A Flexible Bayesian Parametric Proportional Hazard Model: Simulation and Applications to Right-Censored Healthcare Data.

Survival analysis is a collection of statistical techniques which examine the time it takes for an event to occur, and it is one of the most important fields in biomedical sciences and other variety of scientific disciplines. Furthermore, the computational rapid advancements in recent decades have advocated the application of Bayesian techniques in this field, giving a powerful and flexible alternative to the classical inference. The aim of this study is to consider the Bayesian inference for the generalized log-logistic proportional hazard model with applications to right-censored healthcare data sets. We assume an independent gamma prior for the baseline hazard parameters and a normal prior is placed on the regression coefficients. We then obtain the exact form of the joint posterior distribution of the regression coefficients and distributional parameters. The Bayesian estimates of the parameters of the proposed model are obtained using the Markov chain Monte Carlo (McMC) simulation technique. All computations are performed in Bayesian analysis using Gibbs sampling (BUGS) syntax that can be run with Just Another Gibbs Sampling (JAGS) from the R software. A detailed simulation study was used to assess the performance of the proposed parametric proportional hazard model. Two real-survival data problems in the healthcare are analyzed for illustration of the proposed model and for model comparison. Furthermore, the convergence diagnostic tests are presented and analyzed. Finally, our research found that the proposed parametric proportional hazard model performs well and could be beneficial in analyzing various types of survival data.

Bayesian and Classical Inference for the Generalized Log-Logistic Distribution with Applications to Survival Data.

The generalized log-logistic distribution is especially useful for modelling survival data with variable hazard rate shapes because it extends the log-logistic distribution by adding an extra parameter to the classical distribution, resulting in greater flexibility in analyzing and modelling various data types. We derive the fundamental mathematical and statistical properties of the proposed distribution in this paper. Many well-known lifetime special submodels are included in the proposed distribution, including the Weibull, log-logistic, exponential, and Burr XII distributions. The maximum likelihood method was used to estimate the unknown parameters of the proposed distribution, and a Monte Carlo simulation study was run to assess the estimators' performance. This distribution is significant because it can model both monotone and nonmonotone hazard rate functions, which are quite common in survival and reliability data analysis. Furthermore, the proposed distribution's flexibility and usefulness are demonstrated in a real-world data set and compared to its submodels, the Weibull, log-logistic, and Burr XII distributions, as well as other three-parameter parametric survival distributions, such as the exponentiated Weibull distribution, the three-parameter log-normal distribution, the three-parameter (or the shifted) log-logistic distribution, the three-parameter gamma distribution, and an exponentiated Weibull distribution. The proposed distribution is plausible, according to the goodness-of-fit, log-likelihood, and information criterion values. Finally, for the data set, Bayesian inference and Gibb's sampling performance are used to compute the approximate Bayes estimates as well as the highest posterior density credible intervals, and the convergence diagnostic techniques based on Markov chain Monte Carlo techniques were used.

Bayesian Spatial Modeling of Anemia among Children under 5 Years in Guinea.

Anemia is a major public health problem in Africa, affecting an increasing number of children under five years. Guinea is one of the most affected countries. In 2018, the prevalence rate in Guinea was 75% for children under five years. This study sought to identify the factors associated with anemia and to map spatial variation of anemia across the eight (8) regions in Guinea for children under five years, which can provide guidance for control programs for the reduction of the disease. Data from the Guinea Multiple Indicator Cluster Survey (MICS5) 2016 was used for this study. A total of 2609 children under five years who had full covariate information were used in the analysis. Spatial binomial logistic regression methodology was undertaken via Bayesian estimation based on Markov chain Monte Carlo (MCMC) using WinBUGS software version 1.4. The findings in this study revealed that 77% of children under five years in Guinea had anemia, and the prevalences in the regions ranged from 70.32% (Conakry) to 83.60% (NZerekore) across the country. After adjusting for non-spatial and spatial random effects in the model, older children (48-59 months) (OR: 0.47, CI [0.29 0.70]) were less likely to be anemic compared to those who are younger (0-11 months). Children whose mothers had completed secondary school or above had a 33% reduced risk of anemia (OR: 0.67, CI [0.49 0.90]), and children from household heads from the Kissi ethnic group are less likely to have anemia than their counterparts whose leaders are from Soussou (OR: 0.48, CI [0.23 0.92]).

The spatial epidemiology of leprosy in Kenya: A retrospective study.

BACKGROUND: Leprosy elimination defined as a registered prevalence rate of less than 1 case per 10,000 persons was achieved in Kenya at the national level in 1989. However, there are still pockets of leprosy in some counties where late diagnosis and consequent physical disability persist. The epidemiology of leprosy in Kenya for the period 2012 through to 2015 was defined using spatial methods. METHODS: This was a retrospective ecological correlational study that utilized leprosy case based data extracted from the National Leprosy Control Program database. Geographic information system and demographic data were obtained from Kenya National Bureau of Statistics (KNBS). Chi square tests were carried out to check for association between sociodemographic factors and disease indicators. Two Spatial Poisson Conditional Autoregressive (CAR) models were fitted in WinBUGS 1.4 software. The first model included all leprosy cases (new, retreatment, transfers from another health facility) and the second one included only new leprosy cases. These models were used to estimate leprosy relative risks per county as compared to the whole country i.e. the risk of presenting with leprosy given the geographical location. PRINCIPAL FINDINGS: Children aged less than 15 years accounted for 7.5% of all leprosy cases indicating active leprosy transmission in Kenya. The risk of leprosy notification increased by about 5% for every 1 year increase in age, whereas a 1% increase in the proportion of MB cases increased the chances of new leprosy case notification by 4%. When compared to the whole country, counties with the highest risk of leprosy include Kwale (relative risk of 15), Kilifi (RR;8.9) and Homabay (RR;4.1), whereas Turkana had the lowest relative risk of 0.005. CONCLUSION: Leprosy incidence exhibits geographical variation and there is need to institute tailored local control measures in these areas to reduce the burden of disability.

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.

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).

Low adverse event rates following voluntary medical male circumcision in a high HIV disease burden public sector prevention programme in South Africa.

INTRODUCTION: The provision of voluntary medical male circumcision (VMMC) services was piloted in three public sector facilities in a high HIV disease burden, low circumcision rate province in South Africa to inform policy and operational guidance for scale-up of the service for HIV prevention. We report on adverse events (AEs) experienced by clients following the circumcision procedure. METHODS: Prospective recruitment of HIV-negative males aged 12 and older volunteering to be circumcised at three select public health facilities in KwaZulu-Natal between November 2010 and May 2011. Volunteers underwent standardized medical screening including a physical assessment prior to the surgical procedure being performed. AEs were monitored at three time intervals over a 21-day period post-operatively to determine safety outcomes in this pilot demonstration programme. RESULTS: A total of 602 volunteers participated in this study. The median age of the volunteers was 22 years (range 12-56). Most participants (75.6%) returned for the 48-hour post-operative visit; 51.0% for day seven visit and 26.1% for the 21st day visit. Participants aged 20-24 were most likely to return. The AE rate was 0.2% intra-operatively. The frequency of moderate AEs was 0.7, 0.3 and 0.6% at 2-, 7- and 21-day visits, respectively. The frequency of severe AEs was 0.4, 0.3 and 0.6% at 2-, 7- and 21-day visits, respectively. Swelling and wound infection were the most common AEs with mean appearance duration of seven days. Clients aged between 35 and 56 years presented with most AEs (3.0%). CONCLUSIONS: VMMC can be delivered safely at resource-limited settings. The intensive three-visit post-operative review practice may be unfeasible due to high attrition rates over time, particularly amongst older men.

Prevalence and risk factors of anaemia among children aged between 6 months and 14 years in Kenya.

BACKGROUND: Anaemia is one of the significant public health problems among children in the world. Understanding risk factors of anaemia provides more insight to the nature and types of policies that can be put up to fight anaemia. We estimated the prevalence and risk factors of anaemia in a population-based, cross-sectional survey. METHODOLOGY: Blood samples from 11,711 children aged between 6 months and 14 years were collected using a single-use, spring-loaded, sterile lancet to make a finger prick. Anaemia was measured based on haemoglobin concentration level. The generalized linear model framework was used to analyse the data, in which the response variable was either a child was anemic or not anemic. RESULTS: The overall prevalence of anaemia among the children in Kenya was estimated to be 28.8%. Across each band of age within which the definition of anaemia remained constant (0­4, 5­11, and 12­14 years old), the prevalence of anaemia declined with each year of age. [corrected]. The risk of anaemia was significantly higher in male than female children. Mothers with secondary and above education had a protective effect on the risk of anaemia on their children. Malaria diagnosis status of a child was positively associated with risk anaemia. CONCLUSION: Controlling co-morbidity of malaria and improving maternal knowledge are potential options for reducing the burden of anaemia.

Bayesian spatial semi-parametric modeling of HIV variation in Kenya.

Spatial statistics has seen rapid application in many fields, especially epidemiology and public health. Many studies, nonetheless, make limited use of the geographical location information and also usually assume that the covariates, which are related to the response variable, have linear effects. We develop a Bayesian semi-parametric regression model for HIV prevalence data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (McMC). The model is applied to HIV prevalence data among men in Kenya, derived from the Kenya AIDS indicator survey, with n = 3,662. Past studies have concluded that HIV infection has a nonlinear association with age. In this study a smooth function based on penalized regression splines is used to estimate this nonlinear effect. Other covariates were assumed to have a linear effect. Spatial references to the counties were modeled as both structured and unstructured spatial effects. We observe that circumcision reduces the risk of HIV infection. The results also indicate that men in the urban areas were more likely to be infected by HIV as compared to their rural counterpart. Men with higher education had the lowest risk of HIV infection. A nonlinear relationship between HIV infection and age was established. Risk of HIV infection increases with age up to the age of 40 then declines with increase in age. Men who had STI in the last 12 months were more likely to be infected with HIV. Also men who had ever used a condom were found to have higher likelihood to be infected by HIV. A significant spatial variation of HIV infection in Kenya was also established. The study shows the practicality and flexibility of Bayesian semi-parametric regression model in analyzing epidemiological data.