While the toll of the COVID-19 pandemic is often measured in statistics of cases, hospitalizations, and deaths, the impact of COVID-19 goes far beyond these measurements. In early 2020, as public attention and the public health system's priorities, funding, and staff shifted acutely toward COVID-19 emergency response efforts, the exceptionalism of COVID-19 diverted resources and attention from non–COVID-19-related issues and public health initiatives.1,2 For example, because of “stay-home” government orders and messaging, many in-person medical appointments were reassigned to telehealth platforms or skipped altogether.3 In addition, fears of acquiring COVID-19 contributed to overall decreases in emergency department (ED) usage, primary and preventive care visits and their accompanying routine immunizations and screenings, communicable disease testing including for sexually transmitted infections (STIs) and HIV infection, and myriad other essential health services.4,5 These upheavals were paralleled by increases in reported mental health symptoms,6,7 especially anxiety and depression, and increases in reported economic, food, and housing insecurity. Together, these massive disruptions fundamentally exacerbated inequities in the social determinants of health.8
Despite some returns to prepandemic norms, the long-term collateral impacts of the COVID-19 pandemic on health outcomes will likely be profound and observed for years to come. There is considerable research measuring the impact of the pandemic on health across a range of diseases and conditions, as well as predictions about possible long-term consequences. For example, a number of studies have looked at specific diseases and conditions, including the impact on HIV viral suppression rates,9 cancer early detection rates,5 and child blood lead poisoning.10
In addition, local and state health departments and social service agencies are uniquely positioned to take advantage of existing public health surveillance systems to prospectively access timely quantitative data on a wide array of topics including vital statistics, disease incidence, and public assistance, allowing a better understanding of trends across domains and more integrated public policy decisions. For example, the Washington State Department of Health publishes weekly behavioral health impact reports11 summarizing surveillance data on a range of indicators including ED syndromic surveillance, mental/behavioral health, firearm weapons background checks, and alcohol sales.
In 2017, the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) created a surveillance system to monitor the health impacts of the Trump administration's policies and rhetoric on NYC residents.12 It was quickly apparent at the outset of the COVID-19 pandemic that the system could no longer meet its intended purpose as any indications of such impacts13 were confounded by the massive disruptions of the pandemic. In addition, it was clear that pandemic-related surveillance for COVID-19 and its direct outcomes needed to be accompanied by surveillance for collateral impacts, specifically non–COVID-19-related diseases, conditions, and health outcomes that were affected by the pandemic. As a result, the existing system was restructured to monitor the collateral impacts of COVID-19, or what the DOHMH 2020-2021 strategic plan referred to as “parallel pandemics” (https://www1.nyc.gov/assets/doh/downloads/pdf/public/doh-values-mission-vision-strategy.pdf). This article describes the process and framework for selecting indicators; how data are collected and used; key findings; considerations for analysis and dissemination; and implications for public health practice and policy.
Methods
Framework
During summer 2020, a small working group convened to update the surveillance system to reflect the change of focus on the collateral impacts of the COIVD-19 pandemic and to reflect the Health Commissioner's priorities. We organized the surveillance indicators into 6 core domains based on the broad areas we expected to be most impacted by the COVID-19 pandemic and the feasibility of measurement: access to care, chronic disease, sexual and reproductive health, food and economic insecurity, mental/behavioral health, and environmental health (Figure 1). Access to care was chosen because of shifts toward the use of telemedicine accompanied by outright postponement of care, including limited in-person services and nonessential care to accommodate COVID-19 patients and slow transmission.14,15 We hypothesized these changes would lead to increases in undetected and untreated chronic diseases, another domain, the full impact of which will continue for years to come. Sexual and reproductive health was included to reflect an array of changes such as closures of Health Department sexual health clinics resulting in reduced availability of testing for STIs and family planning resources, as well as changes happening at the federal level and in neighboring states possibly impacting access to induced terminations of pregnancy (abortions). Food and economic insecurity was added as a domain because of the closure of nonessential businesses and increased unemployment rates leading to widespread financial, food, and housing insecurity and increased usage of social safety nets.8 Mental/behavioral health was included as a domain to prospectively monitor the impacts of pandemic-related stressors.16 Finally, we included environmental health as a domain to monitor, among other things, screening for blood lead in children.
;)
FIGURE 1: Framework for Design of Surveillance System and Selection of IndicatorsAbbreviations: A1c, hemoglobin A
1c; SNAP, Supplemental Nutrition Assistance Program; STI, sexually transmitted infection; WIC, Special Supplemental Nutrition Program for Women, Infants and Children. This figure is available in color online (
www.JPHMP.com).
Within each domain, we used previously existing indicators or identified new ones. Criteria for selection of indicators included relevance to public health priorities in NYC, measurable using existing data sources without additional data collection, measurable monthly or quarterly, and representative of the NYC population. Supplemental Digital Content Tables A and B (available at https://links.lww.com/JPHMP/B131) include data source descriptions and indicator descriptions, respectively.
Strata
Each indicator in the system is stratified, where possible, by race/ethnicity, nativity, and poverty (see Supplemental Digital Content Table B, available at https://links.lww.com/JPHMP/B131). Depending on the indicator and available strata, we use both individual and area-based measures. For poverty, individual indicators include individual income or Medicaid coverage as a proxy for income level. We also used the recommended DOHMH area-based poverty measure17 for some indicators. Neighborhood poverty is defined as the percentage of residents with incomes below 100% of the Federal Poverty Level, per American Community Survey (2016-2020), and is divided into 4 categories for analysis: low (<10%; wealthiest neighborhoods), medium (10%-19%), high (20%-29%), very high (≥30%; poorest neighborhoods). For nativity, some data sources included information about whether a person was born outside of the United States; this individual-level measure was used when possible. For data sources that do not collect information on a person's country of birth but had the zip code of a person's residence, we used an area-based measure to classify neighborhoods based on the percentage of the residents born outside of the United States who were not citizens. We used data from the American Community Survey (2016-2020) to determine the percentage of noncitizen residents in each zip code tabulation area (ZCTA) born outside of the United States. We divided the ZCTAs into quartiles for analysis. Race/ethnicity was included when data sources collected this information directly for each case/participant.
Data processing
For most data managed by Health Department programs, data are submitted to the surveillance coordinator using provided Microsoft Excel data templates. A few indicators are sourced directly from publicly available data published online. Automated Microsoft Outlook calendar reminders and reusable program code ensure minimal work for data stewards to contribute to data collection. We created a data processing program in SAS Enterprise Guide (version 7.15 HF8), which automatically imports, cleans, and merges all data files (including those submitted without usage of our preferred standardized data template) into a master file, requiring the surveillance coordinator to simply click to run the program and check for pop-ups indicating flags or errors.
Trend analysis
Automated statistical analysis to detect possible trends is performed on most monthly and quarterly indicators using the R Surveillance package (https://cran.r-project.org/web/packages/surveillance/surveillance.pdf) and reported for the most recent time period. We primarily use the improved Farrington algorithm, selected because it has the highest probability of aberrant trend detection compared with other commonly used algorithms,18 accounting for seasonality using historical data starting from March 2020. For indicators without predicted seasonal trends, we use the Early AbeRration detection System (EARS) C1. Questions and identification of possible inflection points are communicated with programs for more detailed analyses as needed.
This project was deemed as public health surveillance and not under the purview of the NYC-204 DOHMH Institutional Review Board.
Dissemination
Beginning in mid-2020, we compiled monthly summary reports using Microsoft PowerPoint to share with Health Department leadership. In September 2022, we transitioned to data dissemination via an interactive online dashboard we custom created for this project using Tableau Desktop (version 2021.1.20) accessible to internal staff. The interactive dashboard allows users to filter graphs according to parameters of interest (eg, viewing specific time frames or strata) and identify precise values by hovering over data points with their cursor.
Results
We observed profound disruptions across all domains and most indicators coinciding with the COVID-19 pandemic. Data for selected indicators are shared in Figures 2 to 4 (additional indicators are included in Supplemental Digital Content Figures A-E, available at https://links.lww.com/JPHMP/B132) to illustrate some of the more immediate and consequential collateral impacts of the pandemic.
FIGURE 2: (A) Total Number of ED Visits per Month, NYC Syndromic Surveillance System. (B) Total Number of ED Visits per Month for Ambulatory Care–Sensitive Conditions, NYC Syndromic Surveillance System. (C) Percentage of ED Visits for Ambulatory Care–Sensitive Conditions Admitted per Month, NYC Syndromic Surveillance System Abbreviations: ED, emergency department; NYC, New York City.
Immediately following the first hospitalization for COVID-19 in NYC on February 29, 2020, total visits to NYC EDs dropped by approximately 50%; by April 2021, the number of visits had nearly rebounded but remained below prepandemic levels until late 2021, when a sharp increase occurred toward the start of a wave of COVID-19 infections caused by the omicron variant (Figure 2A). Similar trends were observed for all race/ethnicity groups and by all levels of neighborhood poverty and nativity (data not shown). ED visits for ambulatory care–sensitive conditions also declined substantially from a monthly low of 55 343 during August 2019 to a low of 25 461 during May 2020 (Figure 2B). Notably, severity of ED visits for ambulatory care–sensitive conditions, as measured by the percentage of visits that resulted in admission to the hospital, increased during the peak of the first wave (Figure 2C; April 2020 first wave, 34%; February 2021 second wave, 28%; February 2022 omicron wave, 23%).
In late March 2020, 54.1% of NYC residents reported probable anxiety (Figure 3) and 36.8% reported probable depression. In November 2021, 25.2% of NYC residents reported probable anxiety and 18.5% reported probable depression.
FIGURE 3: Percentage of Health Opinion Poll Respondents Who Reported Possible Anxiety or Depression, NYC Health Opinion Polla Abbreviation: NYC, New York City.aThe evaluation of change over time should be interpreted with caution. Prior to HOP March 2021, HOP surveys were implemented using non-probability online panels, while surveys from March 2021 onward were completed by members of a probability-based panel. Therefore, any measured change might reflect a change in methodology, rather than a true change over time.
Finally, the percentage of births with babies born less than 2500 g increased for mothers who identified as Asian/Pacific Islander (API) and those who identified as Black (Figure 4), widening a preexisting disparity in birth outcomes. In addition, the percentage of births with babies born less than 2500 g increased for mothers born outside of the United States (data not shown).
FIGURE 4: Percentage of Births With Birth Weight Under 2500 g by Race, NYC DOHMH Bureau of Vital Statisticsa Abbreviations: DOHMH, Department of Health and Mental Hygiene; NYC, New York City.a2021 and 2022 data are provisional.
Discussion
We established a surveillance system to monitor the collateral impacts of the COVID-19 pandemic in NYC. Initial findings include major disruptions in emergency and routine care associated with the COVID-19 pandemic in NYC. These findings align with literature from other localities and nationally that have shown a decline in care seeking for various conditions and services.19,20 These data also suggest that forgone care among people with serious conditions may have contributed to the excess deaths not directly due to COVID-19 that were observed during the early months of the pandemic as people postponed or avoided seeking essential care due to fears of COVID-19 infection.21,22 This highlights the possible long-term impacts of delaying primary prevention and treatment, both realized and to come.23–25 Public health institutions face the cumulative challenge of having to catch up for lost ground by returning to prepandemic indicator levels, as well as making up for what was lost in the interim when non–COVID-19-related public health and health care services were drastically diminished.26 Having established surveillance to help guide these efforts is essential.
The observation that preterm birth rates increased among API and Black females since the outset of the pandemic warrants further research and preventive programs.27,28 A previous study demonstrated an increase in preterm births in NYC as a result of sociopolitical stressors following the 2016 presidential election.29 The increase of hate crimes against Asian Americans during the pandemic and national Black Lives Matter protests against racism and police violence likely elevated stress levels among API and Black females in NYC, including among pregnant females. This stress might have resulted in an increase of preterm or low-birth-weight births. Preterm birth rates for Black females already were twice the rates for White females before the pandemic, and these data suggest the impact of the pandemic on widening preexisting inequities. These findings contributed to DOHMH's launch of a new family home visiting program, emphasizing elements related to prenatal care and services (https://www1.nyc.gov/site/doh/about/press/pr2021/home-visiting-services-for-new-parents.page).
In addition to the direct health impacts, there have been myriad economic consequences of the pandemic, including a massive, inequitable increase in food insecurity, demonstrated in NYC Health Opinion Polls (HOPs)30 (data not shown) and other studies.31 The number of SNAP recipients in NYC (see Supplemental Digital Content Figure E, available at https://links.lww.com/JPHMP/B132) increased steeply immediately following stay-at-home order implementation in NYC. In March 2020, there were 1 483 230 recipients, which increased to a peak of 1 734 160 in mid-2021, also reflecting, at least in part, the considerable impact of pandemic-related policies mitigating economic and food insecurity. For example, temporary changes to federal SNAP requirements granted states more flexibility to respond to the surge in food insecurity through program modifications including waived interview requirements and extended certification periods.32 Surveillance data helped support DOHMH community health workers who rallied to address food insecurity needs, creating new city food security programs such as GetFoodNYC and continuing to support existing initiatives such as Harlem Health Advocacy Partners (HHAP).30
Having an established surveillance framework to monitor the collateral impacts of the COVID-19 pandemic is a necessary tool for prospective monitoring of trends. Like the system from which it is derived, the current system was designed to minimize additional data collection or labor and maintain surveillance with minimal staff inputs. Given the extensive diversion of staff to support the COVID-19 emergency response and limited available time for this work, such a design was essential. Although the technical programming and coordination necessary to establish automation were a full-time endeavor for approximately 6 months, after the initial investment, the system requires only a few hours to maintain each month. Although we initially pivoted to focus on COVID-19 collateral impacts using the indicators already under surveillance, as the pandemic moved into its second year, we added indicators (eg, cash assistance recipients, homelessness) to better align with emerging challenges and as new data sources became available (eg, NYC HOP data on anxiety and depression). We also dropped indicators for various reasons including the new surveillance focus (eg, long-acting reversible contraception), program priorities and resources to provide data (eg, childhood immunizations), cost (eg, data from federally qualified health centers), and timeliness (eg, premature mortality).
We updated the frequency of reporting from quarterly to monthly, given the need for more regular data updates during the Health Department's ongoing, multiyear emergency response to the COVID-19 pandemic. Finally, race/ethnicity was added as a primary stratification variable (in addition to nativity and poverty, which had been the foci of the system as initially designed) to describe the profoundly inequitable impacts of the pandemic on people of color in NYC. This further highlighted areas of inequity among people of color and contributed to shaping the Board of Health (BOH) resolution on racism as a public health crisis (https://www1.nyc.gov/assets/doh/downloads/pdf/boh/racism-public-health-crisis-resolution.pdf) and supported policy advocacy around safety-net health systems to address inequitable access to care for economically disadvantaged communities and communities of color.33
This surveillance system has informed and shaped DOHMH's public health communication, practice, and policy during the COVID-19 pandemic by serving as a key mechanism to monitor progress and adjust our work in alignment with the agency's strategic priorities. For example, data are presented periodically to the agency's leadership and additional analyses are performed as required related to unexpected findings such as trend deviations (eg, the change in low-birth-weight trends for API and Black birthing parents described earlier).
This work informed our public messaging, inspiring Commissioner-led Public Service Announcements encouraging holistic approaches to health (https://www.youtube.com/watch?v=j3iYw97wkuE&t=1s) and the launch of a public health campaign raising awareness of loneliness in response to concerning data on mental health outcomes. Relatedly, data on the growing prevalence of mental and behavioral health concerns supported the need for increased attention to ensuring sufficient resources for the scale-up of NYC Well and the opening of overdose prevention centers.34,35
Strengths of the system and framework include flexibility and use of existing data sources with validated and reliable indicators. Similarly, the use of some data sources, such as syndromic surveillance of all ED visits, to monitor chronic diseases and conditions is a relatively novel way to obtain information on acuity that cannot be obtained from existing systems at this time. In addition, this system facilitates production of snapshots across multiple disease and health topics; because NYC has such a large health department, this central coordination is needed to bring together data collected and analyzed disparately in a way that allows for observation of trends in a more centralized and timely manner. The dashboard facilitates data accessibility presenting compiled data and analysis results, computed through a standardized approach, that might otherwise go uncirculated.
Despite the many strengths of the surveillance system, we recognize its limitations and the areas we are unable to monitor at this time (eg, telehealth visits, condition-specific mortality rates) due to the time it would take to extract the data, permissions or data ownership considerations, or the absence of the desired data in a format that would be representative of the NYC population. Changes to HOP survey methodology in March 2021 necessitate caution when inferring changes over time. We are also in the process of refining our trend analysis to better capture decreasing trend aberrations in addition to the increasing trend detection currently performed. A final major limitation, inherent in surveillance, is the inability of the system to fully attribute impacts observed to the pandemic or disentangle exactly which policies/aspects of the pandemic response enacted at the local, state, or federal levels were most influential on the observed trends. It adds value as an early warning system providing a basis for further inquiry but was not designed as a decision-making tool; rather, it serves as one of many sources to ensure decisions are based on high-quality public health data.
As the COVID-19 pandemic continues into its third year, public health priorities have begun returning to non–COVID-19-related diseases and conditions. These data from NYC highlight the work that is needed to return to even a suboptimal baseline in other aspects of health and well-being. Local surveillance efforts are essential to effectively allocate resources, develop interventions, and guide public communications when needed. Our framework is a valuable starting place for identifying where policies and programs are needed and where future resources can be best allocated.
Implications for Policy & Practice
- The COVID-19 pandemic has had extensive, deleterious collateral impacts on virtually every aspect of public health. As the COVID-19 emergency response transitions to endemic disease control, allowing more capacity for non–COVID-19-related priorities, public health agencies must address the concerning retrogression in many health outcomes along with the loss of expected progress made in the past 3 years of the pandemic. The NYC DOHMH adapted an existing surveillance system to monitor collateral health outcomes of the COVID-19 pandemic in NYC across 6 domains: access to care, chronic disease, sexual/reproductive health, food/economic insecurity, mental/behavioral health, and environmental health.
- Local health departments are uniquely positioned to benefit from such surveillance systems, given their timely access to data and their capacity to set priorities and shape policies in their communities. Our surveillance system's wide scope, compiled into a user-friendly, interactive online dashboard encourages a comprehensive review of trends and patterns that might otherwise go unexamined. Our surveillance system framework offers a flexible and practical way to ensure policies and programs are data informed.
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