Population Health Metrics最新文献

Background: Current measures to monitor population health include indicators of (i) average length-of-life (life expectancy), (ii) average length-of-life spent in good health (health expectancy), and (iii) variability in length-of-life (lifespan inequality). What is lacking is an indicator measuring the extent to which healthy lifespans are unequally distributed across individuals (the so-called 'healthy lifespan inequality' indicators).

Methods: We combine information on age-specific survival with the prevalence of functional limitation or disability in Spain (2014-2017) by sex and level of education to estimate age-at-disability onset distributions. Age-, sex- and education-specific prevalence rates of adult individuals' daily activities limitations were based on the GALI index derived from Spanish National Health Surveys held in 2014 and 2017. We measured inequality using the Gini index.

Results: In contemporary Spain, education differences in health expectancy are substantial and greatly exceed differences in life expectancy. The female advantage in life expectancy disappears when considering health expectancy indicators, both overall and across education groups. The highly educated exhibit lower levels of lifespan inequality, and lifespan inequality is systematically higher among men. Our new healthy lifespan inequality indicators suggest that the variability in the ages at which physical daily activity limitations start are substantially larger than the variability in the ages at which individuals die. Healthy lifespan inequality tends to decrease with increasing educational attainment, both for women and for men. The variability in ages at which physical limitations start is slightly higher for women than for men.

Conclusions: The suggested indicators uncover new layers of health inequality that are not traceable with currently existing approaches. Low-educated individuals tend to not only die earlier and spend a shorter portion of their lives in good health than their highly educated counterparts, but also face greater variation in the eventual time of death and in the age at which they cease enjoying good health-a multiple burden of inequality that should be taken into consideration when evaluating the performance of public health systems and in the elaboration of realistic working-life extension plans and the design of equitable pension reforms.

Background: Despite substantial improvements in prevention and therapy, myocardial infarction (MI) remains a frequent health event, causing high mortality and serious health impairments. Previous research lacks evidence on how social inequalities in incidence and mortality risks developed over time, and on how these developments affect the lifespan free of MI and after MI in different social subgroups. This study investigates income inequalities in MI-free life years and life years after MI and whether these inequalities widened or narrowed over time.

Methods: The analyses are based on claims data of a large German health insurance provider insuring approximately 2.8 million individuals in the federal state Lower Saxony. Trends in income inequalities in incidence and mortality were assessed for all subjects aged 60 years and older by comparing the time periods 2006-2008 and 2015-2017 using multistate survival models. Trends in the number of life years free of MI and after MI were calculated separately for income groups by applying multistate life table analyses.

Results: MI incidence and mortality risks decreased over time, but declines were strongest among men and women in the higher-income group. While life years free of MI increased in men and women with higher incomes, no MI-free life years were gained in the low-income group. Among men, life years after MI increased irrespective of income group.

Conclusions: Income inequalities in the lifespan spent free of MI and after MI widened over time. In particular, men with low incomes are disadvantaged, as life years spent after MI increased, but no life years free of MI were gained.

Background: Value of a Statistical Life Year (VSLY) provides an important economic measure of an individual's trade-off between health risks and other consumption, and is a widely used policy parameter. Measuring VSLY is complex though, especially in low-income and low-literacy communities.

Methods: Using a large randomized experiment (N = 3027), we study methodological aspects of stated-preference elicitation with payment cards (price lists) in an extreme poverty context. In a 2 × 2 design, we systematically vary whether buying or selling prices are measured, crossed with the range of the payment card.

Results: We find substantial effects of both the pricing method and the list range on elicited VSLY. Estimates of the gross domestic product per capita multiplier for VSLY range from 3.5 to 33.5 depending on the study design. Importantly, all estimates are economically and statistically significantly larger than the current World Health Organization threshold of 3.0 for cost-effectiveness analyses.

Conclusions: Our results inform design choice in VSLY measurements, and provide insight into the potential variability of these measurements and possibly robustness checks.

Background: Poor data quality is limiting the use of data sourced from routine health information systems (RHIS), especially in low- and middle-income countries. An important component of this data quality issue comes from missing values, where health facilities, for a variety of reasons, fail to report to the central system.

Methods: Using data from the health management information system in the Democratic Republic of the Congo and the advent of COVID-19 pandemic as an illustrative case study, we implemented seven commonly used imputation methods and evaluated their performance in terms of minimizing bias in imputed values and parameter estimates generated through subsequent analytical techniques, namely segmented regression, which is widely used in interrupted time series studies, and pre-post-comparisons through paired Wilcoxon rank-sum tests. We also examined the performance of these imputation methods under different missing mechanisms and tested their stability to changes in the data.

Results: For regression analyses, there were no substantial differences found in the coefficient estimates generated from all methods except mean imputation and exclusion and interpolation when the data contained less than 20% missing values. However, as the missing proportion grew, k-NN started to produce biased estimates. Machine learning algorithms, i.e. missForest and k-NN, were also found to lack robustness to small changes in the data or consecutive missingness. On the other hand, multiple imputation methods generated the overall most unbiased estimates and were the most robust to all changes in data. They also produced smaller standard errors than single imputations. For pre-post-comparisons, all methods produced p values less than 0.01, regardless of the amount of missingness introduced, suggesting low sensitivity of Wilcoxon rank-sum tests to the imputation method used.

Conclusions: We recommend the use of multiple imputation in addressing missing values in RHIS datasets and appropriate handling of data structure to minimize imputation standard errors. In cases where necessary computing resources are unavailable for multiple imputation, one may consider seasonal decomposition as the next best method. Mean imputation and exclusion and interpolation, however, always produced biased and misleading results in the subsequent analyses, and thus, their use in the handling of missing values should be discouraged.

Background: Alcohol use has widespread effects on health and contributes to over 200 detrimental conditions. Although the pattern of heavy episodic drinking independently increases the risk for injuries and transmission of some infectious diseases, long-term average consumption is the fundamental predictor of risk for most conditions. Population surveys, which are the main source of data on alcohol exposure, suffer from bias and uncertainty. This article proposes a novel triangulation method to reduce bias by rescaling consumption estimates by sex and age to match country-level consumption from administrative data.

Methods: We used data from 17 population surveys to estimate age- and sex-specific trends in alcohol consumption in the adult population of South Africa between 1998 and 2016. Independently for each survey, we calculated sex- and age-specific estimates of the prevalence of drinkers and the distribution of individuals across consumption categories. We used these aggregated results, together with data on alcohol production, sales and import/export, as inputs of a Bayesian model and generated yearly estimates of the prevalence of drinkers in the population and the parameters that characterise the distribution of the average consumption among drinkers.

Results: Among males, the prevalence of drinkers decreased between 1998 and 2009, from 56.2% (95% CI 53.7%; 58.7%) to 50.6% (49.3%; 52.0%), and increased afterwards to 53.9% (51.5%; 56.2%) in 2016. The average consumption from 52.1 g/day (49.1; 55.6) in 1998 to 42.8 g/day (40.0; 45.7) in 2016. Among females the prevalence of current drinkers rose from 19.0% (17.2%; 20.8%) in 1998 to 20.0% (18.3%; 21.7%) in 2016 while average consumption decreased from 32.7 g/day (30.2; 35.0) to 26.4 g/day (23.8; 28.9).

Conclusions: The methodology provides a viable alternative to current approaches to reconcile survey estimates of individual alcohol consumption patterns with aggregate administrative data. It provides sex- and age-specific estimates of prevalence of drinkers and distribution of average daily consumption among drinkers in populations. Reliance on locally sourced data instead of global and regional trend estimates better reflects local nuances and is adaptable to the inclusion of additional data. This provides a powerful tool to monitor consumption, develop burden of disease estimates and inform and evaluate public health interventions.

Background: When Service Provision Assessment (SPA) surveys on primary health service delivery are combined with the nationally representative household survey-Demographic and Health Survey (DHS), they can provide key information on the access, utilization, and equity of health service availability in low- and middle-income countries. However, existing linkage methods have been established only at aggregate levels due to known limitations of the survey datasets.

Methods: For the linkage of two data sets at a disaggregated level, we developed a geostatistical approach where SPA limitations are explicitly accounted for by identifying the sites where health facilities might be present but not included in SPA surveys. Using the knowledge gained from SPA surveys related to the contextual information around facilities and their spatial structure, we made an inference on the service environment of unsampled health facilities. The geostatistical linkage results on the availability of health service were validated using two criteria-prediction accuracy and classification error. We also assessed the effect of displacement of DHS clusters on the linkage results using simulation.

Results: The performance evaluation of the geostatistical linkage method, demonstrated using information on the general service readiness of sampled health facilities in Tanzania, showed that the proposed methods exceeded the performance of the existing methods in terms of both prediction accuracy and classification error. We also found that the geostatistical linkage methods are more robust than existing methods with respect to the displacement of DHS clusters.

Conclusions: The proposed geospatial approach minimizes the methodological issues and has potential to be used in various public health research applications where facility and population-based data need to be combined at fine spatial scale.

Background: The concept of "premature mortality" is at the heart of many national and global health measurement and benchmarking efforts. However, despite the intuitive appeal of its underlying concept, it is far from obvious how to best operationalise it. The previous work offers at least two basic approaches: an absolute and a relative one. The former-and far more widely used- approach sets a unique age threshold (e.g. 65 years), below which deaths are defined as premature. The relative approach derives the share of premature deaths from the country-specific age distribution of deaths in the country of interest. The biggest disadvantage of the absolute approach is that of using a unique, arbitrary threshold for different mortality patterns, while the main disadvantage of the relative approach is that its estimate of premature mortality strongly depends on how the senescent deaths distribution is defined in each country.

Method: We propose to overcome some of the downsides of the existing approaches, by combining features of both, using a hierarchical model, in which senescent deaths distribution is held constant for each country as a pivotal quantity and the premature mortality distribution is allowed to vary across countries. In this way, premature mortality estimates become more comparable across countries with similar characteristics.

Results: The proposed hierarchical models provide results, which appear to align with related evidence from  specific countries. In particular, we find a relatively high premature mortality for the United States and Denmark.

Conclusions: While our hybrid approach overcomes some of the problems of previous measures, some issues require further research, in particular the choice of the group of countries that a given country is assigned to and the choice of the benchmarks within the groups. Hence, our proposed method, combined with further study addressing these issues, could provide a valid alternative way to measure and compare premature mortality across countries.

Background: In many high-income countries, life expectancy (LE) has increased, with women outliving men. This gender gap in LE (GGLE) has been explained with biological factors, healthy behaviours, health status, and sociodemographic characteristics, but little attention has been paid to the role of public health policies that include/affect these factors. This study aimed to assess the contributions of avoidable causes of death, as a measure of public health policies and healthcare quality impacts, to the GGLE and its temporal changes in the UK. We also estimated the contributions of avoidable causes of death into the gap in LE between countries in the UK.

Methods: We obtained annual data on underlying causes of death by age and sex from the World Health Organization mortality database for the periods 2001-2003 and 2014-2016. We calculated LE at birth using abridged life tables. We applied Arriaga's decomposition method to compute the age- and cause-specific contributions into the GGLE in each period and its changes between two periods as well as the cross-country gap in LE in the 2014-2016 period.

Results: Avoidable causes had greater contributions than non-avoidable causes to the GGLE in both periods (62% in 2001-2003 and 54% in 2014-2016) in the UK. Among avoidable causes, ischaemic heart disease (IHD) followed by injuries had the greatest contributions to the GGLE in both periods. On average, the GGLE across the UK narrowed by about 1.0 year between 2001-2003 and 2014-2016 and three avoidable causes of IHD, lung cancer, and injuries accounted for about 0.8 years of this reduction. England & Wales had the greatest LE for both sexes in 2014-2016. Among avoidable causes, injuries in men and lung cancer in women had the largest contributions to the LE advantage in England & Wales compared to Northern Ireland, while drug-related deaths compared to Scotland in both sexes.

Conclusion: With avoidable causes, particularly preventable deaths, substantially contributing to the gender and cross-country gaps in LE, our results suggest the need for behavioural changes by implementing targeted public health programmes, particularly targeting younger men from Scotland and Northern Ireland.