The effects of early-treated phenylketonuria on volumetric measures of the cerebellum

Past murine studies of phenylketonuria (PKU) have documented significant effects on cerebellum at both the gross and cellular levels. The profile of neurocognitive and motor difficulties associated with early-treated PKU (ETPKU) is also consistent with potential cerebellar involvement. Previous neuroanatomical studies of cerebellum in patients with PKU, however, have yielded mixed results. The objective of the present study was to further examine potential differences in cerebellar morphometry between individuals with and without ETPKU. To this end, we analyzed high resolution T1-weighted MR images from a sample of 20 individuals with ETPKU and an age-matched comparison group of 20 healthy individuals without PKU. Measurements of whole brain volume, whole cerebellum volume, cerebellar gray matter volume, and cerebellar white matter volume were collected by means of semiautomatic volumetric analysis. Data analysis revealed no significant group differences in whole brain volume, whole cerebellar volume, or cerebellar white matter volume. A significant reduction in cerebellar gray matter volume, however, was observed for the ETPKU group compared to the non-PKU comparison group. These findings expand on previous animal work suggesting that cerebellar gray matter is impacted by PKU. It is also consistent with the hypothesis that the cognitive difficulties experienced by individuals with ETPKU may be related to disruptions in gray matter. Additional studies are needed to fully elucidate the timing and extent of the impact of ETPKU on cerebellum and the associated neurocognitive consequences

FGF/FGFR Signaling Coordinates Skull Development by Modulating Magnitude of Morphological Integration: Evidence from Apert Syndrome Mouse Models

The fibroblast growth factor and receptor system (FGF/FGFR) mediates cell communication and pattern formation in many tissue types (e.g., osseous, nervous, vascular). In those craniosynostosis syndromes caused by FGFR1-3 mutations, alteration of signaling in the FGF/FGFR system leads to dysmorphology of the skull, brain and limbs, among other organs. Since this molecular pathway is widely expressed throughout head development, we explore whether and how two specific mutations on Fgfr2 causing Apert syndrome in humans affect the pattern and level of integration between the facial skeleton and the neurocranium using inbred Apert syndrome mouse models Fgfr2+/S252W and Fgfr2+/P253R and their non-mutant littermates at P0. Skull morphological integration (MI), which can reflect developmental interactions among traits by measuring the intensity of statistical associations among them, was assessed using data from microCT images of the skull of Apert syndrome mouse models and 3D geometric morphometric methods. Our results show that mutant Apert syndrome mice share the general pattern of MI with their non-mutant littermates, but the magnitude of integration between and within the facial skeleton and the neurocranium is increased, especially in Fgfr2+/S252W mice. This indicates that although Fgfr2 mutations do not disrupt skull MI, FGF/FGFR signaling is a covariance-generating process in skull development that acts as a global factor modulating the intensity of MI. As this pathway evolved early in vertebrate evolution, it may have played a significant role in establishing the patterns of skull MI and coordinating proper skull development

Facial phenotypes in subgroups of prepubertal boys with autism spectrum disorders are correlated with clinical phenotypes

<p>Abstract</p> <p>Background</p> <p>The brain develops in concert and in coordination with the developing facial tissues, with each influencing the development of the other and sharing genetic signaling pathways. Autism spectrum disorders (ASDs) result from alterations in the embryological brain, suggesting that the development of the faces of children with ASD may result in subtle facial differences compared to typically developing children. In this study, we tested two hypotheses. First, we asked whether children with ASD display a subtle but distinct facial phenotype compared to typically developing children. Second, we sought to determine whether there are subgroups of facial phenotypes within the population of children with ASD that denote biologically discrete subgroups.</p> <p>Methods</p> <p>The 3dMD cranial System was used to acquire three-dimensional stereophotogrammetric images for our study sample of 8- to 12-year-old boys diagnosed with essential ASD (<it>n </it>= 65) and typically developing boys (<it>n </it>= 41) following approved Institutional Review Board protocols. Three-dimensional coordinates were recorded for 17 facial anthropometric landmarks using the 3dMD Patient software. Statistical comparisons of facial phenotypes were completed using Euclidean Distance Matrix Analysis and Principal Coordinates Analysis. Data representing clinical and behavioral traits were statistically compared among groups by using χ²tests, Fisher's exact tests, Kolmogorov-Smirnov tests and Student's <it>t</it>-tests where appropriate.</p> <p>Results</p> <p>First, we found that there are significant differences in facial morphology in boys with ASD compared to typically developing boys. Second, we also found two subgroups of boys with ASD with facial morphology that differed from the majority of the boys with ASD and the typically developing boys. Furthermore, membership in each of these distinct subgroups was correlated with particular clinical and behavioral traits.</p> <p>Conclusions</p> <p>Boys with ASD display a facial phenotype distinct from that of typically developing boys, which may reflect alterations in the prenatal development of the brain. Subgroups of boys with ASD defined by distinct facial morphologies correlated with clinical and behavioral traits, suggesting potentially different etiologies and genetic differences compared to the larger group of boys with ASD. Further investigations into genes involved in neurodevelopment and craniofacial development of these subgroups will help to elucidate the causes and significance of these subtle facial differences.</p

Facial Structure Analysis Separates Autism Spectrum Disorders Into Meaningful Clinical Subgroups

Varied cluster analysis were applied to facial surface measurements from 62 prepubertal boys with essential autism to determine whether facial morphology constitutes viable biomarker for delineation of discrete Autism Spectrum Disorders (ASD) subgroups. Earlier study indicated utility of facial morphology for autism subgrouping (Aldridge et al. in Mol Autism 2(1):15, 2011). Geodesic distances between standardized facial landmarks were measured from three-dimensional stereo-photogrammetric images. Subjects were evaluated for autism-related symptoms, neurologic, cognitive, familial, and phenotypic variants. The most compact cluster is clinically characterized by severe ASD, significant cognitive impairment and language regression. This verifies utility of facially-based ASD subtypes and validates Aldridge et al.\u27s severe ASD subgroup, notwithstanding different techniques. It suggests that language regression may define a unique ASD subgroup with potential etiologic differences

Landmarking the brain for geometric morphometric analysis: An error study

Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm-5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm-3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes. © 2014 Chollet et al

Comparative effectiveness of in-person vs. remote delivery of the Common Elements Treatment Approach for addressing mental and behavioral health problems among adolescents and young adults in Zambia: protocol of a three-arm randomized controlled trial

Background In low- and middle-income countries (LMIC), there is a substantial gap in the treatment of mental and behavioral health problems, which is particularly detrimental to adolescents and young adults (AYA). The Common Elements Treatment Approach (CETA) is an evidence-based, flexible, transdiagnostic intervention delivered by lay counselors to address comorbid mental and behavioral health conditions, though its effectiveness has not yet been tested among AYA. This paper describes the protocol for a randomized controlled trial that will test the effectiveness of traditional in-person delivered CETA and a telehealth-adapted version of CETA (T-CETA) in reducing mental and behavioral health problems among AYA in Zambia. Non-inferiority of T-CETA will also be assessed. Methods This study is a hybrid type 1 three-arm randomized trial to be conducted in Lusaka, Zambia. Following an apprenticeship model, experienced non-professional counselors in Zambia will be trained as CETA trainers using a remote, technology-delivered training method. The new CETA trainers will subsequently facilitate technology-delivered trainings for a new cohort of counselors recruited from community-based partner organizations throughout Lusaka. AYA with mental and behavioral health problems seeking services at these same organizations will then be identified and randomized to (1) in-person CETA delivery, (2) telehealth-delivered CETA (T-CETA), or (3) treatment as usual (TAU). In the superiority design, CETA and T-CETA will be compared to TAU, and using a non-inferiority design, T-CETA will be compared to CETA, which is already evidence-based in other populations. At baseline, post-treatment (approximately 3–4 months post-baseline), and 6 months post-treatment (approximately 9 months post-baseline), we will assess the primary outcomes such as client trauma symptoms, internalizing symptoms, and externalizing behaviors and secondary outcomes such as client substance use, aggression, violence, and health utility. CETA trainer and counselor competency and cost-effectiveness will also be measured as secondary outcomes. Mixed methods interviews will be conducted with trainers, counselors, and AYA participants to explore the feasibility, acceptability, and sustainability of technology-delivered training and T-CETA provision in the Zambian context. Discussion Adolescents and young adults in LMIC are a priority population for the treatment of mental and behavioral health problems. Technology-delivered approaches to training and intervention delivery can expand the reach of evidence-based interventions. If found effective, CETA and T-CETA would help address a major barrier to the scale-up and sustainability of mental and behavioral treatments among AYA in LMIC. Trial registration ClinicalTrials.gov NCT03458039 . Prospectively registered on May 10, 202

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation