The Effect of Socioeconomic Factors and Indoor Residual Spraying on Malaria in Mangaluru, India: A Case-Control Study.
Konrad SiegertWelmoed van LoonPrabhanjan P GaiJessica Lee RohmannMarco PiccininniAnatol-Fiete NäherArchith BoloorDamodara ShenoyChakrapani MSuyamindra S KulkarniArun KumarJacob WedamPramod GaiRajeshwari DeviAnimesh JainTobias KurthFrank P MockenhauptPublished in: International journal of environmental research and public health (2021)
India faces 0.5 million malaria cases annually, including half of all Plasmodium vivax malaria cases worldwide. This case-control study assessed socioeconomic determinants of urban malaria in coastal Mangaluru, Karnataka, southwestern India. Between June and December 2015, we recruited 859 malaria patients presenting at the governmental Wenlock Hospital and 2190 asymptomatic community controls. We assessed clinical, parasitological, and socioeconomic data. Among patients, p. vivax mono-infection (70.1%) predominated. Most patients were male (93%), adult (median, 27 years), had no or low-level education (70.3%), and 57.1% were daily labourers or construction workers. In controls (59.3% male; median age, 32 years; no/low-level education, 54.5%; daily labourers/construction workers, 41.3%), 4.1% showed asymptomatic Plasmodium infection. The odds of malaria was reduced among those who had completed 10th school grade (aOR, 0.3; 95% CI, 0.26-0.42), lived in a building with a tiled roof (aOR, 0.71; 95% CI, 0.53-0.95), and reported recent indoor residual spraying (aOR, 0.02; 95% CI, 0.01-0.04). In contrast, migrant status was a risk factor for malaria (aOR, 2.43; 95% CI, 1.60-3.67). Malaria in Mangaluru is influenced by education, housing condition, and migration. Indoor residual spraying greatly contributes to reducing malaria in this community and should be promoted, especially among its marginalised members.
Keyphrases
- plasmodium falciparum
- healthcare
- end stage renal disease
- chronic kidney disease
- ejection fraction
- mental health
- air pollution
- physical activity
- newly diagnosed
- machine learning
- magnetic resonance imaging
- emergency department
- prognostic factors
- magnetic resonance
- particulate matter
- quality improvement
- climate change
- health risk
- risk assessment
- big data
- human health
- mental illness
- drinking water