Altman Z-score and its application to Federally Qualified Health Centers

Introduction

Federally Qualified Health Centers (FQHCs) are the cornerstone of the U.S. healthcare system’s safety-net network. These community-based facilities provide comprehensive care to more than 31.5 million medically underserved people via their 13,000 service sites (1-3). FQHCs receive funding from Section 330 federal grants as well as revenues in the form of reimbursement from Medicare, Medicaid, and commercial health insurance payers (1). Despite the funding FQHCs receive, approximately one-half of FQHCs operated with narrow operating margins (i.e., <5%) in 2020, and most operated with negative margins (1). In addition, recent developments, such as the expected loss of Medicaid coverage, i.e., Medicaid unwinding for an estimated 15 million people over the latter part of 2024 and 2025, pose significant financial and operational challenges for these centers (2). According to a 2023 survey conducted by the National Association of Community Health Centers (NACHC), 85% of centers anticipate facing financial and operational strain due to the end of coronavirus disease 2019 (COVID-19)-related funding, with over 50% expecting to reduce staffing and scale back services on account of these developments (3).

Although FQHCs comprise a crucial component of the US healthcare system, their evaluation has often been neglected in policy and academia (4). For instance, only recently has the centers’ financial performance started to be evaluated regarding sustainability (5,6). For example, a previous study analyzed the financial performance of FQHCs using ratio analysis (6). FQHCs’ financial viability and sustainability are of utmost importance, considering they face potential grant funding cuts and delays, changes in Medicaid revenue rates, and uncertainties regarding the 340B drug program. Such changes can put FQHCs’ financial well-being in jeopardy, especially since many of them already operate on slim margins (5). Consequently, there is a need to predict whether FQHCs may be in a financially distressed position that could put their operations at risk of alteration or cessation.

Financial distress can be defined as “the late stage of an organization’s financial decline”. If unresolved, it may result in ceased business activities or reorganization of the entity (7). In response to the need to predict the financial health of operating organizations, the modified Altman Z-score has been proposed to predict a firm’s degree of financial distress (8-11).

The Altman Z-score has been used over the past five decades in multiple industries (i.e., railroad, retail, and service) to predict organizations’ financial distress (8-12). The score represents a weighted combination of liquidity, profitability, efficiency, productivity, and asset turnover ratios (8). In 1993, the model was used to examine general service organizations, including not-for-profit entities, and resulted in a modified version. This modified Altman Z-score has been used in the healthcare industry, both in hospitals and nursing homes, to forecast the financial health of these organizations (11,13,14). This study will be the first to apply it to FQHCs. The modified Altman Z-Score formula is reflected below:

Z=6.56X1+3.26X2+6.72X3+1.05X4[1]

where Z = overall index score; X1 = working capital/total assets (liquidity); X2 = retained earnings/total assets (profitability); X3 = earnings before interest and taxes/total assets (efficiency); X4 = book value total equity/total liabilities (productivity).

According to Altman’s recommendation for evaluating scores in the service industries, organizations with a score equal to or less than 1.1 are considered to be in “financial distress”. Those with a score greater than 1.1 but less than or equal to 2.6 are deemed to be in an area of “financial concern”, and those with a score greater than 2.6 are considered to be “financially sound” (8,15-17). The modified Z-score has been shown to have an accuracy of between 80% and 90% for predicting whether a company is headed for bankruptcy (18).

Our study explores whether the modified Altman Z-score can effectively predict financial distress in FQHCs that ultimately alter their operations, i.e., close or consolidate their operations. This investigation complements Weinman’s guidance for FQHCs to prepare for disproportionate impacts from anticipated funding reductions, which emphasizes how both external factors and internal capabilities significantly influence FQHC financial performance and long-term sustainability (19).

Methods

This study was a retrospective, longitudinal quantitative analysis using secondary datasets. The first dataset was sourced from the Uniform Data System (UDS). FQHCs provide the UDS data to the Health Resources and Services Administration (HRSA) annually. The UDS data is collected at the Center level and contains census data for all FQHCs operational in the country. It includes patient demographics, service scope, technology used, payer mix, outcomes, etc.

A total of 1,260 FQHCs reported data in 2017, 2018, and 2019. We excluded centers with missing data for any year in our study period (n=113). In addition, to reduce skewness in the dataset caused by outliers, we trimmed 0.25% of the sample bidirectionally (n=6). Our sample size was 1,141 FQHCs with 3,414 FQHC-year observations over the 3 years of analysis.

To conduct our analysis, we calculated the modified Altman Z-scores according to the above formula for each of the 1,141 centers for 2017, 2018, and 2019. A mean score was then calculated for each FQHC in our sample by averaging their score across the 3 years. The financial data used in our analysis was sourced from Candid, a non-profit organization that provides financial data for all non-profit organizations using Internal Revenue Service (IRS) 990 form filings. We categorized FQHCs into three categories for our analysis based on their calculated mean modified Altman Z-score. The first category comprises centers with a mean modified Altman Z-score less than or equal to 1.1, identified as a sign of financial distress (8). The second category comprised centers with a mean Altman score greater than 1.1 but less than or equal to 2.6, associated with financial concern. The last category comprised centers with a mean Altman score greater than 2.6, a sign of sound financial health. Data was merged using grant ID and zip codes as the identifiers.

To explore whether our model was predictive of altered operations (i.e., closure, consolidation, etc.) for FQHCs, we examined the data from 2022. Centers that ceased reporting their operations to HRSA within that period were identified. The 2-year lag period was chosen to allow for a meaningful influence of previous financial performance on the organization’s sustainability or lack thereof (20). A study team member manually confirmed the nature of the altered operations (i.e., closed, merged, consolidated, etc.) by analyzing the website for each FQHC. Financial distress, closure, or consolidation of FQHCs can significantly affect patient care and health equity. Financial instability can lead to reduced services, loss of employment opportunities, or clinic closures, disrupting the continuity of care, exacerbating barriers to access, and worsening health disparities. Provider mergers have been shown to have a negative effect on local populations, often similar to closures (21,22). Therefore, the two categories (closure and merger) were consolidated into one category: altered operations.

An unadjusted logistic regression was performed, with the dependent variable being a dichotomous variable defined as altered operations (i.e., closure or consolidation). If the FQHC reported altered operations as of 2022, it was coded as 1; if reporting continued operations, it was coded as 0. Further, we performed an adjusted logistic regression that included additional covariates representing several organizational and demographic characteristics.

The organizational variables included the geographic location of the center, grants dependence ratio, and the age of the center. Demographic factors included the percentage of the county population over the age of 65 years, the percentage of uninsured at the county level, and the Medicaid expansion status of the state where each Center was located. These variables have previously been used as control variables in studies analyzing the financial performance of FQHCs (5,23). Data management and variable calculations were conducted using MS Excel and Python’s Pandas package (24,25). All analyses were performed using StataNow/SE 18.5, standard errors were clustered at the state level, and a confidence interval (CI) of 95% was used to report the statistical significance of our findings (26).

Results

Table 1 reports our descriptive statistics. Our sample consisted of 1,141 centers (3,414 FQHC-year observations) with a mean modified Altman Z-score of 9.56, which ranged from −11.36 to 51.43. A total of 23 centers reported a negative mean modified Altman Z-scores, representing 2.02% of our total sample.

As reflected in Table 1, 91.5% (n=1,044) of the FQHCs in our sample were considered to be financially sound, with a modified Altman Z-score greater than 2.6. However, 8.5% (n=97) of all centers had a mean modified Altman Z-score lower than or equal to 2.6, which denotes a cause for concern. Of these 97 centers, 45.36% (n=44) reported mean scores lower than or equal to 1.1, defined as financially distressed.

Table 2 reflects the results from our unadjusted logistic regression model. Centers in the financially distressed category (i.e., with an average Altman Z-score lower than or equal to 1.1) had 7.93 greater odds (P=0.001; 95% CI: 2.39–26.37) of altered operations when compared to centers in the financially sound category. Although not statistically significant, centers reporting financially concerning Altman Z-scores had 1.5 times greater odds (OR =1.53; 95% CI: 0.18–13.15) of altered operations compared to centers in the financially sound category.

The adjusted logistic regression model, presented in Table 3, allowed us to complement and extend our findings from the unadjusted logistic regression by testing whether our results held, controlling for organizational and demographic factors. As reflected in Table 3, financially distressed FQHCs reported 9.32 greater odds (P<0.001; 95% CI: 2.87–30.29) of altered operations compared to financially sound FQHCs. FQHCs in the financially concerning category reported 1.68 greater odds (95% CI: 0.16–17.72) of altered operations compared to financially healthy FQHCs, although not statistically significant. The adjusted logistic regression supported the findings of our unadjusted model, indicating that the modified Altman Z-score is a statistically significant predictor of altered operations for FQHCs.

We also performed a sensitivity analysis in which we only included organizational variables sourced from the UDS and Candid (i.e., Form 990s) datasets. This allowed us to expand our sample size to 1170 FQHCs. Our findings largely remain unchanged. In our unadjusted logistics regression sensitivity analysis, centers in the financially distressed category reported 5.67 greater odds (P<0.001; 95% CI: 2.05–15.69) of altered operations when compared to their financially sound counterparts and 5.85 greater odds (P<0.001; 95% CI: 2.01–17.01) of altered operations in the adjusted logistics regression model.

Discussion

The dual analytic approach of utilizing both an unadjusted and an adjusted logistic regression analysis strengthens our understanding of FQHC financial health by identifying centers at risk of altered operations. As previously noted, FQHCs in the financially distressed category had significantly greater odds of experiencing altered operations than centers in the best-performing category when using the unadjusted and adjusted models. This finding is consistent with previous research using the Altman Z-score to predict financial distress and potential failure in various industries, including healthcare providers, such as nursing homes (9,12,27). This result highlights the importance of monitoring and addressing the financial challenges faced by FQHCs to ensure their financial and operational sustainability.

The variability in the modified Altman Z-scores across FQHCs may reflect differences in external resources and internal capabilities. This variability can be influenced by various contextual factors, such as state-level policies (e.g., higher Medicaid reimbursement rates, more generous grant funding), local economic conditions, and patient demographics (e.g., higher poverty rates, uninsured populations, chronic diseases, etc.). For example, FQHCs operating in states with more supportive policies, such as those adopting Medicaid expansion, may have better financial performance than those in states with less favorable policies (5,6). Similarly, FQHCs serving communities with higher rates of poverty, uninsured populations, or chronic diseases may face greater financial challenges due to the increased demand for uncompensated care and complex patient needs (28).

Interestingly, only the percentage of uninsured patients demonstrated statistical significance among the organizational and demographic covariates examined in our adjusted model (P=0.001; OR =0.85; 95% CI: 0.77–0.93). This inverse relationship suggests that FQHCs serving areas with higher uninsured rates had lower odds of experiencing altered operations. This counterintuitive finding may reflect these centers’ enhanced capabilities to secure alternative funding sources, such as grants and donations, to compensate for limited insurance reimbursements or provide less exhaustive services to their patient population to minimize expenses.

The financial vulnerability patterns in our study provide evidence supporting Weinman’s strategic framework for FQHC sustainability (19). Our findings validate his recommendations for centers to reduce dependence on volatile funding sources like section 340-B revenue and federal grants. Centers showing stronger financial resilience align with Weinman’s emphasis on developing data-driven capabilities that demonstrate value to payers. The predictive power of modified Altman Z-scores offers FQHCs an analytical tool to implement Weinman’s guidance that centers must capture accurate financial data, measure performance effectively, and adapt quickly to industry changes—capabilities he identifies as essential for organizations that will “thrive” rather than “struggle, or perhaps even disappear” in an increasingly competitive healthcare environment (19).

The study’s findings have important implications for FQHC governance and leadership. FQHC boards and executive leaders can use the modified Altman Z-score to monitor their organization’s financial health, detect vulnerabilities, make informed decisions to enhance financial sustainability, set financial performance targets, track progress over time, and benchmark their performance against peer organizations. Furthermore, FQHC leaders could use the insights gained from this study to develop and implement strategic plans that prioritize financial stability, such as diversifying revenue streams, improving operational efficiency, and fostering partnerships with key stakeholders (29,30).

Our findings suggest that policymakers should consider providing targeted support and resources to FQHCs at higher risk of financial distress since losing FQHCs in an already underserved community may increase the burden on other safety-net providers, such as emergency departments and free clinics (2). Support could include increased funding, technical assistance, and regulatory flexibility to help these centers adapt to changing market conditions and maintain their mission of serving underserved populations.

Future research can build upon our findings by examining the factors contributing to higher Altman Z-scores in FQHCs, such as leadership characteristics, board composition, and community partnerships. Additionally, exploring the impact of FQHC altered operations on patient access to care, health outcomes, and healthcare costs could provide valuable insights for policymakers and healthcare leaders seeking to optimize the performance of the healthcare safety net system. Furthermore, the financial data for this analysis was sourced before the onset of the COVID-19 pandemic. A follow-up study post-COVID could examine how the pandemic and post-pandemic period affected FQHC sustainability and whether Z-scores were still predictive of altered operations in the sector. Future studies could also investigate the relationship between FQHCs’ modified Altman Z-scores and their ability to achieve and maintain favorable financial outcomes, which is represented by their possession of unique external and internal resources and capabilities.

The modified Altman Z-score can provide the basis for comparative analyses of the external and internal factors that contributed to the financial health of FQHCs in better-performing categories compared to those in financial distress. Also, building on the findings by Davlyatov et al. (5), FQHCs in states that expanded Medicaid under the Affordable Care Act had lower grant ratios. They were less reliant on grant funding for their financial sustainability. Incorporating Medicaid expansion as a variable in future Altman Z-score analyses is encouraged. It could provide additional insights into how external factors impact FQHCs’ financial performance and the likelihood of closure or consolidation.

Our study had several limitations. First, it relied on secondary datasets and focused on Center-level analysis, which may not have captured all the nuances of FQHCs’ financial performance. Second, the specific time examined [2017–2022] may not fully represent longer-term trends that could impact FQHCs’ financial performance. Third, as a quantitative study, we could only establish correlations, and causality was beyond the scope of the current study. Lastly, due to data limitations, we were unable to use state-level fixed effects. However, we did address this issue with the use of state-level control variables such as Medicaid expansion status.

Conclusions

This research demonstrates the modified Altman Z-score’s powerful predictive capability for FQHC financial distress. Our adjusted model revealed that financially distressed centers face more than nine times greater odds of closure or consolidation than financially sound FQHCs. These findings offer FQHC boards and executive leaders a validated financial monitoring tool that can change how they assess organizational stability and implement targeted interventions before operational viability is threatened.

For center leaders and managers of FQHCs, the Altman Z score could be a valuable addition to the dashboard FQHCs use to track their organization’s financial health. For policymakers, our results highlight the need for strategic resource allocation to support vulnerable centers, ensuring continued healthcare access for underserved communities.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Hospital Management and Health Policy for the series “Healthcare Finance: Drivers and Strategies to Improve Performance”. The article has undergone external peer review.

Data Sharing Statement: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-100/dss

Peer Review File: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-100/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-100/coif). The series “Healthcare Finance: Drivers and Strategies to Improve Performance” was commissioned by the editorial office without any funding or sponsorship. N.B. served as the unpaid Guest Editor of the series. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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