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. Author manuscript; available in PMC: 2026 Sep 25.
Published in final edited form as: J Appl Gerontol. 2026 Sep 11;45(10):1924–1931. doi: 10.1177/07334648261420498

Evaluating Differences in Disaster-Related Staffing for Nursing Homes by Ownership Status

Natalia Festa 1,2, Kelsey Alexovitz 1,2, Kendra Davis-Plourde 3, David Dosa 4, Andrew B Cohen 1,2, Thomas M Gill 1
PMCID: PMC13603801  NIHMSID: NIHMS2139877  PMID: 42723429

Abstract

For-profit ownership has been linked to lower levels and skill composition of nursing home staffing, yet it is unclear whether disasters exacerbate these differences. We identified 914 nursing homes in Hurricane Ian’s path (September 2022), of which 665 (72.8%) were for-profit. We applied a comparative interrupted time series design to compare staffing hours per resident per day (HPRPD) before and after landfall. Before the storm, for-profit facilities staffed 9% fewer total HPRPD than not-for-profits (95% Confidence Interval (CI) 0.87–0.95), with similar deficits in the employed (permanent staff) (0.88, 95% CI 0.83–0.93) and licensed (0.90, 95% CI 0.86–0.94) categories. Both for-profit (1.04; 95% CI 1.03–1.05) and not-for-profit facilities (1.03; 95% CI 1.02–1.04) increased total staffing in the post-disaster period, driven by employed personnel; the difference in the magnitude of their responses (1%) was not statistically significant. Baseline staffing differences may affect nursing homes’ capacity to meet disaster-related needs.

Keywords: Nursing Homes, Staffing, Disaster-Response, Ownership

INTRODUCTION

Most nursing homes in the United States operate under private ownership, which has been associated with poorer outcomes in routine (non-disaster) care settings (Comondore et al., 2009; Ryskina et al., 2024; Stevenson et al., 2023). This difference in resident outcomes has been attributed, in large part, to poorer baseline staffing practices within for-profit nursing homes (Braun et al., 2021; Comondore et al., 2009). Indeed, higher staffing levels and skill mix have been associated with improved resident outcomes in routine care settings (Jutkowitz et al., 2023; White and Olsho, 2023). It is unclear whether these differences in staffing practices persist, or are potentially widened, during nursing home response efforts to environmental disasters, given their potential to dynamically influence staffing levels and composition, as well as resident acuity (Brown et al., 2012; Jester et al., 2021, 2022, 2023; Skarha et al., 2021). Because the effectiveness of nursing home disaster response is contingent upon multiple factors, it is important to understand whether ownership-related staffing differences function as a potential stress-multiplier, which could be modified through strengthened emergency preparedness oversight (Hyer et al., 2010). Determining whether disaster-related staffing differs between for-profit and not-for-profit nursing homes can inform whether ownership merits consideration in strategies to improve residents’ post-disaster outcomes (Maxwell, 2023).

There are several operational and structural factors common to for-profit nursing homes, which indicate that ownership status may influence their disaster response practices. First, for-profit nursing homes have been found to exhibit poorer adherence to the federal standards governing routine clinical care, including those pertaining to staffing practices, infection control, and related quality measures (Comondore et al., 2009; Frontz, 2024; Ryskina et al., 2024). Second, because disasters increase nursing homes’ operational costs, for-profit nursing homes may adopt distinct disaster response strategies that are both responsive to their financial incentives and pertinent to resident outcomes (Cai and Song, 2023; Dosa et al., 2012; Thomas et al., 2010). Third, for-profit nursing homes have diminished access to certain public resources that may be relevant to their response capabilities, such as public disaster relief resources (An et al., 2019; Brown et al., 2007). Together, these factors indicate that for-profit nursing homes face distinct operational constraints during disaster response efforts, yet the extent to which ownership affects disaster-related staffing practices remains unclear.

To explore these potential ownership-associated differences in disaster-related staffing, we compared changes in staffing intensity and composition across for-profit and not-for-profit nursing homes exposed to Hurricane Ian (2022) (Bucci et al., 2023). These findings should clarify whether ownership status compounds disaster-response staffing challenges and, if so, provide a basis for targeted preparedness strategies to improve resident safety.

METHODS

Sample and Data Sources

We identified nursing homes exposed to the direct path of Hurricane Ian in four affected states (Florida, Georgia, South Carolina, North Carolina) using their geocoded locations relative to the storm path published by the National Hurricane Center (Bucci et al., 2023). We used the Centers for Medicare & Medicaid Services (CMS) Provider Information file to aggregate operational nursing homes as of September 25, 2022 (Day 0) and obtain their characteristics, including ownership status, size, and quality ratings. We used the 2013 National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme for Counties to ascertain whether nursing homes were located within a rural county, with less than 50,000 inhabitants (Ingram and Franco, 2012). We used multiple imputation by chained equations to impute missing values for the fewer than 2% of nursing homes with at least one missing data element (Supplementary Table S1) (van Buuren and Groothuis-Oudshoorn, 2011).

We defined Day 0 as 72-hours before the United States landfall for Hurricane Ian, corresponding to the point at which facilities have been shown to implement operational decisions related to evacuation and staffing that are attributable to the storm (Dosa et al., 2007, 2020). We used the CMS Payroll Based Journal to link information regarding pre- (Days −30–0) and post-disaster (Days 1–7) staffing to each facility (Centers for Medicare & Medicaid Services, 2022; Geng et al., 2019). The 30-day pre-event period enabled us to account for potential baseline trends, while the shorter post-event period captured immediate staffing changes that were likely attributable to Hurricane Ian. We extended the follow-up period to 30-days in sensitivity analyses.

For-Profit Classification

We used the CMS Provider Information File to determine ownership status for each nursing home. Following the CMS classification framework, we defined for-profit nursing homes as facilities owned by corporations, individuals, partnerships, or limited liability corporations, and classified those owned by governmental or nonprofit organizations as not-for-profit. In a subgroup analysis, we restricted our sample to for-profit nursing homes, comparing those under corporate chain ownership to alternative for-profit ownership arrangements.

Outcome Definitions

We assessed three measures that capture distinct facets of nursing home staffing. First, we examined staffing intensity, defined as the total hours per resident per day (HPRPD) collectively provided by registered nurses (RNs), licensed practical nurses (LPNs), and certified nursing assistants (CNAs) (Medicare and Medicaid Programs; Minimum Staffing Standards for Long-Term Care Facilities and Medicaid Institutional Payment Transparency Reporting, 2024; White and Olsho, 2023). Second, we measured staffing by licensed nurses, defined as the HPRPD provided by RNs and LPNs (Medicare and Medicaid Programs; Minimum Staffing Standards for Long-Term Care Facilities and Medicaid Institutional Payment Transparency Reporting, 2024; White and Olsho, 2023). Third, we evaluated staffing by employed (permanent) staff, defined as the HPRPD provided by individuals who are not independent contractors (Adams et al., 2025).

Statistical Analysis

We used a comparative interrupted time series approach to assess how staffing changed in the post-disaster period and whether this change differed for nursing homes under for-profit, compared to not-for-profit ownership. For each staffing measure (outcome), we estimated a separate generalized linear mixed model with a gamma distribution and log link to accommodate the positive skewness of these continuous measures. For each staffing measure, we considered both the pre-disaster level and the change from pre- to post-disaster, with the primary parameter of interest being the difference in these changes between for-profit and not-for-profit facilities. Each model included an indicator for the post-disaster period, an indicator for ownership classification, and an interaction between these two variables. The interaction term quantifies the amount by which post-disaster staffing changes differed between for-profit and not-for-profit facilities, independent of baseline staffing levels. We included a variable for the date relative to Day 0 to account for underlying time trends and a fixed effect for the day of the week to capture cyclical staffing patterns. We adjusted for nursing home size and CMS 5-Star quality ratings, as well as location within a rural area. We included random intercepts at the facility and state levels to account for potential unmeasured confounding and to adjust for correlations among outcomes within these clusters. The model coefficients were exponentiated to report relative differences, expressed as percentage-point estimates with 95% confidence intervals (CIs), to aid interpretation. We used a 2-tailed p-value threshold of .05 to determine statistical significance.

To evaluate potential differences in disaster-related staffing practices within for-profit nursing homes, we restricted our sample to nursing homes under for-profit arrangements and compared staffing changes between those under chain ownership versus alternative ownership arrangements (described above).

To evaluate whether our findings were robust to the length of follow-up, we repeated our analyses using a 30-day post-disaster observation.

We conducted data preparation and analyses using R version 4.4.3 and ArcGIS Pro version 3.4.

RESULTS

Of the 914 nursing homes in states affected by Hurricane Ian, 665 (72.8%) operated under for-profit ownership (Table 1). Of the for-profit nursing homes, 420 (63.2%) operated under chain ownership. The average bed capacity was 115.6 (standard deviation (SD) 47.3) and CMS 5-Star Quality Rating was 3.8 (SD 1.1). Not-for-profit nursing homes had higher average CMS 5-Star ratings, compared to for-profit nursing homes (standardized mean difference (SMD) 0.21). Figure 1 and Supplementary Table S2 present unadjusted staffing levels and pre- to post-disaster changes by ownership. In for-profit nursing homes, the mean total staffing intensity increased by 0.10 HPRPD (2.84%) during the post-disaster period relative to the pre-disaster baseline, while employed staffing increased by 0.12 HPRPD (3.85%). The levels of licensed staffing in for-profit nursing homes increased by 0.02 HPRPD (1.96%) during the post-disaster period, while staffing by certified nurse assistants increased by 0.07 HPRPD (3.33%). In not-for-profit nursing homes, the mean total staffing also increased by 0.10 HPRPD (2.55%), with an increase in employed staffing of 0.14 HPRPD (3.88%). The levels of licensed staffing in not-for-profit nursing homes also increased by 0.04 HPRPD (2.96%), while staffing by certified nurse assistants increased by 0.06 HPRPD (2.31%).

Table 1.

Characteristics of Nursing Homes Exposed to Hurricane Ian

Characteristic Overall
N = 914		 Not-For-Profit
N = 249 (27.2%)		 For-Profit
N = 665 (72.8%)		 SMD
Chain Ownership, n (%) 420 (46.0%) - 420 (63.2%) -
Bed Capacity, mean (SD) 115.6 (47.3) 109.9 (58.4) 117.7 (42.3) −0.15
CMS 5-Star Quality Rating, mean (SD) 3.8 (1.1) 4.0 (1.1) 3.7 (1.1) 0.21
Rural, n (%) 129 (14.1%) 28 (11.2%) 101 (15.2%) −0.12
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(a)

Facilities within ownership strata were compared using standardized mean differences (SMD)

Abbreviations: CMS: Centers for Medicare & Medicaid Services; SD: Standard Deviation

Figure 1. Pre- and Post-Event Disaster-Related Staffing Practices for For-Profit and Not-For-Profit Nursing Homes by Staffing Category.

Figure 1

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The plot displays the unadjusted change (in units of hours per resident per day) in staffing practices from the pre-event (Days −30–0) to the post-event period (Days 1–7), along with their respective standard errors. Pre versus post staffing practices were comparing using the Mann-Whitney U test, with * denoting statistical significance at p<0.05 and ** denoting p<0.01.

Abbreviation(s): CNA: Certified Nurse Assistant

Figure 2 presents adjusted model estimates for pre-disaster staffing differences and the extent to which post-disaster changes differed by ownership. During the pre-event period, for-profit nursing homes demonstrated 9% lower total staffing intensity (0.91, 95% CI 0.87 to 0.95) compared to their not-for-profit counterparts. In the post-event period, for-profit nursing homes increased total staffing intensity by 4% relative to their own pre-event levels (1.04, 95% CI 1.03 to 1.05), while not-for-profit increased total staffing intensity by 3% relative to their own pre-event levels (1.03, 95% CI 1.02 to 1.04). Therefore, in the post-event period, for-profit nursing homes exhibited a 1% relative increase in total staffing intensity (1.01, 95% CI 1.00 to 1.02) compared to not-for-profit nursing homes but this difference did not reach statistical significance. The similar proportional disaster-related staffing increases in for-profit and not-for-profit nursing homes indicate that the baseline difference in total staffing intensity persisted throughout the disaster response period.

Figure 2. Comparative Interrupted Time Series Estimates of Pre- and Post-Event Differences in Nursing Home Staffing by Ownership.

Figure 2

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“Pre-Event Difference” represents the baseline (pre-event) differences in for profit compared to not-for-profit facilities. “For Profit Post-Event” denotes the change from pre- to post-event for for-profit facilities. “Not For Profit Post-Event” denotes the change from pre- to post-event for not-for-profit facilities. “Post-Event Difference” denotes the magnitude of the additional post-event change in for profit compared to not-for-profit facilities. Estimates are reported as percentage changes and are adjusted using comparative interrupted time series models that control for facility characteristics and temporal trends.

Abbreviation(s): CI: Confidence Interval

During the pre-event period, for-profit nursing homes maintained fewer employed staff compared to not-for-profit facilities (0.88; 95% CI 0.83–0.93) (Figure 2). In the post-event period, for-profit nursing homes increased employed staffing by 4% relative to their own pre-event levels (1.04, 95% CI 1.03 to 1.05), while not-for-profit increased employed staffing by the same amount. There was not an observable post-event difference in employed staffing between for-profit and not-for-profit nursing homes (1.00; 95% CI 0.98–1.01).

During the pre-event period, for-profit nursing homes maintained fewer licensed staff compared to not-for-profit facilities (0.90; 95% CI 0.86–0.94) (Figure 2). In the post-event period, for-profit nursing homes increased licensed staffing by 3% relative to their own pre-event levels (1.03, 95% CI 1.02 to 1.04), while not-for-profit nursing homes increased licensed staffing by 2% (1.02; 95% CI 1.00–1.03). The difference in post-event differences in licensed staffing between for-profit and not-for-profit nursing homes did not reach statistical significance (1.01; 95% CI 1.00–1.03).

Subgroup Analysis

We observed no pre-event differences for any staffing category between nursing homes under chain ownership compared to alternative for-profit ownership arrangements. We observed that nursing homes under both chain and alternative for-profit ownership increased their staffing levels but did not differ in the magnitude of these increases (Supplementary Tables S3 and S4).

Sensitivity Analysis

We observed similar results when we applied an extended 30-day post-event follow-up period (Supplementary Table S5).

DISCUSSION

Previous research indicates that for-profit nursing homes often maintain lower staffing levels than not-for-profit facilities (Braun et al., 2021; Comondore et al., 2009), with potential implications for residents’ susceptibility to adverse post-disaster outcomes. Consistent with this literature, we observed that for-profit nursing homes exposed to Hurricane Ian exhibited lower total staffing intensity and maintained fewer employed and fewer licensed staff compared to not-for-profit nursing homes in the pre-event period. These baseline staffing differences persisted but did not widen during the post-event period, as both for-profit and not-for-profit facilities demonstrated similar proportional increases in staffing, primarily through greater mobilization of their employed workforce. Assuming that the staffing-outcome associations previously observed in routine care settings translate to disaster contexts, ownership-related baseline staffing differences may contribute to differences in resident susceptibility to adverse post-disaster outcomes, although these differences do not appear to be exacerbated by disaster-related staffing adjustments.

The absence of differential staffing adjustments by ownership status may reflect several factors. First, regulatory flexibilities commonly activated during federally-declared emergencies enable nursing homes across ownership categories to rapidly mobilize additional staffing, thus reducing potential differences attributable to administrative or regulatory factors that may influence staffing in routine-care settings (Levitt and Ling, 2020; Peterson, 2025). Second, Hurricane Ian’s severity and extensive geographic impact likely exerted similar constraints across local labor markets for all facilities, limiting opportunities for differential staffing adjustments (Bucci et al., 2023; Jester et al., 2023). Third, although chain ownership comprised approximately two-thirds of studied for-profit nursing homes and could theoretically facilitate internal redistribution of staffing resources (Banaszak-Holl et al., 2018), we did not observe differences in disaster-related staffing strategies between chain-affiliated and for-profit homes under alternative ownership arrangements. Thus, chain affiliation alone did not appear to influence the magnitude or composition of staffing responses, suggesting that other structural or organizational factors may have contributed to the similarity in staffing adaptations observed across nursing homes of different ownership categories.

While statutory interpretation and legislation governing nursing home staffing standards continue to evolve (Medicare and Medicaid Programs; Minimum Staffing Standards for Long-Term Care Facilities and Medicaid Institutional Payment Transparency Reporting, 2024; One Big Beautiful Bill Act, Pub. L. No. 119–21, § 71111, 2025), our finding that ownership-based baseline staffing differences persist throughout disaster response periods remains salient to nursing home oversight. The federal standards established in the 2024 Minimum Staffing Rule have since been repealed, with federal requirements reverting to the 2016 final rule, under which facilities retain greater discretion in determining staffing above a fixed floor for RN coverage (Department of Health and Human Services, 2016, 2025). Considering these developments, our findings may inform both future federal deliberations and nursing-home-level preparedness planning. The persistence of for-profit baseline staffing shortfalls throughout the disaster response, albeit without exacerbation, suggests that residents of these facilities may remain at elevated risk for adverse post-disaster outcomes independent of short-term staffing adjustments (Dosa et al., 2008; Thomas et al., 2012). Consequently, preparedness strategies that solely emphasize surge staffing may prove insufficient without concurrent attention to baseline staffing deficits.

Limitations

Five limitations merit consideration. First, our study evaluated staffing responses to a single event, Hurricane Ian (2022), in the southeastern United States, and the findings may therefore not generalize to other time periods, events, or distinct geographic contexts. Additional studies involving distinct events across different geographies and time periods are required to confirm these findings. Second, although the benefits of increased employed staffing and skill composition within nursing homes are well established in routine care settings, evidence is limited regarding how these staffing-outcome associations translate to disaster contexts. Because the relationship between staffing and resident outcomes may differ during emergencies—due to disrupted care processes or abrupt increases in resident acuity— future research should evaluate whether persistent ownership-based staffing differences during disasters lead to differential resident outcomes. Third, we applied a dichotomous ownership classification in the primary analysis, which may obscure within-group variation among for-profit facilities (Braun et al., 2020, 2021); we addressed this through a subgroup analysis in which we compared for-profit nursing homes across ownership distinctions. Fourth, we were unable to identify for-profit nursing homes that had undergone private equity acquisition due to data limitations. Given the influence of private equity acquisition on baseline staffing levels (Braun et al., 2020, 2021), future research should directly investigate how private equity ownership shapes disaster-related staffing decisions. Fifth, the data sources used for this analysis lacked day-level measures of resident acuity, and alternative baseline acuity measures (LTCFocus) were unavailable for the studied time period. While we employed random intercepts to account for stable, facility-level differences, including baseline acuity, this approach does not eliminate the potential for residual confounding due to dynamic shifts in acuity during disasters.

Conclusion

In this comparative interrupted time series analysis, we demonstrated that baseline staffing deficits observed in for-profit nursing homes relative to not-for-profit facilities persisted in response to Hurricane Ian, yet did not change in magnitude after disaster-related staffing adjustments were implemented. These findings indicate that, if the staffing-outcome associations observed in routine care extend to disaster contexts, residents of for-profit nursing homes may be susceptible to poorer post-disaster outcomes due to differences in baseline staffing, rather than distinct disaster-related staffing strategies. Disaster preparedness strategies designed to safeguard nursing home residents should therefore emphasize sustained improvements in everyday staffing practices, particularly within for-profit facilities, and prioritize supports that enable existing personnel to maintain continuity of care during emergencies.

Supplementary Material

1

What This Paper Adds to the Existing Literature

  • Provides novel, initial evidence that ownership-based staffing differences persist but do not widen in disaster-response.

  • Demonstrates that employed nursing home staff, rather than temporary contractors, were the primary contributors to staffing mobilization following disaster exposure, irrespective of ownership type.

Applications of the Findings to Gerontological Practice and Policy

  • Regulators may wish to target baseline nursing home staffing levels as a key lever for improving disaster preparedness.

  • Future research should investigate whether these persistent baseline staffing deficits lead to differential impacts on post-disaster outcomes for nursing home residents.

Funding/Support:

National Institute on Aging (NIA) R03AG088893 (NF)

Claude D. Pepper Older Adults Independence Center P30AG021342 (NF, ABC, TG)

The Patrick and Catherine Weldon Donaghue Medical Research Foundation Another Look Award with Stakeholder Organization The John A. Hartford Foundation (NF)

Health Resources & Services Administration (HRSA) Geriatric Workforce Education Program U1QHP28745 (NF)

Footnotes

Disclaimer: The contents of this manuscript are the sole responsibility of the authors and do not represent the views of the NIA, HRSA, The Patrick and Catherine Weldon Donaghue Medical Research Foundation or The John A. Hartford Foundation.

References

  1. Adams S, Dempsey M, Frazier L, Tayebali Z, Squillace M, Dey J, and Oliveira I (2025). Use of Contract Staff in Nursing Homes Remains High After the COVID-19 Pandemic. https://aspe.hhs.gov/sites/default/files/documents/0efd4ee9d0d0e9e67c5442141053d71c/contract-staff-nhs-high-after-covid.pdf
  2. An R, Qiu Y, Guan C, Xiang X, and Ji M (2019). Impact of Hurricane Katrina on Mental Health among US Adults. American Journal of Health Behavior, 43(6), 1186–1199. 10.5993/AJHB.43.6.15 [DOI] [PubMed] [Google Scholar]
  3. Banaszak-Holl J, Intrator O, Li J, Zheng Q, Stevenson DG, Grabowski DC, and Hirth RA (2018). The Impact of Chain Standardization on Nursing Home Staffing. Medical Care, 56(12), 994–1000. 10.1097/MLR.0000000000000998 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Braun RT, Jung HY, Casalino LP, Myslinski Z, and Unruh MA (2021). Association of Private Equity Investment in US Nursing Homes With the Quality and Cost of Care for Long-Stay Residents. JAMA Health Forum, 2(11), e213817–e213817. 10.1001/JAMAHEALTHFORUM.2021.3817 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Braun RT, Yun H, Casalino LP, Myslinski Z, Kuwonza FM, Jung HY, and Unruh MA (2020). Comparative Performance of Private Equity-Owned US Nursing Homes during the COVID-19 Pandemic. JAMA Network Open, 3(10). 10.1001/JAMANETWORKOPEN.2020.26702 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Brown LM, Dosa D, Thomas K, Hyer K, Feng Z, and Mor V (2012). The effects of evacuation on nursing home residents with dementia. American Journal of Alzheimer’s Disease and Other Dementias, 27(6), 406–412. 10.1177/1533317512454709 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Brown LM, Hyer K, and Polivka-West LM (2007). A comparative study of laws, rules, codes and other influences on nursing homes’ disaster preparedness in the Gulf Coast states. Behavioral Sciences & the Law, 25(5), 655–675. 10.1002/BSL.785 [DOI] [PubMed] [Google Scholar]
  8. Bucci L, Alaka L, Hagen A, Delgado S, and Beven J (2023). National Hurricane Center Tropical Cyclone Report: Hurricane Ian (AL092022). https://www.nhc.noaa.gov/data/tcr/AL092022_Ian.pdf
  9. Cai C, and Song Z (2023). A Policy Framework for the Growing Influence of Private Equity in Health Care Delivery. JAMA, 329(18), 1545–1546. 10.1001/JAMA.2023.2801 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Centers for Medicare & Medicaid Services. (2022). Staffing Data Submission Payroll Based Journal (PBJ). https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Staffing-Data-Submission-PBJ
  11. Comondore VR, Devereaux PJ, Zhou Q, Stone SB, Busse JW, Ravindran NC, Burns KE, Haines T, Stringer B, Cook DJ, Walter SD, Sullivan T, Berwanger O, Bhandari M, Banglawala S, Lavis JN, Petrisor B, Schünemann H, Walsh K, … Guyatt GH (2009). Quality of care in for-profit and not-for-profit nursing homes: systematic review and meta-analysis. The BMJ, 339(7717), 381–384. 10.1136/BMJ.B2732 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Department of Health and Human Services. (2016, October 4). 42 CFR Part 405, 431, 447, 482, 483, 485, 488, 489: Medicare and Medicaid Programs; Reform of Requirements for Long-Term Care Facilities. Federal Register. https://www.federalregister.gov/documents/2016/10/04/2016-23503/medicare-and-medicaid-programs-reform-of-requirements-for-long-term-care-facilities [PubMed] [Google Scholar]
  13. Department of Health and Human Services. (2025, December 3). 42 CFR Part 483: Medicare and Medicaid Programs; Repeal of Minimum Staffing Standards for Long-Term Care Facilities. Federal Register. https://www.federalregister.gov/documents/2025/12/03/2025-21792/medicare-and-medicaid-programs-repeal-of-minimum-staffing-standards-for-long-term-care-facilities [Google Scholar]
  14. Dosa D, Grossman N, Wetle T, and Mor V (2007). To Evacuate or Not to Evacuate: Lessons Learned From Louisiana Nursing Home Administrators Following Hurricanes Katrina and Rita. Journal of the American Medical Directors Association, 8(3), 142–149. 10.1016/J.JAMDA.2006.11.004 [DOI] [PubMed] [Google Scholar]
  15. Dosa D, Hyer K, Brown LM, Artenstein AW, Polivka-West LM, and Mor V (2008). The controversy inherent in managing frail nursing home residents during complex hurricane emergencies. Journal of the American Medical Directors Association, 9(8), 599–604. 10.1016/J.JAMDA.2008.05.007 [DOI] [PubMed] [Google Scholar]
  16. Dosa D, Hyer K, Thomas K, Swaminathan S, Feng Z, Brown L, and Mor V (2012). To Evacuate or Shelter in Place: Implications of Universal Hurricane Evacuation Policies on Nursing Home Residents. Journal of the American Medical Directors Association, 13(2), 190.e1. 10.1016/J.JAMDA.2011.07.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Dosa D, Skarha J, Peterson LJ, Jester DJ, Sakib N, Ogarek J, Thomas KS, Andel R, and Hyer K (2020). Association Between Exposure to Hurricane Irma and Mortality and Hospitalization in Florida Nursing Home Residents. JAMA Network Open, 3(10), e2019460. 10.1001/JAMANETWORKOPEN.2020.19460 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Frontz AJ (2024). Office of Inspector General: Certain For-Profit Nursing Homes May Not Have Complied with Federal Requirements Regarding the Infection Preventionist Position (Issue August). https://oig.hhs.gov/documents/audit/9967/A-01-22-00001.pdf
  19. Geng F, Stevenson DG, and Grabowski DC (2019). Daily nursing home staffing levels highly variable, often below CMS expectations. Health Affairs, 38(7), 1095–1100. 10.1377/hlthaff.2018.05322 [DOI] [PubMed] [Google Scholar]
  20. Hyer K, Brown LM, West LMP, and Berman A (2010). Helping nursing homes prepare for disasters. Health Affairs (Project Hope), 29(10), 1961–1965. 10.1377/HLTHAFF.2010.0665 [DOI] [PubMed] [Google Scholar]
  21. Ingram DD, and Franco SJ (2012). NCHS urban-rural classification scheme for counties. Vital and Health Statistics. Series 2, Data Evaluation and Methods Research, 154, 1–65. https://europepmc.org/article/med/22783637 [PubMed] [Google Scholar]
  22. Jester DJ, Peterson LJ, Andel R, and Dosa D (2023). Winter Storm Uri Reduced Availability of Daily Direct-Care Nurse Staff in Texas Nursing Homes. Journal of Applied Gerontology : The Official Journal of the Southern Gerontological Society, 42(10), 2148. 10.1177/07334648231175428 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Jester DJ, Peterson LJ, Thomas KS, Dosa D, and Andel R (2022). Nursing Home Compare Star Rating and Daily Direct-Care Nurse Staffing During Hurricane Irma. Journal of the American Medical Directors Association, 23(8), 1409–1412.e1. 10.1016/J.JAMDA.2021.09.038 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Jester DJ, Thomas KS, Peterson LJ, Dosa D, Andel R, and Hyer K (2021). Effect of Hurricane Irma on daily direct-care nurse staffing in nursing homes. Journal of the American Geriatrics Society, 69(8), 2298–2305. 10.1111/JGS.17220 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Jutkowitz E, Landsteiner A, Ratner E, Shippee T, Madrigal C, Ullman K, Linskens E, Wilt TJ, and Duan-Porter W (2023). Effects of Nurse Staffing on Resident Outcomes in Nursing Homes: A Systematic Review. Journal of the American Medical Directors Association, 24(1), 75–81.e11. 10.1016/J.JAMDA.2022.11.002 [DOI] [PubMed] [Google Scholar]
  26. Levitt AF, and Ling SM (2020). COVID-19 in the Long-Term Care Setting: The CMS Perspective. Journal of the American Geriatrics Society, 68(7), 1366–1369. 10.1111/JGS.16562 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Maxwell A (2023). Nursing Homes Reported Wide-Ranging Challenges Preparing for Public Health Emergencies and Natural Disasters.
  28. Medicare and Medicaid Programs; Minimum Staffing Standards for Long-Term Care Facilities and Medicaid Institutional Payment Transparency Reporting, Pub. L. No. 42 CFR Parts 438, 442, 483 (2024). https://www.federalregister.gov/documents/2024/05/10/2024-08273/medicare-and-medicaid-programs-minimum-staffing-standards-for-long-term-care-facilities-and-medicaid
  29. One Big Beautiful Bill Act, Pub. L. No. 119–21, § 71111 (2025). https://www.congress.gov/bill/119th-congress/house-bill/1/text
  30. Peterson LJ (2025). Navigating the Unexpected: Preparing for Challenges in Long-Term Care Quality and Safety. In Liu D and Castle NG (Eds.), Managing Quality and Safety in Long-Term Care (pp. 125–165). Springer Publishing Company. 10.1891/9780826149350 [DOI] [Google Scholar]
  31. Ryskina KL, Tu E, Liang J, Kim S, and Werner RM (2024). Nursing Home Compare star ratings before versus after a change in nursing home ownership. Journal of the American Geriatrics Society, 72(10). 10.1111/JGS.19104 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Skarha J, Gordon L, Sakib N, June J, Jester DJ, Peterson LJ, Andel R, and Dosa D (2021). Association of Power Outage With Mortality and Hospitalizations Among Florida Nursing Home Residents After Hurricane Irma. JAMA Health Forum, 2(11), e213900–e213900. 10.1001/JAMAHEALTHFORUM.2021.3900 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Stevenson D, Peterson H, Skinner R, Ndrianasy E, Braun RT, Unruh M, and Fernandez R (2023). Trends in Ownership Structures of U.S. Nursing Homes and the Relationship with Facility Traits and Quality of Care (2013–2022). https://aspe.hhs.gov/sites/default/files/documents/29b280bc8ec7632e5742ab466f5429d2/ownership-structures-nh-facility-traits.pdf [PubMed]
  34. Thomas KS, Dosa D, Hyer K, Brown LM, Swaminathan S, Feng Z, and Mor V (2012). The Impact of Forced Transitions on the Most Functionally Impaired Nursing Home Residents. Journal of the American Geriatrics Society, 60(10), 1895. 10.1111/J.1532-5415.2012.04146.X [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Thomas KS, Hyer K, Brown LM, Polivka-West L, and Branch LG (2010). Florida’s model of nursing home Medicaid reimbursement for disaster-related expenses. The Gerontologist, 50(2), 263–270. 10.1093/GERONT/GNP132 [DOI] [PubMed] [Google Scholar]
  36. van Buuren S, and Groothuis-Oudshoorn K (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1–67. 10.18637/JSS.V045.I03 [DOI] [Google Scholar]
  37. White AJ, and Olsho LEW (2023). Nursing Home Staffing Study. https://edit.cms.gov/files/document/nursing-home-staffing-study-final-report-appendix-june-2023.pdf

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