Population Health Metrics最新文献

Background: A comprehensive understanding of the impact of the COVID-19 pandemic on childhood nutrition is crucial for devising effective mitigation strategies. However, existing knowledge regarding the pandemic's effect on childhood nutritional status remains limited. Furthermore, research focusing on young children aged 0-3 years is scarce.

Methods: Leveraging the outbreak that originated in Wuhan in Dec 2019, the epicenter of China's first and largest outbreak, and national survey and statistical yearbook data, this study conducted a natural experimental analysis with the consideration of geographical exposure, temporal exposure and survey cohort effects to investigate the pandemic's impacts on varying nutritional indicators of infants and toddlers aged 0-36 months. A comprehensive set of sensitivity analyses, robustness checks and falsification tests were conducted. The potential heterogeneities across socioeconomic and age groups were also examined.

Results: The pandemic was robustly predictive of a higher weight-for-age z-score (WAZ) and length/height-for-age z-score (HAZ), and a lower likelihood of underweight. The effects of the pandemic on increasing WAZ and reducing underweight were more pronounced among children from economically disadvantaged backgrounds or aged 0-12 months. Additionally, the increasing HAZ was primarily among children from households with lower family income. Moreover, the pandemic was negatively linked to the body mass index (BMI)-for-age z-score (BAZ) of children residing in more developed cities, and positively linked to overweight/obesity among children aged 13-24 months.

Conclusions: This study adds to a more comprehensive understanding of the impact of the COVID-19 pandemic on childhood nutrition. Notably, the findings highlight that weight gain attributable to the pandemic was predominantly among vulnerable children from disadvantaged backgrounds and younger age groups, who were already at a higher risk of overweight/obesity before the pandemic. Consequently, our findings imply the necessity of greater caution to the widened gap in childhood malnutrition post-pandemic. Furthermore, the study emphasizes the importance of implementing adaptable strategies with the consideration of social justice to safeguard all children's right to optimal growth from exogenous shocks and to achieve the children-related SDGs by 2030.

Background: In low- and middle-income countries with limited death registration statistics, adult mortality rates are commonly estimated through sibling survival histories (SSH). In full SSH, respondents are asked about either the age, or the age and time of death, of each of their siblings in turn. Full SSH allow direct mortality estimation but can be time-consuming to collect. In this study, we introduce a new indirect estimation method using summary SSH, requiring only a limited set of questions to produce recent mortality estimates.

Methods: We developed a set of 192 microsimulations representing a wide range of fertility and mortality patterns, and reconstructed summary SSH within these simulations as if they had been collected from adults aged 15-49. For each age group of respondents, we calculated coefficients that convert the proportion of adult siblings who died in the previous 5 years into age-specific mortality rates. We then evaluated the performance of this new method with real data, using 154 Demographic and Health Surveys.

Results: The new indirect method provides mortality rates that are consistent with direct estimates from full SSH. Across all DHS, the mean absolute percentage error in the risk of dying in adulthood (ages 15-49) is 6% for both men and women. In all but one survey, 95% confidence intervals around the direct and indirect estimates overlap. As with direct estimates of adult mortality from SSH, the indirect estimates remain, however, lower than those of the Population Division of the United Nations.

Conclusions: Summary questions on sibling survival can be included in censuses and rapid turn-around surveys for the measurement of recent adult mortality.

Background: The proliferation of instruments that define instrument-specific metrics impedes progress in comparative assessment across populations. This paper explores a method to extract a common metric from related but different instruments and transform the original measurements into scores with a standard unit of measurement.

Methods: Existing data from four assessment instruments of child development, collected from three different samples of children, were used to create "equate clusters" of items that measure the same behaviour in (slightly) different ways. A probability model was formulated to identify best items and groups to serve as anchors linking the instruments, assuming that items in an anchoring or "active" equate cluster are psychometrically equivalent. Quantification and inspection of item characteristic curves were used to resolve which equate clusters should be active. We simulated the impact of various analytic choices.

Results: Simulation confirmed the feasibility of creating a common metric from data collected with different instruments from respondent samples with different abilities. The method performed as expected in an application in early childhood development.

Conclusions: The use of equate clusters is an intuitive and flexible way to establish a common metric across instruments and facilitates the transformation of measurements obtained to a standardized scale. Standardizing instrument scores to a common metric allows for population-level comparisons on a global scale.

Background: Mapping health outcomes related to environmental health hazards at the county level can lead to a simplification of risks experienced by populations in that county. The Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program has developed sub-county geographies that aggregate census tracts to allow for stable, minimally suppressed data to be displayed. This helps to highlight more local variation in environmental health outcomes and risk data. However, we wanted to understand whether the aggregation method used was aggregating sociodemographically similar or dissimilar areas with one another. This analysis attempts to explore whether the distributions of select people who may be at increased risk for exposure to environmental health hazards as identified by the Tracking Program are preserved in these sub-county geographies with the census tracts used as the foundation to create them.

Methods: Mean values of three sociodemographic characteristics (persons aged 65 years and older, people from racial and ethnic minority groups, and population below the poverty level) for each sub-county geography in five states were calculated and placed into five break groups. Differences in break groups were determined and compared for each sub-county geography and census tract.

Results: The sociodemographic characteristics among the census tracts and two aggregated sub-county geographies were similar. In some instances, census tracts with a low population or a highly skewed population (e.g., very high percentage of population aged 65 years and older) were aggregated with dissimilar census tracts out of necessity to meet the requirements set by the Tracking Program's aggregation methodology. This pattern was detected in 2.41-6.59% of census tracts within the study area, depending on the sociodemographic variable and aggregation level.

Conclusions: The Tracking Program's sub-county aggregation methodology aggregates census tracts with similar characteristics. The two new sub-county geographies can serve as a potential option for health officials and policymakers to develop targeted interventions using finer resolution health outcome and environmental hazard data compared to coarser resolution county-level data.

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.