Evaluation of Allocation Schemes of COVID-19 Testing Resources in a Community-Based Door-to-Door Testing Program

Abstract

Importance: Overcoming social barriers to COVID-19 testing is an important issue, especially given the demographic disparities in case incidence rates and testing. Delivering culturally appropriate testing resources using data-driven approaches in partnership with community-based health workers is promising, but little data are available on the design and effect of such interventions.

Objectives: To assess and evaluate a door-to-door COVID-19 testing initiative that allocates visits by community health workers by selecting households in areas with a high number of index cases, by using uncertainty sampling for areas where the positivity rate may be highest, and by relying on local knowledge of the health workers.

Design setting and participants: This cohort study was performed from December 18, 2020, to February 18, 2021. Community health workers visited households in neighborhoods in East San Jose, California, based on index cases or uncertainty sampling while retaining discretion to use local knowledge to administer tests. The health workers, also known as promotores de salud (hereinafter referred to as promotores) spent a mean of 4 days a week conducting door-to-door COVID-19 testing during the 2-month study period. All residents of East San Jose were eligible for COVID-19 testing. The promotores were selected from the META cooperative (Mujeres Empresarias Tomando Acción [Entrepreneurial Women Taking Action]).

Interventions: The promotores observed self-collection of anterior nasal swab samples for SARS-CoV-2 reverse transcriptase-polymerase chain reaction tests.

Main outcomes and measures: A determination of whether door-to-door COVID-19 testing was associated with an increase in the overall number of tests conducted, the demographic distribution of the door-to-door tests vs local testing sites, and the difference in positivity rates among the 3 door-to-door allocation strategies.

Results: A total of 785 residents underwent door-to-door testing, and 756 were included in the analysis. Among the 756 individuals undergoing testing (61.1% female; 28.2% aged 45-64 years), door-to-door COVID-19 testing reached different populations than standard public health surveillance, with 87.6% (95% CI, 85.0%-89.8%) being Latinx individuals. The closest available testing site only reached 49.0% (95% CI, 48.3%-49.8%) Latinx individuals. Uncertainty sampling provided the most effective allocation, with a 10.8% (95% CI, 6.8%-16.0%) positivity rate, followed by 6.4% (95% CI, 4.1%-9.4%) for local knowledge, and 2.6% (95% CI, 0.7%-6.6%) for index area selection. The intervention was also associated with increased overall testing capacity by 60% to 90%, depending on the testing protocol.

Conclusions and relevance: In this cohort study of 785 participants, uncertainty sampling, which has not been used conventionally in public health, showed promising results for allocating testing resources. Community-based door-to-door interventions and leveraging of community knowledge were associated with reduced demographic disparities in testing.

Figure 1.. Poverty and COVID-19 Positivity Rates in Santa Clara County, California

A, The poverty rate is calculated as percentage of residents living below the poverty line, from a Santa Clara County Public Health Department report from the 2015 American Community Survey. B, The positivity rate is calculated as the percentage of all conclusive tests returning positive results at the census tract level from the California Reportable Disease Information Exchange database. Both rates were aggregated from November 18 to December 18, 2020, 4 weeks before the intervention began. The red rectangle indicates the area of East San Jose in which the intervention took place.

Figure 2.. Total Number of Tests Conducted in Assigned and Control Segments

The number of tests conducted by the community health workers (promotores) from the laboratory data is restricted to risk selection (tests based on local knowledge are not shown). The 95% CIs in the error bars are based on SEs.