Migration from Epi Info to District Health Information Software 2 for Vaccine-Preventable Disease Surveillance - World Health Organization African Region, 2019-2023.

High-quality vaccine-preventable disease (VPD) surveillance data are critical for timely outbreak detection and response. In 2019, the World Health Organization (WHO) African Regional Office (AFRO) began transitioning from Epi Info, a free, CDC-developed statistical software package with limited capability to integrate with other information systems, affecting reporting timeliness and data use, to District Health Information Software 2 (DHIS2). DHIS2 is a free and open-source software platform for electronic aggregate Integrated Disease Surveillance and Response (IDSR) and case-based surveillance reporting. A national-level reporting system, which provided countries with the option to adopt this new system, was introduced. Regionally, the Epi Info database will be replaced with a DHIS2 regional data platform. This report describes the phased implementation from 2019 to the present. Phase one (2019-2021) involved developing IDSR aggregate and case-based surveillance packages, including pilots in the countries of Mali, Rwanda, and Togo. Phase two (2022) expanded national-level implementation to 27 countries and established the WHO AFRO DHIS2 regional data platform. Phase three (from 2023 to the present) activities have been building local capacity and support for country reporting to the regional platform. By February 2024, eight of 47 AFRO countries had adopted both the aggregate IDSR and case-based surveillance packages, and two had successfully transferred VPD surveillance data to the AFRO regional platform. Challenges included limited human and financial resources, the need to establish data-sharing and governance agreements, technical support for data transfer, and building local capacity to report to the regional platform. Despite these challenges, the transition to DHIS2 will support efficient data transmission to strengthen VPD detection, response, and public health emergencies through improved system integration and interoperability.

Prompt treatment-seeking behaviour varies within communities among guardians of children with malaria-related fever in Malawi.

BACKGROUND: In Malawi, malaria is responsible for 40% of hospital deaths. Prompt diagnosis and effective treatment within 24 h of fever onset is critical to prevent progression from uncomplicated to severe disease and to reduce transmission. METHODS: As part of the large evaluation of the malaria vaccine implementation programme (MVIP), this study analysed survey data to investigate whether prompt treatment-seeking behaviour is clustered at community-level according to socio-economic demographics. RESULTS: From 4563 households included in the survey, 4856 children aged 5-48 months were enrolled. Out of 4732 children with documented gender, 52.2% were female and 47.8% male. Among the 4856 children, 33.8% reported fever in the two weeks prior to the survey. Fever prevalence was high in communities with low socio-economic status (SES) (38.3% [95% CI: 33.7-43.5%]) and low in areas with high SES (29.8% [95% CI: 25.6-34.2%]). Among children with fever, 648 (39.5%) sought treatment promptly i.e., within 24 h from onset of fever symptoms. Children were more likely to be taken for prompt treatment among guardians with secondary education compared to those without formal education (aOR:1.37, 95% CI: 1.11-3.03); in communities with high compared to low SES [aOR: 2.78, 95% CI: 1.27-6.07]. Children were less likely to be taken for prompt treatment if were in communities far beyond 5 km to health facility than within 5 km [aOR: 0.44, 95% CI: 0.21-0.92]. CONCLUSION: The high heterogeneity in prevalence of fever and levels of prompt treatment-seeking behaviour underscore the need to promote community-level malaria control interventions (such as use of long-lasting insecticide-treated nets (LLINs), indoor residual spraying (IRS), intermittent preventive therapy (IPT), presumptive treatment and education). Programmes aimed at improving treatment-seeking behaviour should consider targeting communities with low SES and those far from health facility.

Comparison of statistical methods for the analysis of recurrent adverse events in the presence of non-proportional hazards and unobserved heterogeneity: a simulation study.

BACKGROUND: In preventive drug trials such as intermittent preventive treatment for malaria prevention during pregnancy (IPTp), where there is repeated treatment administration, recurrence of adverse events (AEs) is expected. Challenges in modelling the risk of the AEs include accounting for time-to-AE and within-patient-correlation, beyond the conventional methods. The correlation comes from two sources; (a) individual patient unobserved heterogeneity (i.e. frailty) and (b) the dependence between AEs characterised by time-dependent treatment effects. Potential AE-dependence can be modelled via time-dependent treatment effects, event-specific baseline and event-specific random effect, while heterogeneity can be modelled via subject-specific random effect. Methods that can improve the estimation of both the unobserved heterogeneity and treatment effects can be useful in understanding the evolution of risk of AEs, especially in preventive trials where time-dependent treatment effect is expected. METHODS: Using both a simulation study and the Chloroquine for Malaria in Pregnancy (NCT01443130) trial data to demonstrate the application of the models, we investigated whether the lognormal shared frailty models with restricted cubic splines and non-proportional hazards (LSF-NPH) assumption can improve estimates for both frailty variance and treatment effect compared to the conventional inverse Gaussian shared frailty model with proportional hazard (ISF-PH), in the presence of time-dependent treatment effects and unobserved patient heterogeneity. We assessed the bias, precision gain and coverage probability of 95% confidence interval of the frailty variance estimates for the models under varying known unobserved heterogeneity, sample sizes and time-dependent effects. RESULTS: The ISF-PH model provided a better coverage probability of 95% confidence interval, less bias and less precise frailty variance estimates compared to the LSF-NPH models. The LSF-NPH models yielded unbiased hazard ratio estimates at the expense of imprecision and high mean square error compared to the ISF-PH model. CONCLUSION: The choice of the shared frailty model for the recurrent AEs analysis should be driven by the study objective. Using the LSF-NPH models is appropriate if unbiased hazard ratio estimation is of primary interest in the presence of time-dependent treatment effects. However, ISF-PH model is appropriate if unbiased frailty variance estimation is of primary interest. TRIAL REGISTRATION: ClinicalTrials.gov; NCT01443130.

Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: application to artemisinin-based treatment during pregnancy clinical trial.

BACKGROUND: In drug trials, clinical adverse events (AEs), concomitant medication and laboratory safety outcomes are repeatedly collected to support drug safety evidence. Despite the potential correlation of these outcomes, they are typically analysed separately, potentially leading to misinformation and inefficient estimates due to partial assessment of safety data. Using joint modelling, we investigated whether clinical AEs vary by treatment and how laboratory outcomes (alanine amino-transferase, total bilirubin) and concomitant medication are associated with clinical AEs over time following artemisinin-based antimalarial therapy. METHODS: We used data from a trial of artemisinin-based treatments for malaria during pregnancy that randomized 870 women to receive artemether-lumefantrine (AL), amodiaquine-artesunate (ASAQ) and dihydroartemisinin-piperaquine (DHAPQ). We fitted a joint model containing four sub-models from four outcomes: longitudinal sub-model for alanine aminotransferase, longitudinal sub-model for total bilirubin, Poisson sub-model for concomitant medication and Poisson sub-model for clinical AEs. Since the clinical AEs was our primary outcome, the longitudinal sub-models and concomitant medication sub-model were linked to the clinical AEs sub-model via current value and random effects association structures respectively. We fitted a conventional Poisson model for clinical AEs to assess if the effect of treatment on clinical AEs (i.e. incidence rate ratio (IRR)) estimates differed between the conventional Poisson and the joint models, where AL was reference treatment. RESULTS: Out of the 870 women, 564 (65%) experienced at least one AE. Using joint model, AEs were associated with the concomitant medication (log IRR 1.7487; 95% CI: 1.5471, 1.9503; p < 0.001) but not the total bilirubin (log IRR: -0.0288; 95% CI: - 0.5045, 0.4469; p = 0.906) and alanine aminotransferase (log IRR: 0.1153; 95% CI: - 0.0889, 0.3194; p = 0.269). The Poisson model underestimated the effects of treatment on AE incidence such that log IRR for ASAQ was 0.2118 (95% CI: 0.0082, 0.4154; p = 0.041) for joint model compared to 0.1838 (95% CI: 0.0574, 0.3102; p = 0.004) for Poisson model. CONCLUSION: We demonstrated that although the AEs did not vary across the treatments, the joint model yielded efficient AE incidence estimates compared to the Poisson model. The joint model showed a positive relationship between the AEs and concomitant medication but not with laboratory outcomes. TRIAL REGISTRATION: ClinicalTrials.gov: NCT00852423.

Systematic review of statistical methods for safety data in malaria chemoprevention in pregnancy trials.

BACKGROUND: Drug safety assessments in clinical trials present unique analytical challenges. Some of these include adjusting for individual follow-up time, repeated measurements of multiple outcomes and missing data among others. Furthermore, pre-specifying appropriate analysis becomes difficult as some safety endpoints are unexpected. Although existing guidelines such as CONSORT encourage thorough reporting of adverse events (AEs) in clinical trials, they provide limited details for safety data analysis. The limited guidelines may influence suboptimal analysis by failing to account for some analysis challenges above. A typical example where such challenges exist are trials of anti-malarial drugs for malaria prevention during pregnancy. Lack of proper standardized evaluation of the safety of antimalarial drugs has limited the ability to draw conclusions about safety. Therefore, a systematic review was conducted to establish the current practice in statistical analysis for preventive antimalarial drug safety in pregnancy. METHODS: The search included five databases (PubMed, Embase, Scopus, Malaria in Pregnancy Library and Cochrane Central Register of Controlled Trials) to identify original English articles reporting Phase III randomized controlled trials (RCTs) on anti-malarial drugs for malaria prevention in pregnancy published from January 2010 to July 2019. RESULTS: Eighteen trials were included in this review that collected multiple longitudinal safety outcomes including AEs. Statistical analysis and reporting of the safety outcomes in all the trials used descriptive statistics; proportions/counts (n = 18, 100%) and mean/median (n = 2, 11.1%). Results presentation included tabular (n = 16, 88.9%) and text description (n = 2, 11.1%). Univariate inferential methods were reported in most trials (n = 16, 88.9%); including Chi square/Fisher's exact test (n = 12, 66.7%), t test (n = 2, 11.1%) and Mann-Whitney/Wilcoxon test (n = 1, 5.6%). Multivariable methods, including Poisson and negative binomial were reported in few trials (n = 3, 16.7%). Assessment of a potential link between missing efficacy data and safety outcomes was not reported in any of the trials that reported efficacy missing data (n = 7, 38.9%). CONCLUSION: The review demonstrated that statistical analysis of safety data in anti-malarial drugs for malarial chemoprevention in pregnancy RCTs is inadequate. The analyses insufficiently account for multiple safety outcomes potential dependence, follow-up time and informative missing data which can compromise anti-malarial drug safety evidence development, based on the available data.

Systematic review of analytical methods applied to longitudinal studies of malaria.

BACKGROUND: Modelling risk of malaria in longitudinal studies is common, because individuals are at risk for repeated infections over time. Malaria infections result in acquired immunity to clinical malaria disease. Prospective cohorts are an ideal design to relate the historical exposure to infection and development of clinical malaria over time, and analysis methods should consider the longitudinal nature of the data. Models must take into account the acquisition of immunity to disease that increases with each infection and the heterogeneous exposure to bites from infected Anopheles mosquitoes. Methods that fail to capture these important factors in malaria risk will not accurately model risk of malaria infection or disease. METHODS: Statistical methods applied to prospective cohort studies of clinical malaria or Plasmodium falciparum infection and disease were reviewed to assess trends in usage of the appropriate statistical methods. The study was designed to test the hypothesis that studies often fail to use appropriate statistical methods but that this would improve with the recent increase in accessibility to and expertise in longitudinal data analysis. RESULTS: Of 197 articles reviewed, the most commonly reported methods included contingency tables which comprised Pearson Chi-square, Fisher exact and McNemar's tests (n = 102, 51.8%), Student's t-tests (n = 82, 41.6%), followed by Cox models (n = 62, 31.5%) and Kaplan-Meier estimators (n = 59, 30.0%). The longitudinal analysis methods generalized estimating equations and mixed-effects models were reported in 41 (20.8%) and 24 (12.2%) articles, respectively, and increased in use over time. A positive trend in choice of more appropriate analytical methods was identified over time. CONCLUSIONS: Despite similar study designs across the reports, the statistical methods varied substantially and often represented overly simplistic models of risk. The results underscore the need for more effort to be channelled towards adopting standardized longitudinal methods to analyse prospective cohort studies of malaria infection and disease.

Bayesian random effects modelling with application to childhood anaemia in Malawi.

BACKGROUND: Epidemiological studies in Malawi on child anaemia have neglected the community spatial effect to childhood anaemia. Neglecting the community spatial effect in the model ignores the influence of unobserved or unmeasured contextual variables, and at the same time the resultant model may under estimate model parameter standard errors which can result in erroneous significance of covariates. We aimed at investigating risk factors of childhood anaemia in Malawi with focus on geographical spatial effect. METHODS: We adopted a Bayesian random effect model for child anaemia with district as spatial effect using the 2010 Malawi demographic healthy survey data. We fitted the binary logistic model for the two categories outcome (anaemia (Hb < 11), and no anaemia (Hb ≥ 11)). Continuous covariates were modelled by the penalized splines and spatial effects were smoothed by the two dimensional spline. RESULTS: Residual spatial patterns reveal Nsanje, Chikhwawa, Salima, Nkhota-kota, Mangochi and Machinga increasing the risk of childhood anaemia. Karonga, Chitipa, Rumphi, Mzimba, Ntchisi, and Chiradzulu reduce the risk of childhood anaemia. Known determinants such as maternal anaemia, child stunting, and child fever, have a positive effect on child anaemia. Furthermore childhood anaemia decreases with child age. It also decreases with wealth index. There is a U relationship between child anaemia and mother age. CONCLUSION: Strategies in childhood anaemia control should be tailored to local conditions, taking into account the specific etiology and prevalence of anaemia.

Material deprivation affects high sexual risk behavior among young people in urban slums, South Africa.

Young people in urban slums adopt HIV risk behaviors influenced by their neighborhood factors. Three critical factors in urban slums of Southern and Eastern Africa--the region most affected by the HIV epidemic in the world--are unmet needs of housing, food, and health care, which are associated with HIV sexual risks. Yet, there has been limited attention on how the combination of unmet needs of housing, food, and health care--i.e., material deprivation-relates to sexual risk behavior among young people in urban slums. Cross-sectional data were extracted from the LoveLife survey in South African four provinces--KwaZulu Natal, Mpumalanga, Eastern Cape, and Gauteng, to examine the association between material deprivation and sexual risk behavior among young people aged 18-23 years (263 males, 267 females) in urban slums. Adjusted logistic regression models showed that material deprivation was significantly associated with increased odds of high sexual risk taking for young men (adjusted OR = 1.20; 95 % CI = 1.10, 5.58) and young women (adjusted OR = 1.43; 95 % CI = 1.35, 3.28). Financial difficulty--a proxy for other deprivations--was the most salient influence on young women's high sexual risk taking (adjusted OR = 2.11; 95 % CI = 1.66, 2.70). Localized behavioral HIV prevention interventions should target young people in deprived households.

Modeling time to death for under-five children in Malawi using 2015/16 Demographic and Health Survey: a survival analysis.

BACKGROUND: Malawi has one of the highest under-five mortality rates in Sub Sahara Africa. Understanding the factors that contribute to child mortality in Malawi is crucial for the development and implementation of effective interventions to reduce child mortality. The aim of this study is to use survival analysis in modeling time to death for under-five children in Malawi. In turn, identify potential risk factors for child mortality and inform the development of interventions to reduce child mortality in the country. METHOD: This study used data from all births that occurred in the five years leading up to the 2015/16 Malawi Demographic and Health Survey. The Frailty hazard model was applied to predict infant survival in Malawi. In this analysis, the outcome of interest was death and it had two possible outcomes: "dead" or "alive". Age at death was regarded as the survival time variable. Infants who were still alive at the time of the study as of the day of the interview were considered as censored observations in the analysis. RESULTS: A total of 17,286 live births born during the 5 years preceding the survey were analysed. The study found that the risk of death was higher among children born to mothers aged 30-39 and 40 or older compared to teen mothers. Infants whose mothers attended fewer than four antenatal care visits were also found to be at a higher risk of death. On the other hand, the study found that using mosquito nets and early breastfeeding were associated with a lower risk of death, as were being male and coming from a wealthier household. CONCLUSION: The study reveals a notable decline in infant mortality rates as under-five children age, underscoring the challenge of ensuring newborn survival. Factors such as maternal age, birth order, socioeconomic status, mosquito net usage, early breastfeeding initiation, geographic location, and child's sex are key predictors of under-five mortality. To address this, public health strategies should prioritize interventions targeting these predictors to reduce under-five mortality rates.

Assessing the Vulnerability and Adaptation Needs of Mozambique's Health Sector to Climate: A Comprehensive Study.

Climate change poses severe consequences, particularly in sub-Saharan Africa, where poverty rates may escalate by 2050 without significant climate and development action. The health impacts are diverse, encompassing communicable and non-communicable diseases. Mozambique, a climate-vulnerable nation, has experienced significant natural disasters in the past 42 years, impacting its health system. This study aims to assess Mozambique's health sector's vulnerability and adaptation needs to climate change. Following a methodology proposed by the World Health Organization and the Intergovernmental Panel for Climate Change, a six-step vulnerability and adaptation assessment was conducted to conduct the Health Vulnerability Index (HVI) for Mozambique's regions (n=161). The HVI integrates historical climate, epidemiological, and socio-economic data at the district level, and was computed using exposure, sensitivity, and adaptive capacity dimensions. The results revealed spatial patterns in exposure to climate variables, extreme weather events, and variations in sensitivity and adaptive capacity across the country. The HVI mirrored the exposure findings. Notably, high vulnerability was observed in several districts, while major urban centers displayed lower vulnerability. These findings highlight the country's vulnerability to climate change and underscore the potential for adverse impacts on livelihoods, the economy, and human health. The study provides a foundation for developing strategies and adaptation actions.

Surveillance Programme of IN-patients and Epidemiology (SPINE): implementation of an electronic data collection tool within a large hospital in Malawi.

Miguel Sanjoaquin and colleagues describe their experience of setting up an electronic patient records system in a large referral hospital in Blantyre, Malawi.

Competing risks modeling of length of hospital stay enhances risk-stratification of patient care: application to under-five children hospitalized in Malawi.

Introduction: Length of hospital stay (LOS), defined as the time from inpatient admission to discharge, death, referral, or abscondment, is one of the key indicators of quality in patient care. Reduced LOS lowers health care expenditure and minimizes the chance of in-hospital acquired infections. Conventional methods for estimating LOS such as the Kaplan-Meier survival curve and the Cox proportional hazards regression for time to discharge cannot account for competing risks such as death, referral, and abscondment. This study applied competing risk methods to investigate factors important for risk-stratifying patients based on LOS in order to enhance patient care. Methods: This study analyzed data from ongoing safety surveillance of the malaria vaccine implementation program in Malawi's four district hospitals of Balaka, Machinga, Mchinji, and Ntchisi. Children aged 1-59 months who were hospitalized (spending at least one night in hospital) with a medical illness were consecutively enrolled between 1 November 2019 and 31 July 2021. Sub-distribution-hazard (SDH) ratios for the cumulative incidence of discharge were estimated using the Fine-Gray competing risk model. Results: Among the 15,463 children hospitalized, 8,607 (55.7%) were male and 6,856 (44.3%) were female. The median age was 22 months [interquartile range (IQR): 12-33 months]. The cumulative incidence of discharge was 40% lower among HIV-positive children compared to HIV-negative (sub-distribution-hazard ratio [SDHR]: 0.60; [95% CI: 0.46-0.76]; P < 0.001); lower among children with severe and cerebral malaria [SDHR: 0.94; (95% CI: 0.86-0.97); P = 0.04], sepsis or septicemia [SDHR: 0.90; (95% CI: 0.82-0.98); P = 0.027], severe anemia related to malaria [SDHR: 0.54; (95% CI: 0.48-0.61); P < 0.001], and meningitis [SDHR: 0.18; (95% CI: 0.09-0.37); P < 0.001] when compared to non-severe malaria; and also 39% lower among malnourished children compared to those that were well-nourished [SDHR: 0.61; (95% CI: 0.55-0.68); P < 0.001]. Conclusions: This study applied the Fine-Gray competing risk approach to more accurately model LOS as the time to discharge when there were significant rates of in-hospital mortality, referrals, and abscondment. Patient care can be enhanced by risk-stratifying by LOS based on children's age, HIV status, diagnosis, and nutritional status.

Effect of adverse events on non-adherence and study non-completion in malaria chemoprevention during pregnancy trial: A nested case control study.

BACKGROUND: In drug trials, adverse events (AEs) burden can induce treatment non-adherence or discontinuation. The non-adherence and discontinuation induce selection bias, affecting drug safety interpretation. Nested case-control (NCC) study can efficiently quantify the impact of the AEs, although choice of sampling approach is challenging. We investigated whether NCC study with incidence density sampling is more efficient than NCC with path sampling under conditional logistic or weighted Cox models in assessing the effect of AEs on treatment non-adherence and participation in preventive antimalarial drug during pregnancy trial. METHODS: Using data from a trial of medication to prevent malaria in pregnancy that randomized 600 women to receive chloroquine or sulfadoxine-pyrimethamine during pregnancy, we conducted a NCC study assessing the role of prospectively collected AEs, as exposure of interest, on treatment non-adherence and study non-completion. We compared estimates from NCC study with incidence density against those from NCC with path sampling under conditional logistic and weighted Cox models. RESULTS: Out of 599 women with the outcomes of interest, 474 (79%) experienced at least one AE before delivery. For conditional logistic model, the hazard ratio for the effect of AE occurrence on treatment non-adherence was 0.70 (95% CI: 0.42, 1.17; p = 0.175) under incidence density sampling and 0.68 (95% CI: 0.41, 1.13; p = 0.137) for path sampling. For study non-completion, the hazard ratio was 1.02 (95% CI: 0.56, 1.83; p = 0.955) under incidence density sampling and 0.85 (95% CI: 0.45, 1.60; p = 0.619) under path sampling. We obtained similar hazard ratios and standard errors under incidence density sampling and path sampling whether weighted Cox or conditional logistic models were used. CONCLUSION: NCC with incidence density sampling and NCC with path sampling are practically similar in efficiency whether conditional logistic or weighted Cox analytical methods although path sampling uses more unique controls to achieve the similar estimates. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01443130.

Analysis of Recurrent Times-to-Clinical Malaria Episodes and Plasmodium falciparum Parasitemia: A Joint Modeling Approach Applied to a Cohort Data.

Background: Recurrent clinical malaria episodes due to Plasmodium falciparum parasite infection are common in endemic regions. With each infection, acquired immunity develops, making subsequent disease episodes less likely. To capture the effect of acquired immunity to malaria, it may be necessary to model recurrent clinical disease episodes jointly with P. falciparum parasitemia data. A joint model of longitudinal parasitemia and time-to-first clinical malaria episode (single-event joint model) may be inaccurate because acquired immunity is lost when subsequent episodes are excluded. This study's informativeness assessed whether joint modeling of recurrent clinical malaria episodes and parasitemia is more accurate than a single-event joint model where the subsequent episodes are ignored. Methods: The single event joint model comprised Cox Proportional Hazards (PH) sub-model for time-to-first clinical malaria episode and Negative Binomial (NB) mixed-effects sub-model for the longitudinal parasitemia. The recurrent events joint model extends the survival sub-model to a Gamma shared frailty model to include all recurrent clinical episodes. The models were applied to cohort data from Malawi. Simulations were also conducted to assess the performance of the model under different conditions. Results: The recurrent events joint model, which yielded higher hazard ratios of clinical malaria, was more precise and in most cases produced smaller standard errors than the single-event joint model; hazard ratio (HR) = 1.42, [95% confidence interval [CI]: 1.22, 2.03] vs. HR = 1.29, [95% CI:1.60, 2.45] among participants who reported not to use LLINs every night compared to those who used the nets every night; HR = 0.96, [ 95% CI: 0.94, 0.98] vs. HR = 0.81, [95% CI: 0.75, 0.88] for each 1-year increase in participants' age; and HR = 1.36, [95% CI: 1.05, 1.75] vs. HR = 1.10, [95% CI: 0.83, 4.11] for observations during the rainy season compared to the dry season. Conclusion: The recurrent events joint model in this study provides a way of estimating the risk of recurrent clinical malaria in a cohort where the effect of immunity on malaria disease acquired due to P. falciparum parasitemia with aging is captured. The simulation study has shown that if correctly specified, the recurrent events joint model can give risk estimates with low bias.

Developing excellence in biostatistics leadership, training and science in Africa: How the Sub-Saharan Africa Consortium for Advanced Biostatistics (SSACAB) training unites expertise to deliver excellence.

The increase in health research in sub-Saharan Africa (SSA) has generated large amounts of data and led to a high demand for biostatisticians to analyse these data locally and quickly.  Donor-funded initiatives exist to address the dearth in statistical capacity, but few initiatives have been led by African institutions. The Sub-Saharan African Consortium for Advanced Biostatistics (SSACAB) aims to improve biostatistical capacity in Africa according to the needs identified by African institutions, through (collaborative) masters and doctoral training in biostatistics. We describe the SSACAB Consortium, which comprises 11 universities and four research institutions- supported by four European universities. SSACAB builds on existing resources to strengthen biostatistics for health research with a focus on supporting biostatisticians to become research leaders; building a critical mass of biostatisticians, and networking institutions and biostatisticians across SSA.  In 2015 only four institutions had established Masters programmes in biostatistics and SSACAB supported the remaining institutions to develop Masters programmes. In 2019 the University of the Witwatersrand became the first African institution to gain Royal Statistical Society accreditation for a Biostatistics MSc programme. A total of 150 fellows have been awarded scholarships to date of which 123 are Masters fellowships (41 female) of which with 58 have already graduated. Graduates have been employed in African academic (19) and research (15) institutions and 10 have enrolled for PhD studies. A total of 27 (10 female) PhD fellowships have been awarded; 4 of them are due to graduate by 2020. To date, SSACAB Masters and PhD students have published 17 and 31 peer-reviewed articles, respectively. SSACAB has also facilitated well-attended conferences, face-to-face and online short courses. Pooling the limited biostatistics resources in SSA, and combining with co-funding from external partners is an effective strategy for the development and teaching of advanced biostatistics methods, supervision and mentoring of PhD candidates.

Joint spatial modelling of common morbidities of childhood fever and diarrhoea in Malawi.

Availability of geo-referenced data has increased applications of spatially explicit models to understand important health problems in developing countries. This study aims to investigate joint and disease-specific spatial clusters of fever and diarrhoea at a highly disaggregate level, while simultaneously estimating the influence of other covariates. Using the 2000 Malawi DHS, a logistic model was fitted with spatial random effects partitioned into shared and specific effects. Results indicated that the shared area-specific effects were persistently high in the central and southern regions. Fever-specific effects were high along the lakeshore areas of the country, while diarrhoea-specific effects were excessive in the central region and south-eastern zones of the country. The prevalence of fever and diarrhoea was also associated with individual, familial and community risk factors. Our findings present an opportunity for an integrated disease control approach for reducing childhood morbidity and mortality.

An application of mixed-effect models to analyse contraceptive use in Malawian women.

In Malawi, the current approach to family planning using contraceptive methods is individualised, yet studies have shown that variability in contraceptive-use still remains after accounting for it at individual and household levels. Therefore, this study assessed variability at higher levels such as enumeration areas, districts and regions. Biasness of the estimates was addressed by the use of Bayesian approach. The study used 2015-16 Malawi Demographic Health Survey women data. After ascertaining the significance of association of all explanatory variables with contraceptive use, the top-down (backward) stepwise model selection method was followed in the Bayesian framework using Markov Chain Monte Carlo and defuse priors. Models were compared on the basis of Deviance Information Criteria and significance of parameter estimates was checked via credible intervals while that of cross-cluster variances was checked by examining their diagnostic plots. All the selected socio-demographic factors were strongly associated with contraceptive-use (p-value< 0.001). These factors include; region, place-of-residence, age, parity, education, occupation, marital-status and religion. It was also found that about 15 and 2.3% of the variation in contraceptive-use was attributed to enumeration area and district clustering, respectively. The single-level model underestimated the parameter estimates by at least 4% for both models. And parity-enumeration area, age-enumeration area and age-district random effects were significant in their respective models. It was also noted that most young women aged between 15 and 24 years were not using any contraceptive methods. The study indicated that there exist significant enumeration area and district heterogeneity on contraceptive use in Malawian women and that random-effect models are the most appropriate models other than single-level models. Thus family planning programs focusing on contraceptive-use should switch to inclusive approach and statistical analyses should consider including enumeration area and district heterogeneity while controlling for the above significant factors. Stakeholders may also consider encouraging young women to use contraceptive methods, if Malawi is to minimize problems due to overpopulation.

Joint modelling of time-to-clinical malaria and parasite count in a cohort in an endemic area.

BACKGROUND: In malaria endemic areas such as sub-Saharan Africa, repeated exposure to malaria results in acquired immunity to clinical disease but not infection. In prospective studies, time-to-clinical malaria and longitudinal parasite count trajectory are often analysed separately which may result in inefficient estimates since these two processes can be associated. Including parasite count as a time-dependent covariate in a model of time-to-clinical malaria episode may also be inaccurate because while clinical malaria disease frequently leads to treatment which may instantly affect the level of parasite count, standard time-to-event models require that time-dependent covariates be external to the event process. We investigated whether jointly modelling time-to-clinical malaria disease and longitudinal parasite count improves precision in risk factor estimates and assessed the strength of association between the hazard of clinical malaria and parasite count. METHODS: Using a cohort data of participants enrolled with uncomplicated malaria in Malawi, a conventional Cox Proportional Hazards (PH) model of time-to-first clinical malaria episode with time-dependent parasite count was compared with three competing joint models. The joint models had different association structures linking a quasi-Poisson mixed-effects of parasite count and event-time Cox PH sub-models. RESULTS: There were 120 participants of whom 115 (95.8%) had >1 follow-up visit and 100 (87.5%) experienced the episode. Adults >15 years being reference, log hazard ratio for children <5 years was 0.74 (95% CI: 0.17, 1.26) in the joint model with best fit vs. 0.62 (95% CI: 0.04, 1.18) from the conventional Cox PH model. The log hazard ratio for the 5-15 years was 0.72 (95% CI: 0.22, 1.22) in the joint model vs.0.63 (95% CI: 0.11, 1.17) in the Cox PH model. The area under parasite count trajectory was strongly associated with the risk of clinical malaria, with a unit increase corresponding to-0.0012 (95% CI: -0.0021, -0.0004) decrease in log hazard ratio. CONCLUSION: Jointly modelling longitudinal parasite count and time-to-clinical malaria disease improves precision in log hazard ratio estimates compared to conventional time-dependent Cox PH model. The improved precision of joint modelling may improve study efficiency and allow for design of clinical trials with relatively lower sample sizes with increased power.

Applications of Bayesian approach in modelling risk of malaria-related hospital mortality.

BACKGROUND: Malaria is a major public health problem in Malawi, however, quantifying its burden in a population is a challenge. Routine hospital data provide a proxy for measuring the incidence of severe malaria and for crudely estimating morbidity rates. Using such data, this paper proposes a method to describe trends, patterns and factors associated with in-hospital mortality attributed to the disease. METHODS: We develop semiparametric regression models which allow joint analysis of nonlinear effects of calendar time and continuous covariates, spatially structured variation, unstructured heterogeneity, and other fixed covariates. Modelling and inference use the fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulation techniques. The methodology is applied to analyse data arising from paediatric wards in Zomba district, Malawi, between 2002 and 2003. RESULTS AND CONCLUSION: We observe that the risk of dying in hospital is lower in the dry season, and for children who travel a distance of less than 5 kms to the hospital, but increases for those who are referred to the hospital. The results also indicate significant differences in both structured and unstructured spatial effects, and the health facility effects reveal considerable differences by type of facility or practice. More importantly, our approach shows non-linearities in the effect of metrical covariates on the probability of dying in hospital. The study emphasizes that the methodological framework used provides a useful tool for analysing the data at hand and of similar structure.

Harmful lifestyles' clustering among sexually active in-school adolescents in Zambia.

BACKGROUND: HIV is a leading cause of morbidity and mortality in Zambia. Like many other African nations with high HIV burden, heterosexual intercourse is the commonest mode of HIV spread. The estimation of prevalence and factors associated with sexual intercourse among in-school adolescents has potential to inform public health interventions aimed at reducing the burden of sex-related diseases in Zambia. METHODS: We carried out secondary analysis of the Zambia Global School-Based Health Survey (GSHS) 2004; a cross sectional survey that aims to study health-related behaviors among in-school adolescents. We estimated frequencies of relevant socio-demographic variables. The associations between selected explanatory variables and self-reported history of sexual intercourse within the last 12 months were assessed using logistic regression analysis. RESULTS: Data from 2136 in-school adolescents who participated in the Zambia Global School-Based Health Survey of 2004 were available for analysis. Out of these respondents, 13.4% reported that they had sexual intercourse in the past 12 months prior to the survey; 16.4% and 9.7% among males and females respectively. In multivariable logistic regression analysis, with age less than 15 years as the referent the adjusted odds ratio (AOR) of having engaged in sexual intercourse in adolescents of age 15 years, and those aged 16 years or more were 1.06 (95% CI 1.03-1.10) and 1.74 (95% 1.70-1.79) respectively. Compared to adolescents who had no close friends, adolescents who had one close friend were more likely to have had sexual intercourse, AOR = 1.28 (95% CI 1.24-1.32). Compared to adolescents who were not supervised by their parents, adolescents who were rarely or sometimes supervised by their parents were likely to have had sexual intercourse, and adolescents who were most of the time/always supervised by their parents were less likely to have had sexual intercourse; AORs 1.26 (95% CI 1.23-1.26) and 0.92 (95% CI 0.90-0.95) respectively. Compared to adolescents who did not smoke dagga, adolescents who smoked dagga 1 or 2 times, and those who smoked dagga 3 or more times in their lifetime were 70% and 25% more likely to have had sexual intercourse, respectively. Adolescents who drank alcohol in 1 or 2 days, and those who took alcohol in 3 or more days in a month preceding the survey were 12% and 9% more likely to have had sexual intercourse, respectively, compared to adolescents who did not drink alcohol in the 30 days prior to the survey. Furthermore, adolescents who had been drunk 1 or 2 times, and who had been drunk 3 or more times in a life time were 14% and 13% more likely to have had sexual intercourse compared to those who have never been drunk in their lifetime. CONCLUSION: We identified a constellation of potentially harmful behaviours among adolescents in Zambia. Public health interventions aimed at reducing prevalence of sexual intercourse may be designed and implemented in a broader sense having recognized that sexually active adolescents may also be exposed to other problem behaviours.