Evaluation of the Homeless Management Information System for COVID-19 Surveillance Among People Experiencing Homelessness.

BACKGROUND: Variable and incomplete reporting of housing status creates challenges in the surveillance of coronavirus disease 2019 (COVID-19) among the homeless population in Los Angeles County (LA County) and nationwide. METHODS: We developed standard investigation procedures to assess the housing status of LA County COVID-19 patients. Using data sharing procedures, we matched COVID-19 patients to Homeless Management Information System (HMIS) client profiles and supplemented with additional data sources for contributory data points and to further housing status ascertainment. RESULTS: We identified 10 586 COVID-19 patients among people experiencing homelessness (PEH) between 30 March 2020 and 30 December 2021; 2801 (26.5%) patients were first identified from HMIS profile matches, 1877 (17.7%) from quarantine/isolation housing intake rosters, 573 (5.4%) from hospital records, 749 (7.1%) from case and contact interviews, 3659 (34.6%) directly from PEH medical and service providers, and 927 (8.8%) had unknown sources. Among COVID-19 patients matched to HMIS profiles, 5351 (42.5%) were confirmed to be PEH at the time of COVID-19 diagnosis. CONCLUSIONS: Interoperability between public health data, HMIS, and external partners have been critical components in evaluating the impact of COVID-19 among the LA County homeless population. No one data source was complete for COVID-19 surveillance in this population.

Audit of the Sydney Local Health District Public Health Unit notification and contact tracing system during the first wave of COVID-19.

OBJECTIVE: To conduct a real-time audit to assess a Continuous Quality Improvement (CQI) activity to improve the quality of public health data in the Sydney Local Health District (SLHD) Public Health Unit during the first wave of COVID-19. METHODS: A real-time audit of the Notifiable Conditions Information Management System was conducted for positive cases of COVID-19 and their close contacts from SLHD. After recording missing and inaccurate data, the audit team then corrected the data. Multivariable regression models were used to look for associations with workload and time. RESULTS: A total of 293 cases were audited. Variables measuring completeness were associated with improvement over time (p<0.0001), whereas those measuring accuracy reduced with increased workload (p=0.0003). In addition, the audit team achieved 100% data quality by correcting data. CONCLUSION: Utilising a team, separate from operational staff, to conduct a real-time audit of data quality is an efficient and effective way of improving epidemiological data. Implications for public health: Implementation of CQI in a public health unit can improve data quality during times of stress. Auditing teams can also act as an intervention in their own right to achieve high-quality data at minimal cost. Together, this can result in timely and high-quality public health data.