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dc.contributor.authorBurger, Rulof P.
dc.contributor.authorMcLaren, Zoe
dcterms.creatorhttps://orcid.org/0000-0003-2515-9731en_US
dc.date.accessioned2021-11-23T19:26:51Z
dc.date.available2021-11-23T19:26:51Z
dc.date.issued2017-08-29
dc.description.abstractThe problem of sample selection complicates the process of drawing inference about populations. Selective sampling arises in many real world situations when agents such as doctors and customs officials search for targets with high values of a characteristic. We propose a new method for estimating population characteristics from these types of selected samples. We develop a model that captures key features of the agent's sampling decision. We use a generalized method of moments with instrumental variables and maximum likelihood to estimate the population prevalence of the characteristic of interest and the agents' accuracy in identifying targets. We apply this method to tuberculosis (TB), which is the leading infectious disease cause of death worldwide. We use a national database of TB test data from South Africa to examine testing for multidrug resistant TB (MDR-TB). Approximately one quarter of MDR-TB cases was undiagnosed between 2004 and 2010. The official estimate of 2.5% is therefore too low, and MDR-TB prevalence is as high as 3.5%. Signal-to-noise ratios are estimated to be between 0.5 and 1. Our approach is widely applicable because of the availability of routinely collected data and abundance of potential instruments. Using routinely collected data to monitor population prevalence can guide evidence-based policy making.en_US
dc.description.sponsorshipWe thank Sue Candy, Ananta Nanoo, Michelle Potgeiter and Andrew Whitelaw for assistance with the data and helpful comments. We thank Michael Budros, Elizabeth Brouwer, David Ederer, Kathryn Fischer, Alana Sharp and Jifang Zhou for research assistance. We thank Jacob Bor, Florian Marx, Tyler McCormick, Tom Moultrie, Edward Norton, Kate Schnippel, Margaret Triyana, Sean Wasserman, Mark Wilson, seminar audiences at the University of Michigan, Stellenbosch University and Yale School of Public Health, and participants at the Union for African Population Studies conference, University of KwaZulu Natal Microeconomic Analysis of South Africa conference and European Workshop on Econometrics and Health Economics and two anonymous referees for helpful feedback. Financial support was provided by the University of Michigan School of Public Health Global Public Health Program, the Center for Global Health and the University of Michigan Health Management and Policy Department McNerney Award.en_US
dc.description.urihttps://onlinelibrary.wiley.com/doi/10.1002/hec.3547en_US
dc.format.extent47 pagesen_US
dc.genrejournal articlesen_US
dc.genrepostprintsen_US
dc.identifierdoi:10.13016/m2h3t4-ljoo
dc.identifier.citationBurger, Rulof P.; McLaren, Zoe; An econometric method for estimating population parameters from non-random samples: An application to clinical case finding; Health Economics, 26, 9, 1110-1122, 29 August, 2017; https://doi.org/10.1002/hec.3547en_US
dc.identifier.urihttps://doi.org/10.1002/hec.3547
dc.identifier.urihttp://hdl.handle.net/11603/23468
dc.language.isoen_USen_US
dc.publisherWiley Online Libraryen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC School of Public Policy Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.rightsThis is the peer reviewed version of the following article: Burger, Rulof P.; McLaren, Zoe; An econometric method for estimating population parameters from non-random samples: An application to clinical case finding; Health Economics, 26, 9, 1110-1122, 29 August, 2017; https://doi.org/10.1002/hec.3547, which has been published in final form at https://doi.org/10.1002/hec.3547. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited
dc.titleAn econometric method for estimating population parameters from non-random samples: An application to clinical case findingen_US
dc.typeTexten_US


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