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Mortality among US veterans after emergency visits to Veterans Affairs and other hospitals: retrospective cohort study

Abstract Objective To measure and compare mortality outcomes between dually eligible veterans transported by ambulance to a Veterans Affairs hospital and those transported to a non-Veterans Affairs hospital. Design Retrospective cohort study using data from medical charts and administrative files. Setting Emergency visits by ambulance to 140 Veteran Affairs and 2622 non-Veteran Affairs hospitals across 46 US states and the District of Columbia in 2001-18. Participants National cohort of 583 248 veterans (aged ≥65 years) enrolled in both the Veterans Health Administration and Medicare programs, who resided within 20 miles of at least one Veterans Affairs hospital and at least one non-Veterans Affairs hospital, in areas where ambulances regularly transported patients to both types of hospitals. Intervention Emergency treatment at a Veterans Affairs hospital. Main outcome measure Deaths in the 30 day period after the ambulance ride. Linear probability models of mortality were used, with adjustment for patients’ demographic characteristics, residential zip codes, comorbid conditions, and other variables. Results Of 1 470 157 ambulance rides, 231 611 (15.8%) went to Veterans Affairs hospitals and 1 238 546 (84.2%) went to non-Veterans Affairs hospitals. The adjusted mortality rate at 30 days was 20.1% lower among patients taken to Veterans Affairs hospitals than among patients taken to non-Veterans Affairs hospitals (9.32 deaths per 100 patients (95% confidence interval 9.15 to 9.50) v 11.67 (11.58 to 11.76)). The mortality advantage associated with Veterans Affairs hospitals was particularly large for patients who were black (−25.8%), were Hispanic (−22.7%), and had received care at the same hospital in the previous year. Conclusions These findings indicate that within a month of being treated with emergency care at Veterans Affairs hospitals, dually eligible veterans had substantially lower risk of death than those treated at non-Veterans Affairs hospitals. The nature of this mortality advantage warrants further investigation, as does its generalizability to other types of patients and care. Nonetheless, the finding is relevant to assessments of the merit of policies that encourage private healthcare alternatives for veterans.


Table of Contents
The 31 Elixhauser comorbidities and the 20 most prevalent presenting conditions (at VA hospitals) are sorted by their prevalence in the sampling frame of "All dual enrollees." The last column indicates the final analytic sample of rides, after all exclusions.  The bottom row of this table shows the number of ambulance rides in our final study sample, and indicates the number of patients, ambulance companies, patient zip codes, VA hospitals, and non-VA hospitals represented by those rides. The other rows in this table indicate the counts of each of these same units of measurement through the successive stages of sample derivation that were described in Figure 1. The 31 Elixhauser comorbidities and the 20 most prevalent presenting conditions at VA hospitals are sorted by prevalence in VA hospitals.

Figure S2. Comparison of VA and non-VA predicted mortality distributions
This figure shows probability distributions functions of predicted mortality for patients in the analysis sample. Distributions are plotted separately for patients taken to VA versus non-VA hospitals. A two-sample Kolmogorov-Smirnov test for equality of the distribution functions rejects equality with a p-value less than 0.001 and a D statistic of 0.058.
Details of how we computed predicted mortality are provided beneath Table 1.

Table S6. Results of multivariable regression analysis of 30-day mortality at VA and non-VA hospitals
This table provides details of the regression estimates that are displayed in Figure 2 of the manuscript. These estimates come from ordinary least squares models of the form where ! ! is an indicator for 30-day mortality, &' ! is an indicator for a ride to a VA hospital, and ) ! are control variables in our preferred specification (details of which appear in Table S3). We fit this model in the overall study sample ("All" row) and within various subsamples (remaining rows).
The rows headed "Mort. Risk -Low," "Mort. Risk -Medium," and "Mort. Risk -High" refer to rides transporting patients with first-, second-, and third-tercile predicted mortality risk, respectively. We predicted each patient's 30day mortality risk using an ordinary least squares model with 30-day mortality as the dependent variable and the following as independent variables: patient age, gender, and race; year, month, and day of the week in which the ride occurred; the patient's utilization of health care in the prior year; and indicators for Elixhauser comorbidities. For details of how these variables were specified, see  This table provides estimates from the regression analyses reported graphically in Figure 3, plus additional estimates not reported there. To derive these estimates, we assigned each ride in our analysis sample to one of the 16 categories defined by the first two columns of the above table. These categories indicate types of care received at the index hospital in the prior year.
We then fit the following model: where ! ! is an indicator for 30-day mortality, ,-./012! " are indicator variables for the 16 categories of index hospital type interacted with prior utilization at that hospital, and ) ! are control variables in our preferred specification (see Table S3). We then computed adjusted 30-day mortality rates by using the estimates from this model and setting control variables to their means. Standard errors were clustered at the zip code level.

Figure S3. Adjusted mortality differences by calendar year
This figure plots our mortality estimates separately by calendar year. To derive these estimates, we fit 18 ordinary least square models (one per calendar year) of the form where ! ! is an indicator for 30-day mortality, &' ! is an indicator for being taken to a VA hospital, and ) ! are control variables in our preferred specification (see Table S3). Standard errors were clustered at the zip code level. This table provides details of the regression results that are shown in Figure 4. To derive these estimates, we fit 31 ordinary least square models (one per Elixhauser comorbidity) of the form The analysis used to produce these estimates follows the same approach as was used in the main analysis, details of which are provided in the notes beneath Table 2.  Table 2) -2.32 (-2.51 to -2.12) Preferred specification + hospital characteristics -2.21 (-2.46 to -1.95) Estimates differ slightly from the one reported in the paper because a different analytic sample was used. The unavailability of hospital information for some hospital-years reduced the analytic sample for this comparison by 39,098 rides (2.7%), and for consistency and comparative accuracy we used identical samples for the two analyses shown above.
VA and non-VA hospitals may differ systematically in characteristics such as teaching status, available equipment, and clinical capabilities that affect their performance in caring for patients with acute and chronic conditions. Hospital characteristics may also imply different patterns of patient selection, including selection based on patient characteristics that we do not observe in the data. Such systematic differences may impair our ability to make true head-to-head comparisons between quality of care delivered in VA and non-VA hospitals. To test whether such differences may have confounded our estimates, we re-fit our main regression model, adding covariates to adjust for various characteristics of the destination hospitals, and compared the results with those from a model without them. The sample size differs slightly with that of the "All dual enrollees" sample in Table S4 because of missing data that was dropped prior to running the regression. In order to look at the effect of the VA at 18 months, this study sample has an additional restriction that ambulance rides must occur before May 20, 2018 (to allow for at least 18 months of mortality data after all rides).
This table shows how our estimate of the effect of the VA on 30-day mortality varies according to different sample construction rules. The first five rows of the table track the incremental exclusions applied to derive the study sample (Figure 1) with the fifth row ("After exclusion set #4 (other rides in prior 30 days)") indicating our study sample. The bottom three rows vary the sample according to the stringency with which patients who have had ambulance rides prior to the index ride are excluded.
The analysis used to produce these estimates follows the same approach as was used in the main analysis, details of which are provided in the notes beneath Table 2. This table tests whether our mortality effect is robust to inclusion of missing variables. In the first row, we show the mortality estimate from our preferred specification. In the second row, we include all observations with missing data (shown in Table S13) and use missing category indicator variables to adjust the mortality effect.
Aside from including these indicators, we use the same approach that was used in the main analysis, details of which are provided in the notes beneath Table 2. This table provides details on the number of observations with missing data, tabulated separately for each variable in our analysis dataset. The total number of observations with any missing data was 200,367.
Variables not shown in this table had complete data. This table compares our mortality estimates using OLS (as in our main specification) and logistic regression (which accounts for the binary nature of our outcome variable).
Indicator variables for many 5-digit zip codes perfectly predicted mortality, which made it infeasible to control for 5-digit zip code in logistic regressions run on our full analysis sample. We therefore compared OLS and logistic regression models that control for coarser levels of geography (HRR and 3-digit zip code), which reduced the number of observations that were dropped due to perfect prediction.
Aside from choice of geographic control, the analysis used to produce these estimates follows the same approach as was used in the main analysis, details of which are provided in the notes beneath Table 2.

Figure S4. VA vs. non-VA difference in adjusted mortality at different time horizons through 546 days
This figure expands the time horizons used to estimate mortality rates beyond the 30-day interval examined in Table S9. The figure shows adjusted differences calculated at 7-day increments, from the day of the emergency department visit through 546 days after the visit.
These estimates use the sample described in the second-to-last row of Table S11 (i.e., "Exclusion sets #1-3 + no rides in prior 546 days), which consists of 767,900 rides. Otherwise, the analysis used to produce these estimates follows the same approach as was used in the main analysis, details of which are provided in the notes beneath Table 2.