| Title |
Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys
|
|---|---|
| Published in |
Malaria Journal, November 2017
|
| DOI | 10.1186/s12936-017-2127-y |
| Pubmed ID | |
| Authors |
Victor A. Alegana, Jim Wright, Claudio Bosco, Emelda A. Okiro, Peter M. Atkinson, Robert W. Snow, Andrew J. Tatem, Abdisalan M. Noor |
| Abstract |
One pillar to monitoring progress towards the Sustainable Development Goals is the investment in high quality data to strengthen the scientific basis for decision-making. At present, nationally-representative surveys are the main source of data for establishing a scientific evidence base, monitoring, and evaluation of health metrics. However, little is known about the optimal precisions of various population-level health and development indicators that remains unquantified in nationally-representative household surveys. Here, a retrospective analysis of the precision of prevalence from these surveys was conducted. Using malaria indicators, data were assembled in nine sub-Saharan African countries with at least two nationally-representative surveys. A Bayesian statistical model was used to estimate between- and within-cluster variability for fever and malaria prevalence, and insecticide-treated bed nets (ITNs) use in children under the age of 5 years. The intra-class correlation coefficient was estimated along with the optimal sample size for each indicator with associated uncertainty. Results suggest that the estimated sample sizes for the current nationally-representative surveys increases with declining malaria prevalence. Comparison between the actual sample size and the modelled estimate showed a requirement to increase the sample size for parasite prevalence by up to 77.7% (95% Bayesian credible intervals 74.7-79.4) for the 2015 Kenya MIS (estimated sample size of children 0-4 years 7218 [7099-7288]), and 54.1% [50.1-56.5] for the 2014-2015 Rwanda DHS (12,220 [11,950-12,410]). This study highlights the importance of defining indicator-relevant sample sizes to achieve the required precision in the current national surveys. While expanding the current surveys would need additional investment, the study highlights the need for improved approaches to cost effective sampling. |
X Demographics
The data shown below were collected from the profiles of 29 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 6 | 21% |
| United States | 6 | 21% |
| Australia | 2 | 7% |
| Panama | 1 | 3% |
| Germany | 1 | 3% |
| Nigeria | 1 | 3% |
| Unknown | 12 | 41% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 21 | 72% |
| Scientists | 4 | 14% |
| Science communicators (journalists, bloggers, editors) | 2 | 7% |
| Practitioners (doctors, other healthcare professionals) | 2 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 60 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 12 | 20% |
| Researcher | 10 | 17% |
| Student > Ph. D. Student | 6 | 10% |
| Student > Postgraduate | 5 | 8% |
| Other | 4 | 7% |
| Other | 9 | 15% |
| Unknown | 14 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 13 | 22% |
| Nursing and Health Professions | 6 | 10% |
| Social Sciences | 5 | 8% |
| Economics, Econometrics and Finance | 4 | 7% |
| Agricultural and Biological Sciences | 3 | 5% |
| Other | 12 | 20% |
| Unknown | 17 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 17. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 June 2018.
All research outputs
#2,062,071
of 24,580,204 outputs
Outputs from Malaria Journal
#393
of 5,786 outputs
Outputs of similar age
#45,749
of 447,692 outputs
Outputs of similar age from Malaria Journal
#6
of 104 outputs
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