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BackgroundObservational health data are collected in different formats and structures, making it challenging to analyze with common tools. The Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM) is a standardized data model that can harmonize observational health data.ObjectiveThis paper demonstrates the use of the OMOP CDM to harmonize COVID-19 sero-surveillance data from the Nairobi Urban Health and Demographic Surveillance System (HDSS).MethodsIn this study, we extracted data from the Nairobi Urban HDSS COVID-19 sero-surveillance database and mapped it to the OMOP CDM. We used open-source Observational Health Data Sciences and Informatics (OHDSI) tools like WhiteRabbit, RabbitInAHat, and USAGI. The steps included data profiling (scanning), mapping the vocabularies using the offline USAGI and online ATHENA, and designing the extract, transform, and load (ETL) process using RabbitInAHat. The ETL process was implemented using Pentaho Data Integration community edition software and structured query language (SQL). The target OMOP CDM can now be used to analyze the prevalence of COVID-19 antibodies in the Nairobi Urban HDSS population.ResultsWe successfully mapped the Nairobi Urban HDSS COVID-19 sero-surveillance data to the OMOP CDM. The standardized dataset included information on demographics, COVID-19 symptoms, vaccination, and COVID-19 antibody test results.ConclusionsThe OMOP CDM is a valuable tool for harmonizing observational health data. Using the OMOP CDM facilitates the sharing and analysis of observational health data, leading to a better understanding of disease conditions and trends and improving evidence-based population health strategies.

BackgroundCOVID-19 pandemic had devastating socio-economic and health effects, including mental health. This study examines the intersectionality between gender and mental health outcomes among Kenyan adults in informal settlements of Nairobi, Kisumu, and Kilifi Counties during the COVID-19 crisis. This is necessary to inform mental health response in case of another pandemic.MethodsWe analyzed data collected in a longitudinal survey between July 2020 (fourth round) and February 2021 (fifth round). The data covered COVID-19-related effects on job loss, food insecurity, access to health services, and mental health. Participants were randomly sampled from existing cohorts at the Population Council. The outcomes of interest were depressive and anxiety disorders, combined into a binary composite outcome variable. Descriptive statistics included means for continuous variables and frequencies and proportions for categorical variables. Chi-square tests were used to examine the differences between groups. The relationship between the gendered COVID-19 socio-economic effects and participants’ mental health was examined using modified Poisson regression.ResultsA total of 4,050 participants were interviewed, 66% female and median age 38 [interquartile range (IQR): 29–46]. Complete income loss was strongly associated with negative mental health outcomes in multiple intersections with varied magnitudes. The association was larger among older females (≥50 years) (PR = 1.33, 95% CI = 1.17–1.51, p

BackgroundMetadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. ObjectiveTo address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. MethodsThe framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. ResultsThe implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. ConclusionsThe adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

Objectives We aimed to capture evidence on enablers and barriers to improving equal opportunity and effective organisational interventions that can advance women’s leadership in India and Kenya’s health sectors.Methods We systematically searched JSTOR, PubMed, SCOPUS and Web of Science databases, reference lists of selected articles and Google Scholar using string searches. We included studies that were published in English from 2000 to 2022 in peer-reviewed journals or grey literature, focused on paid, formal health professionals in India or Kenya, described factors relating to women’s representation/leadership.Results We identified 26 studies, 15 from India and 11 from Kenya. From each country, seven studies focused on nursing. Participants included women and men health sector workers. Seven studies used mixed methods, 11 were qualitative, 5 were quantitative and 3 were commentaries. Factors influencing women’s career progression at individual/interpersonal levels included family support, personal attributes (knowledge/skills) and material resources. Factors at the organisational level included capacity strengthening, networking, organisational policies, gender quotas, work culture and relationships, flexibility, and work burden. Nursing studies identified verbal/sexual harassment and professional hierarchies as barriers to career progression. Structural barriers included a lack of infrastructure (training institutes and acceptable working environments). Normative themes included occupational segregation by gender (particularly in nursing), unpaid care work burden for women and gender norms.Studies of interventions to improve women’s career progression and sex-disaggregated workforce data in India or Kenya were limited, especially on leadership within career pathways. The evidence focuses on enablers and barriers at work, rather than on organisations/systems to support women’s leadership or address gender norms.Conclusions Women in India and Kenya’s health sectors face multiple impediments in their careers, which impact their advancement to leadership. This calls for gender-transformative interventions to tackle discrimination/harassment, provide targeted training/mentorship, better parental leave/benefits, flexible/remote working, family/coworker support and equal-opportunity policies/legislation.

Objectives This paper examines the availability of legal provisions, or the lack thereof, that support women to progress equitably into leadership positions within the health workforce in India and Kenya.Methods We adapted the World Bank’s Women, Business and Law framework of legal domains relevant to gender equality in the workplace and applied a ‘law cube’ to analyse the comprehensiveness, accountability and equity and human rights considerations of 27 relevant statutes in India and 11 in Kenya that apply to people in formal employment within the health sector. We assessed those laws against 30 research-validated good practice measures across five legal domains: (1) pay; (2) workplace protections; (3) pensions; (4) care, family life and work–life balance; and (5) reproductive rights. In India, the pension domain and related measures were not assessed because the pension laws do not apply to the public and private sector equally.Results Several legal domains are addressed inadequately or not at all, including pay in India, reproductive rights in Kenya and the care, family life and the work–life balance domain in both countries. Additionally, we found that among the Kenyan laws reviewed, few specify accountability mechanisms, and equity and human rights measures are mainly absent from the laws assessed in both countries. Our findings highlight inadequacies in the legal environments in India and Kenya may contribute to women’s under-representation in leadership in the health sector. The absence of specified accountability mechanisms may impact the effective implementation of legislation, undermining their potential to promote equal opportunities.Conclusions Government action is needed in both countries to ensure that legislation addresses best practice provisions, equity and human rights considerations, and provides for independent review mechanisms to ensure accountability for implementation of existing and future laws. This would contribute to ensuring that legal environments uphold the equality of opportunity necessary for realising gender justice in the workplace for the health workforce.Primary source of funding Bill & Melinda Gates Foundation (INV-031372).

Abstract Background Essential health services can be disrupted due to several naturally occurring public health emergencies such as drought, flood, earthquake and outbreak of infectious diseases. However, little evidence exists on the status of essential health services delivery under the effect of drought and food insecurity. North-east Uganda is severely affected by prolonged drought that significantly affected the livelihood of the residents. Therefore, we aimed to determine the current status of essential health services and quality improvement (QI) actions in health facilities in north-east Uganda. Methods We used a descriptive cross-sectional study design to assess the availability of essential health service and quality improvement activities in drought and food insecurity affected districts of north-east Uganda. We included a total of 150 health facilities from 15 districts with proportionated multistage sampling method. We interviewed health facilities’ managers and services focal persons using structured questionnaire and observation checklist. We used a descriptive statistic to analyze the data with SPSS version 22. Results A few health facilities (8.7%) had mental health specialist. There was also lack of capacity building training on essential health services. Considerable proportion of health facilities had no non-communicable diseases (38.3%), mental health (47.0%), and basic emergency obstetric care (40.3%) services. Stock out of essential medicines were observed in 20% of health facilities. There was lack of supportive supervision, and poor documentation of QI activities. Conclusion Essential health service and QI were suboptimal in drought and food insecure emergency affected districts. Human resource deployment (especially mental health specialist), provision of capacity building training, improving non-communicable diseases, mental health and basic emergency obstetric care services are required to improve availability of essential health services. Supporting supply chain management to minimize stock out of medicines, and promoting QI activities are also vital to assure quality of health service in drought and food insecurity affected districts in north-Eastern Uganda.

The housing market in Dar es Salaam, Tanzania, is expanding and with it a need for increased market transparency to guide investors and other stakeholders. The objective of this paper is to evaluate machine learning (ML) methods to appraise real estate in formal and informal housing markets in this nascent market sector. Various advanced ML models are applied with the aim of improving property value estimates in a market with limited access to information. The dataset used included detailed property characteristics and transaction data from both market types. Regression, decision trees, neural networks, and ensemble methods were employed to refine property appraisals across these settings. The findings indicate significant differences between formal and informal market valuations, demonstrating ML’s effectiveness in handling limited data and complex market dynamics. These results emphasise the potential of ML techniques in emerging markets where traditional valuation methods often fail due to the scarcity of transaction data.

Background The completion of case-based surveillance forms was vital for case identification during COVID-19 surveillance in Malawi. Despite significant efforts, the resulting national data suffered from gaps and inconsistencies which affected its optimal usability. The objectives of this study were to investigate the processes of collecting and reporting COVID-19 data, to explore health workers’ perceptions and understanding of the collection tools and processes, and to identify factors contributing to data quality. Methods A total of 75 healthcare professionals directly involved in COVID-19 data collection from the Malawi Ministry of Health in Lilongwe and Blantyre participated in Focus Group Discussions and In-Depth Interviews. We collected participants’ views on the effectiveness of surveillance forms in collecting the intended data, as well as on the data collection processes and training needs. We used MAXQDA for thematic and document analysis. Results Form design significantly influenced data quality and, together with challenges in applying case definitions, formed 44% of all issues raised. Concerns regarding processes used in data collection and training gaps comprised 49% of all the issues raised. Language issues (2%) and privacy, ethical, and cultural considerations (4%), although mentioned less frequently, offered compelling evidence for further review. Conclusions Our study highlights the integral connection between data quality and the design and utilization of data collection forms. While the forms were deemed to contain the most relevant fields, deficiencies in format, order of fields, and the absence of an addendum with guidelines, resulted in large gaps and errors. Form design needs to be reviewed so that it appropriately fits into the overall processes and systems that capture surveillance data. This study is the first of its kind in Malawi, offering an in-depth view of the perceptions and experiences of health professionals involved in disease surveillance on the tools and processes they use.

Background: Hypoxia is a common presentation in the Emergency Department (ED) worldwide. It affects 9-12% of hospitalized adults in Sub-Saharan Africa. Timely diagnosis of the multiple causes such as pneumonia, heart failure among others is challenging. Chest X-Ray (CXR), one of the most utilized imaging modalities has many limitations, and the gold standard (Computed Tomography scan) is inaccessible. Point of care ultrasound (PoCUS) is more available and increasingly being used, however little is known of its performance in resource limited EDs. The study aimed to assess the diagnostic performance of PoCUS compared with CXR in identifying the causes of hypoxia in the medical ED. Methods: 49 adults presenting with hypoxia (SP02 ≤ 88%) in the medical ED were evaluated. Ultrasound of the lungs and heart (PoCUS) was done, then CXR obtained. Lung ultrasound (LUS) was compared with CXR (first reference standard). Chest X-Ray and PoCUS were each compared to the physician diagnosis (second reference standard) to determine agreement using an acceptable disagreement cut-off of 15%. Results: 31% more abnormalities were identified by LUS than CXR. Lung ultrasound findings agreed with CXR in 86% of the participants with moderate reliability (ĸ=0.75). There was no significant difference between the actual findings of the two tests (X2= 2, p 0.1). Using the second reference, 82% of the CXRs were similar with weak reliability (ĸ=0.5) compared to 98% of PoCUS findings with strong reliability (ĸ=0.9). Compared to PoCUS, CXRs significantly differed from the physician diagnosis (X2= 0.85, p 0.38 vs X2= 8.5, p 0.004 respectively). Conclusion: Overall, PoCUS was not inferior to CXR when compared to final physician diagnosis in identifying causes of hypoxia, and LUS and CXR had comparable performance. Significantly more abnormalities were identified on PoCUS and it demonstrated better agreement and strong reliability with the physician diagnosis than CXR. We recommend PoCUS use in patients with hypoxia attending resource limited in- and pre-hospital settings.

Background: Greater blood pressure variability has detrimental effects on clinical outcome after a stroke; its effects are controversial and have not been evaluated in Sub-Saharan Africa (SSA). Methods: We conducted a prospective study of patients with CT head confirmed ischemic and hemorrhagic strokes admitted to a tertiary hospital within 7 days of onset of unilateral neurological deficits. Blood pressure variability indices, standard deviation (SD) and coefficient of variation (CV) of systolic and diastolic blood pressure between day 0 and day 7, were calculated with a subsequent modified Rankin Scale (mRS) score on day 14 post-stroke. Linear regression was performed to determine the exponential coefficients of mortality at 14 days post- stroke. Results: Out of 120 patients, 51.7% were female, 52.5% had ischemic stroke and the overall median age was 65 (IQR 54–80) years. Twenty (16.7%) patients died within a median survival time of 7 days, while 32 (26.7%) died by day 14 post-stroke. Patients with hemorrhagic stroke had an overall SDSBP of 16.44 mmHg while those with ischemic stroke had an overall SDSBP of 14.05 mmHg. In patients with ischemic stroke, SDSBP had adjusted coefficients of 1, p = 0.004 with C·I: 1.01–1.04 and NIHSS had adjusted coefficients of 1, p = 0.019 with C·I: 1.00–1.03 while in patients with hemorrhagic stroke, SDSBP had adjusted coefficients of 1, p = 0.045 with C·I: 1.00–1.04 and NIHSS had adjusted coefficients of 1, p ≤0.001 with C·I: 1.01–1.03. Conclusion: Exponential increase in Blood Pressure Variability (BPV) and stroke severity scale were independently associated with early mortality among all stroke patients in our study. We recommend future studies to evaluate whether controlling BPV among patients with stroke in Sub-Saharan Africa can reduce mortality.

Abstract Background Involuntary childlessness is a global phenomenon that negatively impacts the couple, or the family involved. The experiences of women living with involuntary childlessness have not been well documented in the literature, specifically in the Ugandan context. The purpose of the study was to explore the experiences of women living with involuntary childlessness in Uganda. Methods A qualitative phenomenological approach was used. Fifteen in-depth interviews were conducted among women experiencing involuntary childlessness attending a National Referral Hospital. Purposive sampling was, and data saturation determined the actual sample size. Thematic analysis was used for data analysis. The results are presented in the form of text and narrative quotes from participants. Results Six themes emerged (i) Inadequate social support (ii) psychological torture (iii) continued grief (iv) marital instability (v) failure attributed to childlessness and (vi) financial constraints. Inadequate social support was in the form of having an unsupportive partner, altered social relation, and altered social status, while women experienced name-calling, emotional abuse, stigma, and blame under the psychological torture theme. Women experienced feelings of distress and grief, including anger, irritability, sadness, stress, and feelings of despair. Women with involuntary childlessness recounted experiencing unstable marriages characterized by infidelity, divorce, abandonment, and polygamous marriages. Some women coped positively, while others employed negative coping strategies such as social withdrawal and isolation. Women who their partners and families well supported coped positively. In contrast, those who did not receive as much support were stressed, sad, angry, and had lost hope of pregnancy. Conclusions In this study, women with involuntary childlessness lacked social support amidst experiences of marital turmoil, psychological torture, feelings of distress and grief, unfulfilled motherhood expectations, and financial constraints while seeking treatment, therefore, there is a need to screen the women for psychological / mental illness symptoms and provide empathetic care and counseling. The prevalence of involuntary childlessness is not well documented in Uganda and a study can be done to determine its extent.

The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting and disseminating COVID-19 related data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-19 data. The INSPIRE PaaS for COVID-19 data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM. Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP's federated model. The INSPIRE platform for evaluation of COVID-19 Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens' participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP. These AI technologies can be used to discover and evaluate pathways that COVID-19 cohorts take through public health interventions and treatments. By using both the data mapping and terminology mapping, we construct ETLs that populate the data and/or metadata elements of the CDM, making the hub both a central model and a distributed model.

Abstract Worldwide, there is a wide gap between what women can contribute to the economy and what they actually contribute. One of the main barriers to women’s engagement in the labor market and productivity at work is the societal expectation that they should take care of their children in addition to meeting the demands of employment. Furthermore, those in informal employment face difficulties due to long working hours and environments that are not appropriate for childcare. To address this, Kidogo runs an innovative “Hub & Spoke” model for low-income communities. Here, we present a study protocol aimed at evaluating whether the provision of quality childcare opportunities for working women through the Kidogo model is feasible and acceptable and whether it contributes to improvements in their incomes and productivity at work. The study reported in this protocol which is currently ongoing, employed a quasi-experimental design with two study arms: primary caregivers who use childcare services were recruited into the intervention (n = 170) and comparison groups (n = 170). Both groups are being followed up for one year. We are using a mixed-methods approach. Appropriate statistical methods including a difference-in-differences (DID) estimator will be used to analyze the effects of the intervention. We expect that the intervention will improve the quality of childcare services which in turn will improve the incomes of the center providers. We expect that providing improved childcare services will enhance women’s economic empowerment. Trial registration: PACTR202107762759962.

Abstract Background Left ventricular diastolic dysfunction (LVDD) is a recognized complication of diabetes mellitus that precedes and is a risk factor for heart failure. We aimed to determine the prevalence of LVDD and its association with body mass index in ambulatory adults with diabetes mellitus in rural Uganda. Methods We conducted a cross-sectional study, over 5 months, to enroll 195 ambulatory Ugandan adults living with diabetes mellitus for at least five years at Mbarara Regional Referral Hospital. We collected demographic, and clinical data and measured body mass index (BMI). Echocardiography was performed to determine LVDD by assessing the mitral inflow ventricular filling velocities (E/A and E/è ratios), tricuspid regurgitant jet peak velocity, and left atrium maximum volume index. We used logistic regression to estimate the odds ratio for the association of LVDD with BMI and evaluated the variation of associations by age and hypertension status. Results Of the 195 participants, 141 (72.31%) were female, the mean age was 62 [standard deviation, 11.50] years, and the median duration of diabetes diagnosis was 10 [interquartile range, 7, 15] years. Eighty-six percent (n = 168) had LVDD with the majority (n = 127, 65.1%) of participants in the grade 1 category of LVDD. In the adjusted model, the odds of LVDD for each 1 kg/m2 increase in BMI was 1.11 [95% confidence interval 1.00, 1.25, p = 0.04]. The adjusted odds of LVDD among individuals aged ≥ 50 years with BMI ≥ 25 kg/m2 was 13.82 times the odds of LVDD in individuals aged

IntroductionIn some communities, rationalization of men's controlling attitudes is associated with the justification of gender norms such as wife-beating as a method of correcting spouse behaviour. In this quasi-experimental study, we investigate the causal effects of the acceptability of gender norms justifying wife-beating on experiences of sexual, emotional, and physical intimate partner violence (IPV) among Ugandan men and women.Methods and materialsWe analysed the 2016 Uganda Demographic and Health Survey data using propensity-score matching. The exposure variable is the acceptability of gender norms justifying wife-beating measured on a binary scale and the outcomes are the respondent's lifetime experiences of sexual, physical, and emotional IPV. We matched respondents who accepted gender norms justifying wife-beating with those that never through a 1:1 nearest-neighbour matching with a caliper to achieve comparability on selected covariates. We then estimated the causal effects of acceptability of gender norms justifying wife-beating on the study outcomes using a logistic regression model.ResultsResults showed that a total of 4,821 (46.5%) out of 10,394 respondents reported that a husband is justified in beating his wife for specific reasons. Among these, the majority (3,774; 78.3%) were women compared to men (1,047; 21.7%). Overall, we found that men and women who accept gender norms justifying wife-beating are more likely to experience all three forms of IPV. In the sub-group analysis, men who justify wife-beating were more likely to experience emotional and physical IPV but not sexual IPV. However, women who justify wife-beating were more likely to experience all three forms of IPV.ConclusionsIn conclusion, the acceptability of gender norms justifying wife-beating has a positive effect on experiences of different forms of IPV by men and women in Uganda. There is, therefore, a need for more research to study drivers for acceptance of gender norms justifying wife-beating to enable appropriate government agencies to put in place mechanisms to address the acceptability of gender norms justifying wife-beating at the societal level.

Introduction: The prevalence of obesity among people diagnosed with Type 2 Diabetes Mellitus (T2DM) has been widely documented. However, the specific composition of this body weight remains largely unknown. The study aimed to understand the body composition of T2DM patients using the bioelectric impedance analysis technique, comparing findings to sex and age-matched controls. Materials and Methods: A comparative case-control study was carried out among 139 known cases of Type 2 diabetes aged 18 to 78 years randomly sampled from the diabetic clinic of Mbarara Regional Referral Hospital. We matched them to 139 hospital controls who were healthy non-diabetic attendants. Body composition parameters were computed and summarized as medians and interquartile ranges. Differences in the medians of body composition parameters were further assessed using the Mann-Whitney U test. Fat-free and fat mass indices were derived to offer a precise estimation of body composition parameters adjusted for height differences among study participants. Results: Cases had significantly higher median systolic blood pressure, pulse rate, weight, Body Mass Index (BMI), Waist-Hip Ratio (WHR), total fat percentage, fat mass amount, Fat Mass Index, visceral fat, and metabolic age than their counterparts, whereas controls had significantly higher median total body water percentage versus cases. The highest significant differences occurred in fat percentage composition (Cases: β: 6.9 (95% C.I: 4.4, 9.4); Controls: Ref) followed by visceral fat (Cases: β: 3.5 (95% C.I: 2.5, 4.4); controls: Ref) and Fat Mass Index (Cases: 95% C.I: 2.6 (95% C.I: 1.6, 3.7). Cases had significantly higher Fat Mass Index, visceral fat and fat percentage (all p0.05) than controls. Conclusions: Routine assessment of body composition of T2DM patients needs to be done to assess the amount, type and pattern of weight gain to prevent increases in adiposity.

The housing market in Dar es Salaam, Tanzania, is expanding and with it a need for increased market transparency to guide investors and other stakeholders. The objective of this paper is to evaluate machine learning (ML) methods to appraise real estate in formal and informal housing markets in this nascent market sector. Various advanced ML models are applied with the aim of improving property value estimates in a market with limited access to information. The dataset used included detailed property characteristics and transaction data from both market types. Regression, decision trees, neural networks, and ensemble methods were employed to refine property appraisals across these settings. The findings indicate significant differences between formal and informal market valuations, demonstrating ML's effectiveness in handling limited data and complex market dynamics. These results emphasise the potential of ML techniques in emerging markets where traditional valuation methods often fail due to the scarcity of transaction data. [ABSTRACT FROM AUTHOR]

Abstract Background Traditional malaria vector sampling techniques bias collections towards female mosquitoes. Comprehensive understanding of vector dynamics requires balanced vector sampling of both males and females. Male mosquito sampling is also necessary for population size estimations by male-based mark-release-recapture (MRR) studies and for developing innovations in mosquito control, such as the male-targeted sterile insect technique and other genetic modification approaches. This study evaluated a range of collection methods which show promise in providing a more equal, or even male-biased, sex representation in the sample. Results Swarms were found at all study sites and were more abundant and larger at the peak of the wet season. Swarm sampling caught the most males, but when man/hour effort was factored in, sampling of eaves by aspiration was the more efficient method and also provided a representative sample of females. Grass-roofed houses were the most productive for eave collections. Overall few mosquitoes were caught with artificial resting traps (clay pots and buckets), although these sampling methods performed better at the start of the wet season than at its peak, possibly because of changes in mosquito ecology and an increased availability of natural resting sites later in the season. Aspiration of bushes was more productive at the peak of the wet season than at the start. Conclusions The results of this study demonstrate that eave aspiration was an efficient and useful male mosquito collection method at the study sites and a potentially powerful aid for swarm location and MRR studies. The methods evaluated may together deliver more sex-balanced mosquito captures and can be used in various combinations depending on the aims and ecological parameters of a given study.

Abstract Background Orthostatic hypotension (OH) and orthostatic hypertension (OHT) are often unrecognized in clinical care for diabetic individuals, yet they are associated with increased risk for adverse cardiovascular outcomes. We aimed to determine the prevalence of the abnormal orthostatic blood pressure (BP) responses, and associated factors among diabetic individuals in ambulatory care for diabetes in southwestern Uganda. Methods We conducted a cross-sectional study among diabetic individuals aged 18–65 years at Mbarara Regional Referral Hospital, southwestern Uganda from November 2018 to April 2019. We obtained demographic and clinical data including a detailed medical history, and glycemic profile. BP measurements were taken in supine position and within 3 min of standing. We defined OH in participants with either ≥ 20 mmHg drop in systolic BP (SBP) or ≥ 10 mmHg drop in diastolic BP (DBP) after assuming an upright position. OHT was defined in participants with either a ≥ 20 mmHg rise in SBP, or ≥ 10 mmHg rise in DBP after assuming an upright position. Multivariate logistic regression was used to identify factors associated with OH and OHT. Results We enrolled 299 participants, with a mean age of 50 years (SD ± 9.8), and mean HbA1c of 9.7% (SD ± 2.6); 70% were female. Of the 299 participants, 52 (17.4%; 95% CI 13.3–22.2%) met the definition of OH and 43 (14.4%; 95% CI 10.6–18.9%) were classified as having OHT. In multivariable models, factors associated with diabetic OH were older age (OR = 2.40 for 51–65 years vs 18–50 years, 95% CI 1.02–5.67, P = 0.046), diabetic retinopathy (OR = 2.51; 95% CI 1.14–5.53, P = 0.022), higher resting SBP ≥ 140 mmHg (OR = 3.14; 95% CI 1.31–8.7.56, P = 0.011), and history of palpitations (OR = 2.31; 95% CI 1.08–4.92, P = 0.031). Self-report of palpitations (OR = 3.14; 95% CI 1.42–6.95, P = 0.005), and higher resting SBP ≥ 140 mmHg (OR = 22.01; 95% CI 1.10–4.42, P = 0.043) were associated with OHT. Conclusion OH and OHT are common among diabetic individuals in ambulatory diabetes care in southwestern Uganda. Orthostatic BP measurements should be considered as part of routine physical examination to improve detection of OH and OHT, especially among older diabetics with complications of the disease. Future studies to assess the health and prognostic implications of OH and OHT among diabetics in the region are warranted.

HIV testing continues to be a challenge among the young population in Tanzania. As of 2017, only 30% of 15–19-year-olds reported getting tested and receiving their results. This study will examine the demographic and socio-behavioral characteristics associated with HIV testing among adolescents and young adults in Tanzania. Interview data from the 2016–2017 Tanzania HIV Impact Survey (THIS) were analyzed on 10,128 adolescents and young adults 15–24 years of age, representing 10.5 million youth in Tanzania. Weighted logistic regression was used to model the relationship of HIV testing with demographic and socio-behavioral characteristics. Half (50%) of respondents reported ever having been tested for HIV. HIV testing was significantly lower among males compared with females (AOR = 0.5;95% confidence interval [CI] = 0.5–0.6; p

In Africa, antiretroviral therapy (ART) is delivered with limited laboratory monitoring, often none. In 2003-2004, investigators in the Development of Antiretroviral Therapy in Africa (DART) Trial randomized persons initiating ART in Uganda and Zimbabwe to either laboratory and clinical monitoring (LCM) or clinically driven monitoring (CDM). CD4 cell counts were measured every 12 weeks in both groups but were only returned to treating clinicians for management in the LCM group. Follow-up continued through 2008. In observational analyses, dynamic marginal structural models on pooled randomized groups were used to estimate survival under different monitoring-frequency and clinical/immunological switching strategies. Assumptions included no direct effect of randomized group on mortality or confounders and no unmeasured confounders which influenced treatment switch and mortality or treatment switch and time-dependent covariates. After 48 weeks of first-line ART, 2,946 individuals contributed 11,351 person-years of follow-up, 625 switches, and 179 deaths. The estimated survival probability after a further 240 weeks for post-48-week switch at the first CD4 cell count less than 100 cells/mm(3) or non-Candida World Health Organization stage 4 event (with CD4 count