The Health Plan Environment In California Contributed To Differential Use Of Telehealth During The COVID-19 Pandemic.

The COVID-19 pandemic has led to substantial increases in the use of telehealth and virtual care in the US. Differential patient and provider access to technology and resources has raised concerns that existing health disparities may be extenuated by shifts to virtual care. We used data from one of the largest providers of employer-sponsored insurance, the California Public Employees' Retirement System, to examine potential disparities in the use of telehealth. We found that lower-income, non-White, and non-English-speaking people were more likely to use telehealth during the period we studied. These differences were driven by enrollment in a clinically and financially integrated care delivery system, Kaiser Permanente. Kaiser's use of telehealth was higher before and during the pandemic than that of other delivery models. Access to integrated care may be more important to the adoption of health technology than patient-level differences.

Group Testing in a Pandemic: The Role of Frequent Testing, Correlated Risk, and Machine Learning

Group testing increases efficiency by pooling patient specimens and clearing the entire group with one negative test. Optimal grouping strategy is well studied in one-off testing scenarios with reasonably well-known prevalence rates and no correlations in risk. We discuss how the strategy changes in a pandemic environment with repeated testing, rapid local infection spread, and highly uncertain risk. First, repeated testing mechanically lowers prevalence at the time of the next test. This increases testing efficiency, such that increasing frequency by x times only increases expected tests by around vx rather than x. However, this calculation omits a further benefit of frequent testing: infected people are quickly removed from the population, which lowers prevalence and generates further e?ciency. Accounting for this decline in intra-group spread, we show that increasing frequency can paradoxically reduce the total testing cost. Second, we show that group size and e?ciency increases with intra-group risk correlation, which is expected in natural test groupings based on proximity. Third, because optimal groupings depend on uncertain risk and correlation, we show how better estimates from machine learning can drive large efficiency gains. We conclude that frequent group testing, aided by machine learning, is a promising and inexpensive surveillance strategy.