Routine health data describe adherence and persistence patterns for oral diabetes medication for a virtual cohort in the Khayelitsha sub-district of Cape Town, South Africa.

Type 2 diabetes mellitus (T2DM) is managed with combined lifestyle modifications and antidiabetic drugs, but people on treatment often fail to reach glycaemic control. Adherence is important for achieving optimal glycaemic control, and management of diabetes with drugs is a lifelong process, so understanding adherence through analysis of longitudinal medications data is important. Using retrospective routine health data and metformin dispensing records as a proxy for medication use, we describe longitudinal persistence and adherence to oral diabetes medication in a virtual cohort of 10541 people with diabetes (PLWD) in Khayelitsha subdistrict, Cape Town. Adherence was measured in 120-day sliding windows over two years and used to estimate metformin adherence trajectories. Multinomial logistic regression identified factors influencing these trajectories. Analysis of pharmacy dispensing records showed varying medication refill patterns: while some PLWD refilled prescriptions consistently, others had treatment gaps with periods of non-persistence and multiple treatment episodes-from one to five per individual across two years. There was a general trend of decreasing adherence over time across all sliding windows in the two-year period, with only 25% of the study population achieved medication adherence (> = 80% adherence) after two years. Four adherence trajectories; 'low adherence gradual decline (A), 'high adherence rapid decline' (B), 'low adherence gradual increase (C) and 'adherent' (D) were identified. Only trajectory D represented participants who were adherent at treatment start and remained adherent after two years. Taking HIV antiretroviral treatment before or concurrently with diabetes treatment and taking metformin in combination with sulphonylurea and/or insulin were associated with the long-term adherence (trajectory D). Routine data shows real life medication implementation patterns which might not be seen under controlled study conditions. This study illustrates the utility of these data in describing longitudinal adherence patterns at both an individual and population level.

Polygenic risk scores for disease risk prediction in Africa: current challenges and future directions.

Early identification of genetic risk factors for complex diseases can enable timely interventions and prevent serious outcomes, including mortality. While the genetics underlying many Mendelian diseases have been elucidated, it is harder to predict risk for complex diseases arising from the combined effects of many genetic variants with smaller individual effects on disease aetiology. Polygenic risk scores (PRS), which combine multiple contributing variants to predict disease risk, have the potential to influence the implementation for precision medicine. However, the majority of existing PRS were developed from European data with limited transferability to African populations. Notably, African populations have diverse genetic backgrounds, and a genomic architecture with smaller haplotype blocks compared to European genomes. Subsequently, growing evidence shows that using large-scale African ancestry cohorts as discovery for PRS development may generate more generalizable findings. Here, we (1) discuss the factors contributing to the poor transferability of PRS in African populations, (2) showcase the novel Africa genomic datasets for PRS development, (3) explore the potential clinical utility of PRS in African populations, and (4) provide insight into the future of PRS in Africa.

Multiple modes of data sharing can facilitate secondary use of sensitive health data for research.

Evidence-based healthcare relies on health data from diverse sources to inform decision-making across different domains, including disease prevention, aetiology, diagnostics, therapeutics and prognosis. Increasing volumes of highly granular data provide opportunities to leverage the evidence base, with growing recognition that health data are highly sensitive and onward research use may create privacy issues for individuals providing data. Concerns are heightened for data without explicit informed consent for secondary research use. Additionally, researchers-especially from under-resourced environments and the global South-may wish to participate in onward analysis of resources they collected or retain oversight of onward use to ensure ethical constraints are respected. Different data-sharing approaches may be adopted according to data sensitivity and secondary use restrictions, moving beyond the traditional Open Access model of unidirectional data transfer from generator to secondary user. We describe collaborative data sharing, facilitating research by combining datasets and undertaking meta-analysis involving collaborating partners; federated data analysis, where partners undertake synchronous, harmonised analyses on their independent datasets and then combine their results in a coauthored report, and trusted research environments where data are analysed in a controlled environment and only aggregate results are exported. We review how deidentification and anonymisation methods, including data perturbation, can reduce risks specifically associated with health data secondary use. In addition, we present an innovative modularised approach for building data sharing agreements incorporating a more nuanced approach to data sharing to protect privacy, and provide a framework for building the agreements for each of these data-sharing scenarios.

Developing Clinical Phenotype Data Collection Standards for Research in Africa.

Modern biomedical research is characterised by its high-throughput and interdisciplinary nature. Multiproject and consortium-based collaborations requiring meaningful analysis of multiple heterogeneous phenotypic datasets have become the norm; however, such analysis remains a challenge in many regions across the world. An increasing number of data harmonisation efforts are being undertaken by multistudy collaborations through either prospective standardised phenotype data collection or retrospective phenotype harmonisation. In this regard, the Phenotype Harmonisation Working Group (PHWG) of the Human Heredity and Health in Africa (H3Africa) consortium aimed to facilitate phenotype standardisation by both promoting the use of existing data collection standards (hosted by PhenX), adapting existing data collection standards for appropriate use in low- and middle-income regions such as Africa, and developing novel data collection standards where relevant gaps were identified. Ultimately, the PHWG produced 11 data collection kits, consisting of 82 protocols, 38 of which were existing protocols, 17 were adapted, and 27 were novel protocols. The data collection kits will facilitate phenotype standardisation and harmonisation not only in Africa but also across the larger research community. In addition, the PHWG aims to feed back adapted and novel protocols to existing reference platforms such as PhenX.

An e-consent framework for tiered informed consent for human genomic research in the global south, implemented as a REDCap template.

Research involving human participants requires their consent, and it is common practice to capture consent information on paper and store those hard copies, presenting issues such as long-term storage requirements, inefficient retrieval of consent forms for reference or future use, and the potential for transcription errors when transcribing captured informed consent. There have been calls to move to electronic capture of the consent provided by research participants (e-consent) as a way of addressing these issues. A tiered framework for e-consent was designed using the freely available features in the inbuilt REDCap e-consent module. We implemented 'branching logic', 'wet signature' and 'auto-archiver' features to the main informed consent and withdrawal of consent documents. The branching logic feature streamlines the consent process by making follow-up information available depending on participant response, the 'wet signature' feature enables a timestamped electronic signature to be appended to the e-consent documents and the 'auto-archiver' allows for PDF copies of the e-consent documents to be stored in the database. When designing the content layout, we provided example participant information text which can be modified as required. Emphasis was placed on the flow of information to optimise participant understanding and this was achieved by merging the consent and participant information into one document where the consent questions were asked immediately after the corresponding participant information. In addition, we have provided example text for a generic human genomic research study, which can be easily edited and modified according to specific requirements. Building informed consent protocols and forms without prior experience can be daunting, so we have provided researchers with a REDCap template that can be directly incorporated into REDCap databases. It prompts researchers about the types of consent they can request for genomics studies and assists them with suggestions for the language they might use for participant information and consent questions. The use of this tiered e-consent module can ensure the accurate and efficient electronic capture and storage of the consents given by participants in a format that can be easily queried and can thus facilitate ethical and effective onward sharing of data and samples whilst upholding individual participant preferences.

Coronavirus Host Genetics South Africa (COHG-SA) database-a variant database for gene regions associated with SARS-CoV-2 outcomes.

The SARS-CoV-2 virus is responsible for the COVID-19 global public health emergency, and the disease it causes is highly variable in its clinical presentation. Clinical phenotypes are heterogeneous both in terms of presentation of symptoms in the host and response to therapy. Several studies and initiatives have been established to analyse and review host genetic epidemiology associated with COVID-19. Our research group curated these articles into a web-based database using the python application-server framework Django. The database provides a searchable research tool describing current literature surrounding COVID-19 host genetic factors associated with disease outcome. This paper describes the COHG-SA database and provides an overview of the analyses that can be derived from these data.

A framework for the promotion of ethical benefit sharing in health research.

There is an increasing recognition of the importance of including benefit sharing in research programmes in order to ensure equitable and just distribution of the benefits arising from research. Whilst there are global efforts to promote benefit sharing when using non-human biological resources, benefit sharing plans and implementation do not yet feature prominently in research programmes, funding applications or requirements by ethics review boards. Whilst many research stakeholders may agree with the concept of benefit sharing, it can be difficult to operationalise benefit sharing within research programmes. We present a framework designed to assist with identifying benefit sharing opportunities in research programmes. The framework has two dimensions: the first represents microlevel, mesolevel and macrolevel stakeholders as defined using a socioecological model; and the second identifies nine different types of benefit sharing that might be achieved during a research programme. We provide an example matrix identifying different types of benefit sharing that might be undertaken during genomics research, and present a case study evaluating benefit sharing in Africa during the SARS-CoV-2 pandemic. This framework, with examples, is intended as a practical tool to assist research stakeholders with identifying opportunities for benefit sharing, and inculcating intentional benefit sharing in their research programmes from inception.

Risk factors for COVID-19 hospitalisation and death in people living with diabetes: A virtual cohort study from the Western Cape Province, South Africa.

BACKGROUND: COVID-19 outcomes and risk factors, including comorbidities and medication regimens, in people living with diabetes (PLWD) are poorly defined for low- and middle-income countries. METHODS: The Provincial Health Data Centre (Western Cape, South Africa) is a health information exchange collating patient-level routine health data for approximately 4 million public sector health care seekers. Data from COVID-19 patients diagnosed between March and July 2020, including PLWD, were analysed to describe risk factors, including dispensed diabetes medications and comorbidities, and their association with COVID-19 outcomes in this population. FINDINGS: There were 64,476 COVID-19 patients diagnosed. Of 9305 PLWD, 44.9% were hospitalised, 4.0% admitted to ICU, 0.6% received ventilation and 15.4% died. In contrast, proportions of COVID-19 patients without diabetes were: 12.2% hospitalised, 1.0% admitted, 0.1% ventilated and 4.6% died. PLWD were significantly more likely to be admitted (OR:3.73, 95 %CI: 3.53, 3.94) and to die (OR:3.01, 95 %CI: 2.76,3.28). Significant hospitalised risk factors included HIV infection, chronic kidney disease, current TB, male sex and increasing age. Significant risk factors for mortality were CKD, male sex, HIV infection, previous TB and increasing age. Pre-infection use of insulin was associated with a significant increased risk for hospitalisation (OR:1·39, 95 %CI:1·24,1·57) and mortality (OR1·49, 95 %CI:1·27; 1·74) and metformin was associated with a reduced risk for hospitalisation (OR:0·62,95 %CI:0·55, 0·71) and mortality (OR 0·77, 95 %CI:0·64; 0·92). INTERPRETATION: Using routine health data from this large virtual cohort, we have described the association of infectious and noncommunicable comorbidities as well as pre-infection diabetes medications with COVID-19 outcomes in PLWD in the Western Cape, South Africa. FUNDING: This research was funded in part, by the Wellcome Trust 203135/Z/16/Z, through support of NT. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The Wellcome Centre for Infectious Diseases Research in Africa is supported by core funding from the Wellcome Trust [203135/Z/16/Z]. NT receives funding from the CIDRI-Africa Wellcome Trust grant (203135/Z/16/Z), and NT and TT receive funding from the NIH H3ABioNET award (U24HG006941). NT receives funding from the UKRI/MRC (MC_PC_MR/T037733/1).

Diabetes in a TB and HIV-endemic South African population: Analysis of a virtual cohort using routine health data.

BACKGROUND: It is widely accepted that people living with diabetes (PLWD) are at increased risk of infectious disease, yet there is a paucity of epidemiology studies on the relationship between diabetes and infectious disease in SSA. In a region with a high burden of infectious disease, this has serious consequences for PLWD. METHODS AND FINDINGS: Using routinely collected longitudinal health data, we describe the epidemiology of diabetes in a large virtual cohort of PLWD who have a high burden of HIV and TB, from the Khayelitsha subdistrict in the Western Cape Province in South Africa. We described the relationship between previous TB, newly diagnosed TB disease and HIV infection on diabetes using HbA1c results as an outcome measure. The study population was predominately female (67%), 13% had a history of active TB disease and 18% were HIV positive. The HIV positive group had diabetes ascertained at a significantly younger age (46 years c.f. 53 years respectively, p<0.001) and in general had increased HbA1c values over time after their HIV diagnosis, when compared to the HIV-negative group. There was no evidence of TB disease influencing the trajectory of glycaemic control in the long term, but diabetes patients who developed active TB had higher mortality than those without TB (12.4% vs 6.7% p-value < 0.001). HIV and diabetes are both chronic diseases whose long-term management includes drug therapy, however, only 52.8% of the study population with an HIV-diabetes comorbidity had a record of diabetes treatment. In addition, the data suggest overall poor glycaemic control in the study population with only 24.5% of the participants having an HbA1c <7% at baseline despite 85% of the study population being on diabetes treatment. CONCLUSION: The epidemiologic findings in this exploratory study highlight the need for further research into diabetes outcomes in a high TB and HIV burden setting and demonstrate that routine health data are a valuable resource for understanding disease epidemiology in the general population.

Accessing HIV care may lead to earlier ascertainment of comorbidities in health care clients in Khayelitsha, Cape Town.

Successful antiretroviral rollout in South Africa has greatly increased the health of the HIV-positive population, and morbidity and mortality in PLHIV can increasingly be attributed to comorbidities rather than HIV/AIDS directly. Understanding this disease burden can inform health care planning for a growing population of ageing PLHIV. Anonymized routine administrative health data were analysed for all adults who accessed public health care in 2016-2017 in Khayelitsha subdistrict (Cape Town, South Africa). Selected comorbidities and age of ascertainment for comorbidities were described for all HIV-positive and HIV-negative healthcare clients, as well as for a subset of women who accessed maternal care. There were 172 937 adult individuals with a median age of 37 (IQR:30-48) years in the virtual cohort, of whom 48% (83 162) were HIV-positive. Median age of ascertainment for each comorbidity was lower in HIV-positive compared to HIV-negative healthcare clients, except in the case of tuberculosis. A subset of women who previously accessed maternal care, however, showed much smaller differences in the median age of comorbidity ascertainment between the group of HIV-positive and HIV-negative health care clients, except in the case of chronic kidney disease (CKD). Both HIV-positive individuals and women who link to maternal care undergo routine point-of-care screening for common diseases at younger ages, and this analysis suggests that this may lead to earlier diagnosis of common comorbidities in these groups. Exceptions include CKD, in which age of ascertainment appears lower in PLHIV than HIV-negative groups in all analyses suggesting that age of disease onset may indeed be earlier; and tuberculosis for which age of incidence has previously been shown to vary according to HIV status.

SufT is required for growth of Mycobacterium smegmatis under iron limiting conditions.

Iron-sulphur (FeS) clusters are versatile cofactors required for a range of biological processes within cells. Due to the reactive nature of the constituent molecules, assembly and delivery of these cofactors requires a multi-protein machinery in vivo. In prokaryotes, SufT homologues are proposed to function in the maturation and transfer of FeS clusters to apo-proteins. This study used targeted gene deletion to investigate the role of SufT in the physiology of mycobacteria, using Mycobacterium smegmatis as a model organism. Deletion of the sufT gene in M. smegmatis had no impact on growth under standard culture conditions and did not significantly alter activity of the FeS cluster dependent enzymes succinate dehydrogenase (SDH) and aconitase (ACN). Furthermore, the ΔsufT mutant was no more sensitive than the wild-type strain to the redox cycler 2,3-dimethoxy-1,4-naphthoquinone (DMNQ), or the anti-tuberculosis drugs isoniazid, clofazimine or rifampicin. In contrast, the ΔsufT mutant displayed a growth defect under iron limiting conditions, and an increased requirement for iron during biofilm formation. This data suggests that SufT is an accessory factor in FeS cluster biogenesis in mycobacteria which is required under conditions of iron limitation.

The effect of sputum quality and volume on the yield of bacteriologically-confirmed TB by Xpert MTB/RIF and smear.

INTRODUCTION: The World Health Organization endorsed (2010) the use of Xpert MTB/RIF and countries are shifting from smear microscopy (smear)-based to Xpert MTB/RIF-based tuberculosis (TB) diagnostic algorithms. As with smear, sputum quality may predict the likelihood of obtaining a bacteriologically-confirmed TB when using Xpert MTB/RIF. METHODS: From 08/12-11/2014, all people living with HIV were recruited at 22 clinics. For patients screened positive using the four TB symptoms their sputa were tested by Xpert MTB/RIF and smear. Laboratorians assessed and recorded sputum appearance and volume. The yield of bacteriologically-positive sputum evaluated using Xpert MTB/RIF and smear, likelihood-ratios were calculated. RESULTS: Among 6,041 patients enrolled 2,296 were presumptive TB, 1,305 (56.8%) had > 1 sputa collected and 644/1,305 (49.3%) had both Xpert MTB/RIF and smear results. Since >1 sputa collected from 644 patients 954 sputa were tested by Xpert MTB/RIF and smear. Bacteriologically-positive sputum was two-fold higher with Xpert MTB/RIF 11.4% versus smear 5.3%, p < 0.001. Sputum appearance and quantity were not predictive of bacteriologically-positive results, except volume of 2ml to < 3ml, tested by Xpert MTB/RIF LR+= 1.26 (95% CI, 1.05-1.50). CONCLUSION: Xpert MTB/RIF test yield to bacteriologically-positive sputum was superior to smear. Sputum quality and quantity, however, were not consistently predictive of bacteriologically-positive results by Xpert MTB/RIF or smear.

Diagnosis of pulmonary tuberculosis and assessment of treatment response through analyses of volatile compound patterns in exhaled breath samples.

OBJECTIVES: We determined the performance of a sensor array (an electronic nose) made of 8 metalloporphyrins coated quartz microbalances sensors for the diagnosis and prognosis of pulmonary tuberculosis (TB) using exhaled breath samples. METHODS: TB cases and healthy controls were prospectively enrolled. Signals from volatile organic compounds (VOCs) in breath samples were measured at days 0, 2, 7, 14, and 30 of TB therapy and correlated with clinical and microbiological measurements. RESULTS: Fifty one pulmonary TB cases and 20 healthy HIV-uninfected controls were enrolled in the study. 31 (61%) of the 51 pulmonary TB cases were coinfected with HIV. At day 0 (before TB treatment initiation) the sensitivity of our device was estimated at 94.1% (95% confidence interval [CI], 83.8-98.8%) and specificity was 90.0% (95% CI, 68.3-98.8%) for distinguishing TB cases from controls. Time-dependent changes in the breath signals were identified as time on TB treatment progressed. Time-dependent signal changes were more pronounced among HIV-uninfected patients. CONCLUSION: The identification of VOCs' signals in breath samples using a sensor array achieved high sensitivity and specificity for the diagnosis of TB and allowed following signal changes during TB treatment.

Identification of a Large Pool of Microorganisms with an Array of Porphyrin Based Gas Sensors.

The association between volatile compounds (VCs) and microorganisms, as demonstrated by several studies, may offer the ground for a rapid identification of pathogens. To this regard, chemical sensors are a key enabling technology for the exploitation of this opportunity. In this study, we investigated the performance of an array of porphyrin-coated quartz microbalance gas sensors in the identification of a panel of 12 bacteria and fungi. The porphyrins were metal complexes and the free base of a functionalized tetraphenylporphyrin. Our results show that the sensor array distinguishes the VC patterns produced by microorganisms in vitro. Besides being individually identified, bacteria are also sorted into Gram-positive and Gram-negative.