An Evaluation of Projection Methods for Detailed Small Area Projections: An Application and Validation to King County, Washington.

Population projections are used by a number of local agencies to better prepare for the future resource needs of counties, ensuring that educational, health, housing, and economic demands of individuals are met. Meeting the specific needs of a county's population, such as what resources to provide, where to target resources, and ensure an equitable distribution of those resources, requires population projections which are both demographically detailed, such as by age, race, and ethnicity, and geographically precise, such as at the census tract level. Despite this need, an evaluation of which methods are best suited to produce population projections at this level are lacking. In this study, we evaluate the accuracy of several cohort-based methods for small area population projections by race and ethnicity. We apply these methods to population projections of King County, Washington and assess the validity of projections using past population estimates. We find a clear pattern that demonstrates while simplified methods perform well in near term forecasts, methods which employ smoothing strategies perform better in long-term forecasting scenarios. Furthermore, we demonstrate that model's incorporating multiple stages of smoothing can provide detailed insights into the projected population size of King county and the places and groups which will most contribute to this growth. Detailed projections, such as those provided by multi-stage smoothing methods, enable city planners and policy makers a detailed view of the future structure of their county's population and provide for them a resource to better meet the needs of future populations.

COVID-19 Burden in Adult Correctional or Detention Facilities and the Surrounding Communities, January 1, 2020-July 20, 2021.

We estimated the COVID-19 burden in adult correctional or detention facilities and associated counties by state, facility jurisdiction, and county urbanicity. COVID-19 cumulative incidence (cases per 1,000 persons) for each U.S. correctional or detention facility and people ages 18 years and older in the associated county was estimated between January 1, 2020 and July 20, 2021. Across 46 U.S. states, 1,083 correctional or detention facilities in 718 counties were included. The median COVID-19 incidence rate was higher in facilities than in associated counties for 42 of 46 states and for all facility jurisdictions and county urbanicity categories. COVID-19 burden was higher in most facilities than in associated counties. Implementing COVID-19 mitigation measures in correctional settings is needed to prevent SARS-CoV-2 transmission in facilities and associated counties.

Racial And Ethnic Inequalities In COVID-19 Mortality Within Carceral Settings: An Analysis Of Texas Prisons.

The COVID-19 pandemic has taken a heightened toll on people incarcerated in prisons in the United States, with those incarcerated experiencing a higher rate of infection and mortality than the US population more generally. What is less well known is the degree to which COVID-19 outcomes differ among incarcerated populations, especially by race and ethnicity, where significant differences have been found among the US population as a whole. This knowledge gap is, in part, due to a lack of reporting of COVID-19 outcomes by race and ethnicity by most state prison systems. To shed light on this topic, we analyzed mortality patterns of the population incarcerated in Texas state prison facilities during both the year before (beginning April 1, 2019) and the first year of (beginning April 1, 2020) the COVID-19 pandemic. We used a unique data set of roster information from the Texas Department of Criminal Justice and medical examiner records. COVID-19 mortality was 1.61 and 2.12 times higher for Black and Hispanic populations, respectively, when compared with the White population in Texas prisons. Strategies for COVID-19 mitigation in carceral settings, such as vaccination and decarceration, should include an equity component to minimize disparities.

Life Expectancy and COVID-19 in Florida State Prisons.

INTRODUCTION: The heightened risk of COVID-19 infection and mortality in prisons is well documented, but COVID-19's impact on all-cause mortality in incarcerated populations has not yet been studied. This study analyzed mortality records from the Florida State Department of Corrections prison system population to evaluate the impact COVID-19 had on all-cause mortality and compare mortality rates and life expectancy with that of the overall state of Florida population. METHODS: Population age and sex data for Florida State Department of Corrections were ascertained from the Florida State Department of Corrections Offender Based Information System. Death data by age, sex, and cause of death were acquired from medical records and Florida State Department of Corrections offender reports. The state of Florida demographic and death data were collected from the Census Bureau, Florida Department of Health, and Centers for Disease Control and Prevention. Age- and sex-standardized life table measures were calculated, and COVID-19 contributions to changes in life expectancy were assessed using Arriaga's decomposition. RESULTS: The standardized mortality rate in the Florida State Department of Corrections population increased by 45% between 2019 and 2020, causing an overall 4.0-year decline in life expectancy. Over the same period, the state of Florida population's standardized mortality increased by 19%, resulting in an overall 2.7-year decline. Within the Florida State Department of Corrections population, life expectancy decline could be attributed exclusively to COVID-19 mortality. CONCLUSIONS: The state of Florida prison population saw a substantial increase in mortality driven solely by COVID-19 mortality, leading to an overall 4-year decline in life expectancy. Given the findings and continued threat of COVID-19 outbreaks, Florida State Department of Corrections and other prison systems should strive to increase vaccination uptake, decrease prison populations, and commit to COVID-19 data transparency.

Factors associated with delays in the search for care in under-5 deaths in Yucatán, Mexico / Factores asociados con demoras en la búsqueda de atención en muertes de menores de cinco años en Yucatán, México

Abstract: Objective: We examined delays during the search for care and associations with mother, child, or health services characteristics, and with symptoms reported prior to death. Materials and methods: Cross-sectional study comprising household interviews with 252 caregivers of children under-5 who died in the state of Yucatán, Mexico, during 2015-2016. We evaluated the three main delays: 1) time to identify symptoms and start search for care, 2) transport time to health facility, and 3) wait time at health facility. Results: Children faced important delays including a mean time to start the search for care of 4.1 days. The mean transport time to the first facility was longer for children enrolled in Seguro Popular and there were longer wait times at public facilities, especially among children who also experienced longer travel time Conclusions: Providing resources to enable caregivers to access health services in a timely manner may reduce delays in seeking care.

Resumen: Objetivo: Analizar las demoras en la búsqueda de atención y su asociación con características de la madre, del niño y los servicios de salud, así como con los síntomas reportados antes de la defunción. Material y métodos: Diseño transversal con entrevistas a 252 cuidadores que se encargaron de niños menores de cinco años que fallecieron en el estado de Yucatán, México, durante 2015-2016. Se evaluaron tres demoras: 1) tiempo en identificar la complicación e iniciar el proceso de búsqueda; 2) tiempo de transporte; y 3) tiempo de espera en la unidad de salud. Resultados: Los niños enfrentaron demoras importantes en la búsqueda de atención. La media de tiempo para iniciar la búsqueda de atención fue de 4.1 días. La media de tiempo de transporte a la primera unidad de atención fue mayor para niños inscritos en el Seguro Popular y hubo tiempos de espera más largos en unidades de salud del sector público, especialmente entre niños que tuvieron tiempos de transporte largos. Conclusión: Proporcionar recursos que permitan a los cuidadores acceder a los servicios de salud de manera oportuna puede reducir las demoras en la búsqueda de atención.

Factors associated with delays in the search for care in under-5 deaths in Yucatán, Mexico.

OBJECTIVE: We examined delays during the search for care and associations with mother, child, or health services characteristics, and with symptoms reported prior to death. MATERIALS AND METHODS: Cross-sectional study compris-ing household interviews with 252 caregivers of children under-5 who died in the state of Yucatán, Mexico, during 2015-2016. We evaluated the three main delays: 1) time to identify symptoms and start search for care, 2) transport time to health facility, and 3) wait time at health facility. RESULTS: Children faced important delays including a mean time to start the search for care of 4.1 days. The mean transport time to the first facility was longer for children enrolled in Seguro Popular and there were longer wait times at public facilities, especially among children who also experienced longer travel time. CONCLUSIONS: Providing resources to enable caregiv-ers to access health services in a timely manner may reduce delays in seeking care.

Assessing the Mortality Impact of the COVID-19 Pandemic in Florida State Prisons.

BACKGROUND: The increased risk of COVID-19 infection among incarcerated individuals due to environmental hazards is well known and recent studies have highlighted the higher rates of infection and mortality prisoners in the United States face due to COVID-19. However, the impact of COVID-19 on all-cause mortality rates in incarcerated populations has not been studied. METHODS: Using data reported by the Florida Department of Corrections on prison populations and mortality events we conducted a retrospective cohort study of all individuals incarcerated in Florida state prisons between 2015 and 2020. We calculated excess deaths by estimating age-specific expected deaths from mortality trends in 2015 through 2019 and taking the difference between observed and expected deaths during the pandemic period. We calculated life table measures using standard demographic techniques and assessed significant yearly changes using bootstrapping. FINDINGS: The Florida Department of Corrections reported 510 total deaths from March 1, 2020 to December 31, 2020 among the state prison population. This was 42% higher (rate ratio 1.42, 95% CI 1.15 to 1.89) than the expected number of deaths in light of mortality rates for previous years. Reported COVID-19 deaths in a month were positively correlated with estimated excess deaths (80.4%, p <.01). Using age-specific mortality estimates, we found that life expectancy at age 20 declined by 4 years (95% CI 2.06-6.57) between 2019 and 2020 for the Florida prison population. INTERPRETATION: The Florida prison population saw a significant increase in all-cause mortality during the COVID-19 pandemic period, leading to a decrease in life expectancy of more than four years. Life years lost by the Florida prison population were likely far greater than those lost by the general United States population, as reported by other studies. This difference in years lost highlights the need for increased interventions to protect vulnerable incarcerated populations during pandemics. FUNDING: Vital Projects Fund, Arnold Ventures, US Centers for Disease Control, Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Early life predictors of positive change during the coronavirus disease pandemic.

BACKGROUND: The COVID-19 pandemic is a crisis unprecedented in its size and scope. Yet studies of resilience suggest most individuals will successfully negotiate this challenge and some may even experience growth and positive change. Some evidence suggests that the capacity to enact positive change in the face of adversity may be shaped by early life experiences. METHODS: In a subset of 374 participants (57% female, mean age = 29 years) in the Study of Early Child Care and Youth Development (SECCYD), a longitudinal, birth cohort, prospective models were tested to determine whether early life adversities in family and neighborhood contexts predict positive change events in response to the COVID-19 pandemic. Childhood family and neighborhood contexts were assessed using a combination of self-report questionnaires and US Census data. Adulthood positive change events (e.g., becoming more appreciative of things usually taken for granted) were assessed using the Epidemic-Pandemic Impacts Inventory (EPII). RESULTS: In regression analyses, neighborhood disadvantage in childhood, measured both by objective and subjective assessments, predicted a higher number of positive change events in response to the COVID-19 pandemic (ß = .18, p = .004 and ß = .15, p = .006, respectively). Examination of the positive change event subscales showed neighborhood disadvantage in childhood predicted increases in events related to 'perspective taking and charitable giving' (ß = .20, p = .022 and ß = .17, p = .002, respectively) and improved 'social relationships' (ß = .18, p = .004 and ß = .13, p = .020, respectively), but not to positive 'health behaviors' (ps > .05). All associations were independent of sociodemographic factors and childhood family dysfunction. CONCLUSIONS: Findings suggest that neighborhood disadvantage in childhood may shape prosocial responses to stress in adulthood, potentially through early life adaptions to stress that are protective when facing adversity. There are several notable implications of the study findings. Although adversity in early life has clear negative impacts, it is possible that adversity experiences may also provide opportunities to develop adaptive strategies that foster resilience and growth when facing stress. Intervention efforts should consider leveraging such stress-adapted strengths to reduce the many negative impacts of early life adversity.

Harmonizing child mortality data at disparate geographic levels.

There is an increasing focus on reducing inequalities in health outcomes in developing countries. Subnational variation is of particular interest, with geographically-indexed data being used to understand the spatial risk of detrimental outcomes and to identify who is at greatest risk. While some health surveys provide observations with associated geographic coordinates (point data), many others provide data that have their locations masked and instead only report the strata (polygon information) within which the data resides (masked data). How to harmonize these data sources for spatial analysis has been previously considered although only ad hoc methods and comparison of methods is lacking. In this paper, we present a new method for analyzing masked survey data, using a method that is consistent with the data-generating process. In addition, we critique two previously proposed approaches to analyzing masked data and illustrate that they are fundamentally flawed methodologically. To validate our method, we compare our approach with previously formulated solutions in several realistic simulation environments in which the underlying structure of the risk field is known. We simulate samples from spatiotemporal fields in a way that mimics the sampling frame implemented in the most common health surveys in low- and middle-income countries, the Demographic and Health Surveys and Multiple Indicator Cluster Surveys. In simulations, the newly proposed approach outperforms previously proposed approaches in terms of minimizing error while increasing the precision of estimates. The approaches are subsequently compared using child mortality data from the Dominican Republic where our findings are reinforced. The ability to accurately increase precision of child mortality estimates, and health outcomes in general, by leveraging various types of data, improves our ability to implement precision public health initiatives and better understand the landscape of geographic health inequalities.

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017.

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2-to end preventable child deaths by 2030-we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000-2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.

Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories.

BACKGROUND: Understanding potential trajectories in health and drivers of health is crucial to guiding long-term investments and policy implementation. Past work on forecasting has provided an incomplete landscape of future health scenarios, highlighting a need for a more robust modelling platform from which policy options and potential health trajectories can be assessed. This study provides a novel approach to modelling life expectancy, all-cause mortality and cause of death forecasts -and alternative future scenarios-for 250 causes of death from 2016 to 2040 in 195 countries and territories. METHODS: We modelled 250 causes and cause groups organised by the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) hierarchical cause structure, using GBD 2016 estimates from 1990-2016, to generate predictions for 2017-40. Our modelling framework used data from the GBD 2016 study to systematically account for the relationships between risk factors and health outcomes for 79 independent drivers of health. We developed a three-component model of cause-specific mortality: a component due to changes in risk factors and select interventions; the underlying mortality rate for each cause that is a function of income per capita, educational attainment, and total fertility rate under 25 years and time; and an autoregressive integrated moving average model for unexplained changes correlated with time. We assessed the performance by fitting models with data from 1990-2006 and using these to forecast for 2007-16. Our final model used for generating forecasts and alternative scenarios was fitted to data from 1990-2016. We used this model for 195 countries and territories to generate a reference scenario or forecast through 2040 for each measure by location. Additionally, we generated better health and worse health scenarios based on the 85th and 15th percentiles, respectively, of annualised rates of change across location-years for all the GBD risk factors, income per person, educational attainment, select intervention coverage, and total fertility rate under 25 years in the past. We used the model to generate all-cause age-sex specific mortality, life expectancy, and years of life lost (YLLs) for 250 causes. Scenarios for fertility were also generated and used in a cohort component model to generate population scenarios. For each reference forecast, better health, and worse health scenarios, we generated estimates of mortality and YLLs attributable to each risk factor in the future. FINDINGS: Globally, most independent drivers of health were forecast to improve by 2040, but 36 were forecast to worsen. As shown by the better health scenarios, greater progress might be possible, yet for some drivers such as high body-mass index (BMI), their toll will rise in the absence of intervention. We forecasted global life expectancy to increase by 4·4 years (95% UI 2·2 to 6·4) for men and 4·4 years (2·1 to 6·4) for women by 2040, but based on better and worse health scenarios, trajectories could range from a gain of 7·8 years (5·9 to 9·8) to a non-significant loss of 0·4 years (-2·8 to 2·2) for men, and an increase of 7·2 years (5·3 to 9·1) to essentially no change (0·1 years [-2·7 to 2·5]) for women. In 2040, Japan, Singapore, Spain, and Switzerland had a forecasted life expectancy exceeding 85 years for both sexes, and 59 countries including China were projected to surpass a life expectancy of 80 years by 2040. At the same time, Central African Republic, Lesotho, Somalia, and Zimbabwe had projected life expectancies below 65 years in 2040, indicating global disparities in survival are likely to persist if current trends hold. Forecasted YLLs showed a rising toll from several non-communicable diseases (NCDs), partly driven by population growth and ageing. Differences between the reference forecast and alternative scenarios were most striking for HIV/AIDS, for which a potential increase of 120·2% (95% UI 67·2-190·3) in YLLs (nearly 118 million) was projected globally from 2016-40 under the worse health scenario. Compared with 2016, NCDs were forecast to account for a greater proportion of YLLs in all GBD regions by 2040 (67·3% of YLLs [95% UI 61·9-72·3] globally); nonetheless, in many lower-income countries, communicable, maternal, neonatal, and nutritional (CMNN) diseases still accounted for a large share of YLLs in 2040 (eg, 53·5% of YLLs [95% UI 48·3-58·5] in Sub-Saharan Africa). There were large gaps for many health risks between the reference forecast and better health scenario for attributable YLLs. In most countries, metabolic risks amenable to health care (eg, high blood pressure and high plasma fasting glucose) and risks best targeted by population-level or intersectoral interventions (eg, tobacco, high BMI, and ambient particulate matter pollution) had some of the largest differences between reference and better health scenarios. The main exception was sub-Saharan Africa, where many risks associated with poverty and lower levels of development (eg, unsafe water and sanitation, household air pollution, and child malnutrition) were projected to still account for substantive disparities between reference and better health scenarios in 2040. INTERPRETATION: With the present study, we provide a robust, flexible forecasting platform from which reference forecasts and alternative health scenarios can be explored in relation to a wide range of independent drivers of health. Our reference forecast points to overall improvements through 2040 in most countries, yet the range found across better and worse health scenarios renders a precarious vision of the future-a world with accelerating progress from technical innovation but with the potential for worsening health outcomes in the absence of deliberate policy action. For some causes of YLLs, large differences between the reference forecast and alternative scenarios reflect the opportunity to accelerate gains if countries move their trajectories toward better health scenarios-or alarming challenges if countries fall behind their reference forecasts. Generally, decision makers should plan for the likely continued shift toward NCDs and target resources toward the modifiable risks that drive substantial premature mortality. If such modifiable risks are prioritised today, there is opportunity to reduce avoidable mortality in the future. However, CMNN causes and related risks will remain the predominant health priority among lower-income countries. Based on our 2040 worse health scenario, there is a real risk of HIV mortality rebounding if countries lose momentum against the HIV epidemic, jeopardising decades of progress against the disease. Continued technical innovation and increased health spending, including development assistance for health targeted to the world's poorest people, are likely to remain vital components to charting a future where all populations can live full, healthy lives. FUNDING: Bill & Melinda Gates Foundation.

Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study.

IMPORTANCE: Cancer is the second leading cause of death worldwide. Current estimates on the burden of cancer are needed for cancer control planning. OBJECTIVE: To estimate mortality, incidence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 32 cancers in 195 countries and territories from 1990 to 2015. EVIDENCE REVIEW: Cancer mortality was estimated using vital registration system data, cancer registry incidence data (transformed to mortality estimates using separately estimated mortality to incidence [MI] ratios), and verbal autopsy data. Cancer incidence was calculated by dividing mortality estimates through the modeled MI ratios. To calculate cancer prevalence, MI ratios were used to model survival. To calculate YLDs, prevalence estimates were multiplied by disability weights. The YLLs were estimated by multiplying age-specific cancer deaths by the reference life expectancy. DALYs were estimated as the sum of YLDs and YLLs. A sociodemographic index (SDI) was created for each location based on income per capita, educational attainment, and fertility. Countries were categorized by SDI quintiles to summarize results. FINDINGS: In 2015, there were 17.5 million cancer cases worldwide and 8.7 million deaths. Between 2005 and 2015, cancer cases increased by 33%, with population aging contributing 16%, population growth 13%, and changes in age-specific rates contributing 4%. For men, the most common cancer globally was prostate cancer (1.6 million cases). Tracheal, bronchus, and lung cancer was the leading cause of cancer deaths and DALYs in men (1.2 million deaths and 25.9 million DALYs). For women, the most common cancer was breast cancer (2.4 million cases). Breast cancer was also the leading cause of cancer deaths and DALYs for women (523 000 deaths and 15.1 million DALYs). Overall, cancer caused 208.3 million DALYs worldwide in 2015 for both sexes combined. Between 2005 and 2015, age-standardized incidence rates for all cancers combined increased in 174 of 195 countries or territories. Age-standardized death rates (ASDRs) for all cancers combined decreased within that timeframe in 140 of 195 countries or territories. Countries with an increase in the ASDR due to all cancers were largely located on the African continent. Of all cancers, deaths between 2005 and 2015 decreased significantly for Hodgkin lymphoma (-6.1% [95% uncertainty interval (UI), -10.6% to -1.3%]). The number of deaths also decreased for esophageal cancer, stomach cancer, and chronic myeloid leukemia, although these results were not statistically significant. CONCLUSION AND RELEVANCE: As part of the epidemiological transition, cancer incidence is expected to increase in the future, further straining limited health care resources. Appropriate allocation of resources for cancer prevention, early diagnosis, and curative and palliative care requires detailed knowledge of the local burden of cancer. The GBD 2015 study results demonstrate that progress is possible in the war against cancer. However, the major findings also highlight an unmet need for cancer prevention efforts, including tobacco control, vaccination, and the promotion of physical activity and a healthy diet.

Neonatal face-to-face interactions promote later social behaviour in infant rhesus monkeys.

In primates, including humans, mothers engage in face-to-face interactions with their infants, with frequencies varying both within and across species. However, the impact of this variation in face-to-face interactions on infant social development is unclear. Here we report that infant monkeys (Macaca mulatta) who engaged in more neonatal face-to-face interactions with mothers have increased social interactions at 2 and 5 months. In a controlled experiment, we show that this effect is not due to physical contact alone: monkeys randomly assigned to receive additional neonatal face-to-face interactions (mutual gaze and intermittent lip-smacking) with human caregivers display increased social interest at 2 months, compared with monkeys who received only additional handling. These studies suggest that face-to-face interactions from birth promote young primate social interest and competency.