Subnational governments are important players in the financial markets, both as issuers of debt securities that pay for infrastructure and as investors that invest funds in the private markets to meet their cash management and employee retirement system needs. The financial markets changed dramatically in the late 2000s and these changes affected the state and local sector. After a discussion of the aggregate balance sheet of state and local governments, this article gives a brief reprise of the financial-market developments in the early 2000s and the subsequent meltdowns of 2007 and 2008, which affected state and local governments. As was true with the entire economy, the financial system debacle was followed by a widespread downturn in activity and this had a disproportionate impact on state and local government revenues. The financial stresses growing from the Great Recession led to major changes in the municipal bond market.

Objectives: To create a straightforward scoring procedure based on widely available, inexpensive financial data that provides an assessment of the financial health of a hospital. Design: Methodological study. Setting: Multicenter study. Participants: All hospitals and health systems reporting the required financial metrics in 2017 were included for a total of 1,075 participants. Interventions: We examined a list of 232 hospital financial indicators and used existing models and financial literature to select 30 metrics that sufficiently describe hospital operations. In a set of hospital financial data from 2017, we used Principal Coordinate Analysis to assess collinearity among variables and eliminated redundant variables. We isolated 10 unique variables, each assigned a weight equal to the share of its coefficient in a regression onto Moody's Credit Rating, our predefined gold standard. The sum of weighted variables is a single composite score named the Yale Hospital Financial Score (YHFS). Primary Outcome Measures: Ability to reproduce both financial trends from a "gold standard" metric and known associations with non-fiscal data. Results: The validity of the YHFS was evaluated by: (1) assessing its reproducibility with previously excluded data; (2) comparing it to existing models; and, (3) replicating known associations with non-fiscal data. Ten percent of the initial dataset had been reserved for validation and was not used in creating the model; the YHFS predicts 96.7% of the variation in this reserved sample, demonstrating reproducibility. The YHFS predicts 90.5% and 88.8% of the variation in Moody's and Standard and Poor's bond ratings, respectively, supporting its validity. As expected, larger hospitals had higher YHFS scores whereas a greater share of Medicare discharges correlated with lower YHFS scores. Conclusions: We created a reliable and publicly available composite score of hospital financial stability.

ObjectivesTo create a straightforward scoring procedure based on widely available, inexpensive financial data that provides an assessment of the financial health of a hospital.DesignMethodological study.SettingMulticentre study.ParticipantsAll hospitals and health systems reporting the required financial metrics in the USA in 2017 were included for a total of 1075 participants.InterventionsWe examined a list of 232 hospital financial indicators and used existing models and financial literature to select 30 metrics that sufficiently describe hospital operations. In a set of hospital financial data from 2017, we used principal coordinate analysis to assess collinearity among variables and eliminated redundant variables. We isolated 10 unique variables, each assigned a weight equal to the share of its coefficient in a regression onto Moody’s Credit Rating, our predefined gold standard. The sum of weighted variables is a single composite score named the Yale Hospital Financial Score (YHFS).Primary outcome measuresAbility to reproduce both financial trends from a ‘gold-standard’ metric and known associations with non-fiscal data.ResultsThe validity of the YHFS was evaluated by: (1) cross-validating it with previously excluded data; (2) comparing it to existing models and (3) replicating known associations with non-fiscal data. Ten per cent of the initial dataset had been reserved for validation and was not used in creating the model; the YHFS predicts 96.7% of the variation in this reserved sample, demonstrating reproducibility. The YHFS predicts 90.5% and 88.8% of the variation in Moody’s and Standard and Poor’s bond ratings, respectively, supporting its validity. As expected, larger hospitals had higher YHFS scores whereas a greater share of Medicare discharges correlated with lower YHFS scores.ConclusionsWe created a reliable and publicly available composite score of hospital financial stability.

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