Building capacity of community health centers to overcome data challenges with the development of an agile COVID-19 public health registry: a multistate quality improvement effort

ObjectiveDuring the coronavirus disease 2019 (COVID-19) pandemic, federally qualified health centers rapidly mobilized to provide SARS-CoV-2 testing, COVID-19 care, and vaccination to populations at increased risk for COVID-19 morbidity and mortality. We describe the development of a reusable public health data analytics system for reuse of clinical data to evaluate the health burden, disparities, and impact of COVID-19 on populations served by health centers.Materials and MethodsThe Multistate Data Strategy engaged project partners to assess public health readiness and COVID-19 data challenges. An infrastructure for data capture and sharing procedures between health centers and public health agencies was developed to support existing capabilities and data capacities to respond to the pandemic.ResultsBetween August 2020 and March 2021, project partners evaluated their data capture and sharing capabilities and reported challenges and preliminary data. Major interoperability challenges included poorly aligned federal, state, and local reporting requirements, lack of unique patient identifiers, lack of access to pharmacy, claims and laboratory data, missing data, and proprietary data standards and extraction methods.DiscussionEfforts to access and align project partners’ existing health systems data infrastructure in the context of the pandemic highlighted complex interoperability challenges. These challenges remain significant barriers to real-time data analytics and efforts to improve health outcomes and mitigate inequities through data-driven responses.ConclusionThe reusable public health data analytics system created in the Multistate Data Strategy can be adapted and scaled for other health center networks to facilitate data aggregation and dashboards for public health, organizational planning, and quality improvement and can inform local, state, and national COVID-19 response efforts.     

Risk factor redistribution of the national HIV/AIDS surveillance data: an alternative approach

OBJECTIVE: The purpose of this study was to assess an alternative statistical approach-multiple imputation-to risk factor redistribution in the national human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) surveillance system as a way to adjust for missing risk factor information. METHODS: We used an approximate model incorporating random variation to impute values for missing risk factors for HIV and AIDS cases diagnosed from 2000 to 2004. The process was repeated M times to generate M datasets. We combined results from the datasets to compute an overall multiple imputation estimate and standard error (SE), and then compared results from multiple imputation and from risk factor redistribution. Variables in the imputation models were age at diagnosis, race/ethnicity, type of facility where diagnosis was made, region of residence, national origin, CD-4 T-lymphocyte cell count within six months of diagnosis, and reporting year. RESULTS: In HIV data, male-to-male sexual contact accounted for 67.3% of cases by risk factor redistribution and 70.4% (SE = 0.45) by multiple imputation. Also among males, injection drug use (IDU) accounted for 11.6% and 10.8% (SE = 0.34), and high-risk heterosexual contact for 15.1% and 13.0% (SE = 0.34) by risk factor redistribution and multiple imputation, respectively. Among females, IDU accounted for 18.2% and 17.9% (SE = 0.61), and high-risk heterosexual contact for 80.8% and 80.9% (SE = 0.63) by risk factor redistribution and multiple imputation, respectively. CONCLUSIONS: Because multiple imputation produces less biased subgroup estimates and offers objectivity and a semiautomated approach, we suggest consideration of its use in adjusting for missing risk factor information.