At the time of writing, it is unclear how the COVID-19 pandemic will play out in rapidly urbanising regions of the world. In these regions, the realities of large overcrowded informal settlements, a high burden of infectious and non-communicable diseases, as well as malnutrition and precarity of livelihoods, have raised added concerns about the potential impact of the COVID-19 pandemic in these contexts. COVID-19 infection control measures have been shown to have some effects in slowing down the progress of the pandemic, effectively buying time to prepare the healthcare system. However, there has been less of a focus on the indirect impacts of these measures on health behaviours and the consequent health risks, particularly in the most vulnerable. In this current debate piece, focusing on two of the four risk factors that contribute to >80% of the NCD burden, we consider the possible ways that the restrictions put in place to control the pandemic, have the potential to impact on dietary and physical activity behaviours and their determinants. By considering mitigation responses implemented by governments in several LMIC cities, we identify key lessons that highlight the potential of economic, political, food and built environment sectors, mobilised during the pandemic, to retain health as a priority beyond the context of pandemic response. Such whole-of society approaches are feasible and necessary to support equitable healthy eating and active living required to address other epidemics and to lower the baseline need for healthcare in the long term.

Introduction: Low household socioeconomic status is associated with unhealthy behaviours including poor diet and adverse health outcomes. Different methods leading to variations in SES classification has the potential to generate spurious research findings or misinform policy. In low and middle-income countries, there are additional complexities in defining household SES, a need for fieldwork to be conducted efficiently, and a dearth of information on how classification could impact estimation of disease risk. Methods: Using cross-sectional data from 200 households in Kisumu County, Western Kenya, we compared three approaches of classifying households into low, middle, or high SES: fieldworkers (FWs), Community Health Volunteers (CHVs), and a Multiple Correspondence Analysis econometric model (MCA). We estimated the sensitivity, specificity, inter-rater reliability and misclassification of the three methods using MCA as a comparator. We applied an unadjusted generalized linear model to determine prevalence ratios to assess the association of household SES status with a self-reported diagnosis of diabetes or hypertension for one household member. Results: Compared with MCA, FWs successfully classified 21.7% (95%CI = 14.4%-31.4%) of low SES households, 32.8% (95%CI = 23.2–44.3) of middle SES households, and no high SES households. CHVs successfully classified 22.5% (95%CI = 14.5%-33.1%) of low SES households, 32.8% (95%CI = 23.2%-44.3%) of middle SES households, and no high SES households. The level of agreement in SES classification was similar between FWs and CHVs but poor compared to MCA, particularly for high SES. None of the three methods differed in estimating the risk of hypertension or diabetes. Conclusions: FW and CHV assessments are community-driven methods for SES classification. Compared to MCA, these approaches appeared biased towards low or middle SES households and not sensitive to high household SES. The three methods did not differ in risk estimation for diabetes and hypertension. A mix of approaches and further evaluation to refine SES classification methodology is recommended.

Background: Natural language processing (NLP) has the potential to promote public health. However, applying these technologies in African health systems faces challenges, including limited digital and computational resources to support the continent’s diverse languages and needs. Objective: This scoping review maps the evidence on NLP technologies for public health in Africa, addressing the following research questions: (1) What public health needs are being addressed by NLP technologies in Africa, and what unmet needs remain? (2) What factors influence the availability of public health NLP technologies across African countries and languages? (3) What stages of deployment have these technologies reached, and to what extent have they been integrated into health systems? (4) What measurable impact has these technologies had on public health outcomes, where such data are available? (5) What recommendations have been proposed to enhance the quality, cost, and accessibility of health-related NLP technologies in Africa? Methods: This scoping review includes academic studies published between January 1, 2013, and October 3, 2024. A systematic search was conducted across databases, including MEDLINE via PubMed, ACL Anthology, Scopus, IEEE Xplore, and ACM Digital Library, supplemented by gray literature searches. Data were extracted and the NLP technology functions were mapped to the World Health Organization’s list of essential public health functions and the United Nations’ sustainable development goals (SDGs). The extracted data were analyzed to identify trends, gaps, and areas for future research. This scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines, and its protocol is publicly available. Results: Of 2186 citations screened, 54 studies were included. While existing NLP technologies support a subset of essential public health functions and SDGs, language coverage remains uneven, with limited support for widely spoken African languages, such as Kiswahili, Yoruba, Igbo, and Zulu, and no support for most of Africa’s >2000 languages. Most technologies are in prototyping phases, with only one fully deployed chatbot addressing vaccine hesitancy. Evidence of measurable impact is limited, with 15% (8/54) studies attempting health-related evaluations and 4% (2/54) demonstrating positive public health outcomes, including improved participants’ mood and increased vaccine intentions. Recommendations include expanding language coverage, targeting local health needs, enhancing trust, integrating solutions into health systems, and adopting participatory design approaches. The gray literature reveals industry- and nongovernmental organizations–led projects focused on deployable NLP applications. However, these projects tend to support only a few major languages and specific use cases, indicating a narrower scope than academic research. Conclusions: Despite growth in NLP research for public health, major gaps remain in deployment, linguistic inclusivity, and health outcome evaluation. Future research should prioritize cross-sectoral and needs-based approaches that engage local communities, align with African health systems, and incorporate rigorous evaluations to enhance public health outcomes. International Registered Report Identifier (IRRID): RR2-doi:10.1101/2024.07.02.24309815

To synthesize evidence on the impact of context-based interventions on the participation of children with disabilities in the community.A systematic review was conducted using the American Academy ...

Background: The gap between research and its practical application in community settings limits its impact on public health. Closing this gap has the potential to improve the well-being of underserved groups, such as children with disabilities. Mobile health has the potential to improve access to community resources and support for underserved populations, thereby encouraging improved health behaviors. Objective: In this feasibility pilot study, we describe the development of the mobile app Jooay. Jooay was developed in partnership with stakeholders to facilitate access to leisure and physical activity community programs for children and youth with disabilities. We also reflect on the lessons learned throughout the implementation process that are relevant for improving the health behaviors of children with disabilities. Methods: We used a participatory action research approach to develop the app. We also administered a survey to current Jooay users and analyzed various app usage indicators to explore use patterns, user feedback, and preferences. Finally, we critically appraised the implementation process through a best practices for implementation research framework. Results: We developed a product that responds to users’ identified need to find information and follows accessibility and user-centered design standards. The analysis of usage data revealed that access to the Jooay app is concentrated in urban areas. Perceptions, attitudes, and information needs varied according to the type of user. The use of the mobile app changed over time, and usage decreased after the app was downloaded, indicating a need for the sustained engagement of app users. Users found value in the ability to identify activities that they would not otherwise know about. However, app use alone was not sufficient to improve participation. Although the app was developed based on users’ active input in multiple iterations, we encountered challenges with survey recruitment and attrition, suggesting the need for more seamless and engaging means for collecting data within this population. Conclusions: Interactions between users and the app can sustain user engagement and behavior change. We will improve the app’s next iterations by using the information gained from this study to conduct a larger study to assess the relationship among social and material deprivation, urban design, and access to inclusive and adaptive leisure programs. This study will inform the improvement of app listings to improve the use of Jooay by different user groups and promote health through mobile apps for marginalized groups.

We highlight aspects of the ordinary – known interactions between urban environments and physical activity – that are amplified by the extraordinary – the COVID-19 pandemic in Lagos, Nigeria. Leisure physical activity is shaped by local contexts, and pandemic-related changes may have altered existing trade-offs between the benefits and risks of undertaking leisure physical activity in the city. We note the dynamic and inequitable ways that urban spaces are used during the pandemic and the urgent need for local evidence and solutions. Our reflections in this commentary illustrate the possibility of rethinking urban development and the reforms necessary to address health inequalities post-pandemic.