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.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12963-022-00285-4.

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

Background: The use of videogames as a public health tool is rapidly expanding. Accurate assessment of the efficacy of such games is complicated by many factors. We describe challenges associated with measuring the impact of playing a videogame with information about human sexual anatomy and reproduction and discuss motivations for, and solutions to, these challenges. Methods: The My Future Family Game (MFF) is a validated tool for collecting data about family planning intentions which includes information about human anatomy and sexual reproduction. We sought to assess the efficacy of the game as a tool for teaching sexual education using a pre-post model which was deployed in three schools in and around Chennai, India in summer of 2018. Results: The MFF game was successfully modified to collect data about players' pre-gameplay knowledge of sexual anatomy and processes. The post gameplay assessment process we used did not effectively assess knowledge gain. Designing assessments for games dealing with sexuality presents challenges including: effectively communicating about biological parts and processes, designing usable and intuitive interfaces with minimal text, ensuring that all parts of the process are fun, and integrating assessments into the game in a way that makes them invisible. Conclusion: Games can be an effective means of gathering data about knowledge of sex and reproduction that it is difficult to obtain through other means. Assessing knowledge about human sexual reproduction is complicated by cultural norms and taboos, and technical hurdles which can be addressed through careful design. This study adds to the sparse literature in the field by providing information about pitfalls to avoid and best practices in this evolving area.

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.

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

Background: The use of videogames as a public health tool is rapidly expanding. Accurate assessment of the efficacy of such games is complicated by many factors. We describe challenges associated with measuring the impact of playing a videogame with information about human sexual anatomy and reproduction and discuss motivations for, and solutions to, these challenges. Methods: The My Future Family Game (MFF) is a validated tool for collecting data about family planning intentions which includes information about human anatomy and sexual reproduction. We sought to assess the efficacy of the game as a tool for teaching sexual education using a pre-post model which was deployed in three schools in and around Chennai, India in summer of 2018. Results: The MFF game was successfully modified to collect data about players' pre-gameplay knowledge of sexual anatomy and processes. The post gameplay assessment process we used did not effectively assess knowledge gain. Designing assessments for games dealing with sexuality presents challenges including: effectively communicating about biological parts and processes, designing usable and intuitive interfaces with minimal text, ensuring that all parts of the process are fun, and integrating assessments into the game in a way that makes them invisible. Conclusion: Games can be an effective means of gathering data about knowledge of sex and reproduction that it is difficult to obtain through other means. Assessing knowledge about human sexual reproduction is complicated by cultural norms and taboos, and technical hurdles which can be addressed through careful design. This study adds to the sparse literature in the field by providing information about pitfalls to avoid and best practices in this evolving area.

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.

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.

Travel time to health facilities in areas of outbreak potential: maps for guiding local preparedness and response

BACKGROUND

Repeated outbreaks of emerging pathogens underscore the need for preparedness plans to prevent, detect, and respond. As countries develop and improve National Action Plans for Health Security, addressing subnational variation in preparedness is increasingly important. One facet of preparedness and mitigating disease transmission is health facility accessibility, linking infected persons with health systems and vice versa. Where potential patients can access care, local facilities must ensure they can appropriately diagnose, treat, and contain disease spread to prevent secondary transmission; where patients cannot readily access facilities, alternate plans must be developed. Here, we use travel time to link facilities and populations at risk of viral hemorrhagic fevers (VHFs) and identify spatial variation in these respective preparedness demands.

METHODS AND FINDINGS

We used geospatial resources of travel friction, pathogen environmental suitability, and health facilities to determine facility accessibility of any at-risk location within a country. We considered in-country and cross-border movements of exposed populations and highlighted vulnerable populations where current facilities are inaccessible and new infrastructure would reduce travel times. We developed profiles for 43 African countries. Resulting maps demonstrate gaps in health facility accessibility and highlight facilities closest to areas at risk for VHF spillover. For instance, in the Central African Republic, we identified travel times of over 24 h to access a health facility. Some countries had more uniformly short travel times, such as Nigeria, although regional disparities exist. For some populations, including many in Botswana, access to areas at risk for VHF nationally was low but proximity to suitable spillover areas in bordering countries was high. Additional analyses provide insights for considering future resource allocation. We provide a contemporary use case for these analyses for the ongoing Ebola outbreak.

CONCLUSIONS

These maps demonstrate the use of geospatial analytics for subnational preparedness, identifying facilities close to at-risk populations for prioritizing readiness to detect, treat, and respond to cases and highlighting where gaps in health facility accessibility exist. We identified cross-border threats for VHF exposure and demonstrate an opportunity to improve preparedness activities through the use of precision public health methods and data-driven insights for resource allocation as part of a country's preparedness plans.

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.

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).

Lessons from a decade of suicide surveillance in India: who, why and how?

Background

This paper investigates trends in suicide rate, the reasons for and means of suicide and the occupation of deceased, to prioritize suicide prevention activities in India and to highlight the limitations to data quality for surveillance.

Methods

Data available in the public domain from the National Crimes Record Bureau (NCRB) were analysed from 2001 to 2010 at the national and sub-national levels, split by age groups and sex for ages 15 years and above.

Results

The reported suicide rate was 14.9 and 15.4 suicides per 100 000 population in 2001 and 2010, respectively. More developed states reported significantly higher suicide rates than the less developed (mean 20.5 versus 8.16), but neither experienced large changes over time. Among males, the reported suicide rate changed slightly (17.8 to 19.5); it remained almost similar for females (11.9 to 11.1). Housewives accounted for the highest proportion of suicide deaths over the decade. Distribution of the reasons for suicide remained almost constant over time; most suicides (33.7%) were due to personal/social reasons, followed by health at 24.3% and unknown reasons at 16.4%; differences were observed between the more and less developed states. Marriage-related suicides were higher for females, and health reasons increased with increasing age. Nationally, poison/overdose with drugs/pesticides was the leading means of suicide through the decade, although the gap between this and hanging decreased over time. The state level data showed considerable heterogeneity in the quality of data across the indicators assessed.

Conclusions

These data provide a range of information to identify vulnerable groups, to formulate appropriate suicide prevention strategies. Addressing the limitations in data quality would facilitate further utility of surveillance data to prevent suicides.

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 2018, 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).

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.

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 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.

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.

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.

Trends and Patterns of Disparities in Cancer Mortality Among US Counties, 1980-2014

Introduction

Cancer is a leading cause of morbidity and mortality in the United States and results in a high economic burden.

Objective

To estimate age-standardized mortality rates by US county from 29 cancers.

Design and Setting

Deidentified death records from the National Center for Health Statistics (NCHS) and population counts from the Census Bureau, the NCHS, and the Human Mortality Database from 1980 to 2014 were used. Validated small area estimation models were used to estimate county-level mortality rates from 29 cancers: lip and oral cavity; nasopharynx; other pharynx; esophageal; stomach; colon and rectum; liver; gallbladder and biliary; pancreatic; larynx; tracheal, bronchus, and lung; malignant skin melanoma; nonmelanoma skin cancer; breast; cervical; uterine; ovarian; prostate; testicular; kidney; bladder; brain and nervous system; thyroid; mesothelioma; Hodgkin lymphoma; non-Hodgkin lymphoma; multiple myeloma; leukemia; and all other cancers combined.

Exposure

County of residence.

Main Outcomes and Measures

Age-standardized cancer mortality rates by county, year, sex, and cancer type.

Results

A total of 19 511 910 cancer deaths were recorded in the United States between 1980 and 2014, including 5 656 423 due to tracheal, bronchus, and lung cancer; 2 484 476 due to colon and rectum cancer; 1 573 593 due to breast cancer; 1 077 030 due to prostate cancer; 1 157 878 due to pancreatic cancer; 209 314 due to uterine cancer; 421 628 due to kidney cancer; 487 518 due to liver cancer; 13 927 due to testicular cancer; and 829 396 due to non-Hodgkin lymphoma. Cancer mortality decreased by 20.1% (95% uncertainty interval [UI], 18.2%-21.4%) between 1980 and 2014, from 240.2 (95% UI, 235.8-244.1) to 192.0 (95% UI, 188.6-197.7) deaths per 100 000 population. There were large differences in the mortality rate among counties throughout the period: in 1980, cancer mortality ranged from 130.6 (95% UI, 114.7-146.0) per 100 000 population in Summit County, Colorado, to 386.9 (95% UI, 330.5-450.7) in North Slope Borough, Alaska, and in 2014 from 70.7 (95% UI, 63.2-79.0) in Summit County, Colorado, to 503.1 (95% UI, 464.9-545.4) in Union County, Florida. For many cancers, there were distinct clusters of counties with especially high mortality. The location of these clusters varied by type of cancer and were spread in different regions of the United States. Clusters of breast cancer were present in the southern belt and along the Mississippi River, while liver cancer was high along the Texas-Mexico border, and clusters of kidney cancer were observed in North and South Dakota and counties in West Virginia, Ohio, Indiana, Louisiana, Oklahoma, Texas, Alaska, and Illinois.

Conclusions and Relevance

Cancer mortality declined overall in the United States between 1980 and 2014. Over this same period, there were important changes in trends, patterns, and differences in cancer mortality among US counties. These patterns may inform further research into improving prevention and treatment.

US County-Level Trends in Mortality Rates for Major Causes of Death, 1980-2014

IMPORTANCE

County-level patterns in mortality rates by cause have not been systematically described but are potentially useful for public health officials, clinicians, and researchers seeking to improve health and reduce geographic disparities.

OBJECTIVES

To demonstrate the use of a novel method for county-level estimation and to estimate annual mortality rates by US county for 21 mutually exclusive causes of death from 1980 through 2014.

DESIGN, SETTING, AND PARTICIPANTS

Redistribution methods for garbage codes (implausible or insufficiently specific cause of death codes) and small area estimation methods (statistical methods for estimating rates in small subpopulations) were applied to death registration data from the National Vital Statistics System to estimate annual county-level mortality rates for 21 causes of death. These estimates were raked (scaled along multiple dimensions) to ensure consistency between causes and with existing national-level estimates. Geographic patterns in the age-standardized mortality rates in 2014 and in the change in the age-standardized mortality rates between 1980 and 2014 for the 10 highest-burden causes were determined.

EXPOSURE

County of residence.

MAIN OUTCOMES AND MEASURES

Cause-specific age-standardized mortality rates.

RESULTS

A total of 80 412 524 deaths were recorded from January 1, 1980, through December 31, 2014, in the United States. Of these, 19.4 million deaths were assigned garbage codes. Mortality rates were analyzed for 3110 counties or groups of counties. Large between-county disparities were evident for every cause, with the gap in age-standardized mortality rates between counties in the 90th and 10th percentiles varying from 14.0 deaths per 100 000 population (cirrhosis and chronic liver diseases) to 147.0 deaths per 100 000 population (cardiovascular diseases). Geographic regions with elevated mortality rates differed among causes: for example, cardiovascular disease mortality tended to be highest along the southern half of the Mississippi River, while mortality rates from self-harm and interpersonal violence were elevated in southwestern counties, and mortality rates from chronic respiratory disease were highest in counties in eastern Kentucky and western West Virginia. Counties also varied widely in terms of the change in cause-specific mortality rates between 1980 and 2014. For most causes (eg, neoplasms, neurological disorders, and self-harm and interpersonal violence), both increases and decreases in county-level mortality rates were observed.

CONCLUSIONS AND RELEVANCE

In this analysis of US cause-specific county-level mortality rates from 1980 through 2014, there were large between-county differences for every cause of death, although geographic patterns varied substantially by cause of death. The approach to county-level analyses with small area models used in this study has the potential to provide novel insights into US disease-specific mortality time trends and their differences across geographic regions.

Global and National Burden of Diseases and Injuries Among Children and Adolescents Between 1990 and 2013: Findings From the Global Burden of Disease 2013 Study

IMPORTANCE

The literature focuses on mortality among children younger than 5 years. Comparable information on nonfatal health outcomes among these children and the fatal and nonfatal burden of diseases and injuries among older children and adolescents is scarce.

OBJECTIVE

To determine levels and trends in the fatal and nonfatal burden of diseases and injuries among younger children (aged <5 years), older children (aged 5-9 years), and adolescents (aged 10-19 years) between 1990 and 2013 in 188 countries from the Global Burden of Disease (GBD) 2013 study.

EVIDENCE REVIEW

Data from vital registration, verbal autopsy studies, maternal and child death surveillance, and other sources covering 14,244 site-years (ie, years of cause of death data by geography) from 1980 through 2013 were used to estimate cause-specific mortality. Data from 35,620 epidemiological sources were used to estimate the prevalence of the diseases and sequelae in the GBD 2013 study. Cause-specific mortality for most causes was estimated using the Cause of Death Ensemble Model strategy. For some infectious diseases (eg, HIV infection/AIDS, measles, hepatitis B) where the disease process is complex or the cause of death data were insufficient or unavailable, we used natural history models. For most nonfatal health outcomes, DisMod-MR 2.0, a Bayesian metaregression tool, was used to meta-analyze the epidemiological data to generate prevalence estimates.

FINDINGS

Of the 7.7 (95% uncertainty interval [UI], 7.4-8.1) million deaths among children and adolescents globally in 2013, 6.28 million occurred among younger children, 0.48 million among older children, and 0.97 million among adolescents. In 2013, the leading causes of death were lower respiratory tract infections among younger children (905.059 deaths; 95% UI, 810,304-998,125), diarrheal diseases among older children (38,325 deaths; 95% UI, 30,365-47,678), and road injuries among adolescents (115,186 deaths; 95% UI, 105,185-124,870). Iron deficiency anemia was the leading cause of years lived with disability among children and adolescents, affecting 619 (95% UI, 618-621) million in 2013. Large between-country variations exist in mortality from leading causes among children and adolescents. Countries with rapid declines in all-cause mortality between 1990 and 2013 also experienced large declines in most leading causes of death, whereas countries with the slowest declines had stagnant or increasing trends in the leading causes of death. In 2013, Nigeria had a 12% global share of deaths from lower respiratory tract infections and a 38% global share of deaths from malaria. India had 33% of the world's deaths from neonatal encephalopathy. Half of the world's diarrheal deaths among children and adolescents occurred in just 5 countries: India, Democratic Republic of the Congo, Pakistan, Nigeria, and Ethiopia.

CONCLUSIONS AND RELEVANCE

Understanding the levels and trends of the leading causes of death and disability among children and adolescents is critical to guide investment and inform policies. Monitoring these trends over time is also key to understanding where interventions are having an impact. Proven interventions exist to prevent or treat the leading causes of unnecessary death and disability among children and adolescents. The findings presented here show that these are underused and give guidance to policy makers in countries where more attention is needed.

Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: quantifying the epidemiological transition

BACKGROUND

The Global Burden of Disease Study 2013 (GBD 2013) aims to bring together all available epidemiological data using a coherent measurement framework, standardised estimation methods, and transparent data sources to enable comparisons of health loss over time and across causes, age-sex groups, and countries. The GBD can be used to generate summary measures such as disability-adjusted life-years (DALYs) and healthy life expectancy (HALE) that make possible comparative assessments of broad epidemiological patterns across countries and time. These summary measures can also be used to quantify the component of variation in epidemiology that is related to sociodemographic development.

METHODS

We used the published GBD 2013 data for age-specific mortality, years of life lost due to premature mortality (YLLs), and years lived with disability (YLDs) to calculate DALYs and HALE for 1990, 1995, 2000, 2005, 2010, and 2013 for 188 countries. We calculated HALE using the Sullivan method; 95% uncertainty intervals (UIs) represent uncertainty in age-specific death rates and YLDs per person for each country, age, sex, and year. We estimated DALYs for 306 causes for each country as the sum of YLLs and YLDs; 95% UIs represent uncertainty in YLL and YLD rates. We quantified patterns of the epidemiological transition with a composite indicator of sociodemographic status, which we constructed from income per person, average years of schooling after age 15 years, and the total fertility rate and mean age of the population. We applied hierarchical regression to DALY rates by cause across countries to decompose variance related to the sociodemographic status variable, country, and time.

FINDINGS

Worldwide, from 1990 to 2013, life expectancy at birth rose by 6·2 years (95% UI 5·6-6·6), from 65·3 years (65·0-65·6) in 1990 to 71·5 years (71·0-71·9) in 2013, HALE at birth rose by 5·4 years (4·9-5·8), from 56·9 years (54·5-59·1) to 62·3 years (59·7-64·8), total DALYs fell by 3·6% (0·3-7·4), and age-standardised DALY rates per 100 000 people fell by 26·7% (24·6-29·1). For communicable, maternal, neonatal, and nutritional disorders, global DALY numbers, crude rates, and age-standardised rates have all declined between 1990 and 2013, whereas for non-communicable diseases, global DALYs have been increasing, DALY rates have remained nearly constant, and age-standardised DALY rates declined during the same period. From 2005 to 2013, the number of DALYs increased for most specific non-communicable diseases, including cardiovascular diseases and neoplasms, in addition to dengue, food-borne trematodes, and leishmaniasis; DALYs decreased for nearly all other causes. By 2013, the five leading causes of DALYs were ischaemic heart disease, lower respiratory infections, cerebrovascular disease, low back and neck pain, and road injuries. Sociodemographic status explained more than 50% of the variance between countries and over time for diarrhoea, lower respiratory infections, and other common infectious diseases; maternal disorders; neonatal disorders; nutritional deficiencies; other communicable, maternal, neonatal, and nutritional diseases; musculoskeletal disorders; and other non-communicable diseases. However, sociodemographic status explained less than 10% of the variance in DALY rates for cardiovascular diseases; chronic respiratory diseases; cirrhosis; diabetes, urogenital, blood, and endocrine diseases; unintentional injuries; and self-harm and interpersonal violence. Predictably, increased sociodemographic status was associated with a shift in burden from YLLs to YLDs, driven by declines in YLLs and increases in YLDs from musculoskeletal disorders, neurological disorders, and mental and substance use disorders. In most country-specific estimates, the increase in life expectancy was greater than that in HALE. Leading causes of DALYs are highly variable across countries.

INTERPRETATION

Global health is improving. Population growth and ageing have driven up numbers of DALYs, but crude rates have remained relatively constant, showing that progress in health does not mean fewer demands on health systems. The notion of an epidemiological transition--in which increasing sociodemographic status brings structured change in disease burden--is useful, but there is tremendous variation in burden of disease that is not associated with sociodemographic status. This further underscores the need for country-specific assessments of DALYs and HALE to appropriately inform health policy decisions and attendant actions.

FUNDING

Bill & Melinda Gates Foundation.

Projected growth of the adult congenital heart disease population in the United States to 2050: an integrative systems modeling approach

Background

Mortality for children with congenital heart disease (CHD) has declined with improved surgical techniques and neonatal screening; however, as these patients live longer, accurate estimates of the prevalence of adults with CHD are lacking.

Methods

To determine the prevalence and mortality trends of adults with CHD, we combined National Vital Statistics System data and National Health Interview Survey data using an integrative systems model to determine the prevalence of recalled CHD as a function of age, sex, and year (by recalled CHD, we mean positive response to the question “has a doctor told you that (name) has congenital heart disease?”, which is a conservative lower-bound estimate of CHD prevalence). We used Human Mortality Database estimates and US Census Department projections of the US population to calculate the CHD-prevalent population by age, sex, and year. The primary outcome was prevalence of recalled CHD in adults from 1970 to 2050; the secondary outcomes were birth prevalence and mortality rates by sex and women of childbearing age (15–49 years).

Results

The birth prevalence of recalled CHD in 2010 for males was 3.29 per 1,000 (95 % uncertainty interval (UI) 2.8–3.6), and for females was 3.23 per 1,000 (95 % UI 2.3–3.6). From 1968 to 2010, mortality among zero to 51-week-olds declined from 170 to 53 per 100,000 person years. The estimated number of adults (age 20–64 years) with recalled CHD in 1968 was 118,000 (95 % UI 72,000–150,000). By 2010, there was an increase by a factor of 2.3 (95 % UI 2.2–2.6), to 273,000 (95 % UI 190,000–330,000). There will be an estimated 510,000 (95 % UI: 400,000–580,000) in 2050. The prevalence of adults with recalled CHD will begin to plateau around the year 2050. In 2010, there were 134,000 (95 % UI 69,000–160,000) reproductive-age females (age 15–49 years) with recalled CHD in the United States.

Conclusion

Mortality rates have decreased in infants and the prevalence of adults with CHD has increased but will slow down around 2050. This population requires adult medical systems with providers experienced in the care of adult CHD patients, including those familiar with reproduction in women with CHD.

Electronic supplementary material

The online version of this article (doi:10.1186/s12963-015-0063-z) contains supplementary material, which is available to authorized users.

Populations adapt to fluctuating selection using derived and ancestral allelic diversity

Populations can adapt to changing environments by using allelic diversity, yet whether diversity is recently derived or ancestral is often debated. Although evolution could productively use both types of diversity in a changing environment, their relative frequency has not been quantified. We address this question experimentally using budding yeast strains that harbor a tandem repeat containing URA3 gene, which we expose to cyclical selection and counterselection. We characterize and quantify the dynamics of frameshift events in the URA3 gene in eight populations over 12 cycles of selection and find that ancestral alleles account for 10-20% of all adaptive events. Using a general model of fluctuating selection, we determine how these results depend on mutation rates, population sizes, and fluctuation timescales. We quantify the contribution of derived alleles to the adaptation process using the de novo mutation rate along the population's ancestral lineage, a novel measure that is applicable in a wide range of settings. We find that the adaptive dynamics undergoes a sharp transition from selection on ancestral alleles to selection on derived alleles as fluctuation timescales increase. Our results demonstrate that fluctuations can select between different modes of adaptation over evolutionary timescales.

Global, regional, and national levels and causes of maternal mortality during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013

BACKGROUND

The fifth Millennium Development Goal (MDG 5) established the goal of a 75% reduction in the maternal mortality ratio (MMR; number of maternal deaths per 100,000 livebirths) between 1990 and 2015. We aimed to measure levels and track trends in maternal mortality, the key causes contributing to maternal death, and timing of maternal death with respect to delivery.

METHODS

We used robust statistical methods including the Cause of Death Ensemble model (CODEm) to analyse a database of data for 7065 site-years and estimate the number of maternal deaths from all causes in 188 countries between 1990 and 2013. We estimated the number of pregnancy-related deaths caused by HIV on the basis of a systematic review of the relative risk of dying during pregnancy for HIV-positive women compared with HIV-negative women. We also estimated the fraction of these deaths aggravated by pregnancy on the basis of a systematic review. To estimate the numbers of maternal deaths due to nine different causes, we identified 61 sources from a systematic review and 943 site-years of vital registration data. We also did a systematic review of reports about the timing of maternal death, identifying 142 sources to use in our analysis. We developed estimates for each country for 1990-2013 using Bayesian meta-regression. We estimated 95% uncertainty intervals (UIs) for all values.

FINDINGS

292,982 (95% UI 261,017-327,792) maternal deaths occurred in 2013, compared with 376,034 (343,483-407,574) in 1990. The global annual rate of change in the MMR was -0·3% (-1·1 to 0·6) from 1990 to 2003, and -2·7% (-3·9 to -1·5) from 2003 to 2013, with evidence of continued acceleration. MMRs reduced consistently in south, east, and southeast Asia between 1990 and 2013, but maternal deaths increased in much of sub-Saharan Africa during the 1990s. 2070 (1290-2866) maternal deaths were related to HIV in 2013, 0·4% (0·2-0·6) of the global total. MMR was highest in the oldest age groups in both 1990 and 2013. In 2013, most deaths occurred intrapartum or postpartum. Causes varied by region and between 1990 and 2013. We recorded substantial variation in the MMR by country in 2013, from 956·8 (685·1-1262·8) in South Sudan to 2·4 (1·6-3·6) in Iceland.

INTERPRETATION

Global rates of change suggest that only 16 countries will achieve the MDG 5 target by 2015. Accelerated reductions since the Millennium Declaration in 2000 coincide with increased development assistance for maternal, newborn, and child health. Setting of targets and associated interventions for after 2015 will need careful consideration of regions that are making slow progress, such as west and central Africa.

FUNDING

Bill & Melinda Gates Foundation.