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Testing for active SARS-CoV-2 infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies in the presence of limited resources. Inthis paper, we provide a mathematical framework for defining anoptimal strategy for allocating viral tests. The framework accounts for imperfect test results, selective testing in certain high-risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the purpose of testing. Our method is not only useful for detecting infected cases, but can also be used for long-time surveillance to monitor for new outbreaks, which will be especially important during ongoing vaccine distribution across the world. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We illustrate our framework using historical data from the initial wave of the COVID-19 outbreak in New York City. We extend our proposed method to address the challenge of allocating two different types of tests with different costs and accuracy (for example, the expensive but more accurate RT-PCR test versus the cheap but less accurate rapid antigen test), administered under budget constraints. We show how this latter framework can be useful to reopening of college campuses where university administrators are challenged with finite resources for community surveillance. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision-making at a community level and adapting to different stages of an epidemic, and it has broader relevance beyond the COVID-19 outbreak.
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ImportanceThe diagnostic tests for COVID-19 have a high false negative rate, but not everyone with an initial negative result is re-tested. Michigan Medicine, being one of the primary regional centers accepting COVID-19 cases, provided an ideal setting for studying COVID-19 repeated testing patterns during the first wave of the pandemic. ObjectiveTo identify the characteristics of patients who underwent repeated testing for COVID-19 and determine if repeated testing was associated with patient characteristics and with downstream outcomes among positive cases. DesignThis cross-sectional study described the pattern of testing for COVID-19 at Michigan Medicine. The main hypothesis under consideration is whether patient characteristics differed between those tested once and those who underwent multiple tests. We then restrict our attention to those that had at least one positive test and study repeated testing patterns in patients with severe COVID-19 related outcomes (testing positive, hospitalization and ICU care). SettingDemographic and clinical characteristics, test results, and health outcomes for 15,920 patients presenting to Michigan Medicine between March 10 and June 4, 2020 for a diagnostic test for COVID-19 were collected from their electronic medical records on June 24, 2020. Data on the number and types of tests administered to a given patient, as well as the sequences of patient-specific test results were derived from records of patient laboratory results. ParticipantsAnyone tested between March 10 and June 4, 2020 at Michigan Medicine with a diagnostic test for COVID-19 in their Electronic Health Records were included in our analysis. ExposuresComparison of repeated testing across patient demographics, clinical characteristics, and patient outcomes Main Outcomes and MeasuresWhether patients underwent repeated diagnostic testing for SARS CoV-2 in Michigan Medicine ResultsBetween March 10th and June 4th, 19,540 tests were ordered for 15,920 patients, with most patients only tested once (13596, 85.4%) and never testing positive (14753, 92.7%). There were 5 patients who got tested 10 or more times and there were substantial variations in test results within a patient. After fully adjusting for patient and neighborhood socioeconomic status (NSES) and demographic characteristics, patients with circulatory diseases (OR: 1.42; 95% CI: (1.18, 1.72)), any cancer (OR: 1.14; 95% CI: (1.01, 1.29)), Type 2 diabetes (OR: 1.22; 95% CI: (1.06, 1.39)), kidney diseases (OR: 1.95; 95% CI: (1.71, 2.23)), and liver diseases (OR: 1.30; 95% CI: (1.11, 1.50)) were found to have higher odds of undergoing repeated testing when compared to those without. Additionally, as compared to non-Hispanic whites, non-Hispanic blacks were found to have higher odds (OR: 1.21; 95% CI: (1.03, 1.43)) of receiving additional testing. Females were found to have lower odds (OR: 0.86; 95% CI: (0.76, 0.96)) of receiving additional testing than males. Neighborhood poverty level also affected whether to receive additional testing. For 1% increase in proportion of population with annual income below the federal poverty level, the odds ratio of receiving repeated testing is 1.01 (OR: 1.01; 95% CI: (1.00, 1.01)). Focusing on only those 1167 patients with at least one positive result in their full testing history, patient age in years (OR: 1.01; 95% CI: (1.00, 1.03)), prior history of kidney diseases (OR: 2.15; 95% CI: (1.36, 3.41)) remained significantly different between patients who underwent repeated testing and those who did not. After adjusting for both patient demographic factors and NSES, hospitalization (OR: 7.44; 95% CI: (4.92, 11.41)) and ICU-level care (OR: 6.97; 95% CI: (4.48, 10.98)) were significantly associated with repeated testing. Of these 1167 patients, 306 got repeated testing and 1118 tests were done on these 306 patients, of which 810 (72.5%) were done during inpatient stays, substantiating that most repeated tests for test positive patients were done during hospitalization or ICU care. Additionally, using repeated testing data we estimate the "real world" false negative rate of the RT-PCR diagnostic test was 23.8% (95% CI: (19.5%, 28.5%)). Conclusions and RelevanceThis study sought to quantify the pattern of repeated testing for COVID-19 at Michigan Medicine. While most patients were tested once and received a negative result, a meaningful subset of patients (2324, 14.6% of the population who got tested) underwent multiple rounds of testing (5,944 tests were done in total on these 2324 patients, with an average of 2.6 tests per person), with 10 or more tests for five patients. Both hospitalizations and ICU care differed significantly between patients who underwent repeated testing versus those only tested once as expected. These results shed light on testing patterns and have important implications for understanding the variation of repeated testing results within and between patients. Key PointsO_ST_ABSQuestionC_ST_ABSDoes having repeated diagnostic tests for the novel coronavirus (COVID-19) depend on patient characteristics and disease outcomes? FindingsThis cross-sectional study of testing patterns with 15,920 patients tested for SARS-CoV-2 virus at Michigan Medicine found significant differences in testing rates across patient age, body mass index, sex, race/ethnicity, neighborhood poverty level, prior history of circulatory diseases, any cancer, Type 2 diabetes, kidney, and liver diseases. Higher hospitalization rates and intensive care unit admissions were associated with repeated testing as expected. MeaningThe results of this study describe diagnostic testing patterns for the novel COVID-19 virus at Michigan Medicine, and how they relate to patient characteristics and COVID-19 outcomes.
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Purpose@#Capsular contracture is a major cause of poor postoperative satisfaction in augmentation mammaplasty. It is unclear whether round or anatomical textured breast implants lead to differences in the rates of capsular contracture. Accordingly, the objective of this study was to compare capsular contracture rates between round and anatomical textured breast implants. @*Methods@#A total of 1,282 cases (2,564 breasts) of augmentation mammaplasty using textured type implants, performed at MD clinic between January 2012 and December 2015, were retrospectively reviewed. A total of 703 cases used round textured implants while 579 used anatomical textured implants. The median follow-up period for the round textured implant group was 9 months, and 7 months for the anatomical textured group. @*Results@#The overall capsular contracture rates in the first year was 2.0% in the round textured group and 1.5% in the anatomical textured group (p=0.609). Capsular contracture rates in the first year in those who underwent primary surgery were 1.6% in the round textured group and 1.3% in the anatomical textured group (p=0.187). The rates in those who underwent revision surgery were 5.2% in the round textured group and 2.6% in the anatomical textured group (p=0.178). Conclusion: Although the risk for capsular contracture in the anatomical textured groups appeared to be slightly lower than in the round textured groups, the difference was not significant. Further studies with longer follow-up periods are needed to assess potential differences.
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Purpose@#Capsular contracture is a major cause of poor postoperative satisfaction in augmentation mammaplasty. It is unclear whether round or anatomical textured breast implants lead to differences in the rates of capsular contracture. Accordingly, the objective of this study was to compare capsular contracture rates between round and anatomical textured breast implants. @*Methods@#A total of 1,282 cases (2,564 breasts) of augmentation mammaplasty using textured type implants, performed at MD clinic between January 2012 and December 2015, were retrospectively reviewed. A total of 703 cases used round textured implants while 579 used anatomical textured implants. The median follow-up period for the round textured implant group was 9 months, and 7 months for the anatomical textured group. @*Results@#The overall capsular contracture rates in the first year was 2.0% in the round textured group and 1.5% in the anatomical textured group (p=0.609). Capsular contracture rates in the first year in those who underwent primary surgery were 1.6% in the round textured group and 1.3% in the anatomical textured group (p=0.187). The rates in those who underwent revision surgery were 5.2% in the round textured group and 2.6% in the anatomical textured group (p=0.178). Conclusion: Although the risk for capsular contracture in the anatomical textured groups appeared to be slightly lower than in the round textured groups, the difference was not significant. Further studies with longer follow-up periods are needed to assess potential differences.
