Changing dynamics of the drug overdose epidemic in the United States from 1979 through 2016.
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ABSTRACT:
SUBMITTER: Jalal H
PROVIDER: S-EPMC8025225 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background:The epidemic of drug overdose deaths in the United States has led to an increase in organ donors. Objective:To characterize donors who died of overdose and to analyze outcomes among transplant recipients. Design:Prospective observational cohort study. Setting:Scientific Registry of Transplant Recipients, 1 January 2000 to 1 September 2017. Participants:138 565 deceased donors; 337 934 transplant recipients at 297 transplant centers. Measurements:The primary exposure was donor mechanism of death (overdose-death donor [ODD], trauma-death donor [TDD], or medical-death donor [MDD]). Patient and graft survival and organ discard (organ recovered but not transplanted) were compared using propensity score-weighted standardized risk differences (sRDs). Results:A total of 7313 ODDs and 19 897 ODD transplants (10 347 kidneys, 5707 livers, 2471 hearts, and 1372 lungs) were identified. Overdose-death donors accounted for 1.1% of donors in 2000 and 13.4% in 2017. They were more likely to be white (85.1%), aged 21 to 40 years (66.3%), infected with hepatitis C virus (HCV) (18.3%), and increased-infectious risk donors (IRDs) (56.4%). Standardized 5-year patient survival was similar for ODD organ recipients compared with TDD organ recipients (sRDs ranged from 3.1% lower to 3.9% higher survival) and MDD organ recipients (sRDs ranged from 2.1% to 5.2% higher survival). Standardized 5-year graft survival was similar between ODD and TDD grafts (minimal difference for kidneys and lungs, marginally lower [sRD, -3.2%] for livers, and marginally higher [sRD, 1.9%] for hearts). Kidney discard was higher for ODDs than TDDs (sRD, 5.2%) or MDDs (sRD, 1.5%); standardization for HCV and IRD status attenuated this difference. Limitation:Inability to distinguish between opioid and nonopioid overdoses. Conclusion:In the United States, transplantation with ODD organs has increased dramatically, with noninferior outcomes in transplant recipients. Concerns about IRD behaviors and hepatitis C among donors lead to excess discard that should be minimized given the current organ shortage. Primary Funding Source:National Institutes of Health.
| S-EPMC6205229 | biostudies-literature
Project description:The United States has experienced a dramatic rise in opioid addiction and opioid overdose deaths in recent years. We investigate the effect of the opioid epidemic at the local level on nonmarital fertility using aggregate- and individual-level analyses. Opioid overdose death rates and prescriptions per capita are used as indicators of the intensity of the opioid epidemic. We estimate area fixed-effects models to test the effect of the opioid epidemic on nonmarital birth rates obtained from vital statistics for 2000-2016. We find an increase in nonmarital birth rates in communities that experienced a rise in opioid overdose deaths and higher prescription rates. Our analyses also show that the local effect of the opioid epidemic is not driven by a reduction in marriage rates and that marital birth rates are unaffected. Individual-level data from the ACS 2008-2016 are then used to further assess the potential causal mechanisms and to test heterogeneous effects by education and race/ethnicity. Our findings suggest that the opioid epidemic increased nonmarital birth rates through social disruptions primarily affecting unmarried women but not through changes in their economic condition.
| S-EPMC8363233 | biostudies-literature
Project description:Using a regional measure of gender norms from the General Social Surveys together with marital histories from the Panel Study of Income Dynamics, this study explored how gender norms were associated with women's marriage dynamics between 1968 and 2012. Results suggested that a higher prevalence of egalitarian gender norms predicted a decline in marriage formation. This decline was, however, only true for women without a college degree. For college-educated women, the association between gender norms and marriage formation became positive when gender egalitarianism prevailed. The findings also revealed an inverted U-shaped relationship between gender norms and divorce: an initial increase in divorce was observed when gender norms were predominantly traditional. The association, however, reversed as gender norms became egalitarian. No differences by education were found for divorce. The findings partially support the gender revolution framework but also highlight greater barriers to marriage for low-educated women as societies embrace gender equality.
| S-EPMC5766036 | biostudies-literature
Project description:The United States opioid epidemic is a nationwide public health crisis. Initially driven by increased consumption and availability of pharmaceutical opioids, an increasing number of opioid overdoses are now related to heroin and illicitly manufactured fentanyl and fentanyl analogs. Addressing this epidemic requires addressing the stigma associated with opioid use disorders and its treatment, improving access to efficacious treatment options, specifically methadone and buprenorphine, and reducing opioid overdose fatalities with distribution of the opioid antagonist and overdose reversal agent naloxone.
| S-EPMC6578581 | biostudies-literature
Project description:OBJECTIVES:To identify levels and trends in life expectancy at age 65 (e65) by geographic region and metropolitan status in the United States. METHODS:Using county-level data on population and deaths from the Census and National Center for Health Statistics, we consider spatial inequality in e65 across 4 metropolitan types and 10 geographic regions from 2000 to 2016. We examine whether changes in e65 are driven by mortality developments in metro types or geographic regions, and compare spatial patterns in the United States to mortality trends in other Organization of Economic Cooperation and Development (OECD) countries. We use decomposition and regression methods to estimate the contributions of 10 causes of death to changes and inequalities in e65. RESULTS:Life expectancy at age 65 increased in all spatial units from 2000 to 2016. Areas with higher e65 in 2000 also experienced larger gains. Longevity increases were greatest in large metropolitan areas and coastal regions. Nonmetropolitan areas and the interior lagged far behind not only other parts of the United States but all OECD comparison countries. Metropolitan status was a better predictor of mortality changes than geographic region. Circulatory diseases and diseases associated with smoking were the principal sources of life expectancy gains and spatial differentiation in those gains. Larger gains in smoking-related mortality accounted for greater improvements among men than women. DISCUSSION:Even at advanced ages, large geographic disparities in life expectancy remain. And as mortality has declined, these disparities have widened. Public health efforts should pay special attention to identifying and ameliorating the sources of lagging life expectancy in nonmetropolitan regions.
| S-EPMC7161363 | biostudies-literature
Project description:BackgroundZika virus (ZIKV) is an emerging mosquito-borne arbovirus that can produce serious public health consequences. In 2016, ZIKV caused an epidemic in many countries around the world, including the United States. ZIKV surveillance and vector control is essential to combating future epidemics. However, challenges relating to the timely publication of case reports significantly limit the effectiveness of current surveillance methods. In many countries with poor infrastructure, established systems for case reporting often do not exist. Previous studies investigating the H1N1 pandemic, general influenza and the recent Ebola outbreak have demonstrated that time- and geo-tagged Twitter data, which is immediately available, can be utilized to overcome these limitations.MethodsIn this study, we employed a recently developed system called Cloudberry to filter a random sample of Twitter data to investigate the feasibility of using such data for ZIKV epidemic tracking on a national and state (Florida) level. Two auto-regressive models were calibrated using weekly ZIKV case counts and zika tweets in order to estimate weekly ZIKV cases 1 week in advance.ResultsWhile models tended to over-predict at low case counts and under-predict at extreme high counts, a comparison of predicted versus observed weekly ZIKV case counts following model calibration demonstrated overall reasonable predictive accuracy, with an R2 of 0.74 for the Florida model and 0.70 for the U.S.ModelTime-series analysis of predicted and observed ZIKV cases following internal cross-validation exhibited very similar patterns, demonstrating reasonable model performance. Spatially, the distribution of cumulative ZIKV case counts (local- & travel-related) and zika tweets across all 50?U.S. states showed a high correlation (r?=?0.73) after adjusting for population.ConclusionsThis study demonstrates the value of utilizing Twitter data for the purposes of disease surveillance. This is of high value to epidemiologist and public health officials charged with protecting the public during future outbreaks.
| S-EPMC6570872 | biostudies-literature
Project description:In recent years, many studies have drawn attention to the important role of collective awareness and human behaviour during epidemic outbreaks. A number of modelling efforts have investigated the interaction between the disease transmission dynamics and human behaviour change mediated by news coverage and by information spreading in the population. Yet, given the scarcity of data on public awareness during an epidemic, few studies have relied on empirical data. Here, we use fine-grained, geo-referenced data from three online sources-Wikipedia, the GDELT Project and the Internet Archive-to quantify population-scale information seeking about the 2016 Zika virus epidemic in the U.S., explicitly linking such behavioural signal to epidemiological data. Geo-localized Wikipedia pageview data reveal that visiting patterns of Zika-related pages in Wikipedia were highly synchronized across the United States and largely explained by exposure to national television broadcast. Contrary to the assumption of some theoretical epidemic models, news volume and Wikipedia visiting patterns were not significantly correlated with the magnitude or the extent of the epidemic. Attention to Zika, in terms of Zika-related Wikipedia pageviews, was high at the beginning of the outbreak, when public health agencies raised an international alert and triggered media coverage, but subsequently exhibited an activity profile that suggests nonlinear dependencies and memory effects in the relation between information seeking, media pressure, and disease dynamics. This calls for a new and more general modelling framework to describe the interaction between media exposure, public awareness and disease dynamics during epidemic outbreaks.
| S-EPMC7067377 | biostudies-literature
Project description:Coronavirus 2019 (COVID-19) is causing a severe pandemic that has resulted in millions of confirmed cases and deaths around the world. In the absence of effective drugs for treatment, non-pharmaceutical interventions are the most effective approaches to control the disease. Although some countries have the pandemic under control, all countries around the world, including the United States (US), are still in the process of controlling COVID-19, which calls for an effective epidemic model to describe the transmission dynamics of COVID-19. Meeting this need, we have extensively investigated the transmission dynamics of COVID-19 from 22 January 2020 to 14 February 2021 for the 50 states of the United States, which revealed the general principles underlying the spread of the virus in terms of intervention measures and demographic properties. We further proposed a time-dependent epidemic model, named T-SIR, to model the long-term transmission dynamics of COVID-19 in the US. It was shown in this paper that our T-SIR model could effectively model the epidemic dynamics of COVID-19 for all 50 states, which provided insights into the transmission dynamics of COVID-19 in the US. The present study will be valuable to help understand the epidemic dynamics of COVID-19 and thus help governments determine and implement effective intervention measures or vaccine prioritization to control the pandemic.
| S-EPMC8305610 | biostudies-literature
Project description:Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection dynamics, presumably via heterosubtypic cross-immunity.
| S-EPMC10593063 | biostudies-literature
Project description:Opioid overdose deaths in the U.S. rose dramatically after 1999, but also exhibited substantial geographic variation. This has largely been explained by differential availability of prescription and non-prescription opioids, including heroin and fentanyl. Recent studies explore the underlying role of socioeconomic factors, but overlook the influence of job loss due to international trade, an economic phenomenon that disproportionately harms the same regions and demographic groups at the heart of the opioid epidemic. We used OLS regression and county-year level data from the Centers for Disease Controls and the Department of Labor to test the association between trade-related job loss and opioid-related overdose death between 1999 and 2015. We find that the loss of 1000 trade-related jobs was associated with a 2.7 percent increase in opioid-related deaths. When fentanyl was present in the heroin supply, the same number of job losses was associated with a 11.3 percent increase in opioid-related deaths.
| S-EPMC6606896 | biostudies-literature