Anthony T. LoSasso, PhD

Using a large natural experiment among 39 employers, we examined the effect of adding financial incentives to workplace wellness programs. 

 The 39 study employers used the same national insurer to administer their wellness programs, allowing us to observe preventive and health-promoting behaviors before and after financial incentives were implemented. Fifteen treatment employers introduced financial incentives into their wellness programs over 3 years, providing variation in the start dates, whereas 24 employers did not introduce financial incentives. These incentives were attached to specific health actions, including annual preventive visits, biometric screening, and selected screening services for diabetes, heart disease, and cancer. 

 Using multivariate regression, we examined employees and their adult dependents who had insurance coverage for at least 12 months and were offered a wellness program. Outcomes include utilization of annual preventive visits, low-density lipoprotein cholesterol testing, fasting blood sugar (FBS) testing, and breast, cervical, and colon cancer screens.

 Financial incentives increased annual preventive visits by 7.7 percentage points, cholesterol testing by 7.9 percentage points, and FBS testing by 7.1 percentage points (

<.05 for each). Compared with baseline rates, these changes represent significant improvements of 21% to 29%. Increases for cancer screening were smaller: 2.7 percentage points for mammograms and 2.2 percentage points for colorectal cancer screening, which correspond to increases over baseline rates of 5.5% and 7.3%, respectively. We did not detect an impact on cervical cancer screening. 

The addition of financial incentives to wellness programs increases their impact on selected preventive care services. 

The addition of financial incentives to workplace wellness programs has a notable impact on whether individuals receive preventive care services. Modest financial incentive programs in workplace settings can be effective; however, individuals who did not receive services in the past year respond less than others. Because targeting financial incentives to selected subgroups is challenging within the Affordable Care Act framework, wellness programs may require additional outreach efforts.Several strategies have been suggested to promote use of preventive services, including expanded incentives and greater involvement of employers.

 Under the Affordable Care Act (ACA), new incentives were created to promote employer wellness programs and encourage employers and employees to support healthier workplaces. The law enables employers to offer financial rewards to employees who participate in wellness programs or meet certain health-related targets.

 Increasingly, employers offering wellness programs are incorporating these financial incentives with the belief that they will boost the impact of their programs; however, little is known about the effectiveness of these incentive programs. 

In 2015, 14% of all employers, half of employers with 200 or more employees, and two-thirds of employers with 1000 or more employees offered wellness programs.

 These programs include health screenings that collect self-reported health risk information and biometric data from an in-person health examination conducted by a medical professional.

 Many employers incorporate financial incentives into their wellness programs. Thirty-two percent of employers with biometric screening programs incorporate a financial incentive for employees who complete the biometric screens; among large employers (≥200 employees) with wellness programs, 56% do so.

The use of incentives in conjunction with wellness programs is largely driven by employers’ beliefs that programs with incentives are somewhat (64% of employers who use incentives) or very effective (27%). Incentives are disbursed in a variety of ways, including premium discounts, waivers of cost sharing, or additional covered benefits. In practice, typical incentives range from $150 to $500, while the average annual premium for single coverage is $6251.

Despite their widespread use, systematic evidence about the effectiveness of wellness programs with financial incentives is lacking. One literature review concluded that incentives were effective at increasing participation in self-reported health assessments, but was unable to assess their impact on the completion of preventive screenings.

 In randomized controlled settings, financial rewards not paired with wellness programs have been effective at increasing participation in health risk assessment completion,

 Randomized trials provide significant proof of concept that targeted financial incentives can be effective; however, as with all randomized trials in which participation requires an opt-in consent, it is difficult to assess generalizability, as many such trials only enroll 10% to 15% of potentially eligible individuals and participants likely differ from those who do not volunteer to participate on unmeasured characteristics, such as motivation.

The current study takes advantage of a sizeable natural experiment in which 39 large employers within the United States initially offered wellness programs without financial incentives. Over time, 15 of them added financial incentives and the remaining 24 did not. Financial incentives were attached to specific health actions, including annual preventive visits, biometric screening, and selected screening services for diabetes, heart disease, and cancer. Employees received personalized scorecards, both online and mailed to their home, to help them track their progress toward receiving incentives, which were awarded as premium reductions, cash, or gift cards. To our knowledge, ours is the largest study of wellness programs with financial incentives to date and includes over 1.4 million insured enrollees. A key strength of our design is the ability to examine pre-post effects of the introduction of incentives with a contemporaneous wellness program control group that did not introduce incentives. 

In addition, we considered whether the financial incentive effect is greatest for nonregular users of preventive services or for users who had received the service in the prior year. The financial incentive program is designed to be broad-based rather than targeted. Nonetheless, a program is more economically efficient if it entices new or nonregular users of prevention services rather than rewarding individuals who likely would have received the services in any case. 

The wellness program we studied allows enrollees to earn dollars or points for adopting better health behaviors. Enrollees are provided a personalized scorecard, which includes health actions that were completed, as well as those that were not, as an aid to maintain or improve their health behaviors. The points earned for various activities can be converted into cash rewards, premium reductions, or gift cards in a manner set by the employer. We examined clinical screening outcomes that can be measured via claims data. The incentive for any given clinical screening was the same for all covered individuals within an employer, but could vary across employers. Incentive amounts ranged from $0 to $80 for preventive visits, $0 to $100 for low-density lipoprotein cholesterol (LDL-C) tests, $0 to $100 for blood sugar ascertainment with glycated hemoglobin, and $0 to $75 for cancer screening tests. The maximum annual award that an individual could earn for receiving all of these services ranged from $250 to $900 across employers. By contrast, employers without financial incentives in their wellness program promoted and measured the same outcomes, but employees did not receive an explicit financial reward.

All employers used the same insurer to administer their wellness programs, allowing us to observe preventive and health promoting behaviors before and after financial incentives were implemented. The 15 treatment employers introduced financial incentives into their wellness programs between 2010 and 2012, providing variation in the start dates. Individuals’ outcomes were observed as long as they were covered by the insurer. In many cases, the insurer was the sole provider of coverage for the employers. The staggered implementation by employer is illustrated in the 

. Our data span January 2009 through December 2013. 

We used de-identified healthcare claims and health plan enrollment and wellness program data from the insurer. Outcome variables were obtained from claims data based on standard claims codes. These include utilization of annual preventive visits, LDL-C tests, fasting blood sugar tests, and breast, cervical, and colon cancer screens. Coding details are provided in 

]. We were able to determine chronic conditions from claims, but not body mass index (BMI). From enrollment data, we obtained individual age, gender, dates and type of coverage, and whether the insured member was an employee or adult dependent. Insurance enrollment data typically have limited demographic information. However, through a vendor, the insurer obtained imputed information on race, ethnicity, and education of covered members, all potentially important factors that could influence an individual’s propensity to seek preventive care. 

Our sample was restricted to adult (aged 18-64 years) employees who were covered by the insurer administering the wellness programs for at least 12 continuous months (1 full plan year). Spouses and domestic partners were included if they, in addition to the employee, were offered the wellness program. All sample members were included in models that examined annual preventive visits, LDL-C screening, and fasting blood sugar screening. Different subgroups were examined for each preventive screening (ie, women aged 40-64 years for breast cancer, women aged 18-64 years for cervical cancer, and men and women aged 50-64 years for colorectal cancer). Individuals older than 64 years were excluded because they were eligible for Medicare coverage. 

Our study employed a panel-data difference-in-differences (DID) design in which 39 employers offered wellness programs without financial incentives at baseline, 15 employers added financial incentives and the remaining 24 comparison employers did not.

 This is also commonly referred to as a stepped-wedge design. Because all employers used the same national insurer to administer their wellness programs, we observed preventive and health promoting behaviors before and after financial incentives were implemented based on common data collection. 

The effect of the incentive is estimated for each preventive service outcome using variations on equation 1, which reflects a staggered DID or stepped-wedge regression model. Equation: 

The observations are at the individual-year level, where 

 denotes the year. The dependent variable, 

, is an indicator variable that equals 1 if the relevant preventive service was received by individual 

represents a vector of individual covariates, including age; gender; imputed race, ethnicity, and education; whether the covered individual was an employee or dependent; whether the individual had asthma, coronary artery disease, congestive heart failure, chronic obstructive pulmonary disease, diabetes, or hypertension; and whether the individual was offered a high-deductible health plan with a health savings account or health reimbursement arrangement. Year and employer indicators are also included. Including employer indicators allows us to control for the average differences across employers in any time-invariant observable or unobservable employer-level predictors. The error term is represented by 

Our key independent variable is represented in equation 1 by “

 is a binary variable that takes the value 1 in any year that the individual is offered a wellness plan with incentives and 0 in all other years. Controlling for individual and other plan characteristics, the coefficient on this variable is interpreted as the average change across enrollees in employers with financial incentives relative to the average change for those with standard wellness programs. Standard errors were clustered by employer, and all reported 

values were for 2-tailed statistical tests.

We estimated multivariate linear probability models to isolate the degree to which offering a wellness program with financial incentives in a given year influenced the probability of an employee receiving a selected preventive visit, cancer screening test, or blood test in that year relative to employees of employers that offered wellness programs without financial incentives. We selected linear probability models because their coefficients can be interpreted as marginal effects of treatment and because our treatment variable was binary. 

In order to identify whether the program effect was greater for nonregular users of services versus users who had recently received the service, we repeated our models adding an indicator for receipt of the service in the prior year and its interaction with the incentive indicator. Although we used all years of data for each employer, we restricted the models to individuals present in the data for 24 continuous months. Introducing a lagged dependent variable (ie, prior receipt) could lead to bias if there is serial correlation in unobserved individual-level factors and it is greater or lesser at employers offering financial incentives.

We addressed observable differences between treatment and comparison employers with treatment-on-the-treated propensity score weighting. Our outcome models were weighted using inverse probability weights obtained from a series of propensity score models. The weighting models were estimated using boosted regression, as implemented in the “Toolkit for Weighting and Analysis of Nonequivalent Groups” package in R,

 which predicted the probability of being a treated observation in the year before those employers added financial incentives based on on age, gender, race and ethnicity, Census region, urban location, chronic conditions, and offer of high-deductible coverage with a health savings or health reimbursement account. Because the study used a DID comparison design, we weighted both treatment and control observations for each year to match the baseline year for the treatment group in order to balance the distribution of covariates both over time for each treatment group and between the treatment groups.

 This allowed us to control for any compositional changes over time in the treatment or comparison group as well as provide appropriate weights to observations in the control group. Separate weighting models were run for each treatment group by year combination. The high-deductible health plan variable was not balanced by weighting in all years and was removed from 6 of the 11 propensity score models, but was included in all outcome models. Weighting models were re-estimated for each of the cancer screening eligibility subgroups defined by age and gender. The propensity score weights were applied in calculating all reported results except sample sizes.

The characteristics of individuals in the treated employers were substantively similar to those in the control employers in the baseline year after weighting (

). Baseline rates for receiving a full biometric tests and breast cancer screening were 3 to 5 percentage points lower in the employers that did not introduce incentives. Thus, introducing financial incentives does not appear to be a selected response by employers to low baseline rates of targeted services. Covariate balance from propensity score weighting is typically measured by standardized mean differences (

). The balance was successful; among 365 comparisons, all were below 0.12 and only 4 were greater than 0.10.

Preventive Visits and Blood Tests for Disease Screening

Baseline rates for preventive visits were an average 36.1% in the treatment group. In years when employers offered a wellness program with financial incentives, members were 7.7 percentage points more likely to have wellness visits (

), a 21.3% increase. They were also 7.9 percentage points more likely to have cholesterol screenings and 7.1 percentage points more likely to have blood sugar tests for diabetes (

<.05). Results for the blood sugar and LDL-C tests were similar, and 95% of members who received the LDL-C test also received a blood sugar screening test. When considered as a set, the financial incentives resulted in 8.1% more individuals having all 3 biometric components (ie, a preventive visit and the 2 blood screenings) over a baseline rate of 19.0%, a 42.6% increase over baseline. Full regression results are shown in 

The wellness program with financial incentives was associated with smaller increases in cancer screening rates (Table 2). Financial incentives were associated with a 2.7 percentage-point increase in mammography (

<.05) and a 2.2 percentage-point increase in colorectal cancer screening (

<.01). Relative to baseline rates in the treatment group, these changes represent 5% to 7% improvements in screening rates. No differences were detected for cervical cancer screening rates over time in employers who offered incentives compared with those who did not.

Individuals who sought services in a given year were 17% to 30% more likely than others to receive them in the following year, controlling for other characteristics (

). Estimates of the incentive program’s effect by prior receipt of service are mixed, but 2 results emerge. The program had a significantly greater impact on receiving the full biometric screen for those who had previously had one, by 6.1 percentage points (

<.05). In contrast, for the cervical and breast cancer screening tests, the program had a greater effect on individuals who did not receive screening in the prior year, meaning that here the incentives had a stronger effect on nonregular service users. The effect of the incentive on nonregular users was 3.1% (

<.01) for breast cancer. We did not detect different impacts of incentives on colorectal cancer screening by prior year receipt. 

For all preventive tests, we found a strong association between having the test in the prior year and repeating it, independent of the incentive. Although federal screening recommendations do not support annual cancer screens for all individuals, we note that our data include a mix of individuals for whom an annual cancer screen would not be recommended and others who may have had positive results in the past and for whom annual exams are recommended.

Within a single national insurer, workplace wellness programs paired with financial incentives were associated with increased utilization of targeted preventive services relative to worksite wellness programs without incentives. Increases ranged from 3% to 42% over baseline rates. The largest impacts were seen for receiving a full biometric screen (ie, preventive visit and 2 lab tests), as our study found that adding financial incentives to wellness programs nearly doubled the number of individuals receiving a full biometric screening exam. These results represent the first national data on the impact of adding financial incentives to wellness programs affecting all employees within a set of employers. Although data from randomized trials show significant impacts of financial incentives on health behaviors for self-selected populations of participants, these data speak to the impact across entire employer populations of implementing wellness programs with financial incentives geared toward increasing prevention and screening.

Efficiency and equity are potential challenges in wellness programs with financial incentives. Rewarding individuals who would receive services regardless of a financial reward is not an efficient use of resources, yet under the ACA, wellness programs implemented in employer settings are required to apply to all “similarly situated” employees and therefore do not allow programs to target just those individuals who would otherwise be unlikely to get a given program. Given this, it is interesting that we found that the incentive effect was similar for individuals who did or did not receive preventive visits, screening blood tests, and colorectal cancer screens in the past year and that it was more effective for individuals receiving prior-year services for the full biometric test. Thus, the financial incentives were not more systematically effective at bringing in “new” or infrequent users than “prior” or more frequent users for these services. This was not the case for cervical and breast cancer, where those who had not received screening in the prior year were more likely to be impacted by the reward.

Our study has several limitations. First, we evaluated the incentive programs as they were implemented, which resulted in a limited range of incentive values. We cannot assess the impact of larger incentives. Second, we have taken several steps to mitigate potential selection bias on observed characteristics, yet we cannot rule out bias on unobserved characteristics that would occur if the trends in targeted service use systematically differed for the treatment versus comparison groups for reasons other than the addition of financial incentives. We were, however, able to rule out that employers with lower baseline use of targeted services were more likely to add financial incentives to their wellness programs. Third, because our analysis required at least 12 months of continuous enrollment, we lost members to attrition. If sicker employees were more likely to leave one set of employers than the other, this could bias our results. We addressed this issue partially through the use of propensity score weighting. Fourth, claims data have been found to underreport preventive services relative to medical records.

 Because our sample was composed of privately insured, continuously enrolled individuals, we believe this problem is attenuated. Moreover, any measurement error caused by our claims data should not differ systematically across the treatment and comparison employers or bias our results. Despite being the largest of its kind, the current study is limited to 15 intervention and 24 comparison employers, limiting the generalizability of the results. Finally, we were not able to assess impacts on health outcomes, as these were not included in the claims data.

We find that the addition of financial incentives to workplace wellness programs has a statistically significant impact on the receipt of targeted preventive care services. Although the magnitude of the incentives evaluated in this study were well below the federally determined maximum of 30% of premiums for outcomes-based incentives, the effectiveness of these programs signals that modest financial incentive programs in workplace settings can drive health behavior in desired directions. Because targeting financial incentives to particular groups, such as individuals who have not had preventive services in the past year, is challenging within the ACA framework, wellness programs may need to rely on other outreach efforts. Overall, our results highlight the potential promise of ACA-induced movement toward greater use of wellness programs in employment-based settings when they are paired with financial incentives.

The authors thank Tanya Lewis-Walls for her descriptions of the UnitedHealthcare United Personal Rewards program. Preliminary results were presented at the 2014 American Society of Health Economists meeting in Los Angeles, CA, and at the 2015 National Institutes of Health, Economics of Prevention Workshop in Bethesda, MD. Dr Cuellar (principal investigator) had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

 George Mason University (AC), Fairfax, VA; Carnegie Mellon University (AMH, HW), Pittsburgh, PA; Lehigh University (SR-S), Bethlehem, PA; University of Illinois at Chicago (ATL, AA); Independent consultant (MS), Chicago, IL; Perelman School of Medicine and the Wharton School, University of Pennsylvania (KGV), Philadelphia, PA; Philadelphia VA Medical Center (KGV), Philadelphia, PA.

 Funding for the evaluation was provided by Eunice Kennedy Shriver National Institute of Child Health and Human Development (5R01HD075118, Cuellar, PI). 

 Ms Shah is a former employee of UnitedHealthcare and owns stock in UnitedHealth Group. Dr Volpp is a principal in VAL Health, a behavioral economics consulting firm, and has also served as a consultant for CVS Health. He has also received research funding from CVS, Humana, Hawaii Medical Services Association, and the Vitality Institute. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Articles

Financial incentives created under the Affordable Care Act can help promote employer wellness programs and support preventive services utilization.

Boosting Workplace Wellness Programs With Financial Incentives

 To investigate whether market competition is a potential driver of hospital performance on the key evidence-based Joint Commission heart-failure (HF) quality indicators of angiotensin-converting enzyme inhibitor/angiotensin receptor blocker prescribed, left ventricular function assessment, smoking-cessation counseling, and discharge instructions.

 Hospital performance data for HF was obtained from The Joint Commission’s ORYX program from 2003 to 2006. The performance data were linked with hospital characteristics from the American Hospital Association Annual Survey and area-level sociodemographic information from the Area Resource File. Healthcare markets

	were defined as hospital referral regions (HRRs) and market competition intensity was defined by the Herfindahl-Hirschman Index. Hospital-level and HRR-level ordinary least squares fixed effects regression models were used to estimate the relationship between market competition and performance.

A paired comparison indicated that there was a significant change in the mean hospital-level performance over time on all of the HF quality indicators. From the multivariate analyses, hospitals in the least competitive markets (Quintile 5) performed slightly better (2.9%) than the most competitive markets (Quintile 1) for left ventricular

 <.01). At the HRR level, however, the least competitive markets (Quintile 5) performed moderately worse (5.1%) on the discharge-instructions quality indicator compared with the most competitive markets (Quintile 1) (

Market competition intensity was associated with only small differences in hospital performance. The level of market competitiveness may produce only marginal incremental benefits to inpatient HF care.

	This study empirically examines whether market competition is a potential driver of hospital performance on The Joint Commission heart-failure quality indicators.

		Given the growth of performance measures as a way to stimulate quality improvement efforts, this is a timely and policy-relevant article.

		 This study adds to the existing literature by examining whether quality improvements made by hospitals based on public performance reports are occurring through the mechanism of market competition.

		 We include data from all patients 18 years and older who were treated for heart failure at a Joint Commission—accredited hospital and were eligible for the performance measure.

The significant chasm between the quality of care that heart failure (HF) patients 

 receive has been widely documented. Previous studies have reported on the extensive variation in the treatment and management of HF patients in hospitals across the country.

Because of the substantial geographic practice variations and underuse of appropriate HF therapies, payers and accrediting bodies have begun to measure hospital performance as a way to stimulate quality-improvement efforts. The Joint Commission now requires that hospitals submit information on their performance for the core conditions of heart attacks, HF, pneumonia, and others as part of the ORYX program, which is publicly reported on Quality Check (http://www.qualitycheck.org).

ORYX was first developed by The Joint Commission in 1997 as a way to integrate performance and outcomes measures into a continuous accreditation process.

The ORYX quality indicators are aligned with the Centers for Medicare & Medicaid Services (CMS) Hospital Compare performance measures. Since the ORYX program was initiated, there have been substantial improvements in hospital quality, although there remains a wide variation in performance across individual hospitals and states.

Despite the enormous progress that has been made to narrow the quality gap, the underlying motivation for hospitals to increase their compliance with the standardized quality indicators and the reasons for the wide heterogeneity in performance across hospitals and geographic areas is unclear. Previous research suggests that hospitals might be motivated to act on publicly reported performance data due to market competition, professional standards, and/or to preserve or enhance their reputation.

In this study, we sought to empirically test whether market competition is a potential driver of hospital performance on the key evidence- based Joint Commission HF quality indicators. We focus specifically on HF because it is one of the core conditions that are measured by The Joint Commission and it is a common and costly chronic condition.

Pathways to Performance on Quality Indicators

There are 3 possible pathways through which hospitals might be motivated to act on publicly reported quality indicators.4 Hospitals may be motivated to improve their performance on quality indicators due to market forces because they would like to hold on to or increase their market share of patients. Hospitals’ awareness of quality deficits might also be enough to stimulate quality improvement efforts because of professional standards. Lastly, hospitals may be motivated to improve quality because they are concerned about protecting or enhancing their public image, since consumers may form certain opinions about a hospital.

 In this study, we focus on market competition as a potential driver of hospital performance. We hypothesize that if hospitals compete on quality, then we expect hospitals in competitive markets will provide a higher level of quality and hospitals in concentrated (less competitive) markets will provide a lower level of quality.

The Joint Commission accounts for more than 3000 hospitals that represent approximately 80% of hospitals in the United States and comprises more than 90% of all acutecare hospital beds.

 Quarterly data from The Joint Commission’s ORYX hospital performance measurement program for HF from 2003 to 2006 was used. The 4 evidence-based HF quality indicators examined were (1) angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB) prescribed at discharge, (2) left ventricular (LV) function assessment, (3) smoking-cessation counseling, and (4) discharge instructions. Only yearly data points for the ACEI or ARB quality indicator in 2003 and 2004 were available, with the exception of Quarters 1 and 2 of 2003, since the definition for this quality indicator changed in 2005 to include ARB. A total of 3011 non-Federal, shortstay, Joint Commission—accredited acute-care hospitals were used as the primary units of analysis over 16 quarters (n = 48,176), with a secondary analysis that aggregated hospitals to 306 hospital referral regions (HRRs) over 16 quarters (n = 4896). Patients 18 years and older with LV systolic dysfunction HF defined by

 International Classification of Diseases, Ninth Revision, Clinical Modification 

diagnosis codes 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, and 428.XX, who were admitted to Joint Commission— accredited hospitals and who met the reporting requirements, comprised the data elements for the individual HF quality indicators.

The ORYX data were linked with hospital characteristics data from the American Hospital Association Annual Survey and sociodemographic and market-level characteristics from the Area Resource File to serve as control variables. Market share was derived from the Medicare Provider and Analysis Review (MedPAR) file based on the total number of Medicare HF patients.

As part of our secondary analysis, we aggregated hospitals to the HRR level to mitigate the potential issue of the small number of cases at a particular hospital that may adversely impact a hospital’s performance, as well as the endogeneity problem of patient selection of hospitals based on perceived or other unobservable characteristics that may be correlated with quality of care. A fixed-effects approach was used to examine within unit changes over time so that the results would not be affected by any heterogeneous differences across HRRs that might be constant over time.

We used HRRs to construct the measure of the healthcare market according to the crosswalk methodology of the 

. HRRs are a naturally occurring healthcare market and they represent a geographic area where a significant proportion of medical care is provided by a referral hospital(s) serving an entire region.

A quarterly measure of market competition was constructed based on the Herfindahl-Hirschman Index (HHI). The HHI is a standard measure of market competition and it has been widely applied for hospitals in HRRs. The HHI measure was created using Medicare HF patient volume derived from the MedPAR data file. Hospital market share was calculated as the total number of Medicare HF patients at a hospital divided by the total number of Medicare HF patients within an HRR, and it was scaled by 100.

The HHI was then determined by taking the sum of the square of market shares for Medicare HF patients for all hospitals within an HRR.

The HHI ranges from 0, which represents an infinitesimally small number of competitors in a market, to 10,000, which represents a monopoly. In our analyses, however, we used quintiles of HHI where Quintile 1 represents the most competitive markets and Quintile 5 represents the least competitive markets.

Hospital-level performance on the quality indicators was used in the primary analyses. For the HRR-level analyses, the weighted average of performance based on the total number of eligible HF patients at each hospital for the quality indicators (ACEI or ARB prescribed at discharge, LV function assessment, smoking-cessation counseling, and discharge instructions) was calculated. By using the HRR-level weighted average of hospital performance, hospitals in a market that had a higher volume of HF patients had their performance score weighted more highly than hospitals that had a lower volume of HF patients.

We first examined descriptive statistics on the hospitallevel performance and market intensity data. We then used a paired t test to assess the change in the mean hospital-level performance on the HF quality indicators from Quarter 1 of 2003 to Quarter 4 of 2006. We specified hospital-level ordinary least squares (OLS) fixed-effects regression models with Quintile 1 (most competitive markets) serving as our reference group to estimate the relationship between market competition and performance and account for the time invariant—omitted variables. We lagged market competition by one quarter in our models because we would reasonably expect a delayed response between the time when the HF performance data are made publicly available and the time when the effect of market competition occurs. Additionally, the temporal sequence of the lagged model would make it less likely for an external shock in the future to affect past performance.

A set of time indicator variables for the quarter of the study was included in the regression models, with the first quarter serving as the reference. We controlled for a number of hospital characteristics including: bed size, teaching status, profit status, presence of a cardiac intensive-care unit, percentage Medicare, percentage Medicaid, and number of nurses to 1000 patient-days. Market-level sociodemographic variables of gender, race, age, per capita income, unemployment rate, number of physicians, and health maintenance organization penetration rate were included. We also included in our models the volume of HF patients and total admissions to control for the quality-volume relationship. We adjusted for the hospital market share of patients from the first quarter to control for the potential shifting of market shares over time. The regression models were specified for hospital-level fixed effects and clustering by HRR to take into account the correlation of the HRR-level independent variables. The multivariate analyses also specified for robust standard errors.

We also explored if the rate of adherence to the HF quality indicators varied over time by market competition intensity whereby competitive markets might conform

more quickly to the HF quality indicators because of market forces and competitive pressure. Therefore, a time by lagged market-competition interaction variable was later included in the regression models.

The models were repeated as HRR-level fixed effects to account for the potential endogeneity problem of patient selection of hospitals based on perceived quality of care. In the HRR-level analyses, the market-weighted average of the covariates was included for each HRR.

 shows the change in average hospital performance for the HF quality indicators, and average number of eligible patients by quality indicator per hospital. The largest increase in average hospital performance over the study period was observed for the behavioral measures of smoking-cessation counseling (45%) and discharge instructions (38%), while smaller progress was made on the clinical measures of ACEI/ARB prescribed at discharge and on LV function assessment.

 shows the distribution of markets by the level of market competition intensity at the first and last quarters of the study. The distribution shifted slightly over time as markets became slightly less competitive.

A paired t-test was conducted to examine the changes in average performance over time. The paired comparisons for each of the quality indicators were found to be highly significant (

 <.001), indicating that there was a significant change in the mean hospital-level performance (Table 1).

This study empirically examines whether market competition is a potential driver of hospital performance on the key evidence-based Joint Commission heart-failure quality indicators.

Anthony T. LoSasso, PhD

Articles

Boosting Workplace Wellness Programs With Financial Incentives

Effect of Market Competition on Hospital Performance for Heart Failure