Population Health Metrics最新文献

Introduction: Pregnant women in sub-Saharan Africa have high risk of HIV acquisition, yet approaches for measuring maternal HIV incidence using routine surveillance systems are undefined. We used programmatic data from routine antenatal care (ANC) HIV testing in Botswana to measure real-world HIV incidence during pregnancy.

Methods: From January 2018 to September 2019, the Botswana Ministry of Health and Wellness implemented an HIV testing program at 139 ANC clinics. The program captured information on testers' age, testing date and result, and antiretroviral treatment (ART) initiation. In our analysis, we excluded individuals who previously tested HIV-positive prior to their first ANC visit. We defined incident HIV infection as testing HIV-positive at an ANC visit after a prior HIV-negative result within ANC.

Results: Overall, 29,570 pregnant women (median age 26 years, IQR 22-31) tested for HIV at ANC clinics: 3% (836) tested HIV-positive at their first recorded ANC visit and 97% tested HIV-negative (28,734). Of those who tested HIV-negative, 28% (7940/28,734) had a repeat HIV test recorded at ANC. The median time to HIV re-testing was 92 days (IQR 70-112). In total, 17 previously undiagnosed HIV infections were detected (HIV incidence 8 per 1000 person-years, 95% CI 0.5-1.3). ART initiation among women newly diagnosed with HIV at ANC (853) was 88% (671/762).

Conclusions: In Botswana, real-world HIV incidence among pregnant women at ANC remains above levels of HIV epidemic control (≤ 1 per 1000 person-years). This study shows how HIV programmatic data can answer timely population-level epidemiological questions and inform ongoing implementation of HIV prevention and treatment programs.

Introduction: Diabetes and chronic kidney diseases are associated with a large health burden in the USA and globally.

Objective: To estimate age-standardized mortality rates by county from diabetes mellitus and chronic kidney disease.

Design and setting: Validated small area estimation models were applied to de-identified death records from the National Center for Health Statistics (NCHS) and population counts from the census bureau, NCHS, and the Human Mortality Database to estimate county-level mortality rates from 1980 to 2014 from diabetes mellitus and chronic kidney disease (CKD).

Exposures: County of residence.

Main outcomes and measures: Age-standardized mortality rates by county, year, sex, and cause.

Results: Between 1980 and 2014, 2,067,805 deaths due to diabetes were recorded in the USA. The mortality rate due to diabetes increased by 33.6% (95% UI: 26.5%-41.3%) between 1980 and 2000 and then declined by 26.4% (95% UI: 22.8%-30.0%) between 2000 and 2014. Counties with very high mortality rates were found along the southern half of the Mississippi river and in parts of South and North Dakota, while very low rates were observed in central Colorado, and select counties in the Midwest, California, and southern Florida. A total of 1,659,045 deaths due to CKD were recorded between 1980 and 2014 (477,332 due to diabetes mellitus, 1,056,150 due to hypertension, 122,795 due to glomerulonephritis, and 2,768 due to other causes). CKD mortality varied among counties with very low mortality rates observed in central Colorado as well as some counties in southern Florida, California, and Great Plains states. High mortality rates from CKD were observed in counties throughout much of the Deep South, and a cluster of counties with particularly high rates was observed around the Mississippi river.

Conclusions and relevance: This study found large inequalities in diabetes and CKD mortality among US counties. The findings provide insights into the root causes of this variation and call for improvements in risk factors, access to medical care, and quality of medical care.

Background: The HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated interventions. While information on the prevalence of HIV at regional and national levels is readily available, the burden of the disease at smaller area levels, where health services are organized and delivered, is not well documented. This affects the targeting of HIV resources. There is need, therefore, for studies to estimate HIV prevalence at appropriate levels to improve HIV-related planning and resource allocation.

Methods: We estimated the district-level prevalence of HIV using Small-Area Estimation (SAE) technique by utilizing the 2016 Zambia Population-Based HIV Impact Assessment Survey (ZAMPHIA) data and auxiliary data from the 2010 Zambian Census of Population and Housing and the HIV sentinel surveillance data from selected antenatal care clinics (ANC). SAE models were fitted in R Programming to ascertain the best HIV predicting model. We then used the Fay-Herriot (FH) model to obtain weighted, more precise and reliable HIV prevalence for all the districts.

Results: The results revealed variations in the district HIV prevalence in Zambia, with the prevalence ranging from as low as 4.2% to as high as 23.5%. Approximately 32% of the districts (n = 24) had HIV prevalence above the national average, with one district having almost twice as much prevalence as the national level. Some rural districts have very high HIV prevalence rates.

Conclusions: HIV prevalence in Zambian is highest in districts located near international borders, along the main transit routes and adjacent to other districts with very high prevalence. The variations in the burden of HIV across districts in Zambia point to the need for a differentiated approach in HIV programming within the country. HIV resources need to be prioritized toward districts with high population mobility.

Introduction: Vital registration is an important element in health information systems which can inform policy and strengthen health systems. Mexico has a well-functioning vital registration system; however, there is still room for improvement, especially for deaths of children under 5. This study assesses the quality of the vital registration system in capturing deaths and evaluates the quality of cause of death certification in under-5 deaths in Yucatan, Mexico.

Methods: We collected information on under-5 deaths that occurred in 2015 and 2016 in Yucatan, Mexico. We calculated the Vital Statistics Performance Index (VSPI) to have a general assessment of the vital registration performance. We examined the agreement between vital registration records and medical records at the individual and population levels using the chance-corrected concordance (CCC) and cause-specific mortality fraction (CSMF) accuracy as quality metrics.

Results: We identified 966 records from the vital registry for all under-5 deaths, and 390 were linked to medical records of deaths occurring at public hospitals. The Yucatan vital registration system captured 94.8% of the expected under-5 deaths, with an overall VSPI score of 87.2%. Concordance between underlying cause of death listed in the vital registry and the cause determined by the medical record review varied substantially across causes, with a mean overall chance-corrected concordance across causes of 6.9% for neonates and 46.9% for children. Children had the highest concordance for digestive diseases, and neonates had the highest concordance for meningitis/sepsis. At the population level, the CSMF accuracy for identifying the underlying cause listed was 35.3% for neonates and 67.7% for children.

Conclusions: Although the vital registration system has overall good performance, there are still problems in information about causes of death for children under 5 that are related mostly to certification of the causes of death. The accuracy of information can vary substantially across age groups and causes, with causes reported for neonates being generally less reliable than those for older children. Results highlight the need to implement strategies to improve the certification of causes of death in this population.

Background: Simulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities. We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply.

Methods: We developed a disaggregation algorithm that iteratively rescales mortality, incidence and case-fatality rates by time-step of the model to ensure correct total population counts were retained at each step. To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality & morbidity rates, coronary heart disease incidence & case fatality rates; stroke incidence & case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups. The three interventions were then run on top of these scaled BAU scenarios.

Results: The algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (HALYs) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population.

Conclusion: Policy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models.

Background: Many low- and middle-income countries cannot measure maternal mortality to monitor progress against global and country-specific targets. While the ultimate goal for these countries is to have complete civil registrations systems, other interim strategies are needed to provide timely estimates of maternal mortality.

Objective: The objective is to inform on potential options for measuring maternal mortality.

Methods: This paper uses a case study approach to compare methodologies and estimates of pregnancy-related mortality ratio (PRMR)/maternal mortality ratio (MMR) obtained from four different data sources from similar time periods in Bangladesh, Mozambique, and Bolivia-national population census; post-census mortality survey; household sample survey; and sample vital registration system (SVRS).

Results: For Bangladesh, PRMR from the 2011 census falls closely in line with the 2010 household survey and SVRS estimates, while SVRS' MMR estimates are closer to the PRMR estimates obtained from the household survey. Mozambique's PRMR from household survey method is comparable and shows an upward trend between 1994 and 2011, whereas the post-census mortality survey estimated a higher MMR for 2007. Bolivia's DHS and post-census mortality survey also estimated comparable MMR during 1998-2003.

Conclusions: Overall all these data sources presented in this paper have provided valuable information on maternal mortality in Bangladesh, Mozambique, and Bolivia. It also outlines recommendations to estimate maternal mortality based on the advantages and disadvantages of several approaches.

Contribution: Recommendations in this paper can help health administrators and policy planners in prioritizing investment for collecting reliable and contemporaneous estimates of maternal mortality while progressing toward a complete civil registration system.

Background: Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and conflict resolution.

Methods: We describe here a 'small-area estimation' method to circumvent these data gaps and quantify both total and excess (i.e. crisis-attributable) death rates and tolls, both overall and for granular geographic (e.g. district) and time (e.g. month) strata. The method is based on analysis of data previously collected by national and humanitarian actors, including ground survey observations of mortality, displacement-adjusted population denominators and datasets of variables that may predict the death rate. We describe the six sequential steps required for the method's implementation and illustrate its recent application in Somalia, South Sudan and northeast Nigeria, based on a generic set of analysis scripts.

Results: Descriptive analysis of ground survey data reveals informative patterns, e.g. concerning the contribution of injuries to overall mortality, or household net migration. Despite some data sparsity, for each crisis that we have applied the method to thus far, available predictor data allow the specification of reasonably predictive mixed effects models of crude and under 5 years death rate, validated using cross-validation. Assumptions about values of the predictors in the absence of a crisis provide counterfactual and excess mortality estimates.

Conclusions: The method enables retrospective estimation of crisis-attributable mortality with considerable geographic and period stratification, and can therefore contribute to better understanding and historical memorialisation of the public health effects of crises. We discuss key limitations and areas for further development.

Background: The mortality pattern from birth to age five is known to vary by underlying cause of mortality, which has been documented in multiple instances. Many countries without high functioning vital registration systems could benefit from estimates of age- and cause-specific mortality to inform health programming, however, to date the causes of under-five death have only been described for broad age categories such as for neonates (0-27 days), infants (0-11 months), and children age 12-59 months.

Methods: We adapt the log quadratic model to mortality patterns for children under five to all-cause child mortality and then to age- and cause-specific mortality (U5ACSM). We apply these methods to empirical sample registration system mortality data in China from 1996 to 2015. Based on these empirical data, we simulate probabilities of mortality in the case when the true relationships between age and mortality by cause are known.

Results: We estimate U5ACSM within 0.1-0.7 deaths per 1000 livebirths in hold out strata for life tables constructed from the China sample registration system, representing considerable improvement compared to an error of 1.2 per 1000 livebirths using a standard approach. This improved prediction error for U5ACSM is consistently demonstrated for all-cause as well as pneumonia- and injury-specific mortality. We also consistently identified cause-specific mortality patterns in simulated mortality scenarios.

Conclusion: The log quadratic model is a significant improvement over the standard approach for deriving U5ACSM based on both simulation and empirical results.

Background: Significant levels of funding have been provided to low- and middle-income countries for development assistance for health, with most funds coming through direct bilateral investment led by the USA and the UK. Direct attribution of impact to large-scale programs funded by donors remains elusive due the difficulty of knowing what would have happened without those programs, and the lack of detailed contextual information to support causal interpretation of changes.

Methods: This study uses the synthetic control analysis method to estimate the impact of one donor's funding (United States Agency for International Development, USAID) on under-five mortality across several low- and middle-income countries that received above average levels of USAID funding for maternal and child health programs between 2000 and 2016.

Results: In the study period (2000-16), countries with above average USAID funding had an under-five mortality rate lower than the synthetic control by an average of 29 deaths per 1000 live births (year-to-year range of - 2 to - 38). This finding was consistent with several sensitivity analyses.

Conclusions: The synthetic control method is a valuable addition to the range of approaches for quantifying the impact of large-scale health programs in low- and middle-income countries. The findings suggest that adequately funded donor programs (in this case USAID) help countries to reduce child mortality to significantly lower rates than would have occurred without those investments.