Spatial analysis and mapping of sexually transmitted diseases to optimise intervention and prevention strategies

A Bayesian maximum entropy model for predicting tsetse ecological distributions

BACKGROUND

African trypanosomiasis is a tsetse-borne parasitic infection that affects humans, wildlife, and domesticated animals. Tsetse flies are endemic to much of Sub-Saharan Africa and a spatial and temporal understanding of tsetse habitat can aid surveillance and support disease risk management. Problematically, current fine spatial resolution remote sensing data are delivered with a temporal lag and are relatively coarse temporal resolution (e.g., 16 days), which results in disease control models often targeting incorrect places. The goal of this study was to devise a heuristic for identifying tsetse habitat (at a fine spatial resolution) into the future and in the temporal gaps where remote sensing and proximal data fail to supply information.

METHODS

This paper introduces a generalizable and scalable open-access version of the tsetse ecological distribution (TED) model used to predict tsetse distributions across space and time, and contributes a geospatial Bayesian Maximum Entropy (BME) prediction model trained by TED output data to forecast where, herein the Morsitans group of tsetse, persist in Kenya, a method that mitigates the temporal lag problem. This model facilitates identification of tsetse habitat and provides critical information to control tsetse, mitigate the impact of trypanosomiasis on vulnerable human and animal populations, and guide disease minimization in places with ephemeral tsetse. Moreover, this BME analysis is one of the first to utilize cluster and parallel computing along with a Monte Carlo analysis to optimize BME computations. This allows for the analysis of an exceptionally large dataset (over 2 billion data points) at a finer resolution and larger spatiotemporal scale than what had previously been possible.

RESULTS

Under the most conservative assessment for Kenya, the BME kriging analysis showed an overall prediction accuracy of 74.8% (limited to the maximum suitability extent). In predicting tsetse distribution outcomes for the entire country the BME kriging analysis was 97% accurate in its forecasts.

CONCLUSIONS

This work offers a solution to the persistent temporal data gap in accurate and spatially precise rainfall predictions and the delayed processing of remotely sensed data collectively in the - 45 days past to + 180 days future temporal window. As is shown here, the BME model is a reliable alternative for forecasting future tsetse distributions to allow preplanning for tsetse control. Furthermore, this model provides guidance on disease control that would otherwise not be available. These 'big data' BME methods are particularly useful for large domain studies. Considering that past BME studies required reduction of the spatiotemporal grid to facilitate analysis. Both the GEE-TED and the BME libraries have been made open source to enable reproducibility and offer continual updates into the future as new remotely sensed data become available.

Progression of a large syphilis outbreak in rural North Carolina through space and time: Application of a Bayesian Maximum Entropy graphical user interface

In 2001, the primary and secondary syphilis incidence rate in rural Columbus County, North Carolina was the highest in the nation. To understand the development of syphilis outbreaks in rural areas, we developed and used the Bayesian Maximum Entropy Graphical User Interface (BMEGUI) to map syphilis incidence rates from 1999-2004 in seven adjacent counties in North Carolina. Using BMEGUI, incidence rate maps were constructed for two aggregation scales (ZIP code and census tract) with two approaches (Poisson and simple kriging). The BME maps revealed the outbreak was initially localized in Robeson County and possibly connected to more urban endemic cases in adjacent Cumberland County. The outbreak spread to rural Columbus County in a leapfrog pattern with the subsequent development of a visible low incidence spatial corridor linking Roberson County with the rural areas of Columbus County. Though the data are from the early 2000s, they remain pertinent, as the combination of spatial data with the extensive sexual network analyses, particularly in rural areas gives thorough insights which have not been replicated in the past two decades. These observations support an important role for the connection of micropolitan areas with neighboring rural areas in the spread of syphilis. Public health interventions focusing on urban and micropolitan areas may effectively limit syphilis indirectly in nearby rural areas.

[Spatial analysis as a tool for identification of priority intervention areas for syphilis prevention]

The spatial analysis of syphilis constitutes a tool capable of contributing to the establishment of guidelines for action in priority geographic areas for preventive intervention. The scope of the article was to describe the scientific evidence that used geoprocessing as a tool to identify risk areas for syphilis. This is an integrative review of the literature, carried out in the Medline/PubMed, Scopus, Web of Science, Lilacs, Ibecs, Cochrane Library Portal, SciELO, Cuiden and Bdenf databases through cross-referencing between the key words "syphilis," "spatial analysis," "geographical information system," "health education" and "geographical mapping." A total of 13 articles were analyzed and in most of them syphilis cases were distributed in a heterogeneous manner, not obeying a unique epidemiological profile in relation to the units of analysis. Discordant ecological and spatial effects between syphilis and HIV and the viability of the integrated screening of syphilis with other diseases was revealed. Efficacy and ability of spatial analysis to target specific educational interventions for each reality were revealed, avoiding investment in geographically non-priority areas for syphilis control.

Quantifying geographic heterogeneity in TB incidence and the potential impact of geographically targeted interventions in South and North City Corporations of Dhaka, Bangladesh: a model-based study

In rapidly growing and high-burden urban centres, identifying tuberculosis (TB) transmission hotspots and understanding the potential impact of interventions can inform future control and prevention strategies. Using data on local demography, TB reports and patient reporting patterns in Dhaka South City Corporation (DSCC) and Dhaka North City Corporation (DNCC), Bangladesh, between 2010 and 2017, we developed maps of TB reporting rates across wards in DSCC and DNCC and identified wards with high rates of reported TB (i.e. 'hotspots') in DSCC and DNCC. We developed ward-level transmission models and estimated the potential epidemiological impact of three TB interventions: active case finding (ACF), mass preventive therapy (PT) and a combination of ACF and PT, implemented either citywide or targeted to high-incidence hotspots. There was substantial geographic heterogeneity in the estimated TB incidence in both DSCC and DNCC: incidence in the highest-incidence wards was over ten times higher than in the lowest-incidence wards in each city corporation. ACF, PT and combined ACF plus PT delivered to 10% of the population reduced TB incidence by a projected 7%-9%, 13%-15% and 19%-23% over five years, respectively. Targeting TB hotspots increased the projected reduction in TB incidence achieved by each intervention 1.4- to 1.8-fold. The geographical pattern of TB notifications suggests high levels of ongoing TB transmission in DSCC and DNCC, with substantial heterogeneity at the ward level. Interventions that reduce transmission are likely to be highly effective and incorporating notification data at the local level can further improve intervention efficiency.

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.

The burden of pre-extensively and extensively drug-resistant tuberculosis among MDR-TB patients in the Amhara region, Ethiopia

BACKGROUND

The emergence of pre-extensively and extensively drug-resistant tuberculosis (Pre-XDR/XDR-TB) is the major hurdle for TB prevention and care programs especially in developing countries like Ethiopia. The less emphasis on universal access to laboratory techniques for the rapid diagnosis of TB and drug susceptibility testing (DST) makes the management of MDR-TB a challenge. Early detection of second line anti-TB drugs resistance is essential to reduce transmission of Pre-XDR/XDR-TB strains and adjusting the treatment regimen in MDR-TB.

OBJECTIVE

To determine the prevalence and resistance pattern of Pre-XDR- and XDR-TB among MDR-TB patients in the Amhara region, Ethiopia.

METHODS

A cross sectional study was carried out in nine MDR-TB treatment centers in the Amhara region. Sputum samples were collected from all pulmonary rifampicin resistant (RR) or MDR-TB patients prior to anti-TB treatment. Lӧwenstein-Jensen (LJ) culture, Ziehl Neelsen (ZN) smear, MTBDRplus and MTBDRsl assays were performed according to the standard procedures. Data were analyzed using SPSS 20 software. Chi-square and/or Fishers exact test was employed.

RESULTS

Overall, 6.3% of MDR-TB isolates were resistant to at least one second line drugs. Pre-XDR-TB and XDR-TB isolates accounted 5.7% and 0.6% respectively. Moreover, 3.4% were resistant to FQs and 3.4% were resistant to second line injectable drugs. All isolates were susceptible for low level kanamycin. Almost all pre-XDR-TB strains (90%) were previously treated with anti-TB drugs. Drug resistant Mycobacterium tuberculosis isolates were disproportionately distributed in districts of the Amhara region and the majorities were concentrated in urban areas.

CONCLUSIONS

The high proportion of MDR-TB patients resistant to at least one second line drug is alarming. Strengthening the laboratory facilities to monitor pre-XDR and XDR-TB patients is crucial. The TB programs need to give emphasis on the effective and rational use of second line drugs for newly diagnosed MDR-TB patients to prevent the emergence of pre-XDR/XDR-TB strains.

Measuring and Visualizing Chlamydia and Gonorrhea Inequality: An Informatics Approach Using Geographical Information Systems

BACKGROUND

Health inequality measurements are vital in understanding disease patterns in identifying high-risk patients and implementing effective intervention programs to treat and manage sexually transmitted diseases.

OBJECTIVES

To measure and identify inequalities among chlamydia and gonorrhea rates using Gini coefficient measurements and spatial visualization mapping from geographical information systems. Additionally, we seek to examine trends of disease rate distribution longitudinally over a ten-year period for an urbanized county.

METHODS

Chlamydia and gonorrhea data from January 2005 to December 2014 were collected from the Indiana Network for Patient Care, a health information exchange system that gathers patient data from electronic health records. The Gini coefficient was used to calculate the magnitude of inequality in disease rates. Spatial visualization mapping and decile categorization of disease rates were conducted to identify locations where high and low rates of disease persisted and to visualize differences in inequality. A multiple comparisons ANOVA test was conducted to determine if Gini coefficient values were statistically different between townships and time periods during the study.

RESULTS

Our analyses show that chlamydia and gonorrhea rates are not evenly distributed. Inequalities in disease rates existed for different areas of the county with higher disease rates occurring near the center of the county. Inequality in gonorrhea rates were higher than chlamydia rates. Disease rates were statistically different when geographical locations or townships were compared to each other (p < 0.0001) but not for different years or time periods (p = 0.5152).

CONCLUSION

The ability to use Gini coefficients combined with spatial visualization techniques presented a valuable opportunity to analyze information from health information systems in investigating health inequalities. Knowledge from this study can benefit and improve health quality, delivery of services, and intervention programs while managing healthcare costs.

Trends in the Epidemiology of Sexually Transmitted Disease, Acquired Immune Deficiency Syndrome (AIDS), Gonorrhea, and Syphilis, in the 31 Provinces of Mainland China

BACKGROUND This study aimed to investigate trends in the epidemiology of the leading sexually transmitted diseases (STDs), acquired immune deficiency syndrome (AIDS), gonorrhea, and syphilis, in the 31 provinces of mainland China. MATERIAL AND METHODS This retrospective study analyzed the incidence data of STDs from official reports in China between 2004 and 2016. The grey model first order one variable, or GM (1,1), time series forecasting model for epidemiological studies predicted the incidence of STDs based on the annual incidence reports from 31 Chinese mainland provinces. Hierarchical cluster analysis was used to group the prevalence of STDs within each province. RESULTS The prediction accuracy of the GM (1,1) model was high, based on data during the 13 years between 2004 and 2016. The model predicted that the incidence rates of AIDS and syphilis would continue to increase over the next two years. Cluster analysis showed that 31 provinces could be classified into four clusters according to similarities in the incidence of STDs. Group A (Sinkiang Province) had the highest reported prevalence of syphilis. Group B included provinces with a higher incidence of gonorrhea, mainly in the southeast coast of China. Group C consisted of southwest provinces with a higher incidence of AIDS. CONCLUSIONS The GM (1,1) model was predictive for the incidence of STDs in 31 provinces in China. The predicted incidence rates of AIDS and syphilis showed an upward trend. Regional distribution of the major STDs highlights the need for targeted prevention and control programs.

Applying core theory and spatial analysis to identify hepatitis C virus infection "core areas" in British Columbia, Canada

"Core areas" of transmission for bacterial sexually transmitted infections have been identified. However, it is unclear whether core areas apply to viral infections, such as hepatitis C virus (HCV). We used geographic mapping and spatial analysis to identify distinct core areas of HCV infection in British Columbia (BC) using the BC Hepatitis Testers Cohort (BC-HTC), 1990-2013. The BC-HTC includes all BC residents tested for HCV (~1.5 million; 1990-2013). Core HCV infection areas were identified spatially and temporally for five time periods (1990-1993, 1994-1998, 1999-2003, 2004-2008 and 2009-2013) through thematic mapping, Kernel Density Estimation, Hotspot analysis and cluster analysis at the Census dissemination area level in ArcGIS and SatScan. HCV infection core areas were consistently identified. HCV core areas expanded from the downtown of major cities in different regions of BC (Metro Vancouver, Vancouver Island, and Northern BC; 1990-1998), to smaller cities in Metro Vancouver and Interior BC (2000 onwards). Statistically significant clusters, or hotspots, were also observed for downtown Vancouver, Northern BC (Prince George) and Vancouver Island from 1990 to 2008 with expansion to other urban areas in Metro Vancouver from 1990-2013. Statistically significant clusters persisted after adjustment for injection drug use, number of HCV tests, age, sex, material and social deprivation. Persistence of areas with high HCV diagnoses rates in Vancouver and Prince George supports the theory of core areas of HCV transmission. Identification of core areas can inform prevention, care and treatment programme interventions and evaluate their impact over time.

Regional differences in chlamydia and gonorrhoeae positivity rate among heterosexual STI clinic visitors in the Netherlands: contribution of client and regional characteristics as assessed by cross-sectional surveillance data

OBJECTIVES

To assess to what extent triage criteria, client and regional characteristics explain regional differences in Chlamydia trachomatis (Ct) and Neisseria gonorrhoeae (Ng) positivity in sexually transmitted infection (STI) clinics.

DESIGN

Retrospective cross-sectional study on the Dutch STI surveillance database of all 24 STI clinics.

PARTICIPANTS

STI clinic visits of heterosexual persons in 2015 with a Ct (n=101 495) and/or Ng test (n=101 081).

PRIMARY OUTCOME MEASURE

Ct and Ng positivity and 95% CI was assessed for each STI clinic. Two-level logistic regression analyses were performed to calculate the percentage change in regional variance (PCV) after adding triage criteria (model 1), other client characteristics (model 2) and regional characteristics (model 3) to the empty model. The contribution of single characteristics was determined after removing them from model 3.

RESULTS

Ct positivity was 14.9% and ranged from 12.6% to 20.0% regionally. Ng positivity was 1.7% and ranged from 0.8% to 3.8% regionally. For Ct, the PCV was 11.7% in model 1, 32.2% in model 2% and 59.3% in model 3. Age, notified for Ct (triage), level of education (other characteristics) and regional degree of urbanisation (region) explained variance most. For Ng, the PCV was 38.7% in model 1, 61.2% in model 2% and 69.1% in model 3. Ethnicity (triage), partner in risk group, level of education and neighbourhood (other characteristics) and regional socioeconomic status (SES) explained variance most. A significant part of regional variance remained unexplained.

CONCLUSIONS

Regional variance was explained by differences in client characteristics, indicating that triage and self-selection influence positivity rates in the surveillance data.Clustering of Ng in low SES regions additionally explained regional variance in Ng; targeted interventions in low SES regions may assist Ng control. Including educational level as triage criterion is recommended. Studies incorporating prevalence data are needed to assess whether regional clustering underlies unexplained regional variance.

Associations between county-level voter turnout, county-level felony voter disenfranchisement, and sexually transmitted infections among women in the Southern United States

PURPOSE

Voting may play a critical role in the allocation of social and structural resources to communities, which in turn shapes neighborhood environments, and ultimately, an individual's sexually transmitted infection (STI) risk. We assessed relationships among county-level voter turnout and felony voter disenfranchisement, and STIs.

METHODS

This cross-sectional multilevel analysis included 666 women in Alabama, Florida, Georgia, Mississippi, and North Carolina enrolled in the Women's Interagency HIV Study between 2013 and 2015. Having a baseline bacterial STI (chlamydia, gonorrhea, trichomoniasis, or early syphilis) was determined by laboratory testing. We used generalized estimating equations to test relationships between county-level voter turnout in the 2012 general election, county-level percentage of felony disenfranchised voters, and STI prevalence.

RESULTS

Eleven percent of participants had an STI. Higher voter turnout corresponded to lower STI prevalence (prevalence ratio = 0.84, 95% confidence interval = 0.73-0.96 per 4 percentage point higher turnout). Greater felony voter disenfranchisement corresponded to higher STI prevalence (prevalence ratio = 1.89, 95% confidence interval = 1.10-3.24 per 4 percentage point higher disenfranchisement).

CONCLUSIONS

STI prevalence was inversely associated with voter turnout and positively associated with felony voter disenfranchisement. Research should assess causality and mechanisms through which civic engagement shapes sexual health. Expanding political participation, including eliminating discriminatory voting laws, could influence sexual health.

Neighborhood Health Care Access and Sexually Transmitted Infections Among Women in the Southern United States: A Cross-Sectional Multilevel Analysis

INTRODUCTION

The United States has experienced an increase in reportable sexually transmitted infections (STIs) while simultaneously experiencing a decline in safety net services for STI testing and treatment. This multilevel study assessed relationships between neighborhood-level access to health care and STIs among a predominantly Human Immunodeficiency Virus (HIV)-seropositive cohort of women living in the south.

METHODS

This cross-sectional multilevel analysis included baseline data from HIV-seropositive and HIV-seronegative women enrolled in the Women's Interagency HIV Study sites in Alabama, Florida, Georgia, Mississippi, and North Carolina between 2013 and 2015 (N = 666). Administrative data (eg, United States Census) described health care access (eg, percentage of residents with a primary care provider, percentage of residents with health insurance) in the census tracts where women lived. Sexually transmitted infections (chlamydia, gonorrhea, trichomoniasis, or early syphilis) were diagnosed using laboratory testing. Generalized estimating equations were used to determine relationships between tract-level characteristics and STIs. Analyses were conducted using SAS 9.4.

RESULTS

Seventy percent of participants were HIV-seropositive. Eleven percent of participants had an STI. A 4-unit increase in the percentage of residents with a primary care provider was associated with 39% lower STI risk (risk ratio, 0.61, 95% confidence interval, 0.38-0.99). The percentage of tract residents with health insurance was not associated with STIs (risk ratio, 0.98, 95% confidence interval, 0.91-1.05). Relationships did not vary by HIV status.

CONCLUSIONS

Greater neighborhood health care access was associated with fewer STIs. Research should establish the causality of this relationship and pathways through which neighborhood health care access influences STIs. Structural interventions and programs increasing linkage to care may reduce STIs.

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.

One-step estimation of networked population size: Respondent-driven capture-recapture with anonymity

Size estimation is particularly important for populations whose members experience disproportionate health issues or pose elevated health risks to the ambient social structures in which they are embedded. Efforts to derive size estimates are often frustrated when the population is hidden or hard-to-reach in ways that preclude conventional survey strategies, as is the case when social stigma is associated with group membership or when group members are involved in illegal activities. This paper extends prior research on the problem of network population size estimation, building on established survey/sampling methodologies commonly used with hard-to-reach groups. Three novel one-step, network-based population size estimators are presented, for use in the context of uniform random sampling, respondent-driven sampling, and when networks exhibit significant clustering effects. We give provably sufficient conditions for the consistency of these estimators in large configuration networks. Simulation experiments across a wide range of synthetic network topologies validate the performance of the estimators, which also perform well on a real-world location-based social networking data set with significant clustering. Finally, the proposed schemes are extended to allow them to be used in settings where participant anonymity is required. Systematic experiments show favorable tradeoffs between anonymity guarantees and estimator performance. Taken together, we demonstrate that reasonable population size estimates are derived from anonymous respondent driven samples of 250-750 individuals, within ambient populations of 5,000-40,000. The method thus represents a novel and cost-effective means for health planners and those agencies concerned with health and disease surveillance to estimate the size of hidden populations. We discuss limitations and future work in the concluding section.

Modelling individual vulnerability to sexually transmitted infections to optimise intervention strategies: analysis of surveillance data from Kalamazoo County, Michigan, USA

OBJECTIVE

We modelled individual vulnerability to STI using personal history of infection and neighbourhood characteristics.

METHODS

Retrospective chlamydia and gonorrhoea data of reported confirmed cases from Kalamazoo County, Michigan for 2012 through 2014 were analysed. Unique IDs were generated from the surveillance data in collaboration with local health officials to track the individual STI histories. We then examine the concept that individuals with similar STI histories form a 'peer' group. These peer group include: (1) individuals with a single chlamydia; (2) individuals with single gonorrhoea; (3) individuals with repeated cases of one type of STI and (4) individuals that were diagnosed with both infections during the study period. Using Kernel density estimation, we generated densities for each peer group and assigned the intensity of the infection to the location of the individual. Finally, the individual vulnerability was characterised through ordinary least square regression (OLS) using demographics and socioeconomic variables.

RESULTS

In an OLS regression adjusted for frequency of infection, individual vulnerability to STI was only consistently significant for race and neighbourhood-level socioeconomic status (SES) in all the models under consideration. In addition, we identified six areas in three townships in Kalamazoo County that could be considered for unique interventions based on overlap patterns among peer groups.

CONCLUSIONS

The results provide evidence that individual vulnerability to STI has some dependency on individual contextual (race) and exogenous factors at the neighbourhood level such as SES, regardless of that individual's personal history of infection. We suggest place-based intervention strategies be adopted for planning STI interventions instead of current universal screening of at-risk populations.

Assessing Spatial Relationships Between Rates of Crime and Rates of Gonorrhea and Chlamydia in Chicago, 2012

Sexually transmitted infections (STIs) remain serious public health problems particularly in urban environments in the USA. Despite accumulating research into the role of aggregate rates of crime in shaping rates of STIs, few studies account for spatial dependence in the structure of geographical data. Using multiple spatial analysis methodologies, the following study investigated spatial patterns in community area rates of violent, drug, and property crimes and rates of infection of gonorrhea and chlamydia in 77 community areas in Chicago. Moran's I analyses confirmed global spatial dependence and statistically significant clusters of STI. Spatial lag regression analyses found that greater rates of drug crimes were associated with higher rates of chlamydia and gonorrhea after adjusting for percent in poverty and racial composition. Finally, a weighted geographic regression identified regions in the urban environment in which local regression coefficient values diverged from their global estimates. Spatial heterogeneity of STIs suggest that public health interventions must be targeted to specific areas of the urban environment with particular attention to substance use.

Relationships between neighbourhood characteristics and current STI status among HIV-infected and HIV-uninfected women living in the Southern USA: a cross-sectional multilevel analysis

OBJECTIVES

Neighbourhood characteristics (eg, high poverty rates) are associated with STIs among HIV-uninfected women in the USA. However, no multilevel analyses investigating the associations between neighbourhood exposures and STIs have explored these relationships among women living with HIV infection. The objectives of this study were to: (1) examine relationships between neighbourhood characteristics and current STI status and (2) investigate whether the magnitudes and directions of these relationships varied by HIV status in a predominantly HIV-infected cohort of women living in the Southern USA.

METHODS

This cross-sectional multilevel analysis tests relationships between census tract characteristics and current STI status using data from 737 women enrolled at the Women's Interagency HIV Study's southern sites (530 HIV-infected and 207 HIV-uninfected women). Administrative data (eg, US Census) described the census tract-level social disorder (eg, violent crime rate) and social disadvantage (eg, alcohol outlet density) where women lived. Participant-level data were gathered via survey. Testing positive for a current STI was defined as a laboratory-confirmed diagnosis of chlamydia, gonorrhoea, trichomoniasis or syphilis. Hierarchical generalised linear models were used to determine relationships between tract-level characteristics and current STI status, and to test whether these relationships varied by HIV status.

RESULTS

Eleven per cent of participants tested positive for at least one current STI. Greater tract-level social disorder (OR=1.34, 95% CI 0.99 to 1.87) and social disadvantage (OR=1.34, 95% CI 0.96 to 1.86) were associated with having a current STI. There was no evidence of additive or multiplicative interaction between tract-level characteristics and HIV status.

CONCLUSIONS

Findings suggest that neighbourhood characteristics may be associated with current STIs among women living in the South, and that relationships do not vary by HIV status. Future research should establish the temporality of these relationships and explore pathways through which neighbourhoods create vulnerability to STIs.

TRIAL REGISTRATION NUMBER

NCT00000797; results.

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

PURPOSE

We used a combination of hot spot analysis (HSA) and spatial regression to examine county-level hot spot correlates for the most commonly reported nonviral sexually transmitted infections (STIs) in the 48 contiguous states in the United States (US).

METHODS

We obtained reported county-level total case rates of chlamydia, gonorrhea, and primary and secondary (P&S) syphilis in all counties in the 48 contiguous states from national surveillance data and computed temporally smoothed rates using 2008-2012 data. Covariates were obtained from county-level multiyear (2008-2012) American Community Surveys from the US census. We conducted HSA to identify hot spot counties for all three STIs. We then applied spatial logistic regression with the spatial error model to determine the association between the identified hot spots and the covariates.

RESULTS

HSA indicated that ≥84% of hot spots for each STI were in the South. Spatial regression results indicated that, a 10-unit increase in the percentage of Black non-Hispanics was associated with ≈42% (P < 0.01) [≈22% (P < 0.01), for Hispanics] increase in the odds of being a hot spot county for chlamydia and gonorrhea, and ≈27% (P < 0.01) [≈11% (P < 0.01) for Hispanics] for P&S syphilis. Compared with the other regions (West, Midwest, and Northeast), counties in the South were 6.5 (P < 0.01; chlamydia), 9.6 (P < 0.01; gonorrhea), and 4.7 (P < 0.01; P&S syphilis) times more likely to be hot spots.

CONCLUSION

Our study provides important information on hot spot clusters of nonviral STIs in the entire United States, including associations between hot spot counties and sociodemographic factors.

Spatial patterns of multidrug resistant tuberculosis and relationships to socio-economic, demographic and household factors in northwest Ethiopia

Background

Understanding the geographical distribution of multidrug-resistant tuberculosis (MDR-TB) in high TB burden countries such as Ethiopia is crucial for effective control of TB epidemics in these countries, and thus globally. We present the first spatial analysis of multidrug resistant tuberculosis, and its relationship to socio-economic, demographic and household factors in northwest Ethiopia.

Methods

An ecological study was conducted using data on patients diagnosed with MDR-TB at the University of Gondar Hospital MDR-TB treatment centre, for the period 2010 to 2015. District level population data were extracted from the Ethiopia National and Regional Census Report. Spatial autocorrelation was explored using Moran’s I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. A multivariate Poisson regression model was developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using a Bayesian Markov chain Monte Carlo (MCMC) simulation approach with Gibbs sampling, in WinBUGS.

Results

A total of 264 MDR-TB patients were included in the analysis. The overall crude incidence rate of MDR-TB for the six-year period was 3.0 cases per 100,000 population. The highest incidence rate was observed in Metema (21 cases per 100,000 population) and Humera (18 cases per 100,000 population) districts; whereas nine districts had zero cases. Spatial clustering of MDR-TB was observed in districts located in the Ethiopia-Sudan and Ethiopia-Eritrea border regions, where large numbers of seasonal migrants live. Spatial clustering of MDR-TB was positively associated with urbanization (RR: 1.02; 95%CI: 1.01, 1.04) and the percentage of men (RR: 1.58; 95% CI: 1.26, 1.99) in the districts; after accounting for these factors there was no residual spatial clustering.

Conclusion

Spatial clustering of MDR-TB, fully explained by demographic factors (urbanization and percent male), was detected in the border regions of northwest Ethiopia, in locations where seasonal migrants live and work. Cross-border initiatives including options for mobile TB treatment and follow up are important for the effective control of MDR-TB in the region.

Influence of Detection Method and Study Area Scale on Syphilis Cluster Identification in North Carolina

BACKGROUND

Identifying geographical clusters of sexually transmitted infections can aid in targeting prevention and control efforts. However, detectable clusters can vary between detection methods because of different underlying assumptions. Furthermore, because disease burden is not geographically homogenous, the reference population is sensitive to the study area scale, affecting cluster outcomes. We investigated the influence of cluster detection method and geographical scale on syphilis cluster detection in Mecklenburg County, North Carolina.

METHODS

We analyzed primary and secondary syphilis cases reported in North Carolina (2003-2010). Primary and secondary syphilis incidence rates were estimated using census tract-level population estimates. We used 2 cluster detection methods: local Moran's I using an areal adjacency matrix and Kulldorff's spatial scan statistic using a variable size moving circular window. We evaluated 3 study area scales: North Carolina, Piedmont region, and Mecklenburg County. We focused our investigation on Mecklenburg, an urban county with historically high syphilis rates.

RESULTS

Syphilis clusters detected using local Moran's I and Kulldorff's scan statistic overlapped but varied in size and composition. Because we reduced the scale to a high-incidence urban area, the reference syphilis rate increased, leading to the identification of smaller clusters with higher incidence. Cluster demographic characteristics differed when the study area was reduced to a high-incidence urban county.

CONCLUSIONS

Our results underscore the importance of selecting the correct scale for analysis to more precisely identify areas with high disease burden. A more complete understanding of high-burden cluster location can inform resource allocation for geographically targeted sexually transmitted infection interventions.

Confidentiality considerations for use of social-spatial data on the social determinants of health: Sexual and reproductive health case study

Understanding whether and how the places where people live, work, and play are associated with health behaviors and health is essential to understanding the social determinants of health. However, social-spatial data which link a person and their attributes to a geographic location (e.g., home address) create potential confidentiality risks. Despite the growing body of literature describing approaches to protect individual confidentiality when utilizing social-spatial data, peer-reviewed manuscripts displaying identifiable individual point data or quasi-identifiers (attributes associated with the individual or disease that narrow identification) in maps persist, suggesting that knowledge has not been effectively translated into public health research practices. Using sexual and reproductive health as a case study, we explore the extent to which maps appearing in recent peer-reviewed publications risk participant confidentiality. Our scoping review of sexual and reproductive health literature published and indexed in PubMed between January 1, 2013 and September 1, 2015 identified 45 manuscripts displaying participant data in maps as points or small-population geographic units, spanning 26 journals and representing studies conducted in 20 countries. Notably, 56% (13/23) of publications presenting point data on maps either did not describe approaches used to mask data or masked data inadequately. Furthermore, 18% (4/22) of publications displaying data using small-population geographic units included at least two quasi-identifiers. These findings highlight the need for heightened education for researchers, reviewers, and editorial teams. We aim to provide readers with a primer on key confidentiality considerations when utilizing linked social-spatial data for visualizing results. Given the widespread availability of place-based data and the ease of creating maps, it is critically important to raise awareness on when social-spatial data constitute protected health information, best practices for masking geographic identifiers, and methods of balancing disclosure risk and scientific utility. We conclude with recommendations to support the preservation of confidentiality when disseminating results.

Assessment: A Core Function for Implementing Effective Interventions in Sexually Transmitted Disease Control Programs

Assessment is a core function in sexually transmitted disease (STD) prevention and control programs. Assessment is more than reviewing case report data; it includes taking into consideration an array of data of various sources and types to be able to respond to emerging disease threats, align human and financial resources, and plan for the future. In this article, we outline key assessment domains, data sources, activities, and methods for STD programs. We present an illustrative case study of how assessment can be used to identify effective interventions for STD control.

Improving Surveillance of Sexually Transmitted Diseases through Geocoded Morbidity Assignment

OBJECTIVES

We assessed the added value of using a geocoder to improve sexually transmitted disease (STD) surveillance data and decision support through redistribution of inaccurately assigned morbidity in Richmond, Virginia.

METHODS

Virginia initiated geocoding of STD data as a data quality tool in 2002. Geocoded output files were assessed and discordant proportions of reported gonorrhea and chlamydia morbidity were reassigned appropriately for the city of Richmond, Chesterfield County, and Henrico County (2002 to 2006). We used Chi-square analysis to compare assignment proportions and calculated crude odds ratios for 2006 data to estimate increased case reassignment likelihood.

RESULTS

From 2002 to 2006, 149,229 cases of gonorrhea and chlamydia were reported within the Commonwealth of Virginia. Of the reported morbidity, 81% of cases (n=120,875) were successfully geocoded; 7% (n=8,461) of geocoded addresses were reassigned. Approximately 76% (n=6,412) of all reassigned cases occurred within Richmond and Chesterfield and Henrico counties. In 2006, 84% (n=654) of reassigned cases in this tri-city/county area were initially reported as Richmond morbidity. Data quality improvements reduced Richmond's artificially inflated morbidity by 18% and increased Chesterfield and Henrico morbidity by 17% and 55%, respectively. Richmond morbidity was three times more likely to be reassigned than Chesterfield cases (odds ratio [OR] = 2.93, 95% confidence interval [CI] 2.21, 3.90), and two times more likely than Henrico cases (OR=2.12, 95% CI 1.63, 2.76). Richmond's number one national rank for STD rates was reduced beginning in 2002.

CONCLUSIONS

Declining rates of STDs were statistically associated with geocoded morbidity reassignments. Implementation of this data quality business process has improved epidemiologic analyses, prevention planning, and assessment of resource allocations. The reduction in Richmond's national STD rankings is indicative of the effect geocoding can have on surveillance data.

Strategies and methodological basics for prevention of sexually transmitted infections in the high-risk subpopulations

A review article presents theoretical basics of the development of innovative STI prevention programs for high-risk groups Urgent need for integrated multidisciplinary preventive strategies aimed at correcting risky behaviors and adapted for the particular vulnerable populations is justified.

Spatial distribution and cluster analysis of retail drug shop characteristics and antimalarial behaviors as reported by private medicine retailers in western Kenya: informing future interventions

Background

Efforts to improve malaria case management in sub-Saharan Africa have shifted focus to private antimalarial retailers to increase access to appropriate treatment. Demands to decrease intervention cost while increasing efficacy requires interventions tailored to geographic regions with demonstrated need. Cluster analysis presents an opportunity to meet this demand, but has not been applied to the retail sector or antimalarial retailer behaviors. This research conducted cluster analysis on medicine retailer behaviors in Kenya, to improve malaria case management and inform future interventions.

Methods

Ninety-seven surveys were collected from medicine retailers working in the Webuye Health and Demographic Surveillance Site. Survey items included retailer training, education, antimalarial drug knowledge, recommending behavior, sales, and shop characteristics, and were analyzed using Kulldorff’s spatial scan statistic. The Bernoulli purely spatial model for binomial data was used, comparing cases to controls. Statistical significance of found clusters was tested with a likelihood ratio test, using the null hypothesis of no clustering, and a p value based on 999 Monte Carlo simulations. The null hypothesis was rejected with p values of 0.05 or less.

Results

A statistically significant cluster of fewer than expected pharmacy-trained retailers was found (RR = .09, p = .001) when compared to the expected random distribution. Drug recommending behavior also yielded a statistically significant cluster, with fewer than expected retailers recommending the correct antimalarial medication to adults (RR = .018, p = .01), and fewer than expected shops selling that medication more often than outdated antimalarials when compared to random distribution (RR = 0.23, p = .007). All three of these clusters were co-located, overlapping in the northwest of the study area.

Conclusion

Spatial clustering was found in the data. A concerning amount of correlation was found in one specific region in the study area where multiple behaviors converged in space, highlighting a prime target for interventions. These results also demonstrate the utility of applying geospatial methods in the study of medicine retailer behaviors, making the case for expanding this approach to other regions.

Where You Live Matters: Structural Correlates of HIV Risk Behavior Among Young Men Who Have Sex with Men in Metro Detroit

Structural characteristics are linked to HIV/STI risks, yet few studies have examined the mechanisms through which structural characteristics influence the HIV/STI risk of young men who have sex with men (YMSM). Using data from a cross-sectional survey of YMSM (ages 18-29) living in Detroit Metro (N = 328; 9 % HIV-positive; 49 % Black, 27 % White, 15 % Latino, 9 % Other race), we used multilevel modeling to examine the association between community-level characteristics (e.g., socioeconomic disadvantage; distance to LGBT-affirming institutions) and YMSM's HIV testing behavior and likelihood of engaging in unprotected anal intercourse with serodiscordant partner(s). We accounted for individual-level factors (race/ethnicity, poverty, homelessness, alcohol and marijuana use) and contextual factors (community acceptance and stigma regarding same-sex sexuality). YMSM in neighborhoods with greater disadvantage and nearer to an AIDS Service Organization were more likely to have tested for HIV and less likely to report serodiscordant partners. Community acceptance was associated with having tested for HIV. Efforts to address YMSM's exposure to structural barriers in Detroit Metro are needed to inform HIV prevention strategies from a socioecological perspective.

Spatial epidemiology of the syphilis epidemic in Toronto, Canada

BACKGROUND

Urban centers across Canada and the United States have battled syphilis epidemics with high rates of human immunodeficiency virus (HIV) coinfection for over a decade. We examined the spatial epidemiology of syphilis over time for Toronto (Canada) with the intention of forming new insights and strategies for restoring low syphilis rates.

METHODS

Syphilis incidence rates, HIV-syphilis coinfection, and sexual risk behavior prevalences were estimated and mapped from primary, secondary, early latent syphilis cases reported to Toronto Public Health between January 1, 2006, and December 31, 2010, using ArcGIS 9.0. Geographic clusters of significantly elevated syphilis incidence rates were identified using SaTScan 9.0. The relationship between syphilis incidence rates and sociocultural factors was modeled using the Besag, York, and Mollie model.

RESULTS

Between 2006 and 2010, syphilis incidence rates were high in Toronto's downtown core area, intensified, and spread outward initiating 3 independent outbreak areas. HIV coinfection was high (47%); however, no spatial clustering was identified. Syphilis incidence rates, HIV coinfection, and behavioral risk factors promoting sexually transmitted infection transmission were high outside the core area, suggesting that peripheral sexual networks may be influencing high syphilis infection rates both inside and outside the core.

CONCLUSIONS

Toronto's syphilis epidemic is mature. Response, resources, and intervention activities should target core and noncore areas.

Spatiotemporal approaches to analyzing pedestrian fatalities: the case of Cali, Colombia

OBJECTIVE

Injuries among pedestrians are a major public health concern in Colombian cities such as Cali. This is one of the first studies in Latin America to apply Bayesian maximum entropy (BME) methods to visualize and produce fine-scale, highly accurate estimates of citywide pedestrian fatalities. The purpose of this study is to determine the BME method that best estimates pedestrian mortality rates and reduces statistical noise. We further utilized BME methods to identify and differentiate spatial patterns and persistent versus transient pedestrian mortality hotspots.

METHODS

In this multiyear study, geocoded pedestrian mortality data from the Cali Injury Surveillance System (2008 to 2010) and census data were utilized to accurately visualize and estimate pedestrian fatalities. We investigated the effects of temporal and spatial scales, addressing issues arising from the rarity of pedestrian fatality events using 3 BME methods (simple kriging, Poisson kriging, and uniform model Bayesian maximum entropy). To reduce statistical noise while retaining a fine spatial and temporal scale, data were aggregated over 9-month incidence periods and censal sectors. Based on a cross-validation of BME methods, Poisson kriging was selected as the best BME method. Finally, the spatiotemporal and urban built environment characteristics of Cali pedestrian mortality hotspots were linked to intervention measures provided in Mead et al.'s (2014) pedestrian mortality review.

RESULTS

The BME space-time analysis in Cali resulted in maps displaying hotspots of high pedestrian fatalities extending over small areas with radii of 0.25 to 1.1 km and temporal durations of 1 month to 3 years. Mapping the spatiotemporal distribution of pedestrian mortality rates identified high-priority areas for prevention strategies. The BME results allow us to identify possible intervention strategies according to the persistence and built environment of the hotspot; for example, through enforcement or long-term environmental modifications.

CONCLUSIONS

BME methods provide useful information on the time and place of injuries and can inform policy strategies by isolating priority areas for interventions, contributing to intervention evaluation, and helping to generate hypotheses and identify the preventative strategies that may be suitable to those areas (e.g., street-level methods: pedestrian crossings, enforcement interventions; or citywide approaches: limiting vehicle speeds). This specific information is highly relevant for public health interventions because it provides the ability to target precise locations.

Maximising the effect of combination HIV prevention through prioritisation of the people and places in greatest need: a modelling study

BACKGROUND

Epidemiological data show substantial variation in the risk of HIV infection between communities within African countries. We hypothesised that focusing appropriate interventions on geographies and key populations at high risk of HIV infection could improve the effect of investments in the HIV response.

METHODS

With use of Kenya as a case study, we developed a mathematical model that described the spatiotemporal evolution of the HIV epidemic and that incorporated the demographic, behavioural, and programmatic differences across subnational units. Modelled interventions (male circumcision, behaviour change communication, early antiretoviral therapy, and pre-exposure prophylaxis) could be provided to different population groups according to their risk behaviours or their location. For a given national budget, we compared the effect of a uniform intervention strategy, in which the same complement of interventions is provided across the country, with a focused strategy that tailors the set of interventions and amount of resources allocated to the local epidemiological conditions.

FINDINGS

A uniformly distributed combination of HIV prevention interventions could reduce the total number of new HIV infections by 40% during a 15-year period. With no additional spending, this effect could be increased by 14% during the 15 years-almost 100,000 extra infections, and result in 33% fewer new HIV infections occurring every year by the end of the period if the focused approach is used to tailor resource allocation to reflect patterns in local epidemiology. The cumulative difference in new infections during the 15-year projection period depends on total budget and costs of interventions, and could be as great as 150,000 (a cumulative difference as great as 22%) under different assumptions about the unit costs of intervention.

INTERPRETATION

The focused approach achieves greater effect than the uniform approach despite exactly the same investment. Through prioritisation of the people and locations at greatest risk of infection, and adaption of the interventions to reflect the local epidemiological context, the focused approach could substantially increase the efficiency and effectiveness of investments in HIV prevention.

FUNDING

The Bill & Melinda Gates Foundation and UNAIDS.

Use of a Google Map Tool Embedded in an Internet Survey Instrument: Is it a Valid and Reliable Alternative to Geocoded Address Data?

Background

Men who have sex with men (MSM) in the United States are at high risk for human immunodeficiency virus (HIV) and poor HIV related outcomes. Maps can be used to identify, quantify, and address gaps in access to HIV care among HIV-positive MSM, and tailor intervention programs based on the needs of patients being served.

Objective

The objective of our study was to assess the usability of a Google map question embedded in a Web-based survey among Atlanta-based, HIV-positive MSM, and determine whether it is a valid and reliable alternative to collection of address-based data on residence and last HIV care provider.

Methods

Atlanta-based HIV-positive MSM were recruited through Facebook and from two ongoing studies recruiting primarily through venue-based sampling or peer referral (VBPR). Participants were asked to identify the locations of their residence and last attended HIV care provider using two methods: (1) by entering the street address (gold standard), and (2) “clicking” on the locations using an embedded Google map. Home and provider addresses were geocoded, mapped, and compared with home and provider locations from clicked map points to assess validity. Provider location error values were plotted against home location error values, and a kappa statistic was computed to assess agreement in degree of error in identifying residential location versus provider location.

Results

The median home location error across all participants was 0.65 miles (interquartile range, IQR, 0.10, 2.5 miles), and was lower among Facebook participants (P<.001), whites (P<.001), and those reporting higher annual household income (P=.04). Median home location error was lower, although not statistically significantly, among older men (P=.08) and those with higher educational attainment (P=.05). The median provider location error was 0.32 miles (IQR, 0.12, 1.2 miles), and did not vary significantly by age, recruitment method, race, income, or level of educational attainment. Overall, the kappa was 0.20, indicating poor agreement between the two error measures. However, those recruited through Facebook had a greater level of agreement (κ=0.30) than those recruited through VBPR methods (κ=0.16), demonstrating a greater level of consistency in using the map question to identify home and provider locations for Facebook-recruited individuals.

Conclusions

Most participants were able to click within 1 mile of their home address and their provider’s office, and were not always able to identify the locations on a map consistently, although some differences were observed across recruitment methods. This map tool may serve as the basis of a valid and reliable tool to identify residence and HIV provider location in the absence of geocoded address data. Further work is needed to improve and compare map tool usability with the results from this study.

Travel distance to HIV medical care: a geographic analysis of weighted survey data from the Medical Monitoring Project in Philadelphia, PA

Decisions regarding where patients access HIV care are not well understood. The purpose of this analysis was to examine differences in travel distance to care among persons receiving care in Philadelphia. A multi-stage sampling design was utilized to identify 400 potential participants. 65 % (260/400) agreed to be interviewed. Participants were asked questions about medical care, supportive services, and geographic location. Distances were calculated between residence and care location. 46.3 % travelled more than three miles beyond the nearest facility. Uninsured travelled further (6.9 miles, 95 % CI 3.9-9.8) than persons with public insurance (3.3 miles, 2.9-3.6). In multivariate analyses, no insurance (20/260) was associated with increased distance (p = 0.0005) and Hispanic ethnicity was associated with decreased distance (p = 0.0462). Persons without insurance travel further but insurance status alone does not explain the variability in distance travelled to care. In Philadelphia, Hispanic populations, and providers that may be most accessible to them, are spatially contained.

Spatial analysis of primary and secondary syphilis incidence in China, 2004-2010

China has recently experienced an increase in the incidence of syphilis. Effective spatial monitoring of syphilis incidence is important for successful implementation of control and prevention programmes. This study monitored county-level primary and secondary (P&S) syphilis incidence rates for all of mainland China by examining spatial patterns. Exploratory spatial data analysis (ESDA) methods were used to characterize the spatial distribution pattern of syphilis cases. During the seven-year study period, the average annual P&S syphilis incidence was 8.82 cases per 100,000 people. Using Empirical Bayes smoothed rates, the local Moran test identified many areas of high syphilis risk (all P values ≤0.01). The number of high-risk counties increased from 73 counties in 2004 to 134 counties in 2010. An eastern coastal cluster of high-risk counties persisted throughout 2004-2010. ESDA methods can assist public health officials in identifying high-risk areas. Allocating more resources to high-risk areas could more effectively reduce syphilis incidence.

Simplified method for including spatial correlations in mean-field approximations

Biological systems involving proliferation, migration, and death are observed across all scales. For example, they govern cellular processes such as wound healing, as well as the population dynamics of groups of organisms. In this paper, we provide a simplified method for correcting mean-field approximations of volume-excluding birth-death-movement processes on a regular lattice. An initially uniform distribution of agents on the lattice may give rise to spatial heterogeneity, depending on the relative rates of proliferation, migration, and death. Many frameworks chosen to model these systems neglect spatial correlations, which can lead to inaccurate predictions of their behavior. For example, the logistic model is frequently chosen, which is the mean-field approximation in this case. This mean-field description can be corrected by including a system of ordinary differential equations for pairwise correlations between lattice site occupancies at various lattice distances. In this work we discuss difficulties with this method and provide a simplification in the form of a partial differential equation description for the evolution of pairwise spatial correlations over time. We test our simplified model against the more complex corrected mean-field model, finding excellent agreement. We show how our model successfully predicts system behavior in regions where the mean-field approximation shows large discrepancies. Additionally, we investigate regions of parameter space where migration is reduced relative to proliferation, which has not been examined in detail before and find our method is successful at correcting the deviations observed in the mean-field model in these parameter regimes.

The epidemiology of gonorrhoea in London: a Bayesian spatial modelling approach

Data obtained from genitourinary medicine clinics through a comprehensive surveillance system were used in a Bayesian mixed-effects Poisson regression model to explore socio-demographic individual and ecological risk factors for gonorrhoea in London, as well as its spatial clustering. The spatial analysis was performed at the Middle-layer Super Output Area level (median population size 7200). A total of 12452 individuals were diagnosed during the 2-year study period (2009-2010). The study confirmed the presence of 'core areas' of high incidence, and identified 'core' high-risk groups, in particular young adults (16-29 years), males, black Caribbeans and more deprived areas. The individual (age, sex, ethnicity) and area-level (deprivation, teenage pregnancies, students) model covariates accounted for 48% of the variance. Most of the remaining variance was explained by the spatial effect, thus capturing other spatially distributed factors associated with gonorrhoea, such as local sexual networks. These findings will be useful in identifying areas for targeted interventions, such as STI testing and health promotion.

Does core area theory apply to sexually transmitted diseases in rural environments?

BACKGROUND

Our objective was to determine the extent to which geographical core areas for gonorrhea and syphilis are located in rural areas as compared with urban areas.

METHODS

Incident gonorrhea (January 1, 2005-December 31, 2010) and syphilis (January 1, 1999-December 31, 2010) rates were estimated and mapped by census tract and quarter. Rurality was measured using percent rural and rural-urban commuting area (rural, small town, micropolitan, or urban). SaTScan was used to identify spatiotemporal clusters of significantly elevated rates of infection. Clusters lasting 5 years or longer were considered core areas; clusters of shorter duration were considered outbreaks. Clusters were overlaid on maps of rurality and qualitatively assessed for correlation.

RESULTS

Twenty gonorrhea core areas were identified: 65% were in urban centers, 25% were in micropolitan areas, and the remaining 10% were geographically large capturing combinations of urban, micropolitan, small town, and rural environments. Ten syphilis core areas were identified with 80% in urban centers and 20% capturing 2 or more rural-urban commuting areas. All 10 (100%) of the syphilis core areas overlapped with gonorrhea core areas.

CONCLUSIONS

Gonorrhea and syphilis rates were high for rural parts of North Carolina; however, no core areas were identified exclusively for small towns or rural areas. The main pathway of rural sexually transmitted disease (STI) transmission may be through the interconnectedness of urban, micropolitan, small town, and rural areas. Directly addressing STIs in urban and micropolitan communities may also indirectly help address STI rates in rural and small town communities.

Spatial analysis of county-based gonorrhoea incidence in mainland China, from 2004 to 2009

BACKGROUND

Gonorrhoea is one of the most common sexually transmissible infections in mainland China. Effective spatial monitoring of gonorrhoea incidence is important for successful implementation of control and prevention programs. The county-level gonorrhoea incidence rates for all of mainland China was monitored through examining spatial patterns.

METHODS

County-level data on gonorrhoea cases between 2004 and 2009 were obtained from the China Information System for Disease Control and Prevention. Bayesian smoothing and exploratory spatial data analysis (ESDA) methods were used to characterise the spatial distribution pattern of gonorrhoea cases.

RESULTS

During the 6-year study period, the average annual gonorrhoea incidence was 12.41 cases per 100000 people. Using empirical Bayes smoothed rates, the local Moran test identified one significant single-centre cluster and two significant multi-centre clusters of high gonorrhoea risk (all P-values <0.01).

CONCLUSIONS

Bayesian smoothing and ESDA methods can assist public health officials in using gonorrhoea surveillance data to identify high risk areas. Allocating more resources to such areas could effectively reduce gonorrhoea incidence.

Geographic information system-based screening for TB, HIV, and syphilis (GIS-THIS): a cross-sectional study

OBJECTIVE

To determine the feasibility and case detection rate of a geographic information systems (GIS)-based integrated community screening strategy for tuberculosis, syphilis, and human immunodeficiency virus (HIV).

DESIGN

Prospective cross-sectional study of all participants presenting to geographic hot spot screenings in Wake County, North Carolina.

METHODS

The residences of tuberculosis, HIV, and syphilis cases incident between 1/1/05-12/31/07 were mapped. Areas with high densities of all 3 diseases were designated "hot spots." Combined screening for tuberculosis, HIV, and syphilis were conducted at the hot spots; participants with positive tests were referred to the health department.

RESULTS AND CONCLUSIONS

Participants (N = 247) reported high-risk characteristics: 67% previously incarcerated, 40% had lived in a homeless shelter, and 29% had a history of crack cocaine use. However, 34% reported never having been tested for HIV, and 41% did not recall prior tuberculin skin testing. Screening identified 3% (8/240) of participants with HIV infection, 1% (3/239) with untreated syphilis, and 15% (36/234) with latent tuberculosis infection. Of the eight persons with HIV, one was newly diagnosed and co-infected with latent tuberculosis; he was treated for latent TB and linked to an HIV provider. Two other HIV-positive persons had fallen out of care, and as a result of the study were linked back into HIV clinics. Of 27 persons with latent tuberculosis offered therapy, nine initiated and three completed treatment. GIS-based screening can effectively penetrate populations with high disease burden and poor healthcare access. Linkage to care remains challenging and will require creative interventions to impact morbidity.

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

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.

Population weighted raster maps can communicate findings of social audits: examples from three continents

Background

Maps can portray trends, patterns, and spatial differences that might be overlooked in tabular data and are now widely used in health research. Little has been reported about the process of using maps to communicate epidemiological findings.

Method

Population weighted raster maps show colour changes over the study area. Similar to the rasters of barometric pressure in a weather map, data are the health occurrence – a peak on the map represents a higher value of the indicator in question. The population relevance of each sentinel site, as determined in the stratified last stage random sample, combines with geography (inverse-distance weighting) to provide a population-weighted extension of each colour. This transforms the map to show population space rather than simply geographic space.

Results

Maps allowed discussion of strategies to reduce violence against women in a context of political sensitivity about quoting summary indicator figures. Time-series maps showed planners how experiences of health services had deteriorated despite a reform programme; where in a country HIV risk behaviours were improving; and how knowledge of an economic development programme quickly fell off across a region. Change maps highlighted where indicators were improving and where they were deteriorating. Maps of potential impact of interventions, based on multivariate modelling, displayed how partial and full implementation of programmes could improve outcomes across a country. Scale depends on context. To support local planning, district maps or local government authority maps of health indicators were more useful than national maps; but multinational maps of outcomes were more useful for regional institutions. Mapping was useful to illustrate in which districts enrolment in religious schools – a rare occurrence - was more prevalent.

Conclusions

Population weighted raster maps can present social audit findings in an accessible and compelling way, increasing the use of evidence by planners with limited numeracy skills or little time to look at evidence. Maps complement epidemiological analysis, but they are not a substitute. Much less do they substitute for rigorous epidemiological designs, like randomised controlled trials.

Incorporating geospatial capacity within clinical data systems to address social determinants of health

Linking electronic health record (EHR) systems with community information systems (CIS) holds great promise for addressing inequities in social determinants of health (SDH). While EHRs are rich in location-specific data that allow us to uncover geographic inequities in health outcomes, CIS are rich in data that allow us to describe community-level characteristics relating to health. When meaningfully integrated, these data systems enable clinicians, researchers, and public health professionals to actively address the social etiologies of health disparities.This article describes a process for exploring SDH by geocoding and integrating EHR data with a comprehensive CIS covering a large metropolitan area. Because the systems were initially designed for different purposes and had different teams of experts involved in their development, integrating them presents challenges that require multidisciplinary expertise in informatics, geography, public health, and medicine. We identify these challenges and the means of addressing them and discuss the significance of the project as a model for similar projects.

Prevalence trends in Sweden for the new variant of Chlamydia trachomatis

In 2006, a new variant of Chlamydia trachomatis (nvCT) was discovered in Sweden that was not detectable with Abbott m2000 (Abbott) and Amplicor/COBAS Amplicor/TaqMan48 (Roche). The proportion of nvCT was 20-64% of the detected Chlamydia cases in counties using Abbott/Roche test systems. Although the ProbeTec system from Becton Dickinson (BD) could detect nvCT, the proportion of nvCT in counties using BD was 7-19%. The objective of the current study was to follow the nvCT proportions from 2007 to 2009 in two counties that used Roche and had introduced test systems able to detect nvCT in late 2006. The nvCT was also followed in two counties that used BD, and in all four counties the effect of nvCT on the serotype distribution of C. trachomatis wild-type strains was analysed. A total of 2576 specimens positive for C. trachomatis were collected in the four counties at three time points, and analysed for nvCT and serotype E. The proportion of nvCT declined significantly in the two counties using Roche, from 65% and 48% in 2007 to 24% for both counties in 2009 (p <0.001). The nvCT proportion increased in Norrbotten county, which used BD, from 9% in 2007 to 19% in 2009 (p 0.03). In Uppsala county, which also used BD but was surrounded by counties using detection systems from Roche, the proportion of nvCT declined from 24% in 2007 to 18% in 2009 (p <0.03). No major difference in the level of serotype E was seen. The proportion of nvCT seems to rapidly converge in the Swedish counties after the selective diagnostic advantage for nvCT has been lost in the Abbott/Roche counties.

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

BACKGROUND

Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level.

RESULTS

In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing.

CONCLUSIONS

Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset.

Sexually transmitted disease core theory: roles of person, place, and time

The authors' purpose was to expand sexually transmitted disease core theory by examining the roles of person, place, and time in differentiating geographic core areas from outbreak areas. The authors mapped yearly census-tract-level syphilis rates for San Francisco, California, based on new primary and secondary syphilis cases reported to the San Francisco City sexually transmitted disease surveillance program between January 1, 1985, and December 31, 2007. SaTScan software (Information Management Services, Inc., Silver Spring, Maryland) was used to identify geographic clusters of significantly elevated syphilis rates over space and time. The authors graphed epidemic curves for 1) core areas, 2) outbreak areas, 3) neither core nor outbreak areas, and 4) noncore areas, where noncore areas included outbreaks, and stratified these curves according to demographic characteristics. Five clusters of significantly elevated primary and secondary syphilis rates were identified. A 5-year threshold was useful for differentiating core clusters from outbreak clusters. Epidemic curves for core areas, outbreak areas, neither core nor outbreak areas, and noncore areas were perfectly synchronized in phase trends and wavelength over time, even when broken down by demographic characteristics. Between epidemics, the occurrence of syphilis affected all demographic groups equally. During an epidemic, a temporary disparity in syphilis occurrence arose and a homogeneous core group of cases could be defined.

Are neighborhood sociocultural factors influencing the spatial pattern of gonorrhea in North Carolina?

PURPOSE

To determine if the spatial pattern of gonorrhea observed for North Carolina was influenced by neighborhood-level sociocultural determinants of health, including race/ethnicity.

METHODS

A generalized linear mixed model with spatially correlated random effects was fit to measure the influence of socio-cultural factors on the spatial pattern of gonorrhea reported to the North Carolina State Health Department (January 1, 2005 to March 31, 2008).

RESULTS

Neighborhood gonorrhea rates increased as the percent single mothers increased (25th to 75th neighborhood percentile Relative Rate 1.18, 95% CI 1.12, 1.25), and decreased as socioeconomic status increased (Relative Rate 0.89, 95% CI 0.84, 0.95). Increasing numbers of men in neighborhoods with more women than men did not change the gonorrhea rate, but was associated with decreased rates in neighborhoods with more men than women. Living in the mountains was protective for all race/ethnicities. Rurality was associated with decreased rates for Blacks and increased rates for Native Americans outside the mountains.

PURPOSE

Neighborhood-level sociocultural factors, primarily those indicative of neighborhood deprivation, explained a significant proportion of the spatial pattern of gonorrhea in both urban and rural communities. Race/ethnicity was an important proxy for social and cultural factors not captured by measures of socioeconomic status.

A spatial analysis of county-level variation in syphilis and gonorrhea in Guangdong Province, China

Background

Sexually transmitted infections (STI) have made a resurgence in many rapidly developing regions of southern China, but there is little understanding of the social changes that contribute to this spatial distribution of STI. This study examines county-level socio-demographic characteristics associated with syphilis and gonorrhea in Guangdong Province.

Methods/Principal Findings

This study uses linear regression and spatial lag regression to determine county-level (n = 97) socio-demographic characteristics associated with a greater burden of syphilis, gonorrhea, and a combined syphilis/gonorrhea index. Data were obtained from the 2005 China Population Census and published public health data. A range of socio-demographic variables including gross domestic product, the Gender Empowerment Measure, standard of living, education level, migrant population and employment are examined. Reported syphilis and gonorrhea cases are disproportionately clustered in the Pearl River Delta, the central region of Guangdong Province. A higher fraction of employed men among the adult population, higher fraction of divorced men among the adult population, and higher standard of living (based on water availability and people per room) are significantly associated with higher STI cases across all three models. Gross domestic product and gender inequality measures are not significant predictors of reported STI in these models.

Conclusions/Significance

Although many ecological studies of STIs have found poverty to be associated with higher reported STI, this analysis found a greater number of reported syphilis cases in counties with a higher standard of living. Spatially targeted syphilis screening measures in regions with a higher standard of living may facilitate successful control efforts. This analysis also reinforces the importance of changing male sexual behaviors as part of a comprehensive response to syphilis control in China.

Spatial clustering of HIV prevalence in Atlanta, Georgia and population characteristics associated with case concentrations

We assessed prevalent HIV cases in Atlanta to examine case distribution trends and population characteristics at the census tract level that may be associated with clustering effects. We calculated cluster characteristics (area and internal HIV prevalence) via Kuldorff's spatial scan method. Subsequent logistic regression analyses were performed to analyze sociodemographics associated with inclusion in a cluster. Organizations offering voluntary HIV testing and counseling services were identified and we assessed average travel time to access these services. One large cluster centralized in downtown Atlanta was identified that contains 60% of prevalent HIV cases. The prevalence rate within the cluster was 1.34% compared to 0.32% outside the cluster. Clustered tracts were associated with higher levels of poverty (OR = 1.19), lower density of multi-racial residents (OR = 1.85), injection drug use (OR = 1.99), men having sex with men (OR = 3.01), and men having sex with men and IV drug use (OR = 1.6). Forty-two percent (N = 11) of identified HIV service providers in Atlanta are located in the cluster with an average travel time of 13 minutes via car to access these services (SD = 9.24). The HIV epidemic in Atlanta is concentrated in one large cluster characterized by poverty, men who have sex with men (MSM), and IV drug usage. Prevention efforts targeted to the population living in this area as well as efforts to address the specific needs of these populations may be most beneficial in curtailing the epidemic within the identified cluster.

Pofessional Practice and Innovation: Geographical Information Systems: An Effective Planning and Decision-Making Platform for Community Health Coalitions in Australia

The development of locally-based healthcare initiatives, such as community health coalitions that focus on capacity building programs and multi-faceted responses to long-term health problems, have become an increasingly important part of the public health landscape. As a result of their complexity and the level of investment, it has become necessary to develop innovative ways to help manage these new healthcare approaches. Geographical Information Systems (GIS) have been suggested as one of the innovative approaches that will allow community health coalitions to better manage and plan their activities. The focus of this paper is to provide a commentary on the use of GIS as a tool for community coalitions and discuss some of the potential benefits and issues surrounding the development of these tools.

Using spatial regression methods to examine the association between county-level racial/ethnic composition and reported cases of Chlamydia and gonorrhea: an illustration with data from the state of Texas

BACKGROUND

Several studies have reported racial/ethnic disparities in the incidence of sexually transmitted diseases. However, very few studies have accounted for potential spatial dependence. Additionally, little is known about the relative magnitudes of the associations between county-level racial/ethnic composition and the 2 most commonly reported sexually transmitted diseases.

METHODS

We used county-level data from the National Electronic Telecommunications System for Surveillance and the 2000 Census data to investigate the association between county-level racial/ethnic composition and reported cases of the 2 most commonly reported sexually transmitted diseases (chlamydia and gonorrhea) in Texas. We also estimated ordinary least square (OLS) models for comparison.

RESULTS

Preliminary results from the spatial regression models indicated that the choice of spatial relationships criteria was important for model specification. The spatial error model (SEM) was superior to the spatial autoregressive model, spatial Durbin model, and OLS. The SEM for the 2 disease equations were further analyzed using a seemingly unrelated regression estimation (SURE) procedure. Although the SEM was superior to all models (using standard criteria), the coefficients were fairly stable across models. Our results showed that a unit change in percent black was associated with 1.6 (1.1 for Hispanic) and 3.3 (0.5 for Hispanic) percent change in chlamydia and gonorrhea rates (on average), respectively, compared with percent white.

CONCLUSION

Although there were no substantial differences in the magnitude of the estimated parameters, spatial regression models are potentially superior to OLS models and should be explored in future sexually transmitted disease studies.

Health GIS and HIV/AIDS studies: Perspective and retrospective

GIS (Geographic Information System) is a useful tool that aids and assists in health research, health education, planning, monitoring and evaluation of health programmes that are meant to control and eradicate certain life threatening diseases and epidemics. HIV/AIDS is one such epidemic that poses a serious challenge and threatens the overall human welfare. This communication is an attempt to link and understand the health scenario in a GIS context with emphasis on HIV/AIDS. Various GIS based functionalities for health studies and their scope in analyzing and controlling epidemiological diseases are explored. Overall scenario of the spread of HIV/AIDS around the world is presented along with the Indian perspective. Finally, we conclude with the general management problems, issues and challenges related to HIV/AIDS prevailing in India.