J40.1 Rainfall Variability and Incidence of Malaria in Infants in Rural Areas of the Abia North Senatorial District, Southeastern Nigeria

Wednesday, 15 January 2020: 12:00 AM
153B (Boston Convention and Exhibition Center)
Felix Ike, Abia State Univ., Uturu, Nigeria; and A. A. Abah, C. R. Ottah, A. Eludoyin, and V. O. Nwaugo

Malaria remains one of the largest deadly disease in Africa, and most of its victims are infants. Malaria is very sensitive to climatic variations, which explains why it is most common in tropical regions. Accumulated rainfall in ponds, lakes and abandoned borrow pits serves as breeding ground for malaria vectors namely Anopheles culicifacies, An. fluviatilis and An. annularis.

This study investigated the effects of variability in rainfall and prevalence of malaria in infants (0-5 years) in rural areas of Abia North District from 2008 to 2018. Monthly counts of clinically diagnosed malaria cases were collected directly from 64 rural primary health care providers. Geographic coordinates (latitude and longitude) of the health care facilities and socioeconomic data (sex and locality) of the infants were also collected. Daily surface rainfall data were obtained from 5 ground based weather stations in the study area.

Spatial clustering using Getis Ord was performed to show hot spots of infant malaria in the study area. The Onset(OS)/ Cessation(CS) dates and Length of the Raining Season(LRS) were estimated on yearly basis(2008-2018) in the malaria hot spot areas using accumulated rainfall model. The Model uses the assumption that after a particular threshold of rainfall, a dry spell that may lead to crop failure or vegetation growth is relatively small.

The analysis indicated that increased malaria incidence in infants is strongly correlated with lags in the onset of rainfall as one progresses from North to South in the study area. The dominant malaria strain in the study area was Plasmodium vivax (Pv) and Plasmodium falciparum (Pf). This study highlighted the benefits of spatial statistical tools as efficient means of analysing rainfall-epidemiological data.

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