Social interactions and malaria preventive behaviors in sub-saharan Africa

Current Status of Malaria Control and Elimination in Africa: Epidemiology, Diagnosis, Treatment, Progress and Challenges

The African continent carries the greatest malaria burden in the world. Falciparum malaria especially has long been the leading cause of death in Africa. Climate, economic factors, geographical location, human intervention and unstable security are factors influencing malaria transmission. Due to repeated infections and early interventions, the proportion of clinically atypical malaria or asymptomatic plasmodium carriers has increased significantly, which easily lead to misdiagnosis and missed diagnosis. African countries have made certain progress in malaria control and elimination, including rapid diagnosis of malaria, promotion of mosquito nets and insecticides, intermittent prophylactic treatment in high-risk groups, artemisinin based combination therapies, and the development of vaccines. Between 2000 and 2022, there has been a 40% decrease in malaria incidence and a 60% reduction in mortality rate in the WHO African Region. However, many challenges are emerging in the fight against malaria in Africa, such as climate change, poverty, substandard health services and coverage, increased outdoor transmission and the emergence of new vectors, and the growing threat of resistance to antimalarial drugs and insecticides. Joint prevention and treatment, identifying molecular determinants of resistance, new drug development, expanding seasonal malaria chemo-prevention intervention population, and promoting the vaccination of RTS, S/AS01 and R21/Matrix-M may help to solve the dilemma. China's experience in eliminating malaria is conducive to Africa's malaria prevention and control, and China-Africa cooperation needs to be constantly deepened and advanced. Our review aims to help the global public develop a comprehensive understanding of malaria in Africa, thereby contributing to malaria control and elimination.

The contribution of risk perception and social norms to reported preventive behaviour against selected vector-borne diseases in Guyana

Preventing vector-borne diseases (VBDs) mainly relies on effective vector control tools and strategies, which in turn depend on population acceptance and adherence. Inspired by the abundant recent literature on SARS-COV-2, we investigate the relationship between risk perception and preventive behaviour for selected VBDs and the extent to which risk perception is determined by social norms. We use cross-sectional data collected from 497 individuals in four regions of Guyana in 2017. We use a conditional mixed process estimator with multilevel coefficients, estimated through a Generalized Linear Model (GLM) framework, applying a simultaneous equation structure. We find robust results on malaria: risk perception was significantly influenced by the risk perception of the reference group across different definitions of the reference group, hinting at the existence of social norms. Risk perception significantly increased the likelihood of passive behaviour by 4.48%. Less clear-cut results were found for dengue. This study applies quantitative social science methods to public health issues in the context of VBDs. Our findings point to the relevance of tailoring communications on health risks for VBDs to groups defined at the intersection of socio-economic and demographic characteristics. Such tailored strategies are expected to align risk perception among reference groups and boost preventive behaviour.

Social network correlates of free and purchased insecticide-treated bed nets in rural Uganda

BACKGROUND

Malaria is a major cause of mortality and morbidity in Uganda. Despite Uganda's efforts to distribute bed nets, only half of households have achieved the World Health Organization (WHO) Universal Coverage Criteria (one bed net for every two household members). The role of peer influence on bed net ownership remains underexplored. Data on the complete social network of households were collected in a rural parish in southwestern Uganda to estimate the association between household bed net ownership and peer household bed net ownership.

METHODS

Data on household sociodemographics, bed net ownership, and social networks were collected from all households across one parish in southwestern Uganda. Bed nets were categorized as either purchased or free. Purchased and free bed net ownership ratios were calculated based on the WHO Universal Coverage Criteria. Using network name generators and complete census of parish residents, the complete social network of households in the parish was generated. Linear regression models that account for network autocorrelation were fitted to estimate the association between households' bed net ownership ratios and bed net ownership ratios of network peer households, adjusting for sociodemographics and network centrality.

RESULTS

One thousand seven hundred forty-seven respondents were interviewed, accounting for 716 households. The median number of peer households to which a household was directly connected was 7. Eighty-six percent of households owned at least one bed net, and 41% of households met the WHO Universal Coverage Criterion. The median bed net ownership ratios were 0.67 for all bed nets, 0.33 for free bed nets, and 0.20 for purchased bed nets. In adjusted multivariable models, purchased bed net ownership ratio was associated with average household wealth among peer households (b = 0.06, 95% CI 0.03, 0.10), but not associated with average purchased bed net ownership ratio of peer households. Free bed net ownership ratio was associated with the number of children under 5 (b = 0.08, 95% CI 0.05, 0.10) and average free bed net ownership ratios of peer households (b = 0.66, 95% CI 0.46, 0.85).

CONCLUSIONS

Household bed net ownership was associated with bed net ownership of peer households for free bed nets, but not for purchased bed nets. The findings suggest that public health interventions may consider leveraging social networks as tools for dissemination, particularly for bed nets that are provided free of charge.

Health and agricultural productivity: Evidence from Zambia

We evaluate the productivity effects of investment in preventive health technology through a randomized controlled trial in rural Zambia. In the experiment, access to subsidized bed nets was randomly assigned at the community level; 516 farmers were followed over a one-year farming period. We find large positive effects of preventative health investment on productivity: among farmers provided with access to free nets, harvest value increased by US$ 76, corresponding to about 14.7% of the average output value. While only limited information was collected on farming inputs, shifts in the extensive and the intensive margins of labor supply appear to be the most likely mechanism underlying the productivity improvements observed.