Incidence estimation and calibration from cross-sectional data of acute infection HIV-1 seroconvertors

ABSTRACT Incidence estimation and calibration from cross-sectional data of acute infection HIV-1 seroconvertors. May 2007 Eustasius Musenge Masters in Medicine in the Field of Biostatistics and Epidemiology Supervised by: Mr E Marinda and Dr A Welte Background: The HIV-1 incidence (a very important measure used as a proxy for disease burden) can be estimated from a cross-sectional study. This incidence estimate has the advantage of reducing on costs and time, thus enabling more timely intervention; it is also ideal for developing nations. A common procedure used in making this estimate utilizes two antibody tests (Sensitive/Less sensitive tests). Due to the long window period of such tests (at least three months), persons classified as recently infected would have been infected more than three months prior to the test date. Detecting acute HIV-1 infection is very important since this is the most infectious stage of the disease. This research report explores a method of estimating incidence using an antibody test and a virological test, Polymerase Chain Reaction Ribonucleic Acid (PCR-RNA).The cross-sectional data used are from the Centre for the AIDS Programme of Research in South Africa (CAPRISA). Methods: Actual follow-up cohort data from CAPRISA acute infection cohort (AIC), comprised of 245 sex workers, were used to estimate the incidence of HIV-1 using a PCR-RNA ,virology test based, incidence formula. The result obtained was compared to the incidence estimate obtained by the classical method of estimating incidence the AIDS Programme of Research in South Africa (CAPRISA). Methods: Actual follow-up cohort data from CAPRISA acute infection cohort (AIC), comprised of 245 sex workers, were used to estimate the incidence of HIV-1 using a PCR-RNA ,virology test based, incidence formula. The result obtained was compared to the incidence estimate obtained by the classical method of estimating incidence (prospective cohort follow-up). As a measure to reduce costs inherent in virological tests (PCR-RNA), multistage pooling was discussed and several pooling strategies simulations were proposed with their uncertainties. Point estimates and interval estimates of the window period, window period prevalence and incidence from crosssectional study of the AIC cohort were computed. Findings: The mean window period was 6.6 days 95% CI: (2.7 – 13.0). The monthly window period prevalence was 0.09423 percent 95 % CI: (0.0193 – 0.1865)%. The incidence from the prospective cohort follow-up was 5.43 percent 95% CI: (3.9 – 9.2) %. The incidence estimate from cross-sectional formulae was 5.21 percent 95% CI: (4.1– 4.6). It was also shown by use of simulations that an optimum pool sample size is obtained when at least half the samples are removed on every run. Interpretation and recommendations: The PCR-RNA test is very sensitive at detecting acute HIV-1 infected persons. The incidence estimate from the crosssectional study formulae was very similar to that obtained from a follow-up study. The number of tests needed can be reduced and a good estimate of the incidence can still be obtained. The calibration was not accurate since the samples used were small and the window period duration was too short, hence, it was difficult to extrapolate to the whole population. Further work still needs to be done on the calibration of the proposed incidence formulae as it could be a very useful public health tool

Effects of shoulder strapping in patients with stroke: A randomised control trial

Background: Disability post stroke remains a global problem, with upper limb involvement playing a key role. Shoulder strapping is one of the techniques used clinically to address this. Objectives: To compare the effect of two shoulder strapping techniques in patients with stroke. Method: A longitudinal randomised controlled trial included baseline, weeks one, two and six assessments of 56 participants with upper limb hemiplegia. The participants were assessed for shoulder subluxation, shoulder pain, upper limb motor function and muscle tone. They were randomised into control, longitudinal strapping or circumferential strapping groups. Results: Longitudinal strapping had a non-significant decrease in shoulder subluxation and pain (p > 0.05). Circumferential strapping had no significant effect on any outcomes; however, it prevented the shoulder pain from worsening as much as in the control group (p > 0.05). General improvement in upper limb motor function was observed for all three groups. Conclusion: Trends in improvement showed that longitudinal strapping could be recommended because it positively influenced shoulder subluxation and pain. Even without significant changes, strapping creates awareness of the limb in patients and caregivers and could be of clinical benefit. Clinical implication: Longitudinal strapping of the shoulder in patients with stroke seems to positively influence shoulder subluxation and pain

HIV Disease Progression Among Antiretroviral Therapy Patients in Zimbabwe: A Multistate Markov Model.

Background: Antiretroviral therapy (ART) impact has prolonged survival of people living with HIV. We evaluated HIV disease progression among ART patients using routinely collected patient-level data between 2004 and 2017 in Zimbabwe. Methods: We partitioned HIV disease progression into four transient CD4 cell counts states: state 1 (CD4 ≥ 500 cells/μl), state 2 (350 cells/μl ≤ CD4 < 500 cells/μl), state 3 (200 cells/μl ≤ CD4 < 350 cells/μl), state 4 (CD4 < 200 cells/μl), and the absorbing state death (state 5). We proposed a semiparametric time-homogenous multistate Markov model to estimate bidirectional transition rates. Covariate effects (age, gender, ART initiation period, and health facility level) on the transition rates were assessed. Results: We analyzed 204,289 clinic visits by 63,422 patients. There were 24,325 (38.4%) patients in state 4 (CD4 < 200) at ART initiation, and 7,995 (12.6%) deaths occurred by December 2017. The overall mortality rate was 3.9 per 100 person-years. The highest mortality rate of 5.7 per 100 person-years (4,541 deaths) was from state 4 (CD4 < 200) compared to other states. Mortality rates decreased with increase in time since ART initiation. Health facility type was the strongest predictor for immune recovery. Provincial or central hospital patients showed a diminishing dose-response effect on immune recovery by state from a hazard ratio (HR) of 8.30 [95% confidence interval (95% CI), 6.64-10.36] (state 4 to 3) to HR of 3.12 (95% CI, 2.54-4.36) (state 2 to 1) compared to primary healthcare facilities. Immune system for male patients was more likely to deteriorate, and they had a 32% increased mortality risk (HR, 1.32; 95% CI, 1.23-1.42) compared to female patients. Elderly patients (45+ years) were more likely to immune deteriorate compared to 25-34 years age group: HR, 1.35; 95% CI, 1.18-1.54; HR, 1.56; 95% CI, 1.34-1.81 and HR, 1.53; 95% CI, 1.32-1.79 for states 1 to 2, state 2 to 3, and states 3 to 4, respectively. Conclusion: Immune recovery was pronounced among provincial or central hospitals. Male patients with lower CD4 cell counts were at a higher risk of immune deterioration and mortality, while elderly patients were more likely to immune deteriorate. Early therapeutic interventions when the immune system is relatively stable across gender and age may contain mortality and increase survival outcomes. Interventions which strengthen ART services in primary healthcare facilities are essential

Loss to Follow-Up Risk among HIV Patients on ART in Zimbabwe, 2009-2016: Hierarchical Bayesian Spatio-Temporal Modeling

Loss to follow-up (LTFU) is a risk factor for poor outcomes in HIV patients. The spatio-temporal risk of LTFU is useful to identify hotspots and guide policy. Secondary data on adult HIV patients attending a clinic in provinces of Zimbabwe between 2009 and 2016 were used to estimate the LTFU risk in each of the 10 provinces. A hierarchical Bayesian spatio-temporal Poisson regression model was fitted using the Integrated Nested Laplace Approximation (INLA) package with LTFU as counts adjusting for age, gender, WHO clinical stage, tuberculosis coinfection and duration on ART. The structured random effects were modelled using the conditional autoregression technique and the temporal random effects were modelled using first-order random walk Gaussian priors. The overall rate of LTFU was 22.7% (95%CI: 22.6/22.8) with Harare (50.28%) and Bulawayo (31.11%) having the highest rates. A one-year increase in the average number of years on ART reduced the risk of LTFU by 35% (relative risk (RR) = 0.651; 95%CI: 0.592-0.712). In general, the provinces with the highest exceedance LTFU risk were Matabeleland South and Matabeleland North. LTFU is one of the drawbacks of HIV prevention. Interventions targeting high-risk regions in the southern and northern regions of Zimbabwe are a priority. Community-based interventions and programmes which mitigate LTFU risk remain essential in the global HIV prevention campaign

Cancer mortality distribution in South Africa, 1997–2016

IntroductionThe mortality data in South Africa (SA) have not been widely used to estimate the patterns of deaths attributed to cancer over a spectrum of relevant subgroups. There is no research in SA providing patterns and atlases of cancer deaths in age and sex groups per district per year. This study presents age-sex-specific geographical patterns of cancer mortality at the district level in SA and their temporal evolutions from 1997 to 2016.MethodsIndividual mortality level data provided by Statistics South Africa were grouped by three age groups (0–14, 15–64, and 65+), sex (male and female), and aggregated at each of the 52 districts. The proportionate mortality ratios (PMRs) for cancer were calculated per 100 residents. The atlases showing the distribution of cancer mortality were plotted using ArcGIS. Spatial analyses were conducted through Moran's I test.ResultsThere was an increase in PMRs for cancer in the age groups 15–64 and 65+ years from 2006 to 2016. Ranges were 2.83 (95% CI: 2.77–2.89) −4.16 (95% CI: 4.08–4.24) among men aged 15–64 years and 2.99 (95% CI: 2.93–3.06) −5.19 (95% CI: 5.09–5.28) among women in this age group. The PMRs in men and women aged 65+ years were 2.47 (95% CI: 2.42–2.53) −4.06 (95% CI: 3.98–4.14), and 2.33 (95% CI: 2.27–2.38) −4.19 (95% CI: 4.11–4.28). There were considerable geographical variations and similarities in the patterns of cancer mortality. For the age group 15–64 years, the ranges were 1.18 (95% CI: 0.78–1.71) −8.71 (95% CI: 7.18–10.47), p &lt; 0.0001 in men and 1.35 (95% CI: 0.92–1.92) −10.83 (95% CI: 8.84–13.14), p &lt; 0.0001 in women in 2016. There were higher PMRs among women in the Western Cape, Northern Cape, North West, and Gauteng compared to other areas. Similar patterns were also observed among men in these provinces, except in North West and Gauteng.ConclusionThe identification of geographical and temporal distributions of cancer mortality provided evidence of periods and districts with similar and divergent patterns. This will contribute to understanding the past, present, future trends and formulating interventions at a local level

Competing risk of mortality on loss to follow-up outcome among patients with HIV on ART: a retrospective cohort study from the Zimbabwe national ART programme.

OBJECTIVE: To determine the loss to follow-up (LTFU) rates at different healthcare levels after antiretroviral therapy (ART) services decentralisation among ART patients who initiated ART between 2004 and 2017 using the competing risk model in addition to the Kaplan-Meier and Cox regressions analysis. DESIGN: A retrospective cohort study. SETTING: The study was done in Zimbabwe using a nationwide routinely collected HIV patient-level data from various health levels of care facilities compiled through the electronic patient management system (ePMS). PARTICIPANTS: We analysed 390 771 participants aged 15 years and above from 538 health facilities. OUTCOMES: The primary endpoint was LTFU defined as a failure of a patient to report for drug refill for at least 90 days from last appointment date or if the patient missed the next scheduled visit date and never showed up again. Mortality was considered a secondary outcome if a patient was reported to have died. RESULTS: The total exposure time contributed was 1 544 468 person-years. LTFU rate was 5.75 (95% CI 5.71 to 5.78) per 100 person-years. Adjustment for the competing event independently increased LTFU rate ratio in provincial and referral (adjusted sub-HRs (AsHR) 1.22; 95% CI 1.18 to 1.26) and district and mission (AsHR 1.47; 95% CI 1.45 to 1.50) hospitals (reference: primary healthcare); in urban sites (AsHR 1.61; 95% CI 1.59 to 1.63) (reference: rural); and among adolescence and young adults (15-24 years) group (AsHR 1.19; 95% CI 1.16 to 1.21) (reference: 35-44 years). We also detected overwhelming association between LTFU and tuberculosis-infected patients (AsHR 1.53; 95% CI 1.45 to 1.62) (reference: no tuberculosis). CONCLUSIONS: We have observed considerable findings that 'leakages' (LTFU) within the ART treatment cascade persist even after the decentralisation of health services. Risk factors for LTFU reflect those found in sub-Saharan African studies. Interventions that retain patients in care by minimising any 'leakages' along the treatment cascade are essential in attaining the 90-90-90 UNAIDS fast-track targets

Markov modelling of viral load adjusting for CD4 orthogonal variable and multivariate conditional autoregressive mapping of the HIV immunological outcomes among ART patients in Zimbabwe

BACKGROUND: This study aimed to jointly model HIV disease progression patterns based on viral load (VL) among adult ART patients adjusting for the time-varying "incremental transients states" variable, and the CD4 cell counts orthogonal variable in a single 5-stage time-homogenous multistate Markov model. We further jointly mapped the relative risks of HIV disease progression outcomes (detectable VL (VL ≥ 50copies/uL) and immune deterioration (CD4 < 350cells/uL) at the last observed visit) conditional not to have died or become loss to follow-up (LTFU). METHODS: Secondary data analysis of individual-level patients on ART was performed. Adjusted transition intensities, hazard ratios (HR) and regression coefficients were estimated from the joint multistate model of VL and CD4 cell counts. The mortality and LTFU transition rates defined the extent of patients' retention in care. Joint mapping of HIV disease progression outcomes after ART initiation was done using the Bayesian intrinsic Multivariate Conditional Autoregressive prior model. RESULTS: The viral rebound from the undetectable state was 1.78times more likely compared to viral suppression among patients with VL ranging from 50-1000copies/uL. Patients with CD4 cell counts lower than expected had a higher risk of viral increase above 1000copies/uL and death if their VL was above 1000copies/uL (state 2 to 3 (λ23): HR = 1.83 and (λ34): HR = 1.42 respectively). Regarding the time-varying effects of CD4 cell counts on the VL transition rates, as the VL increased, (λ12 and λ23) the transition rates increased with a decrease in the CD4 cell counts over time. Regardless of the individual's VL, the transition rates to become LTFU decreased with a decrease in CD4 cell counts. We observed a strong shared geographical pattern of 66% spatial correlation between the relative risks of detectable VL and immune deterioration after ART initiation, mainly in Matabeleland North. CONCLUSION: With high rates of viral rebound, interventions which encourage ART adherence and continual educational support on the barriers to ART uptake are crucial to achieve and sustain viral suppression to undetectable levels. Area-specific interventions which focus on early ART screening through self-testing, behavioural change campaigns and social support strategies should be strengthened in heavily burdened regions to sustain the undetectable VL. Sustaining undetectable VL lowers HIV transmission in the general population and this is a step towards achieving zero HIV incidences by 2030

A review of multistate modelling approaches in monitoring disease progression: Bayesian estimation using the Kolmogorov-Chapman forward equations.

There are numerous fields of science in which multistate models are used, including biomedical research and health economics. In biomedical studies, these stochastic continuous-time models are used to describe the time-to-event life history of an individual through a flexible framework for longitudinal data. The multistate framework can describe more than one possible time-to-event outcome for a single individual. The standard estimation quantities in multistate models are transition probabilities and transition rates which can be mapped through the Kolmogorov-Chapman forward equations from the Bayesian estimation perspective. Most multistate models assume the Markov property and time homogeneity; however, if these assumptions are violated, an extension to non-Markovian and time-varying transition rates is possible. This manuscript extends reviews in various types of multistate models, assumptions, methods of estimation and data features compatible with fitting multistate models. We highlight the contrast between the frequentist (maximum likelihood estimation) and the Bayesian estimation approaches in the multistate modeling framework and point out where the latter is advantageous. A partially observed and aggregated dataset from the Zimbabwe national ART program was used to illustrate the use of Kolmogorov-Chapman forward equations. The transition rates from a three-stage reversible multistate model based on viral load measurements in WinBUGS were reported

Policy, law and post-abortion care services in Kenya

Background: Unsafe abortion is still a leading cause of maternal death in most Sub-Saharan African countries. Post-abortion care (PAC) aims to minimize morbidity and mortality following unsafe abortion, addressing incomplete abortion by treating complications, and reducing possible future unwanted pregnancies by providing contraceptive advice. In this article, we draw on data from PAC service providers and patients in Kenya to illustrate how the quality of PAC in healthcare facilities is impacted by law and government policy. Methods: A cross-sectional design was used for this study, with in-depth interviews conducted to collect qualitative data from PAC service providers and seekers in healthcare facilities. Data were analyzed both deductively and inductively, with diverse sub-themes related to specific components of PAC quality. Results: The provision of quality PAC in healthcare facilities in Kenya is still low, with access hindered by restrictions on abortion. Negative attitudes towards abortion result in the continued undirected self-administration of abortifacients. Intermittent service interruptions through industrial strikes and inequitable access to care also drive unsafe terminations. Poor PAC service availability and lack of capacity to manage complications in primary care facilities result in multiple referrals and delays in care following abortion, leading to further complications. Inefficient infection control exposes patients and caregivers to unrelated infections within facilities, and the adequate provision of contraception is a continued challenge. Discussion: Legal, policy and cultural restrictions to access PAC increase the level of complications. In Kenya, there is limited policy focus on PAC, especially at primary care level, and no guidelines for health providers to provide legal, safe abortion. Discrimination at the point of care discourages women from presenting for care, and discourages providers from freely offering post-abortion contraceptive guidance and services. Poor communication between facilities and communities continues to result in delayed care and access-related discrimination. Conclusion: Greater emphasis should be placed on the prevention of unsafe abortion and improved access to post-abortion care services in healthcare facilities. There is a definite need for service guidelines for this to occur

A machine learning approach towards assessing consistency and reproducibility: an application to graft survival across three kidney transplantation eras

BackgroundIn South Africa, between 1966 and 2014, there were three kidney transplant eras defined by evolving access to certain immunosuppressive therapies defined as Pre-CYA (before availability of cyclosporine), CYA (when cyclosporine became available), and New-Gen (availability of tacrolimus and mycophenolic acid). As such, factors influencing kidney graft failure may vary across these eras. Therefore, evaluating the consistency and reproducibility of models developed to study these variations using machine learning (ML) algorithms could enhance our understanding of post-transplant graft survival dynamics across these three eras.MethodsThis study explored the effectiveness of nine ML algorithms in predicting 10-year graft survival across the three eras. We developed and internally validated these algorithms using data spanning the specified eras. The predictive performance of these algorithms was assessed using the area under the curve (AUC) of the receiver operating characteristics curve (ROC), supported by other evaluation metrics. We employed local interpretable model-agnostic explanations to provide detailed interpretations of individual model predictions and used permutation importance to assess global feature importance across each era.ResultsOverall, the proportion of graft failure decreased from 41.5% in the Pre-CYA era to 15.1% in the New-Gen era. Our best-performing model across the three eras demonstrated high predictive accuracy. Notably, the ensemble models, particularly the Extra Trees model, emerged as standout performers, consistently achieving high AUC scores of 0.95, 0.95, and 0.97 across the eras. This indicates that the models achieved high consistency and reproducibility in predicting graft survival outcomes. Among the features evaluated, recipient age and donor age were the only features consistently influencing graft failure throughout these eras, while features such as glomerular filtration rate and recipient ethnicity showed high importance in specific eras, resulting in relatively poor historical transportability of the best model.ConclusionsOur study emphasises the significance of analysing post-kidney transplant outcomes and identifying era-specific factors mitigating graft failure. The proposed framework can serve as a foundation for future research and assist physicians in identifying patients at risk of graft failure