Population Health Metrics最新文献

Background: Concentration index-based measures are one of the most popular tools for estimating socioeconomic-status-related health inequalities. In recent years, several variants of the concentration index have been developed that are designed to correct for deficiencies of the standard concentration index and which are increasingly being used. These variants, which include the Wagstaff index and the Erreygers index, have important technical and normative differences.

Main body: In this study, we provide a non-technical review and critical assessment of these indices. We (i) discuss the difficulties that arise when measurement tools intended for income are applied in a health context, (ii) describe and illustrate the interrelationship between the technical and normative properties of these indices, (iii) discuss challenges that arise when determining whether index estimates are large or of policy significance, and (iv) evaluate the alignment of research practice with the properties of the indices used. Issues discussed in parts (i) and (ii) include the different conceptions of inequality that underpin the indices, the types of changes to a distribution which leave inequality unchanged and the importance of the measurement scale and range of the outcome variable. These concepts are illustrated using hypothetical examples. For parts (iii) and (iv), we reviewed 44 empirical studies published between 2015 and 2017 and find that researchers often fail to provide meaningful interpretations of the index estimates.

Conclusion: We propose a series of questions to facilitate further sensitivity analyses and provide a better understanding of the index estimates. We also provide a guide for researchers and policy analysts to facilitate the critical assessment of studies using these indices, while helping applied researchers to choose inequality measures that have the normative properties they seek.

Background: Chronic pain is a common disease; about 20% of people worldwide suffer from it. While compared with the research on the prevalence and management of chronic pain in developed countries, there is a relative lack of research in this field in China. This research aims to construct the China Pain Health Index (CPHI) to evaluate the current status of the prevalence and management of chronic pain in the Chinese population.

Methods: The dimensions and indicators of CPHI were determined through literature review, Delphi method, and analytical hierarchy process model, and the original values ​​of relevant indicators were obtained by collecting multi-source data. National and sub-provincial scores of CPHI (2020) were calculated by co-directional transformation, standardization, percentage transformation of the aggregate, and weighted summation.

Results: The highest CPHI score in 2020 is Beijing, and the lowest is Tibet. The top five provinces are Beijing (67.64 points), Shanghai (67.04 points), Zhejiang (65.74 points), Shandong (61.16 points), and Tianjin (59.99 points). The last five provinces are Tibet (33.10 points), Ningxia (37.24 points), Guizhou (39.85 points), Xinjiang (39.92 points), and Hainan (40.38 points). The prevalence of chronic pain is severe in Heilongjiang, Chongqing, Guizhou, Sichuan, and Fujian. Guizhou, Hainan, Xinjiang, Beijing, and Guangdong display a high burden of chronic pain. The five provinces of Guangdong, Shanghai, Beijing, Jiangsu, and Zhejiang have better treatment for chronic pain, while Tibet, Qinghai, Jilin, Ningxia, and Xinjiang have a lower quality of treatment. Beijing, Shanghai, Qinghai, Guangxi, and Hunan have relatively good development of chronic pain disciplines, while Tibet, Sichuan, Inner Mongolia, Hebei, and Guizhou are relatively poor.

Conclusion: The economically developed provinces in China have higher CPHI scores, while economically underdeveloped areas have lower scores. The current pain diagnosis and treatment situation in economically developed regions is relatively good, while that in financially underdeveloped areas is rather poor. According to the variations in the prevalence and management of chronic pain among populations in different provinces in China, it is necessary to implement chronic pain intervention measures adapted to local conditions.

Background: Most cancer disability-adjusted life year (DALY) studies worldwide have used broad, generic disability weights (DWs); however, differences exist among populations and types of cancers. Using breast cancer as example, this study aimed to estimate the population-level DALYs in females in China and the impact of screening as well as applying local DWs.

Methods: Using multisource data, a prevalence-based model was constructed. (1) Overall years lived with disability (YLDs) were estimated by using numbers of prevalence cases, stage-specific proportions, and local DWs for breast cancer. Numbers of females and new breast cancer cases as well as local survival rates were used to calculate the number of prevalence cases. (2) Years of life lost (YLLs) were estimated using breast cancer mortality rates, female numbers and standard life expectancies. (3) The prevalence of and mortality due to breast cancer and associated DALYs from 2020 to 2030 were predicted using Joinpoint regression. (4) Assumptions considered for screening predictions included expanding coverage, reducing mortality due to breast cancer and improving early-stage proportion for breast cancer.

Results: In Chinese females, the estimated number of breast cancer DALYs was 2251.5 thousand (of 17.3% were YLDs) in 2015, which is predicted to increase by 26.7% (60.3% among those aged ≥ 65 years) in 2030 (2852.8 thousand) if the screening coverage (25.7%) stays unchanged. However, if the coverage can be achieved to 40.7% in 2030 (deduced from the "Healthy China Initiative"), DALYs would decrease by 1.5% among the screened age groups. Sensitivity analyses found that using local DWs would change the base-case values by ~ 10%.

Conclusion: Estimates of DALYs due to breast cancer in China were lower (with a higher proportion of YLDs) than Global Burden of Disease Study numbers (2527.0 thousand, 8.2% were YLDs), suggesting the importance of the application of population-specific DWs. If the screening coverage remains unchanged, breast cancer-caused DALYs would continue to increase, especially among elderly individuals.

Background: Data that capture implementation strength can be combined in multiple ways across content and health system levels to create a summary measure that can help us to explore and compare program implementation across facility catchment areas. Summary indices can make it easier for national policymakers to understand and address variation in strength of program implementation across jurisdictions. In this paper, we describe the development of an index that we used to describe the district-level strength of implementation of Malawi's national family planning program.

Methods: To develop the index, we used data collected during a 2017 national, health facility and community health worker Implementation Strength Assessment survey in Malawi to test different methods to combine indicators within and then across domains (4 methods-simple additive, weighted additive, principal components analysis, exploratory factor analysis) and combine scores across health facility and community health worker levels (2 methods-simple average and mixed effects model) to create a catchment area-level summary score for each health facility in Malawi. We explored how well each model captures variation and predicts couple-years protection and how feasible it is to conduct each type of analysis and the resulting interpretability.

Results: We found little difference in how the four methods combined indicator data at the individual and combined levels of the health system. However, there were major differences when combining scores across health system levels to obtain a score at the health facility catchment area level. The scores resulting from the mixed effects model were able to better discriminate differences between catchment area scores compared to the simple average method. The scores using the mixed effects combination method also demonstrated more of a dose-response relationship with couple-years protection.

Conclusions: The summary measure that was calculated from the mixed effects combination method captured the variation of strength of implementation of Malawi's national family planning program at the health facility catchment area level. However, the best method for creating an index should be based on the pros and cons listed, not least, analyst capacity and ease of interpretability of findings. Ultimately, the resulting summary measure can aid decision-makers in understanding the combined effect of multiple aspects of programs being implemented in their health system and comparing the strengths of programs across geographies.

Background: This study compares the health gains, costs, and cost-effectiveness of hundreds of Australian and New Zealand (NZ) health interventions conducted with comparable methods in an online interactive league table designed to inform policy.

Methods: A literature review was conducted to identify peer-reviewed evaluations (2010 to 2018) arising from the Australia Cost-Effectiveness research and NZ Burden of Disease Epidemiology, Equity and Cost-Effectiveness Programmes, or using similar methodology, with: health gains quantified as health-adjusted life years (HALYs); net health system costs and/or incremental cost-effectiveness ratio; time horizon of at least 10 years; and 3% to 5% discount rates.

Results: We identified 384 evaluations that met the inclusion criteria, covering 14 intervention domains: alcohol; cancer; cannabis; communicable disease; cardiovascular disease; diabetes; diet; injury; mental illness; other non-communicable diseases; overweight and obesity; physical inactivity; salt; and tobacco. There were large variations in health gain across evaluations: 33.9% gained less than 0.1 HALYs per 1000 people in the total population over the remainder of their lifespan, through to 13.0% gaining > 10 HALYs per 1000 people. Over a third (38.8%) of evaluations were cost-saving.

Conclusions: League tables of comparably conducted evaluations illustrate the large health gain (and cost) variations per capita between interventions, in addition to cost-effectiveness. Further work can test the utility of this league table with policy-makers and researchers.

Background: Timely tracking of health outcomes is difficult in low- and middle-income countries without comprehensive vital registration systems. Community health workers (CHWs) are increasingly collecting vital events data while delivering routine care in low-resource settings. It is necessary, however, to assess whether routine programmatic data collected by CHWs are sufficiently reliable for timely monitoring and evaluation of health interventions. To study this, we assessed the consistency of vital events data recorded by CHWs using two methodologies-routine data collected while delivering an integrated maternal and child health intervention, and data from a birth history census approach at the same site in rural Nepal.

Methods: We linked individual records from routine programmatic data from June 2017 to May 2018 with those from census data, both collected by CHWs at the same site using a mobile platform. We categorized each vital event over a one-year period as 'recorded by both methods,' 'census alone,' or 'programmatic alone.' We further assessed whether vital events data recorded by both methods were classified consistently.

Results: From June 2017 to May 2018, we identified a total of 713 unique births collectively from the census (birth history) and programmatic maternal 'post-delivery' data. Three-fourths of these births (n = 526) were identified by both. There was high consistency in birth location classification among the 526 births identified by both methods. Upon including additional programmatic 'child registry' data, we identified 746 total births, of which 572 births were identified by both census and programmatic methods. Programmatic data (maternal 'post-delivery' and 'child registry' combined) captured more births than census data (723 vs. 595). Both methods consistently classified most infants as 'living,' while infant deaths and stillbirths were largely classified inconsistently or recorded by only one method. Programmatic data identified five infant deaths and five stillbirths not recorded in census data.

Conclusions: Our findings suggest that data collected by CHWs from routinely tracking pregnancies, births, and deaths are promising for timely program monitoring and evaluation. Despite some limitations, programmatic data may be more sensitive in detecting vital events than cross-sectional census surveys asking women to recall these events.

Background: We have previously developed and validated a biomarker-based metric of overall health status using Mahalanobis distance (DM) to measure how far from the norm of a reference population (RP) an individual's biomarker profile is. DM is not particularly sensitive to the choice of biomarkers; however, this makes comparison across studies difficult. Here we aimed to identify and validate a standard, optimized version of DM that would be highly stable across populations, while using fewer and more commonly measured biomarkers.

Methods: Using three datasets (the Baltimore Longitudinal Study of Aging, Invecchiare in Chianti and the National Health and Nutrition Examination Survey), we selected the most stable sets of biomarkers in all three populations, notably when interchanging RPs across populations. We performed regression models, using a fourth dataset (the Women's Health and Aging Study), to compare the new DM sets to other well-known metrics [allostatic load (AL) and self-assessed health (SAH)] in their association with diverse health outcomes: mortality, frailty, cardiovascular disease (CVD), diabetes, and comorbidity number.

Results: A nine- (DM9) and a seventeen-biomarker set (DM17) were identified as highly stable regardless of the chosen RP (e.g.: mean correlation among versions generated by interchanging RPs across dataset of r = 0.94 for both DM9 and DM17). In general, DM17 and DM9 were both competitive compared with AL and SAH in predicting aging correlates, with some exceptions for DM9. For example, DM9, DM17, AL, and SAH all predicted mortality to a similar extent (ranges of hazard ratios of 1.15-1.30, 1.21-1.36, 1.17-1.38, and 1.17-1.49, respectively). On the other hand, DM9 predicted CVD less well than DM17 (ranges of odds ratios of 0.97-1.08, 1.07-1.85, respectively).

Conclusions: The metrics we propose here are easy to measure with data that are already available in a wide array of panel, cohort, and clinical studies. The standardized versions here lose a small amount of predictive power compared to more complete versions, but are nonetheless competitive with existing metrics of overall health. DM17 performs slightly better than DM9 and should be preferred in most cases, but DM9 may still be used when a more limited number of biomarkers is available.