Evidence map›Paper›PMID 40461415›Full record

ArticleTropical medicine & international health : TM & IH2025

Temporal analysis of non-communicable diseases and NCD-HIV/AIDS comorbidity in Malawi: A 4-year retrospective study 2020-2022.

Ansley Kasambara, Mphatso S Kamndaya, Salule J Masangwi, Atupele Mulaga

Abstract read
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Article in Tropical medicine & international health : TM & IH, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Ansley KasambaraDepartment of Mathematical Sciences, School of Science and Technology, Malawi University of Business and Applied Sciences, Blantyre, Malawi.ORCID https://orcid.org/0000-0003-2285-8609
Mphatso S KamndayaDepartment of Mathematical Sciences, School of Science and Technology, Malawi University of Business and Applied Sciences, Blantyre, Malawi.ORCID https://orcid.org/0000-0002-7597-3339
Salule J MasangwiDepartment of Mathematical Sciences, School of Science and Technology, Malawi University of Business and Applied Sciences, Blantyre, Malawi.ORCID https://orcid.org/0000-0001-7895-6780
Atupele MulagaDepartment of Mathematical Sciences, School of Science and Technology, Malawi University of Business and Applied Sciences, Blantyre, Malawi.ORCID https://orcid.org/0000-0002-9172-0366

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveNon-communicable diseases are on the increase in the Sub-Saharan region, which is also the epicentre of HIV/AIDS. There is limited published data on trends and forecasts of the comorbidity of non-communicable diseases and HIV/AIDS in low- and middle-income countries. Hence, the aim of the study was to examine current trends and forecasts for non-communicable diseases and non-communicable disease-HIV/AIDS comorbidity.

methodsData on 30,686 patients from 2019 to 2022 were extracted from the non-communicable diseases Mastercards, from 70 health facilities in Malawi, using a form designed and implemented in KoboToolBox. All cases were aggregated to form weekly counts of non-communicable diseases and non-communicable disease-HIV/AIDS comorbidity and visualised using time series plots. Then the data was subset by the prominent non-communicable disease, which was hypertension and hypertension-HIV/AIDS comorbidity weekly counts. Extreme Gradient Boosting (XGBoost), a machine learning model, was used for model fitting, generating predictions, and forecasting.

resultsThe forecasts showed that the counts of cases per week will range from 137 to 185 cases. Then specifically, hypertension and hypertension-HIV/AIDS comorbidity case counts averaged approximately within the range of 111-124 new cases per week from 2023 to 2030. The hypertension-HIV/AIDS comorbidity had forecasts ranging from 2 to 9 new cases per week. Although the number of new case counts per week had a consistent progression, the number of cases was on an increasing trend over time. Therefore, whether cases in general, hypertension and hypertension-HIV/AIDS comorbidity cases, or hypertension-HIV/AIDS comorbidity cases, the counts per week imply cumulatively higher case counts.

conclusionsDespite the consistency in the projection to 2030, there is a need to consider the upward trend of these cases and implement intervention measures such as sensitisation to control the number of cases. Otherwise, Malawi may not be able to achieve SDG3 targets by 2030.

Indexed as

Acquired Immunodeficiency SyndromeHIV InfectionsNoncommunicable DiseasesAdultComorbidityFemaleForecastingHumansHypertensionMalawiMaleMiddle AgedRetrospective StudiescomorbidityHIV/AIDSmachine learningMalawiNCD‐HIV/AIDSnon‐communicable diseasesXGBoost regression

Identifiers

PMID40461415
PMCPMC12318443

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.