scholarly journals HIV-Prevalence Mapping Using Small Area Estimation in Kenya, Tanzania, and Mozambique at the First Sub-National Level

2021 ◽  
Vol 87 (1) ◽  
pp. 93
Author(s):  
Enrique M. Saldarriaga
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
National Level ◽  
Hiv Prevalence ◽  
Area Estimation

scholarly journals Estimating District HIV Prevalence in Zambia Using Small Area Estimation Methods (SAE)

2021 ◽  
Author(s):  
Chris Mweemba ◽  
Peter Hangoma ◽  
Isaac Fwemba ◽  
Wilbroad Mutale ◽  
Felix Masiye
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
National Level ◽  
Population Based ◽  
Hiv Prevalence ◽  
Estimation Methods ◽  
Population Mobility ◽  
African Region ◽  
Area Estimation ◽  
Very High

Abstract BackgroundThe HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated interventions. While information on the prevalence of HIV at regional and national levels is readily available, the burden of the disease at smaller area levels, where health services are organized and delivered, is not well documented. This affects the targeting of HIV resources. There is need for studies to estimate HIV prevalence at appropriate levels to improve HIV related planning and resource allocation. MethodsWe estimated the district level prevalence of HIV using Small-Area Estimation (SAE) technique by utilizing the 2016 Zambia Population-Based HIV Impact Assessment Survey (ZAMPHIA) data and auxiliary data from the 2010 Zambian Census of Population and Housing and the HIV sentinel surveillance data from selected antenatal care clinics (ANC). SAE Models were fitted in R Programming to ascertain the best HIV predicting model. We then used the Fay-Herriot (FH) model to obtain weighted, more precise and reliable HIV prevalence for all the districts.ResultsThe results revealed variations in the district HIV prevalence in Zambia, with the prevalence ranging from as low as 4.2% to as high as 23.5%. Approximately 35% of the districts (n=26) had HIV prevalence above the national average, with one district having almost twice as much prevalence as the national level. Some rural districts have very high HIV prevalence rates. ConclusionsHIV prevalence in Zambian districts is driven by population mobility Districts located near international borders, along the main transit routes and adjacent to other districts with very high prevalence, tend to have high HIV prevalence. The variations in the burden of HIV across districts in Zambia points to the need for a differentiated approaches in HIV programming in Zambia. HIV resources need to be prioritized towards districts with high population mobility.

scholarly journals Identification of Disaggregated Hotspots of Child Morbidity in Bangladesh: An Application of Small Area Estimation Method

2019 ◽  
Author(s):  
Sumonkanti Das ◽  
Bappi Kumar ◽  
Luthful Alahi Kawsar
Keyword(s):  
Small Area ◽  
Census Data ◽  
Small Area Estimation ◽  
Random Effect ◽  
National Level ◽  
Estimation Method ◽  
Response Variable ◽  
Area Estimation ◽  
Child Morbidity ◽  
Administrative Units

AbstractAcute respiratory infection (ARI) and diarrhoea are two major causes of child morbidity and mortality in Bangladesh. National and regional level prevalence of ARI and diarrhoea are calculated from nationwide surveys; however, prevalence at micro-level administrative units (say, district and sub-district) is not possible due to lack of sufficient data. In such case, small area estimation (SAE) methods can be applied by combining a survey data with a census data. Using a SAE method for dichotomous response variable, this study aims to estimate the proportions of under-5 children experienced with ARI and diarrhoea separately as well as either ARI or diarrhoea within a period of two-week preceding the survey. The ARI and diarrhoea information extracted from Bangladesh Demographic and Health Survey 2011 are used to develop a random effect logistic model for each of the indicators, and then the prevalence is estimated adapting the World Bank SAE approach for the dichotomous response variable using the 5% data of the Census 2011. The estimated prevalence of each indicator significantly varied by district and sub-district (1.4-11.3% for diarrhoea, 2.2-11.8% for ARI and 4.3-16.5% for ARI/diarrhoea at sub-district level). In a number of districts and sub-district, the proportions are found double the national level. District and sub-district levels spatial distributions of the indicators might help the policy makers to identify the vulnerable disaggregated and remote hotspots. Particularly, aid industries can provide effective interventions at the highly vulnerable spots to overcome the gaps between micro and macro level administrative units.

scholarly journals Improving estimates of district HIV prevalence and burden in South Africa using small area estimation techniques

PLoS ONE ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. e0212445 ◽  
Author(s):  
Steve Gutreuter ◽  
Ehimario Igumbor ◽  
Njeri Wabiri ◽  
Mitesh Desai ◽  
Lizette Durand
Keyword(s):  
South Africa ◽  
Small Area ◽  
Small Area Estimation ◽  
Hiv Prevalence ◽  
Estimation Techniques ◽  
Area Estimation

Analysis of the spatial distribution of under-5 mortality rate in local areas of South Africa

2020 ◽  
Vol 36 (4) ◽  
pp. 1161-1173
Author(s):  
Yegnanew A. Shiferaw
Keyword(s):  
South Africa ◽  
Mortality Rate ◽  
Healthcare Service ◽  
Small Area ◽  
Local Level ◽  
Small Area Estimation ◽  
National Level ◽  
Population Census ◽  
Area Estimation ◽  
Local Municipality

Policymakers and healthcare service managers demand reliable, accurate and disaggregated information about child deaths at the sub-national level to plan and monitor healthcare service delivery and health outcomes. In support of this demand, this research aimed at providing reliable local municipality estimates of the under-5 mortality rate (U5MR) in South Africa. The paper used a small area estimation approach to improve the precision of local municipality estimates of U5MR by linking data from the 2016 Community Survey (CS) and the 2011 Population Census (PC). The diagnostic measures and validation of the reliability of the results showed that the local municipality estimates of U5MR produced by small area estimation are more efficient and precise than direct estimates of U5MR based only on the CS data. Further, accurate and cost-effective local municipality estimates of U5MR were produced without the need for more resources through combining the available data sources. This was achievable since the research did not require a separate survey for this purpose. The results can be used to monitor U5MR at the local level in South Africa since they link directly with the Sustainable Development Goals (SDGs).

scholarly journals A Systematic Review of Small Domain Estimation Research in Forestry During the Twenty-First Century From Outside the United States

2021 ◽  
Vol 4 ◽  
Author(s):  
Richard W. Guldin
Keyword(s):  
United States ◽  
Small Area ◽  
Small Area Estimation ◽  
National Level ◽  
The United States ◽  
Area Estimation ◽  
National Statistics ◽  
Forest Inventories ◽  
Small Domain ◽  
Forest Managers

Small domain estimation (SDE) research outside of the United States has been centered in Canada and Europe—both in transnational organizations, such as the European Union, and in the national statistics offices of individual countries. Support for SDE research is driven by government policy-makers responsible for core national statistics across domains. Examples include demographic information about provision of health care or education (a social domain) or business data for a manufacturing sector (economic domain). Small area estimation (SAE) research on forest statistics has typically studied a subset of core environmental statistics for a limited geographic domain. The statistical design and sampling intensity of national forest inventories (NFIs) provide population estimates of acceptable precision at the national level and sometimes for broad sub-national regions. But forest managers responsible for smaller areas—states/provinces, districts, counties—are facing changing market conditions, such as emerging forest carbon markets, and budgetary pressures that limit local forest inventories. They need better estimates of conditions and trends for small sub-sets of a national-scale domain than can be provided at acceptable levels of precision from NFIs. Small area estimation research is how forest biometricians at the science-policy interface build bridges to inform decisions by forest managers, landowners, and investors.

Sae: A Stata Package For Unit Level Small Area Estimation

2018 ◽  
Author(s):  
Minh Cong Nguyen ◽  
Paul Corral ◽  
Joao Pedro Azevedo ◽  
Qinghua Zhao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Unit Level

scholarly journals Small Area Estimation of Smoke-free Workplace Polices and Home Rules in U.S. Counties

2021 ◽  
Author(s):  
Benmei Liu ◽  
Isaac Dompreh ◽  
Anne M Hartman
Keyword(s):  
Tobacco Control ◽  
Small Area ◽  
Small Sample Size ◽  
Health Hazard ◽  
Small Area Estimation ◽  
Small Sample ◽  
County Level ◽  
Area Estimation ◽  
Model Based ◽  
Workplace Policies

Abstract Background The workplace and home are sources of exposure to secondhand smoke (SHS), a serious health hazard for nonsmoking adults and children. Smoke-free workplace policies and home rules protect nonsmoking individuals from SHS and help individuals who smoke to quit smoking. However, estimated population coverages of smoke-free workplace policies and home rules are not typically available at small geographic levels such as counties. Model-based small area estimation techniques are needed to produce such estimates. Methods Self-reported smoke-free workplace policies and home rules data came from the 2014-2015 Tobacco Use Supplement to the Current Population Survey. County-level design-based estimates of the two measures were computed and linked to county-level relevant covariates obtained from external sources. Hierarchical Bayesian models were then built and implemented through Markov Chain Monte Carlo methods. Results Model-based estimates of smoke-free workplace policies and home rules were produced for 3,134 (out of 3,143) U.S. counties. In 2014-2015, nearly 80% of U.S. adult workers were covered by smoke-free workplace policies, and more than 85% of U.S. adults were covered by smoke-free home rules. We found large variations within and between states in the coverage of smoke-free workplace policies and home rules. Conclusions The small-area modeling approach efficiently reduced the variability that was attributable to small sample size in the direct estimates for counties with data and predicted estimates for counties without data by borrowing strength from covariates and other counties with similar profiles. The county-level modeled estimates can serve as a useful resource for tobacco control research and intervention. Implications Detailed county- and state-level estimates of smoke-free workplace policies and home rules can help identify coverage disparities and differential impact of smoke-free legislation and related social norms. Moreover, this estimation framework can be useful for modeling different tobacco control variables and applied elsewhere, e.g., to other behavioral, policy, or health related topics.

scholarly journals [Small Area Estimation: An Appraisal]: Rejoinder

1994 ◽  
Vol 9 (1) ◽  
pp. 90-93 ◽  
Author(s):  
M. Ghosh ◽  
J. N. K. Rao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation
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