14 research outputs found
Identifying groups of people with similar sociobehavioural characteristics in Malawi to inform HIV interventions:a latent class analysis
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 influ-ence the transmission of HIV and the uptake of prevention. In this study, we intended to identify groups of people in Malawiwith similar risk profiles
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
Tuberculosis in the Western Pacific Region: Estimating the burden of disease and return on investment 2020–2030 in four countries
Background: We aimed to estimate the disease burden of Tuberculosis (TB) and return on investment of TB care in selected high-burden countries of the Western Pacific Region (WPR) until 2030.
Methods: We projected the TB epidemic in Viet Nam and Lao People's Democratic Republic (PDR) 2020–2030 using a mathematical model under various scenarios: counterfactual (no TB care); baseline (TB care continues at current levels); and 12 different diagnosis and treatment interventions. We retrieved previous modeling results for China and the Philippines. We pooled the new and existing information on incidence and deaths in the four countries, covering >80% of the TB burden in WPR. We estimated the return on investment of TB care and interventions in Viet Nam and Lao PDR using a Solow model.
Findings: In the baseline scenario, TB incidence in the four countries decreased from 97•0/100,000/year (2019) to 90•1/100,000/year (2030), and TB deaths from 83,300/year (2019) to 71,100/year (2030). Active case finding (ACF) strategies (screening people not seeking care for respiratory symptoms) were the most effective single interventions. Return on investment (2020–2030) for TB care in Viet Nam and Lao PDR ranged US49/dollar spent; additional interventions brought up to US$2•7/dollar spent.
Interpretation: In the modeled countries, TB incidence will only modestly decrease without additional interventions. Interventions that include ACF can reduce TB burden but achieving the End TB incidence and mortality targets will be difficult without new transformational tools (e.g. vaccine, new diagnostic tools, shorter treatment). However, TB care, even at its current level, can bring a multiple-fold return on investment
Enhancing HIV Prediction through Machine Learning and Streamlining Research with LiteRev
High-yield HIV testing strategies are essential for controlling the epidemic in high prevalence, low-resource settings in East and Southern Africa. Similarly, literature reviews are crucial in synthesising relevant research to advance understanding and support decision-making. However, traditional HIV testing methods may not be sufficiently effective in identifying the remaining individuals who are unaware of their HIV-positive status. and traditional literature reviews are time-consuming and quickly outdated.
This thesis combines two significant advancements in public health research: the adoption of highly precise HIV testing methods in East and Southern Africa, and the creation of LiteRev, an automated tool designed to quicken and simplify the literature review process. Both of these developments utilise modern machine learning technologies.
One facet of this thesis focused on predicting the HIV status of individuals in ten African countries using a minimal set of socio-behavioural characteristics. Four algorithms (penalised logistic regression, generalised additive model, support vector machine, and gradient boosting trees) were employed for this analysis. The gradient boosting trees algorithm demonstrated superior performance in predicting HIV status with high precision, underscoring its potential in informing targeted HIV testing strategies and interventions.
Concurrently, LiteRev was applied to the domain of HIV research, specifically examining the burden and care for acute and early HIV infection in sub-Saharan Africa. LiteRev's performance, characterised by its ability to process and synthesise literature efficiently, was compared against traditional manual methods. It exhibited its ability to significantly reduce the workload involved in screening through many research papers.
The integration of advanced computational methods in public health research, as demonstrated by the predictive algorithms for HIV status and LiteRev, showcases the potential of technology in enhancing research efficiency and precision in critical health interventions. These tools not only provide valuable insights for developing targeted health strategies in challenging environments like sub-Saharan Africa but also accelerate and enrich literature reviews.</p
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
Social, Behavioral, and Cultural factors of HIV in Malawi: Semi-Automated Systematic Review.
BACKGROUND
Demographic and sociobehavioral factors are strong drivers of HIV infection rates in sub-Saharan Africa. These factors are often studied in qualitative research but ignored in quantitative analyses. However, they provide in-depth insight into the local behavior and may help to improve HIV prevention.
OBJECTIVE
To obtain a comprehensive overview of the sociobehavioral factors influencing HIV prevalence and incidence in Malawi, we systematically reviewed the literature using a newly programmed tool for automatizing part of the systematic review process.
METHODS
Due to the choice of broad search terms ("HIV AND Malawi"), our preliminary search revealed many thousands of articles. We, therefore, developed a Python tool to automatically extract, process, and categorize open-access articles published from January 1, 1987 to October 1, 2019 in the PubMed, PubMed Central, JSTOR, Paperity, and arXiV databases. We then used a topic modelling algorithm to classify and identify publications of interest.
RESULTS
Our tool extracted 22,709 unique articles; 16,942 could be further processed. After topic modelling, 519 of these were clustered into relevant topics, of which 20 were kept after manual screening. We retrieved 7 more publications after examining the references so that 27 publications were finally included in the review. Reducing the 16,942 articles to 519 potentially relevant articles using the software took 5 days. Several factors contributing to the risk of HIV infection were identified, including religion, gender and relationship dynamics, beliefs, and sociobehavioral attitudes.
CONCLUSIONS
Our software does not replace traditional systematic reviews, but it returns useful results to broad queries of open-access literature in under a week, without a priori knowledge. This produces a "seed dataset" of relevance that could be further developed. It identified known factors and factors that may be specific to Malawi. In the future, we aim to expand the tool by adding more social science databases and applying it to other sub-Saharan African countries
Burden and care for acute and early HIV infection in sub-Saharan Africa: a scoping review protocol
Unadressed acute and early HIV infection (AEHI) contributes to continuous HIV transmission despite global achievements in HIV control. In sub-Saharan Africa, diagnosis and care for AEHI is almost non-existent, and current testing guidelines provide no guidance.
The aim of this scoping review is to summarize the evidence on burden and care provided for acute and early HIV infection in sub-Saharan Africa, to inform policy, practice and research in future
Clusters of sub-Saharan African countries based on sociobehavioural characteristics and associated HIV incidence
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
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.</p
Factors associated with HIV viral suppression among children and adults receiving antiretroviral therapy in Malawi in 2021: Evidence from the Laboratory Management Information System.
OBJECTIVE
To describe the prevalence of HIV viral suppression and assess the factors associated with HIV viral suppression among persons receiving ART in Malawi in 2021.
METHODS
Implementation study using routinely collected patient-level HIV RNA-PCR test result data extracted from the national Laboratory Management Information System (LIMS) database managed by the Department of HIV/AIDS in 2021. We calculated frequencies, proportions and odds ratios (OR) of HIV viral suppression with their associated 95% confidence intervals (95%CI). We performed a random-effects logistic regression to determine the risk factors associated with HIV viral suppression amongst ART patients, controlling for the spatial autocorrelation between districts and adjusting for other variables.
RESULTS
We evaluated 515,797 adults and children receiving ART and having a viral load test in 2021. Of these, 92.8% had HIV viral suppression. ART patients living in urban areas had lower likelihood of HIV viral suppression than those living in rural areas (adjusted OR [aOR]=0.95, 95%CI: 0.92-0.99, P=0.01). There was an increasing trend in HIV viral suppression with increasing ART duration. Routine VL monitoring samples were 39% more likely to have suppressed VL values than confirmatory HIV VL monitoring samples (aOR=1.39; 95%CI:1.34-1.43, P<0.001).
CONCLUSION
This is the first national analysis of Malawi HIV VL data from LIMS. Our findings show the need to particularly consider the urban residents, those below 20 years, males, those on ART for less than a year as well as those on specific ARV regimens in order to persistently suppress HIV VL and consequently achieve the goal of achieving HIV VL suppression by 2030