Mapping regional variability of exclusive breastfeeding and its determinants at different infant's age in Tanzania.

INTRODUCTION: Despite its numerous benefits, exclusive breastfeeding (EBF) remains an underutilized practice. Enhancing EBF uptake necessitates a focused approach targeting regions where its adoption is suboptimal. This study aimed to investigate regional disparities in EBF practices and identify determinants of EBF among infants aged 0-1, 2-3, and 4-5 months in Tanzania. METHODS: This cross-sectional study utilized data from the 2015/16 Tanzania Demographic and Health Survey. A total of 1,015 infants aged 0-5 met the inclusion criteria, comprising 378 aged 0-1 month, 334 at 2-3 months, and 303 at 4-5 months. EBF practices were assessed using a 24-hour recall method. A generalized linear mixed model, with fixed covariates encompassing infant and maternal attributes and clusters for enumeration areas (EAs) and regions, was employed to estimate EBF proportions. RESULTS: Regional disparities in EBF were evident among infants aged 0-1, 2-3, and 4-5 months, with decline in EBF proportions as an infant's age increases. This pattern was observed nationwide. Regional and EA factors influenced the EBF practices at 0-1 and 2-3 months, accounting for 17-40% of the variability at the regional level and 40-63% at the EA level. Literacy level among mothers had a significant impact on EBF practices at 2-3 months (e.g., women who could read whole sentences; AOR = 3.2, 95% CI 1.1,8.8). CONCLUSION: Regional disparities in EBF proportions exist in Tanzania, and further studies are needed to understand their underlying causes. Targeted interventions should prioritize regions with lower EBF proportions. This study highlights the clustering of EBF practices at 0-1 and 2-3 months on both regional and EA levels. Conducting studies in smaller geographical areas may enhance our understanding of the enablers and barriers to EBF and guide interventions to promote recommended EBF practices.

Bayesian spatio-temporal modelling and mapping of malaria and anaemia among children between 0 and 59 months in Nigeria.

BACKGROUND/M&M: A vital aspect of disease management and policy making lies in the understanding of the universal distribution of diseases. Nevertheless, due to differences all-over host groups and space-time outbreak activities, data are subject to intricacies. Herein, Bayesian spatio-temporal models were proposed to model and map malaria and anaemia risk ratio in space and time as well as to ascertain risk factors related to these diseases and the most endemic states in Nigeria. Parameter estimation was performed by employing the R-integrated nested Laplace approximation (INLA) package and Deviance Information Criteria were applied to select the best model. RESULTS: In malaria, model 7 which basically suggests that previous trend of an event cannot account for future trend i.e., Interaction with one random time effect (random walk) has the least deviance. On the other hand, model 6 assumes that previous event can be used to predict future event i.e., (Interaction with one random time effect (ar1)) gave the least deviance in anaemia. DISCUSSION: For malaria and anaemia, models 7 and 6 were selected to model and map these diseases in Nigeria, because these models have the capacity to receive strength from adjacent states, in a manner that neighbouring states have the same risk. Changes in risk and clustering with a high record of these diseases among states in Nigeria was observed. However, despite these changes, the total risk of malaria and anaemia for 2010 and 2015 was unaffected. CONCLUSION: Notwithstanding the methods applied, this study will be valuable to the advancement of a spatio-temporal approach for analyzing malaria and anaemia risk in Nigeria.

Using joint models to study the association between CD4 count and the risk of death in TB/HIV data.

BACKGROUND: The association structure linking the longitudinal and survival sub-models is of fundamental importance in the joint modeling framework and the choice of this structure should be made based on the clinical background of the study. However, this information may not always be accessible and rationale for selecting this association structure has received relatively little attention in the literature. To this end, we aim to explore four alternative functional forms of the association structure between the CD4 count and the risk of death and provide rationale for selecting the optimal association structure for our data. We also aim to compare the results obtained from the joint model to those obtained from the time-varying Cox model. METHODS: We used data from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) AIDS Treatment programme, the Starting Antiretroviral Therapy at Three Points in Tuberculosis (SAPiT) study, an open-label, three armed randomised, controlled trial between June 2005 and July 2010 (N=642). In our analysis, we combined the early and late integrated arms and compared results to the sequential arm. We utilized the Deviance Information Criterion (DIC) to select the final model with the best structure, with smaller values indicating better model adjustments to the data. RESULTS: Patient characteristics were similar across the study arms. Combined integrated therapy arms had a reduction of 55% in mortality (HR:0.45, 95% CI:0.28-0.72) compared to the sequential therapy arm. The joint model with a cumulative effects functional form was chosen as the best association structure. In particular, our joint model found that the area under the longitudinal profile of CD4 count was strongly associated with a 21% reduction in mortality (HR:0.79, 95% CI:0.72-0.86). Where as results from the time-varying Cox model showed a 19% reduction in mortality (HR:0.81, 95% CI:0.77-0.84). CONCLUSIONS: In this paper we have shown that the "current value" association structure is not always the best structure that expresses the correct relationship between the outcomes in all settings, which is why it is crucial to explore alternative clinically meaningful association structures that links the longitudinal and survival processes.

Application of quantile mixed-effects model in modeling CD4 count from HIV-infected patients in KwaZulu-Natal South Africa.

BACKGROUND: The CD4 cell count signifies the health of an individual's immune system. The use of data-driven models enables clinicians to accurately interpret potential information, examine the progression of CD4 count, and deal with patient heterogeneity due to patient-specific effects. Quantile-based regression models can be used to illustrate the entire conditional distribution of an outcome and identify various covariates effects at the respective location. METHODS: This study uses the quantile mixed-effects model that assumes an asymmetric Laplace distribution for the error term. The model also incorporated multiple random effects to consider the correlation among observations. The exact maximum likelihood estimation was implemented using the Stochastic Approximation of the Expectation-Maximization algorithm to estimate the parameters. This study used the Centre of the AIDS Programme of Research in South Africa (CAPRISA) 002 Acute Infection Study data. In this study, the response variable is the longitudinal CD4 count from HIV-infected patients who were initiated on Highly Active Antiretroviral Therapy (HAART), and the explanatory variables are relevant baseline characteristics of the patients. RESULTS: The analysis obtained robust parameters estimates at various locations of the conditional distribution. For instance, our result showed that baseline BMI (at [Formula: see text] 0.05: [Formula: see text]), baseline viral load (at [Formula: see text] 0.05: [Formula: see text] [Formula: see text]), and post-HAART initiation (at [Formula: see text] 0.05: [Formula: see text]) were major significant factors of CD4 count across fitted quantiles. CONCLUSIONS: CD4 cell recovery in response to post-HAART initiation across all fitted quantile levels was observed. Compared to HIV-infected patients with low viral load levels at baseline, HIV-infected patients enrolled in the treatment with a high viral load level at baseline showed a significant negative effect on CD4 cell counts at upper quantiles. HIV-infected patients registered with high BMI at baseline had improved CD4 cell count after treatment, but physicians should not ignore this group of patients clinically. It is also crucial for physicians to closely monitor patients with a low BMI before and after starting HAART.

Modeling of correlated cognitive function and functional disability outcomes with bounded and missing data in a longitudinal aging study.

Longitudinal studies of correlated cognitive and disability outcomes among older adults are characterized by missing data due to death or loss to follow-up from deteriorating health conditions. The Mini-Mental State Examination (MMSE) score for assessing cognitive function ranges from a minimum of 0 (floor) to a maximum of 30 (ceiling). To study the risk factors of cognitive function and functional disability, we propose a shared parameter model to handle missingness, correlation between outcomes, and the floor and ceiling effects of the MMSE measurements. The shared random effects in the proposed model handle missingness (either missing at random or missing not at random) and correlation between these outcomes, while the Tobit distribution handles the floor and ceiling effects of the MMSE measurements. We used data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and a simulation study. By ignoring the MMSE floor and ceiling effects in the analyses of the CLHLS, the association of systolic blood pressure with cognitive function was not significant and the association of age with cognitive function was lower by 16.6% (from -6.237 to -5.201). By ignoring the MMSE floor and ceiling effects in the simulation study, the relative bias in the estimated association of female gender with cognitive function was 43 times higher (from -0.01 to -0.44). The estimated associations obtained with data missing at random were smaller than those with data missing not at random, demonstrating how the missing data mechanism affects the analytic results. Our work underscores the importance of proper model specification in longitudinal analysis of correlated outcomes subject to missingness and bounded values.

Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach.

BACKGROUND: The rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country. METHODS: In this paper we apply the previously validated approach of phenomenological models, fitting several non-linear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level. Using publicly available daily reported cumulative case and death data up until 22 June 2020, we report 5, 10, 15, 20, 25 and 30-day ahead forecasts of cumulative cases and deaths. All predictions are compared to the actual observed values in the forecasting period. RESULTS: We observed that all models for cases provided accurate and similar short-term forecasts for a period of 5 days ahead at the national level, and that the three and four parameter logistic growth models provided more accurate forecasts than that obtained from the Richards model 10 days ahead. However, beyond 10 days all models underestimated the cumulative cases. Our forecasts across the models predict an additional 23,551-26,702 cases in 5 days and an additional 47,449-57,358 cases in 10 days. While the three parameter logistic growth model provided the most accurate forecasts of cumulative deaths within the 10 day period, the Gompertz model was able to better capture the changes in cumulative deaths beyond this period. Our forecasts across the models predict an additional 145-437 COVID-19 deaths in 5 days and an additional 243-947 deaths in 10 days. CONCLUSIONS: By comparing both the predictions of deaths and cases to the observed data in the forecasting period, we found that this modeling approach provides reliable and accurate forecasts for a maximum period of 10 days ahead.

Modelling HIV disease process and progression in seroconversion among South Africa women: using transition-specific parametric multi-state model.

BACKGROUND: HIV infected patients may experience many intermediate events including between-event transition throughout their follow up. Through modelling these transitions, we can gain a deeper understanding of HIV disease process and progression and of factors that influence the disease process and progression pathway. In this work, we present transition-specific parametric multi-state models to describe HIV disease process and progression. METHODS: The data is from an ongoing prospective cohort study conducted amongst adult women who were HIV-infected in KwaZulu-Natal, South Africa. Participants were enrolled during the acute HIV infection phase and then followed up during chronic infection, up to ART initiation. RESULTS: Transition specific distributions for multi-state models, including a variety of accelerated failure time (AFT) models and proportional hazards (PH) models, were presented and compared in this study. The analysis revealed that women enrolling with a CD4 count less than 350 cells/mm3 (severe and advanced disease stages) had a far lower chance of immune recovery, and a considerably higher chance of immune deterioration, compared to women enrolling with a CD4 count of 350 cells/mm3 or more (normal and mild disease stages). Our analyses also showed that older age, higher educational levels, higher scores for red blood cell counts, higher mononuclear scores, higher granulocytes scores, and higher physical health scores, all had a significant effect on a shortened time to immunological recovery, while women with many sex partners, higher viral load and larger family size had a significant effect on accelerating time to immune deterioration. CONCLUSION: Multi-state modelling of transition-specific distributions offers a flexible tool for the study of demographic and clinical characteristics' effects on the entire disease progression pathway. It is hoped that the article will help applied researchers to familiarize themselves with the models, including interpretation of results.

Modelling of viral load dynamics and CD4 cell count progression in an antiretroviral naive cohort: using a joint linear mixed and multistate Markov model.

BACKGROUND: Patients infected with HIV may experience a succession of clinical stages before the disease diagnosis and their health status may be followed-up by tracking disease biomarkers. In this study, we present a joint multistate model for predicting the clinical progression of HIV infection which takes into account the viral load and CD4 count biomarkers. METHODS: The data is from an ongoing prospective cohort study conducted among antiretroviral treatment (ART) naïve HIV-infected women in the province of KwaZulu-Natal, South Africa. We presented a joint model that consists of two related submodels: a Markov multistate model for CD4 cell count transitions and a linear mixed effect model for longitudinal viral load dynamics. RESULTS: Viral load dynamics significantly affect the transition intensities of HIV/AIDS disease progression. The analysis also showed that patients with relatively high educational levels (ß = - 0.004; 95% confidence interval [CI]:-0.207, - 0.064), high RBC indices scores (ß = - 0.01; 95%CI:-0.017, - 0.002) and high physical health scores (ß = - 0.001; 95%CI:-0.026, - 0.003) were significantly were associated with a lower rate of viral load increase over time. Patients with TB co-infection (ß = 0.002; 95%CI:0.001, 0.004), having many sex partners (ß = 0.007; 95%CI:0.003, 0.011), being younger age (ß = 0.008; 95%CI:0.003, 0.012) and high liver abnormality scores (ß = 0.004; 95%CI:0.001, 0.01) were associated with a higher rate of viral load increase over time. Moreover, patients with many sex partners (ß = - 0.61; 95%CI:-0.94, - 0.28) and with a high liver abnormality score (ß = - 0.17; 95%CI:-0.30, - 0.05) showed significantly reduced intensities of immunological recovery transitions. Furthermore, a high weight, high education levels, high QoL scores, high RBC parameters and being of middle age significantly increased the intensities of immunological recovery transitions. CONCLUSION: Overall, from a clinical perspective, QoL measurement items, being of a younger age, clinical attributes, marital status, and educational status are associated with the current state of the patient, and are an important contributing factor to extend survival of the patients and guide clinical interventions. From a methodological perspective, it can be concluded that a joint multistate model approach provides wide-ranging information about the progression and assists to provide specific dynamic predictions and increasingly precise knowledge of diseases.

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women.

BACKGROUND: Ordinal health longitudinal response variables have distributions that make them unsuitable for many popular statistical models that assume normality. We present a multilevel growth model that may be more suitable for medical ordinal longitudinal outcomes than are statistical models that assume normality and continuous measurements. METHODS: The data is from an ongoing prospective cohort study conducted amongst adult women who are HIV-infected patients in Kwazulu-Natal, South Africa. Participants were enrolled into the acute infection, then into early infection subsequently into established infection and afterward on cART. Generalized linear multilevel models were applied. RESULTS: Multilevel ordinal non-proportional and proportional-odds growth models were presented and compared. We observed that the effects of covariates can't be assumed identical across the three cumulative logits. Our analyses also revealed that the rate of change of immune recovery of patients increased as the follow-up time increases. Patients with stable sexual partners, middle-aged, cART initiation, and higher educational levels were more likely to have better immunological stages with time. Similarly, patients having high electrolytes component scores, higher red blood cell indices scores, higher physical health scores, higher psychological well-being scores, a higher level of independence scores, and lower viral load more likely to have better immunological stages through the follow-up time. CONCLUSION: It can be concluded that the multilevel non-proportional-odds method provides a flexible modeling alternative when the proportional-odds assumption of equal effects of the predictor variables at every stage of the response variable is violated. Having higher clinical parameter scores, higher QoL scores, higher educational levels, and stable sexual partners were found to be the significant factors for trends of CD4 count recovery.

Joint modelling of longitudinal and time-to-event data: an illustration using CD4 count and mortality in a cohort of patients initiated on antiretroviral therapy.

BACKGROUND: Modelling of longitudinal biomarkers and time-to-event data are important to monitor disease progression. However, these two variables are traditionally analyzed separately or time-varying Cox models are used. The former strategy fails to recognize the shared random-effects from the two processes while the latter assumes that longitudinal biomarkers are exogenous covariates, resulting in inefficient or biased estimates for the time-to-event model. Therefore, we used joint modelling for longitudinal and time-to-event data to assess the effect of longitudinal CD4 count on mortality. METHODS: We studied 4014 patients from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) who initiated ART between June 2004 and August 2013. We used proportional hazards regression model to assess the effect of baseline characteristics (excluding CD4 count) on mortality, and linear mixed effect models to evaluate the effect of baseline characteristics on the CD4 count evolution over time. Thereafter, the two analytical approaches were amalgamated to form an advanced joint model for studying the effect of longitudinal CD4 count on mortality. To illustrate the virtues of the joint model, the results from the joint model were compared to those from the time-varying Cox model. RESULTS: Using joint modelling, we found that lower CD4 count over time was associated with a 1.3-fold increase in the risk of death, (HR: 1.34, 95% CI: 1.27-1.42). Whereas, results from the time-varying Cox model showed lower CD4 count over time was associated with a 1.2-fold increase in the risk of death, (HR: 1.17, 95% CI: 1.12-1.23). CONCLUSIONS: Joint modelling enabled the assessment of the effect of longitudinal CD4 count on mortality while correcting for shared random effects between longitudinal and time-to-event models. In the era of universal test and treat, the evaluation of CD4 count is still crucial for guiding the initiation and discontinuation of opportunistic infections prophylaxis and assessment of late presenting patients. CD4 count can also be used when immunological failure is suspected as we have shown that it is associated with mortality.

Multivariate multilevel modeling of quality of life dynamics of HIV infected patients.

BACKGROUND: Longitudinal quality of life (QoL) is an important outcome in many chronic illness studies aiming to evaluate the efficiency of care both at the patient and health system level. Although many QoL studies involve multiple correlated hierarchical outcome measures, very few of them use multivariate modeling. In this work, we modeled the long-term dynamics of QoL scores accounting for the correlation between the QoL scores in a multilevel multivariate framework and to compare the effects of covariates across the outcomes. METHODS: The data is from an ongoing prospective cohort study conducted amongst adult women who were HIV-infected and on the treatment in Kwazulu-Natal, South Africa. Independent and related QoL outcome multivariate multilevel models were presented and compared. RESULTS: The analysis showed that related outcome multivariate multilevel models fit better for our data used. Our analyses also revealed that higher educational levels, middle age, stable sex partners and higher weights had a significant effect on better improvements in the rate of change of QoL scores of HIV infected patients. Similarly, patients without TB co-infection, without thrombocytopenia, with lower viral load, with higher CD4 cell count levels, with higher electrolytes component score, with higher red blood cell (RBC) component score and with lower liver abnormality component score, were associated with significantly improved the rate of change of QoL, amongst HIV infected patients. CONCLUSION: It is hoped that the article will help applied researchers to familiarize themselves with the models and including interpretation of results. Furthermore, three issues are highlighted: model building of multivariate multilevel outcomes, how this model can be used to assess multivariate assumptions, involving fixed effects (for example, to examine the size of the covariate effect varying across QoL domain scores) and random effects (for example, to examine the rate of change in one response variable associated to changes in the other).

Modelling immune deterioration, immune recovery and state-specific duration of HIV-infected women with viral load adjustment: using parametric multistate model.

BACKGROUND: CD4 cell and viral load count are highly correlated surrogate markers of human immunodeficiency virus (HIV) disease progression. In modelling the progression of HIV, previous studies mostly dealt with either CD4 cell counts or viral load alone. In this work, both biomarkers are in included one model, in order to study possible factors that affect the intensities of immune deterioration, immune recovery and state-specific duration of HIV-infected women. METHODS: The data is from an ongoing prospective cohort study conducted among antiretroviral treatment (ART) naïve HIV-infected women in the province of KwaZulu-Natal, South Africa. Participants were enrolled in the acute HIV infection phase, then followed-up during chronic infection up to ART initiation. Full-parametric and semi-parametric Markov models were applied. Furthermore, the effect of the inclusion and exclusion viral load in the model was assessed. RESULTS: Inclusion of a viral load component improves the efficiency of the model. The analysis results showed that patients who reported a stable sexual partner, having a higher educational level, higher physical health score and having a high mononuclear component score are more likely to spend more time in a good HIV state (particularly normal disease state). Patients with TB co-infection, with anemia, having a high liver abnormality score and patients who reported many sexual partners, had a significant increase in the intensities of immunological deterioration transitions. On the other hand, having high weight, higher education level, higher quality of life score, having high RBC parameters, high granulocyte component scores and high mononuclear component scores, significantly increased the intensities of immunological recovery transitions. CONCLUSION: Inclusion of both CD4 cell count based disease progression states and viral load, in the time-homogeneous Markov model, assisted in modeling the complete disease progression of HIV/AIDS. Higher quality of life (QoL) domain scores, good clinical characteristics, stable sexual partner and higher educational level were found to be predictive factors for transition and length of stay in sequential adversity of HIV/AIDS.

Adjusting the Effect of Integrating Antiretroviral Therapy and Tuberculosis Treatment on Mortality for Noncompliance: A Time-varying Instrumental Variables Analysis.

BACKGROUND: Using intent-to-treat comparisons, it has been shown that the integration of antiretroviral therapy (ART) and tuberculosis (TB) treatment improves survival. Because the magnitude of the effect of ART initiation during TB treatment on mortality is less well understood owing to noncompliance, we used instrumental variables (IV) analyses. METHODS: We studied 642 HIV-TB co-infected patients from the Starting Antiretroviral Therapy at Three Points in Tuberculosis trial. Patients were assigned to start ART either early or late during TB treatment or after TB treatment completion. We used 2-stage predictor substitution and 2-stage residuals inclusion methods under additive and proportional hazards regressions with a time-fixed measure of compliance defined as the fraction of time on ART during TB treatment. We moreover developed novel IV methods for additive hazards regression with a time-varying measure of compliance. RESULTS: Intent-to-treat results from additive hazards models showed that patients in the early integrated arms had a reduced hazard of -0.05 (95% confidence interval [CI]: -0.09, -0.01) when compared with the sequential arm. Adjustment for noncompliance changed this effect to -0.07 (95% CI: -0.12, -0.01). An additional time-varying IV analysis on the overall effect of ART exposure suggested an effect of -0.29 (95 % CI: -0.54, -0.03). CONCLUSION: IV analyses enable assessment of the effectiveness of TB and ART integration, corrected for noncompliance, and thereby enable a better public health evaluation of the potential impact of this intervention.

Bayesian modeling of spatiotemporal patterns of TB-HIV co-infection risk in Kenya.

BACKGROUND: Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) diseases are globally acknowledged as a public health challenge that exhibits adverse bidirectional relations due to the co-epidemic overlap. To understand the co-infection burden we used the case notification data to generate spatiotemporal maps that described the distribution and exposure hypotheses for further epidemiologic investigations in areas with unusual case notification levels. METHODS: We analyzed the TB and TB-HIV case notification data from the Kenya national TB control program aggregated for forty-seven counties over a seven-year period (2012-2018). Using spatiotemporal poisson regression models within the Integrated Nested Laplace Approach (INLA) paradygm, we modeled the risk of TB-HIV co-infection. Six competing models with varying space-time formulations were compared to determine the best fit model. We then assessed the geographic patterns and temporal trends of coinfection risk by mapping the posterior marginal from the best fit model. RESULTS: Of the total 608,312 TB case notifications, 194,129 were HIV co-infected. The proportion of TB-HIV co-infection was higher in females (39.7%) than in males (27.0%). A significant share of the co-infection was among adults aged 35 to 44 years (46.7%) and 45 to 54 years (42.1%). Based on the Bayesian Defiance Information (DIC) and the effective number of parameters (pD) comparisons, the spatiotemporal model allowing space-time interaction was the best in explaining the geographical variations in TB-HIV coinfection. The model results suggested that the risk of TB-HIV coinfection was influenced by infrastructure index (Relative risk (RR) = 5.75, Credible Interval (Cr.I) = (1.65, 19.89)) and gender ratio (RR = 5.81e-04, Cr. I = (1.06e-04, 3.18e-03). The lowest and highest temporal relative risks were in the years 2016 at 0.9 and 2012 at 1.07 respectively. The spatial pattern presented an increased co-infection risk in a number of counties. For the spatiotemporal interaction, only a few counties had a relative risk greater than 1 that varied in different years. CONCLUSIONS: We identified elevated risk areas for TB/HIV co-infection and fluctuating temporal trends which could be because of improved TB case detection or surveillance bias caused by spatial heterogeneity in the co-infection dynamics. Focused interventions and continuous TB-HIV surveillance will ensure adequate resource allocation and significant reduction of HIV burden amongst TB patients.

Effect of isoniazid preventive therapy on tuberculosis incidence and associated risk factors among HIV infected adults in Tanzania: a retrospective cohort study.

BACKGROUND: Tuberculosis (TB) continues to be the leading cause of morbidity and mortality among human immunodeficiency virus (HIV) infected individuals in Sub Saharan Africa including Tanzania. Provision of isoniazid preventive therapy (IPT) is one of the public health interventions to reduce the burden of TB among HIV infected persons. However there is limited information about the influence of IPT on TB incidence in Tanzania. This study aimed at ascertaining the effect of IPT on TB incidence and to determine risk factors for TB among HIV positive adults in Dar es Salaam region. METHODS: A retrospective cohort study was conducted using secondary data of HIV positive adults receiving care and treatment services in Dar es Salaam region from 2011 to 2014. TB incidence rate among HIV positive adults on IPT was compared to those who were not on IPT during the follow up period. Risk factors for incident TB were estimated using multivariate Cox proportional hazards regression model. RESULTS: A total of 68,378 HIV positive adults were studied. The median follow up time was 3.4 (IQR = 1.9-3.8) years for patients who ever received IPT and 1.3 (IQR = 0.3-1.3) years among those who never received IPT. A total of 3124 TB cases occurred during 114,926 total person-years of follow up. The overall TB incidence rate was 2.7/100 person-years (95%CI; 2.6-2.8). Patients on IPT had 48% lower TB incidence rate compared to patients who were not on IPT (IRR = 0.52, 95%CI; 0.46-0.59). Factors associated with higher risk for incident TB included; being male (aHR = 1.8, 95% CI; 1.6-2.0), WHO stage III (aHR = 2.7, 95% CI; 2.3-3.3) and IV (aHR = 2.4, 95% CI; 1.9-3.1),being underweight (aHR = 1.7, 95% CI; 1.5-1.9) while overweight (aHR = 0.7, 95% CI; 0.6-0.8), obese (aHR = 0.5, 95% CI; 0.4-0.7), having baseline CD4 cell count between 200 and 350 cells/µl (aHR = 0.7, 95% CI; 0.6-0.8) and CD4 count above 350 cells/µl (aHR = 0.5, 95% CI; 0.4-0.6) were associated with lower risk of developing TB. CONCLUSION: Isoniazid preventive therapy (IPT) has shown to be effective in reducing TB incidence among HIV infected adults in Dar es Salaam. More efforts are needed to increase the provision and coverage of IPT.

Time series non-Gaussian Bayesian bivariate model applied to data on HMPV and RSV: a case of Dadaab in Kenya.

BACKGROUND: Human metapneumovirus (HMPV) have similar symptoms to those caused by the respiratory syncytial virus (RSV). The modes of transmission and dynamics of time series data still remain poorly understood. Climatic factors have long been suspected to be implicated in impacting on the number of cases for these epidemics. Currently, only a few models satisfactorily capture the dynamics of time series data of these two viruses. Our objective was to assess the presence of influence of high incidences between the viruses and to ascertain whether higher incidences of one virus are influenced by the other. METHODS: In this study, we used a negative binomial model to investigate the relationship between RSV and HMPV while adjusting for climatic factors. We specifically aimed at establishing the heterogeneity in the autoregressive effect to account for the influence between these viruses. RESULTS: In this study, our findings showed that RSV incidence contributed to the severity of HMPV incidence. This was achieved through comparison of 12 models with different structures, including those with and without interaction between climatic factors. The models with climatic factors out-performed those without. CONCLUSIONS: The study has improved our understanding of the dynamics of RSV and HMPV in relation to climatic cofactors thereby setting a platform to devise better intervention measures to combat the epidemics. We conclude that preventing and controlling RSV infection subsequently reduces the incidence of HMPV.

A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.

BACKGROUND: Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF) are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate. METHODS: In this study, we compare the random survival forest model to the conditional inference model (CIF) using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points). The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB) which consists of mainly categorical covariates with two levels (few split-points). RESULTS: The study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points. CONCLUSION: Although survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question.

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.

Mendelian Randomization versus Path Models: Making Causal Inferences in Genetic Epidemiology.

OBJECTIVE: The term Mendelian randomization is popular in the current literature. The first aim of this work is to describe the idea of Mendelian randomization studies and the assumptions required for drawing valid conclusions. The second aim is to contrast Mendelian randomization and path modeling when different 'omics' levels are considered jointly. METHODS: We define Mendelian randomization as introduced by Katan in 1986, and review its crucial assumptions. We introduce path models as the relevant additional component to the current use of Mendelian randomization studies in 'omics'. Real data examples for the association between lipid levels and coronary artery disease illustrate the use of path models. RESULTS: Numerous assumptions underlie Mendelian randomization, and they are difficult to be fulfilled in applications. Path models are suitable for investigating causality, and they should not be mixed up with the term Mendelian randomization. In many applications, path modeling would be the appropriate analysis in addition to a simple Mendelian randomization analysis. CONCLUSIONS: Mendelian randomization and path models use different concepts for causal inference. Path modeling but not simple Mendelian randomization analysis is well suited to study causality with different levels of 'omics' data.

An assessment of adverse drug reactions among HIV positive patients receiving antiretroviral treatment in South Africa.

BACKGROUND: Antiretroviral treatment (ART) has been effective in reducing HIV/AIDS related morbidity and mortality. However, the use and uptake of ART has resulted in adverse reactions, due mainly to the medicine's toxicity and interactions with other medicines. The timing of adverse drug reactions (ADRs) among these patients is a critical public health issue for antiretroviral (ARV) treatment adherence and retention. Reliable monitoring of HIV patients on ART is through a structured pharmacovigilance surveillance system. However, recurrent nature of these data pose challenges in their analyses. This study aimed at modelling the timing of ADR events in HIV patients on ART using correlated time-to-event models. METHODS: The data concern 590 HIV patients registered onto the Medunsa National ARV Pharmacovigilance Surveillance System within 6 months of ART initiation between February 2007 and July 2011. Recurrent times of ADRs and baseline characteristics: patient gender, and age, ART regimen, clinic and initiation period were extracted from the data. The recurrent ADR events data were modelled using both shared frailty and marginal models on the five patients' characteristics as covariates. RESULTS: Out of 590 patients, 67% were female, 68% started on regimen: Stavudine, Lamivudine and Efavirenz; 37% had experienced at least one ADR and 67% started ART in 2009-2011. Age (p-value = 0.0210), clinic (p-value < 0.0001) and period of ART initiation (p-value = 0.0002) were significantly associated with timing of first ADR. There was a significantly higher rates of ADR recurrences in patients aged 38-44 years [HR = 2.45; 95% CI = (1.47; 4.10)] vs. 30 years and less, patients taking regimen: Zidovudine, Lamivudine and Nevarapine) vs. regimen: Stavudine, Lamivudine and Efavirenz [HR = 2.09; 95% CI = (1.35; 3.22)], while the rate was lower among those who started ART in 2009-2011 vs. those who initiated in 2007-2008 [HR = 0.55; 95% CI = (0.40; 0.76)]. CONCLUSION: More realistic time-to-event models for recurrent events data have been used to analyse timing of ADR events in HIV patients taking ARV treatment. Age, antiretroviral regimen type and period of initiation of ART were associated with the timing of HIV/AIDS drug related adverse reactions regardless of the analysis model used. This study has public health policy implications in addressing the added morbidity among HIV patients taking ARV treatment in the context of universal scaling up of ARV treatment.