Planalp, Colin
Research Fellow

p 612-624-4850
e cplanalp@umn.edu

Minnesota’s COVID-19 vaccine campaign left vulnerable groups with lagging rates

Health inequities are nothing new in Minnesota, but the pandemic placed them in a new light. Numerous studies have reported disparities in how COVID-19 affects many vulnerable groups, often placing them at higher risk of infection, hospitalization, and death. And the inequitable effects of the disease itself are not the only cause for alarm. Once COVID-19 vaccines received authorization, disparities in vaccination rates also became a concern, especially as surveys indicated widespread hesitancy and lagging uptake.

Partnering with the Minnesota Electronic Health Record Consortium, SHADAC delved deep into an analysis of COVID-19 vaccination rates in Minnesota. Unlike many other studies, we were able to examine not only how COVID-19 vaccination rates differed across different demographic groups, but also how those disparities developed over time. This type of analysis was possible because of the Consortium’s unique dataset, which matches data from large health care providers’ electronic health records—including detailed demographics with immunization records from the Minnesota Department of Health, ultimately covering almost all people who received a COVID-19 vaccine in the state.

Understanding the dynamics of vaccination disparities over time was a crucial element of our study. While we present data on COVID-19 vaccination rates for different demographic subpopulations at the end of 2022, we also examined the time it took to reach 50 percent of people in different demographic groups with COVID-19 vaccinations. That measure of time to vaccination is important because, in a public health crisis during which hundreds or even thousands of people were dying each day at its peak in the U.S. alone, the speed with which people were vaccinated was critical to saving lives.

Our approach also allowed us to glean unique insights on COVID-19 vaccination disparities that are hidden below the headline numbers. For instance, although Minnesota’s Black, Latino, and White populations each had similar COVID-19 vaccination rates (61 percent, 61 percent, and 65 percent, respectively) at the end of 2022, they took distinctly different paths to reach that point. Within six months of the first COVID-19 vaccine receiving emergency use authorization, Minnesota vaccinated half of the state’s White population, but it took twice as long (12 months) for the state to vaccinate half of its Black and Latino populations.

Another crucial but under-recognized factor in COVID-19 vaccination disparities is the way public health policies placed some groups as a disadvantage in accessing vaccines in the early days of limited supply. Because experience showed that elderly people were at higher risk of severe disease and death from COVID-19, they were prioritized to be among the first people eligible for vaccines. But nationwide and in Minnesota, the White population skews older, effectively baking racial and ethnic disparities into the vaccine prioritization criteria. For that reason, we also stratified racial and ethnic groups’ COVID vaccination data by age—an approach that uncovered complex dynamics.

We found that disparities in COVID-19 vaccination rates were relatively small among Minnesota’s elderly population. For instance, Minnesota succeeded in vaccinating half of the elderly population across all racial and ethnic groups within three or four months—an example of how health inequities can be minimized. But disparities were stark among young adults, age 19-24. While Minnesota vaccinated half of Asian and Native Hawaiian and Pacific Islander young adults within five months, it took roughly twice as long to vaccinate half of Latino young adults (nine months), White young adults (ten months), and Black young adults (eleven months). And stunningly, Minnesota had failed to reach half of American Indian and Alaska Native young adults by the end of 2022—approximately 24 months after vaccines were first authorized.

We also examined COVID-19 vaccination rates by other demographic categories, finding higher vaccination rates and quicker vaccination progress among older population groups, urban and suburban communities, and females. Additionally, our study found disappointing rates of COVID-19 vaccination among children, particularly younger children. At the end of 2022, less than one-half of children age 5-11 had been vaccinated, despite having been eligible for vaccines for over a year; and less than one-tenth of children age six months to four years had been vaccinated, despite having been eligible for roughly six months. Despite a common, incorrect notion that COVID-19 is harmless for children, other researchers have found that COVID-19 was a top 10 cause of death for children during the pandemic, so those low vaccination rates are needlessly leaving Minnesota kids at risk.

Together, the findings from this new study highlight two main points: First, Minnesota’s COVID-19 vaccination efforts resulted in clear disparities. When looking at high-level data, it is easy to miss those disparities because some of them narrowed over time. But looking at detailed data illustrates the ways that certain groups were left vulnerable to COVID-19 for much longer than others. Second, our findings on disparities in the time Minnesota took to vaccinate half of different subpopulations demonstrate the importance of monitoring such health equity measures over time. Time is critical in an emergency such as the pandemic, and eventually closing gaps in health disparities simply isn’t good enough. Health equity requires urgency.

Access the full issue brief on this analysis here.

Hest, Emily
Communications Associate

e hestx008@umn.edu

Now Available on State Health Compare: One Brand New Measure and Five Updated Measures

Estimates for five measures of health care access, affordability, and use have now been updated on SHADAC’s State Health Compare. One new measure, Had Telehealth Visit, has also been added to State Health Compare. The new and updated measures are all produced using data from the National Health Interview Survey (NHIS), conducted by the National Center for Health Statistics (NCHS). SHADAC produces these state-level measures using restricted-access data through the Minnesota Research Data Center (MnRDC). SHADAC’s State Health Compare is the only source of state-specific data for these measures which are essential for monitoring individuals’ access to and use of medical care, along with their ability to afford care.

These measures now contain data through 2021, using two-year pooled periods (i.e., 2019-2020, 2020-2021). The measures can be broken down by Total, Age, and Coverage Type (Public, Private, Uninsured).

Updated and new measures include:

NEW: Had Telehealth Visit
This measure describes the percent of individuals who had a medical appointment by video or phone during the past twelve months.

Had Usual Source of Medical Care
This measure captures rates of individuals who had a usual place of medical care other than an emergency department during the past twelve months.

Had General Doctor or Provider Visit
This measure provides rates of individuals who had any visit to a general doctor or provider within the last year.

Had Emergency Department Visit
This measure looks at rates of individuals who had any type of visit to an emergency department in the past twelve months.

​Trouble Paying Medical Bills
This measure examines rates of individuals who had trouble paying off medical bills during past twelve months.

Made Changes to Medical Drugs
This measure highlights rates of individuals who were prescribed medication in the past twelve months who made changes to their medical drugs due to cost during the past twelve months. This includes delaying filling a prescription, taking less medicine, or skipping doses to save money.

Click here to explore these measures on State Health Compare!
 

Notes: Data for State Health Compare’s Had Dental Visit measure is only asked in the NHIS rotating core and thus was not asked in the 2021 survey. We will be able to update that measure with new data after both the 2022 and 2023 data are out in mid-2024.

All measures are representative of the civilian noninstitutionalized population.

Data Source: The estimates were produced using restricted NHIS data in the MnRDC. Measures have been updated with data through 2021 using two-year pooled periods, including (a) 2011–2012 through 2017–2018 and (b) 2019-2020 through 2020-2021, except in the case of the telehealth measure for which data is only available from 2020-2021.

Benedict, Maya
Research Assistant

e bened099@umn.edu

Monitoring Broadband Expansion and Disparities

Introduction

The onset of the COVID-19 pandemic underscored the importance of telehealth services to people across the U.S. and the crucial role of broadband internet access in providing those services. Telehealth continues to be a popular and often necessary way for patients to access care, and federal and state governments are now making significant investments to expand and ensure affordable access to broadband internet.¹

For these reasons, it is important to monitor changes in the share of the population with broadband internet as an indicator of access to care. SHADAC analysis of 2021 American Community Survey (ACS) data indicates households’ access to broadband internet has increased by 3.8 percentage points (PP) since 2019, rising to 90.1% (from 86.3%).

State Broadband Efforts

The percentage of households with broadband access varies across the states, ranging from a low of 81.8% in Mississippi to a high of 93.4% in Washington. Though every state experienced some increase in household broadband access, a few states showed larger expansions in access.

Out of all states, Arkansas saw the largest increase at 5.4PP, rising to 85.7% from 80.3%. Former Arkansas Governor Asa Hutchinson and the Arkansas General Assembly had made broadband a “top priority,” working to expand the state Broadband Office by increasing staffing and enhancing services.^2, 3 The state also developed the Arkansas Rural Connect (ARC) grant program to expand broadband access in rural communities, and they recently announced a first-in-the-nation partnership with national non-profit EducationSuperHighway to develop best practices and programs to address broadband affordability.⁴

Rates of broadband access increased by similar amounts in South Carolina, rising to 87.8% from 82.4%. South Carolina has engaged in significant broadband expansion initiatives over the past few years, approving nearly $30 million in broadband expansion projects in early 2021 and spending nearly $50 million in CARES Act funding on broadband-related projects in 2020.⁵

Despite gains in access, disparities by income level remain

Despite overall growth in broadband internet access, there are still sizable disparities in access between households of different income levels.

For example, though 91.1% of Minnesota households have broadband access (higher than the national average), only 73.1% of low-income households in Minnesota (under $25,000 per year) have broadband internet (below the national average of 74.7%).

In general, households with an income under $25,000 per year have the lowest percentage of broadband access, and households with an income over $50,000 per year have the highest percentage, though that difference varies by state. South Dakota shows the largest gap at 26.2PP: only 67.8% of low-income households have broadband access, compared to 94% of high-income households. Mississippi has a sizable disparity between income levels as well, with a 25.4PP difference between high- and low-income households.

Delaware and Oregon see the smallest disparities between income levels, showing a gap of only 15.8PP and 15.9PP between high- and low-income households, respectively. Those two states have some of the highest percentages of broadband access for low-income households as well, with 79.7% of low-income households in Delaware reporting broadband access and 79.5% in Oregon.

Considering these disparities, some states are taking steps to address broadband affordability;

The 2022 Virginia Telecommunication Initiative guidelines include grant scoring criteria that encourage applicants to be aligned with the state’s efforts to bring low-income households affordable access to broadband internet.⁶ 

The Minnesota Office of Broadband Development recently began the process of establishing a statewide digital equity plan which would focus on addressing internet service affordability and reducing gaps in device access, and digital skills.⁷ 

In 2021, California passed a historic law directing $6 billion toward improving broadband access and affordability, with multiple provisions intended to improve internet speed, increase access, and lower internet costs for consumers.^8, 9

Conclusion

On both national and state levels, access to broadband internet improved from 2019 to 2021. States have begun several promising broadband expansion initiatives and are using available federal and state grant funding to bolster broadband infrastructure and affordability. However, as states continue working toward greater broadband access and navigating an influx of funding for expansion projects, it is necessary to ensure those improvements are specifically targeted to address existing disparities in access.

About the Data
The data cited here can be accessed through SHADAC’s online data tool, State Health Compare, using the measure “Percent of households with a broadband internet subscription” for the years 2019-2021. The estimates come from SHADAC’s analysis of the American Community Survey (ACS) Public Use Microdata Sample (PUMS). All differences described are statistically significant at the 95% confidence level unless otherwise specified.

SHADAC Staff

p 612.624.4802
e shadac@umn.edu

Examining Discrimination and Health Care Access by Sexual Orientation in Minnesota

Authors: Natalie Mac Arthur, Jeremy Duval, Kathleen Call

Introduction

Discrimination based on sexual orientation and gender identity (SOGI) from health care providers is a barrier to creating an equitable health care system. Nearly one in five lesbian, gay, bisexual, transgender, and queer (LBGTQ) adults reports avoiding health care due to anticipated discrimination (Casey et al., 2019). Compared with straight adults, lesbian/gay and bisexual adults are more likely to forgo or delay health care (Jackson et al., 2016, Nguyen et al., 2018). However, less is known about the association between reports of SOGI-based discrimination from health care providers and health care access.

We included three sexual orientation categories in this study: straight, lesbian/gay, and bisexual/pansexual. Survey respondents also had the option to select “none of these” and write in their own response. Due to sample size limitations, we excluded observations with responses that we could not recode to the existing categories. We tabulated SOGI-based discrimination and four measures of health care access by sexual orientation for adults in Minnesota. We also examined differences in health care access for respondents who did and did not report discrimination. 

Results

Reports of discrimination from health care providers based on SOGI were significantly higher among lesbian/gay (36.1%) and bisexual/pansexual (26.1%) populations compared with the state average of 6% (Figure 1). Sexual minorities were also more likely to report barriers to health care access when compared with all adults in Minnesota (Figure 2). Low confidence in getting needed health care was significantly above the state average (11.8%) for people who identify as bisexual/pansexual (30.6%). Statewide, over a quarter of adults reported forgone care due to costs (26.2%), which included routine medical care, prescription drugs, dental care, specialists, and mental health care. Rates of forgone care were significantly higher for people who identify as lesbian/gay (45.5%) or bisexual/pansexual (41.9%).

Figure 1. Unfair treatment from health care providers based on gender or sexual orientation in Minnesota

^ Rate significantly different from All Adults at the 95% confidence level.
Source: SHADAC analysis of the 2021 Minnesota Health Access Survey.

Figure 2. Health care access and barriers to care

^ Rate significantly different from All Adults at the 95% confidence level.
† Estimate may be unreliable due to limited data (relative standard error greater than or equal to 30%).
Source: SHADAC analysis of the 2021 Minnesota Health Access Survey.

We found that Minnesotans who experienced SOGI-based discrimination were more likely to have low confidence in getting care and forgone care compared to those who did not experience discrimination (Figure 3). People who experienced discrimination had elevated barriers across all population groups including people identifying as straight, lesbian/gay, or bisexual/pansexual. However, low confidence in care was highest among bisexual/pansexual adults who reported SOGI-based discrimination (39.4%). Half of all adults with SOGI-based discrimination reported forgone care due to costs, while about two-thirds of bisexual/pansexual (69.0%) and lesbian/gay (66.1%) adults who reported SOGI-based discrimination had forgone care.

Figure 3. Experiences of gender-based discrimination associated with barriers to health care access

* Significant difference within a given subpopulation between rates of people who experienced unfair treatment and those who did not.
† Estimate may be unreliable due to limited data (relative standard error greater than or equal to 30%).
Source: SHADAC analysis of the 2021 Minnesota Health Access Survey.

Discussion

Within the health care setting, discrimination based on SOGI was prevalent among lesbian/gay and bisexual/pansexual adults. SOGI-based discrimination from health care providers was reported by over a third of lesbian/gay adults in Minnesota and over a quarter of bisexual/pansexual adults. Barriers to health care access, including low confidence in getting care and forgone care, were also high among lesbian/gay and bisexual/pansexual adults compared with the average rates seen among adults in Minnesota. Further, reports of SOGI-based discrimination correlated with even higher rates of barriers to access among lesbian/gay and bisexual/pansexual adults; a majority of these populations who reported discrimination also had forgone health care due to costs.

Discrimination by health care providers has substantial clinical implications. Across populations, discrimination negatively affects mental and physical health (Pascoe and Richman, 2009). Compared with straight adults, lesbian/gay and bisexual adults experience health disparities including mental and physical health, activity limitations, and chronic conditions (Gonzales and Henning-Smith, 2017). For LBGTQ adults, both discrimination and barriers to health care are associated with worse mental health, behavioral health, and health-related quality of life (Lee 2016 et al., Jung et al., 2023). One recent study suggests that delayed health care partially mediates the connection between discrimination and worse health status among LBGTQ women (Scott et al., 2022). Our work contributes evidence linking provider discrimination to forgone health care and lack of confidence in getting care.

The clinical impact of discrimination is likely to vary across the life course and across the spectrum of intersectional identities including LBGTQ and race/ethnicity. Compared with lesbians, bisexual women are more likely to report poor physical and mental health and disabilities; both groups of women face higher risks than straight women (Fredriksen-Goldsen 2023). Gay Black and Hispanic men face greater barriers to health care access than gay white men (Hsieh et al., 2017). Among older adults, one survey found that nearly four out of five LBGTQ people anticipate encountering discrimination in long-term care services (Dickson et al., 2022).

Differences in health care access and socioeconomic resources may exacerbate the influence of provider discrimination on health outcomes. Although studies have found that delays in health care among lesbian/gay and bisexual adults persist even with insurance coverage, their coverage may not provide comparable affordability of health care relative to straight adults (Jackson et al., 2016, Nguyen et al., 2018,Tabaac et al., 2020). Lesbian/gay and bisexual adults are less likely to have private coverage and more likely to have purchased a plan from the individual market, which may have higher premiums and deductibles. Furthermore, they are also more likely to experience lapses in coverage. These studies indicate that both cost concerns and previous bad health care experiences contribute to delays in care. Our results add to the growing body of literature documenting high rates of forgone care due to cost for lesbian/gay and bisexual/pansexual adults. Additionally, we document lack of confidence in getting health care among these populations and greater barriers to access among those who reported SOGI-based discrimination from a health care provider.

Conclusion

Reports of discrimination among lesbian/gay and bisexual/pansexual Minnesotans are troubling and require a response. The Affordable Care Act, which expanded Medicaid in willing states, also expanded non-discrimination protections based on sexual orientation and gender identity (KFF, 2014). However, these protections are limited in promoting health care access. Relative to other states, Minnesota offers a robust Medicaid program. Barriers to access may be even higher for LBGTQ people in states that did not expand Medicaid and states with fewer protective non-discrimination laws. Socioeconomic policies at the federal and state level are important for addressing gaps in health equity for many members of the LBGTQ community.

Greater availability of data including SOGI measures would strengthen efforts to better understand and address the health care needs of LBGTQ populations (SHADAC, 2021). Direct measures of discrimination are also important to monitor progress in providing equitable access to health care services (Lett et al., 2022). Ongoing research is needed to improve health equity and address barriers to health care for LBGTQ populations.

Check out our companion blog "Examining Gender-Based Discrimination in Health Care Access by Gender Identity in Minnesota". 

References

SHADAC Staff

p 612.624.4802
e shadac@umn.edu

Race/Ethnicity Data in CMS Medicaid (T-MSIS) Analytic Files: 2020 Data Assessment

The Transformed Medicaid Statistical Information System (T-MSIS) is the largest national database of current Medicaid and Children’s Health Insurance Program (CHIP) beneficiary information collected from U.S. states, territories, and the District of Columbia (DC).¹ T-MSIS data are critical for monitoring and evaluating the utilization of Medicaid and CHIP, which together provide health insurance coverage to more than 90 million people.²

T-MSIS data files are challenging to use directly for research and analytic purposes due to their size and complexity. To optimize these files for health services research, CMS repackages them into a user-friendly, research-ready format called T-MSIS Analytic Files (TAF) Research Identifiable Files (RIF). One such file, the Annual Demographic and Eligibility (DE) file, contains race and ethnicity information for Medicaid and CHIP beneficiaries. This information is vital for assessing enrollment, access to services, and quality of care across racial and ethnic subgroups in the Medicaid/CHIP population, whose members are particularly vulnerable due to limited income, physical and cognitive disabilities, old age, complex medical conditions, housing insecurity, and other social, economic, behavioral, and health needs.    

Completeness of race and ethnicity data reported to CMS remains inconsistent among the states, territories, and DC. To guide researchers and other consumers in their use of T-MSIS data, CMS produces data quality assessments of the race and ethnicity data along with other data such as enrollment, claims, expenditures, and service use. The Data Quality (DQ) assessments for race and ethnicity data have been posted for data years 2014 through 2020 and indicate varying levels of “concern” regarding race and ethnicity data completeness. Some data years have multiple data versions (e.g., Preliminary, Release 1, Release 2), each with its own DQ assessment. This blog explores 2020 Data Release 1, the most recent T-MSIS race and ethnicity data for which a DQ assessment is available.

Evaluation of T-MSIS Race and Ethnicity Data
DQ assessments for each year and release of T-MSIS data are housed in the Data Quality Atlas (DQ Atlas), an online evaluation tool developed as a companion to T-MSIS data.³ The DQ Atlas assesses T-MSIS race and ethnicity data using two criteria: the percentage of beneficiaries with missing race and/or ethnicity values in the TAF; and the number of race/ethnicity categories (out of five) that differ by more than ten percentage points between the TAF and American Community Survey (ACS) data. Taken together, these two criteria indicate the level of “concern” (i.e., reliability) for states’ T-MSIS race/ethnicity data. Five “concern” categories appear in the DQ Atlas: Low Concern, Medium Concern, High Concern, Unusable, and Unclassified. States with substantial missing race/ethnicity data or race/ethnicity data that are inconsistent with the ACS – a premier source of demographic data – are grouped into either the High Concern or Unusable categories, whereas states with relatively complete race/ethnicity data or race/ethnicity data that align with ACS estimates are grouped into either the Low Concern or Medium Concern categories. The Unclassified category includes states for which benchmark data are incomplete or unavailable for a given data year and version.  

To construct the external ACS benchmark for evaluating T-MSIS data, creators of the DQ Atlas combine race and ethnicity categories in the ACS to mirror race and ethnicity categories reported in the TAF (see Table 1). More information about the evaluation of T-MSIS race and ethnicity data is available in the DQ Atlas’ Background and Methods Resource.

Table 1. Crosswalk of Race and Ethnicity Variables between the TAF and ACS    

Table 2. Race and Ethnicity Data Quality Assessment, 2020 T-MSIS Analytic File (TAF) Data Release 1

Notes: *T-MSIS includes all 50 states, the District of Columbia (DC), and the U.S. territories of Puerto Rico (PR) and the Virgin Islands (VI). A DQ assessment is not available for VI in the 2020 TAF (Data Version: Release 1) due to incomplete/unavailable data. VI is therefore the only state/territory categorized as “Unclassified” in the 2020 TAF (Data Version: Release 1), and does not appear in Table 2.

Visualizing T-MSIS Data in the DQ Atlas
The DQ Atlas enables users to generate maps that compare the quality of T-MSIS data between states across different topics, such as race/ethnicity, age, income, and gender (see Figure 1). Visualizing T-MSIS data in this manner can help researchers quickly assess the completeness of a single variable as well as the relative completeness (or incompleteness) of certain variables compared to others. For example, in the 2020 TAF Data Release 1, all states and territories received a “low concern” rating for age data, whereas only 29 states and territories received a “low concern” rating for family income.

Figure 1. Data Quality Assessments of Beneficiary Information by U.S. State/Territory

Notes: Green = low concern; yellow = medium concern; orange = high concern; red = unusable; grey = unclassified.
Source: Medicaid.gov. (n.d.). DQ Atlas: Race and Ethnicity [2020 Data set: Version: Release 1]. Available from https://www.medicaid.gov/dq-atlas/landing/topics/single/map?topic=g3m16&tafVersionId=25. Accessed January 5, 2023.

Looking Ahead
Increasingly, a wide diversity of voices from non-profits, health insurers, state-based marketplaces, and policymakers have called for improving the collection of race, ethnicity, and language data, often with the goal of advancing health equity. CMS’s efforts to improve the quality and availability of T-MSIS data reflect this nationwide movement toward data collection practices that more accurately capture the diversity of the U.S. population.

In June 2022, CMS released updated technical instructions for reporting beneficiary race, including clarification on how to report race information for beneficiaries who report multiple races. That same month, the Biden Administration announced its intent to revise the federal government’s standards for surveying race and ethnicity – a policy change that is expected to result in disaggregated categories for Hispanic individuals and people of Middle Eastern or North African descent.⁴

California and New York have enacted historic policies to disaggregate race/ethnicity data within the past year: California now requires state agencies to list a separate category for Black descendants of enslaved people when collecting state employee data; and New York now requires state agencies to disaggregate Asian, Native Hawaiian, and Pacific Islander data into more granular collection categories (e.g., Chinese, Filipino, Hawaiian, Samoan).^5,6 It is likely that other states will follow these data disaggregation practices in the years ahead as public awareness of issues related to diversity, equity, inclusion, and racial justice continues to grow.  

Sources
¹ Medicaid.gov. Transformed Medicaid Statistical Information System (T-MSIS). Retrieved October 20, 2022, from https://www.medicaid.gov/medicaid/data-systems/macbis/transformed-medicaid-statistical-information-system-t-msis/index.html#

² Medicaid.gov. September 2022 Medicaid & CHIP Enrollment Data Highlights. Retrieved on January 5, 2023, from https://www.medicaid.gov/medicaid/program-information/medicaid-and-chip-enrollment-data/report-highlights/index.html

³ Saunders, H., & Chidambaram, P. (April 28, 2022). Medicaid Administrative Data: Challenges with Race, Ethnicity, and Other Demographic Variables. Kaiser Family Foundation. Retrieved October 31, 2022, from https://www.kff.org/medicaid/issue-brief/medicaid-administrative-data-challenges-with-race-ethnicity-and-other-demographic-variables/

⁴ Wang, H.L. (June 15, 2022). Biden officials may change how the U.S. defines racial and ethnic groups by 2024. NPR. Retrieved November 1, 2022, from https://www.npr.org/2022/06/15/1105104863/racial-ethnic-categories-omb-directive-15

⁵ Diaz, J. (August 16, 2022). California becomes the first state to break down Black employee data by lineage. NPR. Retrieved November 1, 2022, from https://www.npr.org/2022/08/16/1117631210/california-becomes-the-first-state-to-break-down-black-employee-data-by-lineage

⁶ The New York State Senate. (December 22, 2021). Assembly Bill A6896A. Retrieved November 2, 2022, from https://www.nysenate.gov/legislation/bills/2021/A689