Bayesian spatial semi-parametric modeling of HIV variation in Kenya

Bayesian spatial modelling of intimate partner violence and associated factors among adult women and men: evidence from 2019/2020 Rwanda Demographic and Health Survey

BACKGROUND

Intimate partner violence (IPV) remains a global public health concern for both men and women. Spatial mapping and clustering analysis can reveal subtle patterns in IPV occurrences but are yet to be explored in Rwanda, especially at a lower small-area scale. This study seeks to examine the spatial distribution, patterns, and associated factors of IPV among men and women in Rwanda.

METHODS

This was a secondary data analysis of the 2019/2020 Rwanda Demographic and Health Survey (RDHS) individual-level data set for 1947 women aged 15-49 years and 1371 men aged 15-59 years. A spatially structured additive logistic regression model was used to assess risk factors for IPV while adjusting for spatial effects. The district-level spatial model was adjusted for fixed covariate effects and was implemented using a fully Bayesian inference within the generalized additive mixed effects framework.

RESULTS

IPV prevalence amongst women was 45.9% (95% Confidence interval (CI): 43.4-48.5%) while that for men was 18.4% (95% CI: 16.2-20.9%). Using a bivariate choropleth, IPV perpetrated against women was higher in the North-Western districts of Rwanda whereas for men it was shown to be more prevalent in the Southern districts. A few districts presented high IPV for both men and women. The spatial structured additive logistic model revealed higher odds for IPV against women mainly in the North-western districts and the spatial effects were dominated by spatially structured effects contributing 64%. Higher odds of IPV were observed for men in the Southern districts of Rwanda and spatial effects were dominated by district heterogeneity accounting for 62%. There were no statistically significant district clusters for IPV in both men or women. Women with partners who consume alcohol, and with controlling partners were at significantly higher odds of IPV while those in rich households and making financial decisions together with partners were at lower odds of experiencing IPV.

CONCLUSION

Campaigns against IPV should be strengthened, especially in the North-Western and Southern parts of Rwanda. In addition, the promotion of girl-child education and empowerment of women can potentially reduce IPV against women and girls. Furthermore, couples should be trained on making financial decisions together. In conclusion, the implementation of policies and interventions that discourage alcohol consumption and control behaviour, especially among men, should be rolled out.

Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

BACKGROUND

The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors.

METHODS

COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian.

RESULTS

The risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR = 4.16; 95% Credible Interval: 4.05-4.27), being on oxygen (aOR = 1.49; 95% Credible Interval: 1.46-1.51) and on invasive mechanical ventilation (aOR = 3.74; 95% Credible Interval: 3.61-3.87). Being admitted in a public hospital (aOR = 3.16; 95% Credible Interval: 3.10-3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts.

CONCLUSION

The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.

Spatial disparities of HIV prevalence in South Africa. Do sociodemographic, behavioral, and biological factors explain this spatial variability?

Background

In 2021, an estimated 38 million people were living with human immunodeficiency virus (HIV) globally, with over two-thirds living in African regions. In South Africa, ~20% of South African adults are living with HIV. Accurate estimation of the risk factors and spatial patterns of HIV risk using individual-level data from a nationally representative sample is invaluable for designing geographically targeted intervention and control programs.

Methods

Data were obtained from the 2016 South Africa Demographic and Health Survey (SDHS16). The study involved all men and women aged 15 years and older, who responded to questions and tested for HIV in the SDHS. Generalized additive models (GAMs) were fitted to our data with a nonparametric bivariate smooth term of spatial location parameters (X and Y coordinates). The GAMs were used to assess the spatial disparities and the potential contribution of sociodemographic, biological, and behavioral factors to the spatial patterns of HIV prevalence in South Africa.

Results

A significantly highest risk of HIV was observed in east coast, central and north-eastern regions. South African men and women who are widowed and divorced had higher odds of HIV as compared to their counterparts. Additionally, men and women who are unemployed had higher odds of HIV as compared to the employed. Surprisingly, the odds of HIV infection among men residing in rural areas were 1.60 times higher (AOR 1.60, 95% CI 1.12, 2.29) as compared to those in urban areas. But men who were circumcised had lower odds of HIV (AOR 0.73, 95% CI 0.52, 0.98), while those who had STI in the last 12 months prior to the survey had higher odds of HIV (AOR 1.76, 95% CI 1.44, 3.68).

Conclusion

Spatial heterogeneity in HIV risk persisted even after covariate adjustment but differed by sex, suggesting that there are plausible unobserved influencing factors contributing to HIV uneven variation. This study's findings could guide geographically targeted public health policy and effective HIV intervention in South Africa.

Spatiotemporal Variation and Predictors of Unsuppressed Viral Load among HIV-Positive Men and Women in Rural and Peri-Urban KwaZulu-Natal, South Africa

Unsuppressed HIV viral load is an important marker of sustained HIV transmission. We investigated the prevalence, predictors, and high-risk areas of unsuppressed HIV viral load among HIV-positive men and women. Unsuppressed HIV viral load was defined as viral load of ≥400 copies/mL. Data from the HIV Incidence District Surveillance System (HIPSS), a longitudinal study undertaken between June 2014 to June 2016 among men and women aged 15−49 years in rural and peri-urban KwaZulu-Natal, South Africa, were analysed. A Bayesian geoadditive regression model which includes a spatial effect for a small enumeration area was applied using an integrated nested Laplace approximation (INLA) function while accounting for unobserved factors, non-linear effects of selected continuous variables, and spatial autocorrelation. The prevalence of unsuppressed HIV viral load was 46.1% [95% CI: 44.3−47.8]. Predictors of unsuppressed HIV viral load were incomplete high school education, being away from home for more than a month, alcohol consumption, no prior knowledge of HIV status, not ever tested for HIV, not on antiretroviral therapy (ART), on tuberculosis (TB) medication, having two or more sexual partners in the last 12 months, and having a CD4 cell count of <350 cells/μL. A positive non-linear effect of age, household size, and the number of lifetime HIV tests was identified. The higher-risk pattern of unsuppressed HIV viral load occurred in the northwest and northeast of the study area. Identifying predictors of unsuppressed viral load in a localized geographic area and information from spatial risk maps are important for targeted prevention and treatment programs to reduce the transmission of HIV.

Kharsany A, Zewotir T, North D. Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women in an HIV Hyperendemic Area of KwaZulu-Natal, South Africa

New HIV infections among young women remains exceptionally high and to prevent onward transmission, UNAIDS set ambitious treatment targets. This study aimed to determine the prevalence, spatial variation and factors associated with unsuppressed HIV viral load at ≥400 copies per mL. This study analysed data from women aged 15–49 years from the HIV Incidence Provincial Surveillance System (HIPSS) enrolled in two sequential cross-sectional studies undertaken in 2014 and 2015 in rural and peri-urban KwaZulu-Natal, South Africa. Bayesian geoadditive model with spatial effect for a small enumeration area was adopted using Integrated Nested Laplace Approximation (INLA) function to analyze the findings. The overall prevalence of unsuppressed HIV viral load was 45.2% in 2014 and 38.1% in 2015. Factors associated with unsuppressed viral load were no prior knowledge of HIV status, had a moderate-to-low perception of acquiring HIV, not on antiretroviral therapy (ART), and having a low CD4 cell count. In 2014, women who ever consumed alcohol and in 2015, ever ran out of money, had two or more lifetime sexual partners, ever tested for tuberculosis, and ever diagnosed with sexually transmitted infection were at higher risk of being virally unsuppressed. The nonlinear effect showed that women aged 15 to 29 years, from smaller households and had fewer number of lifetime HIV tests, were more likely to be virally unsuppressed. High viral load risk areas were the north-east and south-west in 2014, with north and west in 2015. The findings provide guidance on identifying key populations and areas for targeted interventions.

Spatio-temporal modelling of tick life-stage count data with spatially varying coefficients

There is a vast amount of geo-referenced data in many fields of study including ecological studies. Geo-referencing is usually by point referencing; that is, latitudes and longitudes or by areal referencing, which includes districts, counties, states, provinces and other administrative units. The availability of large geo-referenced datasets for modelling has necessitated the development and application of spatial statistical methods. However, spatial varying coefficients models exploring the abundance of tick counts remain limited. In this study we used data that was collected and prepared by researchers in the Department of Biological Sciences from the Old Dominion University, Virginia, USA. We modelled tick life-stage counts and abundance variability from 12 sampling locations, with 5 different habitats (numbered 1-5), three habitat types; namely: woods, edges and grass; collected monthly from May 2009 through December 2018. Spatio-temporal Poisson and spatio-temporal negative binomial (NB) count data models were fitted to the data and compared using the deviance information criteria (DIC). The NB model outperformed the Poisson models with all its DIC values being smaller than those of the Poisson model. Results showed that the covariates varied spatially across counties. There was a decreasing time (in years) effect over the study period. However, even though the time effect was decreasing over the study period, space-time interaction effects were seen to be increasing over time in York County.

Bayesian semi-parametric spatial modelling of intimate partner violence in Namibia using 2013 Demographic Health Survey Data

BACKGROUND

Intimate partner violence (IPV) is an important public health problem with health and socioeconomic consequences and is endemic in Namibia. Studies assessing risk factors for IPV often use logistic and Poisson regression without geographical location information and spatial effects. We used a Bayesian spatial semi-parametric regression model to determine the risk factors for IPV in Namibia; assess the non-linear effects of age difference between partners and determine spatial effects in the different regions on IPV prevalence.

METHODS

We used the couples' dataset of the 2013-2014 Namibia Demographic and Health Survey (DHS) obtained on request from Measure DHS. The DHS domestic violence module included 2226 women. We generated a binary variable measuring IPV from the questions "ever experienced physical, sexual or emotional violence?" Covariates included respondent's educational level, age, couples' age difference, place of residence and partner's educational level. All estimation was done with the full Bayesian approach using R version 3.5.2 implementing the R2BayesX package.

RESULTS

IPV country prevalence was 33.3% (95% CI = 30.1-36.5%); Kavango had the highest [50.6% (95% CI = 41.2-60.1%)] and Oshana the lowest [11.5% (95% CI = 3.2-19.9%)] regional prevalence. IPV prevalence was highest among teenagers [60.8% (95% CI = 36.9-84.7%)]). The spatial semi-parametric model used for adjusted results controlled for regional spatial effects, respondent's age, age difference, respondent's years of education, residence, wealth, and education levels. Women with higher education were 50% less likely to experience IPV [aOR: 0.46, 95% CI = 0.23-0.87]. For non-linear effects, the risk of IPV was high for women ≥ 5 years older or ≥ 25 years younger than their partners. Younger and older women had higher risks of IPV than those between 25 and 45 years. For spatial variation of IPV prevalence, northern regions had low spatial effects while western regions had very high spatial effects.

CONCLUSION

The prevalence of IPV among Namibia women was high especially among teenagers, with higher educational levels being protective. The risk of IPV was lower in rural than urban areas and higher with wide partner age differences. Interventions and policies for IPV prevention in Namibia are needed for couples with wide age differences as well as for younger women, women with lower educational attainment and in urban and western regions.

Geographic Core Areas of Coinfections in Washington, District of Columbia: Recommendations for Planning Prevention-Intervention to Mitigate Human Immunodeficiency Virus Burden

BACKGROUND

Research suggests that human immunodeficiency virus (HIV)-positive individuals with a sexually transmitted infection (STI) may be at increased risk of transmitting HIV to someone else through unprotected sex. The primary aim of the analysis is to identify the high-risk geographic areas of transmission of coinfections and factors that may be associated with poor outcomes of viral suppression within these higher-risk geographic areas, thus important in transmission prevention.

METHODS

We used surveillance data reported by all providers and laboratories in the District of Columbia (DC). Applied discrete Poisson scan model in SaTScan to identify the geographic areas. The relative risk (RR) for the scan statistic was calculated based on events inside the cluster, and P values evaluated statistical significance. We used multinomial logistical regression to explore care and demographical characteristics associated with being virally unsuppressed within and outside the geographic areas.

RESULTS

The coinfected areas (RR, >1; P < .001) were located in the tracts of central and southern DC. Black population (RR, 3.154 [95% confidence interval {CI}, 1.736-5.729]), age 13-19 years (RR, 4.598 [95% CI, 3.176-6.657]), repeat STIs (RR, 1.387 [95% CI, 1.096-1.754]), and not retained in care (RR, 2.546 [95% CI, 1.997-3.245]) were found to be at higher risk of being virally unsuppressed within the coinfected clusters. Those with unknown linkages were found to be at higher risk of being virally unsuppressed outside the coinfected clusters (RR, 5.162 [95% CI, 2.289-11.640]).

CONCLUSIONS

This is DC's first effort to identify the geographic core areas of coinfections and factors that may be sustaining them. These results will be used by the health department to plan for prevention-intervention strategies. This model be replicated by any local jurisdiction similar.

Detecting Spatial Cores and Temporal Trends of Repeat STIs to Plan Pre-exposure Prophylaxis (PrEP) Scale-up in DC

BACKGROUND

Repeat sexually transmitted infections (STIs) in DC primarily results from untreated sexual partners. This analysis aims to identify high-risk areas and temporal trends of repeat STIs for pre-exposure prophylaxis scale-up and STI mitigation in DC.

METHODS

We identified repeat infections in the DC Department of Health STI and HIV data management systems, diagnosed from 2014 to 2018. The cases were geocoded and aggregated by census tracts. Poisson discrete scan statistic was implemented in SaTScan software to find clusters. Weighted moving average was used to compare temporal trends of repeat STIs. We used χ analysis to identify association with demographic variables.

RESULTS

We identified 8535 repeat STIs from 2014 to 2018. Of these, 61.84% were among men, most cases were among blacks (34.75%) and 47.45% represented gonorrhea cases. The high-risk spatial clusters were identified as those tracts that had relative risk (relative risk > 1; P-value < 0.001). We identified one significant radius of risk covering tracts of wards 7 and 8 and parts of wards 5 and 6. We spotted positive temporal trends in cluster 1 and outside the cluster. We found significant associations of repeat STIs with gender (χ = 317.27, P < 0.001), age (χ = 539.26, P < 0.001), HIV coinfections (χ = 352.06, P < 0.001), and year of diagnoses (χ = 1.5, P < 0.01).

CONCLUSIONS

Our findings indicate spatial disparities in DC for repeat STIs. This analysis is critical for pre-exposure prophylaxis planning, STI prevention strategies such as expedited partner therapies and condom distribution strategies in DC should prioritize the high-risk spatial cores.

Social, Behavioral, and Cultural factors of HIV in Malawi: Semi-Automated Systematic Review

BACKGROUND

Demographic and sociobehavioral factors are strong drivers of HIV infection rates in sub-Saharan Africa. These factors are often studied in qualitative research but ignored in quantitative analyses. However, they provide in-depth insight into the local behavior and may help to improve HIV prevention.

OBJECTIVE

To obtain a comprehensive overview of the sociobehavioral factors influencing HIV prevalence and incidence in Malawi, we systematically reviewed the literature using a newly programmed tool for automatizing part of the systematic review process.

METHODS

Due to the choice of broad search terms ("HIV AND Malawi"), our preliminary search revealed many thousands of articles. We, therefore, developed a Python tool to automatically extract, process, and categorize open-access articles published from January 1, 1987 to October 1, 2019 in the PubMed, PubMed Central, JSTOR, Paperity, and arXiV databases. We then used a topic modelling algorithm to classify and identify publications of interest.

RESULTS

Our tool extracted 22,709 unique articles; 16,942 could be further processed. After topic modelling, 519 of these were clustered into relevant topics, of which 20 were kept after manual screening. We retrieved 7 more publications after examining the references so that 27 publications were finally included in the review. Reducing the 16,942 articles to 519 potentially relevant articles using the software took 5 days. Several factors contributing to the risk of HIV infection were identified, including religion, gender and relationship dynamics, beliefs, and sociobehavioral attitudes.

CONCLUSIONS

Our software does not replace traditional systematic reviews, but it returns useful results to broad queries of open-access literature in under a week, without a priori knowledge. This produces a "seed dataset" of relevance that could be further developed. It identified known factors and factors that may be specific to Malawi. In the future, we aim to expand the tool by adding more social science databases and applying it to other sub-Saharan African countries.

A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa

Spatial analysis has become an increasingly used analytic approach to describe and analyze spatial characteristics of disease burden, but the depth and coverage of its usage for health surveys data in Sub-Saharan Africa are not well known. The objective of this scoping review was to conduct an evaluation of studies using spatial statistics approaches for national health survey data in the SSA region. An organized literature search for studies related to spatial statistics and national health surveys was conducted through PMC, PubMed/Medline, Scopus, NLM Catalog, and Science Direct electronic databases. Of the 4,193 unique articles identified, 153 were included in the final review. Spatial smoothing and prediction methods were predominant (n = 108), followed by spatial description aggregation (n = 25), and spatial autocorrelation and clustering (n = 19). Bayesian statistics methods and lattice data modelling were predominant (n = 108). Most studies focused on malaria and fever (n = 47) followed by health services coverage (n = 38). Only fifteen studies employed nonstandard spatial analyses (e.g., spatial model assessment, joint spatial modelling, accounting for survey design). We recommend that for future spatial analysis using health survey data in the SSA region, there must be an improve recognition and awareness of the potential dangers of a naïve application of spatial statistical methods. We also recommend a wide range of applications using big health data and the future of data science for health systems to monitor and evaluate impacts that are not well understood at local levels.

The changing modes of human immunodeficiency virus transmission and spatial variations among women in a minority prefecture in southwest China: An exploratory study

Liangshan Yi Autonomous Prefecture in Southwest China has a high human immunodeficiency virus (HIV) prevalence rate. This study examined the changing modes of HIV transmission among women with new HIV infections and explored the spatial heterogeneities in the factors associated with heterosexual transmission in this minority region.The data consisting of women with new HIV infections from 2011 to 2014 were collected from multiple sources. New infections were identified by BED capture enzyme immunoassay. The Bayesian hierarchical model was used to estimate the proportion of women with new HIV infections via heterosexual transmission across all townships in the Prefecture. A geographically weighted regression (GWR) model was utilized to investigate spatial variations in the sociodemographic characteristics associated with the changing modes of HIV transmission.An analytical sample of 927 women with new HIV infections was constructed and utilized to investigate the changing mode of HIV transmission. The rate of heterosexual transmission among women with new HIV infections in 2011 was below 20%. However, by 2014 this rate dramatically increased to nearly 80%. Among sociodemographic characteristics, GWR results revealed significant ethnic differences in heterosexual HIV transmission between Yi women and women in other ethnic groups, with Yi women demonstrating a lower risk of infection through heterosexual transmission. However, such ethnic differences were observed only in 30% of the townships in the Prefecture. Moreover, having a primary education decreased the odds of heterosexual transmission, which was observed in about 56% of the townships. Also, being involved in occupations other than agriculture or animal husbandry and being single or married decreased the odds of HIV infection through heterosexual contact among women, which did not significantly vary across the Prefecture.Heterosexual transmission was the predominant mode of HIV transmission among women in the Prefecture, and this transformation was clearly marked by a fast-growing trend and a spatial diffusion pattern. Spatial variations also existed in sociodemographic factors that were associated with the changing modes of HIV transmission.

Bayesian Generalized Linear Mixed Modeling of Breast Cancer

BACKGROUND

Breast cancer is one of the most common cancers among women. Breast cancer treatment strategies in Nigeria need urgent strengthening to reduce mortality rate because of the disease. This study aimed to determine the relationship between the ages at diagnosis and established the prognostic factors of modality of treatment given to breast cancer patient in Nigeria.

METHODS

The data was collected for 247 women between years 2011-2015 who had breast cancer in two different hospitals in Ekiti State, Nigeria. Model estimation is based on Bayesian approach via Markov Chain Monte Carlo. A multilevel model based on generalized linear mixed model is used to estimate the random effect.

RESULTS

The mean age of the patients (at the time of diagnosis) was 42.2 yr with 52% of the women aged between 35-49 yr. The results of the two approaches are almost similar but preference is given to Bayesian because the approach is more robust than the frequentist. Significant factors of treatment modality are age, educational level and breast cancer type.

CONCLUSION

Differences in socio-demographic factors such as educational level and age at diagnosis significantly influence the modality of breast cancer treatment in western Nigeria. The study suggests the use of Bayesian multilevel approach in analyzing breast cancer data for the practicality, flexibility and strength of the method.

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.

Improving estimates of district HIV prevalence and burden in South Africa using small area estimation techniques

Many countries, including South Africa, have implemented population-based household surveys to estimate HIV prevalence and the burden of HIV infection. Most household HIV surveys are designed to provide reliable estimates down to only the first subnational geopolitical level which, in South Africa, is composed of nine provinces. However HIV prevalence estimates are needed down to at least the second subnational level in order to better target the delivery of HIV care, treatment and prevention services. The second subnational level in South Africa is composed of 52 districts. Achieving adequate precision at the second subnational level therefore requires either a substantial increase in survey sample size or use of model-based estimation capable of incorporating other pre-existing data. Our purpose is demonstration of the efficacy of relatively simple small-area estimation of HIV prevalence in the 52 districts of South Africa using data from the South African National HIV Prevalence, Incidence and Behavior Survey, 2012, district-level HIV prevalence estimates obtained from testing of pregnant women who attended antenatal care (ANC) clinics in 2012, and 2012 demographic data. The best-fitting model included only ANC prevalence and dependency ratio as out-of-survey predictors. Our key finding is that ANC prevalence was the superior auxiliary covariate, and provided substantially improved precision in many district-level estimates of HIV prevalence in the general population. Inclusion of a district-level spatial simultaneously autoregressive covariance structure did not result in improved estimation.

Joint spatial modelling of disease risk using multiple sources: an application on HIV prevalence from antenatal sentinel and demographic and health surveys in Namibia

Background

In disease mapping field, researchers often encounter data from multiple sources. Such data are fraught with challenges such as lack of a representative sample, often incomplete and most of which may have measurement errors, and may be spatially and temporally misaligned. This paper presents a joint model in the effort to deal with the sampling bias and misalignment.

Methods

A joint (bivariate) spatial model was applied to estimate HIV prevalence using two sources: 2014 National HIV Sentinel survey (NHSS) among pregnant women aged 15–49 years attending antenatal care (ANC) and the 2013 Namibia Demographic and Health Surveys (NDHS).

Results

Findings revealed that health districts and constituencies in the northern part of Namibia were found to be highly associated with HIV infection. Also, the study showed that place of residence, gender, gravida, marital status, number of kids dead, wealth index, education, and condom use were significantly associated with HIV infection in Namibia.

Conclusion

This study had shown determinants of HIV infection in Namibia and had revealed areas at high risk through HIV prevalence mapping. Moreover, a joint modelling approach was used in order to deal with spatially misaligned data. Finally, it was shown that prediction of HIV prevalence using the NDHS data source can be enhanced by jointly modelling other HIV data such as NHSS data. These findings would help Namibia to tailor national intervention strategies for specific regions and groups of population.

Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence

Background: There has been no previous study to classify malignant breast tumor in details based on Markov Chain Monte Carlo (MCMC) convergence in Western, Nigeria. This study therefore aims to profile patients living with benign and malignant breast tumor in two different hospitals among women of Western Nigeria, with a focus on prognostic factors and MCMC convergence. Materials and Methods: A hospital-based record was used to identify prognostic factors for malignant breast cancer among women of Western Nigeria. This paper describes Bayesian inference and demonstrates its usage to estimation of parameters of the logistic regression via Markov Chain Monte Carlo (MCMC) algorithm. The result of the Bayesian approach is compared with the classical statistics. Results: The mean age of the respondents was 42.2 ±16.6 years with 52% of the women aged between 35-49 years. The results of both techniques suggest that age and women with at least high school education have a significantly higher risk of being diagnosed with malignant breast tumors than benign breast tumors. The results also indicate a reduction of standard errors is associated with the coefficients obtained from the Bayesian approach. In addition, simulation result reveal that women with at least high school are 1.3 times more at risk of having malignant breast lesion in western Nigeria compared to benign breast lesion. Conclusion: We concluded that more efforts are required towards creating awareness and advocacy campaigns on how the prevalence of malignant breast lesions can be reduced, especially among women. The application of Bayesian produces precise estimates for modeling malignant breast cancer.

Spatial modeling of HIV and HSV-2 among women in Kenya with spatially varying coefficients

Background

Disease mapping has become popular in the field of statistics as a method to explain the spatial distribution of disease outcomes and as a tool to help design targeted intervention strategies.

Most of these models however have been implemented with assumptions that may be limiting or altogether lead to less meaningful results and hence interpretations. Some of these assumptions include the linearity, stationarity and normality assumptions. Studies have shown that the linearity assumption is not necessarily true for all covariates. Age for example has been found to have a non-linear relationship with HIV and HSV-2 prevalence. Other studies have made stationarity assumption in that one stimulus e.g. education, provokes the same response in all the regions under study and this is also quite restrictive. Responses to stimuli may vary from region to region due to aspects like culture, preferences and attitudes.

Methods

We perform a spatial modeling of HIV and HSV-2 among women in Kenya, while relaxing these assumptions i.e. the linearity assumption by allowing the covariate age to have a non-linear effect on HIV and HSV-2 prevalence using the random walk model of order 2 and the stationarity assumption by allowing the rest of the covariates to vary spatially using the conditional autoregressive model. The women data used in this study were derived from the 2007 Kenya AIDS indicator survey where women aged 15–49 years were surveyed. A full Bayesian approach was used and the models were implemented in R-INLA software.

Results

Age was found to have a non-linear relationship with both HIV and HSV-2 prevalence, and the spatially varying coefficient model provided a significantly better fit for HSV-2. Age-at first sex also had a greater effect on HSV-2 prevalence in the Coastal and some parts of North Eastern regions suggesting either early marriages or child prostitution. The effect of education on HIV prevalence among women was more in the North Eastern, Coastal, Southern and parts of Central region.

Conclusions

The models introduced in this study enable relaxation of two limiting assumptions in disease mapping. The effects of the covariates on HIV and HSV-2 were found to vary spatially. The effect of education on HSV-2 status for example was lower in North Eastern and parts of the Rift region than most of the other parts of the country. Age was found to have a non-linear effect on HIV and HSV-2 prevalence, a linearity assumption would have led to wrong results and hence interpretations. The findings are relevant in that they can be used in informing tailor made strategies for tackling HIV and HSV-2 in different counties. The methodology used here may also be replicated in other studies with similar data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-016-3022-0) contains supplementary material, which is available to authorized users.

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.

Semi-parametric spatial joint modeling of HIV and HSV-2 among women in Kenya

Several diseases have common risk factors. The joint modeling of disease outcomes within a spatial statistical context may provide more insight on the interaction of diseases both at individual and at regional level. Spatial joint modeling allows for studying of the relationship between diseases and also between regions under study. One major approach for joint spatial modeling is the multivariate conditional autoregressive approach. In this approach, it is assumed that all the covariates in the study have linear effects on the multiple response variables. In this study, we relax this linearity assumption and allow some covariates to have nonlinear effects using the penalized regression splines. This model was used to jointly model the spatial variation of human immunodeficiency virus (HIV) and herpes simplex virus-type 2 (HSV-2) among women in Kenya. The model was applied to HIV and HSV-2 prevalence data among women aged 15-49 years in Kenya, derived from the 2007 Kenya AIDS indicator survey. A full Bayesian approach was used and the models were implemented in WinBUGS software. Both diseases showed significant spatial variation with highest disease burdens occurring around the Lake Victoria region. There was a nonlinear association between age of an individual and HIV and HSV-2 infection. The peak age for HIV was around 30 years while that of HSV-2 was about 40 years. A positive significant spatial correlation between HIV and HSV-2 was observed with a correlation of 0.6831(95% CI: 0.3859, 0.871).

Bayesian mapping of HIV infection among women of reproductive age in Rwanda

HIV prevalence is rising and has been consistently higher among women in Rwanda whereas a decreasing national HIV prevalence rate in the adult population has stabilised since 2005. Factors explaining the increased vulnerability of women to HIV infection are not currently well understood. A statistical mapping at smaller geographic units and the identification of key HIV risk factors are crucial for pragmatic and more efficient interventions. The data used in this study were extracted from the 2010 Rwanda Demographic and Health Survey data for 6952 women. A full Bayesian geo-additive logistic regression model was fitted to data in order to assess the effect of key risk factors and map district-level spatial effects on the risk of HIV infection. The results showed that women who had STIs, concurrent sexual partners in the 12 months prior to the survey, a sex debut at earlier age than 19 years, were living in a woman-headed or high-economic status household were significantly associated with a higher risk of HIV infection. There was a protective effect of high HIV knowledge and perception. Women occupied in agriculture, and those residing in rural areas were also associated with lower risk of being infected. This study provides district-level maps of the variation of HIV infection among women of child-bearing age in Rwanda. The maps highlight areas where women are at a higher risk of infection; the aspect that proximate and distal factors alone could not uncover. There are distinctive geographic patterns, although statistically insignificant, of the risk of HIV infection suggesting potential effectiveness of district specific interventions. The results also suggest that changes in sexual behaviour can yield significant results in controlling HIV infection in Rwanda.