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India recorded more than 1.7 million new cases of HIV in the last 10 years between 2011 and 2021. To attain India's commitment to achieve the United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets by 2030 and end the AIDS epidemic as a public health threat by 2030 in line with Sustainable Development Goals, India needs to strengthen the prevention-testing-treatment-care continuum.Leveraging the experience of STAR phases 1 and 2, PATH has been implementing the STAR Phase 3 project in India. The implementation study was conducted to demonstrate the feasibility and acceptability of HIVST across specific populations. The study was conducted in 14 high HIV prevalence states of India through five service delivery models. A detailed analysis plan was developed to examine and review the findings from the study. As the study concluded in June 2022, the process of data analysis was conducted to understand the information collected from the study participants.PATH has developed high-quality, reliable, and ready-reference knowledge products like the summary report and a compendium of success stories on various interventions, lessons learnt and key takeaways of the HIV Self Testing project in India.

Indoor residual spraying (IRS) and insecticide-treated bed nets (ITNs) are cornerstone malaria prevention and control methods in Madagascar. From 2016 to 2020, non-pyrethroid IRS was deployed to complement standard pyrethroid ITNs in 14 districts with high malaria burden, targeting 5 to 9 districts each year. Districts received IRS for 1 to 3 consecutive years during the study period. This retrospective observational study uses routine data to evaluate the impacts of IRS overall, sustained IRS over multiple years, and achieving high (≥85%) IRS coverage (structures sprayed/found). We fit a multilevel mixed effects model to data from all 114 districts of Madagascar from July 2016 to June 2021. We estimated the effect of IRS exposure, consecutive years of IRS, and high IRS coverage on monthly population-adjusted RDT-confirmed malaria cases at health facility level. Facilities missing data, and communes missing geolocations were excluded, leaving 84% of records included. The model controlled for ITN survivorship, mass drug administration (MDA), precipitation, enhanced vegetation index (EVI), month, year, and district. Using the fitted model we predicted malaria cases under observed and no IRS scenarios and estimated the number of cases averted by IRS. IRS was associated with reduced case incidence and an estimated 196,075 (79,879-316,809) cases were averted in targeted districts (~15% of the 1.3m reported cases). The effect varied by district and was associated with ITN survivorship, MDA, precipitation, EVI, month and year. One year of IRS was associated with higher incidence versus two (IRR = 1.15, 95%CI = 1.03-1.29) or three (IRR = 1.16, 95%CI = 1.01-1.33). High coverage (achieved in 94% of IRS areas) was associated with a 12% lower incidence rate (IRR=0.88, CI=0.82-0.95) compared to areas with lower coverage. This study suggests that IRS together with ITNs may substantially reduce malaria incidence over ITNs alone, and high spray coverage and >1 year of IRS may confer additional benefits. This work highlights the value of routine data to evaluate the impact of intervention combinations and to inform future targeting decisions in Madagascar. (French)

PATH has been contributing to Myanmar’s healthcare transformation since 2012 and supports the government’s commitment to achieve universal health coverage by 2030. PATH works with government, non-governmental actors, and private sector to leverage partnerships, policy advocacy, new technologies, and innovative approaches to address inequities in key health areas – nutrition, vaccines and immunization, sexual and reproductive health, infectious diseases, and non-communicable diseases.This factsheet talks about the various completed and ongoing projects under family health.

A iniciativa de Aceleração e Aprendizagem da Utilização de Dados (DUAL) registou as experiências de cinco Países Africanos na digitalização dos seus sistemas de saúde. Muitos Países estão a trabalhar com parceiros para reforçar a utilização de dados de saúde e melhorar os resultados de saúde através da digitalização dos seus sistemas de saúde. No entanto, os investimentos, orientações e políticas de saúde digital que os intervenientes globais promovem nem sempre refletem as prioridades ou progressos dos Países no sentido da transformação digital.A inovação e as lições que emergem a nível Nacional muitas vezes não são incluídas no financiamento, orientação normativa, e abordagens programáticas. O objetivo do DUAL é partilhar as melhores práticas na utilização da transformação digital para acelerar a utilização de dados e melhorar os resultados em termos de saúde. As conclusões da iniciativa constituem a base do modelo "DUAL", que os governos, os decisores políticos, os implementadores e os financiadores dos Países podem utilizar para reforçar a adoção das tecnologias digitais. O modelo DUAL identifica dez elementos de transformação digital, acrescentando dois novos componentes aos blocos de construção da Estratégia de Saúde da OMS-ITU : gestão da mudança e ecossistemas de utilização de dados. O modelo destila os fatores-chave de sucesso para cada elemento e recomenda ações práticas específicas para os Países

L'initiative Data Use Acceleration and Learning (DUAL) a documenté les expériences de cinq pays africains qui numérisent leurs systèmes de santé. De nombreux pays travaillent avec des partenaires pour renforcer l'utilisation des données sur la santé et améliorer les résultats sanitaires en numérisant leurs systèmes de soins de santé. Cependant, les investissements, les directives et les politiques en matière de santé numérique que les parties prenantes mondiales encouragent ne reflètent pas toujours les priorités des pays ou les progrès réalisés en matière de transformation numérique.L'innovation et les leçons qui émergent au niveau national ne sont souvent pas incluses dans les financements, les orientations normatives et les approches programmatiques. L'objectif de DUAL est de partager les meilleures pratiques en matière d'utilisation de la transformation numérique pour accélérer l'utilisation des données et améliorer les résultats en matière de santé. Les résultats de l'initiative constituent la base du modèle « DUAL », que les gouvernements nationaux, les décideurs, les responsables de la mise en œuvre et les bailleurs de fonds peuvent utiliser pour renforcer l'adoption des technologies numériques. Le modèle DUAL identifie dix éléments de transformation numérique, ajoutant deux nouveaux composants aux éléments constitutifs de la stratégie OMS-UIT en matière de santé en ligne : la gestion du changement et les écosystèmes d'utilisation des données. Le modèle distille les facteurs clés de succès pour chaque élément et recommande des actions pratiques spécifiques pour les pays.