ArticlePreventing chronic disease2008
Applying the small-area estimation method to estimate a population eligible for breast cancer detection services.
Article in Preventing chronic disease, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- US county-level estimation for maternal and infant health-related behavior indicators using pregnancy risk assessment monitoring system data, 2016-2018.Population health metrics · 2022Article
- Constructing Statistical Intervals for Small Area Estimates Based on Generalized Linear Mixed Model in Health Surveys.Open journal of statistics · 2022Article
- Using Small-Area Estimation to Calculate the Prevalence of Smoking by Subcounty Geographic Areas in King County, Washington, Behavioral Risk Factor Surveillance System, 2009-2013.Preventing chronic disease · 2016Article
- Addressing the Challenges of Research With Small Populations.American journal of public health · 2015Article
- Measuring and monitoring progress toward health equity: local challenges for public health.Preventing chronic disease · 2014Article
- Self-rated health: small area large area comparisons amongst older adults at the state, district and sub-district level in India.Health & place · 2014Article
- 'In general, how do you feel today?'--self-rated health in the context of aging in India.Global health action · 2014Article
- Small area estimates reveal high cigarette smoking prevalence in low-income cities of Los Angeles county.Journal of urban health : bulletin of the New York Academy of Medicine · 2012Article
- Using small-area estimation method to calculate county-level prevalence of obesity in Mississippi, 2007-2009.Preventing chronic disease · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPopulations eligible for public health programs are often narrowly defined and, therefore, difficult to describe quantitatively, particularly at the local level, because of lack of data. This information, however, is vital for program planning and evaluation. We demonstrate the application of a statistical method using multiple sources of data to generate county estimates of women eligible for free breast cancer screening and diagnostic services through California's Cancer Detection Programs: Every Woman Counts.
methodsWe used the small-area estimation method to determine the proportion of eligible women by county and racial/ethnic group. To do so, we included individual and community data in a generalized, linear, mixed-effect model.
resultsOur method yielded widely varied estimated proportions of service-eligible women at the county level. In all counties, the estimated proportion of eligible women was higher for Hispanics than for whites, blacks, Asian/Pacific Islanders, or American Indian/Alaska Natives. Across counties, the estimated proportions of eligible Hispanic women varied more than did those of women of other races.
conclusionThe small-area estimation method is a powerful tool for approximating narrowly defined eligible or target populations that are not represented fully in any one data source. The variability and reliability of the estimates are measurable and meaningful. Public health programs can use this method to estimate the size of local populations eligible for, or in need of, preventive health services and interventions.
Indexed as
Identifiers
18081999PMC2248789What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.