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Purpose: This paper aims to assess the economic impact of uniform COVID-controlling policies that were implemented by the US government in 2020 and compare it with hypothetical targeted policies that consider the heterogenous effect of COVID-19 on different age groups. Design/methodology/approach: The author began by showing that the adjusted SEQIHR model is a good fit to the US COVID-induced daily death data in that it can capture the nonlinearities of the data very well. Then, he used this model with extra parameters to evaluate the economic effects of COVID-19 through its impact on the job market. Findings: The results show that targeted COVID-controlling policies could reduce the US death rate and GDP loss to 0.03% and 2%, respectively. By comparing these results with uniform COVID-controlling policies, which led to a 0.1% death rate and 3.5% GDP loss, we could conclude that the death rate reduction is 0.07%. Approximately 378,000 Americans died because of COVID-19 during 2020, therefore, reducing the death rate to 0.03% means saving a significant proportion of the COVID-19 casualties, around 280,000 lives. Originality/value: To the best of the author's knowledge, this paper is the first study to assess the economic impacts of COVID-controlling policies by using the multirisk SEQIHR model. [ABSTRACT FROM AUTHOR]

By January 2024, the COVID-19 pandemic claimed more than 1.1 million deaths in the United States (U.S.). People in prison are particularly vulnerable to COVID-19 as they have no ability to socially distance, secure masks, disinfect their environment or have as much access to tests or vaccinations as is available in the community. In addition, many of these individuals reside in crowded conditions with little ventilation, which makes the spread of the virus more likely. In this paper, we used data from two projects, including the UCLA Law COVID Behind Bars Data Project and the COVID Prison Project, and supplemented these with publicly available data to examine the number of deaths and infection rates caused by COVID-19 among people in prison and prison staff in the U.S., as reported by the population of those facilities. We found that the incidence of infections and death rates in prisons were affected by crowding, prison security type (maximum, medium, minimum, or mixed) and level of prison (state or federal). People in prison who were less likely to have as much human contact (e.g., maximum-security prisons) were also less likely to be afflicted with COVID-19. People in prison were twice as likely to be infected by COVID-19 but had a similar death rate compared to the general public. Prison overcrowding increased the infection rate. The most effective state health policy was to quarantine people who had close contact with confirmed, positive cases. Further, state prisons demonstrated a higher death rate compared to federal prisons. Greater efforts to ameliorate COVID-19 and similar pathogens should be directed at state prisons with lower-level security and prisons with closer contact with the community. Quarantining close-contacts and restricting movements were the most effective state-level responses to reduce infections in prisons during April 2020 to April 2022. [ABSTRACT FROM AUTHOR]

This paper evaluates the empirical relevance of a prominent social vulnerability measure to local economic and public health outcomes at two distinct stages of the COVID-19 pandemic. In contrast to traditional least-squares regression, conditional quantile regression offers stronger evidence in support of social vulnerability for predicting the pandemic's uneven economic devastation and death tolls across the United States. An extension of the spatial autoregressive geographically weighted regression model to conditional quantile regression reveals the varying role of social vulnerability within and across broad regions that were exposed to different levels of pandemic impacts. [ABSTRACT FROM AUTHOR]

Purpose: To synthesize the available evidence on the extent to which area-level socioeconomic conditions are associated with drug overdose deaths in the United States. Methods: We performed a systematic review (in MEDLINE, EMBASE, PsychINFO, Web of Science, EconLit) for papers published prior to July 2022. Eligible studies quantitatively estimated the association between an area-level measure of socioeconomic conditions and drug overdose deaths in the US, and were published in English. We assessed study quality using the Effective Public Health Practice Project Quality Assessment Tool. The protocol was preregistered at Prospero (CRD42019121317). Results: We identified 28 studies that estimated area-level effects of socioeconomic conditions on drug overdose deaths in the US. Studies were scored as having moderate to serious risk of bias attributed to both confounding and in analysis. Socioeconomic conditions and drug overdose death rates were moderately associated, and this was a consistent finding across a large number of measures and differences in study designs (e.g., cross-sectional versus longitudinal), years of data analyzed, and primary unit of analysis (e.g., ZIP code, county, state). Conclusions: This review highlights the evidence for area-level socioeconomic conditions are an important factor underlying the geospatial distribution of drug overdose deaths in the US and the need to understand the mechanisms underlying these associations to inform future policy recommendations. The current evidence base suggests that, at least in the United States, employment, income, and poverty interventions may be effective targets for preventing drug overdose mortality rates. [ABSTRACT FROM AUTHOR]

The number of COVID-19 fatalities fluctuated widely across United States (US) counties. The number of deaths is stochastic. When the average number of deaths is equal to the dispersion, the distribution is the usual Poisson. When the average number of deaths is higher than the dispersion, the distribution is an intervened Poisson. When the average number of deaths is lower than the dispersion, the distribution is an incidence-rate-restricted Poisson (IRRP) type. Because dispersion of COVID-19 fatalities in some counties is higher than the average number of fatalities, the underlying model for the chance-oriented mechanism might be IRRP. Understanding where this overdispersion or volatility exists and the implications of it is the topic of this research. In essence, this paper focuses on the number of COVID-19 fatalities that fluctuated widely across United States (US) counties and develops an incidence-rate-restricted Poisson (IRRP) to understand where this overdispersion or volatility exists and the implications of it. [ABSTRACT FROM AUTHOR]

Although the disparities in COVID-19 outcomes have been proved, they have not been explicitly associated with COVID-19 full vaccinations. This paper examines the spatial and temporal patterns of the county-level COVID-19 case rates, fatality rates, and full vaccination rates in the United States from December 24, 2020 through September 30, 2021. Statistical and geospatial analyses show clear temporal and spatial patterns of the progression of COVID-19 outcomes and vaccinations. In the relationship between two time series, the fatality rates series was positively related to past lags of the case rates series. At the same time, case rates series and fatality rates series were negatively related to past lags of the full vaccination rates series. The lag level varies across urban and rural areas. The results of partial correlation, ordinary least squares (OLS) and Geographically Weighted Regression (GWR) also confirmed that the existing COVID-19 infections and different sets of socioeconomic, healthcare access, health conditions, and environmental characteristics were independently associated with COVID-19 vaccinations over time and space. These results empirically identify the geographic health disparities with COVID-19 vaccinations and outcomes and provide the evidentiary basis for targeting pandemic recovery and public health mitigation actions. [ABSTRACT FROM AUTHOR]

Background: COVID-19-associated mucormycosis (CAM) is associated with high morbidity and mortality. MUNCO is an international database used to collect clinical data on cases of CAM in real time. Preliminary data from the Mycotic Infections in COVID-19 (MUNCO) online registry yielded 728 cases from May to September 2021 in four South Asian countries and the United States. A majority of the cases (694; 97.6%) consisted of a mucormycosis infection. The dataset allowed for the analysis of the risk factors for adverse outcomes from CAM and this analysis is presented in this paper. Methods: The submission of cases was aided by a direct solicitation and social media online. The primary endpoints were full recovery or death measured on day 42 of the diagnosis. All patients had histopathologically confirmed CAM. The groups were compared to determine the contribution of each patient characteristic to the outcome. Multivariable logistic regression models were used to model the probability of death after a CAM diagnosis. Results: The registry captured 694 cases of CAM. Within this, 341 could be analyzed as the study excluded patients with an unknown CAM recovery status due to either an interruption or a lack of follow up. The 341 viable cases consisted of 258 patients who survived after the completion of treatment and 83 patients who died during the period of observation. In a multivariable logistic regression model, the factors associated with an increased risk of mortality include old age (OR = 1.04, 95% CI 1.02–1.07, p = 0.001), history of diabetes mellitus (OR 3.5, 95% CI 1.01–11.9, p = 0.02) and a lower BMI (OR 0.9, 95% CI 0.82–0.98, p = 0.03). Mucor localized to sinus disease was associated with 77% reduced odds of death (OR = 0.23, 95% CI 0.09–0.57, p = 0.001), while cerebral mucor was associated with an increased odds of death (OR = 10.96, 95% CI 4.93–24.36, p = ≤0.0001). Conclusion: In patients with CAM, older age, a history of diabetes and a lower body mass index is associated with increased mortality. Disease limited to the sinuses without a cerebral extension is associated with a lower risk of mortality. Interestingly, the use of zinc and azithromycin were not associated with increased mortality in our study. [ABSTRACT FROM AUTHOR]

The United States National Institutes of Health (NIH) imposed a public access policy on all publications for which the research was supported by their grants; the policy was drafted in 2004 and took effect in 2008. The policy is now 11 years old, yet no analysis has been presented to assess whether in fact this largest-scale US-based public access policy affected the vitality of the scholarly publishing enterprise, as manifested in changed mortality or natality rates of biomedical journals. We show here that implementation of the NIH policy was associated with slightly elevated mortality rates and mildly depressed natality rates of biomedical journals, but that birth rates so exceeded death rates that numbers of biomedical journals continued to rise, even in the face of the implementation of such a sweeping public access policy. This Perspective article analyzes whether the US National Institutes of Health open access policy, which from 2008 forced open access on thousands of papers, had a detectable effect on the "demography" of biomedical journals. [ABSTRACT FROM AUTHOR]

Background: In this paper, we conduct an analysis of the COVID-19 data in the United States in 2020 via functional data analysis methods. Through this research, we investigate the effectiveness of the practice of public health measures, and assess the correlation between infections and deaths caused by the COVID-19. Additionally, we look into the relationship between COVID-19 spread and geographical locations, and propose a forecasting method to predict the total number of confirmed cases nationwide. Methods: The functional data analysis methods include functional principal analysis methods, functional canonical correlation analysis methods, an expectation-maximization (EM) based clustering algorithm and a functional time series model used for forecasting. Results: It is evident that the practice of public health measures helps to reduce the growth rate of the epidemic outbreak over the nation. We have observed a high canonical correlation between confirmed and death cases. States that are geographically close to the hot spots are likely to be clustered together, and population density appears to be a critical factor affecting the cluster structure. The proposed functional time series model gives more reliable and accurate predictions of the total number of confirmed cases than standard time series methods. Conclusions: The results obtained by applying the functional data analysis methods provide new insights into the COVID-19 data in the United States. With our results and recommendations, the health professionals can make better decisions to reduce the spread of the epidemic, and mitigate its negative effects to the national public health. [ABSTRACT FROM AUTHOR]

This paper uses the Value of Statistical Life (VSL) literature to weigh the costs and benefits of non-pharmaceutical interventions of the U.S. COVID-19 stay-at-home orders that affected 92 percent of the U.S. workforce at their peak in April 2020. We calculate the prevaccine COVID-19 infection fatality rate to have been 0.85 percent. We find that the stay-at-home orders saved most likely about 71,000 lives and led to a net benefit to the United States of 1.7 percent of GDP after accounting for lives saved and drops in workforce participation. Through October 31, 2021, the VSL of U.S. lives lost to COVID-19 was over $8.4 trillion. [ABSTRACT FROM AUTHOR]

An outbreak of coronavirus disease 2019 (COVID-19) has quickly spread worldwide from December 2019, thus characterizing a pandemic. Until August 2020, the United States of America (U.S.) accounted for almost one-fourth of the total deaths by coronavirus. In this paper, a new regression is constructed to identify the variables that affected the first-wave COVID-19 mortality rates in the U.S. states. The mortality rates in these states are computed by considering the total of deaths recorded on 30, 90, and 180 days from the 10th recorded case. The proposed regression is compared to the Kumaraswamy and unit-Weibull regressions, which are useful in modeling proportional data. It provides the best goodness-of-fit measures for the mortality rates and explains 76.57 % of its variability. The population density, Gini coefficient, hospital beds, and smoking rate explain the median of the COVID-19 mortality rates in these states. We believe that this article's results reveal important points to face pandemic threats by the State Health Departments in the U.S. [ABSTRACT FROM AUTHOR]

This paper combines panel data on monthly mortality rates of US states and daily temperature variables for over a century (1900-2004) to explore the regional evolution of the temperature-mortality relationship and documents two key findings. First, the impact of extreme heat on mortality is notably smaller in states that more frequently experience extreme heat. Second, the difference in the heat-mortality relationship between hot and cold states declined over 1900-2004, though it persisted through 2004. Continuing differences in the mortality consequences of hot days suggests that health motivated adaptation to climate change may be slow and costly around the world. [ABSTRACT FROM AUTHOR]

Objective: There has been steady progress in reducing cancer mortality in the United States; however, this progress hasn't been evenly distributed across regions. This paper assesses trends in cancer mortality salience (CMS), that is, agreeing that getting cancer is a death sentence, over time in the United States and examines correlates of CMS.Methods: Data from three administrations of the Health Information National Trends Survey (HINTS), gathered in 2008, 2013, and 2017, were merged, resulting in a total sample of 10,063 respondents. Trends in changes in CMS over time were examined as well as maps of the distribution of CMS in the United States. A logistic regression model was conducted, regressing CMS on a set of sociodemographic, psychological, health-related, and environmental predictors.Results: The aggregated percentage of US adults who agreed with the CMS statement changed over time and was modified by age. Geographic distribution of agreement with CMS was inconsistent across the United States. In the adjusted logistic model, perceived health (worse health), cancer prevention, fatalism, and confusion, and cancer status (no cancer) were all significantly associated with CMS. There was also a significant interaction between survey year and age.Conclusion: Despite recent information that cancer mortality rates are decreasing, most US adults still see cancer as a death sentence and this is especially an issue in certain subgroups. These findings have ramifications for groups of people who may be at risk for developing cancer given their attitudes and beliefs that there isn't much they can do to prevent or control it. [ABSTRACT FROM AUTHOR]

We estimate the distribution of COVID-19 mortality (measured as daily deaths) from the start of the pandemic until July 31st, 2022, for six European countries and the USA. We use the Pareto, the stretched exponential, the log-normal and the log-logistic distributions as well as mixtures of the log-normal and log-logistic distributions. The main results are that the Pareto does not describe well the data and that mixture distributions tend to offer a very good fit to the data. We also compute Value-at-Risk measures as well as mortality probabilities with our estimates. We also discuss the implications of our results and findings from the point of view of public health planning and modelling. [ABSTRACT FROM AUTHOR]

We conduct this research with a two-fold aim: providing a quantitative analysis of the opioid epidemic in the United States (U.S.), and exploring the impact of the COVID-19 pandemic on opioid-related mortality. The duration and persistence of the opioid epidemic lends itself to the need for an overarching analysis with extensive scope. Additionally, studying the ramifications of these concurrent severe public health crises is vital for informing policies to avoid preventable mortality. Using data from CDC WONDER, we consider opioid-related deaths grouped by Census Region spanning January 1999 to October 2022 inclusive, and later add on a demographic component with gender-stratification. Through the lens of key events in the opioid epidemic, we build an interrupted time series model to reveal statistically significant drivers of opioid-related mortality. We then employ a counterfactual to approximate trends in the absence of COVID-19, and estimate excess opioid-related deaths (defined as observed opioid-related deaths minus projected opioid-related deaths) associated with the pandemic. According to our model, the proliferation of fentanyl contributed to sustained increases in opioid-related death rates across three of the four U.S. census regions, corroborating existing knowledge in the field. Critically, each region has an immediate increase to its opioid-related monthly death rate of at least 0.31 deaths per 100,000 persons at the start of the pandemic, highlighting the nationwide knock-on effects of COVID-19. There are consistent positive deviations from the expected monthly opioid-related death rate and a sizable burden from cumulative excess opioid-related deaths, surpassing 60,000 additional deaths nationally from March 2020 to October 2022, ∼70% of which were male. These results suggest that robust, multi-faceted measures are even more important in light of the COVID-19 pandemic to prevent overdoses and educate users on the risks associated with potent synthetic opioids such as fentanyl. [ABSTRACT FROM AUTHOR]

This paper examines if industries with higher economic policy uncertainty (EPU) sensitivity also respond differently to the evolution of COVID-19 pandemic. Initially, industries are allocated into decile portfolios according to their sensitivity to the US-EPU shocks, then portfolio returns are conditioned against changes in daily cases and deaths, respectively. After controlling for the standard risk-factors of equity returns, neither the cases nor deaths can load significantly against the returns of portfolio with the highest negative EPU exposure. However, industries which respond positively to the US-EPU shocks also respond positively to increases in cases and deaths. [ABSTRACT FROM AUTHOR]

The article presents a response by Benjamin S. Bradshaw, David W, Smith and Stephen Blanchard to the analysis of Andrew Noymer to their paper "A Cohort Study of Tuberculosis and Influenza Mortality in the Twentieth Century." According to them, Noymer sees their paper in contrast to his work regarding a change in the sex difference in life expectancy following the 1918-19 influenza pandemic in the U.S. Moreover, they argue that the alteration in tuberculosis death rate that occurred in 1945 support the theory that the rise in tuberculosis mortality in 1918 was promoted by wartime mobilization.

Many predictions of the economic and social costs of a modern-day pandemic are based on the effects of the influenza pandemic of 1918. Despite killing 675,000 people in the United States and 40 million worldwide, the influenza of 1918 has been nearly forgotten. The purpose of this paper is to provide an overview of the influenza pandemic of 1918 in the United States, its economic effects, and its implications for a modern-day pandemic. The paper provides a brief historical background as well as detailed influenza mortality statistics for cities and states, including those in the Eighth Federal Reserve District, that account for differences in race, income, and place of residence. Information is obtained from two sources: (i) newspaper articles published during the pandemic and (ii) a survey of economic research on the subject. (JEL I1, N0, R0) [ABSTRACT FROM AUTHOR]

Given two million deaths annually in the U. S., social work education and training have been cited as woefully inadequate in end-of-life care. In response, two of the authors developed two post-Masters programs in end-of-life care for social work. This paper describes their curricula and the methods used to evaluate both programs, including measurements of the participants' self-efficacy before and after the programs. The analysis of evaluative data from the classroom and first-year field experiences of each program shows that participants increased their levels of skill, knowledge, values, and self-efficacy. [ABSTRACT FROM AUTHOR]

Background: Transforming spatial data from one scale to another is a challenge in geographic analysis. As part of a larger, primary study to determine a possible association between travel barriers to pediatric cancer facilities and adolescent cancer mortality across the United States, we examined methods to estimate mortality within zones at varying distances from these facilities: (1) geographic centroid assignment, (2) population-weighted centroid assignment, (3) simple areal weighting, (4) combined population and areal weighting, and (5) geostatistical areal interpolation. For the primary study, we used county mortality counts from the National Center for Health Statistics (NCHS) and population data by census tract for the United States to estimate zone mortality. In this paper, to evaluate the five mortality estimation methods, we employed address-level mortality data from the state of Georgia in conjunction with census data. Our objective here is to identify the simplest method that returns accurate mortality estimates. Results: The distribution of Georgia county adolescent cancer mortality counts mirrors the Poisson distribution of the NCHS counts for the U.S. Likewise, zone value patterns, along with the error measures of hierarchy and fit, are similar for the state and the nation. Therefore, Georgia data are suitable for methods testing. The mean absolute value arithmetic differences between the observed counts for Georgia and the five methods were 5.50, 5.00, 4.17, 2.74, and 3.43, respectively. Comparing the methods through paired t-tests of absolute value arithmetic differences showed no statistical difference among the methods. However, we found a strong positive correlation (r = 0.63) between estimated Georgia mortality rates and combined weighting rates at zone level. Most importantly, Bland--Altman plots indicated acceptable agreement between paired arithmetic differences of Georgia rates and combined population and areal weighting rates. Conclusions: This research contributes to the literature on areal interpolation, demonstrating that combined population and areal weighting, compared to other tested methods, returns the most accurate estimates of mortality in transforming small counts by county to aggregated counts for large, non-standard study zones. This conceptually simple cartographic method should be of interest to public health practitioners and researchers limited to analysis of data for relatively large enumeration units. [ABSTRACT FROM AUTHOR]

Background: Fatal mass shootings, defined as four or more people killed by gunfire, excluding the perpetrator, account for a small percentage of firearm homicide fatalities. Research has not extensively focused on the role of domestic violence (DV) in mass shootings in the United States. This study explores the role of DV in mass shootings in the United States. Methods: Using 2014–2019 mass shooting data from the Gun Violence Archive, we indexed our data by year and mass shooting and collected the number of deaths and injuries. We reviewed news articles for each mass shooting to determine if it was 1) DV-related (i.e., at least one victim of a mass shooting was a dating partner or family member of the perpetrator); 2) history of DV (i.e., the perpetrator had a history of DV but the mass shooting was not directed toward partners or family members); or 3) non-DV-related (i.e., the victims were not partners or family members, nor was there mention of the perpetrator having a history of DV). We conducted descriptive analyses to summarize the percent of mass shootings that were DV-related, history of DV, or non-DV-related, and analyzed how many perpetrators died during the incidents. We conducted one-way ANOVA to examine whether there were differences in the average number of injuries or fatalities or the case fatality rates (CFR) between the three categories. One outlier and 17 cases with unknown perpetrators were excluded from our main analysis. Results: We found that 59.1% of mass shootings between 2014 and 2019 were DV-related and in 68.2% of mass shootings, the perpetrator either killed at least one partner or family member or had a history of DV. We found significant differences in the average number of injuries and fatalities between DV and history of DV shootings and a higher average case fatality rate associated with DV-related mass shootings (83.7%) than non-DV-related (63.1%) or history of DV mass shootings (53.8%). Fifty-five perpetrators died during the shootings; 39 (70.9%) died by firearm suicide, 15 (27.3%) were killed by police, and 1 (1.8%) died from an intentional overdose. Conclusions: Most mass shootings are related to DV. DV-related shootings had higher CFR than those unrelated to DV. Given these findings, restricting access to guns by perpetrators of DV may affect the occurrence of mass shootings and associated casualties. [ABSTRACT FROM AUTHOR]

The paper presents safety data for the period 1977-86 from more than 30 U.S. domestic airlines and 80 international flag carriers. These statistics are examined in conjunction with others from an earlier MIT study about the previous two decades. The primary safety measure used is "death risk per flight," which weights each fatal accident by the proportion of passengers killed and exploits statistical evidence that the risk arising from a nonstop flight is uncorrelated with its route length. The main conclusions of the analysis are: (1) The U.S. domestic trunklines can continue to lay claim to being the safest group of airlines in the world. In 1977-86, the death risk per flight on these carriers was roughly one in eleven million. This represents a factor-of-four improvement since the early 1970's and a factor-of-ten improvement since the early 1960's. (2) But while U.S. jet travel has become safer in recent years, the improvement would probably have been greater still in the absence of airline deregulation. The reason for this assessment relates to the new jet carriers spawned by deregulation. Most such carriers have perfect safety records so far but, collectively, the recent entrants averaged twelve times the death risk per flight in 1979-86 of the established trunklines. This statistically significant excess cut by nearly one-half the recent progress in domestic jet safety. (3) International air travel was also far safer in the last decade than in preceding ones. But the flag carriers of Communist bloc and Third-World nations averaged eight times the death risk per flight as those from industrialized First-World nations. This factor-of-eight discrepancy, which has persisted with little change throughout the last quarter century, cannot be explained by differences in route structure. [ABSTRACT FROM AUTHOR]

The article presents a paper that evaluates the quality of vital statistics and census data for estimating African-American mortality. Demographers are certain that a serious difficulty in estimating African-American mortality levels results from age misreporting, especially at the older ages. The seriousness of this problem was revealed clearly in a direct matching of 1960 death certificates to records from the 1960 Census of Population. In only 44.7% of nonwhite male and 36.9% of nonwhite female matched cases was the same year of age reported in the census as on the death certificate. So, in the paper, the investigators have employed intercensal cohort comparisons and extinct generation estimates to demonstrate that conventionally constructed African-American death rates may be seriously flawed as early as age 50. Using the crude death rate at ages 50 + for 1978-1982 in conjunction with estimated growth rates and two model life table systems, the investigators have estimated black age-specific death rates in 1978-1982. The results suggest that if a racial crossover in death rates occurs, the age pattern of mortality among African-Americans must be far outside the range observed in populations with more accurate data.

This paper studies the design effect of counting rules, for linking deaths to housing units where they are enumerated in the survey, on the sampling variance of dual system and single system estimators of death registration completeness. It investigates estimators based on conventional rules that uniquely link each death to a single housing unit as well as estimators based on multiplicity rules which permit deaths to be linked to more than one housing unit. Sampling variance formulas are derived containing parameters that reflect the efficiency of the counting rule. Estimates of these parameters for different counting rules are compared utilizing information that was collected in a mortality survey experiment. Finally, the design of a national death registration test is considered and the sample size implications of different counting rules are compared. [ABSTRACT FROM AUTHOR]

This paper documents a marked increase in the all-cause mortality of middle-aged white non-Hispanic men and women in the United States between 1999 and 2013. This change reversed decades of progress in mortality and was unique to the United States; no other rich country saw a similar turnaround. The midlife mortality reversal was confined to white non-Hispanics; black non-Hispanics and Hispanics at midlife, and those aged 65 and above in every racial and ethnic group, continued to see mortality rates fall. This increase for whites was largely accounted for by increasing death rates from drug and alcohol poisonings, suicide, and chronic liver diseases and cirrhosis. Although all education groups saw increases in mortality from suicide and poisonings, and an overall increase in external cause mortality, thosewith less education saw the most marked increases. Rising midlife mortality rates of white non-Hispanics were paralleled by increases in midlife morbidity. Self-reported declines in health, mental health, and ability to conduct activities of daily living, and increases in chronic pain and inability to work, as well as clinically measured deteriorations in liver function, all point to growing distress in this population. We comment on potential economic causes and consequences of this deterioration. [ABSTRACT FROM AUTHOR]

National estimates of the all-cause and pneumonia and influenza (P&I) mortality burden derived from U.S. influenza surveillance data treat all missing or unreported values as zero counts. The effect of this methodological decision is to undercount influenza deaths, thus biasing estimates downward and producing underestimates of the true mortality burden. In this paper, a regression-based procedure is proposed to impute missing values and thus produce a more accurate estimate of mortality. Several model specifications are considered and evaluated to predict weekly death counts by city, calendar week, calendar year and age group. Revised all-cause, P&I and excess mortality estimates are calculated by imputing the missing data. The impact of the treatment of unreported mortality data on national estimates is evaluated by comparing the estimates obtained using data with and without imputation. This comparison reflects some differences in mortality burden, excess deaths, and trends over time. The model presented is a useful approach to impute missing counts and improve inference in situations with modest occurrence of missing data. [ABSTRACT FROM AUTHOR]

This paper uses the rollout of the first Community Health Centers (CHCs) to study the longer-term health effects of increasing access to primary care. Within ten years, CHCs are associated with a reduction in age-adjusted mortality rates of 2 percent among those 50 and older. The implied 7 to 13 percent decrease in one-year mortality risk among beneficiaries amounts to 20 to 40 percent of the 1966 poor/non-poor mortality gap for this age group. Large effects for those 65 and older suggest that increased access to primary care has longer-term benefits, even for populations with near universal health insurance. (JEL H75, I12, I13, I18, I32, I38, J14) [ABSTRACT FROM AUTHOR]

COVID-19, known as Coronavirus disease 2019, is caused by a coronavirus called SARS-CoV-2. As coronavirus restrictions ease and cause changes to social and business activities around the world, and in the United States in particular, including social distancing, reopening states, reopening schools, and the face mask mandates, COVID-19 outbreaks are on the rise in many states across the United States and several other countries around the world. The United States recorded more than 1.9 million new infections in July, which is nearly 36 percent of the more than 5.4 million cases reported nationwide since the pandemic began, including more than 170,000 deaths from the disease, according to data from Johns Hopkins University as of 16 August 2020. In April 2020, the author of this paper presented a model to estimate the number of deaths related to COVID-19, which assumed that there would be no significant change in the COVID-19 restrictions and guidelines in the coming days. This paper, which presents the evolved version of the previous model published in April, discusses a new explicit mathematical model that considers the time-dependent effects of various pandemic restrictions and changes related to COVID-19, such as reopening states, social distancing, reopening schools, and face mask mandates in communities, along with a set of selected indicators, including the COVID-19 recovered cases and daily new cases. We analyzed and compared the modeling results to two recent models based on several model selection criteria. The model could predict the death toll related to the COVID-19 virus in the United States and worldwide based on the data available from Worldometer. The results show the proposed model fit the data significantly better for the United States and worldwide COVID-19 data that were available on 16 August 2020. The results show very encouraging predictability that reflected the time-dependent effects of various pandemic restrictions for the proposed model. The proposed model predicted that the total number of U.S. deaths could reach 208,375 by 1 October 2020, with a possible range of approximately 199,265 to 217,480 deaths based on data available on 16 August 2020. The model also projected that the death toll could reach 233,840 by 1 November 2020, with a possible range of 220,170 to 247,500 American deaths. The modeling result could serve as a baseline to help decision-makers to create a scientific framework to quantify their guidelines related to COVID-19 affairs. The model predicted that the death toll worldwide related to COVID-19 virus could reach 977,625 by 1 October 2020, with a possible range of approximately 910,820 to 1,044,430 deaths worldwide based on data available on 16 August 2020. It also predicted that the global death toll would reach nearly 1,131,000 by 1 November 2020, with a possible range of 1,030,765 to 1,231,175 deaths. The proposed model also predicted that the global death toll could reach 1.47 million deaths worldwide as a result of the SARS CoV-2 coronavirus that causes COVID-19. We plan to apply or refine the proposed model in the near future to further study the COVID-19 death tolls for India and Brazil, where the two countries currently have the second and third highest total COVID-19 cases after the United States. [ABSTRACT FROM AUTHOR]

Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (n = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (n = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies. [ABSTRACT FROM AUTHOR]

BACKGROUND Factors including smoking, drinking, substance abuse, obesity, and health care have all been shown to affect health and longevity. The relative importance of each of these factors is disputed in the literature, and has been assessed through a number of methods. OBJECTIVE This paper uses a novel approach to identify factors responsible for interstate mortality variation. It identifies factors through their imprint on mortality patterns and can therefore identify factors that are difficult or impossible to measure directly, such as sensitive health behaviors. METHODS The analysis calculates age-standardized death rates by cause of death from 2000-2009 for white men and women separately. Only premature deaths between ages 20-64 are included. Latent variables responsible for mortality variation are then identified through a factor analysis conducted on a death-rate-by-state matrix. These unobserved latent variables are inferred from observed mortality data and interpreted based on their correlations with individual causes of death. RESULTS Smoking and obesity, substance abuse, and rural/urban residence are the three factors that make the largest contributions to state-level mortality variation among males. The same factors are at work for women but are less vividly revealed. The identification of factors is supported by a review of epidemiologic studies and strengthened by correlations with observable behavioral variables. Results are not sensitive to the choice of factor-analytic method used. CONCLUSIONS The majority of interstate variation in mortality among white working-age adults in the United States is associated with a combination of smoking and obesity, substance abuse and rural/urban residence. [ABSTRACT FROM AUTHOR]

We use three indexes to identify how age-specific mortality rates in the United States compare to those in a composite of five large European countries since 2000. First, we examine the ratio of agespecific death rates in the United States to those in Europe. These show a sharp deterioration in the US position since 2000. Applying European age-specific death rates in 2017 to the US population, we then show that adverse mortality conditions in the United States resulted in 400,700 excess deaths that year. Finally, we show that these excess deaths entailed a loss of 13.0 My of life. In 2017, excess deaths and years of life lost in the United States represent a larger annual loss of life than that associated with the COVID-19 epidemic in 2020. [ABSTRACT FROM AUTHOR]

Background: Rapid increases in drug overdose deaths in the United States since 2014 have been highly regionally stratified, with the largest increases occurring in the eastern and northeastern states. By contrast, many western states saw overdose deaths plateau. This paper shows how the differential influx of fentanyl and fentanyl analogues in the drug supply has reshaped the geography and demography of the overdose crisis in the United States.Methods: Using all state lab drug seizures obtained by Freedom of Information Act request, I analyze the regionally distinctive presence of fentanyl in the US drug supply with descriptive plots and statistical models. Main analyses explore state-year overdose trends using two-way fixed effects ordinary least squares (OLS) regression and two-stage least squares regression (2SLS) instrumenting for fentanyl exposure with state-longitude times a linear trend.Results: First, fentanyl exposure is highly correlated with geography and only weakly explained by overdose rates prior to 2014. States in the east (higher degrees longitude) are much more heavily affected. Second, fentanyl exposure exhibits a statistically significant and important effect on overdose mortality, with model-predicted deaths broadly consistent with official death statistics. Third, fentanyl exposure explains most of the variation in increased overdose mortality between 2011 and 2017. Consequently, the epicenter of the overdose crisis shifted towards the eastern United States over these years.Conclusion: These findings shed light on the "third-wave" of the overdose epidemic, characterized by rapid and geographically disparate changes in drug supply that heighten the risk of overdose. Above all, they underscore the urgency of adopting evidence-based policies to combat addiction in light of the rapidly changing drug environment. [ABSTRACT FROM AUTHOR]

Abstract Investigating the "physics" of food crises consists in identifying features which are common to all large-scale food crises. One element which stands out is the fact that during a food crisis there is not only a surge in deaths but also a correlative temporary decline in conceptions and therefore in subsequent births nine months later. This scenario is studied in three cases of large-scale food crises: Finland (1868), India (1867–1907 and 1942–1944), China (1960–1961). It turns out that between the regional amplitudes of death spikes on one hand and the amplitudes of birth troughs on the other hand there is a power law relationship. This confirms the same phenomenon already observed in the wake of the epidemic of 1918 in the United States (Richmond et al., 2018b). In the second part of the paper we explain and demonstrate on three case studies how this relationship can be used for the investigation of mass-mortality episodes in cases where direct death data are either uncertain, suspicious or nonexistent. Highlights • Mass mortality episodes are followed by birth troughs 9 months later. • There is a power law relationship between death and birth amplitudes. • It can give estimates for the death toll of major disasters. • The method is tested on 3 case studies. [ABSTRACT FROM AUTHOR]

Acetaminophen overdoses are the number one cause of acute liver failure (ALF) in the United States; they account for 50% of all cases of ALF and carry a 30% mortality. Nevertheless, acetaminophen is a highly successful product with sales easily exceeding a billion dollars annually. A narcotic-containing acetaminophen preparation is the number one generic drug sold in the United States. Historically, a strong association was claimed with alcohol as a co-factor and the accidental nature of ingestions was emphasized but the association with alcohol remains a matter of debate. Recently, a very reliable assay has been developed which detects a byproduct of the toxic reaction, acetaminophen-CYS adducts, protein-acetaminophen compounds that are released from damaged hepatocytes in similar fashion to aminotransferases. The US Acute Liver Failure Study Group is a multi-center network established to gather data prospectively on all forms of acute liver failure, a rare but often fatal condition. The group has data on more than 500 acetaminophen cases and described 275 of them in detail in a recent paper. The clinical pattern of presentation is somewhat different in the US compared with the UK, since nearly half are considered to be unintentional, the result of overuse of acetaminophen-containing compounds for pain relief, without suicidal intent. Despite the relatively good prognosis for acetaminophen overdoses, 30% die once acute liver failure occurs and this is the largest cause of death in the entire ALF registry. [ABSTRACT FROM AUTHOR]

In this paper, I examine temporal changes in U.S. adult mortality by chronic disease cause of death and by sex over a 40-year period in the second half of the twentieth century. I apply age-period-cohort (APC) analyses that combine conventional approaches and a new method of model estimation to simultaneously account for age, period, and cohort variations in mortality rates for four leading causes of deaths, including heart disease, stroke, lung cancer, and breast cancer. The results show that large reductions in mortality since the late 1960s continued well into the late 1990s and that these reductions were predominately contributed by cohort effects. Cohort effects are found to differ by specific causes of death examined, but they generally show substantial survival improvements. Implications of these results are discussed with regard to demographic theories of mortality reductions, differential cohort accumulation of health capital and lifetime exposures to socioeconomic and behavioral risk factors, and period changes in diagnostic techniques and medical treatment. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Breast cancer is the most common form of cancer among women in the United States and black women bear a larger burden from this disease when compared to white women. They experience a lower incidence yet their death rate is higher. Despite these facts, survival for both black and white women is improving but what are missing from the literature are descriptions of black women experiences from a broader perspective. METHODS: This paper reviews the literature on black women with breast cancer from a broad, multidimensional perspective. RESULTS: Five dimensions, physical, psychological, social, economic, and spiritual and existential, form the guiding framework of the paper. CONCLUSIONS: The knowledge gained from this literature review can focus research, enhance education and improve nursing and health care to the increasingly culturally diverse patient populations found in the United States, making the care that is provided culturally proficient. [ABSTRACT FROM AUTHOR]

During the World War II and Korean War era, the U.S. military freely distributed cigarettes to overseas personnel and provided low-cost tobacco products on domestic military bases. In fact, even today the military continues to sell subsidized tobacco products on its bases. Using a variety of instrumental variables approaches to deal with nonrandom selection into the military and into smoking, we provide substantial evidence that cohorts with higher military participation rates subsequently suffered more premature mortality. More importantly, we show that a large fraction, 35 to 79 percent, of the excess veteran deaths due to heart disease and lung cancer are attributable to military-induced smoking. [ABSTRACT FROM AUTHOR]

Unlabelled: The public health infrastructure of the former Soviet Union was impacted by both the Chernobyl disaster in 1986 and the Soviet breakup in 1991. This paper examines mortality patterns among children aged 1-14 years within the Mogilev region of Belarus between 1980 and 2000. This study utilized a regional cohort design that included all childhood deaths (ages 1-14 years) occurring among persons residing within the Mogilev oblast of Belarus between 1980 and 2000. Patterns of death and death rates were examined across three intervals: 1980-1985 (pre-Chernobyl), 1986-1991 (post-Chernobyl and pre-Former Soviet Union (FSU) breakup) and 1992-2000 (post-Chernobyl and post-FSU breakup) based upon administrative death files. Annual death rates among children aged 1-4 years declined between 1980 and 2000, while mortality rates among children aged 5-9 years and 10-14 years remained steady over this time period. Average annual mortality rates among males aged 5-9 years and 10-14 years increased significantly between 1986 and 1991. Compared to the baseline interval, mortality among both males and females aged 1-4 years was significantly decreased during 1986-1990 and 1992-2000. In general, mortality rates among males were 24%-95% greater compared to females. Injuries and poisonings accounted for the largest proportion of deaths across all age and gender groups examined. Subsequent to the Chernobyl disaster, significant decreases were noted in mortality rates among children aged 1-4 years while mortality rates among children aged 5-9 and 10-14 remained stable.Conclusion: Similar to the findings in other countries, injuries and poisonings predominated as the leading cause of death among Belarussian children. [ABSTRACT FROM AUTHOR]

Background: Race and ethnicity, socioeconomic class, and geographic location are well-known social determinants of health in the US. Studies of population mortality often consider two, but not all three of these risk factors. Objectives: To disarticulate the associations of race (whiteness), class (socioeconomic status), and place (county) with risk of cause-specific death in the US. Design: We conducted a retrospective analysis of death certificate data. Bayesian regression models, adjusted for age and race/ethnicity from the American Community Survey and the county Area Deprivation Index, were used for inference. Main Measures: County-level mortality for 11 leading causes of death (1999–2019) and COVID-19 (2020–2021). Key Results: County "whiteness" and socioeconomic status modified death rates; geospatial effects differed by cause of death. Other factors equal, a 20% increase in county whiteness was associated with 5–8% increase in death from three causes and 4–15% reduction in death from others, including COVID-19. Other factors equal, advantaged counties had significantly lower death rates, even when juxtaposed with disadvantaged ones. Patterns of residual risk, measured by spatial county effects, varied by cause of death; for example: cancer and heart disease death rates were better explained by age, socioeconomic status, and county whiteness than were COVID-19 and suicide deaths. Conclusions: There are important independent contributions from race, class, and geography to risk of death in the US. [ABSTRACT FROM AUTHOR]

Mortality due to extreme temperatures is one of the most worrying impacts of climate change. In this analysis, we use historic mortality and temperature data from 106 cities in the United States to develop a model that predicts deaths attributable to temperature. With this model and projections of future temperature from climate models, we estimate temperature‐related deaths in the United States due to climate change, changing demographics, and adaptation. We find that temperature‐related deaths increase rapidly as the climate warms, but this is mainly due to an expanding and aging population. For global average warming below 3°C above pre‐industrial levels, we find that climate change slightly reduces temperature‐related mortality in the U.S. because the reduction of cold‐related mortality exceeds the increase in heat‐related deaths. Above 3°C warming, whether the increase in heat‐related deaths exceeds the decrease in cold‐related deaths depends on the level of adaptation. Southern U.S. cities are already well adapted to hot temperatures and the reduction of cold‐related mortality drives overall lower mortality. Cities in the Northern U.S. are not well adapted to high temperatures, so the increase in heat‐related mortality exceeds the reduction in cold‐related mortality. Thus, while the total number of climate‐related mortality may not change much, climate change will shift mortality in the U.S. to higher latitudes. Plain Language Summary: Deaths due to extreme temperatures is one of the most important impacts of climate change. Here we estimate future temperature related deaths in the US using climate projection, population projection, and adaptation assumptions. We find that in 3°C of global warming level, temperature‐related deaths will increase by a factor of five, mostly due to aging and increasing population. Below 3°C warming, impact of climate change slightly decreases temperature‐related deaths, because the decrease in cold‐related deaths exceeds the increase in heat‐related deaths. Above 3°C warming, impact of climate change depends on the level of adaptation. Furthermore, we find that temperature‐related mortality shifts Northward. This is due to increase of heat‐related deaths in Northern cities, which are currently poorly adapted to heat. Key Points: Temperature‐related deaths in the U.S. will increase by a factor of 5 with 3°C of warming, mostly due to aging and increasing populationImpact of climate change is not significant until 3°C of global warming, and it depends on the level of adaptation beyond that pointTemperature‐related deaths will shift Northward, due to increasing heat‐related deaths in poorly heat‐adapted Northern cities [ABSTRACT FROM AUTHOR]

Background Starting in 2018, national death certificates included a new racial classification system that accounts for multiple-race decedents and separates Native Hawaiian and Pacific Islander (NHPI) individuals from Asian individuals. We estimated cancer death rates across updated racial and ethnic categories, sex, and age. Methods Age-standardized US cancer mortality rates and rate ratios from 2018 to 2020 among individuals aged 20 years and older were estimated with national death certificate data by race and ethnicity, sex, age, and cancer site. Results In 2018, there were approximately 597 000 cancer deaths, 598 000 in 2019, and 601 000 in 2020. Among men, cancer death rates were highest in Black men (298.2 per 100 000; n = 105 632), followed by White (250.8; n = 736 319), American Indian/Alaska Native (AI/AN; 249.2; n = 3376), NHPI (205.6; n = 1080), Latino (177.2; n = 66 167), and Asian (147.9; n = 26 591) men. Among women, Black women had the highest cancer death rates (206.5 per 100 000; n = 104 437), followed by NHPI (192.1; n = 1141), AI/AN (189.9; n = 3239), White (183.0; n = 646 865), Latina (128.4; n = 61 579), and Asian (111.4; n = 26 396) women. The highest death rates by age group occurred among NHPI individuals aged 20-49 years and Black individuals aged 50-69 and 70 years and older. Asian individuals had the lowest cancer death rates across age groups. Compared with Asian individuals, total cancer death rates were 39% higher in NHPI men and 73% higher in NHPI women. Conclusions There were striking racial and ethnic disparities in cancer death rates during 2018-2020. Separating NHPI and Asian individuals revealed large differences in cancer mortality between 2 groups that were previously combined in vital statistics data. [ABSTRACT FROM AUTHOR]

Introduction: Over the past 2 decades, drug-related deaths have grown to be a major U.S. public health problem. County-level differences in drug-related mortality rates are large. The relative contributions of social determinants of health to this variation, including the economic, social, and healthcare environments, are unknown.Methods: Using data from the U.S. Centers for Disease Control and Prevention Multiple-Cause of Death Files (2006-2015, analyzed in 2017); U.S. Census Bureau; U.S. Department of Agriculture Economic Research Service; Agency for Healthcare Research and Quality; and Northeast Regional Center for Rural Development, this paper modeled associations between county-level drug-related mortality rates and economic, social, and healthcare environments. Spatial autoregressive models controlled for state fixed effects and county demographic characteristics.Results: The average county-level age-adjusted drug-related mortality rate was 16.6 deaths per 100,000 population (2006-2015), but there were substantial geographic disparities in rates. Controlling for county demographic characteristics, average mortality rates were significantly higher in counties with greater economic and family distress and in counties economically dependent on mining. Average mortality rates were significantly lower in counties with a larger presence of religious establishments, a greater percentage of recent in-migrants, and counties with economies reliant on public (government) sector employment. Healthcare supply factors did not contribute to between-county disparities in mortality rates.Conclusions: Drug-related mortality rates are not randomly distributed across the U.S. Future research should consider the specific pathways through which economic, social, and healthcare environments are associated with drug-related mortality. [ABSTRACT FROM AUTHOR]

This article reports on the results of a study to establish accurate ages at death for the African-American population at advanced ages. The basic sampling universe for the study in this paper consists of African-Americans born in the United States and dying in 1985 at a reported age of 65 or older. Each member of the family is to be listed on the family card, with full name and information on his or her relation to household head, age, sex, state of birth, and month and year of birth. Items on the death certificate that were critical in establishing a match were the individual's name, father's name, mother's name, and state of birth. Other useful information on the death certificate included city or county of birth, which was available on certificates from selected states; month of birth, available for matching only to the 1900 census; and sex. Whether or not a racial crossover exists above age 95 it is shown that African-American death rates computed from conventional death registration are seriously understated at the very advanced ages.

This paper applies methods of seasonal ARIMA time series analysis to U. S. monthly birth and death rates for January 1 950-December 1978. For model selection, the conventional Box-Jenkins (1970, 1976) diagnostic checks are used in conjunction with some suggestions of Nerlove et al. (1979). This results in final models that contain significant second-order autoregressive components, as well as seasonal moving-average components, for both series. In addition, some evidence is found in the birth rate series for weekly periodicities. These findings imply the existence of a seasonal adjustment procedure that improves on the Census X- 11 program currently used by vital statistics agencies. [ABSTRACT FROM AUTHOR]

Among the various types of mortality, motor vehicle fatalities (MVF) and suicides are two that are very responsive to social, economic, and psychological factors. The analysis of the temporal patterns of suicides and MVF points to important but neglected forces affecting them. This paper examines the daily patterns of MVF and suicides for the United States from 1972 to 1976. The effects of the day of the week, month, year, and holidays on these types of mortality are estimated. Total MVF and suicides, as well as daily differentials by sex, age, and race, are analyzed with regression techniques. The results show that temporal variations are stronger in MVF than in suicides but are present in both. MVF tend to peak on Saturday, in the summer months, in 1972 and 1973, and on holidays. Suicides are generally highest on Monday, in the spring months, in 1975 and 1976, and on nonholidays. Differentials in the temporal patterns are evident. For instance, whites generally have a sharper decline in suicides on holidays than do nonwhites. The holiday peak in MVF for the 60 and over population is Christmas, while it is New Year's for the younger age groups. Males exhibit a greater increase in suicides from March to May than do females. Finally, the results of a comparison of the daily patterns of MVF and suicides revealed a negative correlation. This contrasted with the strong positive relationship between MVF and suicides generally found in cross-sectional studies. The implications of these findings are briefly discussed. [ABSTRACT FROM AUTHOR]

It is difficult to obtain direct empirical estimates of chronic disease prevalence in the U.S. population. The available estimates are usually derived from epidemiological studies of selected populations. In this paper we present strategies for estimating morbidity distributions in the national population using auxiliary biomedical evidence and theory to estimate transitions to morbidity states from a cohort mortality time series. We present computational methods which employ these estimates of morbid state transitions to produce life table functions for both primary (morbidity) and secondary (mortality) decrements. These methods are illustrated using data on stomach cancer mortality for nine white male cohorts, aged 30 to 70 in 1950, observed for a 28-year period (1950 to 1977). [ABSTRACT FROM AUTHOR]

This paper presents the results of an attempt to determine, through the use of demographic analysis, the approximate magnitude of the resident illegal alien population in the United States. The method described is the comparative analysis of trends in age-specific death rates in the United States and selected States, 1950 to 1975. The procedure depends on two assumptions: (a) that few or no illegal aliens are included in decennial census counts or current population estimates; and (b) that the reported statistics on deaths include deaths to all or nearly all illegal aliens. If the illegal alien population has increased by several million since 1970, then death rates in geographic areas where illegal aliens are most concentrated should show substantial excesses over the rates for the remainder of the country. A roughly similar downward trend in the death rates of all age groups since 1970 in all areas of the United States is apparent and hence does not support the view that many millions of illegal residents (perhaps over 6 million) are living here. However, the finding of a persistent deviation in the death rate trend for 10 selected States since 1960 for whites males, ages 20 to 44, and for no other group, suggests that the deaths of illegal aliens are being recorded in our vital statistics system. A range of estimates of the illegal alien population based on this analysis is presented and compared with the results of other studies. [ABSTRACT FROM AUTHOR]