Disclaimer: The Care Quality Index (CQI) is a proprietary composite score developed by The Care Ratings and is not a metric published or endorsed by the Centers for Medicare & Medicaid Services (CMS). CMS star ratings are norm-referenced within each state, meaning a 3-star facility in Texas is not directly comparable to a 3-star facility in Minnesota. Penalty-free rates reflect a three-year lookback period, and enforcement intensity varies significantly by state. All data reflects a specific point-in-time snapshot and is subject to quarterly revision. This article is intended for informational and research purposes only and does not constitute medical, legal, or financial advice. Correlation findings reported herein do not imply causation.
Introduction: The Demographic Imperative
The United States is confronting a long-anticipated demographic reckoning. According to the 2024 American Community Survey, more than 61.2 million Americans—approximately 18 percent of the total population—are now aged 65 or older [1]. This proportion is not merely large; it is growing at a rate not seen since the late nineteenth century. The U.S. Census Bureau documented a 38.6 percent increase in the 65-and-over population between 2010 and 2020 alone [2]. Looking further ahead, the direct care workforce research organization PHI projects that the population of adults aged 65 and older will grow from 57.8 million in 2022 to 88.8 million by 2060—a 53.6 percent increase in less than four decades [3].
This demographic wave has profound implications for the nation’s long-term care infrastructure. As of July 2025, the United States operated 14,742 certified nursing facilities, a figure that has declined by approximately 6 percent since 2015 even as the population requiring care has grown [4]. These facilities collectively house approximately 1.24 million residents, a number that itself represents a recovery from the COVID-19 pandemic low of 1.10 million in 2021. The convergence of rising demand and declining supply creates a structural pressure that amplifies the importance of quality—because when families have fewer choices, the quality of the choices available matters more.
It is within this context that The Care Ratings undertook a comprehensive, multi-dimensional analysis of nursing home quality across all 50 states. Our analysis synthesizes millions of federal inspection records, staffing hour logs, and civil money penalty records into a single, transparent composite metric: the Care Quality Index (CQI). The findings reveal a system deeply divided along geographic, economic, and regulatory lines—one in which the state where a person resides can be as determinative of their care quality as any individual facility characteristic.
Methodology: The Care Quality Index
Composite Score Construction
The CQI is a 100-point composite index constructed from three independently validated dimensions of nursing home performance. Each dimension was selected based on its established relationship to resident outcomes in peer-reviewed literature and its availability as a standardized, publicly auditable federal data element.
The three components and their respective weights are as follows:
| Component | Weight | Data Source |
|---|---|---|
| Average Overall CMS Five-Star Rating | 40% | CMS Provider Data Catalog |
| 3-Year Zero-Penalty Rate (% of facilities penalty-free) | 35% | CMS Enforcement Data |
| Staffing Hours vs. Federal Minimums | 25% | CMS Payroll-Based Journal (PBJ) |
The composite formula is expressed as:
CQI = (Avg. CMS Rating × 0.40) + (Penalty-Free Rate × 0.35) + (Staffing Compliance Score × 0.25)
Each component is normalized to a 0–100 scale prior to weighting. The CMS star rating dimension uses the state-level average of the Overall Five-Star Rating, which itself is a composite of Health Inspection, Staffing, and Quality Measure sub-ratings. The penalty-free dimension captures the proportion of a state’s facilities that received zero civil money penalties or payment denials over the most recent three-year period. The staffing dimension measures the degree to which facilities in a given state meet or exceed the federal minimum staffing thresholds established by CMS.
Limitations and Caveats
Several important limitations govern the interpretation of CQI scores. First, because CMS star ratings are norm-referenced—meaning they are distributed relative to national performance curves rather than against absolute benchmarks—a state’s average star rating reflects its relative standing within the national distribution at a given point in time, not an absolute measure of care quality. Second, penalty-free rates are sensitive to enforcement intensity, which varies substantially by state and CMS regional office. A high penalty-free rate may reflect either genuinely compliant facilities or a less aggressive inspection regime. Third, Alaska is flagged throughout this analysis with a small-sample caveat (†), as its nursing home population comprises fewer than 30 facilities, rendering its CQI score statistically imprecise. Fourth, the staffing component reflects compliance with federal minimums as they existed at the time of data collection; the Biden administration’s April 2024 minimum staffing final rule—which mandated 3.48 hours of total nursing care per resident per day and a minimum of 0.55 registered nurse hours—was subsequently rescinded in December 2025, introducing uncertainty into this dimension’s future comparability [4].
National Findings: A System at the Midpoint of Adequacy
Descriptive Statistics
The national CQI dataset yields the following key descriptive statistics (all 50 states included unless otherwise noted):
| Statistic | Value |
|---|---|
| National Mean CQI (excluding Alaska†) | 48.3 |
| National Median CQI | 50.0 |
| Standard Deviation | 7.8 |
| Minimum CQI | 30 (Illinois) |
| Maximum CQI (excluding Alaska†) | 63 (Alabama) |
| Maximum CQI (including Alaska†) | 74 (Alaska) |
| Interquartile Range (Q1–Q3) | 44–53 |
| National Mean Avg. CMS Rating | 3.03 stars |
| National Mean Penalty-Free Rate | 51.1% |
| Std. Dev. of Penalty-Free Rate | 14.3 percentage points |
The national mean CQI of 48.3 is sobering. On a 100-point scale designed to capture the full range of possible performance, the average American state scores just below the midpoint. The interquartile range of 44 to 53 indicates that the middle 50 percent of states are clustered within a relatively narrow 9-point band, while the tails of the distribution—particularly the lower tail—exhibit more extreme variation.
The average CMS star rating of 3.03 across all states is consistent with the national distribution that CMS’s norm-referencing methodology is designed to produce (approximately equal numbers of 1-, 2-, 3-, 4-, and 5-star facilities nationally). However, this national average masks enormous state-level variation, ranging from a low of 2.4 stars in Louisiana to a high of 3.6 stars in Hawaii.
The penalty-free rate exhibits the widest dispersion of any component, with a standard deviation of 14.3 percentage points. Rhode Island records the lowest penalty-free rate in the nation at 25 percent, while Alabama records the highest at 80 percent—a 55-percentage-point spread that reflects the vast differences in state enforcement philosophies and regulatory capacity.
Distribution by Performance Tier
The CQI distribution across the four performance tiers reveals a system in which the majority of states cluster in the middle ranges, with a pronounced skew toward underperformance at the bottom:
Tier 1 — Excellent (CQI ≥ 60): 5 states
Alaska (74†), Alabama (63), Hawaii (60), Maine (60), Minnesota (60)
Tier 2 — Good (CQI 50–59): 20 states
Arizona (59), Arkansas (56), North Dakota (56), Wisconsin (56), Iowa (55), Idaho (55), Nebraska (55), Delaware (53), Indiana (53), Michigan (53), Oregon (53), Florida (52), New Hampshire (52), Kansas (51), Maryland (51), New Jersey (51), Nevada (51), New York (51), California (50), Ohio (50)
Tier 3 — Fair (CQI 40–49): 19 states
Kentucky (49), Utah (49), Virginia (49), Mississippi (48), Pennsylvania (48), Connecticut (47), Tennessee (47), Washington (46), Colorado (45), Massachusetts (44), Rhode Island (44), South Carolina (44), Wyoming (44), Montana (43), South Dakota (43), Georgia (42), Oklahoma (40), Vermont (40), West Virginia (40)
Tier 4 — Poor (CQI < 40): 6 states
North Carolina (39), Missouri (37), New Mexico (36), Louisiana (34), Texas (31), Illinois (30)
The fact that 25 states—exactly half the nation—fall in the “Fair” or “Poor” categories is a finding that should concern policymakers and families alike. It suggests that the current regulatory and financing framework is insufficient to drive quality above a mediocre baseline in a significant portion of the country.
Distribution of States by Care Quality Index Score
Source: The Care Ratings CQI Dataset, Q1 2026
Regional Analysis: The Geography of Care Quality
Census Region Rankings
Applying the nine U.S. Census Bureau geographic divisions to the CQI dataset reveals clear regional patterns. The following table presents each region’s average CQI, average CMS star rating, and average penalty-free rate, ranked from highest to lowest CQI:
Average Care Quality Index by U.S. Census Region
Source: The Care Ratings CQI Dataset, Q1 2026 | thecareratings.com
| Census Region | Avg. CQI | Avg. CMS Rating | Avg. Penalty-Free % |
|---|---|---|---|
| Pacific (excl. Alaska) | 52.2 | 3.25 | 43.5% |
| East South Central | 51.8 | 2.90 | 64.5% |
| West North Central | 51.0 | 2.97 | 50.1% |
| Mid-Atlantic | 50.0 | 3.10 | 59.0% |
| East North Central | 48.4 | 3.00 | 55.6% |
| New England | 47.8 | 2.98 | 47.0% |
| Mountain | 47.8 | 3.12 | 46.5% |
| South Atlantic | 46.2 | 3.00 | 50.1% |
| West South Central | 40.2 | 2.80 | 47.5% |
The Pacific region’s leadership is driven by Hawaii’s high CMS rating (3.6 stars) and Oregon’s strong compliance record. The East South Central region’s second-place ranking is heavily influenced by Alabama’s anomalously high penalty-free rate of 80 percent, a finding that warrants careful interpretation (discussed in detail below). The West South Central region’s last-place finish is consistent with the well-documented quality challenges in Texas and Louisiana, which together account for two of the six “Poor” tier states.
The West South Central Crisis
The West South Central region—Arkansas, Louisiana, Oklahoma, and Texas—presents the most acute quality challenge in the nation, with an average CQI of 40.2. Texas alone operates 1,177 certified nursing facilities, the largest inventory of any state in the country [5]. This scale creates both an opportunity and a challenge: with so many facilities, even marginal improvements in average quality could benefit hundreds of thousands of residents, but the sheer volume of facilities also makes comprehensive oversight more difficult.
Louisiana’s CQI of 34 is particularly alarming. The state records the lowest average CMS star rating in the nation at 2.4 stars, and only 45 percent of its facilities have remained penalty-free over the three-year lookback period. Louisiana’s challenges are compounded by high Medicaid dependency—the state has one of the highest proportions of Medicaid-funded nursing home residents in the country—and chronic underfunding of Medicaid reimbursement rates, which limits facilities’ ability to invest in staffing and quality improvement.
The Pacific Advantage
The Pacific region’s relative strength is not accidental. California, Hawaii, Oregon, and Washington have all enacted state-level staffing mandates and quality standards that exceed federal minimums. California, for example, requires a minimum of 3.5 nursing hours per resident per day—a standard that predates and exceeds the now-rescinded federal rule [6]. Hawaii’s geographic isolation, while limiting the number of facilities (and thus creating a small-sample caveat similar to Alaska's), may also concentrate higher-quality facilities in urban centers where oversight is more consistent.
The Penalty-Free Paradox: Enforcement Intensity vs. Care Quality
Statistical Relationship Between Metrics
One of the most analytically significant findings of this study is the weak correlation between a state’s average CMS Star Rating and its Penalty-Free Percentage. Pearson correlation analysis of the 49-state dataset (excluding Alaska) yields the following coefficients:
| Correlation Pair | Pearson r | Interpretation |
|---|---|---|
| CQI ↔ Avg. CMS Rating | 0.716 | Strong positive |
| CQI ↔ Penalty-Free % | 0.608 | Moderate positive |
| Avg. CMS Rating ↔ Penalty-Free % | 0.195 | Very weak positive |
The strong correlation between CQI and both of its major components (CMS rating and penalty-free rate) validates the composite index’s internal consistency. However, the near-zero correlation between the CMS star rating and the penalty-free rate (r = 0.195) is the most revealing finding. It demonstrates that these two dimensions are measuring largely independent aspects of nursing home performance—and that a state can score well on one while performing poorly on the other.
CQI Score vs. Average CMS Star Rating by State (excl. Alaska)
Source: The Care Ratings CQI Dataset, Q1 2026 | CMS Provider Data Catalog
Case Study: Alabama
Alabama’s CQI of 63 places it second in the nation (excluding Alaska), driven primarily by its extraordinary penalty-free rate of 80 percent—the highest of any state. Yet Alabama’s average CMS star rating of 3.0 is precisely at the national mean, suggesting that its facilities are not delivering care that is measurably above average on the clinical quality dimensions captured by the star rating system.
Several hypotheses may explain this divergence. First, Alabama may have a less aggressive inspection and enforcement regime, resulting in fewer citations and penalties even when care quality is marginal. The Long Term Care Community Coalition’s data on regional enforcement patterns is instructive here: CMS Region 4 (Atlanta), which oversees Alabama, has historically had lower rates of harm-level citations than regions like Region 5 (Chicago) or Region 8 (Denver) [7]. Second, Alabama’s high penalty-free rate may reflect a genuine improvement trend in regulatory compliance that has not yet translated into higher star ratings, which are updated on a rolling basis. Third, the state’s relatively lower population density and smaller total number of nursing facilities may make comprehensive oversight more manageable, reducing the likelihood of enforcement actions.
Case Study: Illinois
Illinois presents the inverse of the Alabama paradox. With a CQI of 30—the lowest in the nation—Illinois combines a dismal average CMS rating of 2.5 stars with a penalty-free rate of just 29 percent. The LTCCC’s July 2024 data confirms that Illinois nursing homes received the highest total civil money penalties of any state over the preceding three years: $74 million in fines, with an average fine of $34,000 per action [7]. CMS Region 5 (Chicago), which oversees Illinois, recorded the highest total fines of any CMS region nationally at $177 million.
This combination of high enforcement activity and low quality ratings suggests that penalties in Illinois are not functioning as an effective deterrent or quality improvement mechanism. Research on the effectiveness of civil money penalties in nursing home quality improvement has consistently found that fines must be sufficiently large and consistently applied to produce behavioral change among facility operators [8]. The data suggests that while Illinois is aggressive in its enforcement, the underlying structural conditions—including high for-profit ownership concentration, high Medicaid dependency, and workforce shortages in the Chicago metropolitan area—are not being adequately addressed by the penalty system alone.
Case Study: Maine
Maine’s CQI of 60 places it in the top tier nationally, and it is notable for a different reason: the LTCCC data identifies Maine as the state with the lowest total civil money penalties of any state in the nation [7]. This finding, combined with a penalty-free rate of 60 percent and an average CMS rating of 3.0, suggests a regulatory environment in which facilities are generally compliant and enforcement actions are infrequent—not because oversight is lax, but because facilities are meeting standards.
Maine’s performance is particularly impressive given its demographic context. As of 2024, nearly 25 percent of Maine’s population is aged 65 or older, making it the oldest state in the nation by proportion [9]. This creates extraordinary demand pressure on nursing facilities, yet Maine’s facilities maintain above-average quality metrics. Research suggests that states with older populations often develop more robust long-term care policy frameworks in response to constituent demand, creating a virtuous cycle of investment and accountability.
Structural Drivers: Ownership, Staffing, and Medicaid
The For-Profit Ownership Effect
The ownership structure of nursing facilities is one of the most consistently documented predictors of care quality in the academic literature. A landmark systematic review and meta-analysis by Comondore et al., published in the BMJ in 2009 and cited over 600 times, found that not-for-profit nursing homes deliver higher quality care than for-profit facilities across a range of outcome measures, including staffing levels, deficiency citations, and resident satisfaction [10]. This finding has been replicated in numerous subsequent studies.
According to the Kaiser Family Foundation’s 2025 analysis, 73 percent of U.S. nursing facilities are for-profit entities, 20 percent are non-profit, and 7 percent are government-owned [4]. The states that rank lowest on the CQI—Illinois, Texas, Louisiana, and Missouri—all have above-average concentrations of for-profit facilities. Conversely, several of the top-performing states, including Maine and Minnesota, have higher-than-average proportions of non-profit and government-owned facilities.
The mechanism through which ownership affects quality is primarily financial. For-profit facilities are subject to shareholder return expectations that can create pressure to minimize labor costs—the largest single expense in nursing home operations. Staffing reductions, particularly in registered nurse hours, are a common cost-control measure that directly impacts care quality. The KFF’s 2025 analysis found that average nursing care hours per resident per day have declined from 4.13 in 2015 to 3.85 in 2025, with registered nurse hours declining by 19 percent over the same period [4].
The Staffing Crisis
The staffing dimension of the CQI captures one of the most acute operational challenges facing the nursing home sector. Research by Bostick et al. established through systematic review that staffing levels are strongly and positively correlated with quality outcomes across multiple domains, including pressure ulcer rates, restraint use, deficiency citations, and resident satisfaction [11]. More recent work by Mukamel et al., published in JAMA Network Open in 2022, found that daily variation in staffing levels—not just average staffing—is significantly associated with Five-Star Survey and Quality Measure scores [12].
The Biden administration’s April 2024 final rule establishing minimum staffing standards—3.48 total nursing hours per resident per day and 0.55 registered nurse hours—was intended to address this crisis. However, the rule was challenged in federal court (with a Texas judge overturning key elements in May 2025) and ultimately rescinded in December 2025 [4]. At the time of the rule’s passage, only 19 percent of nursing facilities nationally met its requirements, indicating the scale of the staffing gap that the rule sought to address.
The states that score lowest on the staffing component of the CQI are disproportionately concentrated in the South and Midwest, regions that also face significant healthcare workforce shortages. The direct care workforce is projected to need to grow substantially to meet future demand: PHI estimates that by 2060, the number of direct care workers needed will increase dramatically as the 65-and-over population nearly doubles [3].
The Medicaid Dependency Problem
A critical structural factor that does not appear directly in the CQI but underlies many of its findings is Medicaid dependency. According to the KFF, Medicaid is the primary payer for approximately 63 percent of nursing home residents nationally [4]. Medicaid reimbursement rates are set by states and are often below the actual cost of care, creating a persistent funding gap that facilities must absorb or offset through other means.
Research by Mor et al., published in the Milbank Quarterly and cited over 630 times, documented a “driven to tiers” phenomenon in which nursing homes serving high proportions of Medicaid-dependent residents—who tend to be lower-income and disproportionately minority—consistently deliver lower quality care than facilities serving primarily Medicare and private-pay residents [13]. This finding has profound equity implications: the residents who are most dependent on the public system for their care are systematically receiving lower quality care.
The 2025 federal budget reconciliation process introduced an additional threat to Medicaid-dependent nursing facilities, with proposed cuts of $911 billion to Medicaid over 10 years [4]. If enacted, these cuts would disproportionately impact the states and facilities that already score lowest on the CQI, potentially widening the quality gap further.
Outlier Analysis: States That Defy Regional Trends
Alabama: The Southern Anomaly
Alabama’s CQI of 63 makes it a striking outlier within the South Atlantic and East South Central regions, where most states score in the 40s. Several factors may contribute to this anomalous performance. Alabama has a relatively small total number of nursing facilities compared to its population, which may make oversight more manageable. The state also has a higher-than-average proportion of non-profit and faith-based nursing facilities, which the literature consistently associates with better quality outcomes. Additionally, Alabama’s low per-capita income may paradoxically reduce the demand for premium-priced facilities, concentrating residents in a smaller number of facilities that receive more consistent regulatory attention.
Hawaii: The Island Advantage
Hawaii’s CQI of 60, driven by the highest average CMS star rating in the dataset (3.6 stars), reflects the unique characteristics of an island healthcare system. Geographic isolation limits the total number of nursing facilities, concentrating care in a smaller number of urban facilities in Honolulu and the other major islands. This concentration may facilitate more consistent oversight and create competitive pressure among a smaller number of providers. Hawaii also has one of the highest per-capita incomes in the nation and a strong tradition of community-based elder care rooted in its multicultural heritage, which may reduce demand for institutional care and concentrate nursing home residents among those with more complex needs—paradoxically driving up the average quality of care delivered.
North Carolina: The Underperforming Neighbor
North Carolina’s CQI of 39 places it in the “Poor” tier, making it a notable outlier within the South Atlantic region, where several neighboring states score in the high 40s. North Carolina’s penalty-free rate of 39 percent is among the lowest in the region, and its average CMS rating of 2.9 stars falls below the national mean. The state has a large and rapidly growing elderly population, with significant rural-urban disparities in facility quality. Research on rural nursing home quality consistently finds that rural facilities have higher rates of deficiencies and lower staffing levels than their urban counterparts [14], and North Carolina’s large rural population may be a contributing factor.
Implications for Families and Policymakers
For Families: Using the CQI as a Starting Point
The CQI is designed to serve as a high-level geographic orientation tool for families beginning the process of evaluating nursing home options. A state’s CQI score provides important context about the regulatory environment and average quality standards within which individual facilities operate. However, families should always evaluate individual facilities using the full suite of data available on The Care Ratings, including facility-level CMS star ratings, recent inspection reports, staffing data, and resident and family reviews.
Families in “Poor” tier states (CQI < 40) should be particularly diligent in their facility-level research, as the average quality floor in these states is lower and the variance between facilities may be higher. Conversely, families in “Excellent” tier states should not assume that all facilities within the state are high-quality; even in top-performing states, individual facilities can fall well below the state average.
For Policymakers: Targeted Interventions
The regional clustering of low-performing states in the West South Central region suggests that targeted federal and regional interventions may be more effective than uniform national policies. The CMS regional office structure provides a natural mechanism for such interventions: CMS Region 6 (Dallas), which oversees Texas, Louisiana, Oklahoma, and New Mexico, oversees four of the six “Poor” tier states. Targeted technical assistance, enhanced oversight, and Medicaid rate reform in this region could have an outsized impact on national average quality.
The Illinois case demonstrates the limitations of enforcement-only approaches to quality improvement. While aggressive civil money penalties are necessary for accountability, they are insufficient to address the structural drivers of low quality—particularly for-profit ownership concentration, Medicaid dependency, and workforce shortages. Comprehensive reform in Illinois and similar states will require a multi-pronged approach that addresses financing, workforce development, and ownership accountability simultaneously.
Frequently Asked Questions
What is the Care Quality Index (CQI) and how is it calculated?
The Care Quality Index is a proprietary 100-point composite score developed by The Care Ratings to provide a standardized, state-level measure of nursing home quality. It is calculated by weighting three components: the Average Overall CMS Five-Star Rating (40%), the 3-Year Zero-Penalty Rate (35%), and Staffing Hours versus Federal Minimums (25%). Each component is normalized to a 0–100 scale before weighting. The CQI is updated quarterly as new CMS data becomes available.
Why is Alaska ranked #1 but flagged with a dagger (†)?
Alaska’s CQI of 74 is the highest in the nation, but it is flagged because Alaska has fewer than 30 certified nursing facilities—a sample size too small to be statistically representative of a complex, large-scale healthcare system. Alaska’s high score is driven by a combination of a 70% penalty-free rate and a 3.5-star average CMS rating. However, the small number of facilities means that a single outlier facility can significantly skew the state average. Alaska’s geographic remoteness also limits the frequency of federal oversight inspections, which may affect the comparability of its data to more densely populated states.
Does a high Penalty-Free Percentage always mean better care?
No. Our analysis identified what we term the “Penalty-Free Paradox”: a very weak correlation (Pearson r = 0.195) between a state’s average CMS star rating and its penalty-free rate. A high penalty-free rate can reflect either genuinely compliant, high-quality facilities or a less aggressive enforcement environment in which facilities receive fewer citations and penalties regardless of their actual care quality. Alabama’s 80% penalty-free rate paired with only a 3.0-star average CMS rating is the clearest example of this paradox in the dataset.
Why do for-profit nursing homes tend to score lower on quality metrics?
The relationship between for-profit ownership and lower quality is one of the most consistently documented findings in nursing home research. The primary mechanism is financial: for-profit facilities are subject to shareholder return expectations that can create pressure to minimize labor costs, particularly nursing staff, which is the largest expense in nursing home operations. Staffing reductions directly impact care quality across multiple outcome measures. A systematic review by Comondore et al. (2009), cited over 600 times in the academic literature, found that not-for-profit nursing homes deliver higher quality care than for-profit facilities across a range of measures.
How does Medicaid funding affect nursing home quality?
Medicaid is the primary payer for approximately 63% of nursing home residents nationally. Medicaid reimbursement rates are set by states and are frequently below the actual cost of care, creating a funding gap that facilities must absorb. Research has documented a “driven to tiers” phenomenon in which facilities serving high proportions of Medicaid-dependent residents consistently deliver lower quality care than those serving primarily Medicare and private-pay residents. This creates a systemic equity problem: the residents most dependent on public funding for their care are systematically receiving lower quality care.
What happened to the federal minimum staffing rule?
The Biden administration finalized a minimum staffing rule in April 2024, requiring nursing homes to provide at least 3.48 total nursing hours per resident per day, including a minimum of 0.55 registered nurse hours. However, the rule faced significant legal challenges—a federal judge in Texas overturned key elements in May 2025—and was ultimately rescinded by the federal government in December 2025. At the time of the rule’s passage, only 19% of nursing facilities nationally met its requirements, illustrating the scale of the staffing gap that existed.
How often is the CQI updated?
The Care Quality Index is updated quarterly, aligned with CMS’s quarterly release cycle for the Five-Star Quality Rating System data. The penalty-free rate component is updated annually, as it reflects a three-year lookback period. The staffing component is updated monthly using CMS Payroll-Based Journal (PBJ) data. Users should note that significant changes in a state’s CQI score between quarters may reflect changes in the underlying CMS data, changes in the national distribution used for norm-referencing, or changes in enforcement activity.
Can I use the CQI to compare individual nursing homes?
No. The CQI is a state-level aggregate metric and is not designed to compare individual facilities. For facility-level comparisons, please use The Care Ratings’ individual facility profiles, which include facility-specific CMS star ratings, recent health inspection results, staffing data, and resident and family reviews. The CQI should be used as a geographic context tool—a starting point for understanding the regulatory environment and average quality standards in a given state—not as a substitute for facility-level due diligence.
What are the most important factors to consider when choosing a nursing home?
While the CQI provides useful state-level context, the most important factors in choosing an individual nursing home include: (1) the facility’s most recent health inspection report, including the number and severity of deficiencies cited; (2) staffing levels, particularly registered nurse hours per resident per day; (3) the facility’s overall CMS Five-Star Rating and its component sub-ratings; (4) the facility’s ownership structure and any recent changes in ownership; (5) the facility’s Medicaid and Medicare acceptance policies; and (6) reviews and testimonials from current and former residents and their families. The Care Ratings provides all of this information in a single, searchable platform at thecareratings.com.
Where does The Care Ratings get its data?
The Care Ratings synthesizes data from multiple authoritative federal and academic sources, including the CMS Provider Data Catalog (which includes the Five-Star Quality Rating System, Payroll-Based Journal staffing data, and civil money penalty records), the Long Term Care Community Coalition (LTCCC), the Kaiser Family Foundation (KFF), the U.S. Census Bureau, and peer-reviewed academic literature. All data sources are cited in the References section of this article.
References
- U.S. Census Bureau. (2024). Population 65 Years and Over in the United States. 2024 American Community Survey, 1-Year Estimates. data.census.gov
- U.S. Census Bureau. (2021). Older Population and Aging. census.gov
- PHI. (n.d.). Understanding the Direct Care Workforce: Key Facts & FAQ. phinational.org
- Chidambaram, P., & Burns, A. (2025, December 17). A Look at Nursing Facility Characteristics in 2025. Kaiser Family Foundation. kff.org
- Statista. (2026, March 3). Number of nursing homes by U.S. state 2025. statista.com
- California Department of Public Health. (2024). Nursing Home Staffing Requirements. cdph.ca.gov
- Long Term Care Community Coalition (LTCCC). (2024, July 18). LTCCC Alert: New Data on Nursing Home Citations and Penalties. nursinghome411.org
- Gammonley, D. (2019). State variation in nursing home civil money penalties. PMC. pmc.ncbi.nlm.nih.gov
- USAFacts. (2024). America is getting older: Which states have the largest elderly populations? usafacts.org
- Comondore, V. R., et al. (2009). Quality of care in for-profit and not-for-profit nursing homes: systematic review and meta-analysis. BMJ, 339, b2732. doi.org
- Bostick, J. E., Rantz, M. J., Flesner, M. K., & Riggs, C. J. (2006). Systematic review of studies of staffing and quality in nursing homes. Journal of the American Medical Directors Association, 7(6), 366–376.
- Mukamel, D. B., et al. (2022). Daily staffing variation and nursing home quality. JAMA Network Open, 5(1), e2143721.
- Mor, V., et al. (2004). Driven to tiers: Socioeconomic and racial disparities in the quality of nursing home care. Milbank Quarterly, 82(2), 227–256.
- Lutfiyya, M. N., Gessert, C. E., & Lipsky, M. S. (2013). Nursing home quality: A comparative analysis using CMS nursing home compare data to examine differences between rural and nonrural facilities. Journal of the American Medical Directors Association, 14(8), 593–598.
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