The impact of depression and physical multimorbidity on health-related quality of life in China:a national longitudinal quantile regression study

Abstract The co-occurrence of mental and physical chronic conditions is a growing concern and a largely unaddressed challenge in low-and-middle-income countries. This study aimed to investigate the independent and multiplicative effects of depression and physical chronic conditions on health-related quality of life (HRQoL) in China, and how it varies by age and gender. We used two waves of the China Health and Retirement Longitudinal Study (2011, 2015), including 9227 participants aged ≥ 45 years, 12 physical chronic conditions and depressive symptoms. We used mixed-effects linear regression to assess the effects of depression and physical multimorbidity on HRQoL, which was measured using a proxy measure of Physical Component Scores (PCS) and Mental Component Scores (MCS) of the matched SF-36 measure. We found that each increased number of physical chronic conditions, and the presence of depression were independently associated with lower proxy PCS and MCS scores. There were multiplicative effects of depression and physical chronic conditions on PCS (− 0.83 points, 95% CI − 1.06, − 0.60) and MCS scores (− 0.50 points, 95% CI − 0.73, − 0.27). The results showed that HRQoL decreased markedly with multimorbidity and was exacerbated by the presence of co-existing physical and mental chronic conditions

Low physical activity is associated with adverse health outcome and higher costs in Indonesia: A national panel study

AimsTo assess the association between low physical activity, cardiovascular disease (CVD) and risk factors, health service utilization, risk of catastrophic health expenditure, and work productivity in Indonesia.MethodsIn this population-based, panel data analysis, we used data from two waves of the Indonesian Family Life Survey (IFLS) for 2007/2008 and 2014/2015. Respondents aged 40–80 years who participated in both waves were included in this study (n = 5,936). Physical activity was assessed using the International Physical Activity Questionnaire (IPAQ-SF). Multinomial logistic regression model was used to examine factors associated with physical activity levels (low, moderate, and high). We applied a series of multilevel mixed-effect panel regression to examine the associations between physical activity and outcome variables.ResultsThe prevalence of low physical activity increased from 18.2% in 2007 to 39.6% in 2014. Compared with those with high physical activity, respondents with low physical activity were more likely to have a 10-year high CVD risk (AOR: 2.11, 95% CI: 1.51–2.95), use outpatient care (AOR: 1.26, 95% CI: 1.07–1.96) and inpatient care (AOR 1.45, 95% CI: 1.07–1.96), experience catastrophic health expenditure of 10% of total household expenditure (AOR: 1.66, 95% CI: 1.21–2.28), and have lower labor participation (AOR: 0.24, 95% 0.20–0.28).ConclusionsLow physical activity is associated with adverse health outcomes and considerable costs to the health system and wider society. Accelerated implementation of public health policies to reduce physical inactivity is likely to result in substantial population health and economic benefits

The effect of health insurance and socioeconomic status on women’s choice in birth attendant and place of delivery across regions in Indonesia: a multinomial logit analysis

Background Evidence suggests that women gave birth in diverse types of health facilities and were assisted by various types of health providers. This study examines how these choices are influenced by the Indonesia national health insurance programme (Jaminan Kesehatan Nasional (JKN)), which aimed to provide equitable access to health services, including maternal health.Methods Using multinomial logit regression models, we examined patterns and determinants of women’s choice for childbirth, focusing on health insurance coverage, geographical location and socioeconomic disparities. We used the 2018 nationally representative household survey dataset consisting of 41 460 women (15–49 years) with a recent live birth.Results JKN coverage was associated with increased use of higher-level health providers and facilities and reduced the likelihood of deliveries at primary health facilities and attendance by midwives/nurses. Women with JKN coverage were 13.1% and 17.0% (p<0.05) more likely to be attended by OBGYN/general practitioner (GP) and to deliver at hospitals, respectively, compared with uninsured women. We found notable synergistic effects of insurance status, place of residence and economic status on women’s choice of type of birth attendant and place of delivery. Insured women living in Java–Bali and in the richest wealth quintile were 6.4 times more likely to be attended by OBGYN/GP and 4.2 times more likely to deliver at a hospital compared with those without health insurance, living in Eastern Indonesia, and in the poorest income quantile.Conclusion There are large variations in the choice of birth attendant and place of delivery by population groups in Indonesia. Evaluation of health systems reform initiatives, including the JKN programme and the primary healthcare strengthening, is essential to determine their impact on disparities in maternal health services

Out-Of-Pocket Expenditure Associated with Physical Inactivity, Excessive Weight, and Obesity in China: Quantile Regression Approach

Introduction: Previous studies exploring associations of physical inactivity, obesity, and out-of-pocket expenditure (OOPE) mainly used traditional linear regression, and little is known about the effect of both physical inactivity and obesity on OOPE across the percentile distribution. This study aims to assess the effects of physical inactivity and obesity on OOPE in China using a quantile regression approach. Methods: Study participants included 10,687 respondents aged 45 years and older from the recent wave of the China Health and Retirement Longitudinal Study in 2015. Linear regression and quantile regression models were used to examine the association of physical activity, body weight with annual OOPE. Results: Overall, the proportion of overweight and obesity was 33.2% and 5.8%, respectively. The proportion of individuals performing high-level, moderate-level, and low-level physical activity was 55.2%, 12.7%, and 32.1%, respectively. The effects of low-level physical activity on annual OOPE were small at the bottom quantiles but more pronounced at higher quantiles. Respondents with low-level activity had an increased annual OOPE of 26.9 USD, 150.3 USD, and 1,534.4 USD, at the 10th, 50th, and 90th percentiles, respectively, compared with those with high-level activity. The effects of overweight and obesity on OOPE were also small at the bottom quantiles but more pronounced at higher quantiles. Conclusion: Interventions that improve the lifestyles and unhealthy behaviors among people with obesity and physical inactivity are likely to yield substantial financial gains for the individual and health systems in China

Functional limitation as a mediator of the relationship between multimorbidity on health-related quality of life in Australia: evidence from a national panel mediation analysis

Objective: The inverse relationships between chronic disease multimorbidity and health-related quality of life (HRQoL) have been well-documented in the literature. However, the mechanism underlying this relationship remains largely unknown. This is the first study to look into the potential role of functional limitation as a mediator in the relationship between multimorbidity and HRQoL. Methods: This study utilized three recent waves of nationally representative longitudinal Household, Income, and Labor Dynamics in Australia (HILDA) surveys from 2009 to 2017 (n = 6,814). A panel mediation analysis was performed to assess the role of functional limitation as a mediator in the relationship between multimorbidity and HRQoL. The natural direct effect (NDE), indirect effect (NIE), marginal total effect (MTE), and percentage mediated were used to calculate the levels of the mediation effect. Results: This study found that functional limitation is a significant mediator in the relationship between multimorbidity and HRQoL. In the logistic regression analysis, the negative impact of multimorbidity on HRQoL was reduced after functional limitation was included in the regression model. In the panel mediation analysis, our results suggested that functional limitation mediated ~27.2% (p < 0.05) of the link between multimorbidity and the composite SF-36 score for HRQoL. Functional limitation also mediated the relationship between the number of chronic conditions and HRQoL for each of the eight SF-36 dimensions, with a proportion mediated ranging from 18.4 to 28.8% (p < 0.05). Conclusion: Functional status has a significant impact on HRQoL in multimorbid patients. Treatment should concentrate on interventions that improve patients' functioning and mitigate the negative effects of multimorbidity

The relative impact of underweight, overweight, smoking, and physical inactivity on health and associated costs in Indonesia: propensity score matching of a national sample

Background: Indonesia is in the middle of a rapid epidemiological transition with an ageing population and increasing exposure to risk factors for chronic conditions. This study examines the relative impacts of obesity, tobacco consumption, and physical inactivity, on non-communicable diseases multimorbidity, health service use, catastrophic health expenditure (CHE), and loss in employment productivity in Indonesia. Methods: Secondary analyses were conducted of cross-sectional data from adults aged ≥ 40 years (n = 12,081) in the Indonesian Family Life Survey 2014/2015. We used propensity score matching to assess the associations between behavioural risk factors and health service use, CHE, employment productivity, and multimorbidity. Results: Being obese, overweight and a former tobacco user was associated with a higher number of chronic conditions and multimorbidity (p < 0.05). Being a former tobacco user contributed to a higher number of outpatient and inpatient visits as well as CHE incidences and work absenteeism. Physical inactivity relatively increased the number of outpatient visits (30% increase, p < 0.05) and work absenteeism (21% increase, P < 0.06). Although being underweight was associated with an increased outpatient care utilisation (23% increase, p < 0.05), being overweight was negatively associated with CHE incidences (50% decrease, p < 0.05). Conclusion: Combined together, obesity, overweight, physical inactivity and tobacco use contributed to an increased number of NCDs as well as medical costs and productivity loss in Indonesia. Interventions addressing physical and behavioural risk factors are likely to have substantial benefits for individuals and the wider society in Indonesia

Table_1_Impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life of Australians: a population-based longitudinal data analysis.DOCX

BackgroundHealth risk factors, including smoking, excessive alcohol consumption, overweight, obesity, and insufficient physical activity, are major contributors to many poor health conditions. This study aimed to assess the impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life (HRQoL) in Australia.MethodsWe used two waves of the nationally representative Household, Income, and Labor Dynamics in Australia (HILDA) Survey from 2013 and 2017 for the analysis. Healthcare resource utilization included outpatient visits, hospitalisations, and prescribed medication use. Work-related outcomes were assessed through employment status and sick leave. HRQoL was assessed using the SF-6D scores. Generalized estimating equation (GEE) with logit or log link function and random-effects regression models were used to analyse the longitudinal data on the relationship between health risk factors and the outcomes. The models were adjusted for age, sex, marital status, education background, employment status, equilibrium household income, residential area, country of birth, indigenous status, and socio-economic status.ResultsAfter adjusting for all other health risk factors covariates, physical inactivity had the greatest impact on healthcare resource utilization, work-related outcomes, and HRQoL. Physical inactivity increased the likelihood of outpatient visits (AOR = 1.60, 95% CI = 1.45, 1.76 p ConclusionOur study contributed to the growing body of literature on the relative impact of health risk factors for healthcare resource utilization, work-related outcomes and HRQoL. Our results suggested that public health interventions aim at improving these risk factors, particularly physical inactivity and obesity, can offer substantial benefits, not only for healthcare resource utilization but also for productivity.</p

Additional file 1 of The relative impact of underweight, overweight, smoking, and physical inactivity on health and associated costs in Indonesia: propensity score matching of a national sample

Additional file 1: Figure S1. Flowchart of sampling selection for independent variable BMI. Figure S2. Flowchart of sampling selection for independent variable tobacco consumption. Figure S3. Flowchart of sampling selection for independent variable physical activity. Figure S4. Flowchart of sampling selection for independent variable ageing. Table S1. List of variables for 2014 IFLS analysis. Table S2. Sample characteristics stratified by age groups, before matching. Table S3. Sample characteristics stratified by tobacco consumption groups, before matching. Table S4. Sample characteristics stratified by BMI groups, before matching. Table S5. Sample characteristics stratified by physical activity (PA) groups, before matching. Table S6. Mean biases of covariates after matching using individual t-test (age group 50-59 vs 40-49). Table S7. Mean biases of covariates after matching using individual t-test (age group 60-69 vs 40-49). Table S8. Mean biases of covariates after matching using individual t-test (age group 70+ vs 40-49). Table S9. Mean biases of covariates after matching using individual t-test (former vs never use tobacco). Table S10. Mean biases of covariates after matching using individual t-test (light user vs never use tobacco). Table S11. Mean biases of covariates after matching using individual t-test (moderate user vs never use tobacco). Table S12. Mean biases of covariates after matching using individual t-test (heavy user vs never use tobacco). Table S13. Mean biases of covariates after matching using individual t-test (overweight vs normal BMI). Table S14. Mean biases of covariates after matching using individual t-test (obesity vs normal BMI). Table S15. Mean biases of covariates after matching using individual t-test (underweight vs normal BMI). Table S16. Mean biases of covariates after matching using individual t-test (low vs high physical activity. Table S17. Mean biases of covariates after matching using individual t-test (moderate vs high physical activity). Table S18. The ATT of the number of chronic conditions across different matching algorithms. Table S19. The ATT of multimorbidity presence across different matching algorithms. Table S20. The ATT of the number of outpatient visits across different matching algorithms. Table S21. The ATT of the number of inpatient visits across different matching algorithms. Table S22. The ATT of CHE >25% of total household expenditure across different matching algorithms. Table S23. The ATT of CHE >40% of total non-food expenditure across different matching algorithms. Table S24. The ATT of labour participation across different matching algorithms. Table S25. The ATT of the number of days primary activity missed across different matching algorithms. Table S26. Logistic and ZINB regression for the number of chronic condition and presence of multimorbidity. Table S27. Logistic and ZINB regression for the number of outpatient and inpatient visits. Table S28. Logistic regression for CHE >25% and 40%. Table S29. Logistic and ZINB regression for the productivity loss

Prevalence of unmet health care need in older adults in 83 countries: measuring progressing towards universal health coverage in the context of global population ageing

Abstract Current measures for monitoring progress towards universal health coverage (UHC) do not adequately account for populations that do not have the same level of access to quality care services and/or financial protection to cover health expenses for when care is accessed. This gap in accounting for unmet health care needs may contribute to underutilization of needed services or widening inequalities. Asking people whether or not their needs for health care have been met, as part of a household survey, is a pragmatic way of capturing this information. This analysis examined responses to self-reported questions about unmet need asked as part of 17 health, social and economic surveys conducted between 2001 and 2019, representing 83 low-, middle- and high-income countries. Noting the large variation in questions and response categories, the results point to low levels (less than 2%) of unmet need reported in adults aged 60+ years in countries like Andorra, Qatar, Republic of Korea, Slovenia, Thailand and Viet Nam to rates of over 50% in Georgia, Haiti, Morocco, Rwanda, and Zimbabwe. While unique, these estimates are likely underestimates, and do not begin to address issues of poor quality of care as a barrier or contributing to unmet need in those who were able to access care. Monitoring progress towards UHC will need to incorporate estimates of unmet need if we are to reach universality and reduce health inequalities in older populations