Invited commentary: Motivating better methods-and better data collection-for measuring prevalence of drug misuse.

The United States continues to suffer a drug overdose crisis that has resulted in over 100,000 deaths annually since 2021. Despite decades of attention, estimates of the prevalence of drug use at the spatiotemporal resolutions necessary for resource allocation and intervention evaluation are lacking. Current approaches to measure prevalence of drug use, such as population surveys, capture-recapture, and multiplier methods, have significant limitations. Santaella-Tenorio et al. (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX)) use a novel joint Bayesian spatiotemporal modeling approach to estimate county-level opioid misuse prevalence in New York state from 2007 to 2018 and identify significant intra-state variation. By leveraging five data sources and simultaneously modeling different opioid-related outcomes - such as deaths, emergency department visits, and treatment visits - they obtain policy-relevant insights into the prevalence of opioid misuse and opioid-related outcomes at high spatiotemporal resolutions. This study provides future researchers with a sophisticated modeling approach that allows them to incorporate multiple data sources in a rigorous statistical framework. The limitations of the study reflect the constraints of the broader field and underscores the importance of enhancing current surveillance with better, newer, and more timely data that is both standardized and easily accessible to inform public health policies and interventions.

Youth Experiencing Parental Death Due to Drug Poisoning and Firearm Violence in the US, 1999-2020.

Importance: Youth (those aged <18 years) parental death has been associated with negative health outcomes. Understanding the burden of parental death due to drug poisoning (herein, drugs) and firearms is essential for informing interventions. Objective: To estimate the incidence of youth parental death due to drugs, firearms, and all other causes. Design, Setting, and Participants: This cross-sectional observational study was conducted using vital registration, including all US decedents, and census data from January 1990 through December 2020. Data were analyzed from May 30, 2023, to March 28, 2024. Exposures: Parental death due to drug poisoning or firearms. Main Outcomes and Measures: A demographic matrix projection model was used to estimate the number and incidence of youth experiencing parental death, defined as the death of 1 or more parents, per 1000 population aged less than 18 years. Analyses evaluated parental deaths by drugs, firearms, and all other causes from 1999 through 2020 by race and ethnicity. Results: Between 1999 and 2020, there were 931 785 drug poisoning deaths and 736 779 firearm-related deaths with a mean (SD) age of 42.6 (16.3) years. Most deaths occurred among males (73.8%) and White decedents (70.8%) followed by Black (17.5%) and Hispanic (9.5%) decedents. An estimated 759 000 (95% CI, 722 000-800 000) youth experienced parental death due to drugs and an estimated 434 000 (95% CI, 409 000-460 000) youth experienced parental death due to firearms, accounting for 17% of all parental deaths. From 1999 to 2020, the estimated number of youth who experienced parental death increased 345% (95% CI, 334%-361%) due to drugs and 39% (95% CI, 37%-41%) due to firearms compared with 24% (95% CI, 23%-25%) due to all other causes. Black youth experienced a disproportionate burden of parental deaths, based primarily on firearm deaths among fathers. In 2020, drugs and firearms accounted for 23% of all parental deaths, double the proportion in 1999 (12%). Conclusions and Relevance: Results of this modeling study suggest that US youth are at high and increasing risk of experiencing parental death by drugs or firearms. Efforts to stem this problem should prioritize averting drug overdoses and firearm violence, especially among structurally marginalized groups.

A framework to model global, regional, and national estimates of intimate partner violence.

BACKGROUND: Accurate and reliable estimates of violence against women form the backbone of global and regional monitoring efforts to eliminate this human right violation and public health problem. Estimating the prevalence of intimate partner violence (IPV) is challenging due to variations in case definition and recall period, surveyed populations, partner definition, level of age disaggregation, and survey representativeness, among others. In this paper, we aim to develop a sound and flexible statistical modeling framework for global, regional, and national IPV statistics. METHODS: We modeled IPV within a Bayesian multilevel modeling framework, accounting for heterogeneity of age groups using age-standardization, and age patterns and time trends using splines functions. Survey comparability is achieved using adjustment factors which are estimated using exact matching and their uncertainty accounted for. Both in-sample and out-of-sample comparisons are used for model validation, including posterior predictive checks. Post-processing of models' outputs is performed to aggregate estimates at different geographic levels and age groups. RESULTS: A total of 307 unique studies conducted between 2000-2018, from 154 countries/areas, and totaling nearly 1.8 million unique women responses informed lifetime IPV. Past year IPV had a similar number of studies (n = 332), countries/areas represented (n = 159), and individual responses (n = 1.8 million). Roughly half of IPV observations required some adjustments. Posterior predictive checks suggest good model fit to data and out-of-sample comparisons provided reassuring results with small median prediction errors and appropriate coverage of predictions' intervals. CONCLUSIONS: The proposed modeling framework can pool both national and sub-national surveys, account for heterogeneous age groups and age trends, accommodate different surveyed populations, adjust for differences in survey instruments, and efficiently propagate uncertainty to model outputs. Describing this model to reproducible levels of detail enables the accurate interpretation and responsible use of estimates to inform effective violence against women prevention policy and programs, and global monitoring of elimination efforts as part of the Sustainable Development Goals.

Competing Effects on the Average Age of Infant Death.

In recent decades, the relationship between the average length of life for those who die in the first year of life-the life table quantity a10-and the level of infant mortality, on which its calculation is often based, has broken down. The very low levels of infant mortality in the developed world correspond to a range of a10 quantities. We illustrate the competing effect of falling mortality and reduction in preterm births on a10 through two populations with very different levels of premature birth-infants born to non-Hispanic White mothers and infants born to non-Hispanic Black mothers in the United States-using linked birth and infant death cohort data. Through simulation, we further demonstrate that falling mortality reduces a10, while a reduction in premature births increases it. We use these observations to motivate the formulation of a new approximation formula for a10 in low-mortality contexts, which aims to incorporate differences in preterm birth through a proxy measure-the ratio of infant to under-five mortality. Models are built and tested using data from the Human Mortality Database. Model results and validation show that the newly proposed model outperforms existing alternatives.

A Bayesian Cohort Component Projection Model to Estimate Women of Reproductive Age at the Subnational Level in Data-Sparse Settings.

Accurate estimates of subnational populations are important for policy formulation and monitoring population health indicators. For example, estimates of the number of women of reproductive age are important to understand the population at risk of maternal mortality and unmet need for contraception. However, in many low-income countries, data on population counts and components of population change are limited, and so subnational levels and trends are unclear. We present a Bayesian constrained cohort component model for the estimation and projection of subnational populations. The model builds on a cohort component projection framework, incorporates census data and estimates from the United Nation's World Population Prospects, and uses characteristic mortality schedules to obtain estimates of population counts and the components of population change, including internal migration. The data required as inputs to the model are minimal and available across a wide range of countries, including most low-income countries. The model is applied to estimate and project populations by county in Kenya for 1979-2019 and is validated against the 2019 Kenyan census.

Temporal Models for Demographic and Global Health Outcomes in Multiple Populations: Introducing a New Framework to Review and Standardise Documentation of Model Assumptions and Facilitate Model Comparison.

There is growing interest in producing estimates of demographic and global health indicators in populations with limited data. Statistical models are needed to combine data from multiple data sources into estimates and projections with uncertainty. Diverse modelling approaches have been applied to this problem, making comparisons between models difficult. We propose a model class, Temporal Models for Multiple Populations (TMMPs), to facilitate both documentation of model assumptions in a standardised way and comparison across models. The class makes a distinction between the process model, which describes latent trends in the indicator interest, and the data model, which describes the data generating process of the observed data. We provide a general notation for the process model that encompasses many popular temporal modelling techniques, and we show how existing models for a variety of indicators can be written using this notation. We end with a discussion of outstanding questions and future directions.

Trends in Black and White Opioid Mortality in the United States, 1979-2015.

BACKGROUND: Recent research on the US opioid epidemic has focused on the white or total population and has largely been limited to data after 1999. However, understanding racial differences in long-term trends by opioid type may contribute to improving interventions. METHODS: Using multiple cause of death data, we calculated age-standardized opioid mortality rates, by race and opioid type, for the US resident population from 1979 to 2015. We analyzed trends in mortality rates using joinpoint regression. RESULTS: From 1979 to 2015, the long-term trends in opioid-related mortality for Earlier data did not include ethnicity so this is incorrect. It is all black and all white residents in the US. blacks and whites went through three successive waves. In the first wave, from 1979 to the mid-1990s, the epidemic affected both populations and was driven by heroin. In the second wave, from the mid-1990s to 2010, the increase in opioid mortality was driven by natural/semi-synthetic opioids (e.g., codeine, morphine, hydrocodone, or oxycodone) among whites, while there was no increase in mortality for blacks. In the current wave, increases in opioid mortality for both populations have been driven by heroin and synthetic opioids (e.g., fentanyl and its analogues). Heroin rates are currently increasing at 31% (95% confidence interval [CI] = 27, 35) per year for whites and 34% (95% CI = 30, 40) for blacks. Concurrently, respective synthetic opioids are increasing at 79% (95% CI = 50, 112) and 107% (95% CI = -15, 404) annually. CONCLUSION: Since 1979, the nature of the opioid epidemic has shifted from heroin to prescription opioids for the white population to increasing of heroin/synthetic deaths for both black and white populations. See video abstract at, http://links.lww.com/EDE/B377.

A Flexible Bayesian Model for Estimating Subnational Mortality.

Reliable subnational mortality estimates are essential in the study of health inequalities within a country. One of the difficulties in producing such estimates is the presence of small populations among which the stochastic variation in death counts is relatively high, and thus the underlying mortality levels are unclear. We present a Bayesian hierarchical model to estimate mortality at the subnational level. The model builds on characteristic age patterns in mortality curves, which are constructed using principal components from a set of reference mortality curves. Information on mortality rates are pooled across geographic space and are smoothed over time. Testing of the model shows reasonable estimates and uncertainty levels when it is applied both to simulated data that mimic U.S. counties and to real data for French départements. The model estimates have direct applications to the study of subregional health patterns and disparities.

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs.

Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet, the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper, we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001-2020. The key themes covered by the review are extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socioeconomic variables and spatial relationships, 'downscaling' and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used. Supplementary Information: The online version contains supplementary material available at 10.1007/s11113-021-09671-6.

Sociodemographic characteristics of missing data in digital phenotyping.

The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for "digital phenotyping," the collection and analysis of raw phone sensor and log data to study the lived experiences of subjects in their natural environments using their own devices. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. In this meta-study using individual-level data from six different studies, we examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression with study- and user-level random intercepts. Sensitivity analyses including alternative model specification and stratified models were conducted. We found that iOS users had lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection, but rates did not differ by sex, education, or age. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.

Assessment of Changes in the Geographical Distribution of Opioid-Related Mortality Across the United States by Opioid Type, 1999-2016.

Importance: As the opioid epidemic evolves, it is vital to identify changes in the geographical distribution of opioid-related deaths, and the specific opioids to which those deaths are attributed, to ensure that federal and state public health interventions remain appropriately targeted. Objective: To identify changes in the geographical distribution of opioid-related mortality across the United States by opioid type. Design, Setting, and Participants: Cross-sectional study using joinpoint modeling and life table analysis of individual-level data from the National Center for Health Statistics on 351 630 US residents who died from opioid-related causes from January 1, 1999, to December 31, 2016, for all of the United States and the District of Columbia. The analysis was conducted from September 6 to November 23, 2018. Exposures: Deaths involving any opioid, heroin, synthetic opioids, and natural and semisynthetic opioids. Main Outcomes and Measures: Opioid-related mortality rate, annual percent change in the opioid-related mortality rate, and life expectancy lost at age 15 years by state and opioid type. Results: From 1999 to 2016, a total of 231 264 men and 120 366 women died from opioid-related causes across the whole United States. Sixty-six observations were removed owing to missing data on age; therefore, 351 564 US residents were included in this study. The mean (SD) age at death was 39.8 (12.5) years for men and was 43.5 (12.9) years from women. Opioid-related mortality rates, especially from synthetic opioids, rapidly increased in all of the eastern United States. In most states, mortality associated with natural and semisynthetic opioids (ie, prescription painkillers) remained stable. In contrast, 28 states had mortality rates from synthetic opioids that more than doubled every 2 years (ie, annual percent change, ≥41%), including 12 with high mortality rates from synthetic opioids (>10 per 100 000 people). Among these 28 states, the mortality rate from natural and semisynthetic opioids ranged from 2.0 to 18.7 per 100 000 people (with a mean mortality rate of 6.0 per 100 000 people). The District of Columbia had the fastest rate of increase in mortality from opioids, more than tripling every year since 2013 (annual percent change, 228.3%; 95% CI, 169.7%-299.6%; P < .001), and a high mortality rate from synthetic opioids in 2016 (18.8 per 100 000 people); the mortality rate from natural and semisynthetic opioids was 6.9 per 100 000 people. Nationally, overall opioid-related mortality resulted in 0.36 years of life expectancy lost in 2016, which was 14% higher than deaths due to firearms and 18% higher than deaths due to motor vehicle crashes; 0.17 years of the life expectancy lost was due specifically to synthetic opioids. In 2016, New Hampshire and West Virginia lost more than 1 year of life expectancy due to opioid-related mortality. Conclusions and Relevance: Opioid-related mortality, particularly mortality associated with synthetic opioids, has increased in the eastern United States. These findings indicate that policies focused on reducing opioid-related deaths may need to prioritize synthetic opioids and rapidly expanding epidemics in northeastern states and consider the potential for synthetic opioid epidemics outside of the heroin supply.

National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: a systematic analysis.

BACKGROUND: Reducing neonatal mortality is an essential part of the third Sustainable Development Goal (SDG), to end preventable child deaths. To achieve this aim will require an understanding of the levels of and trends in neonatal mortality. We therefore aimed to estimate the levels of and trends in neonatal mortality by use of a statistical model that can be used to assess progress in the SDG era. With these estimates of neonatal mortality between 1990 and 2017, we then aimed to assess how different targets for neonatal mortality could affect the burden of neonatal mortality from 2018 to 2030. METHODS: In this systematic analysis, we used nationally-representative empirical data related to neonatal mortality, including data from vital registration systems, sample registration systems, and household surveys, to estimate country-specific neonatal mortality rates (NMR; the probability of dying during the first 28 days of life) for all countries between 1990 (or the earliest year of available data) and 2017. For our analysis, we used all publicly available data on neonatal mortality from databases compiled annually by the UN Inter-agency Group for Child Mortality Estimation, which were extracted on or before July 31, 2018, for data relating to the period between 1950 and 2017. All nationally representative data were assessed. We used a Bayesian hierarchical penalised B-splines regression model, which allowed for data from different sources to be weighted differently, to account for variable biases and for the uncertainty in NMR to be assessed. The model simultaneously estimated a global association between NMR and under-5 mortality rate and country-specific and time-specific effects, which enabled us to identify countries with an NMR that was higher or lower than expected. Scenario-based projections were made at the county level by use of current levels of and trends in neonatal mortality and historic or annual rates of reduction that would be required to achieve national targets. The main outcome that we assessed was the levels of and trends in neonatal mortality and the global and regional NMRs from 1990 to 2017. FINDINGS: Between 1990 and 2017, the global NMR decreased by 51% (90% uncertainty interval [UI] 46-54), from 36·6 deaths per 1000 livebirths (35·5-37·8) in 1990, to 18·0 deaths per 1000 livebirths (17·0-19·9) in 2017. The estimated number of neonatal deaths during the same period decreased from 5·0 million (4·9 million-5·2 million) to 2·5 million (2·4 million-2·8 million). Annual NMRs vary widely across the world, but west and central Africa and south Asia had the highest NMRs in 2017. All regions have reported reductions in NMRs since 1990, and most regions accelerated progress in reducing neonatal mortality in 2000-17 versus 1990-2000. Between 2018 and 2030, we project that 27·8 million children will die in their first month of life if each country maintains its current rate of reduction in NMR. If each country achieves the SDG neonatal mortality target of 12 deaths per 1000 livebirths or fewer by 2030, we project 22·7 million cumulative neonatal deaths by 2030. More than 60 countries need to accelerate their progress to reach the neonatal mortality SDG target by 2030. INTERPRETATION: Although substantial progress has been made in reducing neonatal mortality since 1990, increased efforts to improve progress are still needed to achieve the SDG target by 2030. Accelerated improvements are most needed in the regions and countries with high NMR, particularly in sub-Saharan Africa and south Asia. FUNDING: Bill & Melinda Gates Foundation, United States Agency for International Development.

Changes in central corneal thickness over time: the ocular hypertension treatment study.

OBJECTIVE: To describe how much change, if any, occurs between central corneal thickness (CCT) measurements performed an average of 3.8 years apart in participants in the Ocular Hypertension Treatment Study (OHTS) and to identify clinical and demographic factors that are associated with changes in CCT, including baseline intraocular pressure, duration and class of ocular hypotensive medication, medical history, and systemic medication. DESIGN: Secondary analysis of data from a randomized clinical trial. PARTICIPANTS: Ocular Hypertension Treatment Study participants. Participants who had undergone incisional intraocular or keratorefractive surgery between CCT measurements were excluded. TESTING: The first CCT measurements were performed starting in 1999, and the second measurements were performed starting in 2002. Measurements were performed by OHTS certified technicians using an ultrasonic pachymeter under a standardized protocol. MAIN OUTCOME MEASURE: Central corneal thickness measurement (micrometers). RESULTS: First and second CCT measurements were available from 73% (1191) of the 1636 OHTS participants randomized. Central corneal thickness decreased a mean rate of -0.74+/-3.5 microm/year between the first and second CCT measurements. The mean medication exposure between first and second CCT measurements in participants originally randomized to observation (n = 595) was 1.1+/-1.6 years, versus 5.0+/-2.7 years in participants originally randomized to medication (n = 596). Central corneal thickness decreased by a mean of 1.0+/-3.4 microm/year among participants originally randomized to observation, compared with 0.5+/-3.5 microm/year among participants originally randomized to medication (P<0.0001). Subgroup analyses suggest that participants treated only with topical prostaglandin analogues (PGAs) between the two CCT measurements had a greater rate of decrease per year than participants treated only with topical beta-blockers. CONCLUSIONS: The rate of CCT decrease over 3.8 years is comparable to the cross-sectional age differences reported in the OHTS at the first measurement (0.6 microm/year) and comparable to other cross-sectional studies. Use of topical PGAs may be associated with a slightly higher rate of thinning. The modest age- and drug-related rates of thinning observed are unlikely to influence tonometry or clinical decision-making substantially in most clinical situations.

Infant mortality among twins born to teenagers in the United States. Black-white disparity.

OBJECTIVE: To determine the magnitude of the disparity in infant mortality between twins born to black and white teenagers in the United States. STUDY DESIGN: Analysis was performed on twins born to adolescents in the United States within the period 1995-1997. The generalized estimating equations framework was used to generate relative risks after capturing the effects of sibling correlations within twin pairs. RESULTS: Infant mortality was 20% higher among black twins as compared to their white counterparts (adjusted OR = 1.20, 95% CI = 1.04-1.39). The black-white disparity in infant mortality occurred exclusively in the neonatal period (adjusted OR = 1.31, 95% CI = 1.11-1.54), with postneonatal estimates comparable (adjusted OR = 0.86, 95% CI = .63-1.17). The higher proportion of low-birth-weight infants--more specifically, those small for gestational age as opposed to preterm--among black twins was the most likely explanation for the lower survival probability among twins born to black teenagers. CONCLUSION: Black-white disparity in infant mortality among twins occurred exclusively during the first 28 days of life rather than throughout infancy. Efforts to bridge the gap should be focused on this critical period and should preferentially target those twins who are small for gestational age.

Survival of triplets who are born to teen mothers in the United States.

OBJECTIVE: The purposes of this study were to assess survival among triplets who are born to teen mothers and to determine whether fetal number influences the mortality rates of the offspring of teen mothers when compared with the offspring of older women. STUDY DESIGN: A retrospective cohort study of 354 triplet births to teenage mothers and 6858 to young mature mothers (20-29 years) who were delivered from 1995 through 1998. We compared the occurrence of stillbirth and neonatal and infant mortality rates between the 2 categories by means of the generalized estimating equation. Similar analyses were conducted for singleton pregnancies and twin pregnancies. RESULTS: Triplets of teenage mothers experienced a higher level of stillbirth (odds ratio, 3.24; 95% CI, 1.44-7.24), neonatal mortality (odds ratio, 2.00; 95% CI, 1.11-3.61), and infant death (odds ratio, 1.66; 95% CI, 1.01-2.87). Moreover, as the plurality increased from singleton infant to triplet, the offspring of teenagers fared progressively worse ( P < .0001). CONCLUSION: This study confirms the association between teenage motherhood and feto-infant death and indicates that this mortality relationship varies in a dose-dependent fashion.

Stillbirths and infant deaths associated with maternal smoking among mothers aged > or =40 years: a population study.

We set out to estimate the association between smoking among pregnant women aged at least 40 years and pregnancy outcome by analyzing singleton live births in the United States between 1995 and 1997. The study group consisted of deliveries to mothers aged 40 years and older with two maternal age categories (20 to 29 and 30 to 39 years) as control. Although risks varied with maternal age, smoking was associated with a higher-than-expected risk for infant mortality in all maternal age categories. The highest rate of infant mortality associated with smoking after adjusting for confounding was among mothers aged 20 to 29 (hazard ratio [HR], 1.49; 95% confidence interval [CI], 1.28 to 1.75), while the lowest was among pregnant mothers in the 40 and above age category (HR, 1.03; 95% CI, 0.87 to 1.23). In utero fetal demise was highest among older smoking mothers (>/=40 years) and declined with decreasing age (p for trend <0.0001). In conclusion, the relationship between maternal smoking and pregnancy outcomes is modified by the age of the mother.