Population Health Metrics最新文献

Background: Diabetes prevalence is increasing worldwide, particularly in developing countries and disadvantaged groups. Alongside this phenomenon, the expansion of educational attainment has led to changes in population educational composition, which can significantly influence social disparities in diabetes and its risk factors, including obesity. This paper explores the role of changing educational composition in shaping the future burden of excess body weight and diabetes in Indonesia, a country with a rapidly growing prevalence of both diabetes and obesity.

Methods: We utilise three data sources as the inputs for our projection model. Panel data from the Indonesia Family Life Survey (IFLS) for 2007 and 2014 were used to compute health transition probabilities by age, sex, and education status using a multinomial logit model. Results from a dried blood test were used to adjust for undiagnosed diabetes in the projection model. The Indonesian National Health Surveys (Riskesdas) in 2007, 2013, and 2018 were used to estimate the prevalence of excess body weight and diabetes by age, sex, and education. Finally, we used projections of Indonesia's population size and composition by age, sex and education level for the period 2010 to 2060 from the Wittgenstein Centre Human Capital Data Explorer version WIC2018 v2. We employ a cohort component model with microsimulation to project the population forward.

Results: The estimated prevalence of diabetes from our projection model incorporating population education composition is 7.8% in 2010 and is expected to reach 16.7% by 2060. The most rapid increase in prevalence (14% growth in 50 years) is estimated among people with primary education, while other groups show smaller rises.

Conclusion: Incorporating population educational composition into projections of the burden of excess body weight and diabetes provides valuable insights into social disparities in diabetes over time. This can inform policy decisions by helping to prioritise healthcare budgets, targeted disease prevention programs, and diabetes treatment for high-risk groups based on educational status.

Introduction: Low socioeconomic status (SES) and unhealthy lifestyle are known risk factors for hypertension, but their impact on early-onset hypertension remains unclear.

Methods: A nationwide cohort study using UK Biobank data included 128,918 adults. SES was classified as high, medium, or low through latent class analysis of household income, employment, and education. A healthy lifestyle score was based on nonsmoking, moderate alcohol use, physical activity, and diet quality. Early-onset hypertension was defined as diagnosis before age 50; late-onset hypertension was diagnosed at 50 or older.

Results: Early-onset hypertension incidence was 2.93%, while late-onset was 14.45%. In early-onset hypertension, incidence increased from 2.03% in high SES to 7.35% in low SES (p < 0.001). For late-onset hypertension, incidence ranged from 9.70 to 20.74% (p < 0.001). Medium and low SES were significant risk factors for both forms of hypertension. The mediation effect of a healthy lifestyle on early-onset hypertension was 3.16%, and 0.42% for late-onset. Low SES combined with an unhealthy lifestyle increased early-onset hypertension risk by 356% and late-onset by 105%. The strength of the associations between risk factors for early-onset hypertension was greater than that for late-onset hypertension, particularly for low SES, obesity, and low education level.

Conclusions: Low SES is a stronger risk factor for early-onset hypertension than late-onset. A healthy lifestyle provides significant protection, especially in low SES groups. Tailored interventions addressing SES, education, and obesity are essential for preventing both early- and late-onset hypertension.

Background: The evolution of family structures in China has led to changes in the living arrangements of older adults, resulting in an increasing prevalence of individuals living alone. This shift has raised concerns about the impact on the well-being of older adults, particularly in relation to loneliness. The objective of this study is to examine the association between living alone and lonely life expectancy (LLE) among older adults, with particular attention to gender differences.

Methods: This study analyzed data from 9664 individuals aged 65-105, drawn from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) conducted in 2008, 2011, 2014, and 2018. A multistate life table approach, coupled with interpolation Markov Chain methods using IMaCh software (version 0.99r43), was employed to estimate age-specific transition probabilities, life expectancy (LE), lonely life expectancy (LLE), and the proportion of LLE to LE (LLE/LE). The analysis was stratified by gender and living arrangements, distinguishing between individuals living alone and those not living alone.

Results: The study identified several key findings. First, while LE has increased among older adults in China, they continue to experience a substantial duration of LLE. Second, older adults living alone experienced both longer LLE and higher LLE/LE ratios. Specifically, the LLE/LE among individuals not living alone was 13.61% higher for males and 15.18% higher for females compared to those living alone. Third, among females living alone, the LLE/LE was the highest, reaching 39.48% at age 65, compared to just 20.96% for males not living alone.

Conclusions: Tackling persistent loneliness is essential for enhancing the quality of life among older adults, highlighting the need to integrate mental well-being into social welfare policies. As China's population ages and family structures continue to evolve, living arrangements emerge as crucial policy indicators. Prioritizing the mental health of older adults, particularly women living alone, is critical for the development of effective social security policies.

Background: In this scoping review, we aimed to examine evidence on methods used to construct disability indicators in linked administrative datasets and describe the approaches used to assess the validity of the indicators.

Methods: Medline (Ovid) and Embase (Ovid) were searched for studies published between January 2010 and June 2023. Original, peer-reviewed studies that aimed to construct a disability indicator using linked administrative data sources were included. Studies identifying any types of disability were included, but not those which defined the target population in terms of specific health conditions. We produced a narrative synthesis of findings related to disability indicator construction methods and validation approaches.

Results: Thirty-six relevant studies were included, with 30 of those identifying a cohort of people with intellectual and/or developmental disability. Health data sources were most commonly used for indicator construction, with 33 of the studies using at least one health data source. Disability and education sector data sources were also commonly used. Diagnostic codes were used for disability identification in 34 of the 36 studies; 16 used diagnostic codes alone and 18 used diagnostic codes along with other information. A subgroup of 19 studies had a primary aim to create a disability cohort or estimate disability prevalence. Thirteen of these 19 studies compared their estimated prevalence rates with previously published estimates. Only five studies conducted testing to investigate the extent to which their derived disability indicator captured the intended target population.

Discussion: We found a paucity of evidence on methods for identifying a target population of people with diverse disabilities. In the existing literature, diagnostic information is relied upon heavily for disability identification, likely due to a lack of other types of disability-relevant information in administrative data sources. Use of derived disability indicators within linked data holds potential to advance research regarding people with disability. It is crucial, however, to conduct and report validation testing to understand the strengths and limitations of the indicators and inform their use for specific purposes.

Background: Pregnancy is a period marked by significant physiological and psychological changes in women and increased fetal nutritional requirements, necessitating maternal adaptation and behavior modifications. Clinicians and health institutions recommend pregnant women engage in healthy practices, such as smoking and alcohol cessation, folic acid consumption, vaccinations, and the like. As behavioral changes in general, the individual's conduct during pregnancy is also influenced not only by personal socio-economic status but also by the socio-economic conditions of the individual's area of residence. This mechanism is recognized by the social epidemiological approach and relates to the concept of neighborhood effect on individual health-related choices. Leveraging such considerations, the work aims to explore the association between selected behaviors recommended by clinicians during pregnancy and specific contextual variables in the residential areas where pregnant women live.

Methods: Data from the "MAMI-MED" cohort, recruiting pregnant women during the first prenatal visit at a hospital in Catania (Italy), were analyzed. The cohort provides a valuable resource for investigating the relationship between various exposures during pregnancy and the health outcomes of both mothers and infants. Geocoding techniques were employed to link individual-level data to selected contextual variables related to education, income, unemployment, and housing costs in the participants' residential areas. Mann-Whitney test, Kruskal-Wallis tests, logistic regressions and mixtures of regressions models with concomitant variables are implemented 1) to investigate the associations between contextual covariates and individual responses, 2) to assess the presence of latent sub-groups of the population reacting differently to the same contextual factors.

Results: The results of Mann-Whitney and Kruskal-Wallis tests, and logistic regressions indicated that neighborhood's socio-economic characteristics, such as educational level and unemployment rate, are associated with women's behaviors during pregnancy, smoking cessation in particular. Results from the logistic regression for BMI showed that obese and overweight individuals tend to live in neighborhoods where the percentage of individuals holding at least a bachelor's degree is comparatively lower. A mixture of regressions predicting individual BMI detected the presence of two latent groups in the population under analysis. The main finding seems to suggest that people living in worse socio-economic environments have a higher sensitivity to changes in education conditions, with respect to individuals living in better-off neighborhoods.

Conclusions: These findings highlight the importance of considering social and contextual dimensions in understanding and promoting healthy behaviors during pregnancy.

Background: Reducing under-five mortality is a global health priority. Countries need specific data on which interventions have the most potential to lead to improvements to inform their programs and policies.

Methods: Group-based trajectory modeling was applied to identify distinct trajectories of under-five mortality from 2000 to 2019 in 78 low and lower-middle income countries. Both health and non-health variables were studied as time-stable covariates of trajectory group membership and as time-varying covariates of mortality rates within groups.

Results: We identified four distinct groups that were primarily distinguished based on trajectory level rather than shape-low mortality, medium mortality, medium-high mortality and high mortality. All four groups had under-five trajectories that declined over time with the highest mortality group having the largest declines. We conducted two sets of bivariate analyses. The first analysis was conducted to understand what factors distinguished the different groups from one another (time stable covariate analysis), and the second analysis was done to understand what factors were significant within a group (time vaying covariate analysis). Results indicated that five factors were associated with all three comparisons of being in the low, medium and medium high groups compared to the highest mortality group. These factors were fertility rate, % of population with an improved water source, female secondary school enrollment rate, diphtheria, pertussis, tetanus dose 3 (DPT3) coverage and % of gross domestic product (GDP) on health expenditures. Results of the modeling of the within group analysis with time-varying factors indicated that higher fertility rates and higher out-of-pocket expenditures were significantly associated with greater mortality rates for all four groups. High DPT3 coverage, greater political stability, high coverage of births in a health facility and a greater % GDP on health expenditures were significantly associated with reduced under-five mortality for all four groups.

Conclusion: Findings from our study revealed the importance of considering both health and non-health factors in understanding trajectories of under-five mortality. In particular the fertility rate and % of GDP on health expenditures were signicant for all three comparisons of the trajectory group membership analysis (time-stable covariates) and were significant for all four groups in the within group analysis (time varying covariates). Other factors were significant for some comparisons and groups. Focusing on a number of key factors relevant to their group could help countries to further improve the health of young children.

Background: The war in Tigray Region, Ethiopia (November 2020 to November 2022) ended with formal commitments to accountability, but these have yet to produce publicly available accounts of the harms caused by the conflict.

Methods: We carried out an online survey of the Tigrayan diaspora to estimate mortality amongst children, adults, and older adults during, and prior to, the war-period. We collected retrospective demographic information on respondents', and their spouses', extended family inside Tigray. To mitigate selection bias, we standardised mortality estimates by rural-urban residence and wealth index.

Results: Of 1011 participant-reported decedents, 810 died within Tigray, and 310 of these individuals died during the war-period. Of the 310 deaths in Tigray during the war-period, 224 (72.3%) died from intentional injuries. The standardised mortality rate for adults (15-49 years) was 21.3 per 1000 person-years (29.4 for men, 14.8 for women) during the war, and 1.0 in the preceding period (2010-2020). The mortality rate amongst older adults (≥ 60 years) was 45.1 per 1000 person-years during the war-period, compared to 22.8 in 2010-2020, and is higher than the period encompassing the Ethiopian Civil War (1974-1991) and Tigray/Wollo Famine (1984-1985). The mortality ratio (men to women) was approximately 2:1 in both adults, and older adults. The mortality rate amongst adults and older adults had been declining across the pre-war periods. Few deaths were reported amongst children. We estimate that the conflict has resulted in more than 102,000 deaths amongst those aged ≥ 15 years.

Conclusions: Our study suggests a significant elevation in all-cause mortality, largely driven by intentional injuries. Although our pre-war-period data are likely under-reported, comparisons with other pre-war estimates corroborate these plausible elevations, particularly amongst adults. The mortality ratio, when compared to those from other settings, does not support assertions that the military strategy primarily involved the targeting of adult males, and instead suggests widespread killing of women and older adults who would not have posed a combat threat.

Background: A composite evaluation that merges various data quality indicators separately enabled the researchers to score the overall data quality of the research. In this context, the objective of the present study is to develop composite anthropometric data quality indices for children under 5 registered on the Brazilian National Food and Nutrition Surveillance System (SISVAN) from 2019 to 2021.

Methods: Anthropometric data quality indicators were generated for 5,210 Brazilian municipalities: coverage, completeness, the ratio between the sexes, age difference index, preference for height and weight digits, biologically implausible z-score values, and standard deviation. Principal component analysis [PCA] was used to generate a composite anthropometric data quality index for standardized height-for-age (HAZ) and weight-for-height z score (WHZ) indices. The municipalities were ranked in descending order, following their anthropometric quality index values: lowest [worst quality] and highest values [best quality].

Results: In total, 29,367,435 records and 8,930,881 children with anthropometric measurement information were identified. The dispersion indicators, the percentage of biologically implausible values [BIV] and the digit preference had the highest factor loadings. We observed that the worst index values were found in municipalities in the country's poorest and most vulnerable regions [North, Northeast, and Central-West]. The correlation between the HAZ and WHZ quality indices was 0.74.

Conclusion: The proposed index provides a coherent measurement to discriminate municipal anthropometric data quality.

Background: Adverse Childhood Experiences have been implicated in a range of health-related risk behaviors in adulthood, but there is limited research on how these patterns manifest among internal migrant workers in China. This study aims to elucidate the mediating role of family functioning and explore generational differences in this relationship.

Methods: A cross-sectional study was conducted among two groups of migrant workers in China: first-generation migrant workers (FGWs) and new-generation migrant workers (NGMWs). A total of 2,187 participants completed surveys that assessed adverse childhood experiences, family functioning, and health-related risk behaviors. Mediation analysis was performed to examine the indirect effects of adverse childhood experiences on health risks through family functioning. Additionally, moderated mediation analysis was conducted to explore potential differences between FGWs and NGMWs.

Results: Adverse childhood experiences significantly predicted higher health-related risk behaviors and lower family functioning (p < 0.001). Family Functioning mediated the relationship between adverse childhood experiences and health-related risks behaviors, which accounted for approximately 16.67% of the total effect (95% CI 0.03 to 0.07, p < 0.001). There was a significant difference in the indirect effects between NGMWs and FGWs (95% CI 0.01 to 0.06, p < 0.05). The indirect effect of adverse childhood experiences through family functioning was significant for NGMWs (p < 0.001), but non-significant for FGWs.

Conclusion: Our study fills a critical gap in understanding the intricate links between adverse childhood experiences, family functioning, and health-related risk behaviors among China migrant workers in China. It highlights the role of family functioning as a significant mediator of the impact of early adverse childhood experiences on adult health-related risks behaviors, particularly in a vulnerable population like migrant workers. More importantly, our findings indicate that this mediation varies significantly between FGWs and NGMWs. Family functioning exhibited a stronger mediating effect between early adverse childhood experience and adult health-related risks behaviors for NGMWs compared to FGWs.

Health inequalities are an important societal injustice. Understanding their scale and trends, and how they compare internationally, is needed to inform policy and practice, and also in order to evaluate the impacts of different policies. Many studies comparing health inequality trends across Europe have used educational attainment as a means of ranking adult populations, but there have been challenges as a consequence of the educational attainment data being missing, or categorising a very large proportion of the total population into a single group. Janssen et al. have recognised this challenge and have proposed an innovative and helpful method to overcome the problems of missing data. Although these are useful improvements, they still leave > 80% of the population categorised in the same group for some years, limiting the validity of the inequality measure.