Three Essays on Health, Health Care, and Healthy Ageing

This thesis consists of three empirical studies focusing on the health and health care utilisation of older adults using the healthy ageing framework proposed by the World Health Organization in 2015. In Essay 1, I examine the relationships between life-course factors and intrinsic capacity, a break-through and strengths-based composite measure of ageing. I find that unfavourable early-life factors directly decrease late-life intrinsic capacities, particularly cognitive, sensory and psychological capacities rather than locomotor functioning and vitality, and these effects are exacerbated by the cumulative socioeconomic inequalities over a person’s life course. In Essay 2, I employ the method of standardised patients to identify the overuse of health care, document its patterns, and quantify its financial impact on patients in primary care in China. My findings suggest that overuse is pervasive in primary care in China and leads to a significant increase in health care expenditure. The overuse in my setting seems unlikely to be attributable to physician incompetence. My findings shed light on the cost escalation of primary care in China, which is a form of medical inefficiency that should be urgently addressed. In Essay 3, I further investigate the impact of physician over-service on the quality of care provided, since physician over-service can also contribute to physicians’ learning and therefore better health care. I report new evidence that physician over-service is associated with a significant increase in physicians’ investment in learning, such as consultation length, adherence to checklists, and patient-centred communication, but no significant change in giving a correct diagnosis, correct drug prescriptions or a referral. Moreover, over-service in drugs is associated with a significant increase in physicians’ better learning and the provision of correct drugs. However, my findings imply that physician over-service does not improve the accuracy of physicians’ decisions. The higher rate of correct drug prescriptions was mainly explained by the prescription of more drugs

Decomposing inequality in long-term care need among older adults with chronic diseases in China : a life course perspective

Abstract:Background: Chinahasthelargestnumberofagingpeopleinneedoflong-termcare,among whom 70% have chronic diseases. For policy planners, it is necessary to understand the different levels of needs of long-term care and provide long-term care insurance to ensure the long-term care needs of all people can be met. Methods: This study combines the 2013 wave of CHARLS survey and the Life Course Survey of 2014. The combination allows us to factor in both childhood and adulthood data to provide life-course analysis. We identified 7,734 older adults with chronic diseases foranalysis. Theneedforlong-termcareisdefinedbythepresenceoffunctionallimitationsbasedon the performance of basic activities of daily living (ADLs) and of instrumental activities of daily living (IADLs). Two dummy variables, ADLs disability and IADLs disability, and two count variables, ADLs score and IADLs score, were defined to measure incidence and severity of long-term care need, respectively. The concentration index was used to capture the inequality in long-term care need, and a decomposition method based on Probit Regression and Negative Binomial Regression was exploited to identify the contribution of each determination. Results: At least a little difficulty was reported in ADLs and IADLs in 20.44% and 19.25% of respondents, respectively. The concentration index of ADLs disability, ADLs score, IADLs disability, IADLs score were−0.085,−0.109,−0.095 and−0.120, respectively, all of which were statistically significant, indicating the pro-poor inequality in the incidence and severity of long-term care need. Decomposition analyses revealed that family income,educationattainment,aging,andchildhoodexperienceplayedasignificantroleinexplaining the inequalities. Conclusions: The long-term care need among older adults with chronic disease is high in China and low socioeconomic groups had a higher probability of needing long-term care or need more long-term care. It is urgent to implement long-term care insurance, especially for the individuals from lower socioeconomic groups

Association between multimorbidity and informal long-term care use in China: a nationwide cohort study

Background The impact of multimorbidity on long-term care (LTC) use is understudied, despite its well-documented negative effects on functional disabilities. The current study aims to assess the association between multimorbidity and informal LTC use in China. We also explored the socioeconomic and regional disparities. Methods The study included 10,831 community-dwelling respondents aged 45 years and older from the China Health and Retirement Longitudinal Study in 2011, 2015, and 2018 for analysis. We used a two-part model with random effects to estimate the association between multimorbidity and informal LTC use. Heterogeneity of the association by socioeconomic position (education and income) and region was explored via a subgroup analysis. We further converted the change of informal LTC hours associated with multimorbidity into monetary value and calculated the 95% uncertainty interval (UI). Results The reported prevalence of multimorbidity was 60·0% (95% CI: 58·9%, 61·2%) in 2018. We found multimorbidity was associated with an increased likelihood of receiving informal LTC (OR = 2·13; 95% CI: 1·97, 2·30) and more hours of informal LTC received (IRR = 1·20; 95% CI: 1·06, 1·37), ceteris paribus. Participants in the highest income quintile received more hours of informal LTC care (IRR = 1·62; 95% CI: 1·31, 1·99). The estimated monetary value of increased informal LTC hours among participants with multimorbidity was equivalent to 3·7% (95% UI: 2·2%, 5·4%) of China’s GDP in 2018. Conclusion Our findings substantiate the threat of multimorbidity to LTC burden. It is imperative to strengthen LTC services provision, especially among older adults with multimorbidity and ensure equal access among those with lower income

Effect of Health Insurance Policy on the Health Outcomes of the Middle-Aged and Elderly: Progress Toward Universal Health Coverage

This population-based study aims to explore the effect of the integration of the Urban and Rural Residents' Basic Medical Insurance (URRBMI) policy on the health outcomes of the middle-aged and elderly. A total of 13,360 participants in 2011 and 15,082 participants in 2018 were drawn from the China Health and Retirement Longitudinal Study. Health outcomes were evaluated using the prevalence of chronic diseases. A generalized linear mixed model was used to analyze the effect of the URRBMI policy on the prevalence of chronic disease. Prior to the introduction of the URRBMI policy, 67.09% of the rural participants and 73.00% of the urban participants had chronic diseases; after the policy's implementation, 43.66% of the rural participants and 45.48% of the urban participants had chronic diseases. When adjusting for the confounding factors, the generalized linear mixed model showed that the risk of having a chronic disease decreased by 81% [odds ratio (OR) = 0.19; 95% confidence interval (CI): 0.16, 0.23] after the introduction of the policy in the urban participants; in the rural participants, the risk of having a chronic disease was 30% lower (OR = 0.70; 95% CI: 0.60, 0.82) than the risk in the urban participants before the policy and 84% lower (OR = 0.16; 95% CI: 0.14, 0.19) after the implementation of the policy; the differences in the ORs decreased from 0.30 prior to the policy to 0.03 after the policy had been introduced between rural and urban participants when adjusting for the influence of socioeconomic factors on chronic diseases. This study provides evidence of the positive effects of the URRBMI policy on improving the rural population's health outcomes and reducing the gap in health outcomes between rural and urban populations, indicating that the implementation of the URRBMI policy has promoted the coverage of universal health

Blockade of FGF2/FGFR2 Partially Overcomes Bone Marrow Mesenchymal Stromal Cells Mediated Progression of T-cell Acute Lymphoblastic Leukaemia

The development of acute lymphoblastic leuakemia (ALL) is partly attributed to the effects of bone marrow (BM) microenvironment, especially mesenchymal stromal cells (MSCs), which interact bilaterally with leukaemia cells, leading to ALL progression. In order to find MSCs-based microenvironment targeted therapeutic strategies, Notch1-induced T-cell ALL (T-ALL) mice models were used and dynamic alterations of BM-MSCs with increased cell viability during T-ALL development was observed. In T-ALL mice derived stroma-based condition, leukaemia cells showed significantly elevated growth capacity indicating that MSCs participated in leukaemic niche formation. RNA sequence results revealed that T-ALL derived MSCs secreted fibroblast growth factor 2 (FGF2), which combined with fibroblast growth factor receptor 2 (FGFR2) on leukaemia cells, resulting in activation of PI3K/AKT/mTOR signalling pathway in leukaemia cells. In vitro blocking the interaction between FGF2 and FGFR2 with BGJ398 (infigratinib), a FGFR1-3 kinase inhibitor, or knockdown FGF2 in MSCs by interference caused deactivation of PI3K/AKT/mTOR pathway and dysregulations of genes associated with cell cycle and apoptosis in ALL cells, leading to decrease of leukaemia cells. In mouse model received BGJ398, overall survival was extended and dissemination of leukaemia cells in BM, spleen, liver and peripheral blood was decreased. After subcutaneous injection of primary human T-ALL cells with MSCs, tumour growth was suppressed when FGF2/FGFR2 was interrupted. Thus, inhibition of FGF2/FGFR2 interaction appears to be a valid strategy to overcome BM-MSCs mediated progression of T-ALL, and BGJ398 could indeed improve outcomes in T-ALL, which provide theoretical basis of BGJ398 as a BM microenvironment based therapeutic strategy to control disease progression

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic