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

Human immunodeficiency virus epidemic scenery among brazilian women: a spatial analysis study

BACKGROUND

Approximately 37.7 million people worldwide are infected with human immunodeficiency virus (HIV). Although HIV detection among women, they still representing 53% of population living with the virus. Spatial analysis techniques are powerful tools for combating HIV allowing the association of the phenomenon with socioeconomic and political factors. Therefore, the main goal of this study was to spatially analyze HIV prevalence among Brazilian women from 2007 to 2020.

METHODS

ecological study was conducted using secondary databases of the Notifiable Diseases Information System (SINAN) for HIV and Acquired Immunodeficiency Syndrom (AIDS) in Brazilian women 15 years old and over. Age-adjusted HIV/AIDS incidence rates were analyzed using spatial distribution, autocorrelation, and spatiotemporal risk analysis techniques.

RESULTS

During the study period, 119,890 cases of HIV/AIDS were reported among Brazilian women. The southeastern region had a higher age-adjusted HIV/AIDS incidence than other Brazilian regions. Hotspot HIV/AIDS incidence rates decreased in all Brazil. Piauí, Paraná, and Minas Gerais were the only states with an increased number of cold spots. Previous spatiotemporal risk zones were observed in the states of São Paulo, Rio Grande do Sul, and Rio de Janeiro. Belém was a risk zone with a later spatiotemporal risk.

CONCLUSIONS

The efficiency of public policies fighting HIV has not been uniform among municipalities, although HIV/AIDS cases have decreased among Brazilian women. The social determinants of health in each municipality should be considered when local health authorities implement policies. Women empowerment should be promoted, and access to preventive, diagnostic, and treatment healthcare places should be expanded and guaranteed.

A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.

Exploring regional and sociodemographic disparities associated with unenrollment for the disease management program for type 2 Diabetes Mellitus using Bayesian spatial modelling

BACKGROUND

The disease management program (DMP) for type 2 Diabetes Mellitus (T2DM) is the largest DMP in Germany. Our goal was to analyze regional differences in unenrollment rates, suggest areas for intervention and provide background information, which population groups in which locations are currently not enrolled in the DMP for T2DM.

METHODS

In this study, we used data of the 1.7 mil. insurants of the AOK Nordost health insurance. For the visualization of enrollment potential, we used the Besag-York-Mollie model (BYM). The spatial scan statistic (SaTScan) was used to detect areas of unusually high rates of unenrolled diabetics to prioritize areas for intervention. To explore sociodemographic associations, we used Bayesian spatial global regression models. A Spatially varying coefficient model (SVC) revealed in how far the detected associations vary over space.

RESULTS

The proportion of diabetics currently not enrolled in the DMP T2DM was 36.8% in 2019 and varied within northeastern Germany. Local clusters were detected mainly in Mecklenburg-West-Pomerania and Berlin. The main sociodemographic variables associated with unenrollment were female sex, younger age, being unemployed, foreign citizenship, small household size and the proportion of persons commuting to work outside their residential municipality. The SVC model revealed important spatially varying effects for some but not all associations.

CONCLUSION

Lower socioeconomic status and foreign citizenship had an ubiquitous effect on not being enrolled. The DMP T2DM therefore does currently not reach those population groups, which have a higher risk for secondary diseases and possible avoidable hospitalizations. Logically, future interventions should focus on these groups. Our methodology clearly suggests areas for intervention and points out, which population group in which locations should be specifically approached.

Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique

Most analyses of spatial patterns of disease risk using health survey data fail to adequately account for the complex survey designs. Particularly, the survey sampling weights are often ignored in the analyses. Thus, the estimated spatial distribution of disease risk could be biased and may lead to erroneous policy decisions. This paper aimed to present recent statistical advances in disease-mapping methods that incorporate survey sampling in the estimation of the spatial distribution of disease risk. The methods were then applied to the estimation of the geographical distribution of child malnutrition in Malawi, and child fever and diarrhoea in Mozambique. The estimation of the spatial distributions of the child disease risk was done by Bayesian methods. Accounting for sampling weights resulted in smaller standard errors for the estimated spatial disease risk, which increased the confidence in the conclusions from the findings. The estimated geographical distributions of the child disease risk were similar between the methods. However, the fits of the models to the data, as measured by the deviance information criteria (DIC), were different.

Spatial pattern and determinants of anemia among women of childbearing age in Nigeria

BACKGROUND

The risk of anemia in Nigeria is of public health importance, with an increasing number of women of reproductive age being anemic. This study sought to identify the spatial distribution and examine the geographical variation of anemia risk at a regional level while accounting for risk factors associated with anemia among women of childbearing age in Nigeria. The significant interest in spatial statistics lies in identifying associated risk factors that enhance the risk of infection. However, most studies make no or limited use of the data's spatial structure and possible non-linear effects of the risk factors.

METHODS

The data used in this study were extracted from the 2015 Nigeria Demographic and Health Survey (NDHS). A full Bayesian semi-parametric regression model was fitted to data to accomplish the aims of the study. Model estimation and the inference was fully Bayesian approach via integrated nested Laplace approximations (INLA). The fixed effects were modeled parametrically; non-linear effects were modeled non-parametrically using second-order random walk priors.

RESULTS

Wealth index, level of education, type of residence, and unprotected drinking water source were found to be the risk factors associated with anemia. The risk of anemia was found to vary across different regions, with North Central, North East, and North West regions having the highest number of cases and South East with the least number of cases. The spatial analysis result indicated that statistically high hot-spots of anemia were observed in all the northern parts of the country.

CONCLUSION

The study revealed associations between anemia risk and women residing in rural settlements, wealth index, women with no formal education, and unprotected drinking water sources. Community and household-related change interventions should, therefore, be pertinent to the prevention of anemia. The spatial analysis further revealed a significant anemia risk towards the Northern areas of Nigeria. We propose that interventions targeting women of reproductive age should initially focus on these regions and subsequently spread across Nigeria.

A note on the impact of late diagnosis on HIV/AIDS dynamics: a mathematical modelling approach

Objectives:

The global incidence of HIV infection is not significantly decreasing, especially in sub-Saharan African countries. Though there is availability and accessibility of free HIV services, people are not being diagnosed early for HIV, and hence HIV-related mortality remains significantly high. We formulate a mathematical model for the spread of HIV using non linear ordinary differential equations in order to investigate the impact of late diagnosis of HIV on the spread of HIV.

Results:

The results suggest the need to encourage early initiation into HIV treatment as well as promoting HIV self-testing programs that enable more undiagnosed people to know their HIV status in order to curtail the continued spread of HIV.

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.

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.

Education, early screening and treatment of STIs could reduce infertility among women in Kenya

In Kenya, sexually transmitted infections (STIs) such as Chlamydia trachomatis, Neisseria gonorrhoea, HIV, herpes simplex virus type 2 (HSV-2), syphilis and trichomoniasis tend to be prevalent, especially in women. Further, the research shows that women who test positive for STIs (other than HIV), have little knowledge of these infections. Of particular concern, is that there has been little attention on the part of government to educate the general public about STIs, yet these diseases can have devastating consequences on women's and men's health. In women, STIs can produce sequelae such as tubal infertility. To help reduce female factor infertility, the Kenya government should conduct a nationwide campaign to educate the public about the importance of screening and treatment of STIs.