benefits of paternal smoking cessation prior to conception remain understudied.

Methods: The current study involved non-smoking reproductive-aged Chinese women who participated two times in the National Free Prepregnancy Checkups Project during 2010-2018. A total of 158 986 pregnancies were included, with husbands reporting smoking at the preconception examination stage during their first participation. The primary exposure was paternal smoking cessation before pregnancy. And the primary outcome was spontaneous abortion (SAB) recorded in the second participation. Inverse-probability-weighted (IPW) logistic regression was used to estimate ORs and their 95% CIs of SAB with paternal smoking cessation before pregnancy. Instrumental variable (IV) analyses were further used to estimate the association.

Results: Compared with continued paternal smoking, paternal smoking cessation before pregnancy was associated with a reduced risk of SAB (IPW-adjusted OR 0.86 (95% CI 0.81 to 0.91); IV-estimated OR 0.79 (95% CI 0.76 to 0.82)). Additionally, a decrease in paternal smoking was also associated with a lower risk of SAB. Notably, the risk of SAB was still higher than that of those without paternal smoking in IV analysis (OR 1.21 (95% CI 1.26 to 1.32)).

Conclusions: Paternal smoking cessation prior to conception is linked to a reduced risk of SAB. However, the risk of SAB among women with paternal smoking cessation was still higher than that among those without paternal smoking. Preconception counselling should advise fathers who smoke to quit.

Background: Falls can repeatedly occur as people age, which leads to injury, disability and mortality in older adults. Sleep duration may be a modifiable factor, but longitudinal evidence on its association with recurrent falls is limited.

Methods: We analysed data from two prospective cohorts: the China Health and Retirement Longitudinal Study (CHARLS) and the English Longitudinal Study of Ageing (ELSA). Baseline self-reported sleep duration was classified as short (<6 hours), normal (6-10 hours) and long (>10 hours). Fall status was assessed in each follow-up wave and analysed as recurrent events. HRs and 95% CIs were estimated using Andersen-Gill models. Non-linear associations were explored using restricted cubic splines (RCS).

Results: A total of 11 603 participants from CHARLS and 8083 from ELSA were included. During median follow-ups of 9.0 years and 9.1 years, 7783 and 6472 recurrent falls were reported, respectively. Compared with normal sleep, short sleep was associated with higher fall risk (CHARLS: HR 1.127, 95% CI 1.066 to 1.191; ELSA: HR 1.115, 95% CI 1.041 to 1.195). Long sleep also showed increased risk (CHARLS: HR 1.293, 95% CI 1.020 to 1.640; ELSA: HR 1.413, 95% CI 1.027 to 1.946). RCS analysis revealed non-linear relationships, with the lowest risk observed at 7-8 hours.

Conclusion: Both short and long sleep durations are associated with increased risk of recurrent falls in adults aged 50 and above. A sleep duration of 7-8 hours appears to represent the lowest risk. Sleep-focused interventions may be a valuable strategy for fall prevention in public health and geriatric care.

Background: Cold weather remains a serious health threat in the UK and elsewhere, particularly for older adults. The Winter Fuel Payment has been a key government strategy to mitigate health risks linked to cold homes in the UK, but recent policy shifts have raised questions about whether income-based eligibility criteria effectively identify those most at risk.

Methods: We analysed cold-related mortality in adults aged ≥75 across 324 local authority districts in England (2007-2019) using distributed lag non-linear models in a spatial Bayesian framework. Multivariate meta-regression was used to evaluate modification of cold effects by deprivation, income-based pension credit uptake, home energy efficiency and fuel poverty.

Results: Areas in the highest quartile of fuel poverty had significantly greater cold-related mortality risk than those in the lowest quartile, with a 15.3% versus 13.1% increase in mortality risk at the first compared with the 50th percentile of wintertime temperature, ie, an absolute difference of 2.2% (p<0.001). This effect was stronger than the corresponding differences for energy efficiency (1.7%, p=0.04), income as indicated by pension credit uptake (0.6%, p=0.39) and deprivation-based measures, for which differences were minimal. Overall, an estimated 17% of cold-related deaths among people aged ≥75 were attributable to fuel poverty.

Conclusion: Fuel poverty, an indicator designed to capture both low-income and housing energy efficiency, is a stronger predictor of cold-related mortality than income (as indicated by pension credit update) or deprivation-based indicators alone. Winter energy support schemes should consider fuel poverty metrics in their targeting to more effectively reduce health risks associated with cold homes and improve equity.

Health inequities are unnecessary, avoidable and unjust differences in health across social groups. Addressing them is a priority for governments and health systems worldwide, requiring not only specific interventions targeting inequity but also embedding equity across all decision-making. Systematic reviews of interventions underpin health decision-making and could, therefore, be a key mechanism to address inequities, but most reviews are limited in their approach to considering equity and often only conclude data for subgroup analyses are unavailable. While some guidance is available, it largely focuses on reviews of interventions specifically seeking to reduce inequities and is published in disparate literature. We describe approaches to incorporate an equity perspective relevant to all systematic reviews of interventions, even when equity is not the primary review focus.Consideration of equity may be needed at all stages of the review process. Planning the review involves examining theory, using logic models, involving relevant people and organisations, and considering if additional sources of evidence are needed. Investigating the data requires examining the external validity of primary studies, including who was involved in the primary studies, and the reach of interventions. The synthesis process includes selecting appropriate analysis, considering the implications of reporting absolute or relative equity effects of the intervention, exploring and understanding mechanisms and assessing certainty of the evidence in relation to equity. Interpreting results involves linking theory with evidence and discussing implications and limitations. We hope this article helps review authors make best use of the available evidence to incorporate equity into systematic reviews.

Background: Adverse health conditions may serve as intermediate states linking loneliness and social isolation with excess mortality. We examined whether loneliness and social isolation are primarily associated with the risk of developing an adverse health condition or with prognosis after diagnosis.

Methods: A cohort of 236 879 individuals from the UK Biobank was followed from baseline in 2006-2010 until death or administrative end of follow-up in 2022. The incidence of three broad categories of adverse health conditions, (1) neoplasms, (2) endocrine, nutritional and metabolic diseases and (3) circulatory diseases, was recorded through self-report or register-based data. Progressive multistate Markov proportional hazards models were used to examine the associations of loneliness and social isolation with transitions from a healthy state to adverse health conditions and mortality, while adjusting for confounding.

Results: Loneliness and social isolation were associated with all three transitions: from a healthy state to adverse health conditions (except for neoplasms), from a healthy state to mortality and from adverse health conditions to mortality. In transitions to adverse health conditions, the HRs were larger for loneliness (HR range 1.12-1.17 for loneliness, 1.05-1.08 for social isolation). In subsequent transitions to mortality, the HRs were larger for social isolation (1.05-1.13 for loneliness, 1.28-1.42 for social isolation).

Conclusion: While both loneliness and social isolation were associated with the onset and prognosis of adverse health conditions, our findings highlight the associations of social isolation, in particular, with mortality.

Background: HIV literature shows that gay, bisexual and men who have sex with other men (GBMSM) experience inequities across social and contextual factors. Given growing inequities, this study used complex systems theory, a scientific approach to understanding the interconnected parts, to identify and visualise the system of factors that shape the emergence or (re)production of HIV risk among GBMSM.

Methods: A meta-synthesis of systematic reviews and meta-analyses was conducted to examine risk factors for HIV in alignment with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria and quality assessments using A Measurement Tool to Assess Systematic Reviews 2. After screening 255 studies, data were synthesised and visualised from 29 articles with moderate-quality or high-quality assessments. Study characteristics and risk factors for HIV were extracted, and data were thematically analysed into higher-order themes and respective subthemes aligned with Bronfenbrenner's socio-ecological model. Kumu.io, a system mapping software, was used to visualise the system of factors.

Results: Our thematic analysis and visualisation portray a dynamic and complex web of HIV risk that GBMSM experience implicated across all levels of the socio-ecological model: individual, interpersonal, community, institutional/organisational and structural/policy levels. These risk factors, in tandem, interact with one another to create pathways and patterns that generate feedback loops, such that the systems of factors create the emergence of GBMSM's HIV risk beyond that accounted for at the individual level.

Conclusion: GBMSM's HIV risk is socially patterned by a diversity of multilevel and interacting risk factors, which creates a dynamic and reinforcing system of HIV risk that requires attention in its totality to fully address HIV risk.