Spatial distribution and cluster analysis of sexual risk behaviors reported by young men in Kisumu, Kenya

The role of high-risk geographies in the perpetuation of the HIV epidemic in rural South Africa: A spatial molecular epidemiology study

In this study, we hypothesize that HIV geographical clusters (geospatial areas with significantly higher numbers of HIV positive individuals) can behave as the highly connected nodes in the transmission network. Using data come from one of the most comprehensive demographic surveillance systems in Africa, we found that more than 70% of the HIV transmission links identified were directly connected to an HIV geographical cluster located in a peri-urban area. Moreover, we identified a single central large community of highly connected nodes located within the HIV cluster. This module was composed by nodes highly connected among them, forming a central structure of the network that was also connected with the small sparser modules located outside of the HIV geographical cluster. Our study supports the evidence of the high level of connectivity between HIV geographical high-risk populations and the entire community.

Premarital sexual practice and associated factors among adolescents in the refugee camps in Tigray, northern Ethiopia

OBJECTIVE

Premarital sexual practice during adolescence time may lead to different sexual and reproductive health problems including HIV/AIDS. This study was aimed to assess the prevalence of premarital sexual practice and associated factors among adolescents living in the refugee camps in Tigray, northern Ethiopia.

RESULT

The prevalence of premarital sexual practice was 47.6% [95% CI (43.3%, 51.9%)]. The age (mean ± SD) of the study participants was 17.4 ± 2.71 (15-24 years) and 68.8% of them were males. Being in the age group 14-19 years [AOR = 4.50, 95% CI (2.29-8.84)] or 20-24 years [AOR = 9.76, CI (4.58-20.82)], living with friends [AOR = 5.24, 95% CI (2.28-12.05)] or alone [AOR = 5.72, 95% CI (2.69-12.19)], being in primary school [AOR = 6.93, 95% CI (3.08-15.57)] or college [AOR = 4.33, CI (1.44-13.02)], getting pocket money [AOR = 4.14, 95% CI (2.31-7.41)], parents being either divorced [AOR = 5.11, 95% CI (2.42-10.80)] or widowed [AOR = 3.52, 95% CI (1.69-7.33)], alcohol consumption [AOR = 1.99, 95% CI (1.20-3.38)] were independent predictors of premarital sexual practice among the adolescents.

Geographic Information Systems, spatial analysis, and HIV in Africa: A scoping review

INTRODUCTION

Geographic Information Systems (GIS) and spatial analysis are emerging tools for global health, but it is unclear to what extent they have been applied to HIV research in Africa. To help inform researchers and program implementers, this scoping review documents the range and depth of published HIV-related GIS and spatial analysis research studies conducted in Africa.

METHODS

A systematic literature search for articles related to GIS and spatial analysis was conducted through PubMed, EMBASE, and Web of Science databases. Using pre-specified inclusion criteria, articles were screened and key data were abstracted. Grounded, inductive analysis was conducted to organize studies into meaningful thematic areas.

RESULTS AND DISCUSSION

The search returned 773 unique articles, of which 65 were included in the final review. 15 different countries were represented. Over half of the included studies were published after 2014. Articles were categorized into the following non-mutually exclusive themes: (a) HIV geography, (b) HIV risk factors, and (c) HIV service implementation. Studies demonstrated a broad range of GIS and spatial analysis applications including characterizing geographic distribution of HIV, evaluating risk factors for HIV, and assessing and improving access to HIV care services.

CONCLUSIONS

GIS and spatial analysis have been widely applied to HIV-related research in Africa. The current literature reveals a diversity of themes and methodologies and a relatively young, but rapidly growing, evidence base.

Spatial distribution of HIV, HCV, and co-infections among drug users in the southwestern border areas of China (2004-2014): a cohort study of a national methadone maintenance treatment program

Background

A methadone maintenance treatment (MMT) program to curb the dual epidemics of HIV/AIDS and drug use has been administered by China since 2004. Little is known regarding the geographic heterogeneity of HIV and hepatitis C virus (HCV) infections among MMT clients in the resource-constrained context of Chinese provinces, such as Guangxi. This study aimed to characterize the geographic distribution patterns and co-clustered epidemic factors of HIV, HCV and co-infections at the county level among drug users receiving MMT in Guangxi Zhuang Autonomous Region, located in the southwestern border area of China.

Methods

Baseline data on drug users’ demographic, behavioral and biological characteristics in the MMT clinics of Guangxi Zhuang Autonomous Region during the period of March 2004 to December 2014 were obtained from national HIV databases. Residential addresses were entered into a geographical information system (GIS) program and analyzed for spatial clustering of HIV, HCV and co-infections among MMT clients at the county level using geographic autocorrelation analysis and geographic scan statistics.

Results

A total of 31,015 MMT clients were analyzed, and the prevalence of HIV, HCV and co-infections were 13.05%, 72.51% and 11.96% respectively. Both the geographic autocorrelation analysis and geographic scan statistics showed that HIV, HCV and co-infections in Guangxi Zhuang Autonomous Region exhibited significant geographic clustering at the county level, and the Moran’s I values were 0.33, 0.41 and 0.30, respectively (P < 0.05). The most significant high-risk overlapping clusters for these infections were restricted to within a 10.95 km² radius of each of the 13 locations where P county was the cluster center. These infections also co-clustered with certain characteristics, such as being unmarried, having a primary level of education or below, having used drugs for more than 10 years, and receptive sharing of syringes with others. The high-risk clusters for these characteristics were more likely to reside in the areas surrounding P county.

Conclusions

HIV, HCV and co-infections among MMT clients in Guangxi Zhuang Autonomous Region all presented substantial geographic heterogeneity at the county level with a number of overlapping significant clusters. The areas surrounding P county were effective in enrolling high-risk clients in their MMT programs which, in turn, might enable people who inject drugs to inject less, share fewer syringes, and receive referrals for HIV or HCV treatment in a timely manner.

Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data

The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns.

Feasibility of Using HIV Care-Continuum Outcomes to Identify Geographic Areas for Targeted HIV Testing

BACKGROUND

Improved detection and linkage to care of previously undiagnosed HIV infections require innovative approaches to testing. We sought to determine the feasibility of targeted HIV testing in geographic areas, defined by continuum of care parameters, to identify HIV-infected persons needing linkage or engagement in care.

METHODS

Using HIV surveillance data from Washington, DC, we identified census tracts that had an HIV prevalence >1% and were either above (higher risk areas-HRAs) or below (lower risk areas-LRAs) the median for 3 indicators: monitored viral load, proportion of persons out of care (OOC), and never in care. Community-based HIV rapid testing and participant surveys were conducted in the 20 census tracts meeting the criteria. Areas were mapped using ArcGIS, and descriptive and univariate analyses were conducted comparing the areas and participants.

RESULTS

Among 1471 persons tested, 28 (1.9%) tested HIV positive; 2.1% in HRAs vs. 1.7% in LRAs (P = 0.57). Higher proportions of men (63.7% vs. 56.7%, P = 0.007) and fewer blacks (91.0% vs. 94.6%, P = 0.008) were tested in LRAs vs. HRAs; no differences were observed in risk behaviors between the areas. Among HIV-positive participants, 54% were new diagnoses (n = 9) or OOC (n = 6), all were Black, 64% were men with a median age of 51 years.

CONCLUSIONS

Although significant differences in HIV seropositivity were not observed between testing areas, our approach proved feasible and enabled identification of new diagnoses and OOC HIV-infected persons. This testing paradigm could be adapted in other locales to identify areas for targeted HIV testing and other reengagement efforts.

Examining the role of socioeconomic deprivation in ethnic differences in sexually transmitted infection diagnosis rates in England: evidence from surveillance data

Differences by ethnic group in STI diagnosis rates have long been recognized in England. We investigated whether these may be explained by ethnic disparities in socioeconomic deprivation (SED). Data on all diagnoses made in sexual health clinics in England in 2013 were obtained from the mandatory STI surveillance system. Poisson regression was used to calculate incidence rate ratios (IRRs) of STIs, by ethnicity, with and without adjustment for index of multiple deprivation (IMD) a measure of area-level deprivation. Unadjusted IRRs (95% confidence intervals) were highest for gonorrhoea [8·18 (7·77-8·61) and 5·76 (5·28-6·29)] and genital herpes [4·24 (3·99-4·51) and 3·58 (3·23-3·98)] for people of black Caribbean and non-Caribbean/non-African black ethnicity and IRRs were highest for syphilis [8·76 (7·97-9·63)] and genital warts [2·23 (2·17-2·29)] for people of non-British/non-Irish white ethnicity compared to white British ethnicity. After adjustment for IMD, IRRs for gonorrhoea [5·76 (5·47-6·07)] and genital herpes [3·73 (3·50-3·97)] declined but remained highest for black Caribbeans and IRRs for syphilis [7·35 (6·68-8·09)] and genital warts [2·10 (2·04-2·16)] declined but remained highest for non-British/non-Irish white compared to white British. In England, ethnic disparities in STI diagnosis rates are partially explained by SED, but behavioural and contextual factors likely contribute. Clinic and community-based interventions should involve social peer networks to ensure they are targeted and culturally sensitive.

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.

Spatial Distributions of HIV Infection in an Endemic Area of Western Kenya: Guiding Information for Localized HIV Control and Prevention

HIV is still a major health problem in developing countries. Even though high HIV-risk-taking behaviors have been reported in African fishing villages, local distribution patterns of HIV infection in the communities surrounding these villages have not been thoroughly analyzed. The objective of this study was to investigate the geographical distribution patterns of HIV infection in communities surrounding African fishing villages. In 2011, we applied age- and sex-stratified random sampling to collect 1,957 blood samples from 42,617 individuals registered in the Health and Demographic Surveillance System in Mbita, which is located on the shore of Lake Victoria in western Kenya. We used these samples to evaluate existing antibody detection assays for several infectious diseases, including HIV antibody titers. Based on the results of the assays, we evaluated the prevalence of HIV infection according to sex, age, and altitude of participating households. We also used Kulldorff’s spatial scan statistic to test for HIV clustering in the study area. The prevalence of HIV at our study site was 25.3%. Compared with the younger age group (15–19 years), adults aged 30–34 years were 6.71 times more likely to be HIV-positive, and the estimated HIV-positive population among women was 1.43 times larger than among men. Kulldorff’s spatial scan statistic detected one marginally significant (P = 0.055) HIV-positive and one significant HIV-negative cluster (P = 0.047) in the study area. These results suggest a homogeneous HIV distribution in the communities surrounding fishing villages. In addition to individual behavior, more complex and diverse factors related to the social and cultural environment can contribute to a homogeneous distribution pattern of HIV infection outside of African fishing villages. To reduce rates of transmission in HIV-endemic areas, HIV prevention and control programs optimized for the local environment need to be developed.

Spatial analysis of HIV infection and associated individual characteristics in Burundi: indications for effective prevention

Background

Adequate resource allocation is critical in the battle against HIV/AIDS, especially in Africa. The determination of the location and nature of HIV services to implement must comply with the geographic, social and behavioral characteristics of patients. We therefore investigated the spatial heterogeneity of HIV prevalence in Burundi and then assessed the association of social and behavioral characteristics with HIV infection accounting for the spatial heterogeneity.

Methods

We used data from the 2010 Demographic and Health Survey. We analyzed these data with a geostatistical approach (which takes into account spatial autocorrelation) by i) interpolating HIV data using the kernel density estimation, ii) identifying the spatial clusters with high and low HIV prevalence using the Kulldorff spatial scan statistics, and then iii) performing a multivariate spatial logistic regression.

Results

Overall HIV prevalence was 1.4 %. The interpolated data showed the great spatial heterogeneity of HIV prevalence (from 0 to 10 %), independently of administrative boundaries. A cluster with high HIV prevalence was found in the capital city and adjacent areas (3.9 %; relative risk 3.7, p < 0.001) whereas a cluster with low prevalence straddled two southern provinces (0 %; p = 0.02). By multivariate spatial analysis, HIV infection was significantly associated with the female sex (posterior odds ratio [POR] 1.36, 95 % credible interval [CrI] 1.13-1.64), an older age (POR 1.97, 95 % CrI 1.26-3.08), the level of education (POR 1.50, 95 % CrI 1.22-1.84), the marital status (POR 1.86, 95 % CrI 1.23-2.80), a higher wealth index (POR 2.11, 95 % CrI 1.77-2.51), the sexual activity (POR 1.76, 95 % CrI 1.04-2.96), and a history of sexually transmitted infection (POR 2.03, 95 % CrI 1.56-2.64).

Conclusions

Our study, which shows where and towards which populations HIV resources should be allocated, could help national health policy makers develop an effective HIV intervention in Burundi. Our findings support the strategy of the Joint United Nations Programme on HIV/AIDS (UNAIDS) for country-specific, in-depth analyses of HIV epidemics to tailor national prevention responses.

Spatial epidemiology of HIV-hepatitis co-infection in the State of Michigan: a cohort study

BACKGROUND

Acquired immunodeficiency syndrome (AIDS) is a continuing global public health threat affecting millions of individuals. In 2009, 33.3 million people worldwide were living with human immunodeficiency virus (HIV) infection. HIV-infected individuals are at an increased risk of acquiring hepatitis B and hepatitis C viral (HBV, HCV)infections because of shared transmission routes. The purpose of this study was to identify geographical clusters of HIV-(HBV/HCV) co-infection in the State of Michigan.

METHODS

Retrospective cohort data on HIV-infected individuals were matched to all hepatitis B and C cases in Michigan during the period of January 1, 2006 through December 31, 2009. A prevalence map of HIV infection was created and spatial clusters of HIV-hepatitis B or C co-infection were detected using GeoDa's bivariate local Moran's I and SaTScan's discrete Poisson model.

RESULTS

A bivariate cluster of high prevalence HIV and hepatitis B or C was identified in the Detroit Metropolitan Area and surrounding counties. A Poisson cluster of HIV-hepatitis B or C co-infection was identified, relative risk (RR) = 1.38 (p = 0.029) in the western and northwestern counties of Lower Michigan, controlling for sex, race, and AIDS status.

CONCLUSION

This study identified elevated HIV-hepatitis B or C co-infection unexplained by sex, race or AIDS status in counties outside of the Detroit Metropolitan Area where HIV prevalence was highest in Michigan. The findings from this study may be used to target future public health policy and healthcare interventions for HIV-hepatitis co-infection in these areas.

Spatial analysis of factors associated with HIV infection among young people in Uganda, 2011

Background

The HIV epidemic in East Africa is of public health importance with an increasing number of young people getting infected. This study sought to identify spatial clusters and examine the geographical variation of HIV infection at a regional level while accounting for risk factors associated with HIV/AIDS among young people in Uganda.

Methods

A secondary data analysis was conducted on a survey cross-sectional design whose data were obtained from the 2011 Uganda Demographic and Health Survey (DHS) and AIDS Indicator Survey (AIS) for 7 518 young people aged 15-24 years. The analysis was performed in three stages while incorporating population survey sampling weights. Maximum likelihood-based logistic regression models were used to explore the non-spatially adjusted factors associated with HIV infection. Spatial scan statistic was used to identify geographical clusters of elevated HIV infections which justified modelling using a spatial random effects model by Bayesian-based logistic regression models.

Results

In this study, 309/533 HIV sero-positive female participants were selected with majority residing in the rural areas [386(72%)]. Compared to singles, those currently [Adjusted Odds Ratio (AOR) =3.64; (95% CI; 1.25-10.27)] and previously married [AOR = 5.62; (95% CI: 1.52-20.75)] participants had significantly higher likelihood of HIV infections. Sexually Transmitted Infections [AOR = 2.21; (95% CI: 1.35-3.60)] were more than twice likely associated with HIV infection. One significant (p < 0.05) primary cluster of HIV prevalence around central Uganda emerged from the SaTScan cluster analysis. Spatial analysis disclosed behavioural factors associated with greater odds of HIV infection such as; alcohol use before sexual intercourse [Posterior Odds Ratio (POR) =1.32; 95% (BCI: 1.11-1.63)]. Condom use [POR = 0.54; (95% BCI: 0.41-0.69)] and circumcision [POR = 0.66; (95% BCI: 0.45-0.99)] provided a protective effect against HIV.

Conclusions

The study revealed associations between high-risk sexual behaviour and HIV infection. Behavioural change interventions should therefore be pertinent to the prevention of HIV. Spatial analysis further revealed a significant HIV cluster towards the Central and Eastern areas of Uganda. We propose that interventions targeting young people should initially focus on these regions and subsequently spread out across Uganda.

Spatial analysis of HIV positive injection drug users in San Francisco, 1987 to 2005

Spatial analyses of HIV/AIDS related outcomes are growing in popularity as a tool to understand geographic changes in the epidemic and inform the effectiveness of community-based prevention and treatment programs. The Urban Health Study was a serial, cross-sectional epidemiological study of injection drug users (IDUs) in San Francisco between 1987 and 2005 (N = 29,914). HIV testing was conducted for every participant. Participant residence was geocoded to the level of the United States Census tract for every observation in dataset. Local indicator of spatial autocorrelation (LISA) tests were used to identify univariate and bivariate Census tract clusters of HIV positive IDUs in two time periods. We further compared three tract level characteristics (% poverty, % African Americans, and % unemployment) across areas of clustered and non-clustered tracts. We identified significant spatial clustering of high numbers of HIV positive IDUs in the early period (1987-1995) and late period (1996-2005). We found significant bivariate clusters of Census tracts where HIV positive IDUs and tract level poverty were above average compared to the surrounding areas. Our data suggest that poverty, rather than race, was an important neighborhood characteristic associated with the spatial distribution of HIV in SF and its spatial diffusion over time.

Spatial Analysis of Factors Associated with Household Subscription to the National Health Insurance Scheme in Rural Ghana

The use of health insurance schemes in financing healthcare delivery and to minimize the poverty gap is gaining considerable recognition among the least developed and resource challenged countries around the world. With the implementation of the socialized health insurance scheme, Ghana has taken the lead in Sub-Saharan Africa and now working out further strategies to gain universal coverage among her citizenry. The primary goal of this study is to explore the spatial relationship between the residential homes and demographic features of the people in the Barekese subdistrict in Ghana on the probability to enroll the entire household unit in the National Health Insurance Scheme (NHIS). Household level data were gathered from 20 communities on the enrollment status into the NHIS alongside demographic and socioeconomic indicators and the spatial location of every household that participated in the study. Kulldorff’s purely spatial scan statistic was used to detect geographic clusters of areas with participatory households that have either higher or lower enrollment patterns in the insurance program. Logistic regression models on selected demographic and socioeconomic indicators were built to predict the effect on the odds of enrolling an entire household membership in the NHIS.

Three clusters significantly stood out to have either high or low enrollment patterns in the health insurance program taking into accounts the number of households in those sub-zones of the study region. Households in the Cluster 1 insurance group have very high travel expenses compared to their counterparts in the other idenfied clusters. Travel cost and time to the NHIS registration center to enroll in the program were both significant predictors to participation in the program when controlling for cluster effect. Residents in the High socioeconomic group have about 1.66 [95% CI: 1.27-2.17] times the odds to enroll complete households in the insurance program compared to their counterparts in the Low socioeconomic group.

The study demonstrated the use of spatial analytical tools to identify clusters of household enrollment pattern in the NHIS among residents in rural Ghana. In the face of limited resources, policy makers can therefore use the findings as guideline to strategically channel interventions to areas of most need. Furthermore, these analyses can be repeated annually to assess progress on improving insurance coverage.

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.