Utilizing Geocoded Address Matching for COVID-19 Surveillance in New York City in March-December 2020
Geocoded address matching was a useful tool for estimating the burden of household transmission during the start of the COVID-19 pandemic in NYC, where 43% of COVID-19 cases were associated with a household or building cluster.
Understanding where and how a disease is spreading is a critical component of outbreak response. In New York City (NYC), a densely populated city and the epicenter of the COVID-19 pandemic in the United States during the first wave, it was particularly important to understand how much transmission was occurring in the home. In our article, “Clustering of SARS-CoV-2 in Households in New York City: A Building-Level Analysis, March-December 2020,” we and our NYC Department of Health and Mental Hygiene colleagues explored a novel approach to COVID-19 surveillance by using a cluster detection method of geocoded address matching using a building identification number (BIN). Our goal was to identify the proportion of NYC COVID-19 cases clustered within households and buildings during the first ten months of the pandemic to explore the utility of geocoding techniques for cluster identification, estimate the burden of disease attributable to transmission within people’s homes, and inform targeted prevention strategies.
Among the 399,626 COVID-19 cases reported between March 1, 2020, and December 31, 2020, in NYC, 173,340 (43%) were included in a cluster of 2+ cases in a household or 3+ cases in a building within two weeks. This is indicative of significant COVID-19 transmission within households and buildings during the first ten months of the pandemic. Similar to trends in the overall COVID-19 prevalence in NYC during this time, the proportion of COVID-19 cases associated with a household/building cluster reached an initial peak in April 2020, decreased into the summer months, and increased again into the fall and winter with an additional peak in December. The first peak coincided with the implementation of stay-at-home orders in March and April, which may have reduced opportunities for transmission outside the home.
Describing the characteristics of cluster-associated cases is an important step in addressing the role that racial, economic, and geographic inequality play in the spread of COVID-19. Overall, we found that cluster-associated cases were more frequently Latino, 60 years or older, female, and from medium poverty neighborhoods. This mirrors the findings of other studies that show that low-income communities and people of color are disproportionately burdened by COVID-19 clustering. Women and people of color make up the majority of frontline workers[i] who were not able to stay home to avoid contracting COVID-19 during the start of the pandemic, increasing opportunities for infection and household transmission. Most of the areas in which the majority of cluster-associated cases were identified were in the Bronx and upper Manhattan, which are among the most densely populated neighborhoods in NYC. Residents of these areas may face additional challenges to isolating a household member with COVID-19 to prevent transmission in the home.
Our analysis of household/building clustering of COVID-19 cases in NYC showed that transmission within the home may have contributed to a substantial proportion of the virus’ spread during 2020, before vaccines became widely available. The BIN analysis method may be useful for other jurisdictions to identify the burden of household transmission of future emerging COVID-19 variants or other infectious diseases. A better understanding of where household transmission is occurring is crucial to direct resources and prevention efforts towards the communities most impacted by health disparities. To learn more about the COVID-19 pandemic and cluster-associated cases in New York City in 2020, visit the July issue of JPHMP to read the full research report.
[i] Office of the New York City Comptroller. New York City’s front-line workers. https://comptroller.nyc.gov/wp-content/uploads/documents/Frontline_Workers_032020.pdf. Published March 2020. Accessed May 2023.
You Might Also Like
- July 2023: From the Editor
- Promoting Equitable Healthy Aging in Public Health
- Local Public Health Professionals Report Five Lessons Learned During the COVID Pandemic: The Results of Guided Reflection
- Declining Investment in Community-Building Activities by Nonprofit Hospitals Highlights the Need for Policy Alignment
- Empathy Not Emnity: Responding to COVID-19 Misinformation in a Social Media Feed
- Public Health 3.0 in Action: Pandemic Response Officers Expedite Prevention and Response
- Designing Data Dashboards to Build Community Engagement Around Young People’s Well-being
- Tailoring Outreach Strategies for COVID-19 Vaccination: Using Data Science to Address Vaccine Hesitancy
- Successes and Opportunities for Leadership in State Health Departments for Public Health Professionals of Color
- Do Telemedicine Payment Parity Legislations Matter?
- Racial and Ethnic Disparities in Prenatal Care Utilization During the COVID Pandemic: Comparison Between Medical Expansion and Non-Expansion States
Catherine Gulley, MPH, was a previous Council of State and Territorial Epidemiologists Applied Epidemiology Fellow at the New York City Department of Health and Mental Hygiene, where she worked in the COVID-19 Surveillance and Epidemiology branch. She received a Bachelor’s degree in Public Health and a Master’s in Public Health from the George Washington University. Her current placement is with JBS International as a Research Associate, where she supports health and human services federal contracts.
Kelsey Kepler, MPH, is a Data & Evaluation Manager at the New York City Department of Health and Mental Hygiene. During NYC’s COVID-19 emergency response, she served as an EpiData Analyst in the Surveillance and Epidemiology Branch. Ms. Kepler earned her MPH at Tulane University.