Population Health Metrics最新文献

Background: The burden of disease (BOD) approach, originating with the Global Burden of Disease (GBD) study in the 1990s, has become a cornerstone for population health monitoring. Despite the widespread use of the Disability-Adjusted Life Year (DALY) metric, variations in methodological approaches and reporting inconsistencies hinder comparability across studies. To tackle this issue, we set out to develop guidelines for reporting DALY calculation studies to improve the transparency and comparability of BOD estimates.

Methods and findings: The development of the STROBOD statement began within the European Burden of Disease Network, evolving from initial concepts discussed in workshops and training sessions focused on critical analysis of BOD studies. In 2021, a working group was formed to refine the preliminary version into the final Standardised Reporting of Burden of Disease studies (STROBOD) statement, consisting of 28 items structured across six main sections. These sections cover the title, abstract, introduction, methods, results, discussion, and open science, aiming to ensure transparency and standardization in reporting BOD studies. Notably, the methods section of the STROBOD checklist encompasses aspects such as study setting, data inputs and adjustments, DALY calculation methods, uncertainty analyses, and recommendations for reproducibility and transparency. A pilot phase was conducted to test the efficacy of the STROBOD statement, highlighting the importance of providing clear explanations and examples for each reporting item.

Conclusions: The inaugural STROBOD statement offers a crucial framework for standardizing reporting in BOD research, with plans for ongoing evaluation and potential revisions based on user feedback. While the current version focuses on general BOD methodology, future iterations may include specialized checklists for distinct applications such as injury or risk factor estimation, reflecting the dynamic nature of this field.

Background: The aims of this study were to establish national disability weights based on the health state preferences of a Dutch general population sample, examine the relation between results and respondent's characteristics, and compare disability weights with those estimated in the European disability weights study.

Methods: In this cross-sectional study, a web-based survey was administered to a general population 18-75 years from the Netherlands. The survey included paired comparison questions. Paired comparison data were analysed using probit regression and located results onto the 0-to-1 disability weight scale using non-parametric regression. Bootstrapping was used to estimate 95% uncertainty intervals (95%UI). Spearman's correlation was used to investigate the relation of probit regression coefficients between respondent's characteristics.

Results: 3994 respondents completed the questionnaire. The disability weights ranged from 0.007 (95%UI: 0.003-0.012) for mild distance vision impairment to 0.741 (95% UI: 0.498-0.924) for intensive care unit admission. Spearman's correlation of probit coefficients between sub-groups based on respondent's characteristics were all above 0.95 (p < 0.001). Comparison of disability weights of 140 health states that were included in the Dutch and European disability weights study showed a high correlation (Spearman's correlation: 0.942; p < 0.001); however, for 76 (54.3%) health states the point estimate of the Dutch disability weight fell outside of the 95%UI of the European disability weights.

Conclusions: Respondent's characteristics had no influence on health state valuations with the paired comparison. However, comparison of the Dutch disability weights to the European disability weights indicates that health state preferences of the general population of the Netherlands differ from those of other European countries.

Background: Regional variations in SARS-CoV-2 infection were observed in Canada and other countries. Studies have used multilevel analyses to examine how a context, such as a neighbourhood, can affect the SARS-CoV-2 infection rates of the people within it. However, few multilevel studies have quantified the magnitude of the general contextual effect (GCE) in SARS-CoV-2 infection rates and assessed how it may be associated with individual- and area-level characteristics. To address this gap, we will illustrate the application of the median rate ratio (MRR) in a multilevel Poisson analysis for quantifying the GCE in SARS-CoV-2 infection rates in Ontario, Canada.

Methods: We conducted a population-based, two-level multilevel observational study where individuals were nested into regions (i.e., forward sortation areas [FSAs]). The study population included community-dwelling adults in Ontario, Canada, between March 1, 2020, and May 1, 2021. The model included seven individual-level variables (age, sex, asthma, diabetes, hypertension, congestive heart failure, and chronic obstructive pulmonary disease) and four FSA census-based variables (household size, household income, employment, and driving to work). The MRR is a median value of the rate ratios comparing two patients with identical characteristics randomly selected from two different regions ordered by rate. We examined the attenuation of the MRR after including individual-level and FSA census-based variables to assess their role in explaining the variation in rates between regions.

Results: Of the 11 789 128 Ontario adult community-dwelling residents, 343 787 had at least one SARS-CoV-2 infection during the study period. After adjusting for individual-level and FSA census-based variables, the MRR was attenuated to 1.67 (39% reduction from unadjusted MRR). The strongest FSA census-based associations were household size (RR = 1.88, 95% CI: 1.71-1.97) and driving to work (RR = 0.68, 95% CI: 0.65-0.71).

Conclusions: The individual- and area-level characteristics in our study accounted for approximately 40% of the between-region variation in SARS-CoV-2 infection rates measured by MRR in Ontario, Canada. These findings suggest that population-based policies to address social determinants of health that attenuate the MRR may reduce the observed between-region heterogeneity in SARS-CoV-2 infection rates.

Background: Since the outbreak of the COVID-19 pandemic, the excess mortality P-score has gained prominence as a measure of pandemic burden. The P-score indicates the percentage by which observed deaths deviate from expected deaths. As the P-score is regularly used to compare excess mortality between countries, questions arise regarding the age dependency of the measure. In this paper we present formal and empirical results on the population structure bias of the P-score with a special focus on cross-country comparisons during the COVID-19 pandemic in Europe.

Methods: P-scores were calculated for European countries for 2021, 2022, and 2023 using data from the 2024 revision of the United Nations' World Population Prospects and the HMDs Short Term Mortality Fluctuations data series. The expected deaths for 2021, 2022, and 2023 were estimated using a Lee-Carter forecast model assuming pre-pandemic conditions. P-score differences between countries were decomposed using a Kitagawa-type decomposition into excess-mortality and structural components. To investigate the sensitivity of P-score cross-country rankings to differences in population structure we calculated the rank-correlation between age-standardized and classical P-scores.

Results: The P-score is an average of age-specific percent excess deaths weighted by the age-distribution of expected deaths. It can be shown that the effect of differences in the distribution of deaths only plays a marginal role in a European comparison. In most cases, the excess mortality effect is the dominant effect. P-score rankings among European countries during the COVID-19 pandemic are similar under both age-standardized and classical P-scores.

Conclusions: Although the P-score formally depends on the age-distribution of expected deaths, this structural component only plays a minor role in a European comparison, as the distribution of deaths across the continent is similar. Thus, the P-score is suitable as a measure of excess mortality in a European comparison, as it mainly reflects the differences in excess mortality. However, this finding should not be extrapolated to global comparisons, where countries could have very different death distributions. In situations were P-score comparisons are biased age-standardization can be applied as a solution.

In 2016, the Bloomberg Philanthropies Data for Health initiative assisted the Philippine Statistical Authority in implementing Iris, an automated coding software program that enables medical death certificates to be coded according to international standards. Iris was implemented to improve the quality, timeliness, and consistency of coded data as part of broader activities to strengthen the country's civil registration and vital statistics system. This study was conducted as part of the routine implementation of Iris to ensure that automatically coded cause of death data was of sufficient quality to be released and disseminated as national mortality statistics. Data from medical death certificates coded with Iris between 2017 and 2019 were analysed and evaluated for apparent errors and inconsistencies, and trends were examined for plausibility. Cause-specific mortality distributions were calculated for each of the 3 years and compared for consistency, and annual numeric and percentage changes were calculated and compared for all age groups. The typology, reasons, and proportions of records that could not be coded (Iris 'rejects') were also studied. Overall, the study found that the Philippine Statistical Authority successfully operates Iris. The cause-specific mortality fractions for the 20 leading causes of death showed reassuring stability after the introduction of Iris, and the type and proportion of rejects were similar to international experience. Broadly, this study demonstrates how an automated coding system can improve the accuracy and timeliness of cause of death data-providing critical country experiences to help build the evidence base on the topic.

Background: The Decade of Healthy Aging (2021-2030) emerges as a 10 years strategy to improve the lives of older adults, their families, and the communities in which they live. One of the actions defined in this framework is related to improving the measurement, monitoring, and understanding of characteristics, factors, and needs related to aging and health. The aim was to analyze and assess the process of construction and development of the Strategic Information System on Health, Funcional Dependence and Aging (SIESDE, for its acronym in Spanish). SIESDE will provide strategic information in Mexico at the municipal, state, and national levels that support the public policies on healthy aging.

Methods: The system processes and analyzes the data sources of the Health Information Systems and the National System of Statistical and Geographical Information. SIESDE comprises three components: (1) Design, construction, and evaluation of the indicators; (2) storage, management, and visualization, and (3) diffusion and translation of information.

Results: A total of 135 indicators were built on seven themes: (1) demographic, socioeconomic, and aging conditions, (2) health, (3) functional dependence, (4) healthy aging, (5) health services, (6) social and physical environments, and (7) complex indicators.

Conclusions: SIESDE is an effective system for providing an overall view of health, aging, and functional dependence.

Background: Routine health facility data are an important source of health information in resource-limited settings. Regular quality assessments are necessary to improve the reliability of routine data for different purposes, including estimating facility-based maternal mortality. This study aimed to assess the quality of routine data on deliveries, livebirths and maternal deaths in Kampala City, Uganda.

Methods: We reviewed routine health facility data from the district health information system (DHIS2) for 2016 to 2021. This time period included an upgrade of DHIS2, resulting in two datasets (2016-2019 and 2020-2021) that were managed separately. We analysed data for all facilities that reported at least one delivery in any of the six years, and for a subset of facilities designated to provide emergency obstetric care (EmOC). We adapted the World Health Organization data quality review framework to assess completeness and internal consistency of the three data elements, using 2019 and 2021 as reference years. Primary data were collected to verify reporting accuracy in four purposively selected EmOC facilities. Data were disaggregated by facility level and ownership.

Results: We included 255 facilities from 2016 to 2019 and 247 from 2020 to 2021; of which 30% were EmOC facilities. The overall completeness of data for deliveries and livebirths ranged between 53% and 55%, while it was < 2% for maternal deaths (98% of monthly values were zero). Among EmOC facilities, completeness was higher for deliveries and livebirths at 80%; and was < 6% for maternal deaths. For the whole sample, the prevalence of outliers for all three data elements was < 2%. Inconsistencies over time were mostly observed for maternal deaths, with the highest difference of 96% occurring in 2021.

Conclusions: Routine data from childbirth facilities in Kampala were generally suboptimal, but the quality was better in EmOC facilities. Given likely underreporting of maternal deaths, further efforts to verify and count all facility-related maternal deaths are essential to accurately estimate facility-based maternal mortality. Data reliability could be enhanced by improving reporting practices in EmOC facilities and streamlining reporting processes in private-for-profit facilities. Further qualitative studies should identify critical points where data are compromised, and data quality assessments should consider service delivery standards.

Background: In malaria-endemic countries, asymptomatic carriers of plasmodium represent an important reservoir for malaria transmission. Estimating the burden at a fine scale and identifying areas at high risk of asymptomatic carriage are important to guide malaria control strategies. This study aimed to estimate the prevalence of asymptomatic carriage at the communal level in Burkina Faso, the smallest geographical entity from which a local development policy can be driven.

Methods: The data used in this study came from several open sources: the 2018 Multiple Indicator Cluster Survey on Malaria and the 2019 general census of the population data and environmental. The analysis involved a total of 5489 children under 5 from the malaria survey and 293,715 children under 5 from the census. The Elbers Langjouw and Langjouw (ELL) approach is used to estimate the prevalence. This approach consists of including data from several sources (mainly census and survey data) in a statistical model to obtain predictive indicators at a sub-geographical level, which are not measured in the population census. The method achieves this by finding correlations between common census variables and survey data.

Findings: The findings suggest that the spatial distribution of the prevalence of asymptomatic carriage is very heterogeneous across the communes. It varies from a minimum of 5.1% (95% CI 3.6-6.5) in the commune of Bobo-Dioulasso to a maximum of 41.4% (95% CI 33.5-49.4) in the commune of Djigoué. Of the 341 communes, 208 (61%) had prevalences above the national average of 20.3% (95% CI 18.8-21.2).

Contributions: This analysis provided commune-level estimates of the prevalence of asymptomatic carriage of plasmodium in Burkina Faso. The results of this analysis should help to improve planning of malaria control at the communal level in Burkina Faso.

Background: The COVID-19 pandemic brought greater focus to the rural mortality penalty in the U.S., which describes the greater mortality rate in rural compared to urban areas. Although it is widely thought that issues such as access to care, age structure of the population, and differences in behavior are likely drivers of the rural mortality penalty, few studies have attempted to tie delayed access to care in rural populations to healthcare outcomes quantitatively. Therefore, it is critical to try and understand these factors to enable more effective public health policy.

Methods: We performed a cross-sectional analysis of a population of patients with COVID-19 who were admitted to hospitals in the United States between 3/1/2020 and 2/26/2023 to better understand factors leading to outcome disparities amongst groups that all had some level of access to hospital care. Nevertheless, it is widely thought that rural populations often experience delayed access to care, due to transportation and other constraints. Therefore, we hypothesized that deteriorated patient condition at admission likely explained some of the observed difference in mortality between rural and urban populations.

Results: Our results supported our hypothesis, showing that the rural mortality penalty persists in this population and that by multiple measures, rural patients were likely to be admitted in worse condition, had worse overall health, and were older.

Conclusions: Although the pandemic threw the rural mortality penalty into sharp relief, it is important to remember that it existed prior to the pandemic and will continue to exist until effective interventions are implemented. This study demonstrates the critical need to address the underlying factors that resulted in rural-dwelling patients being admitted to the hospital in worse condition than their urban-dwelling counterparts during the COVID-19 pandemic, which likely affected other healthcare outcomes as well.