Socioeconomic, Demographic, and Environmental Factors May Inform Malaria Intervention Prioritization in Urban Nigeria.

Urban population growth in Nigeria may exceed the availability of affordable housing and basic services, resulting in living conditions conducive to vector breeding and heterogeneous malaria transmission. Understanding the link between community-level factors and urban malaria transmission informs targeted interventions. We analyzed Demographic and Health Survey Program cluster-level data, alongside geospatial covariates, to describe variations in malaria prevalence in children under 5 years of age. Univariate and multivariable models explored the relationship between malaria test positivity rates at the cluster level and community-level factors. Generally, malaria test positivity rates in urban areas are low and declining. The factors that best predicted malaria test positivity rates within a multivariable model were post-primary education, wealth quintiles, population density, access to improved housing, child fever treatment-seeking, precipitation, and enhanced vegetation index. Malaria transmission in urban areas will likely be reduced by addressing socioeconomic and environmental factors that promote exposure to disease vectors. Enhanced regional surveillance systems in Nigeria can provide detailed data to further refine our understanding of these factors in relation to malaria transmission.

An archetypes approach to malaria intervention impact mapping: a new framework and example application.

BACKGROUND: As both mechanistic and geospatial malaria modeling methods become more integrated into malaria policy decisions, there is increasing demand for strategies that combine these two methods. This paper introduces a novel archetypes-based methodology for generating high-resolution intervention impact maps based on mechanistic model simulations. An example configuration of the framework is described and explored. METHODS: First, dimensionality reduction and clustering techniques were applied to rasterized geospatial environmental and mosquito covariates to find archetypal malaria transmission patterns. Next, mechanistic models were run on a representative site from each archetype to assess intervention impact. Finally, these mechanistic results were reprojected onto each pixel to generate full maps of intervention impact. The example configuration used ERA5 and Malaria Atlas Project covariates, singular value decomposition, k-means clustering, and the Institute for Disease Modeling's EMOD model to explore a range of three-year malaria interventions primarily focused on vector control and case management. RESULTS: Rainfall, temperature, and mosquito abundance layers were clustered into ten transmission archetypes with distinct properties. Example intervention impact curves and maps highlighted archetype-specific variation in efficacy of vector control interventions. A sensitivity analysis showed that the procedure for selecting representative sites to simulate worked well in all but one archetype. CONCLUSION: This paper introduces a novel methodology which combines the richness of spatiotemporal mapping with the rigor of mechanistic modeling to create a multi-purpose infrastructure for answering a broad range of important questions in the malaria policy space. It is flexible and adaptable to a range of input covariates, mechanistic models, and mapping strategies and can be adapted to the modelers' setting of choice.

Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence.

Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable routine surveillance data of malaria incidence, typically aggregated to administrative units, is becoming more widely available. Disaggregation regression is an important model framework for estimating high resolution risk maps from aggregated data. However, the aggregation of incidence over large, heterogeneous areas means that these data are underpowered for estimating complex, non-linear models. In contrast, prevalence point-surveys are directly linked to local environmental conditions but are not common in many areas of the world. Here, we train multiple non-linear, machine learning models on Plasmodium falciparum prevalence point-surveys. We then ensemble the predictions from these machine learning models with a disaggregation regression model that uses aggregated malaria incidences as response data. We find that using a disaggregation regression model to combine predictions from machine learning models improves model accuracy relative to a baseline model.

Trends and patterns of disparities in diabetes and chronic kidney disease mortality among US counties, 1980-2014.

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.

Maps and metrics of insecticide-treated net access, use, and nets-per-capita in Africa from 2000-2020.

Insecticide-treated nets (ITNs) are one of the most widespread and impactful malaria interventions in Africa, yet a spatially-resolved time series of ITN coverage has never been published. Using data from multiple sources, we generate high-resolution maps of ITN access, use, and nets-per-capita annually from 2000 to 2020 across the 40 highest-burden African countries. Our findings support several existing hypotheses: that use is high among those with access, that nets are discarded more quickly than official policy presumes, and that effectively distributing nets grows more difficult as coverage increases. The primary driving factors behind these findings are most likely strong cultural and social messaging around the importance of net use, low physical net durability, and a mixture of inherent commodity distribution challenges and less-than-optimal net allocation policies, respectively. These results can inform both policy decisions and downstream malaria analyses.

Indirect effects of the COVID-19 pandemic on malaria intervention coverage, morbidity, and mortality in Africa: a geospatial modelling analysis.

BACKGROUND: Substantial progress has been made in reducing the burden of malaria in Africa since 2000, but those gains could be jeopardised if the COVID-19 pandemic affects the availability of key malaria control interventions. The aim of this study was to evaluate plausible effects on malaria incidence and mortality under different levels of disruption to malaria control. METHODS: Using an established set of spatiotemporal Bayesian geostatistical models, we generated geospatial estimates across malaria-endemic African countries of the clinical case incidence and mortality of malaria, incorporating an updated database of parasite rate surveys, insecticide-treated net (ITN) coverage, and effective treatment rates. We established a baseline estimate for the anticipated malaria burden in Africa in the absence of COVID-19-related disruptions, and repeated the analysis for nine hypothetical scenarios in which effective treatment with an antimalarial drug and distribution of ITNs (both through routine channels and mass campaigns) were reduced to varying extents. FINDINGS: We estimated 215·2 (95% uncertainty interval 143·7-311·6) million cases and 386·4 (307·8-497·8) thousand deaths across malaria-endemic African countries in 2020 in our baseline scenario of undisrupted intervention coverage. With greater reductions in access to effective antimalarial drug treatment, our model predicted increasing numbers of cases and deaths: 224·1 (148·7-326·8) million cases and 487·9 (385·3-634·6) thousand deaths with a 25% reduction in antimalarial drug coverage; 233·1 (153·7-342·5) million cases and 597·4 (468·0-784·4) thousand deaths with a 50% reduction; and 242·3 (158·7-358·8) million cases and 715·2 (556·4-947·9) thousand deaths with a 75% reduction. Halting planned 2020 ITN mass distribution campaigns and reducing routine ITN distributions by 25%-75% also increased malaria burden to a total of 230·5 (151·6-343·3) million cases and 411·7 (322·8-545·5) thousand deaths with a 25% reduction; 232·8 (152·3-345·9) million cases and 415·5 (324·3-549·4) thousand deaths with a 50% reduction; and 234·0 (152·9-348·4) million cases and 417·6 (325·5-553·1) thousand deaths with a 75% reduction. When ITN coverage and antimalarial drug coverage were synchronously reduced, malaria burden increased to 240·5 (156·5-358·2) million cases and 520·9 (404·1-691·9) thousand deaths with a 25% reduction; 251·0 (162·2-377·0) million cases and 640·2 (492·0-856·7) thousand deaths with a 50% reduction; and 261·6 (167·7-396·8) million cases and 768·6 (586·1-1038·7) thousand deaths with a 75% reduction. INTERPRETATION: Under pessimistic scenarios, COVID-19-related disruption to malaria control in Africa could almost double malaria mortality in 2020, and potentially lead to even greater increases in subsequent years. To avoid a reversal of two decades of progress against malaria, averting this public health disaster must remain an integrated priority alongside the response to COVID-19. FUNDING: Bill and Melinda Gates Foundation; Channel 7 Telethon Trust, Western Australia.

Spatiotemporal mapping of malaria prevalence in Madagascar using routine surveillance and health survey data.

Malaria transmission in Madagascar is highly heterogeneous, exhibiting spatial, seasonal and long-term trends. Previous efforts to map malaria risk in Madagascar used prevalence data from Malaria Indicator Surveys. These cross-sectional surveys, conducted during the high transmission season most recently in 2013 and 2016, provide nationally representative prevalence data but cover relatively short time frames. Conversely, monthly case data are collected at health facilities but suffer from biases, including incomplete reporting and low rates of treatment seeking. We combined survey and case data to make monthly maps of prevalence between 2013 and 2016. Health facility catchment populations were estimated to produce incidence rates from the case data. Smoothed incidence surfaces, environmental and socioeconomic covariates, and survey data informed a Bayesian prevalence model, in which a flexible incidence-to-prevalence relationship was learned. Modelled spatial trends were consistent over time, with highest prevalence in the coastal regions and low prevalence in the highlands and desert south. Prevalence was lowest in 2014 and peaked in 2015 and seasonality was widely observed, including in some lower transmission regions. These trends highlight the utility of monthly prevalence estimates over the four year period. By combining survey and case data using this two-step modelling approach, we were able to take advantage of the relative strengths of each metric while accounting for potential bias in the case data. Similar modelling approaches combining large datasets of different malaria metrics may be applicable across sub-Saharan Africa.

Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991-2019.

BACKGROUND: Anti-malarial drugs play a critical role in reducing malaria morbidity and mortality, but their role is mediated by their effectiveness. Effectiveness is defined as the probability that an anti-malarial drug will successfully treat an individual infected with malaria parasites under routine health care delivery system. Anti-malarial drug effectiveness (AmE) is influenced by drug resistance, drug quality, health system quality, and patient adherence to drug use; its influence on malaria burden varies through space and time. METHODS: This study uses data from 232 efficacy trials comprised of 86,776 infected individuals to estimate the artemisinin-based and non-artemisinin-based AmE for treating falciparum malaria between 1991 and 2019. Bayesian spatiotemporal models were fitted and used to predict effectiveness at the pixel-level (5 km × 5 km). The median and interquartile ranges (IQR) of AmE are presented for all malaria-endemic countries. RESULTS: The global effectiveness of artemisinin-based drugs was 67.4% (IQR: 33.3-75.8), 70.1% (43.6-76.0) and 71.8% (46.9-76.4) for the 1991-2000, 2006-2010, and 2016-2019 periods, respectively. Countries in central Africa, a few in South America, and in the Asian region faced the challenge of lower effectiveness of artemisinin-based anti-malarials. However, improvements were seen after 2016, leaving only a few hotspots in Southeast Asia where resistance to artemisinin and partner drugs is currently problematic and in the central Africa where socio-demographic challenges limit effectiveness. The use of artemisinin-based combination therapy (ACT) with a competent partner drug and having multiple ACT as first-line treatment choice sustained high levels of effectiveness. High levels of access to healthcare, human resource capacity, education, and proximity to cities were associated with increased effectiveness. Effectiveness of non-artemisinin-based drugs was much lower than that of artemisinin-based with no improvement over time: 52.3% (17.9-74.9) for 1991-2000 and 55.5% (27.1-73.4) for 2011-2015. Overall, AmE for artemisinin-based and non-artemisinin-based drugs were, respectively, 29.6 and 36% below clinical efficacy as measured in anti-malarial drug trials. CONCLUSIONS: This study provides evidence that health system performance, drug quality and patient adherence influence the effectiveness of anti-malarials used in treating uncomplicated falciparum malaria. These results provide guidance to countries' treatment practises and are critical inputs for malaria prevalence and incidence models used to estimate national level malaria burden.

Quantifying Inequitable Access to Rapid Burn and Reconstructive Care through Geospatial Mapping.

Time-critical pathologies, such as the care of burn-injured patients, rely on accurate travel time data to plan high-quality service provision. Geospatial modeling, using data from the Malaria Atlas Project, together with census data, permits quantification of the huge global discrepancies in temporal access to burn care between high-income and low-resource settings. In this study, focusing on the United Kingdom and Ghana, we found that a 3-fold population difference exists with, respectively, 95.6% and 29.9% of the population that could access specialist burn care within 1-hour travel time. Solutions to such inequalities include upscaling of infrastructure and specialist personnel, but this is aspirational rather than feasible in most low- to middle-income countries. Mixed models of decentralization of care that leverage eHealth strategies, such as telemedicine, may enhance quality of local burns and reconstructive surgical care through skills transfer, capacity building, and expediting of urgent transfers, while empowering local healthcare communities. By extending specialist burn care coverage through eHealth to 8 district hospitals in rural Ghana, we demonstrate the potential to increase specialist population coverage within 1-hour travel time from 29.9% to 45.3%-equivalent to an additional 5.1 million people.

Outcomes assessment pitfalls: challenges to quantifying knowledge gain in a sex education game.

Background: We describe challenges associated with incorporating knowledge assessment into an educational game on a sensitive topic and discuss possible motivations for, and solutions to, these challenges. Methods: The My Future Family Game (MFF) is a tool for collecting data about family planning intentions. The game was expanded to include information about human anatomy and sexual reproduction. To assess the efficacy of the game as a tool for teaching sexual education, we designed a pre-post study with assessments before and after the game which was deployed in three schools in and around Chennai, India in summer of 2018. Results: The pre-post process did not effectively assess knowledge gain and made the game less enjoyable. Although all participants completed the pre-test because it was required to access the main game, many did not complete the post test. As a result, the post-test scores are of limited use in assessing the efficacy of the intervention as an educational tool. This deployment demonstrated that pre-post testing has to be integrated in a way that motivates players to improve their scores in the post-test. The pre-test results did provide useful information about players' knowledge of human anatomy and mechanisms of human reproduction prior to gameplay and validated the tool as a means of data collection. Conclusion: Adding outcomes assessment required asking players questions about sexual anatomy and function with little or no introduction. This process undermined elements of the initial game design and made the process less enjoyable for participants. Understanding these failures has been a vital step in the process of iterative game design. Modifications were made to the pre-post test process for future deployments so that the process of assessment does not diminish enthusiasm for game play or enjoyment and motivates completion of the post-test as part of gameplay.

Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000-17: a spatial and temporal modelling study.

BACKGROUND: Since 2000, the scale-up of malaria control interventions has substantially reduced morbidity and mortality caused by the disease globally, fuelling bold aims for disease elimination. In tandem with increased availability of geospatially resolved data, malaria control programmes increasingly use high-resolution maps to characterise spatially heterogeneous patterns of disease risk and thus efficiently target areas of high burden. METHODS: We updated and refined the Plasmodium falciparum parasite rate and clinical incidence models for sub-Saharan Africa, which rely on cross-sectional survey data for parasite rate and intervention coverage. For malaria endemic countries outside of sub-Saharan Africa, we produced estimates of parasite rate and incidence by applying an ecological downscaling approach to malaria incidence data acquired via routine surveillance. Mortality estimates were derived by linking incidence to systematically derived vital registration and verbal autopsy data. Informed by high-resolution covariate surfaces, we estimated P falciparum parasite rate, clinical incidence, and mortality at national, subnational, and 5 × 5 km pixel scales with corresponding uncertainty metrics. FINDINGS: We present the first global, high-resolution map of P falciparum malaria mortality and the first global prevalence and incidence maps since 2010. These results are combined with those for Plasmodium vivax (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The P falciparum estimates span the period 2000-17, and illustrate the rapid decline in burden between 2005 and 2017, with incidence declining by 27·9% and mortality declining by 42·5%. Despite a growing population in endemic regions, P falciparum cases declined between 2005 and 2017, from 232·3 million (95% uncertainty interval 198·8-277·7) to 193·9 million (156·6-240·2) and deaths declined from 925 800 (596 900-1 341 100) to 618 700 (368 600-952 200). Despite the declines in burden, 90·1% of people within sub-Saharan Africa continue to reside in endemic areas, and this region accounted for 79·4% of cases and 87·6% of deaths in 2017. INTERPRETATION: High-resolution maps of P falciparum provide a contemporary resource for informing global policy and malaria control planning, programme implementation, and monitoring initiatives. Amid progress in reducing global malaria burden, areas where incidence trends have plateaued or increased in the past 5 years underscore the fragility of hard-won gains against malaria. Efforts towards elimination should be strengthened in such areas, and those where burden remained high throughout the study period. FUNDING: Bill & Melinda Gates Foundation.

Impact of mass drug administration campaigns depends on interaction with seasonal human movement.

Background: Mass drug administration (MDA) is a control and elimination tool for treating infectious diseases. For malaria, it is widely accepted that conducting MDA during the dry season results in the best outcomes. However, seasonal movement of populations into and out of MDA target areas is common in many places and could potentially fundamentally limit the ability of MDA campaigns to achieve elimination. Methods: A mathematical model was used to simulate malaria transmission in two villages connected to a high-risk area into and out of which 10% of villagers traveled seasonally. MDA was given only in the villages. Prevalence reduction under various possible timings of MDA and seasonal travel was predicted. Results: MDA is most successful when distributed outside the traveling season and during the village low-transmission season. MDA is least successful when distributed during the traveling season and when traveling overlaps with the peak transmission season in the high-risk area. Mistiming MDA relative to seasonal travel resulted in much poorer outcomes than mistiming MDA relative to the peak transmission season within the villages. Conclusions: Seasonal movement patterns of high-risk groups should be taken into consideration when selecting the optimum timing of MDA campaigns.

Trends and Patterns of Geographic Variation in Mortality From Substance Use Disorders and Intentional Injuries Among US Counties, 1980-2014.

Importance: Substance use disorders, including alcohol use disorders and drug use disorders, and intentional injuries, including self-harm and interpersonal violence, are important causes of early death and disability in the United States. Objective: To estimate age-standardized mortality rates by county from alcohol use disorders, drug use disorders, self-harm, and interpersonal violence in the United States. Design and Setting: Validated small-area estimation models were applied to deidentified death records from the National Center for Health Statistics (NCHS) and population counts from the US Census Bureau, NCHS, and the Human Mortality Database to estimate county-level mortality rates from 1980 to 2014 for alcohol use disorders, drug use disorders, self-harm, and interpersonal violence. Exposures: County of residence. Main Outcomes and Measures: Age-standardized mortality rates by US county (N = 3110), year, sex, and cause. Results: Between 1980 and 2014, there were 2 848 768 deaths due to substance use disorders and intentional injuries recorded in the United States. Mortality rates from alcohol use disorders (n = 256 432), drug use disorders (n = 542 501), self-harm (n = 1 289 086), and interpersonal violence (n = 760 749) varied widely among counties. Mortality rates decreased for alcohol use disorders, self-harm, and interpersonal violence at the national level between 1980 and 2014; however, over the same period, the percentage of counties in which mortality rates increased for these causes was 65.4% for alcohol use disorders, 74.6% for self-harm, and 6.6% for interpersonal violence. Mortality rates from drug use disorders increased nationally and in every county between 1980 and 2014, but the relative increase varied from 8.2% to 8369.7%. Relative and absolute geographic inequalities in mortality, as measured by comparing the 90th and 10th percentile among counties, decreased for alcohol use disorders and interpersonal violence but increased substantially for drug use disorders and self-harm between 1980 and 2014. Conclusions and Relevance: Mortality due to alcohol use disorders, drug use disorders, self-harm, and interpersonal violence varied widely among US counties, both in terms of levels of mortality and trends. These estimates may be useful to inform efforts to target prevention, diagnosis, and treatment to improve health and reduce inequalities.

Trends and Patterns of Differences in Infectious Disease Mortality Among US Counties, 1980-2014.

Importance: Infectious diseases are mostly preventable but still pose a public health threat in the United States, where estimates of infectious diseases mortality are not available at the county level. Objective: To estimate age-standardized mortality rates and trends by county from 1980 to 2014 from lower respiratory infections, diarrheal diseases, HIV/AIDS, meningitis, hepatitis, and tuberculosis. Design and Setting: This study used deidentified death records from the National Center for Health Statistics (NCHS) and population counts from the US Census Bureau, NCHS, and the Human Mortality Database. Validated small-area estimation models were applied to these data to estimate county-level infectious disease mortality rates. Exposures: County of residence. Main Outcomes and Measures: Age-standardized mortality rates of lower respiratory infections, diarrheal diseases, HIV/AIDS, meningitis, hepatitis, and tuberculosis by county, year, and sex. Results: Between 1980 and 2014, there were 4 081 546 deaths due to infectious diseases recorded in the United States. In 2014, a total of 113 650 (95% uncertainty interval [UI], 108 764-117 942) deaths or a rate of 34.10 (95% UI, 32.63-35.38) deaths per 100 000 persons were due to infectious diseases in the United States compared to a total of 72 220 (95% UI, 69 887-74 712) deaths or a rate of 41.95 (95% UI, 40.52-43.42) deaths per 100 000 persons in 1980, an overall decrease of 18.73% (95% UI, 14.95%-23.33%). Lower respiratory infections were the leading cause of infectious diseases mortality in 2014 accounting for 26.87 (95% UI, 25.79-28.05) deaths per 100 000 persons (78.80% of total infectious diseases deaths). There were substantial differences among counties in death rates from all infectious diseases. Lower respiratory infection had the largest absolute mortality inequality among counties (difference between the 10th and 90th percentile of the distribution, 24.5 deaths per 100 000 persons). However, HIV/AIDS had the highest relative mortality inequality between counties (10.0 as the ratio of mortality rate in the 90th and 10th percentile of the distribution). Mortality from meningitis and tuberculosis decreased over the study period in all US counties. However, diarrheal diseases were the only cause of infectious diseases mortality to increase from 2000 to 2014, reaching a rate of 2.41 (95% UI, 0.86-2.67) deaths per 100 000 persons, with many counties of high mortality extending from Missouri to the northeastern region of the United States. Conclusions and Relevance: Between 1980 and 2014, there were declines in mortality from most categories of infectious diseases, with large differences among US counties. However, over this time there was an increase in mortality for diarrheal diseases.

Collecting family planning intentions and providing reproductive health information using a tablet-based video game in India.

Background: In response to a Grand Challenges in Global Health call for action to collect data about family planning intentions and increase the uptake of family planning methods in India, our team designed, developed, and piloted the My Future Family video game in Karnataka Province. The game educates adolescents about human sexuality and reproduction while asking players when they would like to achieve five important family planning milestones.  Participants were also asked to report who influences them the most when making family planning decisions. Methods: Focus groups were conducted and the resulting data used to design the game which was iteratively tested and then piloted in 11 schools in rural and urban areas of southern India. Data was collected throughout gameplay and cross-checked with paper questionnaires.  Results: In August 2017, we successfully piloted the game with 382 adolescents and validated its efficacy both as an educational tool and as an innovative means of accurate data collection.  Conclusion: It has historically been problematic to gather accurate data about adolescents in India on this culturally sensitive topic for a variety of reasons. These include difficulties obtaining consent, developing appropriate survey methods, and framing questions in language that young people can understand. Our game met these challenges by working within a single school system with approval from senior administration, delivering information via a game environment which freed players from societal constraints, and communicating information via images and audio in addition to text in both English and Kannada (the local language).

Variation in life expectancy and mortality by cause among neighbourhoods in King County, WA, USA, 1990-2014: a census tract-level analysis for the Global Burden of Disease Study 2015.

BACKGROUND: Health outcomes are known to vary at both the country and local levels, but trends in mortality across a detailed and comprehensive set of causes have not been previously described at a very local level. Life expectancy in King County, WA, USA, is in the 95th percentile among all counties in the USA. However, little is known about how life expectancy and mortality from different causes of death vary at a local, neighbourhood level within this county. In this analysis, we estimated life expectancy and cause-specific mortality within King County to describe spatial trends, quantify disparities in mortality, and assess the contribution of each cause of death to overall disparities in all-cause mortality. METHODS: We applied established so-called garbage code redistribution algorithms and small area estimation methods to death registration data for King County to estimate life expectancy, cause-specific mortality rates, and years of life lost (YLL) rates from 152 causes of death for 397 census tracts from Jan 1, 1990, to Dec 31, 2014. We used the cause list developed for the Global Burden of Disease 2015 study for this analysis. Deaths were tabulated by age group, sex, census tract, and cause of death. We used Bayesian mixed-effects regression models to estimate mortality overall and from each cause. FINDINGS: Between 1990 and 2014, life expectancy in King County increased by 5·4 years (95% uncertainty interval [UI] 5·0-5·7) among men (from 74·0 years [73·7-74·3] to 79·3 years [79·1-79·6]) and by 3·4 years (3·0-3·7) among women (from 80·0 years [79·7-80·2] to 83·3 years [83·1-83·5]). In 2014, life expectancy ranged from 68·4 years (95% UI 66·9-70·1) to 86·7 years (85·0-88·2) for men and from 73·6 years (71·6-75·5) to 88·4 years (86·9-89·9) for women among census tracts within King County. Rates of YLL by cause also varied substantially among census tracts for each cause of death. Geographical areas with relatively high and relatively low YLL rates differed by cause. In general, causes of death responsible for more YLLs overall also contributed more significantly to geographical inequality within King County. However, certain causes contributed more to inequality than to overall YLLs. INTERPRETATION: This census tract-level analysis of life expectancy and cause-specific YLL rates highlights important differences in health among neighbourhoods in King County that are masked by county-level estimates. Efforts to improve population health in King County should focus on reducing geographical inequality, by targeting those health conditions that contribute the most to overall YLLs and to inequality. This analysis should be replicated in other locations to more fully describe fine-grained local-level variation in population health and contribute to efforts to improve health while reducing inequalities. FUNDING: John W Stanton and Theresa E Gillespie.

Trends and Patterns of Differences in Chronic Respiratory Disease Mortality Among US Counties, 1980-2014.

Importance: Chronic respiratory diseases are an important cause of death and disability in the United States. Objective: To estimate age-standardized mortality rates by county from chronic respiratory diseases. Design, Setting, and Participants: Validated small area estimation models were applied to deidentified death records from the National Center for Health Statistics and population counts from the US Census Bureau, National Center for Health Statistics, and Human Mortality Database to estimate county-level mortality rates from 1980 to 2014 for chronic respiratory diseases. Exposure: County of residence. Main Outcomes and Measures: Age-standardized mortality rates by county, year, sex, and cause. Results: A total of 4 616 711 deaths due to chronic respiratory diseases were recorded in the United States from January 1, 1980, through December 31, 2014. Nationally, the mortality rate from chronic respiratory diseases increased from 40.8 (95% uncertainty interval [UI], 39.8-41.8) deaths per 100 000 population in 1980 to a peak of 55.4 (95% UI, 54.1-56.5) deaths per 100 000 population in 2002 and then declined to 52.9 (95% UI, 51.6-54.4) deaths per 100 000 population in 2014. This overall 29.7% (95% UI, 25.5%-33.8%) increase in chronic respiratory disease mortality from 1980 to 2014 reflected increases in the mortality rate from chronic obstructive pulmonary disease (by 30.8% [95% UI, 25.2%-39.0%], from 34.5 [95% UI, 33.0-35.5] to 45.1 [95% UI, 43.7-46.9] deaths per 100 000 population), interstitial lung disease and pulmonary sarcoidosis (by 100.5% [95% UI, 5.8%-155.2%], from 2.7 [95% UI, 2.3-4.2] to 5.5 [95% UI, 3.5-6.1] deaths per 100 000 population), and all other chronic respiratory diseases (by 42.3% [95% UI, 32.4%-63.8%], from 0.51 [95% UI, 0.48-0.54] to 0.73 [95% UI, 0.69-0.78] deaths per 100 000 population). There were substantial differences in mortality rates and changes in mortality rates over time among counties, and geographic patterns differed by cause. Counties with the highest mortality rates were found primarily in central Appalachia for chronic obstructive pulmonary disease and pneumoconiosis; widely dispersed throughout the Southwest, northern Great Plains, New England, and South Atlantic for interstitial lung disease; along the southern half of the Mississippi River and in Georgia and South Carolina for asthma; and in southern states from Mississippi to South Carolina for other chronic respiratory diseases. Conclusions and Relevance: Despite recent declines in mortality from chronic respiratory diseases, mortality rates in 2014 remained significantly higher than in 1980. Between 1980 and 2014, there were important differences in mortality rates and changes in mortality by county, sex, and particular chronic respiratory disease type. These estimates may be helpful for informing efforts to improve prevention, diagnosis, and treatment.

Trends and Patterns of Geographic Variation in Cardiovascular Mortality Among US Counties, 1980-2014.

IMPORTANCE: In the United States, regional variation in cardiovascular mortality is well-known but county-level estimates for all major cardiovascular conditions have not been produced. OBJECTIVE: To estimate age-standardized mortality rates from cardiovascular diseases by county. DESIGN AND SETTING: Deidentified death records from the National Center for Health Statistics and population counts from the US Census Bureau, the National Center for Health Statistics, and the Human Mortality Database from 1980 through 2014 were used. Validated small area estimation models were used to estimate county-level mortality rates from all cardiovascular diseases, including ischemic heart disease, cerebrovascular disease, ischemic stroke, hemorrhagic stroke, hypertensive heart disease, cardiomyopathy, atrial fibrillation and flutter, rheumatic heart disease, aortic aneurysm, peripheral arterial disease, endocarditis, and all other cardiovascular diseases combined. EXPOSURES: The 3110 counties of residence. MAIN OUTCOMES AND MEASURES: Age-standardized cardiovascular disease mortality rates by county, year, sex, and cause. RESULTS: From 1980 to 2014, cardiovascular diseases were the leading cause of death in the United States, although the mortality rate declined from 507.4 deaths per 100 000 persons in 1980 to 252.7 deaths per 100 000 persons in 2014, a relative decline of 50.2% (95% uncertainty interval [UI], 49.5%-50.8%). In 2014, cardiovascular diseases accounted for more than 846 000 deaths (95% UI, 827-865 thousand deaths) and 11.7 million years of life lost (95% UI, 11.6-11.9 million years of life lost). The gap in age-standardized cardiovascular disease mortality rates between counties at the 10th and 90th percentile declined 14.6% from 172.1 deaths per 100 000 persons in 1980 to 147.0 deaths per 100 000 persons in 2014 (posterior probability of decline >99.9%). In 2014, the ratio between counties at the 90th and 10th percentile was 2.0 for ischemic heart disease (119.1 vs 235.7 deaths per 100 000 persons) and 1.7 for cerebrovascular disease (40.3 vs 68.1 deaths per 100 000 persons). For other cardiovascular disease causes, the ratio ranged from 1.4 (aortic aneurysm: 3.5 vs 5.1 deaths per 100 000 persons) to 4.2 (hypertensive heart disease: 4.3 vs 17.9 deaths per 100 000 persons). The largest concentration of counties with high cardiovascular disease mortality extended from southeastern Oklahoma along the Mississippi River Valley to eastern Kentucky. Several cardiovascular disease conditions were clustered substantially outside the South, including atrial fibrillation (Northwest), aortic aneurysm (Midwest), and endocarditis (Mountain West and Alaska). The lowest cardiovascular mortality rates were found in the counties surrounding San Francisco, California, central Colorado, northern Nebraska, central Minnesota, northeastern Virginia, and southern Florida. CONCLUSIONS AND RELEVANCE: Substantial differences exist between county ischemic heart disease and stroke mortality rates. Smaller differences exist for diseases of the myocardium, atrial fibrillation, aortic and peripheral arterial disease, rheumatic heart disease, and endocarditis.

Inequalities in Life Expectancy Among US Counties, 1980 to 2014: Temporal Trends and Key Drivers.

Importance: Examining life expectancy by county allows for tracking geographic disparities over time and assessing factors related to these disparities. This information is potentially useful for policy makers, clinicians, and researchers seeking to reduce disparities and increase longevity. Objective: To estimate annual life tables by county from 1980 to 2014; describe trends in geographic inequalities in life expectancy and age-specific risk of death; and assess the proportion of variation in life expectancy explained by variation in socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors. Design, Setting, and Participants: Annual county-level life tables were constructed using small area estimation methods from deidentified death records from the National Center for Health Statistics (NCHS), and population counts from the US Census Bureau, NCHS, and the Human Mortality Database. Measures of geographic inequality in life expectancy and age-specific mortality risk were calculated. Principal component analysis and ordinary least squares regression were used to examine the county-level association between life expectancy and socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors. Exposures: County of residence. Main Outcomes and Measures: Life expectancy at birth and age-specific mortality risk. Results: Counties were combined as needed to create stable units of analysis over the period 1980 to 2014, reducing the number of areas analyzed from 3142 to 3110. In 2014, life expectancy at birth for both sexes combined was 79.1 (95% uncertainty interval [UI], 79.0-79.1) years overall, but differed by 20.1 (95% UI, 19.1-21.3) years between the counties with the lowest and highest life expectancy. Absolute geographic inequality in life expectancy increased between 1980 and 2014. Over the same period, absolute geographic inequality in the risk of death decreased among children and adolescents, but increased among older adults. Socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors explained 60%, 74%, and 27% of county-level variation in life expectancy, respectively. Combined, these factors explained 74% of this variation. Most of the association between socioeconomic and race/ethnicity factors and life expectancy was mediated through behavioral and metabolic risk factors. Conclusions and Relevance: Geographic disparities in life expectancy among US counties are large and increasing. Much of the variation in life expectancy among counties can be explained by a combination of socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors. Policy action targeting socioeconomic factors and behavioral and metabolic risk factors may help reverse the trend of increasing disparities in life expectancy in the United States.