Gosia Sylwestrzak, MA

 To examine how health care utilization and spending vary for low-income employees compared with high-income employees enrolled in an employer-sponsored high-deductible health plan (HDHP).

 We use commercial medical claims data and administrative human resource data from a large employer between 2014 and 2018. We link the administrative data, which include details on salary and other benefit choices, to each employee in each year with medical claims. Our variables of interest include medical spending and utilization outcomes grouped into different care settings.

 Using multivariate regressions, we estimate the association between salary buckets and health care utilization and spending, controlling for demographic characteristics, comorbidities of employees, human resource health plan benefits, and geography.

 Employees earning less than $75,000 show lower rates of utilization and spending on preventive measures, such as outpatient visits and prescription drugs, while having higher rates of utilization of preventable and avoidable emergency department visits and inpatient stays, resulting in lower overall health care spending among lower-salary employees.

 Low-salary employees enrolled in HDHPs have higher rates of acute care utilization and spending but lower rates of primary care spending compared with high-salary employees. Results suggest that HDHPs discourage routine physician-patient care among low-salary employees.

Social determinants of health are a growing topic of interest in the health insurance market because they have been shown to be associated with health care spending and health outcomes.

High-deductible health plans shift the cost of care to plan holders. This can lead to reduced utilization of care. High-deductible health plans have potential negative consequences for individuals earning a low salary.

We compared employees along the salary distribution to compare health care spending and utilization and show that low-salary individuals spend less on outpatient care but have more visits and higher spending at emergency departments.

Additional work is needed to align health care needs with access to care and to the appropriate provider setting.

The Medicare Prescription Drug Improvement and Modernization Act of 2003 established tax-sheltered health savings accounts (HSA) for high-deductible health plans (HDHPs) to incentivize individuals to save for health care needs and to consciously shop for health care services. The legislation led to a subsequent shift from traditional plans with low deductibles to HDHPs by employers and health insurance plans, with 15% of all private health plans classified as an HDHP in 2007 and 44% by 2017.

Proponents of HDHPs argue that they tend to have lower total premiums and lower out-of-pocket (OOP) premiums for employees and they retain the traditional value of insurance, protecting employees from large health care expenses with limits on total OOP spending. At the same time, they reduce health care spending by exposing consumers to the full price of care for low levels of medical spending. The RAND Health Insurance Experiment showed that increased cost sharing decreased utilization and spending without resulting in adverse health outcomes.

 A recent study among high-income earners has also shown that switching to an HDHP has resulted in limited differential changes in medical spending across income.

 HDHPs also provide incentives to shop for less costly service providers, given the widely documented variation of health care cost even within the same geographic area.

Opponents of HDHPs argue that exposing employees to the full cost of care discourages potentially necessary and preventable health care services, especially when the deductible for families can reach more than $4000.

 Studies that evaluate increases in cost-sharing provisions have found negative effects on medication adherence and even increases in mortality.

 These effects may be amplified among lower-salary employees, who may avoid or delay health care spending simply because they may not have enough savings to cover relatively minor procedures or because they anticipate high OOP costs, especially in a system that operates with unpredictable pricing and out-of-network billing tactics.

 Additional complications emerge through the well-established link between health and social determinants of health.

 Thus, lower-salary individuals generally have a higher prevalence of chronic conditions, and this factor, combined with OOP spending concerns, suggests that lower-salary individuals should be instead encouraged to utilize more health care services to adequately treat their comorbidities compared with high-salary individuals.

Therefore, the aim of this study was to evaluate health care utilization and spending along the salary distribution for employees enrolled in an HDHP. We use medical and pharmacy claims data linked to employer administrative data that include precise employee salary and a variety of human resource information to adjust for differences in sociodemographic characteristics, health plan benefit design (such as employee medical plan premiums), geography, and baseline health status of employees.

There is limited research describing how individuals continuously covered by an HDHP differentially utilize health care. The best evidence to date uses self-reported salary or US Census estimates.

 For example, one study found that HDHPs affected spending similarly for low-salary employees and high-salary employees.

 Other research has shown that lower-salary families enrolled in HDHPs were more likely than high-salary families to delay or forgo care due to costs.

 Research also found an association between lower socioeconomic status and inappropriate reductions in high-acuity emergency department (ED) visits and longer delays in breast cancer care.

 However, earlier work also found that women of low socioeconomic status enrolled in HDHPs experienced no statistically significant detectable changes in rates of cervical and breast cancer screenings.

We used commercial medical and pharmacy claims and administrative data from a large national employer between 2014 and 2018. The employer operates in the health care industry and has more than 60,000 employees residing in all states, most of whom can be considered as holding professional jobs. For each employee covered by the employer-sponsored health insurance plan, we followed their compensation history with the employer, as well as their health plan enrollment and their medical and pharmacy claims history. Medical and pharmacy claims data for the employer were obtained from the HealthCore Integrated Research Environment, a repository of fully adjudicated claims data for approximately 70 million health plan members across the United States. We limit the data to employees enrolled in a medical plan for the full plan year (January 1 to December 31) between 2014 and 2018.

The employer’s administrative data included demographic information on employee gender, age, annual salary (salary is the actual salary earned by the employee during the calendar year, exclusive of bonuses), employment status (part time vs full time), length of tenure with the company, and race/ethnicity (White, Black, Hispanic, Asian, other). Health plan benefit design elements included the chosen consumer-directed health plan (CDHP) (HDHP with an HSA), the tier (employee vs family plan), associated deductible, coinsurance rate, and OOP maximum. We also observed the health insurance premiums paid by the employee and employer and the HSA and flexible spending account (FSA) contributions by employee and employer.

Employees at the company had the choice of 3 CDHPs with a similar provider network in all years of the study. Plans differed only in cost sharing and employee premiums. Plan A was the most generous plan with the lowest deductible, lowest maximum OOP, and lowest OOP coinsurance rate, but highest employee premium. Plan B had about a 30% higher deductible, 17% higher maximum OOP, 10% higher coinsurance rate, and, on average, 25% lower employee premiums. Plan C was the least generous plan with the highest deductible, the highest maximum OOP, and the least generous coinsurance coverage, although it had the lowest employee premium. The plan tier selection (ie, employee vs family plan) affected the premium, deductible, maximum OOP, and employer contribution to the HSA/health reimbursement account (HRA) (although the employer contribution across all plans was similar and very generous). Premiums also increased with salary, although low-salary employees paid very similar premiums for all 3 plans, which should lead to higher enrollment in the most generous health plan compared with higher-salaried employees’ enrollment in the most generous health plan.

Our independent variables of interest are binary variables indicating the salary level of the employee for the year. We use the employee’s base salary for the year, excluding bonuses. The median salary was $61,013 with an IQR of $42,000 to $85,100, and the highest salaries were truncated at $200,000. We categorize employees into 4 salary bins: those earning less than $50,000, $50,000 to $74,999, $75,000 to $100,000, and more than $100,000. The inclusion of binary variables, rather than a continuous salary variable, has the advantage of allowing for nonlinear effects of salary on health care utilization. Results using alternative bins are presented in 

Our primary outcome variables measure health care utilization and spending trends for each employee in each quarter. Specifically, we create binary and continuous utilization measures for inpatient stays, outpatient visits, ED visits, and prescription fills. Health care spending is defined as the total amount (plan paid, patient paid, and any payments by secondary payers) of medical expenses associated with inpatient stays, outpatient visits, ED visits, and prescription fills. We log the spending variables to account for the skewness of high health care utilizers and to facilitate the ease of interpretation of the results.

To classify ED visits by medical urgency, we used the New York University ED algorithm.

 The algorithm assigns probabilities to ED visit as urgent (ED needed), emergent but preventable or avoidable (preventable ED care where the condition could have been prevented or avoided with timely and effective ambulatory care), emergent but primary care treatable (care could be provided in a primary care setting), and nonemergent (immediate care not required). We allocated each visit to 1 of the 4 categories related to emergency status when the assigned probability of the algorithm was 100% for said category. We also classified inpatient visits based on the Agency for Healthcare Research and Quality’s Prevention Quality Indicators (PQIs), which identify whether an inpatient stay could have been avoided had high-quality ambulatory care been provided.

 Ten different inpatient conditions can be marked as a preventable inpatient stay, and we evaluate the cumulative number of inpatient stays for all PQI conditions.

We create control variables from the medical plan information, including plan choice, tier, coinsurance rate, deductible level, OOP maximum, annual employee premium payment, annual employee and employer HSA contribution, and total FSA contributions. These variables account for differential behavior over time stemming from varying incentives to consume health care.

 We also include age, gender, and the Elixhauser Comorbidity Index score, which indicates diagnosis of a high-expenditure chronic disease or condition (eg, cancer, diabetes, mental health diagnoses).

 The administrative data provide additional control variables that include employees’ full-time employment status (reference group: part time), tenure with the employer (measured in years), and race/ethnicity (measured with binary indicators for White, Black, Hispanic, Asian, and other). We also include binary variables for the state of residence, year, and quarter to account for geographic differences in access and health care spending, seasonality of care seeking and sickness, and year-specific trends.

We used multivariate regression analyses to estimate the association of salary with health care spending and utilization at the employee-quarter level. The first set of regressions estimate linear probability models, where the dependent variable is a binary indicator for whether the employee receives any type of care within a quarter for the aforementioned health care utilization variables. The second set of regressions estimate ordinary least squares (OLS) models, with the number of encounters for each outcome as the dependent variable. For spending outcomes, we use generalized linear models, with a log-link and gamma distribution. We transformed the coefficients of interests with ecoefficient minus 1 to correctly interpret the percentage change. 

 present results from sensitivity analysis for our binary outcomes using logit models for binary outcomes, negative binomial models for continuous utilization outcomes, and log-transformed spending outcome OLS models. Our independent variables of interest are binary variables indicating whether the employee earned less than $50,000, $50,000 to $74,999, or more than $100,000 per year in salary, with $75,000 to $100,000 as the reference category. In all multivariate regressions, we included the full set of control variables to adjust for confounding that cover demographic, region, and health plan benefit elements. State, quarter, and year fixed effects were included to account for differences by state and time in spending and care utilization. We conducted all analyses using Stata 15 (StataCorp LP).

This study was conducted in full compliance with relevant provisions of the Health Insurance Portability and Accountability Act. Because researchers used only analytic files derived from a limited data set to perform the analyses as defined by the Privacy Rule 45 CFR 164.514(e), no waiver of informed consent or exemption was needed from an institutional review board.

In total, our study sample includes 448,012 employee-quarter observations, representing utilization patterns for 33,470 employees between 2014 and 2018. Individuals in the higher-salary groups (ie, salary of $75,000-$100,000 and > $100,000) were more likely to be men (27.5% and 50.2%, respectively) and more likely to identify as White (73.5% and 77.6%) or Asian (7.0% and 10.5%) than those in the lower-salary groups (

). Higher-salary employees ($75,000-$100,000 and > $100,000) were healthier relative to lower-salary employees, with a smaller proportion having 3 or more Elixhauser comorbidities (15.3% and 11.7%). Higher-salary employees were more likely to have family plans that include either their spouse, their children, or both (59% and 69%). In terms of health plan choice, approximately the same percentage of employees across the salary categories were enrolled in health plan B; however, the employees in the highest-salary group were much more likely to be enrolled in the least generous plan C (18.1%). Lastly, those earning more than $100,000 contributed more money to both their HSA/HRA and FSA ($2045 and $401, respectively) compared with lower-salary employees earning less than $50,000 ($1037 and $163).

In terms of health care utilization, 1.6% of employees had an inpatient stay during the quarter, 4.0% had an ED visit, 63% had at least 1 outpatient visit, and 65% had at least 1 prescription filled during the quarter. On average, an employee had 3.4 outpatient visits and 3.6 prescription fills per quarter. Across salary groups, outpatient care utilization was similar, although low-salary employees were more likely to use inpatient and ED care. Mean quarterly medical spending equaled $1535, large seasonality across years in spending between the last quarter of the year and the first quarter of the following year was prevalent (with high spending in the last quarter and low spending in the first quarter), and the majority (64%) of spending occurred in the outpatient setting ($989). Low-salary employees had higher quarterly average spending on inpatient ($500) and ED ($156) visits compared with those in higher-salary groups.

The linear probability model results show that employees earning less than $50,000 had a 0.14-percentage-point lower probability (

 = .01) of having an inpatient stay relative to employees earning $75,000 to $100,000 in a given quarter (

). The lowest-salary group also had a 1.8-percentage-point higher probability of visiting the ED during the quarter (

 < .01), a 4.3-percentage-point lower probability of having an outpatient visit (

 < .001), and a 1.8-percentage-point lower probability of filling a prescription during the quarter (

 < .01). On the other hand, the highest-salary group had a 1.3-percentage-point higher probability of an outpatient visit (

 < .01) and a 1.4-percentage-point higher probability of filling a prescription (

 < .01) relative to the $75,000-to-$100,000 salary group during the quarter.

The lowest-salary group had 0.022 more ED visits (

 < .01) per quarter relative to the reference group (Table 2). The lower 2 salary groups also had significantly fewer outpatient visits per quarter, with 0.43 and 0.16 fewer visits per quarter (

 < .01 for both), whereas the highest-salary group had 0.2 more outpatient visits (

 < .01) during the quarter. Lastly, the lower-salary groups had 0.22 and 0.13 fewer prescription fills (

 < .01 for both), whereas the highest-salary group had 0.12 more prescription fills (

For total medical spending, plan and member paid, only enrollees in the $50,000-to-$74,999 salary group had 6% lower spending (

 < .01) relative to employees earning $75,000 to $100,000. The highest-salary employees spent 8% more on their health care (

 < .01) relative to those earning $75,000 to $100,000 (

). Within service categories, the lowest-salary group spent 40% more on ED care (

 < .01) compared with the reference group, and the lower 2 salary groups spent significantly less on outpatient care (14% and 8%, respectively; 

 < .01 for both) and prescription drugs (10% and 5%; 

 < .02, respectively). The highest-salary group spent 7% more on outpatient visits (

 < .01) and 17% more for prescription drugs (

 < .01) compared with the reference group.

The probability and number of nonemergent ED visits were higher among the lowest-salary employees, with little differential visit trends in all other salary bins (

). Similar results were found for primary care–treatable and preventable ED visits, where the lowest-salary group had higher visit rates compared with the reference group. Findings also reveal that the lowest-salary group had more ED visits that require ED care, suggesting that they may have a higher prevalence of conditions such as chest pain. Comparing preventable inpatient stays across salary groups showed that the lowest-salary group had a higher probability and number of preventable inpatient admissions.

In this study we found that low-salary employees, especially those earning less than $50,000 per year, utilize significantly fewer outpatient services and prescriptions compared with employees earning $75,000 to $100,000 in salary. Low-salary employees, however, have significantly higher inpatient and ED service use. The lowest-salary employees had 40% higher total medical spending on ED care but less spending on outpatient care and prescription drugs. In contrast, those earning more than $100,000 a year were generally more likely to seek outpatient care and prescriptions compared with employees earning $75,000 to $100,000 per year. The utilization and cost results indicate that lower-salary employees utilize care differently than higher-salary employees in a way that suggests suboptimal care-seeking behavior.

Higher ED care use, especially for preventable conditions, and lower outpatient care use for low-salary employees may point to substitution of what would typically be considered a more appropriate setting for nonemergent conditions—physician offices. It is also possible that lower-salaried individuals do not receive the right amount of condition management. These findings are reiterated by the fact that we also found higher preventable inpatient stays. At the same time, a lower probability of pharmacy use and spending among low earners also suggests that the cost of prescriptions may be a barrier to filling prescriptions; however, we observe higher pharmacy fills, potentially implying that those who fill prescriptions are more likely to use cheaper generics. The differentially signed total pharmacy fills and spending results may reflect that those who consider filling a single prescription may be less likely to do so (extensive margin), whereas those who commonly fill prescriptions (intensive margin) are much more likely to fill prescriptions relative to employees earning $75,000 to $100,000. Additional qualitative and quantitative work is needed to understand the mechanism that could explain these differential care patterns between low- and high-salary employees.

To date, few studies have focused on vulnerable populations enrolled in HDHPs, which is important because they are especially exposed to high OOP medical spending relative to their income; this may be because of a lack of individual socioeconomic information matchable to claims data.

 Our findings are in accord with work that found that HDHPs reduced utilization and spending for individuals of low socioeconomic status and evidence that lower-salary families enrolled in HDHPs were more likely to delay or forgo care due to costs than high-salary families.

 Other work analyzed the impact of switching to an HDHP.

 Some studies found negative consequences after the switch, such as reductions in high-acuity ED visits, that were especially pronounced for low socioeconomic status groups, whereas other research did not find statistical evidence for differences in breast cancer and cervical cancer preventive care.

Compared with previous studies that have focused on low-income individuals using geographic socioeconomic data, we perform comprehensive analyses that assess health care utilization and spending patterns by salary for employees in HDHPs across time using precise individual level earnings data.

 We also used a much richer set of control variables compared with previous work to account for factors that influence health care plan choice and health care spending. The closest work to ours was performed by Sherman et al, who utilized wage data in 2014 from self-insured employers, many of them located in the South.

 They found significant disparities in spending, ED use, and hospitalizations, which are partially confirmed in our study. Medical spending was higher for the highest-salary group but lower for the lowest-salary group. We also observe significantly elevated levels of avoidable ED use for low-salary employees. However, the number of hospital admissions for the lowest- and highest-salary groups were both elevated relative to the middle-salary reference group.

The continued shift toward HDHPs can result in significant challenges for low-salary employees. Although CDHPs come with lower premiums for consumers, plans may realize higher medical expenses in the long run based on the complicated nature of their setups, misunderstanding of consumers, and consumers’ liquidity constraints to pay for OOP expenses.

 Additionally, evidence suggests that many employees prefer consumption smoothing (eg, consistent plan premium payments rather than large 1-time OOP medical payments). HDHPs reduce consumption smoothing by shifting high spending onto consumers at the time of care needs, whereas low-deductible plans shelter consumers from high OOP spending but require consistently high monthly premium payments. Consumers might even prefer low-deductible plans when total medical and premium spending would have been lower in an HDHP compared with the total medical and premium spending in a low-deductible plan.

Work by Wharam et al suggests that rising health care costs could exacerbate the negative consequences of HDHPs on preventive care use for individuals of low socioeconomic status.

 Payers and providers can counteract rising medical costs by improving price transparency, especially for preventive services that might reduce costs in the long run. Payers may also reformulate HDHPs by providing generous HSA account contributions that cover most of the deductible for low-income employees. Health insurance literacy interventions, however, might need to accompany such changes to ensure that low-salary employees are aware of the benefits of HSAs.

This study has several limitations. First, the salary data that we use include information for only the employees of the firm, whereas the employee’s household income may be higher due to the employment of other household members (such as the spouse). However, many studies on the value of HDHPs rely on Census estimates based on the geographic location of the employee’s residence. Second, it is important to understand the household decision-making process of employees because health insurance coverage decisions are normally made at the household level, but we do not know the marital status or whether the employee had dependents unless their health plan covers family members. Third, the employees in this study are employed by a single firm in a single sector of the economy. It is possible that employees in different types of jobs and different sectors of the economy use health care differently, although the employer covers a variety of jobs across the United States. Fourth, the study is limited to claims data billed through employer-sponsored coverage, so care and prescriptions purchased without insurance are not captured in our data. Fifth, differences in health care use between low- and high-salary groups may not necessarily suggest that low-salary individuals underutilize care. It is possible that higher-salary individuals overutilize care. However, previous work has shown little difference in health care use among low-salary and higher-salary groups, and in some cases there is evidence of overutilization for low-salary individuals.

This study fills an important gap in the literature by assessing the association of salary and care utilization when a HDHP is present. Understanding how HDHPs influence employees’ health care decision-making, particularly for low-salary employees who may not have the liquidity to pay for high deductibles, is important because associational evidence has shown that HDHPs reduce medical care utilization. In this study, we found that lower-salary employees enrolled in an HDHP with an HSA through their employer spent significantly less on outpatient services and prescription fills compared with higher-salary employees. At the same time, low-salary employees spent significantly more on ED care. These results suggest that low-salary employees may be forgoing outpatient services and instead spending more in the ED compared with higher-salary employees when HDHPs are present. This pattern of health care utilization may lead to delayed diagnosis of health conditions and potentially miss the window and benefits of early diagnosis or prevention.


 Texas A&M University (BU), College Station, TX; Anthem Inc (BU, MO, AD, GS), Indianapolis, IN; HealthCore (SE-P), Wilmington, DE.

 Dr DeVries and Ms Sylwestrzak are employees of HealthCore, a wholly owned research subsidiary of Anthem Inc, and own stock in Anthem Inc. 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.

 Concept and design (BU, MO, AD, GS); acquisition of data (BU, SE-P, AD, GS); analysis and interpretation of data (BU, SE-P, MO, AD, GS); drafting of the manuscript (BU, SE-P, MO, GS); critical revision of the manuscript for important intellectual content (BU, SE-P, GS); statistical analysis (BU); provision of patients or study materials (GS); obtaining funding (GS); administrative, technical, or logistic support (BU, GS); and supervision (BU, GS).

 Benjamin Ukert, PhD, Texas A&M University, 212 Adriance Lab Rd, College Station, TX 77843. Email: bukert@tamu.edu.

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Articles

Lower-salary employees in high-deductible health plans underutilize outpatient care and overutilize emergency departments.

Disparities in Health Care Use Among Low-Salary and High-Salary Employees

 To evaluate the impact of value-based insurance design (VBID), which removed patient cost sharing for primary care visits, on healthcare spending in a large, geographically diverse employer.

 Quasi-experimental, difference-in-differences (DID) design, administrative claims—based study.

 Healthcare spending during the preintervention period (2008 and 2009) was compared with the postintervention period (2011 through 2014) to measure the impact of removing primary care cost sharing. The study population included Anthem commercially insured enrollees with continuous medical eligibility from 2008 to 2014 who were younger than 65 years. The VBID cohort included health plan enrollees from a national large employer that implemented the benefit change. The comparison cohort included other Anthem enrollees who did not have a similar benefit change and were propensity score—matched to the VBID cohort. Utilization of various types of healthcare services was also examined.

 The VBID cohort experienced a $12.0 per member per month relative reduction in overall spending compared with the comparison cohort (

 = .02). The trend was driven by reductions in expenditures for emergency department (ED) visits ($1.3 relative reduction; DID, —10.0%; 

 = .03) and other outpatient services ($7.6 relative reduction; DID, —5.8%; 

 = .02), which aligned with reduced utilization of ED visits (DID, —4.5%; 

 = .07) and other outpatient services (DID, —4.1%; 

 = .004). For physician office visits, the VBID cohort did not experience a significant relative increase compared with the comparison cohort (DID, 0.9%; 

 The attempt to increase primary care access by reducing cost sharing did not produce a negative outcome in terms of total spending for healthcare.

Our study evaluated the impact of removing cost sharing for primary care on healthcare spending and utilization among commercially insured children and adults in a large employer group over a 6-year period.

Previous research demonstrates that increased cost sharing reduces use of low-value services, but it has not demonstrated an increase in use of high-value services.

Patients included in value-based insurance design (VBID) in this study did not experience significantly increased use of primary care.

VBID patients in this study experienced a lower total healthcare spending trend through decreases in medical utilization, driven by reduced utilization of the emergency department.

Recent reports suggest that healthcare expenditures in the United States will continue to grow by 5% to 6% per year, creating additional pressure on employers and insurers to contain spending.

 One approach widely adopted by insurers and employers has been to shift financial responsibility to consumers through higher premiums, co-pays, and deductibles. Based on the RAND Health Insurance Experiment, cost-sharing provisions seem especially promising in curbing “excessive” or potentially unnecessary healthcare utilization without any repercussions for patient health.

 Increasing cost-sharing provisions resulted in ballooning out-of-pocket payments for patients, which grew by 94% between 2012 and 2015.

Another promising approach has been the use of value-based insurance design (VBID), in which cost sharing is removed or reduced for high-value services and retained or increased for low-value services.

 The idea behind VBID is that some healthcare encounters are beneficial in detecting and treating negative health events at an early stage, events that could become compounded later, and they therefore reduce down-the-road hospitalizations and other costly healthcare encounters. So far, VBID has been implemented relatively widely in the area of prescription medications—mostly in managing chronic conditions. One exception is the Mayo Clinic, which coupled cost-sharing reductions in the primary care setting and for general preventive services with increased cost sharing for specialty care and outpatient procedures.

 Other examples include Connecticut, which provided substantial incentives to state employees to lower their out-of-pocket costs for medications and office visits associated with chronic disease.

 More recently, in January 2017, CMS began testing a VBID model in the Medicare Advantage population by offering reduced cost sharing for enrollees falling into specific clinical categories, such as those with diabetes, congestive heart failure, or chronic obstructive pulmonary disease.

Several studies have identified access and continuity of care in the primary care setting as promising high-value opportunities that improve health outcomes while lowering total healthcare expenditures. Primary care utilization improves care continuity, intensifies the patient—provider relationship, emphasizes potential disease prevention, and improves the management of existing conditions, which can reduce unnecessary specialist care, hospitalizations, and emergency department (ED) visits.

In this study, we evaluated the impact of a large national employer’s decision to remove cost sharing for primary care physician (PCP) office visits. Previously, Sepulveda et al evaluated this initiative on the employees’ children during the 2 years following implementation and found a significant increase in PCP visits and a reduction in ED visits while total healthcare expenditures per child were unchanged.

 Our study, intending to examine the long-term impacts of the initiative on a comprehensive population, evaluated how the removal of cost sharing in primary care affects healthcare spending and utilization among commercially insured children and adults over a 6-year period.

This quasi-experimental study with difference-in-differences (DID) design used 6 years of administrative medical claims data from the HealthCore Integrated Research Environment, which links medical claims and eligibility files from 14 commercial health plans across the United States. All data were accessed as a limited data set in a manner compliant with the Health Insurance Portability and Accountability Act of 1996.

The study population consisted of Anthem commercially insured members with continuous medical eligibility from 2008 to 2014, with 2008 and 2009 designated as the preintervention period (before the cost sharing was removed) and 2011 through 2014 designated as the postintervention period. The year 2010 was excluded because it represents the transition period when the benefit change was implemented.

Members 65 years or older were excluded from the study. Members with very complex health scenarios or conditions (eg, organ transplantation, hemophilia, cystic fibrosis, end-stage renal disease, and long-term respiratory failure; detailed list in 

]) were also excluded, as these members were more likely to have their care coordinated by a specialist.

The study subjects were divided into 2 cohorts. The VBID cohort consisted of employees and their family members from the single large national employer that made the decision to remove PCP cost sharing. Other aspects of benefit design remained unchanged over the study period for the VBID cohort. The comparison cohort was selected from the population of other Anthem enrollees who did not undergo a similar benefit change, using 1:1 propensity score matching. To evaluate whether there was a difference in impact by age group, the study population was stratified to subgroups of individuals aged 18 years or younger, 19 to 50 years, and 51 to 64 years.

The primary outcomes measured the utilization rate of physician office visits and the annual total healthcare spending. The total spending was the sum of plan-paid amounts, out-of-pocket amounts, and amounts paid by other insurances for all medical services, including hospitalizations, ED visits, physician office visits, and other outpatient services (ie, services occurring in an outpatient setting that are not physician office visits, such as laboratory tests, imaging, and procedures), for all causes. Other types of all-cause healthcare utilization, including hospitalizations, ED visits, and other outpatient services, were assessed for exploratory purposes. Furthermore, several specific utilization types, such as hospital admissions due to ambulatory care—sensitive conditions and ED visits for primary care–treatable conditions (defined by the New York University Emergency Department Algorithm), were also examined.

 Average annual utilization and spending for the preintervention and postintervention periods were calculated respectively and compared across the cohorts. All spending was adjusted to 2015 values using the Consumer Price Index.

Multivariate logistic regression was used to estimate the association of having PCP cost sharing removed (ie, being in the VBID cohort) with covariates of baseline characteristics, including age, gender, health plan type, residence region, metropolitan statistical area, enhanced Elixhauser Comorbidity Index (ECI) score, comorbid conditions, and healthcare utilization and expenditures during 2008.

 The VBID and comparison cohorts with similar estimated likelihood (maximum permitted difference of 0.0001) were selected using a greedy algorithm, to ensure the baseline comparability of the 2 cohorts after matching.

 For outcome measures, a DID analysis was conducted to compare changes from the preintervention to postintervention period between the 2 cohorts. A generalized estimating equation approach was used with a zero-inflated Poisson distribution and a log link for utilization, and a γ distribution with log link for spending, at the conventional significance level of α <.05. Analyses were also performed on the 3 age subgroups.

Among members in the VBID cohort, 25,725 (53%) met the population inclusion criteria. The main reason for exclusion was lack of continuous eligibility, which accounted for 45% of the population drop. The members who were not included were slightly younger (average age of 31.0 vs 33.5 years), more likely to be from the South region (27.6% vs 15.2%), and more likely to have a mental disorder (3.0% vs 2.2%; all 

 <.0001) compared with those included in the study (

). However, they did not differ in other characteristics. After matching, the VBID and comparison cohorts each had 25,725 subjects with comparable baseline characteristics in 2008—all standardized differences were less than 20% (

). Both cohorts had a mean age of 33.4 to 33.5 years, and most were aged 19 to 50 years. A slight majority of the subjects were men (51.2%-51.3%) and lived in the Midwest or Northeast regions. Both cohorts had the same mean (SD) ECI scores (0.5 [0.9]); more than 68% of subjects had an ECI score of 0. The most common comorbidity was hyperlipidemia (9.7% in VBID cohort vs 10.3% in comparison cohort), followed by hypertension (7.4% vs 7.8%, respectively). All subjects were followed for the entire 6-year study period.

Physician office visits increased in both cohorts from the preintervention to the postintervention period (

): 3588 to 3681 visits per 1000 members per year in the VBID cohort and 3792 to 3866 visits per 1000 members per year in the comparison cohort. However, the magnitudes of increase were similar between the VBID and comparison cohorts (2.6% increase vs 1.7% increase; DID, 0.9%; 

The plan-paid amount for physician visits had the same trend as utilization. Both cohorts had increased plan-paid amounts in the postintervention period, but the VBID cohort had a relatively greater increase compared with the comparison cohort (DID, 9.4%; 

 = .03). Not surprisingly, out-of-pocket amounts for physician visits decreased annually by 12.8% in the VBID cohort, driven by the removal of cost sharing for PCP visits, compared with a 9.8% annual increase in the comparison cohort (DID, —22.6%; 

 <.0001). As the sum of plan-paid and out-of-pocket amounts, the total spending for physician office visits in the VBID cohort had a relative but marginal increase (DID, 2.1%; 

Trends of overall healthcare spending, including the total and out-of-pocket amounts, were favorable for the VBID cohort. Total medical spending for the VBID cohort increased annually by 12.2% compared with a 17.3% annual increase for the comparison cohort (DID, —5.1%; 

). This difference in trend translates to a $12.0 relative reduction in total spent per member per month (PMPM) for the VBID cohort. ED visits and other outpatient services significantly contributed to the trend toward lower spending for the VBID cohort (

 = .02) relative reductions in PMPM spending, respectively. No significant relative reduction was observed for hospitalization expenses ($4.2 PMPM relative reduction; DID, —10.0%; 

 = .17). The aforementioned total spending for physician office visits stayed relatively stable (DID, 2.1%; 

There was a relative reduction of $5.5 in out-of-pocket PMPM spending that favored the VBID cohort (DID, —11.1%; 

 <.0001). Significant relative reductions in out-of-pocket amount were seen for other outpatient services (13.2% increase for VBID vs 24.3% increase for comparison cohort; DID, —11.2%; 

 = .004). The change in overall plan-paid amount was similar between the 2 cohorts (DID, —2.3%; 

 = .39), but VBID had favorable decreasing trends of plan-paid amounts for ED visits (DID, —9.2%; 

 = .001) and other outpatient services (DID, —3.4%; 

The removal of cost sharing increased primary care access and did not negatively affect total cost of care.

Gosia Sylwestrzak, MA

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