Incidence tendencies and projection of climate sensitive diseases in mozambique
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Instituto Nacional de Saúde Mozambique
Instituto Nacional de Saúde Osvaldo Inlamea Mozambique
Publication date: 2023-04-26
Popul. Med. 2023;5(Supplement):A196
Background and objective:
Changes in climate patters have been reported around the world. There is evidence that such changes can affect human health, particularly with climate sensitive diseases like Malaria and diarrhea. This effects are exacerbated in low and middle-income countries such as Mozambique. Thus, this study aimed to determine climate sensibility of Malaria and Diarrhea as well as the behavior they can take in the face of climate change.

Eight districts of Mozambique were selected. Malaria and diarrhea incidence data was obtained at Weekly Epidemiological Bulletins. Pearssons Correlation Coefficient was determined and Poisson’s regression with distributed lags was used to determine relationship between the incidences, temperature and precipitation. Projections of climate change impact were determined based on IPCC emission scenarios. All analyzes performed using the R statistical software.

Positive correlation between diarrhea cases and precipitation and negative correlation between Malaria and maximum temperatures were observed. There is a high probability of an increase in the incidence of diarrhea if precipitation above 50 mm occurs. Thresholds of minimum temperate that favor an increase in Malaria vary from 17oC in Lichinga to 24 oC in Angoche, Beira and Tete. The models indicated to a possible increase in the incidence of Malaria in the low emissions scenario in all districts, except in Angoche and Moamba, where the low emissions scenario show a probable decrease in the incidences. The models also showed to a possible increase of diarrhea in the long term, with up to 35% increase in Mabalane district.

The analyses showed weak to moderate correlation between climatic variables and studied diseases. Precipitation most influences diarrhea cases, while Malaria is most influenced by minimum temperatures. According to the models, the incidence of both diseases will probably increase in most studied districts.

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