Assessing spatial gaps in sexually transmissible infection services and morbidity: an illustration with Texas county-level data from 2007

Sexual Health ◽  
2012 ◽  
Vol 9 (4) ◽  
pp. 334 ◽  
Author(s):  
Kwame Owusu-Edusei ◽  
Sonal R. Doshi
Keyword(s):  
The United States ◽  
High Morbidity ◽  
County Level ◽  
Socioeconomic Characteristics ◽  
Regression Methods ◽  
Texas County ◽  
Level Data ◽  
High Incidence ◽  
Sexually Transmissible Infection ◽  
Transmissible Infection

Background In the United States, sexually transmissible infection (STI) and family planning (FP) clinics play a major role in the detection and treatment of STIs. However, an examination of the spatial distribution of these service sites and their association with STI morbidity and county-level socioeconomic characteristics is lacking. We demonstrate how mapping and regression methods can be used to assess the spatial gaps between STI services and morbidity. Methods: We used 2007 county-level surveillance data on chlamydia (Chlamydia trachomatis), gonorrhoea (Neisseria gonorrhoeae) and syphilis. The geocoded STI service (STI or FP clinic) locations overlaid on the Texas county-level chlamydia, gonorrhoea and syphilis morbidity map indicated that counties with high incidence had at least one STI service site. Logistic regression was used to examine the association between having STI services and county-level socioeconomic characteristics. Results: Twenty-two percent of chlamydia high-morbidity counties (>365 out of 100 000); 32% of gonorrhoea high-morbidity counties (>136 out of 100 000) and 23% of syphilis high-morbidity counties (≥4 out of 100 000 and at least two cases) had no STI services. When we controlled for socioeconomic characteristics, high-morbidity syphilis was weakly associated with having STI services. The percent of the population aged 15–24 years, the percent of Hispanic population, the crime rate and population density were significantly (P < 0.05) associated with having STI services. Conclusion: Our results suggest that having an STI service was not associated with high morbidity. The methods used have demonstrated the utility of mapping to assess the spatial gaps that exist between STI services and demand.

scholarly journals Timing of social distancing policies and COVID-19 mortality: county-level evidence from the U.S.

2021 ◽  
Author(s):  
Catalina Amuedo-Dorantes ◽  
Neeraj Kaushal ◽  
Ashley N. Muchow
Keyword(s):  
New York ◽  
Political Ideology ◽  
Death Rate ◽  
The United States ◽  
County Level ◽  
Level Data ◽  
Level Information ◽  
Alternative Measures ◽  
The U.S ◽  
Pharmaceutical Interventions

AbstractUsing county-level data on COVID-19 mortality and infections, along with county-level information on the adoption of non-pharmaceutical interventions (NPIs), we examine how the speed of NPI adoption affected COVID-19 mortality in the United States. Our estimates suggest that adopting safer-at-home orders or non-essential business closures 1 day before infections double can curtail the COVID-19 death rate by 1.9%. This finding proves robust to alternative measures of NPI adoption speed, model specifications that control for testing, other NPIs, and mobility and across various samples (national, the Northeast, excluding New York, and excluding the Northeast). We also find that the adoption speed of NPIs is associated with lower infections and is unrelated to non-COVID deaths, suggesting these measures slowed contagion. Finally, NPI adoption speed appears to have been less effective in Republican counties, suggesting that political ideology might have compromised their efficacy.

Abstract P636: Social Determinants of Stroke Hospitalization and Mortality in the United States

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Randhir Sagar Yadav ◽  
Durgesh Chaudhary ◽  
Shima Shahjouei ◽  
Jiang Li ◽  
Vida Abedi ◽  
...  
Keyword(s):  
United States ◽  
Income Inequality ◽  
Social Determinants ◽  
Hemorrhagic Stroke ◽  
The United States ◽  
Care Physician ◽  
County Level ◽  
Medicare Beneficiaries ◽  
Level Data ◽  
Control And Prevention

Introduction: Stroke hospitalization and mortality are influenced by various social determinants. This ecological study aimed to determine the associations between social determinants and stroke hospitalization and outcome at county-level in the United States. Methods: County-level data were recorded from the Centers for Disease Control and Prevention as of January 7, 2020. We considered four outcomes: all-age (1) Ischemic and (2) Hemorrhagic stroke Death rates per 100,000 individuals (ID and HD respectively), and (3) Ischemic and (4) Hemorrhagic stroke Hospitalization rate per 1,000 Medicare beneficiaries (IH and HH respectively). Results: Data of 3,225 counties showed IH (12.5 ± 3.4) and ID (22.2 ± 5.1) were more frequent than HH (2.0 ± 0.4) and HD (9.8 ± 2.1). Income inequality as expressed by Gini Index was found to be 44.6% ± 3.6% and unemployment rate was 4.3% ± 1.5%. Only 29.8% of the counties had at least one hospital with neurological services. The uninsured rate was 11.0% ± 4.7% and people living within half a mile of a park was only 18.7% ± 17.6%. Age-adjusted obesity rate was 32.0% ± 4.5%. In regression models, age-adjusted obesity (OR for IH: 1.11; HH: 1.04) and number of hospitals with neurological services (IH: 1.40; HH: 1.50) showed an association with IH and HH. Age-adjusted obesity (ID: 1.16; HD: 1.11), unemployment (ID: 1.21; HD: 1.18) and income inequality (ID: 1.09; HD: 1.11) showed an association with ID and HD. Park access showed inverse associations with all four outcomes. Additionally, population per primary-care physician was associated with HH while number of pharmacy and uninsured rate were associated with ID. All associations and OR had p ≤0.04. Conclusion: Unemployment and income inequality are significantly associated with increased stroke mortality rates.

scholarly journals The evolution of infant mortality inequality in the United States, 1960–2016

2020 ◽  
Vol 6 (29) ◽  
pp. eaba5908
Author(s):  
Nick Turner ◽  
Kaveh Danesh ◽  
Kelsey Moran
Keyword(s):  
United States ◽  
Infant Mortality ◽  
The United States ◽  
Mortality Rates ◽  
County Level ◽  
Level Data ◽  
Antipoverty Programs ◽  
The Relationship ◽  
Over Time ◽  
Mortality Inequality

What is the relationship between infant mortality and poverty in the United States and how has it changed over time? We address this question by analyzing county-level data between 1960 and 2016. Our estimates suggest that level differences in mortality rates between the poorest and least poor counties decreased meaningfully between 1960 and 2000. Nearly three-quarters of the decrease occurred between 1960 and 1980, coincident with the introduction of antipoverty programs and improvements in medical care for infants. We estimate that declining inequality accounts for 18% of the national reduction in infant mortality between 1960 and 2000. However, we also find that level differences between the poorest and least poor counties remained constant between 2000 and 2016, suggesting an important role for policies that improve the health of infants in poor areas.

Religious Environments and the Distribution of Anti-Poverty Nonprofit Organizations

2016 ◽  
Vol 46 (1) ◽  
pp. 156-174 ◽  
Author(s):  
Edward C. Polson
Keyword(s):  
Nonprofit Organizations ◽  
Regression Models ◽  
Spatial Regression ◽  
The United States ◽  
County Level ◽  
Geographic Concentration ◽  
Religious Traditions ◽  
Level Data ◽  
Spatial Regression Models ◽  
Previous Scholarship

Previous scholarship highlights the effect that religious environments have on community-level outcomes such as neighborhood stability, economic development, and crime. In the present study, I extend work on the contextual effects of religion by examining how the religious composition of U.S. counties is related to the distribution of anti-poverty nonprofit organizations. Anti-poverty nonprofits represent an important source of support for communities across the United States, and history suggests that religious people and groups have played a significant role in their development. Still, it is unclear whether some religious environments may be more nurturing of these organizations than others. Utilizing spatial regression models and county-level data, I seek to address this question. I find that the geographic concentration of some religious traditions is related to a more robust presence of anti-poverty nonprofit organizations than others.

scholarly journals Unraveling US National COVID-19 Racial/Ethnic Disparities using County Level Data Among 328 Million Americans

2020 ◽  
Author(s):  
Daniel Li ◽  
Sheila M. Gaynor ◽  
Corbin Quick ◽  
Jarvis T. Chen ◽  
Briana J.K. Stephenson ◽  
...  
Keyword(s):  
Health Disparities ◽  
Social Determinants ◽  
Ethnic Disparities ◽  
The United States ◽  
Vulnerable Groups ◽  
County Level ◽  
Racial And Ethnic Disparities ◽  
Racial Health Disparities ◽  
Personal Protection Equipment ◽  
Level Data

ABSTRACTRacial and ethnic disparities in COVID-19 outcomes reflect the unequal burden experienced by vulnerable communities in the United States (US). Proposed explanations include socioeconomic factors that influence how people live, work, and play, and pre-existing comorbidities. It is important to assess the extent to which observed US COVID-19 racial and ethnic disparities can be explained by these factors. We study 9.8 million confirmed cases and 234,000 confirmed deaths from 2,990 US counties (3,142 total) that make up 99.8% of the total US population (327.6 out of 328.2 million people) through 11/8/20. We found national COVID-19 racial health disparities in US are partially explained by various social determinants of health and pre-existing comorbidities that have been previously proposed. However, significant unexplained racial and ethnic health disparities still persist at the US county level after adjusting for these variables. There is a pressing need to develop strategies to address not only the social determinants but also other factors, such as testing access, personal protection equipment access and exposures, as well as tailored intervention and resource allocation for vulnerable groups, in order to combat COVID-19 and reduce racial health disparities.

scholarly journals Explaining COVID-19 Outbreaks with Reactive SEIRD Models

2021 ◽  
Author(s):  
Kunal Menda ◽  
Lucas Laird ◽  
Mykel J. Kochenderfer ◽  
Rajmonda S. Caceres
Keyword(s):  
Neural Network ◽  
United States ◽  
Infection Rate ◽  
Expectation Maximization ◽  
The United States ◽  
County Level ◽  
Training Set ◽  
Single Model ◽  
Partial Observability ◽  
Level Data

AbstractCOVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. In this work, we seek to explain the diversity in epidemic progressions by considering an extension to the compartmental SEIRD model. The model we propose uses a neural network to predict the infection rate as a function of time and of the prevalence of the disease. We provide a methodology for fitting this model to available county-level data describing aggregate cases and deaths. Our method uses Expectation-Maximization in order to overcome the challenge of partial observability—that the system’s state is only partially reflected in available data. We fit a single model to data from multiple counties in the United States exhibiting different behavior. By simulating the model, we show that it is capable of exhibiting both single peak and multi-peak behavior, reproducing behavior observed in counties both in and out of the training set. We also numerically compare the error of simulations from our model with a standard SEIRD model, showing that the proposed extensions are necessary to be able to explain the spread of COVID-19.

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