Early Effects of the New York City Paid Safe and Sick Leave Law.

CONTEXT: Paid sick leave (PSL) is a public health strategy associated with benefits for workers, businesses, and consumers. In the absence of a federal law, in 2014, New York City (NYC) joined other state and municipal governments with local PSL policies. OBJECTIVES: To examine changes in PSL after the implementation of NYC's 2014 Paid Safe and Sick Leave Law and to assess which communities remain less likely to use PSL. DESIGN: This study uses data from multiple panels of the NYC Longitudinal Survey of Wellbeing (NYC-LSW)-a population-representative study of NYC adults-to track changes in PSL, using data collected before and after NYC's Paid Safe and Sick Leave Law was implemented. We use weighted cross-tabulations and multinomial logistic regression models to assess changes in payment for sick leave since the implementation of the law. SETTING AND PARTICIPANTS: The study includes 2985 NYC adults aged 18 to 64 years who reported working for pay in the year preceding the survey where PSL questions were asked (2014-2019). MAIN OUTCOME MEASURES: Use of sick leave and payment for sick leave. RESULTS: Weighted descriptive results show a 7-percentage-point increase ( P = .02) in the rate of being paid for all sick days and a 6-percentage-point decrease ( P = .02) in not being paid for any sick days. Results from multinomial logistic regression models, adjusting for potential confounders, show that after implementation of the law, workers with low levels of education, who are younger, Latino, and foreign-born remain less likely than their peers to use PSL. CONCLUSIONS: We demonstrate that the PSL mandate expanded access for employees but not evenly across groups. These results offer guidance to other jurisdictions implementing PSL policies, suggesting the need for targeted education and enforcement efforts to ensure policies reach sectors where low-wage workers are most prevalent.

Collateral Impacts of the COVID-19 Pandemic: The New York City Experience.

OBJECTIVE: To adapt an existing surveillance system to monitor the collateral impacts of the COVID-19 pandemic on health outcomes in New York City across 6 domains: access to care, chronic disease, sexual/reproductive health, food/economic insecurity, mental/behavioral health, and environmental health. DESIGN: Epidemiologic assessment. Public health surveillance system. SETTING: New York City. PARTICIPANTS: New York City residents. MAIN OUTCOME MEASURES: We monitored approximately 30 indicators, compiling data from 2006 to 2022. Sources of data include clinic visits, surveillance surveys, vital statistics, emergency department visits, lead and diabetes registries, Medicaid claims, and public benefit enrollment. RESULTS: We observed disruptions across most indicators including more than 50% decrease in emergency department usage early in the pandemic, which rebounded to prepandemic levels by late 2021, changes in reporting levels of probable anxiety and depression, and worsening birth outcomes for mothers who identified as Asian/Pacific Islander or Black. Data are processed in SAS and analyzed using the R Surveillance package to detect possible inflections. Data are updated monthly to an internal Tableau Dashboard and shared with agency leadership. CONCLUSIONS: As the COVID-19 pandemic continues into its third year, public health priorities are returning to addressing non-COVID-19-related diseases and conditions, their collateral impacts, and postpandemic recovery needs. Substantial work is needed to return even to a suboptimal baseline across multiple health topic areas. Our surveillance framework offers a valuable starting place to effectively allocate resources, develop interventions, and issue public communications.

Assessing targeted invitation and response modes to improve survey participation in a diverse New York City panel: Healthy NYC.

BACKGROUND: Healthy NYC is an innovative survey panel created by the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) that offers a cost-effective mechanism for collecting priority and timely health information. Between November 2020 and June 2021, invitations for six different surveys were sent to Healthy NYC panelists by postal mail, email, and text messages. Panelists had the option to complete surveys online or via paper survey. METHODS: We analyzed whether panelists varied by sociodemographic characteristics based on the contact mode they provided and the type of invitation that led to their response using logistic regression models. Poisson regression models were used to determine whether the number of invitations received before participating in a survey was associated with sociodemographic characteristics. RESULTS: Younger age and higher education were positively associated with providing an email or text contact. Furthermore, age, race, and income were significant predictors for invitation modes that led to a survey response. Black panelists had 72% greater odds (OR 1.72 95% CI: 1.11-2.68) of responding to a mail invite and 33% lesser odds (OR 0.67, 95% CI: 0.54-0.83) of responding to an email invite compared with White panelists. Additionally, in five of the six surveys, more than half of the respondents completed surveys after two invites. Email invitations garnered the highest participation rates. CONCLUSIONS: We recommend using targeted invitation modes as an additional strategy to improve participation in panels. For lower-income panelists who do not provide an email address, it may be reasonable to offer additional response options that do not require internet access. Our study's findings provide insight into how panels can tailor outreach to panelists, especially among underrepresented groups, in the most economical and efficient ways.

Prevalence of SARS-CoV-2 Antibodies in New York City Adults, June-October 2020: A Population-Based Survey.

BACKGROUND: Serosurveys help to ascertain burden of infection. Prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serosurveys in New York City (NYC) used nonrandom samples. During June-October 2020, the NYC Health Department conducted a population-based survey estimating SARS-CoV-2 antibody prevalence in NYC adults. METHODS: Participants were recruited from the NYC 2020 Community Health Survey. We estimated citywide and stratified antibody prevalence using a hybrid design: serum tested with the DiaSorin LIAISON SARS-CoV-2 S1/S2 IgG assay and self-reported antibody test results were used together. We estimated univariate frequencies and 95% confidence intervals (CI), accounting for complex survey design. Two-sided P values ≤ .05 were statistically significant. RESULTS: There were 1074 respondents; 497 provided blood and 577 provided only a self-reported antibody test result. Weighted prevalence was 24.3% overall (95% CI, 20.7%-28.3%). Latino (30.7%; 95% CI, 24.1%-38.2%; P < .01) and black (30.7%; 95% CI, 21.9%-41.2%; P = .02) respondents had a higher weighted prevalence compared with white respondents (17.4%; 95% CI, 12.5%-23.7%). CONCLUSIONS: By October 2020, nearly 1 in 3 black and 1 in 3 Latino NYC adults had SARS-CoV-2 antibodies, highlighting unequal impacts of the coronavirus disease 2019 (COVID-19) pandemic on black and Latino NYC adults.

Population-Based Estimates of Coronavirus Disease 2019 (COVID-19)-like Illness, COVID-19 Illness, and Rates of Case Ascertainment, Hospitalizations, and Deaths-Noninstitutionalized New York City Residents, March-April 2020.

Using a population-based, representative telephone survey, ~930 000 New York City residents had COVID-19 illness beginning 20 March-30 April 2020, a period with limited testing. For every 1000 persons estimated with COVID-19 illness, 141.8 were tested and reported as cases, 36.8 were hospitalized, and 12.8 died, varying by demographic characteristics.

A comparison of the four healthy days measures (HRQOL-4) with a single measure of self-rated general health in a population-based health survey in New York City.

BACKGROUND: Data on health-related quality of life (HRQOL) can be used to track health disparities, assess the impact of chronic diseases, and predict mortality. The Centers for Disease Control and Prevention's "Healthy Days Measures" (HRQOL-4) assesses four key domains: self-rated general health, physical health, mental health, and activity limitations. The domains are not easily combined to summarize overall HRQOL, and some evidence suggests that self-rated general health may be an adequate proxy indicator for overall HRQOL. This study compares self-rated general health as a solitary measure of HRQOL with two summary indices of the HRQOL-4 as a predictor of adverse health conditions in a representative sample of adult New York City residents. METHODS: The 2017 NYC Social Determinants of Health survey implemented by the New York City Department of Health and Mental Hygiene collected data from a representative sample of New Yorkers (n = 2335) via phone, mail, and web. We compared the information criteria and predictive power of self-rated general health with two alternative summary indices of the HRQOL-4 in predicting self-reported health conditions (hypertension, diabetes, obesity, non-specific psychological distress, and a summary indicator for at least one those four morbidities). RESULTS: Overall, 19.1% (95% CI: 16.9, 21.5) of respondents reported that they had fair or poor general health. Self-rated general health was significantly associated with days of poor physical health, poor mental health, and activity limitations (p < 0.001 for each). While the Akaike and Bayesian information criteria suggested that the summary indices of the HRQOL-4 produced marginally better models for predicting adverse health conditions, self-rated general health had slightly higher predictive power than did the summary indices in all models of physical health outcomes as measured by Tjur's pseudo-R2 and the area under the curve. CONCLUSION: We found very small differences between self-rated general health and the summary indices of the HRQOL-4 in predicting health conditions, suggesting self-rated general health is an appropriate proxy measure of overall HRQOL. Because it can be measured with a single question rather than four, it might be the most simple, efficient, and cost-effective method of summarizing HRQOL in large population-based surveys.

Association between racial discrimination and health-related quality of life and the impact of social relationships.

PURPOSE: Interpersonal racial discrimination is associated with poor health. Social relationships may moderate the impact of discrimination and represent modifiable behaviors that can be targeted by public health interventions. We described citywide associations between self-reported racial discrimination and health-related quality of life among the overall New York City (NYC) adult residential population and by four main race/ethnicity groups and explored whether social relationships moderated health effects of discrimination. METHODS: We analyzed cross-sectional survey data from 2335 adults weighted to be representative of the NYC population. We measured exposures to lifetime interpersonal racial discrimination in nine domains using a modified version of the Experiences of Discrimination scale. We performed unadjusted and adjusted regression analyses on four self-rated health-related quality of life outcomes including general health, physical health, mental health, and limitations from physical or mental health. RESULTS: Overall, 47% [95% CI 44.5, 50.3] of respondents reported having experienced racial discrimination in at least one domain. In the overall population, significant associations with racial discrimination were noted in adjusted models for poor physical health, poor mental health, and limitations by poor physical and mental health. Among those exposed to racial discrimination, the risk of experiencing poor mental health was lower among those who had contact with family or friends outside their household at least once a week, compared with those who had less frequent social contact. CONCLUSION: This study provides evidence that social relationships may moderate the impact of racial discrimination on mental health and should be integrated into health promotion efforts.

Identifying New York City Neighborhoods at Risk of Being Overlooked for Interventions.

Public health agencies are often faced with difficult decisions about where and how to allocate funding and resources. This question of resource allocation is central to public health policy; however, decisions related to resource allocation are sometimes made via informal or subjective approaches. We walk readers through a process of identifying needs across different neighborhoods in New York City (NYC) by examining community district-level health outcomes using data from published Community Health Profile reports released by the NYC Department of Health and Mental Hygiene (DOHMH) in 2015. In NYC, community districts are represented by community boards that provide a forum for addressing the needs of the community, making them a useful geographic unit for examining health information and turning data into action. We examined prevalence estimates and 95% confidence intervals of health indicators in each community district to identify significant disparities and calculated relative disparities in rates or prevalence to understand the relative magnitude of each disparity. Lastly, we demonstrate an application of this approach by identifying a cluster of neighborhoods with a high chance of being overlooked for public health interventions by conventional methods because of the relative number of disparities that exist in these neighborhoods. We present information on the disparity profile (number of disparities and relative disparity) for each neighborhood within the cluster and discuss potential public health implications. This approach can be applied to other jurisdictions to inform public health planning and resource allocation.

ZIP Code-Level Estimates from a Local Health Survey: Added Value and Limitations.

We assessed the added value and limitations of generating directly estimated ZIP Code-level estimates by aggregating 5 years of data from an annual cross-sectional survey, the New York City Community Health Survey (n = 44,886) from 2009 to 2013, that were designed to provide reliable estimates only of larger geographies. Survey weights generated directly-observed ZIP Code (n = 128) level estimates. We assessed the heterogeneity of ZIP Code-level estimates within coarser United Hospital Fund (UHF) neighborhood areas (n = 34) by using the Rao-Scott Chi-Square test and one-way ANOVA. Orthogonal linear contrasts assessed whether there were linear trends at the UHF level from 2009 to 2013. 22 of 37 health indicators were reliable in over 50% of ZIP Codes. 14 of the 22 variables showed heterogeneity in ≥4 UHFs. Variables for drinking, nutrition, and HIV testing showed heterogeneity in the most UHFs (9-24 UHFs). In half of the 32 UHFs, >20% variables had within-UHF heterogeneity. Flu vaccination and sugary beverage consumption showed significant time trends in the largest number of UHFs (12 or more UHFs). Overall, heterogeneity of ZIP Code-level estimates suggests that there is value in aggregating 5 years of data to make direct small area estimates.

Impact of a New York City supportive housing program on Medicaid expenditure patterns among people with serious mental illness and chronic homelessness.

BACKGROUND: A rapid increase of Medicaid expenditures has been a serious concern, and housing stability has been discussed as a means to reduce Medicaid costs. A program evaluation of a New York City supportive housing program has assessed the association between supportive housing tenancy and Medicaid savings among New York City housing program applicants with serious mental illness and chronic homelessness or dual diagnoses of mental illness and substance use disorder, stratified by distinctive Medicaid expenditure patterns. METHODS: The evaluation used matched data from administrative records for 2827 people. Sequence analysis identified 6 Medicaid expenditure patterns during 2 years prior to baseline among people placed in the program (n = 737) and people eligible but not placed (n = 2090), including very low Medicaid coverage, increasing Medicaid expenditure, low, middle, high, and very high Medicaid expenditure patterns. We assessed the impact of the program on Medicaid costs for 2 years post-baseline via propensity score matching and bootstrapping. RESULTS: The housing program was associated with Medicaid savings during 2 years post-baseline (-$9526, 95% CI = -$19,038 to -$2003). Stratified by Medicaid expenditure patterns, Medicaid savings were found among those with very low Medicaid coverage (-$15,694, 95% CI = -$35,926 to -$7983), increasing Medicaid expenditures (-$9020, 95% CI = -$26,753 to -$1705), and high Medicaid expenditure patterns (-$14,450, 95% CI = -$38,232 to -$4454). Savings were largely driven by shorter psychiatric hospitalizations in the post-baseline period among those placed. CONCLUSIONS: The supportive housing program was associated with Medicaid savings, particularly for individuals with very low Medicaid coverage, increasing Medicaid expenditures, and high Medicaid expenditures pre-baseline.

Workers not Paid for Sick Leave after Implementation of the New York City Paid Sick Leave Law.

This study examined factors associated with being paid for sick leave after implementation of the New York City (NYC) paid sick leave law. A random sample of NYC residents was surveyed by telephone multiple times over a 2-year period. Participants (n = 1195) reported socio-demographics, awareness of the law, income, work hours per week, and payment for sick time off work. In the year after implementation of the law, part-time workers were significantly more likely to attend work while sick than full-time workers (relative risk = 1.25, 95% CI = 1.1, 1.4). Seventy percent of workers who missed work due to illness (n = 249) were paid for sick leave. Part-time workers, respondents not aware of the benefit (30% of workers), and workers without a college degree were the least likely to be paid for sick days. More than one third (37%) of persons not paid for sick leave worked in retail, food service, or health care. Although 70% of respondents were paid for sick leave after implementation of the law, part-time workers and workers with low education were least likely to access the benefit and more likely to work while sick. The disparity in paid sick leave may have public health consequences as many persons not paid for sick leave had occupations that carry a high risk of disease transmission to others.

Public Health and Vulnerable Populations: Morbidity and Mortality Among People Ever Incarcerated in New York City Jails, 2001 to 2005.

The health of people ever incarcerated in New York City (NYC) jails during 2001 to 2005 was characterized by matching jail, shelter, mortality, sexually transmitted infection, HIV, and tuberculosis (TB) data from the NYC Departments of Health and Mental Hygiene, Correction, and Homeless Services. Compared with nonincarcerated people and those living in the lowest income NYC neighborhoods, those ever incarcerated had higher HIV prevalence and HIV case rates. Ever-incarcerated females also had higher rates of gonorrhea and syphilis than nonincarcerated females. Ever-incarcerated people who used the single adult homeless shelter system had higher HIV, gonorrhea, and TB case rates and all-cause mortality rates than ever-incarcerated people without shelter use, when adjusting for other variables. People ever incarcerated in NYC jails are at risk for conditions of public health importance. Sex-specific jail- and community-based interventions are needed.

Temporal and Spatial Patterns in Utilization of Mental Health Services During and After Hurricane Sandy: Emergency Department and Inpatient Hospitalizations in New York City.

OBJECTIVE: Hurricane Sandy made landfall on October 29, 2012, causing a coastal storm surge and extensive flooding, which led to the closure of several health care facilities in New York City (NYC) and prolonged interruptions in service delivery. The impact on mental health-related emergency department (ED) and inpatient hospital service utilization was studied. METHODS: Data came from the New York Statewide Planning and Research Cooperative System. We obtained mental health-related data among NYC residents from 2010 to 2013. Patients were grouped into 5 geographic areas, including service areas of closed hospitals, the Hurricane Sandy evaluation zone, and all of NYC. The Farrington method was used to detect increases in ED visits and hospitalizations for the post-Sandy period. RESULTS: Open hospitals experienced a substantial increase in psychiatric ED visits from patients living in the service areas of closed hospitals. This surge in psychiatric ED visits persisted for 4 to 6 months after Hurricane Sandy. However, the increase in psychiatric hospitalizations was observed for 1 to 3 months. CONCLUSIONS: Several NYC hospitals received a substantially larger number of ED patients from service areas of closed hospitals after Hurricane Sandy, unlike other hospitals that experienced a decrease. Because of potential surges in the number of psychiatric ED visits, resource allocation to hospitals should be considered. (Disaster Med Public Health Preparedness. 2016;10:512-517).

Bias due to sample selection in propensity score matching for a supportive housing program evaluation in New York City.

OBJECTIVES: Little is known about influences of sample selection on estimation in propensity score matching. The purpose of the study was to assess potential selection bias using one-to-one greedy matching versus optimal full matching as part of an evaluation of supportive housing in New York City (NYC). STUDY DESIGN AND SETTINGS: Data came from administrative data for 2 groups of applicants who were eligible for an NYC supportive housing program in 2007-09, including chronically homeless adults with a substance use disorder and young adults aging out of foster care. We evaluated the 2 matching methods in their ability to balance covariates and represent the original population, and in how those methods affected outcomes related to Medicaid expenditures. RESULTS: In the population with a substance use disorder, only optimal full matching performed well in balancing covariates, whereas both methods created representative populations. In the young adult population, both methods balanced covariates effectively, but only optimal full matching created representative populations. In the young adult population, the impact of the program on Medicaid expenditures was attenuated when one-to-one greedy matching was used, compared with optimal full matching. CONCLUSION: Given covariate balancing with both methods, attenuated program impacts in the young adult population indicated that one-to-one greedy matching introduced selection bias.