Evaluating tuberculosis treatment outcomes in Haiti from 2018 to 2019: A competing risk analysis.

Objectives: This study assesses tuberculosis (TB) treatment outcomes in Haiti. Methods: Data from drug-susceptible patients with TB (2018-2019) were analyzed using the Fine & Gray model with multiple imputation. Results: Of the 16,545 patients, 14.7% had concurrent HIV coinfection, with a 66.2% success rate. The median treatment duration was 5 months, with patients averaging 30 years (with an interquartile range of 22-42 years). The estimated hazard of achieving a successful treatment outcome decreased by 2.5% and 8.1% for patients aged 45 and 60 years, respectively, compared with patients aged 30 years. Male patients had a 6.5% lower estimated hazard of success than their female counterparts. In addition, patients coinfected with HIV experienced a 35.3% reduction in the estimated hazard of achieving a successful treatment outcome compared with those with a negative HIV serologic status. Conclusions: Integrated health care approaches should be implemented, incorporating innovative solutions, such as machine learning algorithms combined with geographic information systems and non-conventional data sources (including social media), to identify TB hotspots and high-burden households.

The spatial spread of HIV in Malawi: An individual-based mathematical model.

Background: The prevalence of HIV varies greatly between and within countries. We aimed to build a comprehensive mathematical modelling tool capable of exploring the reasons of this heterogeneity and test its applicability by simulating the Malawian HIV epidemic. Methods: We developed a flexible individual-based mathematical model for HIV transmission that comprises a spatial representation and individual-level determinants. We tested this model by calibrating it to the HIV epidemic in Malawi and exploring whether the heterogeneity in HIV prevalence could be reproduced. We ran the model for 1975-2030 with five alternative realizations of the geographical structure and mobility: (I) no geographical structure; 28 administrative districts including (II) only permanent inter-district relocations, (III) inter-district permanent relocations and casual sexual relationships, or (IV) permanent relocations between districts and to/from abroad and inter-district casual sex; and (V) a grid of 10 × 10km2 cells, with permanent relocations and between-cell casual relationships. We assumed HIV was present in 1975 in the districts with >10 % prevalence in 2010. We calibrated the models to national and district-level prevalence estimates. Results: Reaching the national prevalence required all adults to have at least 22 casual sex acts/year until 1990. Models II, III and V reproduced the geographical heterogeneity in prevalence in 2010 to some extent if between-district relationships were excluded (Model II; 4.9 %-21.1 %). Long-distance casual partnership mixing mitigated the differences in prevalence substantially (range across districts 4.1%-18.9 % in 2010 in Model III; 4.0%-17.6 % in Model V); with international migration the differences disappeared (Model IV; range across districts 6.9%-13.3 % in 2010). National prevalence decreased to 5 % by 2030. Conclusion: Earlier introduction of HIV into the Southern part of Malawi may cause some level of heterogeneity in HIV prevalence. Other factors such as sociobehavioural characteristics are likely to have a major impact and need investigation.

An Automated Literature Review Tool (LiteRev) for Streamlining and Accelerating Research Using Natural Language Processing and Machine Learning: Descriptive Performance Evaluation Study.

BACKGROUND: Literature reviews (LRs) identify, evaluate, and synthesize relevant papers to a particular research question to advance understanding and support decision-making. However, LRs, especially traditional systematic reviews, are slow, resource-intensive, and become outdated quickly. OBJECTIVE: LiteRev is an advanced and enhanced version of an existing automation tool designed to assist researchers in conducting LRs through the implementation of cutting-edge technologies such as natural language processing and machine learning techniques. In this paper, we present a comprehensive explanation of LiteRev's capabilities, its methodology, and an evaluation of its accuracy and efficiency to a manual LR, highlighting the benefits of using LiteRev. METHODS: Based on the user's query, LiteRev performs an automated search on a wide range of open-access databases and retrieves relevant metadata on the resulting papers, including abstracts or full texts when available. These abstracts (or full texts) are text processed and represented as a term frequency-inverse document frequency matrix. Using dimensionality reduction (pairwise controlled manifold approximation) and clustering (hierarchical density-based spatial clustering of applications with noise) techniques, the corpus is divided into different topics described by a list of the most important keywords. The user can then select one or several topics of interest, enter additional keywords to refine its search, or provide key papers to the research question. Based on these inputs, LiteRev performs a k-nearest neighbor (k-NN) search and suggests a list of potentially interesting papers. By tagging the relevant ones, the user triggers new k-NN searches until no additional paper is suggested for screening. To assess the performance of LiteRev, we ran it in parallel to a manual LR on the burden and care for acute and early HIV infection in sub-Saharan Africa. We assessed the performance of LiteRev using true and false predictive values, recall, and work saved over sampling. RESULTS: LiteRev extracted, processed, and transformed text into a term frequency-inverse document frequency matrix of 631 unique papers from PubMed. The topic modeling module identified 16 topics and highlighted 2 topics of interest to the research question. Based on 18 key papers, the k-NNs module suggested 193 papers for screening out of 613 papers in total (31.5% of the whole corpus) and correctly identified 64 relevant papers out of the 87 papers found by the manual abstract screening (recall rate of 73.6%). Compared to the manual full text screening, LiteRev identified 42 relevant papers out of the 48 papers found manually (recall rate of 87.5%). This represents a total work saved over sampling of 56%. CONCLUSIONS: We presented the features and functionalities of LiteRev, an automation tool that uses natural language processing and machine learning methods to streamline and accelerate LRs and support researchers in getting quick and in-depth overviews on any topic of interest.

A Platform for Data-Centric, Continuous Epidemiological Analyses (EpiGraphHub): Descriptive Analysis.

BACKGROUND: Guaranteeing durability, provenance, accessibility, and trust in open data sets can be challenging for researchers and organizations that rely on public repositories of data critical for epidemiology and other health analytics. The required data repositories are often difficult to locate and may require conversion to a standard data format. Data-hosting websites may also change or become unavailable without warning. A single change to the rules in one repository can hinder updating a public dashboard reliant on data pulled from external sources. These concerns are particularly challenging at the international level, because policies on systems aimed at harmonizing health and related data are typically dictated by national governments to serve their individual needs. OBJECTIVE: In this paper, we introduce a comprehensive public health data platform, EpiGraphHub, that aims to provide a single interoperable repository for open health and related data. METHODS: The platform, curated by the international research community, allows secure local integration of sensitive data while facilitating the development of data-driven applications and reports for decision-makers. Its main components include centrally managed databases with fine-grained access control to data, fully automated and documented data collection and transformation, and a powerful web-based data exploration and visualization tool. RESULTS: EpiGraphHub is already being used for hosting a growing collection of open data sets and for automating epidemiological analyses based on them. The project has also released an open-source software library with the analytical methods used in the platform. CONCLUSIONS: The platform is fully open source and open to external users. It is in active development with the goal of maximizing its value for large-scale public health studies.

Validation and Improvement of a Machine Learning Model to Predict Interruptions in Antiretroviral Treatment in South Africa.

INTRODUCTION: Machine learning algorithms are increasingly being used to inform HIV prevention and detection strategies. We validated and extended a previously developed machine learning model for patient retention on antiretroviral therapy in a new geographic catchment area in South Africa. METHODS: We compared the ability of an adaptive boosting algorithm to predict interruption in treatment (IIT) in 2 South African cohorts from the Free State and Mpumalanga and Gauteng and North West (GA/NW) provinces. We developed a novel set of predictive features for the GA/NW cohort using a categorical boosting model. We evaluated the ability of the model to predict IIT over all visits and across different periods within a patient's treatment trajectory. RESULTS: When predicting IIT, the GA/NW and Free State and Mpumalanga models demonstrated a sensitivity of 60% and 61%, respectively, able to correctly predict nearly two-thirds of all missed visits with a positive predictive value of 18% and 19%. Using predictive features generated from the GA/NW cohort, the categorical boosting model correctly predicted 22,119 of a total of 35,985 missed next visits, yielding a sensitivity of 62%, specificity of 67%, and positive predictive value of 20%. Model performance was highest when tested on visits within the first 6 months. CONCLUSIONS: Machine learning algorithms may be useful in informing tools to increase antiretroviral therapy patient retention and efficiency of HIV care interventions. This is particularly relevant in developing countries where health data systems are being strengthened to collect data on a scale that is large enough to apply novel analytical methods.

Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa.

INTRODUCTION: High yield HIV testing strategies are critical to reach epidemic control in high prevalence and low-resource settings such as East and Southern Africa. In this study, we aimed to predict the HIV status of individuals living in Angola, Burundi, Ethiopia, Lesotho, Malawi, Mozambique, Namibia, Rwanda, Zambia and Zimbabwe with the highest precision and sensitivity for different policy targets and constraints based on a minimal set of socio-behavioural characteristics. METHODS: We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). The algorithms were trained and validated on 80% of the data, and tested on the remaining 20%. We compared the predictions based on the F1 score, the harmonic mean of sensitivity and positive predictive value (PPV), and we assessed the generalization of our models by testing them against an independent left-out country. The best performing algorithm was trained on a minimal subset of variables which were identified as the most predictive, and used to 1) identify 95% of people living with HIV (PLHIV) while maximising precision and 2) identify groups of individuals by adjusting the probability threshold of being HIV positive (90% in our scenario) for achieving specific testing strategies. RESULTS: Overall 55,151 males and 69,626 females were included in the analysis. The gradient boosting trees algorithm performed best in predicting HIV status with a mean F1 score of 76.8% [95% confidence interval (CI) 76.0%-77.6%] for males (vs [CI 67.8%-70.6%] for SVM) and 78.8% [CI 78.2%-79.4%] for females (vs [CI 73.4%-75.8%] for SVM). Among the ten most predictive variables for each sex, nine were identical: longitude, latitude and, altitude of place of residence, current age, age of most recent partner, total lifetime number of sexual partners, years lived in current place of residence, condom use during last intercourse and, wealth index. Only age at first sex for male (ranked 10th) and Rohrer's index for female (ranked 6th) were not similar for both sexes. Our large-scale scenario, which consisted in identifying 95% of all PLHIV, would have required testing 49.4% of males and 48.1% of females while achieving a precision of 15.4% for males and 22.7% for females. For the second scenario, only 4.6% of males and 6.0% of females would have had to be tested to find 55.7% of all males and 50.5% of all females living with HIV. CONCLUSIONS: We trained a gradient boosting trees algorithm to find 95% of PLHIV with a precision twice higher than with general population testing by using only a limited number of socio-behavioural characteristics. We also successfully identified people at high risk of infection who may be offered pre-exposure prophylaxis or voluntary medical male circumcision. These findings can inform the implementation of new high-yield HIV tests and help develop very precise strategies based on low-resource settings constraints.

Analysing the reported incidence of COVID-19 and factors associated in the World Health Organization African region as of 31 December 2020.

This study analysed the reported incidence of COVID-19 and associated epidemiological and socio-economic factors in the WHO African region. Data from COVID-19 confirmed cases and SARS-CoV-2 tests reported to the WHO by Member States between 25 February and 31 December 2020 and publicly available health and socio-economic data were analysed using univariate and multivariate binomial regression models. The overall cumulative incidence was 1846 cases per million population. Cape Verde (21 350 per million), South Africa (18 060 per million), Namibia (9840 per million), Eswatini (8151 per million) and Botswana (6044 per million) recorded the highest cumulative incidence, while Benin (260 per million), Democratic Republic of Congo (203 per million), Niger (141 cases per million), Chad (133 per million) and Burundi (62 per million) recorded the lowest. Increasing percentage of urban population (ß = -0.011, P = 0.04) was associated with low cumulative incidence, while increasing number of cumulative SARS-CoV-2 tests performed per 10 000 population (ß = 0.0006, P = 0.006) and the proportion of population aged 15-64 years (adjusted ß = 0.174, P < 0.0001) were associated with high COVID-19 cumulative incidence. With limited testing capacities and overwhelmed health systems, these findings highlight the need for countries to increase and decentralise testing capacities and adjust testing strategies to target most at-risk populations.

Clusters of sub-Saharan African countries based on sociobehavioural characteristics and associated HIV incidence.

INTRODUCTION: HIV incidence varies widely between sub-Saharan African (SSA) countries. This variation coincides with a substantial sociobehavioural heterogeneity, which complicates the design of effective interventions. In this study, we investigated how sociobehavioural heterogeneity in sub-Saharan Africa could account for the variance of HIV incidence between countries. METHODS: We analysed aggregated data, at the national-level, from the most recent Demographic and Health Surveys of 29 SSA countries (2010-2017), which included 594,644 persons (183,310 men and 411,334 women). We preselected 48 demographic, socio-economic, behavioural and HIV-related attributes to describe each country. We used Principal Component Analysis to visualize sociobehavioural similarity between countries, and to identify the variables that accounted for most sociobehavioural variance in SSA. We used hierarchical clustering to identify groups of countries with similar sociobehavioural profiles, and we compared the distribution of HIV incidence (estimates from UNAIDS) and sociobehavioural variables within each cluster. RESULTS: The most important characteristics, which explained 69% of sociobehavioural variance across SSA among the variables we assessed were: religion; male circumcision; number of sexual partners; literacy; uptake of HIV testing; women's empowerment; accepting attitude toward people living with HIV/AIDS; rurality; ART coverage; and, knowledge about AIDS. Our model revealed three groups of countries, each with characteristic sociobehavioural profiles. HIV incidence was mostly similar within each cluster and different between clusters (median (IQR); 0.5/1000 (0.6/1000), 1.8/1000 (1.3/1000) and 5.0/1000 (4.2/1000)). CONCLUSIONS: Our findings suggest that the combination of sociobehavioural factors play a key role in determining the course of the HIV epidemic, and that similar techniques can help to predict the effects of behavioural change on the HIV epidemic and to design targeted interventions to impede HIV transmission in SSA.

Identifying groups of people with similar sociobehavioural characteristics in Malawi to inform HIV interventions: a latent class analysis.

INTRODUCTION: Within many sub-Saharan African countries including Malawi, HIV prevalence varies widely between regions. This variability may be related to the distribution of population groups with specific sociobehavioural characteristics that influence the transmission of HIV and the uptake of prevention. In this study, we intended to identify groups of people in Malawi with similar risk profiles. METHODS: We used data from the Demographic and Health Survey in Malawi (2015 to 2016), and stratified the analysis by sex. We considered demographic, socio-behavioural and HIV-related variables. Using Latent Class Analysis (LCA), we identified groups of people sharing common sociobehavioural characteristics. The optimal number of classes (groups) was selected based on the Bayesian information criterion. We compared the proportions of individuals belonging to the different groups across the three regions and 28 districts of Malawi. RESULTS: We found nine groups of women and six groups of men. Most women in the groups with highest risk of being HIV positive were living in female-headed households and were formerly married or in a union. Among men, older men had the highest risk of being HIV positive, followed by young (20 to 25) single men. Generally, low HIV testing uptake correlated with lower risk of having HIV. However, rural adolescent girls had a low probability of being tested (48.7%) despite a relatively high HIV prevalence. Urban districts and the Southern region had a higher percentage of high-prevalence and less tested groups of individuals than other areas. CONCLUSIONS: LCA is an efficient method to find groups of people sharing common HIV risk profiles, identify particularly vulnerable sub-populations, and plan targeted interventions focusing on these groups. Tailored support, prevention and HIV testing programmes should focus particularly on female household heads, adolescent girls living in rural areas, older married men and young men who have never been married.

Buckling of an Epithelium Growing under Spherical Confinement.

Many organs are formed through folding of an epithelium. This change in shape is usually attributed to tissue heterogeneities, for example, local apical contraction. In contrast, compressive stresses have been proposed to fold a homogeneous epithelium by buckling. While buckling is an appealing mechanism, demonstrating that it underlies folding requires measurement of the stress field and the material properties of the tissue, which are currently inaccessible in vivo. Here, we show that monolayers of identical cells proliferating on the inner surface of elastic spherical shells can spontaneously fold. By measuring the elastic deformation of the shell, we infer the forces acting within the monolayer and its elastic modulus. Using analytical and numerical theories linking forces to shape, we find that buckling quantitatively accounts for the shape changes of our monolayers. Our study shows that forces arising from epithelial growth in three-dimensional confinement are sufficient to drive folding by buckling.

Socio-behavioural characteristics and HIV: findings from a graphical modelling analysis of 29 sub-Saharan African countries.

INTRODUCTION: Socio-behavioural factors may contribute to the wide variance in HIV prevalence between and within sub-Saharan African (SSA) countries. We studied the associations between socio-behavioural variables potentially related to the risk of acquiring HIV. METHODS: We used Bayesian network models to study associations between socio-behavioural variables that may be related to HIV. A Bayesian network consists of nodes representing variables, and edges representing the conditional dependencies between variables. We analysed data from Demographic and Health Surveys conducted in 29 SSA countries between 2010 and 2016. We predefined and dichotomized 12 variables, including factors related to age, literacy, HIV knowledge, HIV testing, domestic violence, sexual activity and women's empowerment. We analysed data on men and women for each country separately and then summarized the results across the countries. We conducted a second analysis including also the individual HIV status in a subset of 23 countries where this information was available. We presented summary graphs showing associations that were present in at least six countries (five in the analysis with HIV status). RESULTS: We analysed data from 190,273 men (range across countries 2295 to 17,359) and 420,198 women (6621 to 38,948). The two variables with the highest total number of edges in the summary graphs were literacy and rural/urban location. Literacy was negatively associated with false beliefs about AIDS and, for women, early sexual initiation, in most countries. Literacy was also positively associated with ever being tested for HIV and the belief that women have the right to ask their husband to use condoms if he has a sexually transmitted infection. Rural location was positively associated with false beliefs about HIV and the belief that beating one's wife is justified, and negatively associated with having been tested for HIV. In the analysis including HIV status, being HIV positive was associated with female-headed household, older age and rural location among women, and with no variables among men. CONCLUSIONS: Literacy and urbanity were strongly associated with several factors that are important for HIV acquisition. Since literacy is one of the few variables that can be improved by interventions, this makes it a promising intervention target.

The mechanical properties of a cell-based numerical model of epithelium.

In this work we use a computational cell-based model to study the influence of the mechanical properties of cells on the mechanics of epithelial tissues. We analyze the effect of the model parameters on the elasticity and the mechanical response of tissues subjected to stress loading application. We compare our numerical results with experimental measurements of epithelial cell monolayer mechanics. Unlike previous studies, we have been able to estimate in physical units the parameter values that match the experimental results. A key observation is that the model parameters must vary with the tissue strain. In particular, it was found that, while the perimeter contractility and the area elasticity of cells remain constant at lower strains (<20%), they must increase to respond to larger strains (>20%). However, above a threshold of 50% extension, the cells stop counteracting the tissue strain and reduce both their perimeter contractility and area elasticity.