Association Between States’ Certificate of Need Laws and Home Health Care Access in the US

Abstract

Home health care has a significant role in the care of Medicare beneficiaries. Certificate of Need (CON) laws for home health agencies limit their number in affected states. This study’s objective was to evaluate the association of CON laws with the availability and service utilization of urban and rural home health agencies in CON states and non-CON states using the generalized estimating equations (GEE) method. The results from this study indicated that CON states have fewer home health agencies for their Medicare population than non-CON states, regardless of urbanicity or rurality. Home health agencies in CON states also have less service utilization in terms of care episodes and visits per patient. This study provides an understanding of the effects of CON laws on Medicare beneficiaries' home health access and experiences.

Keywords

policy home care disparities quantitative methods access

What this paper adds

• Assessments of the availability and service utilization of urban and rural home health agencies in CON states and non-CON states.

• A multivariable regression analysis on a multi-year dataset of home health agencies' performance.

• CON laws have a negative effect on home health service availability and utilization.

Applications of study findings

• Providing evidence to evaluate the implementation of CON laws as it is shown to be associated with limited availability and utilization of home health services in their areas.

• A consideration for policymakers to create incentives to motivate home health availability and recruitment capabilities to address the identified gaps in CON states.

Introduction

Home health care has a significant role in the care of Medicare beneficiaries. Between 2002 and 2016, Medicare Payment Advisory Commission (MedPAC) estimated that Medicare spending on home health services increased by more than 88% (MedPAC, 2020). Among post-acute care services, home health agencies (HHAs) are the second-highest Medicare reimbursement category after skilled nursing facilities, amounting to $7.7 billion in 2019 (Martin et al., 2021). Because home health care constitutes a large and growing budget item for Medicare, the Centers for Medicare and Medicaid Services (CMS) require four conditions for benefit eligibility: (1) Certification from a physician; (2) The beneficiary needs at least one of the following: intermittent skilled nursing care, physical therapy, speech-language therapy, or continue to need occupational therapy; (3) The beneficiary must be homebound; and (4) The HHA is Medicare-certified (CMS, 2020).

The majority of home health service users are 65 years old and over (82%), women (61%), non-Hispanic whites (76%), and need assistance in bathing, walking, and bed transfers (Harris-Kojetin et al., 2019). In 2018, there were 3.4 million Medicare beneficiaries, and 83% had access to at least five HHAs within their residential zip codes (MedPAC, 2020). Nevertheless, rural Medicare beneficiaries were more likely to be served by a single HHA and less likely to utilize it as rurality increased (Mroz et al., 2020; Probst et al., 2014).

In addition to federal policies (e.g., HHA—Federal Conditions of Participation (42 CFR Part 484), states have specific rules to regulate healthcare facilities with the intention of balancing cost control, quality assurance, and accessibility. These regulations may include licensing requirements, quality and safety standards, and reporting obligations, among others. These measures aim to ensure that healthcare facilities meet certain standards, operate within the defined legal and regulatory frameworks, and contribute to the state’s health objectives. The most prominent state regulations for managing healthcare facilities’ availability are the Certificate of Need (CON) laws. CON laws are state regulatory mechanisms that control the number of health care resources in each area (NASHP, 2021; NCSL, 2021). CON laws are enacted with the expectation that overall healthcare spending can be reduced by limiting the quantity of services that are available for consumption. It was first instituted in New York in 1964. The passage of the National Health Planning and Resources Development Act (PL 93-641) in 1974 motivated states to adopt the regulation through federal funds and potential penalties. Eventually, the federal regulation was lifted in 1986, which led to states dropping their CON programs across the years (American Health Planning, 2021; Conover & Bailey, 2020). There are 35 states and the District of Columbia maintaining CON laws with various scope and focus across health care service segments (American Health Planning, 2021; Conover & Bailey, 2020).

Despite Medicare’s status as the primary payer for home health services, states play a pivotal role through the enactment of CON laws. State interest is driven by financial obligations under Medicaid, which also funds home health services, and by the broader mandate to ensure quality, accessibility, and equitable distribution of healthcare. CON laws allow states to regulate growth in the healthcare sector and align service provision with population needs and resources, substantiating their relevance in the home health landscape. Indeed, CON laws gave states the authority to control the entry, expansions, and major capital investments by health care facilities (Conover & Bailey, 2020). With these laws, it is postulated that states may realize cost savings in certain programs by limiting provider’s proliferation, enhance quality by the regionalization of services based on a volume-quality relationship, and prevent provider’s “cream-skimming” practice (Conover & Bailey, 2020). For HHAs, the enactment of CON laws mainly limits the entry of new agencies into the market (American Health Planning, 2021; National Academy for State Health Policy, 2021, National Conference of State Legislatures, 2021). There is limited evidence on how state CON laws might influence the availability and performance of HHAs and more studies are needed to evaluate access of home health services (Atemnkeng, 2020; Conover & Bailey, 2020; Ohsfeldt & Li, 2018; Probst et al., 2014; Wang et al., 2022). In response to these concerns, this study aims to examine the association of CON laws with HHAs' service availability and utilization in the United States (US).

Methods

Study Population

This study used a national panel dataset covering HHAs in the US from 2013 to 2016. The following datasets were sourced from CMS: (1) The CMS Program Statistics—The Medicare Enrollment provided information to measure the availability of HHAs across states; (2) The Home Health Agency Utilization and Payment Public Use File (Home Health Agency PUF) provided information on the utilization of home health care components and organizational characteristics; and (3) Provider of Services files provided information on organizational characteristics. All datasets are publicly available on the CMS Web site.

The identification of states with CON laws used publicly available information on the National Conference of State Legislature (NCSL), American Health Planning Association (AHPA), and National Academy for State Health Policy (NASHP) websites (American Health Planning, 2021; National Academy for State Health Policy, 2021, National Conference of State Legislatures, 2021). These webpages provide policy details (e.g., facilities regulated under CON laws, activities regulated under CON laws, and other CON requirements) with links for references and state regulations comparison tools. The study sample comprises Medicare-certified HHAs that served Medicare beneficiaries from 2013–2016, which amounted to 42,414 HHA-year observations (Table 1).

Table 1.

Home Health Agencies Study Sample.

Year	Home health agencies
Year	Non-CON states	CON states	Total
2013	8885	2117	11,002
2014	8747	2081	10,828
2015	8463	2022	10,485
2016	8164	1935	10,099

Dependent Variables

This study used the Home Health Agency PUF and The Medicare Enrollment File data to calculate a state-level measure by dividing the total number of home health agencies by the total number of Medicare beneficiaries in a state. Home health utilization was calculated as an agency-level measure by dividing the total number of episodes utilized each year by the number of distinct Medicare beneficiaries served by that HHA. This study utilized the home health utilization components for this estimation. An episode is defined as a 60-day home health care period. The beneficiaries are entitled to an unlimited number of subsequent 60-day episodes if they continue to meet the eligibility requirements. We used 60-day episodes in our dataset, and beneficiaries with shorter episodes were not included. The number and type of visits per episode were informed by the available dataset and used in the utilization analysis.

Independent and Control Variables

The independent variable for this study is the CON laws status of the states. Based on the review of states' policy, out of the 35 states with health care CON laws, there are 17 states and the District of Columbia with CON laws for HHAs: Alabama, Arkansas, Georgia, Hawaii, Kentucky, Maryland, Mississippi, Montana, New Jersey, New York, North Carolina, Rhode Island, South Carolina, Tennessee, Vermont, Washington, and West Virginia. The remaining 33 states are classified as Non-CON law states for HHAs. This study used HHA-level measures of Medicare and Medicaid program participation indicator, ownership type, whether an HHA has branches, number of distinct Medicare beneficiaries served, rurality status (using available Core Based Statistical Area (CBSA) indicator), and average Hierarchical Condition Category (HCC) score of Medicare beneficiaries served as control variables to adjust for variations in organization characteristics.

Statistical Analysis

All data were analyzed using STATA 16.0. This study used generalized estimating equations (GEE) to examine the relationship between CON laws and HHAs' availability, utilization, and performance in the US, controlling for other relevant factors. GEE is an analytic tool for evaluating longitudinal or repeated measures and clustered research designs with flexibility to analyze non-normal response variables (Ballinger, 2004; Liang & Zeger, 1986). GEE is frequently used as a strategy to estimate population average models (Hubbard et al., 2010).

The first analysis was conducted to calculate the association of CON laws with HHAs' availability in each state (Equation [1]). For the dependent variable in equation (1), home health agency availability, the study used the Home Health Agency PUF and The Medicare Enrollment File data to calculate a state-level measure by dividing the total number of home health agencies by the total number of Medicare beneficiaries in a state. Furthermore, for the stratified analysis on the rural and urban status of HHA, it was based on the assumption that a HHA located in a particular Core Based Statistical Area (CBSA) will primarily serve Medicare beneficiaries in that area as well. The first analysis was done at the state-level, while utilization analysis were done at the HHA-level. The second analysis were conducted to estimate the effects of CON laws on the utilization of home health care services (Equation [2]). All estimations were conducted using the GEE approach with and without control variables.

Y_{it} = µ + β_{1} {CON}_{it} + β_{2 t} Year + ε_{it}

(1)

Y_{jit} = µ + β_{1} {CON}_{jit} + β_{3} X_{jit} + β_{2 t} Year + ε_{jit}

(2)

Where Y_it in Equation [1] represents the availability of HHAs in state (i) in year (t), Y_jit in Equation [2] represents utilization of home health care services for HHA (j) in state (i) in year (t), μ is the intercept of the model, β1 captures the effect of the state’s CON laws status, β2 is the coefficient of year, and β3 captures the effects of organization characteristics that are control variables (including rurality status). These three coefficients are estimated as fixed effects. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) for reporting purposes.

Results

Home Health Availability

On average, there are about 17 HHAs per 100,000 Medicare beneficiaries across states. Table 2 shows the distribution of HHAs and Medicare beneficiaries in the US. and the relationship between these two variables. Home health availability per 100,000 Medicare beneficiaries was significantly lower in CON states than in Non-CON states.

Table 2.

Descriptive Statistics for Home Health Availability CY. 2013/16.

Variables	All States	Non-CON	CON	p-value
# of Home Health Agencies
All, mean (SD)	207.9 (377.7)	259.5 (452.4)	113.3 (127.3)	.008
Rural HHA, mean (SD)	36.6 (39.4)	38.9 (43.7)	32.3 (29.8)	.26
Urban HHA, mean (SD)	171.1 (352.9)	220.3 (422.9)	80.9 (114.9)	.007
Medicare beneficiaries
All, mean (SD)	1,049,869 (1,090,648)	1,055,029 (1,232,993)	1,040,411 (771,918)	.93
Rural beneficiaries, mean (SD)	191,925 (143,772)	179,554 (135,519)	214,607 (156,231)	.096
Urban beneficiaries, mean (SD)	857,943 (1,029,805)	875,474 (1,173,281)	825,803 (700,438)	.74
Home health availability per 100,000 medicare beneficiaries
All, mean (SD)	17.0 (10.7)	19.8 (11.2)	11.8 (7.5)	<.001
Rural, mean (SD)	16.6 (11.3)	19.1 (11.6)	12.1 (9.1)	<.001
Urban, mean (SD)	15.8 (11.4)	18.6 (12.1)	10.7 (7.8)	<.001

Table 3 shows the results of the GEE model analysis. It shows that, on average, there are about eight (8) fewer HHAs per 100,000 Medicare beneficiaries in CON states compared to those living in non-CON states. This association is consistent regardless of urban or rural location, where approximately seven and eight fewer HHAs per 100,000 Medicare beneficiaries live in their respective settings.

Table 3.

GEE Results for Home Health Availability per 100,000 Medicare Beneficiaries CY. 2013/16 (Ref. Non-CON States).

Variables (N = 204)	Coeff. (SE)
All	−8.03** (2.61)
Rural	−7.04* (2.90)
Urban	−7.97** (2.78)

Note. *p < .05; **p < .01; ***p < .001.

Home Health Utilization

In terms of utilization patterns, Table 4 shows that on average, Medicare beneficiaries who used home health utilized two episodes of care (defined as a 60-day period of care) during the study period. Moreover, Table 4 shows that the total number of episodes per beneficiary was significantly lower in CON states than Non-CON states, as were overall visits, skilled nurse visits, and home health aide visits. However, the opposite pattern was seen for the other types of visits (e.g., physical therapy visit, occupational therapy visit, speech therapy visit).

Table 4.

Descriptive Statistics for Home Health Utilization CY. 2013/16.

Variables	All States (N = 42,414)	Non-CON (N = 34,259)	CON (N = 8155)	p-value
Home Health Utilization
Total episodes per distinct beneficiary, mean (SD)	2.01 (.84)	2.10 (.88)	1.61 (.43)	<.001
Visits per episode
Overall visits, mean (SD)	18.59 (6.69)	18.89 (7.17)	17.35 (3.84)	<.001
Skilled nurse visits, mean (SD)	9.49 (4.81)	10.01 (5.07)	7.29 (2.46)	<.001
Home health aide, mean (SD)	2.62 (3.63)	2.71 (3.77)	2.21 (2.94)	<.001
Physical therapy, mean (SD)	5.16 (2.87)	4.90 (2.88)	6.27 (2.57)	<.001
Occupational therapy, mean (SD)	1.10 (1.15)	1.05 (1.16)	1.27 (1.11)	<.001
Speech therapy, mean (SD)	.18 (.30)	.17 (.30)	.23 (.31)	<.001
Medical social, mean (SD)	.09 (.14)	.08 (.13)	.14 (.17)	<.001

The distribution of control variables is described in Table 5. Specifically, it shows that home health patients have an average of 2.3 HCC risk score, indicating that the expected healthcare costs are 2.3 times the average patient’s cost. As for the organizational characteristics, most HHAs are privately owned, accept both Medicare and Medicaid patients, and can provide six home health services (nursing, physical therapy, occupational therapy, speech therapy, home health aide, and medical social). In contrast, they are less likely to be located in rural areas, have branches, or act as a hospice.

Table 5.

Control Variables for Home Health Utilization CY. 2013/16.

Variables	All States (N = 42,414)	Non-CON (N = 34,259)	CON (N = 8155)	p-value
Patient Severity
HCC score, mean (SD)	2.25 (.5)	2.25 (.5)	2.24 (.4)	.026
Organizational characteristic		N (%)	N (%)
Type of ownership				<.001
Gov. Voluntary	60 (.1%)	40 (.1%)	20 (.2%)
Gov. Local	694 (1.6%)	438 (1.3%)	256 (3.2%)
Gov. State	1409 (3.3%)	757 (2.2%)	652 (8.0%)
Non-profit other	2225 (5.3%)	1520 (4.4%)	705 (8.6%)
Non-profit private	3151 (7.4%)	2190 (6.4%)	961 (11.8%)
Non-profit religious	1446 (3.4%)	1074 (3.1%)	372 (4.6%)
Proprietary	33,394 (78.7%)	28,207 (82.4%)	5187 (63.6%)
Located in rural area	7460 (17.6%)	5134 (15.0%)	2326 (28.5%)	<.001
Accept medicare and medicaid	32,638 (76.9)	25,728 (75.2%)	6910 (84.8%)	<.001
Agencies with branch(es)	6138 (14.5%)	4487 (13.1%)	1651 (20.3%)	<.001
Provide six main services	33,144 (78.1%)	26,948 (80.3%)	6196 (77.0%)	<.001
Act as hospice	2687 (6.3%)	2182 (6.4%)	505 (6.2%)	.55

The GEE estimation showed that, on average, HHAs in CON states delivered fewer episodes of care per beneficiary than HHAs in states without CON laws (Table 6). The results also show that, on average, HHAs in CON states are associated with fewer overall visits per episode, skilled nursing visits, and home health aide visits.

Table 6.

GEE Results for Home Health Utilization CY. 2013/16 (Ref. Non-CON States).

Variables (N = 42,414)	Coef. (Adjusted) (SE)
Home Health Utilization
Total episodes per distinct beneficiary	−.45*** (.01)
Visits per episode
Overall visits	−1.40*** (.10)
Skilled nurse visits	−2.64*** (.07)
Home health aide	−.58*** (.07)
Physical therapy	1.53*** (.06)
Occupational therapy	.19*** (.02)
Speech therapy	.05*** (.01)
Medical social	.05*** (.01)

Note. *p < .05; **p < .01; ***p < .001.

Table 7 provides an in-depth view of the utilization pattern across different settings. The results suggest that, on average, HHAs in rural and urban areas of the CON states are associated with fewer episodes of care per beneficiary than their counterparts in urban non-CON states.

Table 7.

GEE Results for Home Health Utilization in Different Settings CY. 2013/16.

Variables (N = 42,414)	Coef. (Adjusted) (SE)
Total Episodes Per Beneficiary in Different Settings (Ref: HHA in urban non-CON states)
Rural CON states	−.31*** (.02)
Urban CON states	−.47*** (.01)
Rural non-CON states	.01 (.02)

Note. *p < .05; **p < .01; ***p < .001.

Sensitivity

For sensitivity testing, the primary analysis of home health utilization were rerun only for HHAs that served only Medicare beneficiaries. Appendix A show that the results are consistent with the primary results reported above. Also, checks for potential outliers were conducted using scatterplot graphs and studentized residuals for models related to home health utilization outcomes. Based on the studentized residuals, a new binary group was created to identify observations with residuals exceeding 2.5 (UCLA, 2021). Another round of analyses was conducted for the three outcomes of interest (utilization, total visits, and skilled nurse visits) without considering outliers. Appendix B shows that the interpretations for the binary group results are similar to the primary results.

Discussion

The assessment of the availability and utilization of HHAs in CON states in comparison to those in non-CON states has produced valuable insights. First, there is evidence that CON states have fewer HHAs available for their Medicare population than non-CON states. Second, the findings suggest that HHAs in CON states have less utilization of care episodes and visits.

Even though there is an increasing trend for the number of Medicare-certified HHAs (Wang, Leifheit-Limson, et al., 2017), this study suggests their availability for Medicare beneficiaries varies by whether states have adopted CON laws. The analysis on home health availability showed that compared to Non-CON states, fewer HHAs were available for Medicare beneficiaries living in rural and urban areas of CON states. Indeed, in CON states, approvals for new services were sparsely given, which limited the competition and may create more concentrated markets in CON states (Polsky et al., 2014). This condition is concerning because Medicare beneficiaries are the primary users of home health services (Van Houtven & Dawson, 2020).

Furthermore, the US population is aging, and about a quarter of people 65 years old and over are residing in rural areas (Assistance, 2014; Colby & Ortman, 2015). Moreover, the proportion of older adults in the U.S. is notably higher in rural areas, and numerous rural communities across the nation are experiencing an accelerated rate of aging compared to other regions (Smith & Trevelyan, 2019). The restriction of entry for new HHAs seems to have lowered their availability particularly in rural areas and needs to be revisited by states with CON laws. The main objective of CON laws is to control the cost of medical care by regulating the market competition and encouraging efficient care. HHAs provide services that enable care at home instead of admissions to health facilities. It usually has a lower cost than receiving care in institutional settings and offers more convenience for the patients (Landers et al., 2016). By lessening the restrictions, policymakers may improve availability and potentially reduce post-acute care costs (Mitchell et al., 2020).

HHAs located in states with CON laws are also associated with fewer care episodes, total visits per episode, and skilled nursing and home health aide visits per beneficiary receiving services. This finding applies to agencies regardless of urbanicity or rurality. These differences might be driven by the availability of HHAs and home health staff. Lower availability of HHAs in CON states means the available agencies need to manage their personnel resources efficiently to address incoming demands and existing patients, limiting their capability to extend care during episodes.

Post-acute care facility availability has been portrayed as a strong predictor of utilization, and these facilities need to compete with acute care facilities in recruiting medical professionals, especially in rural areas (Buntin et al., 2005; Skillman et al., 2016). States with CON laws may consider creating local incentives similar to rural add-on payments, a policy designed to compensate for additional costs associated with delivering health services in rural areas, contributing to improving the supply of HHAs serving rural areas (Mroz et al., 2020). The recent discontinuation of this incentive may pose as a challenge for HHAs, since they might struggle to cover the higher costs of providing care in rural communities, which can stem from increased travel time and distances, higher overhead costs per patient, and difficulties in recruiting and retaining staff. This situation further highlights the importance of creating similar incentives that may improve home health availability and recruitment capabilities to address the existing gap.

While CON laws are a significant factor in the accessibility and availability of home health services, it is crucial to understand that these services are influenced by a complex interplay of multiple factors. Socioeconomic characteristics, such as income levels, education, and employment status, can influence the demand for and utilization of home health services. Additionally, the state of healthcare infrastructure, marked by the accessibility and standards of medical facilities, as well as the readiness of skilled medical personnel, can influence the referral process and transitions into and out of acute care settings. Moreover, demographic factors, such as age distribution, population density, and the prevalence of chronic diseases, can create varying levels of need for home health services across different regions. Policy and regulatory environment, beyond CON laws, such as Medicare and Medicaid reimbursement policies, can also influence the availability of these services. All these factors underscore the complex and multifaceted nature of home health service availability and utilization across regions. While our study sheds light on the impact of CON laws, a comprehensive understanding of this issue would require a broader examination of these and other potential factors.

Limitations

This study is limited because the latest data is in 2016, restricting our ability to assess the outcomes in more recent years. However, after 2016, new HHA-related policies, such as value-based payment, four statewide home health moratoriums (Florida, Illinois, Michigan, and Texas), and a new reporting system, might make it more challenging to identify the effect of CON laws on HHAs. Another limitation is that the proxies used in measuring agency performance are limited to utilization due to variables available in selected datasets, restricting the interpretation of financial-related indicators. Several cost-effectiveness studies have provided insights into the financial matter, suggesting that home care interventions are likely to be cost-saving and as effective as hospital (Curioni et al., 2023). Another limitation is that the data were aggregated to the organization level, which restricts insights from the beneficiary’s level; still, this study’s objective is to evaluate institutions' performance considering their regulatory environment. Overall, these limitations will not invalidate the current study results and may serve as the basis for future studies.

Conclusions

This study complements previous studies on the effects of CON laws (Atemnkeng, 2020; Conover & Bailey, 2020; Ohsfeldt & Li, 2018; Polsky et al., 2014; Probst et al., 2014). The evidence suggests that the availability of HHAs for Medicare beneficiaries is lower in states with CON laws. Moreover, the results show that CON restrictions are associated with less utilization, which suggests challenges in providing services. Based on this study, the general recommendation for policymakers is to reconsider and reevaluate the implementation of CON laws given the findings that such regulations are associated with limited availability and utilization of home health services in their states. Such findings apply to both urban and rural geographic locations in states with CON laws. For practical recommendations, home health stakeholders should consider creating incentives to improve home health availability and recruitment capabilities to address the existing gap.

Supplemental Material

Supplemental Material - Association Between States’ Certificate of Need Laws and Home Health Care Access in the US

Supplemental Material for Association Between States’ Certificate of Need Laws and Home Health Care Access in the US by M. Muska Nataliansyah, Marcia M. Ward, and Xi Zhu in Journal of Applied Gerontology

Footnotes

Author Contributions

All authors contributed to the study’s conception and design. Material preparation and data collection were performed by M. Muska Nataliansyah. The analysis was conducted by M. Muska Nataliansyah, Marcia M. Ward, and Xi Zhu. The first draft of the manuscript was written by M. Muska Nataliansyah, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Statement

ORCID iD

M. Muska Nataliansyah

Data Availability Statement

Data are publicly available. All data relevant to the study are presented in the article.*

Supplemental Material

Supplemental material for this article is available online.

References

American Health Planning

Association.

(2021). American Health Planning Association: CON overview. Retrieved from.\ http://www.ahpanet.org/copnahpa.html

Assistance

HousingCouncil

. (2014). Housing an aging rural America: Rural seniors and their homes. Retrieved from https://ruralhome.org/wp-content/uploads/storage/documents/publications/rrreports/ruralseniors2014.pdf

Atemnkeng

P. A.

(2020). Association between certificate-of-need laws and the health outcomes of home health patients. Walden Dissertations and Doctoral Studies. (p. 8668). Retrieved from. https://scholarworks.waldenu.edu/dissertations/8668

Ballinger

G. A.

(2004). Using generalized estimating equations for longitudinal data analysis. Organizational Research Methods, 7(2), 127–150. https://doi.org/10.1177/1094428104263672

Buntin

M. B.

Garten

A. D.

Paddock

Saliba

Totten

Escarce

J. J.

(2005). How much is postacute care use affected by its availability? Health Services Research, 40(2), 413–434. https://doi.org/10.1111/j.1475-6773.2005.00365.x

Centers for Medicare and Medicaid Services . (2020). Medicare and home health care. Retrieved from. https://www.medicare.gov/pubs/pdf/10969-medicare-and-home-health-care.pdf

Colby

S. L.

Ortman

J. M.

(2015) Projections of the size and composition of the US Population: 2014 to 2060. Population estimates and projections. Current population reports (pp. 25–1143). US Census Bureau.

Conover

C. J.

Bailey

(2020). Certificate of need laws: A systematic review and cost-effectiveness analysis. BMC Health Services Research, 20(1), 1–29. https://doi.org/10.1186/s12913-020-05563-1

Curioni

Silva

A. C.

Damião

Castro

Huang

Barroso

Araujo

Guerra

(2023). The cost-effectiveness of homecare services for adults and older adults: A systematic review. International Journal of Environmental Research and Public Health, 20(4), 3373. https://doi.org/10.3390/ijerph20043373

10.

Harris-Kojetin

L. D.

Sengupta

Lendon

J. P.

Rome

Valverde

Caffrey

(2019). Long-term care providers and services users in the United States, 2015-2016. National Center for Health Statistics. Vital Health Stat, 3(43).

11.

Hubbard

A. E.

Ahern

Fleischer

N. L.

Van der Laan

Lippman

S. A.

Jewell

Bruckner

Satariano

W. A.

Satariano

W. A.

(2010). To GEE or not to GEE: Comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology, 21(4), 467–474. https://doi.org/10.1097/EDE.0b013e3181caeb90

12.

Landers

Madigan

Leff

Rosati

R. J.

McCann

B. A.

Hornbake

MacMillan

Jones

Bowles

Dowding

Lee

Moorhead

Rodriguez

Breese

(2016). The future of home health care: A strategic framework for optimizing value. Home Health Care Management and Practice, 28(4), 262–278. https://doi.org/10.1177/1084822316666368

13.

Liang

K.-Y.

Zeger

S. L.

(1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13–22. https://doi.org/10.1093/biomet/73.1.13

14.

Martin

A. B.

Hartman

Lassman

Catlin

National Health Expenditure Accounts Team . (2021). National health care spending in 2019: Steady growth for the fourth consecutive year: Study examines national health care spending for 2019. Health Affairs, 40(1), 14–24. https://doi.org/10.1377/hlthaff.2020.02022

15.

Medicare Payment Advisory Commission . (2020). Report to Congress: Home health care services. Retrieved from. http://www.medpac.gov/docs/default-source/reports/mar20_medpac_ch9_sec.pdf?sfvrsn=0

16.

Mitchell

Matthew D.

Amez Droz Elise Parsons Anna

, Phasing Out Certificate-of-Need Laws: A Menu of Options (2020). Mercatus Policy Brief. Retrieved from https://ssrn.com/abstract=3562236 or https://doi.org/10.2139/ssrn.3562236

17.

Mroz

T. M.

Patterson

D. G.

Frogner

B. K.

(2020). The Impact of medicare’s rural add-on payments on supply of home health agencies serving rural counties: Study examines how Medicare rural add-on payments affected the number of home health agencies serving rural counties. Health Affairs, 39(6), 949–957. https://doi.org/10.1377/hlthaff.2019.00952

18.

National Academy for State Health Policy . (2021). 50 state scan of state certificate of need programs. Retrieved from. https://www.nashp.org/50-state-scan-of-state-certificate-of-need-programs/

19.

National Conference of State Legislatures . (2021). National conference of state legislature certificate of need state laws. Retrieved from. https://www.ncsl.org/research/health/con-certificate-of-need-state-laws.aspx

20.

Ohsfeldt

R. L.

(2018). State entry regulation and home health agency quality ratings. Journal of Regulatory Economics, 53(1), 1–19. https://doi.org/10.1007/s11149-018-9351-4

21.

Polsky

David

Yang

Kinosian

Werner

R. M.

(2014). The effect of entry regulation in the health care sector: The case of home health. Journal of Public Economics, 110(Feb 1), 1–14. https://doi.org/10.1016/j.jpubeco.2013.11.003.

22.

Probst

Towne

Mitchell

Bennett

Chen

. (2014) Home health care agency availability in rural counties. South Carolina Rural Health Research Center. Retrieved from https://sc.edu/study/colleges_schools/public_health/research/research_centers/sc_rural_health_research_center/documents/112hhcagencyavailability2014.pdf

23.

Skillman

S. M.

Patterson

D. G.

Couthard

Mroz

T. M.

(2016). Access to rural home health services: Views from the field (No. 152, p. 19). WWAMI Rural Health Research Center. https://www.ruralhealthresearch.org/publications/1003

24.

Smith

A. S.

Trevelyan

(2019). The older population in rural America: 2012-2016. US Department of Commerce, Economics and Statistics Administration, US Census Bureau.

25.

University of California Los Angeles . (2021). Regression with STATA chapter 2 – regression diagnostics. Retrieved from. https://stats.idre.ucla.edu/stata/webbooks/reg/chapter2/stata-webbooksregressionwith-statachapter-2-regression-diagnostics/

26.

Van Houtven

C. H.

Dawson

W. D.

(2020). Medicare and home health: Taking stock in the COVID-19 era. In The Commonwealth Fund Issue Brief. Commonwealth Fund.

27.

Wang

Ying

(2022). Home health agencies with more socially vulnerable patients have poorer experience of care ratings. Journal of Applied Gerontology, 41(3), 661–670. https://doi.org/10.1177/07334648211053859

28.

Wang

Leifheit-Limson

E. C.

Fine

Pandolfi

M. M.

Gao

Liu

Eckenrode

Lichtman

J. H.

Lichtman

J. H.

(2017). National trends and geographic variation in availability of home health care: 2002–2015. Journal of the American Geriatrics Society, 65(7), 1434–1440. https://doi.org/10.1111/jgs.14811

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