scholarly journals Predicting County-Level Overdose Death Rates Amid the Escalating Overdose Crisis in the United States: A Statistical Modeling Approach Predicting Deaths Through 2022

2022 ◽  
Author(s):  
Charles Marks ◽  
Daniela Abramowitz ◽  
Christl A. Donnelly ◽  
Daniel Ciccarone ◽  
Natasha Martin ◽  
...  
Keyword(s):  
Negative Binomial ◽  
The United States ◽  
Similar Efficacy ◽  
Original Model ◽  
Mortality Data ◽  
County Level ◽  
Online Application ◽  
Death Rates ◽  
Overdose Death ◽  
The Us

Aims. U.S. overdose (OD) deaths continue to escalate but are characterized by geographic and temporal heterogeneity. We previously validated a predictive statistical model to predict county-level OD mortality nationally from 2013 to 2018. Herein, we aimed to: 1) validate our model’s performance at predicting county-level OD mortality in 2019 and 2020; 2) modify and validate our model to predict OD mortality in 2022.Methods. We evaluated our mixed effects negative binomial model’s performance at predicting county-level OD mortality in 2019 and 2020. Further, we modified our model which originally used data from the year X to predict OD deaths in the year X+1 to instead predict deaths in year X+3. We validated this modification for the years 2017 through 2019 and generated future-oriented predictions for 2022. Finally, to leverage available, albeit incomplete, 2020 OD mortality data, we also modified and validated our model to predict OD deaths in year X+2 and generated an alternative set of predictions for 2022.Results. Our original model continued to perform with similar efficacy in 2019 and 2020, remaining superior to a benchmark approach. Our modified X+3 model performed with similar efficacy as our original model, and we present predictions for 2022, including identification of counties most likely to experience highest OD mortality rates. There was a high correlation (Spearman’s ρ = 0.93) between the rank ordering of counties for our 2022 predictions using our X+3 and X+2 models. However, the X+3 model (which did not account for OD escalation during COVID) predicted only 62,000 deaths nationwide for 2022, whereas the X+2 model predicted over 87,000.Conclusion. We have predicted county-level overdose death rates for 2022 across the US. These predictions, made publicly available in our online application, can be used to identify counties at highest risk of high OD mortality and support evidence-based OD prevention planning.

scholarly journals 694 Insufficient sleep linked with higher COVID-19 infection cases and deaths in the United States

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A271-A271
Author(s):  
Azizi Seixas ◽  
Nicholas Pantaleo ◽  
Samrachana Adhikari ◽  
Michael Grandner ◽  
Giardin Jean-Louis
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
The United States ◽  
Poor Sleep ◽  
County Level ◽  
Infection Rates ◽  
Insufficient Sleep ◽  
Death Rates ◽  
Rural Counties ◽  
The Relationship

Abstract Introduction Causes of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep <7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US. Methods County-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros. Results Multivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates. Conclusion Insufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates. Support (if any) NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

scholarly journals Exploring Drug Overdose Mortality Data in Harris County, Texas

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric V. Bakota ◽  
Deborah Bujnowski ◽  
Larissa Singletary ◽  
Sherry Onyiego ◽  
NAdia Hakim ◽  
...  
Keyword(s):  
Public Health ◽  
Substance Abuse ◽  
Drug Overdose ◽  
The United States ◽  
Mortality Data ◽  
Harris County ◽  
Death Rates ◽  
Overdose Death ◽  
The Past ◽  
Overdose Mortality

ObjectiveIn this session, we will explore the results of a descriptive analysis of all drug overdose mortality data collected by the Harris County Medical Examiner's Office and how that data can be used to inform public health action.IntroductionDrug overdose mortality is a growing problem in the United States. In 2017 alone over 72,000 deaths were attributed to drug overdose, most of which were caused by fentanyl and fentanyl analogs (synthetic opioids)1. While nearly every community has seen an increase in drug overdose, there is considerable variation in the degree of increase in specific communities. The Harris County community, which includes the City of Houston, has not seen the massive spikes observed in some communities, such as West Virginia, Kentucky, and Ohio. However, the situation in Harris County is complicated in mortality and drug use. From 2010 - 2016 Harris County has seen a fairly stable overdose-related mortality count, ranging from 450 - 618 deaths per year. Of concern, the last two years, 2015-2016, suggest a sharp increase has occurred. Another complexity is that Harris County drug related deaths seem to be largely from polysubstance abuse. Deaths attributed to cocaine, methamphetamine, and benzodiazipine all have risen in the past few years. Deaths associated with methamphetamine have risen from approximately 20 per year in 2010 - 2012 to 119 in 2016. This 6-fold increase is alarming and suggests a large-scale public health response is needed.MethodsData were collected by the Harris County Institute of Forensic Sciences (IFS), which is part of the Harris County Medical Examiner's Office. IFS is the agency responsible for collecting and analyzing human tissue of the deceased for toxicological information about the manner and cause of death. IFS is able to test for the presence of multiple substances, including opioids, benzodiazepines, methamphetamines, cocaine, ethanol, and many others.These data were cleaned and labeled for the presence of opioids, cocaine, benzodiazepine, Z-drug (novel drug), amphetamines, ethanol, and carisoprodol. Explorative descriptive analyses were then completed in R (version 3.4) to identify trends. An RShiny app was created to further explore the data by allowing for rapid filtering and/or subsetting based on various demographic characteristics (e.g., age, sex, race).ResultsWe found that Harris County is experiencing a modest upward trend of drug related overdoses, with 529 observed in 2010 and 618 in 2016. We also found that the increase was not uniform across all classified drugs: amphetamines, cocaine, and ethanol all saw increases. Deaths involving amphetamine increased substantially from 21 in 2010 to 119 in 2016 (Figure 1). Deaths involving cocaine saw the next sharpest increase with 144 in 2010 and 237 in 2016. Deaths associated with opioids remained fairly constant, with 291 deaths in 2010 and 271 deaths in 2016.Differences in mortality across race and sex groups were also observed. The proportion of amphetamine deaths among whites jumped sharply, while the proportion of opioid and benzodiazepine deaths among whites decreased in recent years. The proportion of amphetamine and cocaine deaths among men rose more sharply than with women in the past three years, whereas for opioids, the proportion of women dying has dropped.ConclusionsIt is undeniable that the opioid epidemic is a true public health emergency for the nation. New surveillance tools are needed to better understand the impact and nature of this threat. Additionally, as we have found in Harris County, the threat may be polysubstance in nature.Our report offers two important insights: 1) that mortality data is a useful and actionable surveillance resource in understanding the problem of substance abuse; and 2) public health needs to look at substance abuse from a holistic and comprehensive perspective. Keeping the purview limited to opioids alone may create significant blind spots to the public health threat facing us.References1. National Institute of Health. (2018) Overdose Death Rates. Retreived from https://www.drugabuse.gov/related-topics/trends-statistics/overdose-death-rates

scholarly journals Risk factors for increased COVID-19 case-fatality in the United States: A county-level analysis during the first wave

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258308
Author(s):  
Jess A. Millar ◽  
Hanh Dung N. Dao ◽  
Marianne E. Stefopulos ◽  
Camila G. Estevam ◽  
Katharine Fagan-Garcia ◽  
...  
Keyword(s):  
Generalized Linear Model ◽  
Ecological Study ◽  
Negative Binomial ◽  
Case Fatality ◽  
The United States ◽  
Asthma Prevalence ◽  
County Level ◽  
Increased Risk ◽  
The Us ◽  
Level Analysis

The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.

scholarly journals Risk factors for increased COVID-19 case-fatality in the United States: A county-level analysis during the first wave

2021 ◽  
Author(s):  
Jess A. Millar ◽  
Hanh Dung N. Dao ◽  
Marianne E. Stefopulos ◽  
Camila G. Estevam ◽  
Katharine Fagan-Garcia ◽  
...  
Keyword(s):  
Generalized Linear Model ◽  
Ecological Study ◽  
Negative Binomial ◽  
Case Fatality ◽  
The United States ◽  
Asthma Prevalence ◽  
County Level ◽  
Increased Risk ◽  
The Us ◽  
Level Analysis

AbstractThe ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.

scholarly journals County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States

2020 ◽  
Author(s):  
Jochem O Klompmaker ◽  
Jaime E Hart ◽  
Isabel Holland ◽  
M Benjamin Sabath ◽  
Xiao Wu ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Vegetation Index ◽  
Negative Binomial ◽  
The United States ◽  
Systems Science ◽  
County Level ◽  
Incidence And Mortality ◽  
The Us ◽  
At Home

AbstractBackgroundCOVID-19 is an infectious disease that has killed more than 246,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection.ObjectivesWe evaluated whether greenness is related to COVID-19 incidence and mortality in the United States.MethodsWe downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home order.ResultsAn increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density.DiscussionExposures to NDVI had beneficial impacts on county-level incidence of COVID-19 in the US and may have reduced county-level COVID-19 mortality rates, especially in densely populated counties.

Population-level analysis of antibiotic use and death rates in beef feedlots over ten years in three cattle-feeding regions of the United States

2021 ◽  
pp. 1-7
Author(s):  
Stephanie C. Rutten-Ramos ◽  
Shabbir Simjee ◽  
Michelle S. Calvo-Lorenzo ◽  
Jason L. Bargen
Keyword(s):  
Death Rate ◽  
Live Weight ◽  
Population Level ◽  
Antibiotic Use ◽  
The United States ◽  
Death Rates ◽  
Cattle Feeding ◽  
The Us ◽  
Rate Ratios ◽  
Beef Feedlots

Abstract OBJECTIVE To assess antibiotic use and other factors associated with death rates in beef feedlots in 3 regions of the US over a 10-year period. SAMPLE Data for 186,297 lots (groups) of finished cattle marketed between 2010 and 2019 were obtained from a database representing feedlots in the central, high, and north plains of the US. PROCEDURES Descriptive statistics were generated. Generalized linear mixed models were used to estimate lot death rates for each region, sex (steer or heifer), and cattle origin (Mexico or the US) combination. Death rate was calculated as the (number of deaths/number of cattle placed in the lot) × 100. Lot antibiotic use (TotalActiveMG/KGOut) was calculated as the total milligrams of active antibiotics assigned to the lot per live weight (in kilograms) of cattle marketed from the lot. Rate ratios were calculated to evaluate the respective associations between lot death rate and characteristics of cattle and antibiotic use. RESULTS Mean death rate increased during the 10-year period, peaking in 2018. Mean number of days on feed also increased over time. Mean TotalActiveMG/KGOut was greatest in 2014 and 2015, lowest in 2017, and moderated in 2018 and 2019. Death rate was positively associated with the number of days on feed and had a nonlinear association with TotalActiveMG/KGOut. Feeding medicated feed articles mitigated death rate. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested a balance between disease prevention and control in feedlots for cattle with various risk profiles. Additional data sources are needed to assess TotalActiveMG/KGOut across the cattle lifetime.

scholarly journals What does and does not correlate with COVID-19 death rates

2020 ◽  
Author(s):  
Christopher R. Knittel ◽  
Bora Ozaltun
Keyword(s):  
Linear Regression ◽  
Public Transportation ◽  
Death Rate ◽  
Negative Binomial ◽  
Statistical Significance ◽  
County Level ◽  
Linear Regression Models ◽  
Death Rates ◽  
Socio Economic Variables ◽  
Home Values

AbstractWe correlate county-level COVID-19 death rates with key variables using both linear regression and negative binomial mixed models, although we focus on linear regression models. We include four sets of variables: socio-economic variables, county-level health variables, modes of commuting, and climate and pollution patterns. Our analysis studies daily death rates from April 4, 2020 to May 27, 2020. We estimate correlation patterns both across states, as well as within states. For both models, we find higher shares of African American residents in the county are correlated with higher death rates. However, when we restrict ourselves to correlation patterns within a given state, the statistical significance of the correlation of death rates with the share of African Americans, while remaining positive, wanes. We find similar results for the share of elderly in the county. We find that higher amounts of commuting via public transportation, relative to telecommuting, is correlated with higher death rates. The correlation between driving into work, relative to telecommuting, and death rates is also positive across both models, but statistically significant only when we look across states and counties. We also find that a higher share of people not working, and thus not commuting either because they are elderly, children or unemployed, is correlated with higher death rates. Counties with higher home values, higher summer temperatures, and lower winter temperatures have higher death rates. Contrary to past work, we do not find a correlation between pollution and death rates. Also importantly, we do not find that death rates are correlated with obesity rates, ICU beds per capita, or poverty rates. Finally, our model that looks within states yields estimates of how a given state’s death rate compares to other states after controlling for the variables included in our model; this may be interpreted as a measure of how states are doing relative to others. We find that death rates in the Northeast are substantially higher compared to other states, even when we control for the four sets of variables above. Death rates are also statistically significantly higher in Michigan, Louisiana, Iowa, Indiana, and Colorado. California’s death rate is the lowest across all states.It is important to understand that this research, and other observational analyses like it, only identify correlations: these relationships are not necessarily causal. However, these correlations may help policy makers identify variables that may potentially be causally related to COVID-19 death rates and adopt appropriate policies after understanding the causal relationship.

scholarly journals Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248702
Author(s):  
Brian Neelon ◽  
Fedelis Mutiso ◽  
Noel T. Mueller ◽  
John L. Pearce ◽  
Sara E. Benjamin-Neelon
Keyword(s):  
Social Vulnerability ◽  
Primary Care Physicians ◽  
Negative Binomial ◽  
Temporal Trends ◽  
The United States ◽  
Social Vulnerability Index ◽  
Crossover Effect ◽  
Death Rates ◽  
The Impact ◽  
Over Time

Background Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time. Methods We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19. Results At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties. Conclusions The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.

Abstract 076: Longitudinal Trajectories And Predictors Of County-level Cardiovascular Mortality In The United States (1980-2014)

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Shreya Rao ◽  
Amy E Hughes ◽  
Colby Ayers ◽  
Sandeep R Das ◽  
Ethan A Halm ◽  
...  
Keyword(s):  
Violent Crime ◽  
High Mortality ◽  
Multinomial Logistic Regression ◽  
The United States ◽  
Crime Rates ◽  
Social Characteristics ◽  
Mortality Data ◽  
County Level ◽  
Obesity And Diabetes ◽  
Health Factors

Introduction: CV mortality has declined over 4 decades in the U.S. However, whether declines have been uniformly experienced across U.S. counties, and predictors of CV mortality trajectory are not known. Methods: County-level mortality data from 1980-2014 was obtained from the National Center for Health Statistics. We used a ClustMix approach to identify 3 distinct county phenogroups based on mortality trajectory. Adjusted multinomial logistic regression models were constructed to evaluate the associations between county-level characteristics (demographic, social, and health status) and CV mortality trajectory-based phenogroups. Results: Among 3,133 counties, there were parallel declines in CV mortality in all groups (Fig.1A). High-mortality counties were located in the South and parts of the Ohio and Mississippi River valleys (Fig. 1B). County phenogroups varied significantly in social characteristics such as non-white proportion (low vs. high mortality: 12% vs. 27%), high-school education (11% vs. 20%), and violent crime rates (.01 vs. 0.3/100 population). Disparities in health factors were also observed with higher rates of smoking, obesity, and diabetes in the high (vs. low) mortality groups. A substantial collinearity was observed between social and health factors. In adjusted analysis, social, environmental, and health characteristics explained 56% variance in the county-level CV mortality trajectory. Education status (OR [95% CI]=12.4 [9.4-16.3]), violent crime rates (OR [95% CI] =1.6 [1.3-1.9]), and smoking (OR [95% CI] = 3.9 [3.1- 4.9]) were the strongest predictors of high mortality trajectory phenogroup membership (ref: low mortality). Conclusions: Despite a decline in CV mortality, disparities at the county-level have persisted over the past 4 decades largely driven by differences in social characteristics and smoking prevalence. This highlights the need for multi-domain interventions focusing on safety, education and public health to improve county-level disparities in CV health.

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