Daniel W. Sacks, Nir Menachemi, Peter Embi, Coady Wing
arXiv 1 Aug 2020 · Econometrics · publishedThe Review of Economics and Statistics (2022) · 3 citations (OpenAlex)
arXiv:2008.00298 · PDF · DOI · OpenAlex · Extracted main text
Measuring the prevalence of active SARS-CoV-2 infections in the general population is difficult because tests are conducted on a small and non-random segment of the population. However, people admitted to the hospital for non-COVID reasons are tested at very high rates, even though they do not appear to be at elevated risk of infection. This sub-population may provide valuable evidence on prevalence in the general population. We estimate upper and lower bounds on the prevalence of the virus in the general population and the population of non-COVID hospital patients under weak assumptions on who gets tested, using Indiana data on hospital inpatient records linked to SARS-CoV-2 virological tests. The non-COVID hospital population is tested fifty times as often as the general population, yielding much tighter bounds on prevalence. We provide and test conditions under which this non-COVID hospitalization bound is valid for the general population. The combination of clinical testing data and hospital records may contain much more information about the state of the epidemic than has been previously appreciated. The bounds we calculate for Indiana could be constructed at relatively low cost in many other states.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Richard M. Fairbanks School of Public Health (2020) Iupui, isdh release findings from phase 2 of covid-19 testing in indiana | 1.000 | 6 | 3 | 100% |
| 2 | Menachemi, N., C. Yiannoutsos, B. Dixon, and et al (2020) Population point prevalence of sars-cov-2 infection based on a statewide random sample — indiana, april 25–29, 2020 self | 1.000 | 5 | 3 | 100% |
| 3 | Manski, C. F. and F. Molinari (2020) Estimating the covid-19 infection rate: Anatomy of an inference problem | 0.874 | 7 | 2 | 100% |
| 4 | Armed Forces Health Surveillance Center (2015, October) (2015) Influenza-like illness | 0.737 | 3 | 3 | 67% |
| 5 | Center for Disease Control and Prevention (2020, April) (2020) Icd-10-cm official coding and reporting guidelines april 1, 2020 through september 30, 2020 | 0.737 | 3 | 3 | 67% |
| 6 | Indiana State Department of Health (2020) Novel coronavirus (covid-19) | 0.737 | 3 | 3 | 67% |
| 7 | Imbens, G. W. and C. F. Manski (2004) Confidence intervals for partially identified parameters | 0.644 | 2 | 2 | 100% |
| 8 | Horowitz, J. L. and C. F. Manski (2000) Nonparametric analysis of randomized experiments with missing covariate and outcome data | 0.644 | 2 | 2 | 100% |
| 9 | Manski, C. F (1999) Identification problems in the social sciences | 0.644 | 2 | 2 | 100% |
| 10 | Peci, A., A.-L. Winter, E.-C. King, J. Blair, and J. B. Gubbay (2014) Performance of rapid influenza diagnostic testing in outbreak settings | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Binary Classification Tests, Imperfect Standards, and Ambiguous Information | 0.405 | 1 | 1 |