Spatial Prevalence and Determinants of Malaria among under-five Children in Ghana
Ejigu, B. A.; Wencheko, E.
Show abstract
In Ghana malaria is an endemic disease and the incidence of malaria still accounts for 38.0% of all outpatient attendance with the most vulnerable groups being children under 5 years of age. In order to alleviate this problem, it is essential to design geographically targeted and cost-effective intervention mechanisms guided by up-to-date and reliable data and maps that show the spatial prevalence of the disease. The 2016 Ghana Malaria Indicator Survey data (N = 2,910 under-five children) were analyzed using model-based geostatistical methods with the two objectives to: (1) explore individual-, household-, and community-level determinant variables associated with malaria illness in U5 children, and (2) produce prevalence maps of malaria across the study locations in the country. The overall weighted prevalence of malaria by microscopy blood smear and rapid diagnostic tests were 20.63% (with 95% CI: 18.85% - 22.53%) and 27.82% (with 95% CI: 25.81% - 29.91%), respectively. Across regions of Ghana, the prevalence of malaria ranges from 5% in Greater Accra to 31% in Eastern region. Malaria prevalence was higher in rural areas, increased with child age, and decreased with better household wealth index and higher level of mothers education. Given the high prevalence of childhood malaria observed in Ghana, there is an urgent need for effective and efficient public health interventions in hot spot areas. The determinant variables of malaria infection that have been identified in this study as well as the maps of parasitaemia risk could be used in malaria control program implementation to define priority intervention areas.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Does the Data Tell the True Story? A Modelling Study of Early COVID-19 Pandemic Suppression and Mitigation Strategies in Ghana 96%
- Dynamics of residual malaria transmission in Central Western Senegal: Mapping the breeding sites of Anopheles gambiae s. l. 95%
- Malaria treatment-seeking behaviour and its associated factors: A cross-sectional study in rural East Nusa Tenggara Province, Indonesia 95%
Similar papers in this journal
- Fine scale spatial mapping of urban malaria prevalence for microstratification in an urban area of Ghana 97%
- Impact of the COVID-19 pandemic on the malaria burden in northern Ghana: Analysis of routine surveillance data 96%
- On multifactorial drivers for malaria rebound in Brazil: a spatio-temporal analysis 95%
Similar papers in this journal
- Acceptability and associated factors of indoor residual spraying for Malaria control by households in Luangwa district of Zambia: A multilevel analysis 95%
- Impact of four years of annually repeated indoor residual spraying (IRS) with Actellic 300CS on routinely reported malaria cases in an agricultural setting in Malawi 94%
- Factors associated with the uptake of Intermittent Preventive Treatment (IPTp-SP) for malaria in pregnancy: further analysis of the 2018 Nigeria Demographic and Health Survey 94%
Similar papers in this journal
- Use of routine health data to monitor malaria intervention effectiveness: a scoping review. 94%
- Determinants of disease prevalence and antibiotic consumption for children under five in Nepal: analysis and modelling of demographic health survey data from 2006 to 2016 91%
- Interest of seroprevalence surveys for the epidemiological surveillance of the SARS-CoV-2 pandemic in African populations: insights from the ARIACOV Project in Benin 91%
Similar papers in this journal
- Impact of COVID-19 on Mental Health: A Longitudinal Study Using Penalized Logistic Regression 89%
- A Flexible Framework for Local-Level Estimation of the Effective Reproductive Number in Geographic Regions with Sparse Data 89%
- A systematic review of sample size estimation accuracy on power in malaria cluster randomised trials measuring epidemiological outcomes 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.