The Deterrent Effect of Tort Law: Evidence from Medical Malpractice Reform

Zenon Zabinski

Northwestern University Department of Economics

Bernard S. Black

Northwestern University Law School and Kellogg School of Management

Northwestern University, Institute for Policy Research Working Paper No. 13-xx

Northwestern University Law School

Law and Economics Research Paper No. 13-09

This paper can be downloaded without charge from the Social Science Research Network electronic library at:

http://ssrn.com/abstract=2131362

(Draft April 2014)

The Deterrent Effect of Tort Law: Evidence from Medical Malpractice Reform

Zenon Zabinski

Northwestern University, Department of Economics*

Bernard S. Black Northwestern University, Law School and Kellogg School of Management**

Abstract. A principal goal of tort law is to deter negligent behavior, but there is limited empirical evidence on whether it does so. We study that question for medical malpractice liability. We examine whether medical malpractice reforms affect in-hospital patient safety, using Patient Safety Indicators (PSIs) – measures of adverse events developed by the Agency for Healthcare Research and Quality – as proxies for overall safety. In Difference-in-Differences analyses of five states that adopt caps on non-economic damages during 2003-2005, we find consistent evidence that patient safety generally falls after the reforms, compared to control states.

We thank Michael Frakes, Cynthia Kinnan, Michelle Mello, Burt Weisbrod, and participants in the Robert

Wood Johnson Foundation Public Health Law Research Program 2012 Annual Meeting and workshops at Northwestern Law School and *to come] for helpful comments and suggestions.

* Ph.D. Candidate, Northwestern University, Department of Economics. Email: zzabinski@northwestern.edu.

** Nicholas J. Chabraja Professor, Northwestern University, Law School and Kellogg School of Management. Tel: 312-503-2784, email: bblack@northwestern.edu.

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I. INTRODUCTION

A principal goal of tort law is to deter negligent behavior. However, empirical evidence on a deterrent effect is scarce. To obtain convincing evidence, one needs an external shock to liability risk in one or more “treated” jurisdictions, and good before and after measures of the rate of negligence in both treated and control jurisdictions. Those circumstances are rare.

We study the deterrent effect of liability for negligence for medical malpractice (“med mal”). Med mal is a promising area to study because it provides large numbers of encounters between patients and providers and a reasonably supply of legal reform shocks to the liability regime. We first examine Texas, where we have both a large reform shock and complete patient-level administrative data on in-hospital outcomes. In 2003, the Texas legislature adopted a strict cap on noneconomic damages in med mal lawsuits and imposed other limits on med mal suits. The reforms led to a large drop in med mal claim rates and payouts per claim.1 We study whether lower med mal liability affected patient safety in hospitals. Our proxies for patient safety are Patient Safety Indicators (PSIs) -- standard measures of often preventable adverse events, developed by the Agency for Healthcare Research and Quality (AHRQ).

We use a Difference-in-Differences (“DiD”) research design, in which we measure PSI rates for hospitals in Texas and control states, both before and after the Texas reforms. We rely principally on patient-level data, but obtain similar results with hospital- or state-level data. Prior to reform, PSI rates are stable or declining in Texas, relative to control states. After reform, PSI rates gradually rise, consistent with hospitals gradually relaxing (or doing less to reinforce) patient safety standards. The rise in rates is seen both for most individual PSIs, and for pooled measures.

We then study four other states that adopt caps on non-economic damages (“damage caps”) at about the same time. For Florida, Georgia, and Illinois, we have a 20% random sample of discharges each year from the National Inpatient Sample (NIS); for South Carolina, we have a 100% sample of discharges from the State Inpatient Database (SID). We find consistent results across states. Damage cap adoption is followed by a broad increase in adverse patient safety events. The average rise is 10-15%, depending on the measure. The PSIs with death as the outcome (PSIs 2 and 4) are an exception – the point estimates are positive, but small and statistically insignificant. Thus, the additional adverse events rarely result in death. This may explain why other studies of the impact of med mal reform on healthcare quality, most of which use mortality as their outcome measure, find no significant effects.

Our results are consistent with classic tort law deterrence theory, in which med mal liability spurs healthcare providers to attend to care quality. When liability risk falls, preventable adverse events rise. This suggests that policy attention is needed to provide incentives for hospitals and physicians to prevent adverse events. Financial incentives are already perverse: Hospitals earn more revenue from patients who suffer complications than from patients who do not (Krupka, Sandberg and Weeks, 2012; Eappen et al., 2013). If the incentive provided by med mal liability becomes weaker, something else must take its place, or quality is likely to fall.

1 See Paik, Black, and Hyman et al. (2012, 2013); Carter (2006); Stewart et al. (2011).

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This paper proceeds as follows: Part II reviewing existing literature and describes the med mal reforms we study. Part III describes our data and measures of patient safety. Part IV presents a detailed analysis of Texas. Part V extends the analysis to the other four reform states. Part VI concludes.

II. BACKGROUND

A. General Evidence on the Deterrent Effect of Tort Law

There is limited quantitative evidence on the deterrent impact of tort law. The principal areas where this has been studied are auto accidents and medical malpractice. For auto accidents, the adoption of no-fault insurance by a number of states, principally in the 1970s, provides an opportunity for DiD analysis of how type of insurance affects accident rates. Some studies find that a switch from fault-based to no-fault insurance leads to higher fatal accident rates (e.g., Cummins, Phillips and Weiss, 2001; Cohen and Dehejia, 2004). But the most careful studies find no significant effect on either fatalities or accident rates (Loughran, 2001; Heaton and Helland, 2010).2

One reason for these weak results might be that these studies do not provide a clean test of the impact of care incentives that arise from negligence-based liability. Drivers have personal safety reasons to be careful, which might swamp from the potential impact of an accident on future auto insurance rates or on the remote risk of a personal payment. A shift from fault to no-fault insurance could also affect how insurers adjust insurance rates to reflect past accident history; thus it is not clear that no-fault rates are less sensitive to that history.

Rubin and Shepherd (2007) report that caps on non-economic damages, predict fewer accidental deaths. The mechanism for this counter-intuitive result is unclear.3 Carvell, Currie, and MacLeod (2012) develop theory and supporting evidence for abolition of joint and several liability to reduce accidental death rates. For products liability, a review by Polinsky and Shavell (2010) finds no convincing evidence that liability (as opposed to purchaser demand for greater safety or direct safety regulation) affects the level of care.

B. The Effect of Med Mal Reform on Health Care Outcomes

Medical malpractice may provide a more promising area to study how tort liability affects care. States have been active in reforming their med mal laws, thus providing the shocks needed for DiD analysis. Physicians are generally insured, and out of pocket payments are rare,

2 Of the studies that find higher fatalities after introduction of no-fault insurance, only Cohen and Dehejia

(2004) use a DiD-like design (panel data with state and year fixed effects). However, as Loughran (2001) shows, fault states (i) have higher auto fatality rates than no-fault states prior to adoption of no fault, and (ii) fatality rates were dropping more rapidly in fault states prior to no-fault adoption. This convergence might have continued without adoption of no-fault. Thus, the Cohen and Dehejia specification, with raw fatality rate as the dependent variable, is suspect. Loughran’s specification, with ln(rate) as dependent variable, is more credible.

3 Rubin and Shepherd speculate that med mal reform could induce greater physician supply and hence improve care when accidents occur. The evidence, however, does not support the physician supply premise for this argument. Most studies find that med mal reform has only minor impacts on physician supply (e.g., Matsa, 2007; Klick, and Stratmann, 2007; Hyman et al., 2013).

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but physicians still find lawsuits highly stressful and time consuming, and worry about how a suit will affect future insurance rates, insurability, and hospital willingness to provide privileges. Hospitals, especially larger ones, are typically self-insured in significant part.

However, prior research on the effect of the med mal reform on healthcare outcomes has yielded mixed results. A survey by Kachalia and Mello (2011) finds the existing evidence “too limited to draw conclusions.” Several studies find no effect of reform or only small effects. Kessler and McClellan (1996, 2002) report that the “second wave” of med mal reforms, during the 1980s, predicts lower spending on patients with serious heart disease, but no increase in mortality or complications. Sloan and Shadle (2009) find no effect of damage caps and other “direct” reforms on survival for one year after hospitalization. They find lower survival following indirect reforms (which are generally weaker than direct reforms), but caution that this result is not robust to alternative specifications. Lakdawalla and Seabury (2011) find that higher non-economic damage awards predict lower mortality. In contemporaneous work, Frakes and Jena (2013) find no evidence of an impact of caps on non-economic or total damages (“damage caps”) on health outcomes, but find evidence that change from a local to a national standard of care lead to improved outcomes in states that previously had below-average quality.

For childbirth, the evidence is again mixed. Currie and MacLeod (2008) find higher birth complication rates after med mal reform in some specifications, but Yang et al (2012) find no effect of damage caps on birth outcomes, and Klick and Stratmann (2007) find no effect on infant mortality. Iizuka (2013) uses the NIS to study the effect of med mal reform on childbirth-related PSIs. He finds some evidence that reform predicts higher PSI rates but his evidence is both weak and odd: weak because he find no effect in patient level analyses, and odd because in hospital level analyses, he finds no effect for the most important reform (caps on non-economic or total damages, together “damage caps”), yet positive coefficients for two minor reforms (caps on punitive damages, or “punitive caps”) and collateral source rule reform.4 Frakes and Jena (2013) find no evidence that caps affect maternal trauma rates.

Finally, we are aware of contemporaneous research by Sarah Miller and Christine Jachetta, also using NIS data. They have advised us that they find evidence of higher PSI rates following tort reform.

C. Texas Med Mal Reforms

In 2003, Texas enacted a set of strong med mal reforms, effective September 1, 2003. The core reform was a cap on non-economic damages (“non-econ cap”) in med mal lawsuits. The cap limits non-econ damages against physicians and other individual licensed health care providers to $250,000 (nominal, not adjusted for inflation) for all individuals together. A separate $250,000 (nominal) cap applies to each hospital or other licensed health care facility, with a maximum of $500,000 (nominal) for all health care facilities. These reforms also included a variety of other provisions, including making the separate cap on damages in death cases apply per claim, rather than per defendant, nearly insurmountable evidentiary standards for

4 On the importance of damage caps and unimportance of other reforms, see, for example, Paik, Black and

Hyman (2013). Iizuka also studies PSIs 17-20, even though AHRQ has withdrawn PSI 20 (relating to cesarean deliveries). Our judgment, based on the results he reports, is that some or most of his results for injury to mother (based on PSIs 18-20) would be insignificant if he dropped PSI 20.

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emergency room care, a requirement that plaintiffs file an expert report within 120 days of suit with regard to each defendant’s negligence, and a ten year statute of repose.

The Texas reforms had a profound impact on med mal claim rates and payouts (Paik et al., 2012). The number of “large” paid claims – claims closed with payments greater than $25,000 (in 2011 dollars) fell by 58% from 2003 to 2010, and mean payout per large paid claim dropped by 41%, for a combined drop of about 75% in total payouts. Payout per Texas resident dropped from $25.39 in 2003 to $5.57 in 2010 (2011 dollars).

D. Med Mal Reforms in Other States

We also study Florida, Georgia, Illinois, and South Carolina, relying on the 20% NIS sample. These states, plus Texas, are the only states which adopt non-econ caps, for which we have data on PSIs covering at least two years before reform, to establish a pre-reform baseline. Georgia, Illinois, and South Carolina all adopted non-econ caps in 2005: $350,000; $500,000; and $350,000, respectively. The Georgia and Illinois caps were invalidated in 2010, but this should not affect our analysis, because we end our sample period in 2010.

Florida adopted a weak non-econ cap in 2003. The cap limits are $500,000 for one or more physicians, plus $750,000 for one or more hospitals, but double for cases involving death, vegetative state, “catastrophic injury,” or “manifest injustice.” These exceptions likely cover a majority of paid med mal claims, and a larger majority of claims which might lead to a large non-econ damages award. Thus, from a physician’s perspective, the effective cap is $1 million. However, insurance policy limits already cap recoveries from physicians in most cases (Hyman et al., 2007; Zeiler et al., 2007), and typical physician malpractice insurance limits are $1 million or less. The American Medical Association has cited Florida as an example of “Caps that Do Not Work,” explaining that “the number of claims against physicians affected by [this] cap is likely to be small” (American Medical Association, 2005). Thus, the Florida cap may have a limited effect on physician incentives.

III. PATIENT SAFETY INDICATORS (PSIs)

We use DiD analysis to assess the effect of med mal reform on patient safety. We compare the rate of hospital adverse events in our five treated states relative to rates in control states, which did not adopt damage caps during our sample period.

A. Patient Safety Measures

Our patient safety measures rely on the Patient Safety Indicators (PSIs) developed by AHRQ to detect often avoidable negative health outcomes using standard hospital inpatient datasets. The PSI definitions use ICD-9-CM diagnosis and procedure codes, admission type and source, patient age, time between procedure date and adverse event date, Diagnosis Related Group (DRG), Major Diagnostic Category (MDC), length of stay, and patient discharge type to identify both adverse events and patients at risk for each type of event.

AHRQ amends the PSI definitions with some frequency, partly to keep them in line with revisions to the ICD-9-CM codes. This produces time-inconsistent measures of PSI rates, which might bias our regression results, although including period effects in regressions should reduce this bias. We therefore modify the AHRQ definitions to improve time-consistency for our period

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of study, but obtain similar results in robustness checks using the time-varying definitions. Appendix A details our adjustments to the AHRQ definitions.

PSI 4 deserves special treatment. It measures death among surgical inpatients with a specified set of serious but usually treatable complications. These complications often reflect lapses in patient safety – indeed, some are other PSIs.5 Thus, the PSI 4 denominator is a useful measure of patient safety, and we use it in our analysis. However, reform-induced changes in the denominator makes changes in the PSI-4 rate itself hard to interpret, so we drop PSI-4 in analyses that rely on a cases-at-risk denominator. Below, for convenience, in discussing our data and results we generally use the terms “PSI” and “PSI rates” to include PSI-4 cases at risk.

We compute quarterly PSI rates for Texas and South Carolina using 100% inpatient datasets. These inpatient datasets include inpatient billing records for most hospitals, includes billing, clinical, and demographic data. They do not include patient identifiers, so we cannot track individual patients over time. We have data for 1999-2010. For control states and the other three reform states – Florida, Georgia, and Illinois – we rely on the NIS, an inpatient dataset containing a random subsample of hospitals, totaling approximately 20% of discharges in participating states. We use as controls 27 states which do not adopt a damage cap during our sample period, and which participate in NIS from 2001 on.6

The state inpatient databases on which NIS draws vary in the maximum number of diagnosis and procedure codes reported, both across states and across time. The NIS allows up to 15 diagnosis codes through 2008, this rises to 25 in 2009; NIS also added four fields for codes identifying External Causes of Injury and Poisoning (“E-codes”) beginning in 2003. The NIS also provides up to 15 procedure codes. However, some states databases report as few as nine diagnosis codes and six procedure codes, including Texas before 2004 (after which Texas reports 25 diagnosis and 25 procedure codes). To increase the time consistency of our PSI counts, we keep only the first nine non-E-code diagnosis codes, the first E-code, and the first six procedure codes for all years for both treated and control states. Since most records do not reach the limits on the number of codes, the impact on the number of PSI events is small.

Table 1 provides descriptions and summary statistics for the PSI rates we use in this study, separately for Texas and control states.7 For PSI-4 cases at risk, to avoid double-counting,

5 Some of the PSI-4 denominator measures – for example, hospital-acquired pneumonia – lack sufficient

specificity to be usable as PSIs, but all are related to patient safety. 6 For Texas, we use the Texas Inpatient Public Use Data File, distributed by the Texas Health Care

Information Council. Hospitals in counties with fewer than 35,000 people, and hospitals in rural counties which have fewer than 100 licensed beds, are exempt from reporting. For South Carolina, we use the South Carolina State Inpatient Database (SID), distributed by HCUP (Healthcare Cost and Utilization Project). The South Carolina database is limited to acute care hospitals. NIS is also distributed by HCUP and includes data for non-federal acute care hospitals. The available control states are: Arizona (missing 2002), California, Colorado, Connecticut, Hawaii, Iowa, Kansas, Kentucky, Maine (missing 2003-2006), Maryland, Massachusetts, Michigan, Minnesota, Missouri, Nebraska, New Jersey, New York, North Carolina, Oregon, Rhode Island, Tennessee, Utah, Vermont, Virginia, Washington, West Virginia (missing 2005), and Wisconsin. In robustness checks, we obtain similar results using only the 16 control states with data in NIS throughout 1999-2010.

7 PSIs are numbered 2 through 19, excluding 16. PSI 1 and PSI-20 have been dropped by AHRQ and the frequency of PSI 16 (transfusion reaction) is too low to be informative. PSI-4 cases at risk are numbered 2 through 6.

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we count only cases that are not themselves PSIs. We assign arbitrary numbers 21-25 to the five components of PSI-4 cases at risk. There is significant variation in the fraction of discharges at risk for each PSI as well as in PSI rates. Overall, Texas rates in treated states are similar to rates in control states – higher for some PSIs, lower for others. There are 4.45 PSI events per 1,000 cases at risk in Texas compared to 4.22 in control states. Table 7 provides similar information for the other treatment states.

B. The PSIs as Patient Safety Measures

The PSIs are often-used measures of patient safety at individual hospitals, but are subject to several limitations. First, they are calculated using administrative (billing), rather than clinical, records. If hospitals tend to underreport diagnoses and procedures for which they are not reimbursed, this will introduce noise into the measures, which may not be random if hospital characteristics predict reporting practices. The lack of detail in billing records, plus the limit on the number of reported codes, means that some adverse events will not be captured by the PSI measures. The PSIs were designed for high specificity (if a PSI is found, the likelihood of an actual adverse event is high), at the cost of lower sensitivity (the PSI definitions miss some adverse events). Third, the PSIs only capture a subset of adverse in-hospital events.

Studies of how well PSIs perform as patient safety measures fall into two general categories. The first category focuses on how well the PSI measures do in capturing underlying adverse events. Classen et al. (2011), compares PSIs to adverse events identified using patient records, and find PSI sensitivity of 8.5% and specificity of 98.5%. So PSIs miss many adverse events, but rarely flag non-events. Other similar studies in this vein include Romano et al. (2009) (studying PSIs 10 through 14); Utter (2009) (PSI 15); White et al. (2009) (PSI 12), and several studies of VA hospitals, surveyed by Rosen and Itani (2011).

The second category of studies asks whether PSIs are significant predictors of bad outcomes. In general, they find that patients who have PSIs suffer worse outcomes than similar patients without PSIs. Zhan and Miller (2003) find that PSI events are associated with longer hospital stays and higher mortality. Rivard et al. (2008) and Raleigh et al. (2008) report similar results for VA patients and UK patients, respectively.8

The PSIs are of interest partly for the specific adverse events they measure, but also as proxies for overall patient safety, which we cannot directly observe. Singer et al. (2009) report that PSI rates are associated with survey-based evaluation of hospital patient safety; Yu et al. (2009) suggest that PSI-7 can be used as a “canary measure” due to its high correlation with other PSIs. To further assess whether the PSIs (or PSI-4 cases at risk) are likely to proxy for overall safety, we calculate the correlation among PSI rates within Texas hospitals. We first run PSI-specific regressions of PSI indicators on demographic and clinical controls, quarter dummies, and hospital dummies. We then compute the correlations among the coefficients on the hospital dummies from regressions involving different PSIs. The sample for each PSI is cases

8 In contrast, Isaac and Jha (2008) find generally limited association between PSIs 2, 3, 4, and 7 and three

outcome measures: risk-adjusted mortality, HHS Hospital Compare process measures of quality; and US News hospital rankings. Lower PSI 4 rates predict lower risk-adjusted mortality, but PSI 4 is already a measure of mortality. The HHS Hospital Compare process measures are not good measures of outcomes (Nicholas et al., 2010). And US News rankings primarily measure reputation, which maybe only loosely related to outcomes.

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at risk for that PSI in Texas over 1999-2010; the sample for PSI-4 cases at risk is all discharges. As Table 2 shows, most PSIs are positively correlated with one another, with only a few statistically significant (at 1%) negative correlations. For the 16 PSIs, Cronbach α, a measure of whether different measures capture a common underlying concept (here, overall patient safety), is 0.523.

C. Pooled Measures

To capture the effect of med mal reform on overall patient safety, not just individual PSIs, we need to construct pooled PSI measures. We construct eight pooled measures: Operating room PSIs (5, 15); Infection PSIs (6, 7, 13); Post-surgical PSIs (8, 9, 10, 11, 12, 14); Birth-related PSIs (17, 18, 19); PSIs with death outcome (2, 4) (“Death PSIs”); PSI-4 cases at risk; All PSIs; and All PSIs plus PSI-4 cases at risk.

Our pooled measures are weighted sums of PSI events and PSI-4 cases at risk given by:

1nPSI j

PSI jj∈Jm

∑ (1)

where PSIs and PSI-4 cases at risk are indexed by j, Jm is the set of measures included in the pooled measure, and nPSI j is the total number of PSIj events in the five treated states and control states during our sample period. This approach gives equal weight to each measure and is similar to the numerator weights suggested by AHRQ for composite quality measures (AHRQ 2011).

IV. DIFFERENCE-IN-DIFFERENCES ANALYSIS: TEXAS

We conduct a DiD analysis of whether med mal reform affects patient safety, using several specifications. We present our Texas results in this Part in detail, and our results for the other treated states in Part V in somewhat less detail.

Our primary specifications use patient-level data to assess the probability that a patient, at risk for a particular PSI, incurred that PSI, and whether that probability changed in each treated state after reform, relative to control states. We use an array of patient-specific characteristics to improve the precision of our estimates and allow for changes in patient health over time in treated hospitals, relative to control hospitals. All specifications include quarter dummies to control for changes in PSI rates over time that affect both treated and control states. Our principal specification includes hospital fixed effects (FE). Using hospital FE controls for time-invariant hospital characteristics (for example, does a hospital tend to have especially sick or health patients, in ways not captured by the patient characteristics), and ensures that estimated treatment effects reflect within-hospital changes in quality, rather than a change in the hospitals included in the NIS. Specifications without hospital FE include state dummies. For South Carolina, we lack hospital identifiers, so use a state dummy instead. Standard errors are clustered on state.

A. Annual Differences in PSI Measures

We first assess whether annual PSI rates change in treated states versus control states, relative to a base year, during either the pre-reform or post-reform period. We use the year three years

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before the reform year as the base year; results are not sensitive to this choice. The regression specification for each treated state is:

Yi =ηh +δt + γ k *reformskk=1999

2010

∑ +β * Xi + ei (2)

Here Yi is the safety measure (individual or pooled) for patient i, discharged from a hospital in state s in quarter t; reformsk equals one for the treated state(s) in year k, zero otherwise; ηh and δt are hospital and quarter fixed effects; Xi is a vector of 49 patient characteristics: age (divided into 5 bins: < 29; 30-44; 45-59; 60-74; and 75+), gender, and dummy indicators for 26 Major Diagnostic Categories9 and 17 diagnoses that enter the widely used Charlson comorbidity index;10 and ei is the error. We normalize γt-3 (relative to reform year 0) to zero, so that the γk for other years capture changes in the (treated – control) difference in PSI rates relative to this base year. This can be called a “leads and lags” specification; it is sometimes called an Autor model, following Autor (2003).

A central DiD assumption is “parallel trends”: we must have reason to believe that PSI rates in the treated states would have moved in parallel with rates in control states, but for med mal reform. This assumption is not directly testable, but a core credibility check is to assess whether PSI rates in treated and control states moved in parallel during the pre-reform period. The specification in eqn. (2) lends itself to graphical assessment of parallelism.

Figure 1 plots regression results for Texas for our pooled measures. The dots indicate point estimates, the vertical lines show 90% confidence intervals, and the vertical bar between 2003 and 2004 roughly separates the pre- and post- reform periods.

For all of the pooled measures, the pre-period estimates are either insignificant or barely significant with no evidence that PSI rates in Texas are rising relative to control states prior to reform. If anything, some measures trend downward for Texas, relative to control states. If those trends would have continued into the post-reform period, but for reform, the annual estimates from eqn. (2) will understate the impact of med mal reform on Texas PSI rates. This increases our confidence that the parallel trends assumption is reasonable, and perhaps conservative. Nevertheless, in our regression analyses, we confirm that our results are robust to inclusion of state-specific trends.

In the post-reform period, all of the pooled measures trend upward in Texas relative to the controls. All except Pooled Death are significantly above the 2001 level by 2010. For Pooled Death, the upward trend moves Texas back to near the 2001 level by 2008-2010, after this measure dropped below the 2001 level over 2002-2007. In Panels G and H, we obtain similar results for pooled measure that include (exclude) PSI-4 cases at risk. Taken together, these pre-

9 Major Diagnostic Categories are broad categories of Diagnosis Related Groups, roughly based on medical

specialty, for example respiratory system, circulatory system, digestive system. 10 Charlson et al. (1987). The diagnostic categories are: Myocardial infarction; Congestive heart failure;

Peripheral vascular disease; Cerebrovascular disease; Dementia; Chronic pulmonary disease; Rheumatic disease; Peptic ulcer disease; Mild liver disease; Moderate or severe liver disease; Diabetes without chronic complication; Diabetes with chronic complication; Renal disease; Hemiplegia or paraplegia; Any malignancy, including lymphoma and leukemia, except malignant neoplasm of skin; Metastatic solid tumor; and AIDS/HIV. Quan et al. (2005) provide additional details. We used Stata’s “charlson” command to identify diagnoses in these categories.

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and post-reform patterns provide initial evidence that patient safety deteriorated in Texas after reform, with a several year lag.

B. Distributed Lag Estimates for Individual PSIs

Many DiD analyses assume a one-time jump in the outcome variable, immediately after reform. As Figure 1 shows, that assumption would not be a good fit for our data. We instead use a more flexible “distributed lag” model, which lets the treatment effect vary in each post-reform period. In this model, we modify the leads and lags model in eqn. (2) by replacing the annual reform dummies with a family of quarterly reform dummies covering the post-reform period, which for Texas starts in 4Q2003. The Texas dummy for 4Q2003 equals 1 for Texas in 4Q2003 and after, and is 0 otherwise. The coefficient on this dummy captures the treatment effect in 4Q2003. The next dummy, for 1Q2004 is the same except lagged one quarter – it turns on for Texas in 1Q2004 and stays on after that. This variable captures the incremental treatment effect in 1Q2004. The 2Q2004 dummy turns on for Texas in 2Q2004 and captures the incremental treatment effect in 2Q2004; and so on. The full model, not used in all specifications, is:

Yi =αs +ηh +δt + γ k *reformstk

k=2003Q4

2009Q1

∑ +β * Xi + (τ s * t)+ ei , (3)

where q indicates quarters (relative to the first quarter in the dataset), each reformsqk equals one

when q ≥ k, the τs are state-specific trends, and other variables are defined above. We end the series of lags with 2009Q1. This lets the last lag cumulate over 8 quarters in Texas; if we use additional lags, the last few become increasing noisy, due to apparently random quarterly fluctuations in PSI rates.

Our principal interest is in the cumulative treatment effect, which is the sum of the γk’s The sum will equal the cumulative post-reform change in the Texas PSI rate, averaged over 2009-2010, relative to the control states. We test the following null hypothesis:

H0: 2009 1

2003Q40

Q

kk

γ=

=∑ (4)

In effect, we test whether the cumulative quarterly changes in Texas’ PSI rates, relative to control states, become statistically significant by 2009-2010. Our specification otherwise permits a flexible adjustment path during the post-reform period.11

PSIs 3, 5, and 7 were subject to Medicare rules denying reimbursement for some PSI events which took effect in 4Q2008. Hospitals responded by becoming less likely to code these outcomes in the billing records that are our data source (Farmer, Black, and Bonow, 2013). The switch to lower reporting for these PSIs could have been larger or smaller in a treated state than in control states. This would confound an effort to ascribe any relative changes to med mal

11 In Stata, the lincom command (for linear combination) provides a sum of coefficients on the reform

dummies and allows for clustered standard errors, and a t-test for the significance of the sum relative to the null of zero effect.

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reform. We address this risk by dropping 4Q2008 from the sum in eqn. (4) for these PSIs and pooled measures which include them. The 4Q2008 coefficient will capture both any difference between a treated state and control states in how the reimbursement shock affects PSI reporting and any incremental effect of med mal reform during this quarter. If, as Figure 1 suggests, reform gradually affect PSI rates, excluding 4Q2008 will bias downward our estimate of the total impact of reform. We also drop 1Q1999 (the first quarter with available data) for Texas for PSI-8 (and pooled measures including PSI-8) due to an outlier Texas rate.

We present results for Texas from distributed lag regressions in Table 3 for individual PSIs and in Table 4 for pooled PSI measures. For reasons discussed above, we exclude PSI-4 (death of surgical inpatients from serious treatable complications) from Table 3. Table 3, column (1) shows baseline results without patient or hospital controls. Column (2) adds patient-level controls.

Column (3) adds hospital FE. We lose some effective sample size, since different hospitals are observed in different years, and some are only observed once, or only in the pre-reform or post-reform period. The last row of Table 1 shows the loss of effective size, which is about 12% for Texas and 23% for control states. The results are similar with and without hospital FE.

Focusing on the results in column (3), with both patient-level controls and hospital FE, the estimates for 13 of the 21 measures (PSIs 5, 6, 7, 9, 10, 11, 12, 15, 17, and 18; and PSI-4 cases at risk 21, 22, 24) are positive and statistically significant. Only three coefficients are negative (for PSIs 2, 8, and 19), all of these are small and statistically insignificant, and two of these three coefficients are positive without hospital FE. The estimate for the PSI-4 numerator is also positive, but only marginally significant when hospital FE are included; it is significant at the 5% level without hospital FE.

In column (4), as a robustness check, we add state-specific trends. The coefficient for PSI 2 becomes positive and significant and the coefficients for PSIs 5, 6, 7, 9, 11, and 15 remain significant. However, the coefficients move around a fair bit. The three coefficients which were negative in column (3) are now positive, but three other coefficients turn negative, and two are marginally significant. We observe a similar pattern for PSI-4 cases at risk. In our experience in this and other projects, when one adds state trends to a DiD specification, DiD coefficients of interest often move around, with no apparent pattern. The specification with state trends, in effect, takes what is likely to be only random noise during the pre-reform period (see Figure 1), and assumes it will continue during the post-reform period. Note that standard errors are much higher with state-specific trends.

The last column of Table 3 shows the implied percentage change in PSI rates for Texas, relative to the pre-reform mean, based on regression (3). The percentage changes range from -8% to +88%; the average increase for all 16 PSIs is 18.5%. Thus, the increase in PSI rates is “economically” meaningful as well as statistically significant.

Taken as a whole, Table 3 provides evidence for a general deterioration in patient safety in Texas following med mal reform. However, there is clearly noise in individual measures. For example, we might expect the three birth-related measures (PSIs 17-19) to move together, but instead find a rise in trauma to infant (PSI-17) and trauma to mother for vaginal birth using instruments (PSI-18), but no change in trauma to mother for vaginal birth without instruments (PSI-19). Thus, there is value in considering pooled measures. We do so next.

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B. Distributed Lag Estimates for Pooled Measures

We turn in Table 4 to distributed lag estimates for the pooled PSI measures. Individual patients are at risk for different PSIs. Thus, the sample for each pooled measure is all discharges. In Table 3, in contrast, the sample for each PSI was the cases at risk for that PSI. The broader denominator lets us use a pooled death measure (PSIs 2 and 4), even though we excluded PSI-4 from Table 3.

The columns of Table 4 use specifications similar to the corresponding columns in Table 3. Focusing on column (3), with patient-level controls and hospital FE, the coefficients are positive and statistically significant for all pooled measures except Death, for which the coefficient is close to zero. This suggests the value of using measures other than mortality to assess hospital quality. Any extra medical errors that occur after reform rarely cause death. Thus, our results for mortality are consistent with prior studies that do not find a significant effect of med mal reform on mortality rates (e.g., Kessler and McClellan, 1996; Frakes and Jena, 2013).

The last column of Table 4 shows the implied percentage implied percentage change in the pooled measures, relative to the pre-reform mean, based on regression (3). The increases range from 2% (for Pooled Death) to 34% (for Pooled Operating Room).

In column (4), with state-specific trends, results are broadly similar. The coefficient on Post-surgical outcomes becomes statistically insignificant (due largely to higher standard error); the coefficient for Birth-related PSIs becomes small and insignificant, and the coefficient for Death PSIs jumps and becomes statistically significant.

Overall, the pooled PSI results are consistent with the individual PSI results. They provide strong evidence of a broad rise in PSI rates following med mal reform, affecting many individual PSIs and most pooled measures.

D. Combined Change in Level and Trend

As an alternative to distributed lag regressions, we next consider a simpler specification which allows for a change in PSI level and a change in trend following med mal reform. We estimate the following regression specification:

Yi =αs +ηh +δt +γ1 *reformst +γ2 *reformst *(t − t0 )+β * Xi +τ s * t + ei . (5)

Here reformst equals one for the treated state starting with the first quarter after med mal reforms (in Texas, starting with 4Q2003), and 0 otherwise; and t0 is the quarter after reform. All other variables are defined above. The parameter γ1 will capture a one-time change in the level of PSIs at the time of reform, while γ2 will capture a change in PSI trend. We are interested whether the treated states experienced an overall change in PSI rates due to both effects by the end of the sample period. We test for this versus the null hypothesis of no change:

H0 : γ1 +γ 2 *(4Q2010− t0 ) = 0 (6)

As noted above, PSIs 3, 5, and 7 were subject to Medicare rules denying reimbursement for at

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least some PSI events which took effect in 4Q2008. To address the risk that the reimbursement-induced change in reporting of these PSIs differed between treated and control states, we drop quarters after 3Q2008 from the sample, and replace 4Q2010 with 3Q2008 in the above hypothesis, for these three PSIs and pooled measures that include them.

We present results for individual PSIs in Table 5. Regression (1) shows results with patient-level controls; regression (2) adds hospital FE; regression (3) further adds state-specific trends. The results are very similar to the distributed lag results in Table 3. For example, the same 10 PSIs are positive and significant in Table 5, column (2), as for the similar specification in Table 3, column (3). Likewise, the three coefficients for the PSI-4 cases at risk that were positive and significant in the distributed lag specification continue to be; the estimate for case at risk 23 strengthens and is now significant.

Table 6 presents results using the level-and-trend for the pooled measures. The results are again very similar to the corresponding distributed lag results in Table 4. For example, in Table 6 column (2), all pooled measures are positive and significant except Pooled Death – the same pattern as for the similar distributed lag specification in Table 4, column (3).

V. EFFECT OF MED MAL REFORM IN OTHER STATES

We also conduct a DiD analysis of the impact of med mal reform on patient safety in Florida, Georgia, Illinois, and South Carolina. Our specifications are similar to those discussed above. We present distributed lag results, but obtain consistent results with a level-and-trend specification. As noted above, Florida’s reform was weak and thus might have less effect on provider incentives.

Florida adopted a damage cap in 2003; Georgia, Illinois, and South Carolina did so in 2005. For the 2005 adopters, we have available a shorter post-reform period than in Texas. Two states had their caps invalidated by their state supreme courts during our sample period: Georgia in 2Q2010 and Illinois in 1Q2010. Since providers likely respond to the change in med mal risk with a lag, we continue to include data after the repeals in measuring the effect of the caps. If providers change safety practices in response to the repeals, this will bias against any measured effect of the caps, thus providing conservative estimates of the effect of reform.12 Both the availability of only a 20% sample and the shorter observation period reduces sample size and thus statistical power. Table 7 presents summary statistics for each state. The effective sample size with hospital FE ranges from 3.3M in Georgia (with only 1.4M post-reform discharges) to 7.6M in Florida, compared to 29.7M in Texas.

A. Annual Differences in PSI Measure We extend the analysis from section IV.A to the other states that underwent reform.

Panels A-D in Figure 2 show the results of the regression specified by equation (2) for Florida, Georgia, Illinois, and South Carolina, respectively, with All PSI plus PSI-4 cases at risk as the dependent variable. As with Texas, none of the treated states exhibit strong pre-treatment trends in the safety measure. However, states for which we only have a 20% sample – Florida, Georgia,

12 The quarter of adoption is 4Q2003 in Florida; 2Q2005 in Georgia; 4Q2005 in Illinois; and 3Q2005 in

South Carolina. The Georgia repeal was March 22, 2010, near the end of 1Q2010; we treat the repeal as occurring in 2Q2010. The Illinois repeal was Feb. 4, 2010; we treat it as occurring in 1Q2010.

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and Illinois – have significantly more variation in the pre-period than South Carolina or Texas, for which we have 100% samples. We also observe a statistically significant increase in PSI levels in three of the four additional reform states: Georgia, Illinois, and South Carolina. Any rise in Florida after reform is much less pronounced, possibly due to Florida implementing a weaker reform.

Panels E and F show results for a specification in which we include all five reform states in a single regression. To do this, we modify the specification given by equation (2) to:

Yi =ηh +δt + γ k *reforms,y0 s+kk=1999−y0 s

2010−y0 s

∑ +β * Xi + ei (7)

where y0s is the first year after reform in state s, and reforms,y0 s+k equals one for each treated state

in year y0s+k, zero otherwise. The other variables are defined as before. Once again, we normalize the coefficient three years before reform, γ-3, to zero. The other coefficients γk will now capture the average level of the PSI measure in reform states relative to this base year. Consistent with the other regressions, there is no evidence of a trend in the pre-period; in fact, each of the point estimates is small and not statistically different from zero. We also see a gradual increase in adverse events relative to the control states after reform. Overall, these results give confidence that the parallel trends assumption for DiD analysis may be reasonable for the other reform states as well, and provide initial evidence of deterioration in patient safety in at least three of the other four reform states after reform. B. Distributed Lag Estimates for Individual PSIs

Table 8 shows distributed lag estimates for the effect of damage cap adoption on individual PSI measures and PSI-4 cases at risk in the five reform states. Columns (1)-(5) separately estimate the distributed lag regression specified by equation (3) for each reform state. First, let us consider the results in column (4) for South Carolina, for which we have a 100% inpatient sample. The results are very similar, both in sign and magnitude, to those for Texas described above and reported again in column (5). With the exception of PSI-4 case at risk 24, all of the measures that experience positive and statistically significant effects in Texas are also positive and significant in South Carolina. PSI-8, which was close to zero in Texas, is also positive and statistically significant in South Carolina, but PSI-13 turns negative and significant.

The results are more mixed for Florida, Georgia, and Illinois, and may reflect, at least in part, relying on only a 20% sample for these states. For Illinois, reported in column (3), coefficients for fifteen of the twenty-one measures are positive and statistically significant, but three are negative and significant. For Georgia, reported in column (2), coefficients for eleven of the measures are positive and significant, but three are negative and significant. While there is more variation than in South Carolina or Texas, overall there also seems to be a general trend towards worse outcomes in these states. For Florida, on the other hand, the estimates are positive and statistically significant only for seven measures, and negative and significant for four. Accordingly, the evidence for deterioration in patient safety after reform in Florida is much less compelling.

We also estimate the effect of med mal reform jointly across all five reform states by modifying the specification given by equation (3) as follows:

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Yi =αs +ηh +δt + γ k *reforms,t−t0 sk

k=0

15

∑ +β * Xi + (τ s * t)+ ei (8)

Here, t0s is the first quarter after reform in state s and reforms,t−t0 sk equals one for reform states

when k � t - t0,s. The other variables are defined as above. Accordingly, γ0 captures the incremental effect of reform, averaged across the five reform states, in the first quarter after reform; γ1 then gives the incremental effect in the second period after reform; and so on. We test for an overall effect as the sum of the incremental effects with the hypothesis:

H0: γ k = 0k=0

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∑ (9)

In the single state distributed lag regressions, we omitted the reform lags for 4Q2008 for PSIs 3, 5, and 7 due to the Medicare reimbursement shock which affected hospital billing practices. However, it is unlikely that hospital response to this shock will be systematically correlated with the implementation of med mal reform. Since we are now testing the effect of reform across multiple states, any differential effect of the reimbursement shock on PSI rates should average out across states. Accordingly, we retain the coefficient for 4Q2008 in the multi-state regressions.

The results of this specification are presented in column (6). Eleven of the twenty-one measures (PSIs 5, 6, 7, 8, 9, 10, 12, 15, 17, and 18; and PSI-4 cases at risk 21 and 22) experience statistically significant increases across states after reform. Moreover, coefficients are only negative for two of the measures, and statistically significant for one of them: Pressure Ulcers. The average increase in the PSI measures relative to pre-treatment means in 21.2%, slightly higher than the average from the regressions in which Texas is the only treated state.

C. Distributed Lag Estimates for Pooled Measures

Table 9 shows distributed lag results for the pooled measures in the five reform states, estimated for each state individually as well as jointly in single regression. The specifications are similar to those in Table 8. The broad Pooled All PSI and Pooled All PSI plus PSI-4 cases at risk measures are positive and significant in Georgia, Illinois, and South Carolina in addition to Texas. The coefficients in all three states are substantially larger than for Texas, and are strongly significant. Of the 18 narrower pooled measures in these states (six in each state), 13 are positive and significant; of the three negative coefficients (all in Georgia), only one is significant.

Results are mixed in Florida. The broadest Pooled All PSI plus PSI-4 cases at risk is positive and marginally significant; the Pooled All PSI is negative but insignificant. Of the six narrower pooled measures, four are positive and significant, but the other two are negative and significant. These mixed results may reflect the weakness of the Florida reforms. Our overall Florida results are consistent with the possibility that small shocks to all PSIs from med mal reform are dominated by other independent factors that influence Florida PSI rates over time.

Column (6) gives the results from estimating the effect of reform jointly across all five reform states. Overall, the results are qualitatively the same as for South Carolina and Texas, for

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which we have a 100% patient sample: All of the pooled measures are positive and statistically significant, with the exception of Pooled Death, which is positive, but close to zero and statistically insignificant.

The presence of some negative and significant coefficients suggests caution in inferring causation from one or two measures. There may be sources of state trends, other than med mal reform, that affect specific PSIs. More convincing evidence of an effect of med mal reform on patient safety comes from the overall pattern of many positive and significant coefficients, especially for broader measures; only a few negative coefficients, and even fewer significant, negative coefficients.

VI. DISCUSSION

A. Effect of Hospital Characteristics

In unreported results, we assess whether the apparent response to med mal reform varies based on hospital characteristics: nonprofit versus for-profit, urban versus rural, large versus small, and teaching versus non-teaching. We do not find significant differences in responses in different hospital types.

B. General Deterrence and Gradual Effect

We find a gradual rise in rates for most PSIs after reform, consistent with a gradual relaxation of care, or failure to reinforce care standards over time. The decline is widespread, and applies both to aspects of care that are relatively likely to lead to a malpractice suit (e.g., PSI-5; foreign body left in during surgery), and aspects that are unlikely to do so (e.g., PSI-7; central-line associated bloodstream infection). The broad relaxation of care suggests that med mal liability provides “general deterrence” – an incentive to be careful in general – in addition to any “specific deterrence” it may have for particular actions. Frakes and Jena (2013) report improved maternal safety during childbirth following a shift to a national rather than local standard of care, which is consistent with specific deterrence.

C. Why Do We Find Results, When Others Have Not?

One reason why we find evidence for deterrence, when prior studies generally have not, is likely the combination of very large sample size and use of a broad range of adverse events. The inpatient data we rely on was not available during the prior med mal reform waves of the 1970s and 1980s. If it had been, computing power might well have been a binding constraint. A typical regression takes a day or so to run on a well-powered server, built for this project, and uses over 100GB of memory.

A second reason is that most prior studies use mortality as their principal outcome measure. We find that damage cap adoptions predict a broad decline in patient safety across most PSIs, but not in mortality. A third could be that we allow for a gradual relaxation of care. A more typical DiD strategy, in which the effect simply turns on after reform, would produce weaker results. Unless one uses a post-reform period sufficient for a gradual effect to emerge, this research design might not find an effect at all.

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D. Policy Implications

We find an adverse effect of damage cap adoptions on PSI rates. Frakes and Jena (2013) do not, but they do find that state movement from a local to a national standard of care leads to improved quality in previously lower-quality states. Suppose that we accept the positive results from both studies as correct. What implications would follow for med mal reform?

One combined message is that standards of care affect the behavior of healthcare providers. Higher standards can lead to higher healthcare quality. Reduced liability pressure can lead to lower quality. This suggests that we should look for ways to strengthen care standards. Med mal liability is one avenue, but not the only one. Public reporting of quality information, financial incentives, and liability could all play complementary roles. At the same time, our results suggest that one should be cautious about relaxing tort liability without providing a substitute source of incentives.

E. Limitations

As discussed above, PSIs are imperfect measures of patient safety. They count only some adverse events and rely on hospital billing records, which contain limited information on clinical outcomes. These drawbacks should not bias the DiD estimates as long as the PSI measures are consistent across time or any time inconsistency is random. We made extensive efforts, discussed above, to make the measures as time-consistent as possible. Period effects, included in all regressions should limit the impact of any remaining time inconsistency.

Our data is limited to hospital inpatients. Thus, we cannot assess whether med mal reform also causes a change in level of care in the outpatient setting. We cannot link specific adverse events directly to med mal lawsuits. However, related work finds evidence that high PSI rates are associated with higher rates of paid med mal claims (Greenberg et al., 2010; Black and Zabinski, 2013).

It is possible that some change in the healthcare or regulatory environment, other than med mal reform, is the true driver of the apparent decline in inpatient safety in our seven reform states. We are not aware of other regulatory or other shocks in these states which are likely to affect patient safety. The general consistency of results across PSIs and across states makes such an explanation less likely, but we cannot rule it out.

F. Future Research

One promising avenue for future research on the relationship between med mal reform and healthcare quality involves moving beyond the PSIs to in-depth study of particular procedures which are likely to lead to complications or adverse outcomes, ideally using reform shocks in multiple states and a panel DiD research design. For example, Bilimoria et al. (2013) study the association between med mal reform and complications of colorectal surgery, but have only cross-sectional data.

Our study also illustrates the potential value of very large patient-level datasets. There is no technological reason why the NIS samples hospitals with 20% of the patients in a state, and different hospitals in different years, when it could readily take a random sample of discharges from all hospitals. Nor is there reason why NIS provides a 20% sample of inpatient discharges,

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rather than a 100% sample. There is no technological reason why the standard Medicare dataset available to researchers is a 5% sample, when it could be 20% or 100%. The computer power to handle larger datasets exists, and could be exploited if the datasets were available.

VII. CONCLUSION

We find evidence that reduced risk of med mal litigation, due to state adoption of damage caps, leads to higher rates of preventable adverse patient safety events in hospitals. We focus principally on Texas, but find consistent results for the three other states with available data on adverse events that adopt strong damage caps (Georgia, Illinois and South Carolina). Our study is the first, either for medical malpractice or indeed, in any area of personal injury liability, to find strong evidence consistent with classic tort law deterrence theory – liability for harm induces greater care. The relaxation of care occurs gradually over a number of years following adoption of damage caps.

For med mal reform, our results should form part of the mix of evidence on the social value of reforms designed to limit med mal lawsuits. The available evidence suggests that damage caps substantially reduce both claim rates and payout per claim (e.g., Paik, Black, and Hyman, 2013b), but do not reduce – indeed may increase -- overall healthcare spending (Paik, Black, and Hyman, 2013c). Damage caps also have limited effects on physician supply (e.g., Matsa, 2007; Klick and Stratmann, 2007). Those results, combined with the evidence in this paper on the effect of reform on patient safety, provide a weak policy case for further med mal reform. Med mal reform is good for healthcare providers – but the case for overall social benefit is hard to make.

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Appendix A: Modifications to PSI Measures We summarize here the modifications we made to the time-varying PSI definitions to

enhance time consistency. A full list of modifications is available from the authors on request. To count PSIs, we principally used version 4.3a of the AHRQ PSI definitions,13 but made two sets of modifications to improve time-consistency of the definitions and avoid excluding PSI events without apparent need to do so. First, we reversed the exclusion of certain patients from the cases at risk for each PSI. For example, the PSI definitions exclude cases with missing data on gender even though gender does not enter the PSI definitions; we included these cases. Our changes to the exclusion restrictions are listed below.

PSI Change to Exclusion Criteria % of discharges affected (1999-2010)

All Records with missing gender not excluded. No need to exclude.

5.0%

2, 4 Transfers to acute care facility not excluded. Low frequency. 1.7% 3 Transfers from different hospital, Skilled Nursing Facility,

Intermediate Care Facility, or another health care facility not excluded for time consistency.

5.4% (thru 2007Q3) 14.6% (after 2007Q3)

17, 18, 19 Records with age or principal diagnosis missing not excluded. No need to exclude.

0.07%

We also made numerous changes to the ICD-9-CM codes used to identify PSIs. ICD-9-CM codes are updated annually; often a broader code is replaced by two or more narrower codes. AHRQ modifies the PSI definitions to work with the latest version of the ICD-9-CM codes; thus, more recent definitions will produce time-inconsistent measures if applied to earlier periods.14 The PSI definitions rely on groups of ICD-9-CM codes that identify diagnosis and procedure categories, such as Cancer or Cardiac Surgical Procedure (“code groups”). To improve time consistency, we must usually either broaden a current code group by adding additional codes, or narrow it by omitting codes. We made modifications with the primary goal of improving time consistency, and the secondary goal of making those changes, among the plausible alternatives, that produced the smallest change in PSI frequencies. We exercised judgment on what modification to make and whether to make any modification. If the number of cases affected by a change to the ICD-9CM codes was sufficiently small, we often chose not to modify the current definition.

We considered each code group separately and identified the specific codes that were affected by changes to ICD-9-CM. This includes both entirely new codes and existing codes that were split into new codes. For example, diagnoses that had previously been together under one code may now be reported as one of two new codes. The old code and the two new codes are all affected by the change. If all three codes are within the same code group, no change is needed. If any affected code is outside the code group, then for each affected code, we compute the annual frequency of each code in the data. We use this information to decide which affected

13 Available at: http://www.qualityindicators.ahrq.gov/Archive/PSI_TechSpec_V43a.aspx 14 Changes to the ICD-9-CM through FY2013 are available at:

http://www.cdc.gov/nchs/data/icd9/ICD9CM_FY13_CNVTBL.pdf.

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codes should be included in or excluded from the code group to improve time consistency while minimally affecting the total rate of the codes in each code group.

As an example, consider the Cancer diagnosis code group, which is used for PSIs 2, 7, and 13, and underwent a number of changes between 1999 and 2010. For example, in 4Q2008, code 209.60 (“Benign carcinoid tumor of unknown primary site”) replaced part of code 199.0 (which became “Malignant neoplasm without specification of site: Disseminated”). AHRQ includes code 199.0 in the Cancer code group, but not 209.60. To maintain time-consistency, we need to either include 209.60 in the Cancer code group, or else exclude 199.0. It turns out that 209.60 diagnoses are a small fraction of diagnoses previously coded as 199.0. In 2010, only 46 cases of 209.60 were reported compared to 470 cases of 199.0. We include 209.60 in the Cancer code group, because this is a smaller change to the Cancer code group than excluding 199.0.

As a second example, in 4Q2009, code 209.32 (“Merkel cell carcinoma of the scalp and neck”) replaced part of code 173.4 (“Other malignant neoplasm of skin: Scalp and skin of neck”). Code 209.32 is included in the Cancer code group, but 173.4 is not. Code 209.32 had only 11 cases in 2010 compared to 309 cases of 173.4. We therefore drop 209.32 from the Cancer code group, because this has a smaller impact on the overall frequency of cases in the Cancer code group than including 173.4.

As a third example, consider code 258.02 (“Multiple endocrine neoplasia type IIA”), which replaced parts of 258.0 (“Polyglandular activity in multiple endocrine adenomatosis”) and 193 (“Malignant neoplasm of thyroid gland”) in 4Q2008. Of these three codes, only 193 is included in the Cancer code group. It is not clear, however, what fraction of 258.02 cases came from 258.0 versus 193, and there are an average of only 18 cases per year of 258.02. We decided not to modify the Cancer code group in this case.

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Quan, Hude, Vijaya Sundararajan, Patricia Halfon, Andrew Fong, Bernard Burnand, Jean-Christophe Luthi, L. Duncan Saunders, Cynthia A. Beck, Thomas E. Feasby, and William A. Ghali (2005), Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data 43 Medical Care 1130-1139.

Rubin, Paul H., and Joanna M. Shepherd (2007), Tort Reform and Accidental Deaths, 50 Journal of Law and Economics 221-238.

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Stewart, Ronald M., Kathy Geoghegan, John G. Myers, Kenneth R. Sirinek, Michael G. Corneille, Deborah Mueller, Daniel L. Dent, Steven E Wolf, and Basil A Pruitt, Jr. (2011), Malpractice Risk and Cost are Significantly Reduced After Tort Reform, 212 Journal of the American College of Surgeons 463-467.

Yang, Tony Y., David M. Studdert, S.V. Subramanian, and Michelle M. Mello (2012), Does Tort Law Improve the Health of Newborns, or Miscarry? A Longitudinal Study of the Effect of Liability Pressure on Birth Outcomes, 9 Journal of Empirical Legal Studies 217-245.

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25

Figure 1: Annual differences in pooled PSI measures between Texas and control states

Graphs show coefficients and 90% confidence intervals from patient-level OLS regressions of indicated pooled patient safety measures on year*Texas dummies, hospital and quarter fixed effects, patient age and sex, clinical controls, and constant term. 2001 is treated as the base year and normalized to zero. Pooled measures are �j[1/(no. of PSIj events total) * PSIj dummy (=1 if patient experiences PSIj,)/], where PSIs and PSI-4 cases at risk are indexed by j. Clinical controls are 43 dummy indicators for 26 major diagnostic categories and 17 diagnoses that enter Charlson comorbidity measure. Sample period is 1999-2010. Texas sample is 100% Texas inpatient dataset. Control sample is 20% random sample of discharges from NIS for 27 states with data in NIS from 2001 on. We drop 1Q1999 for measures including PSI-8 due to outlier Texas rate. Vertical line indicates Texas reform (Sept 1, 2003). Standard errors are clustered on state.

26

Figure 1 (cont’d): Annual differences in pooled PSI measures between Texas and control states

27

Figure 2: Annual differences in Pooled PSI measures between reform states and control states

Graphs show coefficients and 90% confidence intervals from patient-level OLS regressions of indicated pooled patient safety measures on year*(reform state) dummies, hospital and quarter fixed effects, patient age and sex, clinical controls, and constant term. The year three years before reform year is treated as the base year and normalized to zero. Panels A-D provide results for Florida, Georgia, Illinois, and South Carolina, respectively. Panels E-F include all five reform states; the year*(reform state) dummies are defined relative to each state’s reform year. Sample period is 1999-2010. Pooled measures, and Texas and control samples are defined in Figure 1. South Carolina sample is 100% sample but lacks hospital identifiers; we use a state dummy instead. Florida, Georgia, and Illinois samples are 20% samples from NIS. Vertical lines separate pre-reform from reform period. Standard errors are clustered on state.

28

Table 1: PSI summary statistics, 1999-2010

Texas Control states

PSIs PSI events

CAR (1,000s)

Rate (x1,000) PSI events CAR

(1,000s) Rate

(x1,000) 2 Death in Low-mortality DRGs 2,407 7,757 0.310 4,239 12,263 0.346 3 Pressure Ulcer 228,579 8,277 27.6 350,084 13,428 26.1 4 Death of Surgical Inpatients with Serious Treatable Complications 27,535 208 132.7 45,106 345 130.7 5 Foreign Body Left in during Procedure 1,952 26,776 0.073 3,749 46,973 0.080 6 Iatrogenic Pneumothorax 11,616 20,693 0.561 20,488 37,765 0.543 7 Central line Associated Bloodstream Infections 29,897 18,540 1.61 43,525 31,012 1.40 8 Postoperative Hip Fracture 1,156 4,580 0.252 1,501 7,888 0.190 9 Postoperative Hemorrhage or Hematoma 16,817 6,757 2.49 30,295 11,985 2.53 10 Postoperative Physiologic and Metabolic Derangement 2,967 3,657 0.811 3,049 5,477 0.557 11 Postoperative Respiratory Failure 24,520 2,902 8.45 31,393 4,378 7.17 12 Postoperative Pulmonary Embolism or Deep Vein Thrombosis 63,794 6,780 9.41 110,950 12,018 9.23 13 Postoperative Sepsis 12,726 884 14.4 14,527 1,262 11.5 14 Postoperative Wound Dehiscence 1,956 1,275 1.53 3,832 2,254 1.70 15 Accidental Puncture or Laceration 60,726 21,451 2.83 126,974 39,249 3.24 17 Birth Trauma – Injury to Neonate 5,820 4,240 1.37 13,642 6,138 2.22 18 Obstetric Trauma – Vaginal Delivery with Instrument 42,423 272 155.7 73,922 426 173.4 19 Obstetric Trauma – Vaginal Delivery without Instrument 77,868 2,646 29.4 122,318 4,032 30.3 Total PSI 612,759 137,695 4.45 999,594 236,894 4.22 PSI-4 Cases at Risk (excluding cases that are PSIs) CAR CAR 21 Deep Vein Thrombosis or Pulmonary Embolism (excluding PSI-12) 2,314 0.0688 3,575 0.0631 22 Hospital-acquired Pneumonia 90,421 2.690 153,612 2.71 23 Sepsis (excluding PSI-13) 24,713 0.735 43,318 0.765 24 Shock or Cardiac Arrest in Hospital 28,658 0.852 44,537 0.787 25 Gastrointestinal Hemorrhage or Acute Ulcer 34,806 1.04 60,444 1.07 Total PSI-4 Cases at Risk 180,912 5.38 305,486 5.40

Discharges 33,617,080 56,611,832 Discharges during Texas reform period (4Q2003-4Q2010) 21,044,296 34,406,081 Effective sample with hospital FE 29,698,785 43,375,356

Table shows, for 1999-2010, PSI events, cases at risk (CAR), and rates per 1,000 CAR; PSI-4 CAR and rates per 1,000 discharges; and total discharges for Texas and control states. Texas sample is 100% Texas inpatient dataset. Control sample is 20% random sample of discharges from NIS for 27 states with data in NIS from 2001 on. Effective sample with hospital FE includes discharges from hospitals observed at least once before and at least once after reform.

29

Table 2: Correlation within Texas hospitals for PSI rates and PSI-4 Cases at Risk PSI 4 Cases at Risk PSI 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17 18 19 21 22 23 24 3 0.13 4 0.09 0.06 5 -0.05 -0.06 0.17 6 0.09 0.08 0.08 0.05 7 0.17 0.61 0.08 -0.04 0.24 8 -0.01 -0.01 -0.01 -0.02 -0.01 0.00 9 -0.07 0.08 -0.09 0.01 0.04 0.01 -0.02 10 0.20 0.11 0.01 -0.02 0.02 0.10 -0.03 -0.10 11 0.17 0.41 0.08 -0.03 0.14 0.50 -0.03 -0.09 0.09 12 0.18 0.54 0.00 -0.03 0.05 0.48 0.04 -0.11 0.19 0.41 13 0.37 0.48 0.12 -0.07 0.15 0.56 -0.01 -0.09 0.20 0.47 0.46 14 -0.01 -0.09 -0.11 -0.02 -0.04 -0.07 -0.03 0.05 -0.03 -0.08 -0.10 -0.04 15 -0.06 -0.16 0.00 0.11 0.12 -0.10 -0.01 0.05 -0.03 -0.08 -0.10 -0.11 -0.01 17 -0.01 0.10 -0.08 0.02 0.03 -0.03 0.06 -0.01 -0.03 0.00 -0.02 0.15 0.03 -0.01 18 -0.06 -0.14 0.03 0.01 0.10 0.07 0.03 0.05 0.00 0.05 0.01 -0.13 -0.06 0.23 0.15 19 0.02 -0.08 0.01 0.04 -0.04 -0.04 -0.04 -0.04 -0.05 -0.04 -0.06 0.10 -0.05 -0.01 -0.04 0.52 PSI 4 Cases at Risk 21 -0.01 0.09 0.16 0.50 -0.02 0.12 -0.01 -0.02 0.00 0.07 0.08 0.11 0.06 -0.03 -0.03 0.07 -0.01 22 0.03 0.33 -0.02 0.03 0.06 0.27 -0.04 -0.04 0.16 0.31 0.26 0.31 0.05 0.03 0.11 0.05 -0.07 0.19 23 -0.02 -0.03 0.16 0.05 0.08 -0.06 -0.02 -0.01 0.05 0.03 0.10 0.02 -0.04 0.10 0.14 -0.01 -0.08 0.08 0.25 24 -0.07 0.12 0.06 0.01 0.08 -0.06 -0.02 0.08 -0.01 0.10 0.21 0.11 0.03 0.07 0.01 0.05 -0.08 0.12 0.45 0.32 25 0.03 0.27 -0.03 0.04 0.12 0.32 -0.05 -0.04 0.15 0.31 0.26 0.30 0.02 0.10 0.00 0.10 -0.01 0.18 0.65 0.09 0.29

Table shows within-hospital correlation among coefficients on hospital dummies from measure-specific OLS regressions of PSI (or PSI-4 cases at risk) indicators on patient age and sex, clinical controls, quarter dummies, and hospital dummies. Clinical controls are dummy indicators for 26 major diagnostic categories and 17 diagnoses that enter Charlson comorbidity measure. Sample for PSIs is cases at risk for that PSI in Texas over 1999-2010; sample for PSI-4 cases at risk is all discharges. We drop 1Q1999 for PSI-8 due to outlier rate. Significant results at 1% level are in bold.

30

Table 3: Individual PSIs and PSI-4 Cases at Risk: Distributed Lag Regressions

PSI

(1) (2) (3) (4) Cases at risk

(1,000s) % change from (3)

2 Death in Low-mortality DRGs -0.041* -0.027 -0.030 0.187*** 20,020 -8.0% (0.022) (0.022) (0.028) (0.062) 3 Pressure Ulcer 1.108 2.665 1.539 0.791 21,705 5.9% (3.740) (3.933) (3.329) (1.485) 5 Foreign Body Left in during Procedure 0.027** 0.031** 0.036*** 0.044** 73,749 49.1% (0.012) (0.012) (0.012) (0.019) 6 Iatrogenic Pneumothorax 0.043* 0.054** 0.073*** 0.204*** 58,458 12.1% (0.022) (0.021) (0.019) (0.066) 7 Central line Associated Bloodstream Infections 0.217** 0.289*** 0.195** 0.752*** 49,552 12.7% (0.078) (0.076) (0.077) (0.164) 8 Postoperative Hip Fracture 0.020 0.013 -0.006 0.154* 12,224 -1.9% (0.018) (0.018) (0.016) (0.086) 9 Postoperative Hemorrhage or Hematoma 0.338*** 0.257** 0.304*** 0.627** 18,742 11.7% (0.095) (0.106) (0.110) (0.256) 10 Postoperative Physiologic and Metabolic Derangement 0.255*** 0.153*** 0.192*** -0.104 9,135 27.7% (0.050) (0.044) (0.058) (0.108) 11 Postoperative Respiratory Failure 2.673*** 1.501*** 2.074*** 1.246** 7,280 31.3% (0.527) (0.522) (0.473) (0.482) 12 Postoperative Pulmonary Embolism or Deep Vein Thrombosis 1.920*** 1.452*** 1.496*** 1.929* 18,798 20.2% (0.387) (0.372) (0.348) (1.122) Demographic and clinical controls No Yes Yes Yes Yes Hospital FE No No Yes Yes Yes State trends No No No Yes No

Upper panel shows sum of coefficients on reform and reform lag indicators (x1,000) from patient-level OLS regressions, for each PSI, of PSIj dummy (=1 if patient experienced PSIj) on reform indicator (=1 if state = TX and quarter ≥ 4Q2003), lags of reform indicator through 1Q2009, quarter dummies, state or hospital dummies (as indicated), demographic and clinical controls (as indicated), and constant. Sample period is 1999-2010; sample for PSIj is cases at risk for that PSI. Lower panel reports coefficients for similar regressions with indicators for PSI-4 components as the dependent variables; sample is all discharges. Regressions (2)-(4) include same demographic (age and sex) and clinical controls as Figure 1; regressions (3)-(4) include hospital fixed effects; regression (4) includes state-specific trends. Last column shows percent change in rate, relative to pre-reform mean, based on regression (3). We drop 1Q1999 for PSI-8 due to outlier Texas rate, and exclude coefficient for 4Q2008 in estimates for PSIs 3, 5, and 7. Standard errors, clustered on state, are in parentheses. Significance: *** = 1%, ** = 5%, * = 10%. Significant results at 5% level are in bold.

31

Table 3 (cont’d): Individual PSIs and PSI-4 Cases at Risk: Distributed Lag Regressions

PSI

(1) (2) (3) (4) Cases at risk

(1,000s) % change from (3)

13 Postoperative Sepsis 2.001 1.497 1.137 2.697 2,146 11.1% (1.311) (1.129) (1.225) (1.672) 14 Postoperative Wound Dehiscence 0.039 0.032 0.058 -0.660* 3,530 3.8% (0.103) (0.105) (0.139) (0.365) 15 Accidental Puncture or Laceration 0.632*** 0.761*** 0.672*** 0.889*** 60,700 23.7% (0.106) (0.095) (0.093) (0.160) 17 Birth Trauma – Injury to Neonate 1.050*** 1.054*** 1.245*** -0.832* 10,378 87.5% (0.196) (0.197) (0.229) (0.472) 18 Obstetric Trauma – Vaginal Delivery with Instrument 9.639* 9.256* 17.266*** 10.310 699 10.0% (5.004) (5.048) (4.325) (7.739) 19 Obstetric Trauma – Vaginal Delivery without Instrument 0.248 0.207 -0.262 1.992 6,679 -0.7% (0.760) (0.777) (0.940) (1.357)

PSI-4 and PSI-4 Cases at Risk Discharges (1,000s)

Mean: 18.5%

PSI-4 Numerator 0.069* 0.080** 0.066* -0.015 90,229 8.3% (0.035) (0.036) (0.035) (0.049) PSI-4 Cases at Risk 0.840** 1.014*** 0.991*** 1.433*** 90,229 18.7% (0.307) (0.299) (0.275) (0.277) 21 Deep Vein Thrombosis or Pulmonary Embolism (excl. PSI-12) 0.016** 0.020** 0.022*** 0.010 90,229 49.3% (0.007) (0.007) (0.005) (0.012) 22 Hospital-acquired Pneumonia 0.427** 0.489** 0.533** 0.612*** 90,229 23.3% (0.203) (0.197) (0.200) (0.157) 23 Sepsis (excluding PSI-13) 0.050 0.056 0.079* 0.164** 90,229 14.4% (0.047) (0.044) (0.043) (0.068) 24 Shock or Cardiac Arrest in Hospital 0.189** 0.204** 0.211** 0.226*** 90,229 35.7% (0.073) (0.081) (0.077) (0.070) 25 Gastrointestinal Hemorrhage or Acute Ulcer 0.058 0.094*** 0.044 0.210** 90,229 3.9% (0.036) (0.032) (0.045) (0.089) Demographic and clinical controls No Yes Yes Yes Yes Hospital FE No No Yes Yes Yes State trends No No No Yes No

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Table 4: Pooled PSIs and PSI-4 Cases at Risk: Distributed Lag Regressions

Pooled measure (1) (2) (3) (4) Discharges (1,000s)

% change from (3)

Operating room 0.434*** 0.504*** 0.540*** 0.679*** 90,229 34.2% (5, 15) (0.144) (0.146) (0.140) (0.221) Infections 0.353** 0.460*** 0.347** 0.959*** 90,229 12.9% (6, 7, 13) (0.157) (0.157) (0.134) (0.204) Post-surgical 0.914*** 1.005*** 1.037*** 0.836 89,619 18.3% (8, 9, 10, 11, 12, 14) (0.167) (0.165) (0.156) (0.639) Birth-related 0.516*** 0.367*** 0.433** -0.068 90,229 13.6% (17, 18, 19) (0.103) (0.098) (0.161) (0.259) Death -0.033 0.029 0.030 0.741*** 90,229 1.6% (2, 4) (0.062) (0.064) (0.082) (0.182) PSI-4 Cases at Risk 0.658** 0.779*** 0.814*** 0.935*** 90,229 23.1%

(0.250) (0.254) (0.222) (0.275) All PSI 1.717*** 1.756*** 1.717*** 2.324** 89,619 10.8% (0.599) (0.607) (0.574) (0.962) All PSI and PSI-4

Cases at Risk 2.375** 2.504** 2.485*** 3.239*** 89,619 12.7% (0.927) (0.935) (0.844) (1.131)

Demographic and clinical controls No Yes Yes Yes Yes

Hospital FE No No Yes Yes Yes State trends No No No Yes No

Table shows sum of coefficients on reform indicator and reform lag coefficients (x108) from separate patient-level OLS regressions of each pooled measure on reform indicator (=1 if state = TX and quarter ≥ 4Q2003), lags of reform indicator through 1Q2009, hospital or state dummies (as indicated), quarter dummies, demographic and clinical controls (as indicated), and constant. Pooled measures and clinical controls are defined in Figure 1. Regressions (2)-(4) include demographic (age and sex) and clinical controls; regressions (3)-(4) include hospital fixed effects; regression (4) includes state-specific trends. Last column shows percent change in rate, relative to pre-reform mean, based on regression (3). Sample is all discharges over 1999-2010. We drop 1Q1999 for measures including PSI-8 due to outlier Texas rate, and exclude coefficient for 4Q2008 in the sum of coefficients for pooled measures including PSIs 3, 5, and 7. Standard errors, clustered at the state level, are in parentheses. Significance: *** = 1%, ** = 5%, * = 10%. Significant results at 5% level are in bold.

33

Table 5: Individual PSIs and PSI-4 Cases at Risk: Level and Trend DiD Regressions (1) (2) (3)

PSI Reform

indicator Reform

trend Combined

effect Reform

indicator Reform

trend Combined

effect Reform

indicator Reform

trend Combined

effect Cases at risk

(1,000s) % chg.

from (2) 2 -0.062** 0.001 -0.027 -0.026 0.000 -0.033 0.046 0.007*** 0.254*** 19,596 -8.9% (0.024) (0.001) (0.024) (0.026) (0.001) (0.023) (0.030) (0.002) (0.071) 3 0.426 0.011 0.631 -0.444 -0.006 -0.566 -0.268 0.015 0.018 17,360 -2.2% (0.969) (0.073) (2.054) (0.642) (0.073) (1.749) (0.515) (0.054) (1.156) 5 -0.018*** 0.002*** 0.015** -0.012** 0.002*** 0.023*** -0.009 0.002*** 0.036** 58,974 30.9% (0.006) (0.000) (0.006) (0.005) (0.000) (0.005) (0.007) (0.001) (0.013) 6 0.021 0.001 0.052** 0.043** 0.001* 0.080*** 0.081*** 0.006** 0.243*** 56,902 13.2% (0.018) (0.001) (0.023) (0.017) (0.001) (0.020) (0.017) (0.002) (0.072) 7 0.014 0.028*** 0.538*** -0.058 0.034*** 0.595*** 0.101** 0.053*** 1.108*** 39,756 38.9% (0.059) (0.004) (0.064) (0.043) (0.003) (0.069) (0.047) (0.006) (0.138) 8 -0.077*** 0.003** 0.011 -0.080*** 0.003** 0.006 -0.038 0.008** 0.188* 12,255 1.9% (0.025) (0.001) (0.019) (0.026) (0.001) (0.017) (0.036) (0.003) (0.101) 9 -0.094 0.016*** 0.367*** -0.066 0.018*** 0.448*** 0.018 0.027*** 0.773*** 18,516 17.2% (0.074) (0.003) (0.104) (0.076) (0.003) (0.107) (0.086) (0.008) (0.261) 10 0.028 0.003 0.117** 0.045 0.004 0.155*** 0.001 -0.003 -0.076 9,086 22.4% (0.038) (0.002) (0.045) (0.050) (0.002) (0.054) (0.054) (0.004) (0.131) 11 0.380* 0.049** 1.751*** 0.149 0.075*** 2.248*** 0.024 0.061*** 1.724*** 7,243 33.9% (0.196) (0.022) (0.569) (0.166) (0.019) (0.521) (0.208) (0.019) (0.602) 12 -0.783 0.076*** 1.347*** -0.410 0.070*** 1.544*** -0.426 0.078 1.758 18,571 20.8% (0.344) (0.022) (0.445) (0.283) (0.021) (0.437) (0.251) (0.047) (1.411) Demographic and

clinical controls Yes Yes Yes

Hospital effects No Yes Yes State trends No No Yes

Upper panel reports OLS regressions for indicated PSIs of PSI event indicator on reform indicator (state = TX, quarter >= 4Q2003), post-reform trend (reform indicator * t, where t = quarter - 4Q2003), quarter dummies, state or hospital dummies (as indicated), demographic (age and sex) and clinical controls as in Figure 1, and a constant. Coefficients on reform indicator and post-reform trend, and combined effect (reform coefficient + post-reform trend coefficient * no. of periods in sample after first reform period) are reported for each PSI (x1,000). Sample period is 1999-2010; sample for PSIj is cases at risk for that PSI. Lower panel reports coefficients for similar regressions with PSI-4 components as the dependent variables; sample is all discharges. Regressions (2)-(3) include hospital fixed effects; regression (3) includes state-specific trends. Last column shows percent change in rate, relative to pre-reform mean, based on regression (2). We drop 1Q1999 for PSI-8 due to outlier Texas rate, and drop quarters >= 4Q2008 for PSIs 3, 5, and 7. Standard errors, clustered at the state level, are in parentheses. Significance: *** = 1%, ** = 5%, * = 10%. Combined effects at 5% level are in bold.

34

Table 5 (cont’d): Individual PSIs and PSI-4 Cases at Risk: Level and Trend DiD Regressions (1) (2) (3)

PSI Reform

indicator Reform

trend Combined

effect Reform

indicator Reform

trend Combined

effect Reform

indicator Reform

trend Combined

effect Cases at risk

(1,000s) % chg.

from (2) 13 0.763 -0.005 0.616 0.222 0.014 0.623 0.880 0.048 2.219 2,127 6.1% (0.577) (0.056) (1.435) (0.462) (0.062) (1.568) (0.536) (0.079) (2.151) 14 -0.124 0.008 0.104 0.026 0.002 0.073 -0.164 -0.019 -0.694* 3,494 4.8% (0.103) (0.006) (0.107) (0.115) (0.006) (0.148) (0.145) (0.011) (0.381) 15 -0.162** 0.036*** 0.852*** 0.005 0.030*** 0.851*** 0.035 0.034*** 0.977*** 59,079 30.1% (0.071) (0.004) (0.101) (0.062) (0.004) (0.099) (0.068) (0.007) (0.206) 17 -0.124 0.045*** 1.141*** 0.039 0.045*** 1.303*** -0.495*** -0.013 -0.849 10,360 91.5% (0.115) (0.009) (0.233) (0.173) (0.009) (0.257) (0.149) (0.018) (0.554) 18 -12.321** 0.895*** 12.752** -2.333 0.857*** 21.675*** -4.817* 0.668** 13.900 697 12.5% (5.069) (0.239) (5.369) (2.395) (0.188) (4.326) (2.803) (0.305) (9.420) 19 -0.502 0.024 0.171 -0.424 0.009 -0.181 -0.160 0.063 1.597 6,648 -0.5% (0.980) (0.031) (0.816) (0.715) (0.017) (0.838) (0.595) (0.049) (1.606) Discharges

(1,000s) Mean: 19.5%

PSI-4 Numerator -0.021 0.004*** 0.092** -0.001 0.003* 0.092** -0.016 0.001 0.024 88,331 11.5% (0.018) (0.001) (0.037) (0.016) (0.002) (0.040) (0.016) (0.002) (0.058) PSI-4 Denom. -0.061 0.039*** 1.041*** 0.146 0.033*** 1.082*** 0.247** 0.045*** 1.495*** 88,331 20.4% (0.128) (0.013) (0.353) (0.086) (0.012) (0.332) (0.098) (0.012) (0.341) 21 0.007 0.001 0.022*** 0.011** 0.000 0.024*** 0.006 0.000 0.004 88,331 54.8% (0.006) (0.000) (0.008) (0.005) (0.000) (0.007) (0.006) (0.000) (0.016) 22 0.004 0.017** 0.485** 0.102* 0.016* 0.562** 0.111* 0.017** 0.590*** 88,331 24.6% (0.071) (0.008) (0.232) (0.059) (0.008) (0.233) (0.062) (0.007) (0.186) 23 0.009 0.002 0.059 0.024 0.003 0.104** 0.039 0.004 0.154** 88,331 18.8% (0.027) (0.002) (0.047) (0.022) (0.002) (0.045) (0.024) (0.003) (0.074) 24 -0.036 0.009*** 0.225** -0.038* 0.010*** 0.242*** -0.022 0.012*** 0.311*** 88,331 41.0% (0.022) (0.003) (0.087) (0.020) (0.003) (0.084) (0.026) (0.003) (0.092) 25 -0.028 0.005** 0.104*** 0.010 0.002 0.054 0.054 0.007* 0.241** 88,331 4.8% (0.027) (0.002) (0.035) (0.024) (0.002) (0.043) (0.038) (0.003) (0.112) Demographic and

clinical controls Yes Yes Yes

Hospital effects No Yes Yes State trends No No Yes

35

Table 6: Pooled Measures: Level and Trend DiD Regressions

(1) (2) (3)

Pooled measure Reform

indicator Reform

trend Total effect

Reform indicator

Reform trend

Total effect

Reform indicator

Reform trend

Total effect

Disch. (1,000s)

% chg. from (2)

Operating room -0.233*** 0.027*** 0.274*** -0.161** 0.032*** 0.447*** -0.113 0.039*** 0.623*** 72,313 28.3% (5, 15) (0.077) (0.006) (0.082) (0.072) (0.006) (0.066) (0.088) (0.009) (0.173) Infections 0.190** 0.012 0.413*** 0.113** 0.021** 0.503*** 0.307*** 0.043*** 1.125*** 72,313 18.7% (6, 7, 13) (0.084) (0.007) (0.147) (0.053) (0.008) (0.143) (0.061) (0.007) (0.137) Post-surgical -0.290 0.048*** 1.068*** -0.145 0.047*** 1.161*** -0.131 0.049** 1.228 88,331 20.5% (8, 9, 10, 11, 12, 14) (0.223) (0.010) (0.178) (0.200) (0.009) (0.176) (0.232) (0.024) (0.764) Birth-related -0.237*** 0.023*** 0.407*** -0.179 0.022*** 0.447*** -0.309*** 0.010 -0.040 88,331 14.0% (17, 18, 19) (0.078) (0.004) (0.120) (0.111) (0.005) (0.159) (0.077) (0.011) (0.336) Death -0.254*** 0.011*** 0.053 -0.150** 0.008* 0.060 0.077 0.032*** 0.971*** 88,331 3.2% (2, 4) (0.069) (0.004) (0.061) (0.068) (0.004) (0.072) (0.079) (0.007) (0.203) PSI 4 Cases at Risk 0.033 0.029*** 0.843*** 0.160* 0.027*** 0.922*** 0.181* 0.030*** 1.016*** 88,331 26.1%

(0.110) (0.010) (0.284) (0.080) (0.009) (0.258) (0.103) (0.010) (0.339) All PSI -0.595* 0.092*** 1.156*** -0.368 0.104*** 1.606*** -0.074 0.140*** 2.592*** 71,727 10.1% (0.319) (0.017) (0.406) (0.246) (0.018) (0.383) (0.189) (0.031) (0.645) All PSI and PSI 4 Cases at Risk

-0.560 0.119*** 1.710*** -0.266 0.136*** 2.314*** 0.046 0.179*** 3.447*** 71,727 11.9% (0.395) (0.024) (0.590) (0.293) (0.024) (0.495) (0.238) (0.039) (0.809)

Demographic and clinical controls Yes Yes Yes Yes

Hospital effects No Yes Yes Yes State trends No No Yes No

Level and trend regressions are separate OLS regressions of each pooled PSI measure on reform indicator (state = TX, quarter >= 4Q2003), post-reform trend (reform indicator * t, where t = quarter - 4Q2003), quarter dummies, state or hospital dummies (as indicated), demographic (age and sex) and clinical controls as in Figure 1, and a constant. Pooled PSI measures are defined in Figure 1. Coefficients on reform indicator and post-reform trend, and total effect (reform coefficient + post-reform trend coefficient * no. of periods in sample after first reform period) are reported for each PSI (x108). Regressions (2)-(3) include hospital fixed effects; regression (3) includes state-specific trends. Last column shows percent change in PSI rate, relative to pre-reform mean, based on regression (2). Sample is all discharges over 1999-2010. We drop 1Q1999 for measures including PSI-8 due to outlier Texas rate. We drop quarters >= 4Q2008 for measures including PSIs 3, 5, and 7. Standard errors, clustered at the state level, are given in parentheses. Significance: *** = 1%, ** = 5%, * = 10%. Significant combined level and trend effects at 5% level in bold.

36

Table 7: PSI summary statistics for other reform states, 1999-2010

Florida Georgia Illinois S. Carolina

PSI PSI events CAR (1,000s)

Rate per 1,000

PSI events

CAR (1,000s)

Rate (x1,000)

PSI events

CAR (1,000s)

Rate (x1,000)

PSI events

CAR (1,000s)

Rate (x1,000)

2 646 1,643 0.393 285 764 0.373 356 1,069 0.333 553 1,347 0.411 3 49,404 2,157 22.9 19,335 818 23.6 31,034 1,170 26.5 39,604 1,716 23.1 4 7,880 58 136.7 3,076 24 130.7 1,478 16 90.0 5,877 44 133.6 5 530 7,097 0.075 237 2,877 0.082 291 4,014 0.072 380 5,476 0.069 6 3,171 5,911 0.536 1,275 2,247 0.567 1,368 3,258 0.420 2,111 4,450 0.474 7 8,385 4,704 1.78 2,609 2,004 1.30 3,640 2,689 1.35 4,838 3,700 1.31 8 271 1,199 0.226 82 489 0.168 92 604 0.152 174 990 0.176 9 4,225 1,807 2.34 1,835 723 2.54 2,438 897 2.72 3,406 1,472 2.31 10 699 927 0.754 281 413 0.681 76 478 0.159 462 708 0.653 11 7,220 730 9.89 2,289 320 7.16 3,566 374 9.54 4,216 583 7.23 12 18,348 1,811 10.1 7,206 724 9.95 9,449 897 10.5 12,986 1,476 8.80 13 3,086 222 13.9 1,279 100 12.8 1,314 123 10.7 1,821 163 11.2 14 549 328 1.67 256 149 1.72 21 179 0.117 545 273 2.00 15 16,552 6,158 2.69 7,772 2,329 3.34 7,835 3,400 2.30 14,076 4,601 3.06 17 1,634 707 2.31 942 430 2.19 1,043 488 2.14 963 626 1.54 18 5,037 36 141.2 4,821 30 161.4 6,669 36 183.8 8,284 54 152.6 19 12,039 450 26.8 9,171 284 32.3 10,058 336 29.9 11,070 392 28.2 Total 139,676 35,943 3.89 62,751 14,723 4.26 80,728 20,029 4.03 111,366 28,071 3.97 PSI-4 Cases at Risk 21 485 0.0584 332 0.0943 645 0.135 485 0.0740 22 25,638 3.09 10,686 3.03 8,155 1.71 20,117 3.07 23 7,792 0.938 2,978 0.846 1,368 0.287 4,844 0.739 24 7,348 0.884 2,979 0.846 2,012 0.422 4,806 0.734 25 10,104 1.22 4,570 1.30 3,583 0.751 8,217 1.25 Total 51,367 6.18 21,545 6.12 15,763 3.30 38,469 5.87 Discharges 8,310,207 3,521,646 4,769,760 6,552,066 Post-reform discharges 4,897,312 1,431,363 1,577,638 3,071,109 Effective hosp. FE sample 7,635,404 3,262,281 3,752,230 N/A Table shows, for 1999-2010, PSI events, cases at risk (CAR), and rates per 1,000 CAR; PSI-4 CAR and rates per 1,000 discharges; and total discharges for Florida, Georgia, Illinois, and South Carolina. Florida, Georgia, and Illinois sample is 20% sample from NIS; South Carolina sample is 100% sample from SID. Effective sample with hospital FE includes discharges from hospitals observed at least once before and at least once after reform.

37

Table 8: PSI in other reform states: Distributed lag regressions

(1) (2) (3) (4) (5) (6) % change PSI Florida Georgia Illinois S. Carolina Texas All states from (6) 2 Death in Low-mortality DRGs -0.155*** -0.024 0.011 0.023 -0.030 -0.020 -5.0% (0.028) (0.026) (0.029) (0.027) (0.028) (0.024) 3 Pressure Ulcer -3.607 -0.598 13.558*** 3.474 1.539 -3.179** -13.1% (3.282) (3.280) (3.256) (3.173) (3.329) (1.494) 5 Foreign Body Left in during Procedure 0.037*** -0.033** 0.081*** 0.071*** 0.036*** 0.025*** 32.9% (0.012) (0.012) (0.012) (0.012) (0.012) (0.007) 6 Iatrogenic Pneumothorax 0.049** -0.062*** 0.186*** 0.040** 0.073*** 0.061*** 10.9% (0.019) (0.018) (0.018) (0.018) (0.019) (0.020) 7 Central line Associated Bloodstream Infections 0.060 0.492*** 0.914*** 0.468*** 0.195** 0.440*** 29.4% (0.078) (0.077) (0.077) (0.077) (0.077) (0.068) 8 Postoperative Hip Fracture -0.014 0.067*** 0.166*** 0.095*** -0.006 0.019 7.3% (0.016) (0.017) (0.017) (0.018) (0.016) (0.026) 9 Postoperative Hemorrhage or Hematoma 0.047 0.516*** 0.215** 0.252*** 0.304*** 0.260** 10.1% (0.101) (0.094) (0.102) (0.091) (0.110) (0.103) 10 Postoperative Physiologic and Metabolic

Derangement -0.134** 0.242*** 0.644*** 0.224*** 0.192*** 0.132** 21.3%

(0.058) (0.057) (0.055) (0.052) (0.058) (0.064) 11 Postoperative Respiratory Failure -3.326*** 1.644*** -1.754*** 1.425*** 2.074*** 0.508 6.8% (0.484) (0.458) (0.461) (0.447) (0.473) (1.071) 12 Postoperative Pulmonary Embolism or Deep Vein

Thrombosis 3.336*** 0.141 5.119*** 1.145*** 1.496*** 1.459*** 18.6%

(0.361) (0.363) (0.347) (0.355) (0.348) (0.433) Demographic and clinical controls Yes Yes Yes Yes Yes Yes Hospital effects Yes Yes Yes Yes Yes Yes Total discharges (1,000s) 64,719 60,110 61,515 62,959 88,331 111,997

Upper panel shows sum of coefficients on reform and reform lag indicators (x1,000) from patient-level OLS regressions, for each PSI, of PSIj dummy (=1 if patient experienced PSIj) on reform indicator (=1 if state = FL, qtr >= 2003Q4; state = GA, qtr >= 2005Q2; state = IL, qtr >= 2005Q4; state = SC, qtr >= 2005Q3; state = TX, qtr >= 4Q2003), lags of reform indicator, hospital dummies, quarter dummies, demographic (age and sex) and clinical controls same as Figure 1, and a constant. Sample period is 1999-2010; sample for PSIj is cases at risk for that PSI. Lower panel reports coefficients for similar regressions with indicators for PSI-4 components as the dependent variables; sample is all discharges. Last column shows percent change in rate, relative to pre-reform mean, based on regression (6). In regressions (1)-(5), the reform indicator is lagged through 1Q2009; in regression (6), the reform indicator is lagged for 15 quarters. In regressions (1)-(5), we exclude the coefficient for 4Q2008 in the sum of coefficients on reform indicator lags for pooled measures including PSIs 3, 5, and 7. We drop 1Q1999 for PSI-8 for Texas due to outlier rate. Standard errors, clustered at the state level, are given in parentheses. Significance: *** = 1%, ** = 5%, * = 10%. Significant results at 5% level are in bold.

38

Table 8 (cont’d): PSI in other reform states: Distributed lag regressions

(1) (2) (3) (4) (5) (6) % change PSI Florida Georgia Illinois S. Carolina Texas All states from (6) 13 Postoperative Sepsis 0.468 -2.854** -2.493** -3.554*** 1.137 -0.455 -4.5% (1.212) (1.247) (1.178) (1.193) (1.225) (1.343) 14 Postoperative Wound Dehiscence 0.298** 0.089 1.760*** -0.075 0.058 0.130 9.0% (0.144) (0.138) (0.140) (0.136) (0.139) (0.144) 15 Accidental Puncture or Laceration 0.061 0.370*** 0.811*** 0.564*** 0.672*** 0.528*** 18.7% (0.094) (0.097) (0.092) (0.094) (0.093) (0.124) 17 Birth Trauma – Injury to Neonate 0.677** 0.512** 0.184 0.648*** 1.245*** 0.762*** 42.7% (0.274) (0.245) (0.209) (0.208) (0.229) (0.240) 18 Obstetric Trauma – Vaginal Delivery with

Instrument 8.321 16.918*** -9.765** 11.737*** 17.266*** 13.129*** 7.6%

(4.262) (4.186) (4.687) (4.188) (4.325) (3.770) 19 Obstetric Trauma – Vaginal Delivery without

Instrument 2.185** 2.558*** -1.954 -0.146 -0.262 0.048 0.1%

(1.015) (0.814) (0.956) (0.766) (0.940) (0.782) Mean:

21.2% PSI-4 Numerator -0.074** -0.012 0.549*** 0.055 0.066* 0.069 8.8% (0.035) (0.036) (0.034) (0.035) (0.035) (0.046) PSI-4 Denominator 0.539* -0.059 3.943*** 0.980*** 0.991*** 0.864** 16.1% (0.272) (0.272) (0.272) (0.267) (0.275) (0.329) 21 Deep Vein Thrombosis or Pulmonary Embolism

(excl. PSI-12) 0.072*** 0.043*** 0.059*** 0.019*** 0.022*** 0.032*** 62.3%

(0.006) (0.006) (0.005) (0.005) (0.005) (0.011) 22 Hospital-acquired Pneumonia 0.368* 0.134 1.923*** 0.729*** 0.533** 0.490** 21.0% (0.199) (0.195) (0.199) (0.191) (0.200) (0.209) 23 Sepsis (excluding PSI-13) -0.072 0.116*** 0.424*** -0.069 0.079* 0.065 11.7% (0.042) (0.041) (0.042) (0.041) (0.043) (0.055) 24 Shock or Cardiac Arrest in Hospital 0.017 -0.043 0.460*** 0.058 0.211** 0.139* 24.7% (0.074) (0.073) (0.073) (0.071) (0.077) (0.076) 25 Gastrointestinal Hemorrhage or Acute Ulcer -0.111** -0.008 0.541*** 0.085* 0.044 0.041 3.5% (0.047) (0.045) (0.047) (0.044) (0.045) (0.048) Demographic and clinical controls Yes Yes Yes Yes Yes Yes Hospital effects Yes Yes Yes Yes Yes Yes Total discharges (1,000s) 64,719 60,110 61,515 62,959 88,331 111,997

39

Table 9: Pooled PSI measures in other reform states: Distributed lag regressions

(1) (2) (3) (4) (5) (6) Pooled measure Florida % chg Georgia % chg Illinois % chg S. Carolina % chg Texas % chg All states % chg Operating room (5, 15)

0.528*** 29.2 -0.200 -10.0 1.109*** 72.0 0.901*** 48.9 0.540*** 34.2 0.440*** 26.2 (0.137) (0.139) (0.137) (0.138) (0.140) (0.104)

Infections (6, 7, 13)

0.457*** 14.9 -0.424*** -15.5 1.058*** 45.8 0.587*** 24.8 0.347** 12.9 0.399*** 15.0 (0.134) (0.135) (0.132) (0.133) (0.134) (0.120)

Post-surgical -0.310** -4.8 0.741*** 13.2 2.901*** 85.1 1.447*** 26.7 1.037*** 18.3 0.807*** 14.8 (8, 9, 10, 11, 12, 14) (0.149) (0.157) (0.156) (0.153) (0.156) (0.277) Birth-related 0.858*** 35.0 0.450*** 12.8 0.098 3.2 0.454*** 17.9 0.433** 13.6 0.339** 11.3 (17, 18, 19) (0.181) (0.162) (0.155) (0.136) (0.161) (0.144) Death -0.376*** -17.0 -0.032 -1.6 0.621*** 51.3 0.082 4.0 0.030 1.6 0.042 2.2 (2, 4) (0.086) (0.078) (0.086) (0.080) (0.082) (0.081) PSI 4 Cases at Risk 0.887*** 21.5 0.674*** 15.2 2.824*** 110.3 0.529*** 13.2 0.814*** 23.1 0.825*** 22.7

(0.215) (0.213) (0.212) (0.210) (0.222) (0.232) All PSI -0.404 -2.4 2.620*** 15.7 6.894*** 55.6 3.379*** 22.5 1.717*** 10.8 1.962*** 12.7 (0.569) (0.579) (0.563) (0.565) (0.574) (0.451) All PSI and PSI 4 Cases at Risk

1.621* 7.7 2.739*** 13.0 10.521*** 70.6 4.199*** 22.1 2.485*** 12.7 2.790*** 14.5 (0.839) (0.846) (0.832) (0.826) (0.844) (0.569)

Demographic and clinical controls Yes Yes Yes Yes Yes Yes

Hospital effects Yes Yes Yes Yes Yes Yes Total discharges (1,000s)

64,719 60,110 61,515 62,959 88,331 111,997

Table shows sum of coefficients on reform and reform lag indicators (x108) from separate patient-level OLS regressions of each pooled PSI measure on reform indicator (=1 if state = FL, qtr >= 2003Q4; state = GA, qtr >= 2005Q2; state = IL, qtr >= 2005Q4; state = SC, qtr >= 2005Q3; state = TX, qtr >= 4Q2003), lags of reform indicator, hospital dummies, quarter dummies, demographic (age and sex) and clinical controls same as in Figure 1, and a constant. In regressions (1)-(5), the reform indicator is lagged through 1Q2009; in regression (6), the reform indicator is lagged for 15 quarters. Pooled PSI measures are defined in Figure 1. Percent changes are changes in rates relative to pre-form mean. Sample is all discharges over 1999-2010. In regressions (1)-(5), we exclude the coefficient for 4Q2008 in the sum of coefficients on reform indicator lags for pooled measures including PSIs 3, 5, and 7. We drop 1Q1999 for Texas measures involving PSI-8 due to outlier rate. Standard errors, clustered at the state level, are given in parentheses. Significance: *** = 1%, ** = 5%, * = 10%. Significant results at 5% level are in bold.