Correlates of county-level nonviral sexually transmitted infection hot spots in the US: application of hot spot analysis and spatial logistic regression

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

STI Testing among Medicaid Enrollees Initiating PrEP for HIV Prevention in Six Southern States

OBJECTIVES

The purpose of this study was to measure sexually transmitted infection (STI) testing among Medicaid enrollees initiating preexposure prophylaxis (PrEP) to prevent human immunodeficiency virus. Secondary data are in the form of Medicaid enrollment and claims data in six states in the US South.

METHODS

Research partnerships in six states in the US South developed a distributed research network to accomplish study aims. Each state identified all first-time PrEP users in fiscal year 2017-2018 (combined N = 990) and measured the presence of STI testing for chlamydia, syphilis, and gonorrhea through 2019. Each state calculated the percentage of individuals with at least one STI test during 3-, 6-, and 12-month follow-up periods.

RESULTS

The proportion of first-time PrEP users that received an STI test varied by state: 37% to 67% of all of the individuals in each state who initiated PrEP received a test within the first 6 months of PrEP treatment and 50% to 77% received a test within the first 12 months.

CONCLUSIONS

Although the Centers for Disease Control and Prevention recommends STI testing at least every 6 months for PrEP users, our analysis of Medicaid data suggests that STI testing occurs less frequently than recommended in populations at elevated risk of syphilis, gonorrhea, and chlamydia.

Spatial and temporal distribution of reported dengue cases and hot spot identification in Quezon City, Philippines, 2010-2017

BACKGROUND

Dengue remains a major public health problem in the Philippines, particularly in urban areas of the National Capital Region. Thematic mapping using geographic information systems complemented by spatial analysis such as cluster analysis and hot spot detection can provide useful information to guide preventive measures and control strategies against dengue. Hence, this study was aimed to describe the spatiotemporal distribution of dengue incidence and identify dengue hot spots by barangay using reported cases from Quezon City, the Philippines from 2010 to 2017.

METHODS

Reported dengue case data at barangay level from January 1, 2010 to December 31, 2017 were obtained from the Quezon City Epidemiology and Surveillance Unit. The annual incidence rate of dengue from 2010 to 2017, expressed as the total number of dengue cases per 10,000 population in each year, was calculated for each barangay. Thematic mapping, global cluster analysis, and hot spot analysis were performed using ArcGIS 10.3.1.

RESULTS

The number of reported dengue cases and their spatial distribution varied highly between years. Local clusters were evident during the study period. Eighteen barangays were identified as hot spots.

CONCLUSIONS

Considering the spatial heterogeneity and instability of hot spots in Quezon City across years, efforts towards the containment of dengue can be made more targeted, and efficient with the application of hot spot analysis in routine surveillance. This may be useful not only for the control of dengue but also for other diseases, and for public health planning, monitoring, and evaluation.

Spatial distribution and machine learning prediction of sexually transmitted infections and associated factors among sexually active men and women in Ethiopia, evidence from EDHS 2016

INTRODUCTION

Sexually transmitted infections (STIs) are the major public health problem globally, affecting millions of people every day. The burden is high in the Sub-Saharan region, including Ethiopia. Besides, there is little evidence on the distribution of STIs across Ethiopian regions. Hence, having a better understanding of the infections is of great importance to lessen their burden on society. Therefore, this article aimed to assess predictors of STIs using machine learning techniques and their geographic distribution across Ethiopian regions. Assessing the predictors of STIs and their spatial distribution could help policymakers to understand the problems better and design interventions accordingly.

METHODS

A community-based cross-sectional study was conducted from January 18, 2016, to June 27, 2016, using the 2016 Ethiopian Demography and Health Survey (EDHS) dataset. We applied spatial autocorrelation analysis using Global Moran's I statistics to detect latent STI clusters. Spatial scan statics was done to identify local significant clusters based on the Bernoulli model using the SaTScan™ for spatial distribution and Supervised machine learning models such as C5.0 Decision tree, Random Forest, Support Vector Machine, Naïve Bayes, and Logistic regression were applied to the 2016 EDHS dataset for STI prediction and their performances were analyzed. Association rules were done using an unsupervised machine learning algorithm.

RESULTS

The spatial distribution of STI in Ethiopia was clustered across the country with a global Moran's index = 0.06 and p value = 0.04. The Random Forest algorithm was best for STI prediction with 69.48% balanced accuracy and 68.50% area under the curve. The random forest model showed that region, wealth, age category, educational level, age at first sex, working status, marital status, media access, alcohol drinking, chat chewing, and sex of the respondent were the top 11 predictors of STI in Ethiopia.

CONCLUSION

Applying random forest machine learning algorithm for STI prediction in Ethiopia is the proposed model to identify the predictors of STIs.

Preconception syphilis seroprevalence and association with duration of marriage and age among married individuals in Guangdong Province, China: A population-based cross-sectional study

BACKGROUND

Duration of marriage (DoM) and age are important characteristics of married individuals, who are the critical population for eliminating mother-to-child transmission (MTCT) of syphilis. A deep understanding of the preconception syphilis seroprevalence (PSS) and its distribution among this population may be able to help to eliminate MTCT. However, few population-based epidemiological studies have been focused on this group, and the association of DoM and age with PSS remains unclear.

METHODOLOGY/PRINCIPAL FINDINGS

This study used data from 4,826,214 married individuals aged 21-49 years who participated in the National Free Preconception Health Examination Project in Guangdong Province, China, between 2014 and 2019. Syphilis was screened using the rapid plasma reagin (RPR) test. The seroprevalence time series, seroprevalence map, and hot spot analysis (HSA) were employed to visualize the spatiotemporal distribution. The restricted cubic spline (RCS) based on multivariate logistic regression was used to model the association of DoM and age with PSS. The interactions on the additive scale of DoM and age were also assessed. The PSS was 266.61 per 100,000 persons (95% CI: 262.03-271.24) and the burden was higher in economically underdeveloped area within the province. A strong J-shaped non-linearity association was observed between age and PSS. Specifically, the risk of seropositivity was relatively flat until 27 years of age among men and increased rapidly afterwards, with an adjusted odds ratio (aOR) of 1.13 (95% CI: 1.12-1.13) per unit. Among women, the risk of seropositivity was relatively flat until 25 years of age and increased rapidly afterwards with an aOR of 1.08 (95% CI: 1.08-1.09) per unit. DoM was negatively associated with PSS among married individuals. Moreover, the combined effects of age and DoM appeared to be synergistic.

CONCLUSIONS/SIGNIFICANCE

Our findings suggest that attention should be paid to preventing syphilis in underdeveloped areas and that syphilis screening in newly married individuals who are in their late 20s or older should be recommended. Additionally, early syphilis prevention strategies should be implemented among young people as early as possible.

Assessing inequities underlying racial disparities of COVID-19 mortality in Louisiana parishes

High COVID-19 mortality among Black communities heightened the pandemic's devastation. In the state of Louisiana, the racial disparity associated with COVID-19 mortality was significant; Black Americans accounted for 50% of known COVID-19-related deaths while representing only 32% of the state's population. In this paper, we argue that structural racism resulted in a synergistic framework of cumulatively negative determinants of health that ultimately affected COVID-19 deaths in Louisiana Black communities. We identify the spatial distribution of social, environmental, and economic stressors across Louisiana parishes using hot spot analysis to develop aggregate stressors. Further, we examine the correlation between stressors, cumulative health risks, COVID-19 mortality, and the size of Black populations throughout Louisiana. We hypothesized that parishes with larger Black populations (percentages) would have larger stressor values and higher cumulative health risks as well as increased COVID-19 mortality rates. Our results suggest two categories of parishes. The first group has moderate levels of aggregate stress, high population densities, predominately Black populations, and high COVID-19 mortality. The second group of parishes has high aggregate stress, lower population densities, predominantly Black populations, and initially low COVID-19 mortality that increased over time. Our results suggest that structural racism and inequities led to severe disparities in initial COVID-19 effects among highly populated Black Louisiana communities and that as the virus moved into less densely populated Black communities, similar trends emerged.

Syphilis in riverine communities: prevalence and associated factors

OBJECTIVE

To estimate the prevalence of syphilis and associated factors in riverine communities.

METHOD

This is a cross-sectional and analytical study carried out with 250 riverside dwellers living in five communities in the city of João Pessoa, state of Paraíba. Data were collected through interviews and rapid screening tests to investigate syphilis. Bivariate, logistic regression and weight of evidence analysis were performed to identify the association between risk factors and behavior variables and rapid test positivity.

RESULTS

he prevalence of syphilis was 11.6% (95%CI: 7.5-15.6). Riverside dwellers who have a previous history of Sexually Transmitted Infection (OR 8.00; 95%CI: 2.76-23.2), history of imprisonment (OR 7.39; 95%CI: 1.61-33.7) and who reported having more than two sexual partners in the last 12 months (OR 4.31; 95%CI: 1.55-11.9) were more likely to be positive for syphilis.

CONCLUSION

High prevalence of syphilis among riverside dwellers and the presence of behavioral factors that increase vulnerability to acquiring the infection. The need to invest in preventive and screening strategies for syphilis in populations considered vulnerable is highlighted.

Identification of most important features based on a fuzzy ensemble technique: Evaluation on joint space narrowing progression in knee osteoarthritis patients

OBJECTIVE

Feature selection (FS) is a crucial and at the same time challenging processing step that aims to reduce the dimensionality of complex classification or regression problems. Various techniques have been proposed in the literature to address this challenge with emphasis to medical applications. However, each one of the existing FS algorithms come with its own advantages and disadvantages introducing a certain level of bias.

MATERIALS AND METHODS

To avoid bias and alleviate the defectiveness of single feature selection results, an ensemble FS methodology is proposed in this paper that aggregates the results of several FS algorithms (filter, wrapper and embedded ones). Fuzzy logic is employed to combine multiple feature importance scores thus leading to a more robust selection of informative features. The proposed fuzzy ensemble FS methodology was applied on the problem of knee osteoarthritis (KOA) prediction with special emphasis on the progression of joint space narrowing (JSN). The proposed FS methodology was integrated into an end-to-end machine learning pipeline and a thorough experimental evaluation was conducted using data from the Osteoarthritis Initiative (OAI) database. Several classifiers were investigated for their suitability in the task of JSN prediction and the best performing model was then post-hoc analyzed by using the SHAP method.

RESULTS

The results showed that the proposed method presented a better and more stable performance in contrast to other competitive feature selection methods, leading to an average accuracy of 78.14% using XG Boost at 31 selected features. The post-hoc explainability highlighted the important features that contribute to the classification of patients with JSN progression.

CONCLUSIONS

The proposed fuzzy feature selection approach improves the performance of the predictive models by selecting a small optimal subset of features compared to popular feature selection methods.

Predicting Future Geographic Hotspots of Potentially Preventable Hospitalisations Using All Subset Model Selection and Repeated K-Fold Cross-Validation

Long-term future prediction of geographic areas with high rates of potentially preventable hospitalisations (PPHs) among residents, or "hotspots", is critical to ensure the effective location of place-based health service interventions. This is because such interventions are typically expensive and take time to develop, implement, and take effect, and hotspots often regress to the mean. Using spatially aggregated, longitudinal administrative health data, we introduce a method to make such predictions. The proposed method combines all subset model selection with a novel formulation of repeated k-fold cross-validation in developing optimal models. We illustrate its application predicting three-year future hotspots for four PPHs in an Australian context: type II diabetes mellitus, heart failure, chronic obstructive pulmonary disease, and "high risk foot". In these examples, optimal models are selected through maximising positive predictive value while maintaining sensitivity above a user-specified minimum threshold. We compare the model's performance to that of two alternative methods commonly used in practice, i.e., prediction of future hotspots based on either: (i) current hotspots, or (ii) past persistent hotspots. In doing so, we demonstrate favourable performance of our method, including with respect to its ability to flexibly optimise various different metrics. Accordingly, we suggest that our method might effectively be used to assist health planners predict excess future demand of health services and prioritise placement of interventions. Furthermore, it could be used to predict future hotspots of non-health events, e.g., in criminology.

Spatiotemporal distribution and sociodemographic and socioeconomic factors associated with primary and secondary syphilis in Guangdong, China, 2005-2017

Background

Previous studies exploring the factors associated with the incidence of syphilis have mostly focused on individual-level factors. However, recent evidence has indicated that social-level factors, such as sociodemographic and socioeconomic factors, also affect the incidence of syphilis. Studies on the sociodemographic and socioeconomic factors associated with syphilis incidence are scarce, and they have rarely controlled for spatial effects, even though syphilis shows spatial autocorrelation.

Methodology/Principal findings

Syphilis data from 21 cities in Guangdong province between 2005 and 2017 were provided by the National Notifiable Infectious Disease Reporting Information System. The incidence time series, incidence map, and space-time scanning data were used to visualize the spatiotemporal distribution. The spatial panel data model was then applied to explore the relationship between sociodemographic factors (population density, net migration rate, male:female ratio, and the number of health institutions per 1,000 residents), socioeconomic factors (gross domestic product per capita, the proportion of secondary/tertiary industry), and the incidence of primary and secondary syphilis after controlling for spatial effects. The incidence of syphilis increased slowly from 2005 (11.91 per 100,000) to 2011 (13.42 per 100,000) and then began to decrease, reaching 6.55 per 100,000 in 2017. High-risk clusters of syphilis tended to shift from developed areas to underdeveloped areas. An inverted U-shaped relationship was found between syphilis incidence and gross domestic product per capita. Moreover, syphilis incidence was significantly associated with population density (β = 2.844, P = 0.006), the number of health institutions per 1,000 residents (β = -0.095, P = 0.007), and the net migration rate (β = -0.219, P = 0.002).

Conclusions/Significance

Our findings suggest that the incidence of primary and secondary syphilis first increase before decreasing as economic development increases further. These results emphasize the necessity to prevent syphilis in regions at the early stages of economic growth.

Syphilis is a sexually transmitted infection that continues to cause morbidity and mortality worldwide. The primary and secondary stages of syphilis are the most transmissive stages in the entire process of the disease. We analyzed primary and secondary (P&S) syphilis data from 2005 to 2017 in Guangzhou, China, provided by the National Notifiable Infectious Disease Reporting Information System. The results showed that the annual incidence rates of P&S syphilis slightly increased from 2005 to 2011 and then began to decrease in 2017. Cases of P&S syphilis were spatially clustered. The high-risk syphilis clusters tended to shift from developed areas to underdeveloped areas. There may be an inverted U-shaped relationship between the level of economic development and the incidence of P&S syphilis, suggesting that the incidence of P&S syphilis first increased before decreasing as the level of economic development increased further. These results emphasize the necessity of preventing syphilis at locations in the early stage of economic growth. Investments in syphilis prevention education for people in regions at early development stages may mitigate the increasing cost of syphilis to future healthcare systems.

Maternal HIV and syphilis are not syndemic in Brazil: Hot spot analysis of the two epidemics

While the annual incidence of HIV diagnosis in pregnancy in Brazil remains relatively stable, rates of maternal syphilis increased over six-fold in the past decade. We hypothesized that maternal HIV and syphilis are two distinct epidemics. Data on all cases of maternal HIV or syphilis detected in pregnancy between January 1, 2010 to December 31, 2018 were requested from the Brazilian Ministry of Health. In order to evaluate how the epidemics evolved over the time period, ArcGIS software was used to generate spatiotemporal maps of annual rates of detection of maternal HIV and syphilis in 2010 and 2018. We utilized Euclidean-distance hot spot analysis to identify state-specific clusters in 2010 and 2018. From 2010 to 2018, there were 66,631 cases of maternal HIV, 225,451 cases of maternal syphilis, and 150,414 cases of congenital syphilis in Brazil. The state of Rio Grande do Sul had the highest rate of maternal HIV detection in both 2010 and 2018. Hot spots of maternal HIV were identified in the three most Southern states in both 2010 and 2018 (99% confidence, z-score >2.58, p <0.01). While syphilis incidence >30 per 1,000 live births in 2018 in four states, only the two coastal states of Rio de Janeiro and Espirito Santo in Southeastern Brazil were significant hot spots (90% confidence, z-score 1.65-1.95, p <0.10). Contrary to the general assumption, HIV and syphilis epidemics in Brazil are not syndemic in pregnant women. There is a spatial cluster of maternal HIV in the South, while syphilis is increasing throughout the country, more recently on the coast. Focusing on maternal HIV hot spots in the Southern states is insufficient to curtail the maternal and congenital syphilis epidemics throughout the country. New strategies, including ongoing hot spot analysis, are urgently needed to monitor, identify and treat maternal syphilis.

County-Level Social Capital and Bacterial Sexually Transmitted Infections in the United States

BACKGROUND

The association between county-level social capital indices (SCIs) and the 3 most commonly reported sexually transmitted infections (STIs) in the United States is lacking. In this study, we determined and examined the association between 2 recently developed county-level SCIs (ie, Penn State Social Capital Index [PSSCI] vs United States Congress Social Capital Index [USCSCI]) and the 3 most commonly reported bacterial STIs (chlamydia, gonorrhea, and syphilis) using spatial and nonspatial regression techniques.

METHODS

We assembled and analyzed multiyear (2012-2016) cross-sectional data on STIs and 2 SCIs (PSSCI vs USCSCI) on counties in all 48 contiguous states. We explored 2 nonspatial regression models (univariate and multiple generalized linear models) and 3 spatial regression models (spatial lag model, spatial error model, and the spatial autoregressive moving average model) for comparison.

RESULTS

Without exception, all the SCIs were negatively associated with all 3 STI morbidities. A 1-unit increase in the SCIs was associated with at least 9% (P < 0.001) decrease in each STI. Our test of the magnitude of the estimated associations indicated that the USCSCI was at least 2 times higher than the estimates for the PSSCI for all STIs (highest P value = 0.01).

CONCLUSIONS

Overall, our results highlight the potential benefits of applying/incorporating social capital concepts to STI control and prevention efforts. In addition, our results suggest that for the purpose of planning, designing, and implementing effective STI control and prevention interventions/programs, understanding the communities' associational life (as indicated by the factors/data used to develop the USCSCI) may be important.

Investigating Multiple-Reported Bacterial Sexually Transmitted Infection Hot Spot Counties in the United States: Ordered Spatial Logistic Regression

PURPOSE

To identify and examine the correlates of multiple bacterial sexually transmitted infection (STI) hot spot counties in the United States.

METHODS

We assembled and analyzed 5 years (2008-2012) of cross-sectional STI morbidity data to identify multiple bacterial STI (chlamydia, gonorrhea, and syphilis) hot spot counties using hot spot analysis. Then, we examined the association between the multi-STI hot spots and select multiyear (2008-2012) sociodemographic factors (data obtained from the American Community Survey) using ordered spatial logistic regression analyses.

RESULTS

Of the 2935 counties, the results indicated that 85 counties were hot spots for all 3 STIs (3-STI hot spot counties), 177 were hot spots for 2 STIs (2-STI hot spot counties), and 145 were hot spots for only 1 STI (1-STI hot spot counties). Approximately 93% (79 of 85) of the counties determined to be 3-STI hot spots were found in 4 southern states--Mississippi (n = 25), Arkansas (n = 22), Louisiana (n = 19), and Alabama (n = 13). Counties determined to be 2 STI hot spots were found in 7 southern states--Arkansas, Louisiana, Mississippi, Alabama, Georgia, and North and South Carolina had at least ten 2-STI hot spot counties each. The multi-STI hot spot classes were significantly (P < 0.05) associated with percent black (non-Hispanic), percent Hispanics, percent American Indians, population density, male-female sex ratio, percent aged 25 to 44 years, and violent crime rate.

CONCLUSIONS

This study provides information on multiple STI hot spot counties in the United States and the associated sociodemographic factors. Such information can be used to assist planning, designing, and implementing effective integrated bacterial STI prevention and control programs/interventions.

Identifying Infectious Diarrhea Hot spots and Associated Socioeconomic Factors in Anhui Province, China

Infectious diarrhea cases have increased during the past years in the Anhui Province of China, but little is known about its spatial cluster pattern and associated socioeconomic factors. We obtained county-level total cases of infectious diarrhea in 105 counties of Anhui in 2016 and computed age-adjusted rates. Socioeconomic factors were collected from the Statistical Yearbook. Hot spot analysis was used to identify hot and cold spot counties for infectious diarrhea incidence. We then applied binary logistic regression models to determine the association between socioeconomic factors and hot spot or cold spot clustering risk. Hot spot analysis indicated there were both significant hot spot (29 counties) and cold spot (18 counties) clustering areas for infectious diarrhea in Anhui (P < 0.10). Multivariate binary logistic regression results showed that infectious diarrhea hot spots were positively associated with per capita gross domestic product (GDP), with an adjusted odds ratio (AOR): 3.51, 95% CI: 2.09-5.91, whereas cold spots clustering were positively associated with the number of medical staffs (AOR: 1.18, 95% CI: 1.08-1.29) and negatively associated with the number of public health physicians (AOR: 0.27, 95% CI: 0.09-0.86). We identified locations for hot and cold spot clusters of infectious diarrhea incidence in Anhui, and the clustering risks were significantly associated with health workforce resources and the regional economic development. Targeted interventions should be carried out with considerations of regional socioeconomic conditions.

Estimating Housing Vacancy Rates in Rural China Using Power Consumption Data

Village hollowing is a growing policy problem globally, but accurately estimating housing vacancy rates is difficult and costly. In this study, we piloted the use of power consumption data to estimate the vacancy rate of rural housing. To illustrate the method used, we took power consumption data in 2014 and 2017 in an area of rural China to analyze the change in housing vacancies. Results indicated that the rural vacancy rates were 5.27% and 8.69%, respectively, while underutilization rates were around 10% in 2014 and 2017. Second, there was significant spatial clustering of vacant rural housing, and the hotspots were mainly distributed in western mountainous areas, whereas villages near urban areas had lower vacancy rates. Third, rural vacancies increased from 2014 to 2017. Compared with other methods, our method proved to be accurate, very cost-effective and scalable, and it can offer timely spatial and temporal information that can be used by policymakers to identify areas with significant village hollowing issues. However, there are challenges in setting the right thresholds that take into consideration regional differences. Therefore, there is also a need for more studies in different regions in order to scale up this method to the national level.

The Spatial Association Between Federally Qualified Health Centers and County-Level Reported Sexually Transmitted Infections: A Spatial Regression Approach

BACKGROUND

The number of categorical sexually transmitted disease (STD) clinics is declining in the United States. Federally qualified health centers (FQHCs) have the potential to supplement the needed sexually transmitted infection (STI) services. In this study, we describe the spatial distribution of FQHC sites and determine if reported county-level nonviral STI morbidity were associated with having FQHC(s) using spatial regression techniques.

METHODS

We extracted map data from the Health Resources and Services Administration data warehouse on FQHCs (ie, geocoded health care service delivery [HCSD] sites) and extracted county-level data on the reported rates of chlamydia, gonorrhea and, primary and secondary (P&S) syphilis (2008-2012) from surveillance data. A 3-equation seemingly unrelated regression estimation procedure (with a spatial regression specification that controlled for county-level multiyear (2008-2012) demographic and socioeconomic factors) was used to determine the association between reported county-level STI morbidity and HCSD sites.

RESULTS

Counties with HCSD sites had higher STI, poverty, unemployment, and violent crime rates than counties with no HCSD sites (P < 0.05). The number of HCSD sites was associated (P < 0.01) with increases in the temporally smoothed rates of chlamydia, gonorrhea, and P&S syphilis, but there was no significant association between the number of HCSD per 100,000 population and reported STI rates.

CONCLUSIONS

There is a positive association between STI morbidity and the number of HCSD sites; however, this association does not exist when adjusting by population size. Further work may determine the extent to which HCSD sites can meet unmet needs for safety net STI services.

Sexually transmitted infections in the Delta Regional Authority: significant disparities in the 252 counties of the eight-state Delta Region Authority

OBJECTIVE

Chlamydia, gonorrhoea and syphilis (primary and secondary) are at high levels in the USA. Disparities by race, gender and sexual orientation have been characterised, but while there are indications that rural poor populations may also be at distinct risk this has been subjected to little study by comparison. The federally designated Delta Regional Authority, similar in structure to the Appalachian Regional Commission, oversees 252 counties within eight Mississippi Delta states experiencing chronic economic and health disparities. Our objective was to identify differences in infection risk between Delta Region (DR)/non-DR counties and examine how they might vary by rurality, population density, primary care access and education attainment.

METHODS

Reported chlamydia/gonorrhoea/syphilis data were obtained from the Centers for Disease Control and Prevention AtlasPlus, county demographic data from the Area Health Resource File and rurality classifications from the Department of Agriculture. Data were subjected to analysis by t-test, χ² and linear regression to assess geographical disparities in incidence and their association with measures of rurality, population and primary care density, and education.

RESULTS

Overall rates for each infection were significantly higher in DR versus non-DR counties (577.8 vs 330.1/100 000 for chlamydia; 142.8 vs 61.8 for gonorrhoea; 3.6 vs 1.7 for syphilis; all P<0.001) and for nearly every infection for every individual state. DR rates for each infection were near-universally significantly increased for every level of rurality (nine levels) and population density (quintiles). Regression found that primary care and population density and HS graduation rates were significantly associated with each, though model predictive abilities were poor.

CONCLUSIONS

The nearly 10 million people living in the DR face significant disparities in the incidence of chlamydia, gonorrhoea and syphilis-in many instances a near-doubling of risk. Our findings suggest that resource-constrained areas, as measured by rurality, should be considered a priority for future intervention efforts.

Spatiotemporal distribution and determinants of gonorrhea infections in mainland China: a panel data analysis

OBJECTIVES

Gonorrhea remains a major public health concern worldwide. This study aims to explore the spatiotemporal distribution and sociodemographic determinants of gonorrhea rates during 2004-2014 in mainland China.

STUDY DESIGN

Space-time scan statistics and spatial panel regression model.

METHODS

The gonorrhea infection data and sociodemographic data during 2004-2014 at the provincial level in mainland China were extracted from the China Public Health Science Data Center and China Statistical Yearbooks, respectively. The space-time scan statistics were used to identify the high-risk clusters of gonorrhea, and the spatial panel regression model was adopted to examine the sociodemographic determinants.

RESULTS

One most likely and five secondary high-risk clusters of gonorrhea rates were identified, which were mainly located in southern and eastern China. The regions with higher GDP per capita, larger floating population, less access to healthcare, higher male-female ratio, and higher divorce rate were more likely to become high-risk areas of gonorrhea.

CONCLUSIONS

Gonorrhea rates were distributed unevenly through space and time and affected by various sociodemographic variables. The space-time scan statistics and spatial panel regression are viable tools for identifying clusters and examining determinants of gonorrhea rates. The findings provide valuable implications for developing targeted prevention and control programs in public health practice.

Mapping hot spots of breast cancer mortality in the United States: place matters for Blacks and Hispanics

PURPOSE

The goals of this study were to identify geographic and racial/ethnic variation in breast cancer mortality, and evaluate whether observed geographic differences are explained by county-level characteristics.

METHODS

We analyzed data on breast cancer deaths among women in 3,108 contiguous United States (US) counties from years 2000 through 2015. We applied novel geospatial methods and identified hot spot counties based on breast cancer mortality rates. We assessed differences in county-level characteristics between hot spot and other counties using Wilcoxon rank-sum test and Spearman correlation, and stratified all analysis by race/ethnicity.

RESULTS

Among all women, 80 of 3,108 (2.57%) contiguous US counties were deemed hot spots for breast cancer mortality with the majority located in the southern region of the US (72.50%, p value < 0.001). In race/ethnicity-specific analyses, 119 (3.83%) hot spot counties were identified for NH-Black women, with the majority being located in southern states (98.32%, p value < 0.001). Among Hispanic women, there were 83 (2.67%) hot spot counties and the majority was located in the southwest region of the US (southern = 61.45%, western = 33.73%, p value < 0.001). We did not observe definitive geographic patterns in breast cancer mortality for NH-White women. Hot spot counties were more likely to have residents with lower education, lower household income, higher unemployment rates, higher uninsured population, and higher proportion indicating cost as a barrier to medical care.

CONCLUSIONS

We observed geographic and racial/ethnic disparities in breast cancer mortality: NH-Black and Hispanic breast cancer deaths were more concentrated in southern, lower SES counties.