Thomas D. Sequist, MD

 To determine whether patients are deferring necessary care for urgent conditions during the coronavirus disease 2019 (COVID-19) pandemic and, if so, to what extent.

 Using billing data from 8 acute care hospitals, we identified 9 principal medical diagnoses from 

International Classification of Diseases, Tenth Revision

 codes across 4 medical specialties (cardiology, gastroenterology, neurology, and urology). In addition, we defined a combined obstetrical falsification end point. We compared daily admission rates during the pandemic period (3/1/2020-4/30/2020) with the same dates in 2019 (3/1/2019-4/30/2019). As a reference, we also compared a prepandemic period in the same years (1/1/2019-2/28/2019 and 1/1/2020-2/29/2020). We compared admission rates between years using 

 There were 3219 admissions for the conditions of interest during the study period in 2019 and 2661 in 2020. There was no difference in prepandemic daily admission rates in 2020 compared with 2019 (29.04 vs 27.63 admissions per day; –4.9%; 

= .50). During the pandemic period, there was a 33.7% decrease in admission rates for all conditions combined in 2020 compared with 2019 (24.68 vs 16.37; –33.7%; 

 = .03). By specialty, the combined gastroenterology (10.22 vs 7.20; –29.6%; 

= .02) and cardiovascular (2.34 vs 1.29; –44.7%; 

= .05) end points demonstrated reduction in daily admission rates.

 Daily admission rates during the COVID-19 pandemic were lower for these acute medical conditions. Public awareness campaigns are urgently needed to reassure the public about the safety of presenting for care.

Patients may be avoiding medical facilities during the coronavirus disease 2019 (COVID-19) pandemic.

 Whether patients are deferring needed care for urgent conditions and to what extent are unknown. We compared admission rates for acute medical conditions during the COVID-19 pandemic with a reference year.

We used billing data from 8 acute care hospitals within a major integrated health system. We selected medical conditions for which incidence should not be affected by COVID-19. We identified 9 principal diagnoses from 

 codes across 4 specialties: cardiovascular disease (ST-segment elevation myocardial infarction [STEMI], aortic dissection), neurology (cerebrovascular accident, seizure), gastroenterology (appendicitis, cholecystitis, pancreatitis, gastrointestinal bleed), and urology (urolithiasis). In addition, we defined a combined obstetrical falsification end point (ectopic pregnancies, miscarriages, vaginal deliveries, and cesarean sections).

We compared daily admission rates during the pandemic period (3/1/2020-4/30/2020) with the same dates in 2019 (3/1/2019-4/30/2019). As a reference, we also compared a prepandemic period in the same years (1/1/2019-2/28/2019 and 1/1/2020-2/29/2020). We compared admission rates between years using 

 tests. We also describe basic demographic data for both 2019 and 2020. This project was undertaken as a Quality Improvement Initiative at Mass General Brigham and as such was not formally supervised by the Institutional Review Board per its policies.

There were 3219 admissions for the conditions of interest during the study period in 2019 and 2661 in 2020. In 2020, patients were 73.4% white, 53.4% male, and 86.8% English speaking, and 60.0% were older than 60 years; 41.5% were Medicare beneficiaries. There was no difference in prepandemic daily admission rates in 2020 compared with 2019 (29.04 vs 27.63 admissions per day; –4.9%; 

). During the pandemic period, there was a 33.7% decrease in admission rates for all conditions combined in 2020 compared with 2019 (24.68 vs 16.37; –33.7%; 

= .03). By specialty, the combined gastroenterology (10.22 vs 7.20; –29.6%; 

= .05) end points showed reduction in daily admission rates. In condition-specific analyses, cholecystitis (3.44 vs 2.10; –38.9; 

= .06), gastrointestinal bleed (3.35 vs 2.37; –29.2%; 

= .08), and aortic dissection (1.36 vs 0.54; –60.3%; 

= .12) showed a trend toward difference. There was no difference in admission volume for the falsification end point (48.3 vs 45.6; –5.5%; 

Daily admission rates during the COVID-19 pandemic were lower for these acute medical conditions. Although combined specialty end points exhibited a decrease in daily admissions, the individual conditions were likely underpowered to identify similar changes. STEMI had an effect estimate similar in magnitude to that found in previous studies, and other conditions demonstrated a trend toward difference.

The observational design limits our ability to draw causal conclusions. One explanation for these findings is that patients are fearful of engaging with medical facilities during the pandemic. Alternatively, changes in emergency department practices may result in fewer admissions or in admissions under different primary diagnosis codes related to COVID-19.

Because a change in incidence is unlikely for these conditions on clinical grounds, these results raise the possibility that patients are not presenting for necessary care. Public awareness campaigns are urgently needed to reassure the public about the safety of presenting for care.

 Cardiology Division, Department of Medicine, Massachusetts General Hospital (ASO, JHW), Boston, MA; Data and Analytics Organization, Partners Healthcare (DN, CK, SM, PYL, JP), Boston, MA; Division of General Internal Medicine, Department of Health Care Policy, Brigham and Women’s Hospital, Harvard Medical School (TDS), Boston, MA.

 Ms Nash, Ms Kim, and Ms Moisuk are employed with Partners Healthcare. Dr Wasfy is employed with Massachusetts General Hospital. 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 (ASO, DN, JP, TDS, JHW); acquisition of data (DN, CK, SM, JP, TDS); analysis and interpretation of data (DN, CK, SM, PYL, JP, JHW); drafting of the manuscript (ASO, JHW); critical revision of the manuscript for important intellectual content (ASO, TDS, JHW); statistical analysis (DN, CK, SM, PYL, JHW); administrative, technical, or logistic support (PYL, JP, JHW); and supervision (PYL, JP, TDS, JHW).

 Jason H. Wasfy, MD, MPhil, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114. Email: jwasfy@mgh.harvard.edu.

1. Garcia S, Albaghdadi MS, Meraj PM, et al. Reduction in ST-segment elevation cardiac catheterization laboratory activations in the United States during COVID-19 pandemic. 

. 2020;75(22):2871-2872. doi:10.1016/j.jacc.2020.04.011

2. Rosenbaum L. The untold toll—the pandemic’s effects on patients without Covid-19. 

. Published online April 17, 2020. doi:10.1056/NEJMms2009984

3. Rodríguez-Leor O, Cid-Álvarez B, Ojeda S, et al; Registro de Código Infarto de la ACI-SEC. Impacto de la pandemia de COVID-19 sobre la actividad asistencial en cardiología intervencionista en España. 

. 2020;2:82-89. doi:10.24875/RECIC.M20000120

Articles

Admission rates during the coronavirus disease 2019 (COVID-19) pandemic were lower than in 2019 for acute medical conditions, suggesting that patients may be deferring necessary medical care.

Changes in Hospital Admissions for Urgent Conditions During COVID-19 Pandemic

A substantial portion of healthcare spending is wasted on services that do not directly improve patient health and that cause harm in some cases. Features of health insurance coverage, including enrollment in high-deductible health plans (HDHPs) or health maintenance organizations (HMOs), may provide financial and nonfinancial mechanisms to potentially reduce overuse of low-value healthcare services.

 Using 2009 to 2013 administrative data from 3 large commercial insurers, we examined patient characteristics and health insurance plan types associated with overuse of 6 healthcare services identified by the Choosing Wisely campaign.

We explored associations between overuse and patient characteristics using multivariate logistic regression models, including patient age, gender, enrollment in an HMO, enrollment in an HDHP, an indicator of primary care fragmentation, and number of outpatient visits as explanatory variables.

Measurement of services highlighted as potential overuse by the Choosing Wisely recommendations revealed low to moderate prevalence, depending on the service. HMO coverage and enrollment in HDHPs were significantly associated with differences in prevalence of all 6 services, albeit differently in terms of the direction of the effects. Primary care fragmentation was significantly associated with higher rates of overuse.

Neither HDHPs nor HMO plans, with their closed networks and referral requirements, consistently reduced overuse, although HMO plans were never associated with higher rates of overuse. As policy makers seek levers for reducing low-value healthcare utilization, health insurance plan features may prove a valuable target, although the effect may be complicated by other factors.

We found low to moderate overuse of 6 low-value health services among patients with commercial insurance.

Health insurance plan type, including enrollment in a health maintenance organization or high-deductible health plan, was significantly associated with differences in prevalence of all 6 services.

Primary care fragmentation was significantly associated with higher rates of overuse.

Health insurance plan features should be considered alongside more targeted mechanisms, like preauthorization and value-based cost sharing, to influence overuse of these low-value services.

Driven by the urgency to achieve affordable healthcare for more Americans, many policy experts are focusing on defining, identifying, and reducing waste in healthcare. Some policy makers and researchers suggest that generous insurance coverage may prompt patients to demand more testing and interventions than they otherwise would, leading to overuse of services. To the extent this is occurring, health insurance plan type, which can include cost-sharing schemes or utilization management, may reduce waste by curbing demand-driven use of low-value services. To begin addressing this issue, the American Board of Internal Medicine Foundation launched the Choosing Wisely campaign in 2012, an initiative aimed at reducing unnecessary use of healthcare services. More than 80 specialty societies created lists of low-value services,

 and the campaign was associated with modest decreases in some services.

The potential impact of health insurance plan type and, more specifically, high-deductible health plans (HDHPs) on minimizing waste remains inconclusive. HDHPs have high levels of initial cost sharing until the deductible is met, potentially reducing inappropriate use by making consumers more sensitive to the prices of healthcare services. Shifts in the employer-sponsored insurance landscape indicate a gradual increase in the number of firms offering and number of employees enrolling in HDHPs in the past 5 years.

 It is thus increasingly important to understand the relationship between health insurance plan type and overuse of care.

Although evidence on the long-term effect of HDHPs on overuse is limited, some results of studies examining the short-term effects on utilization and spending suggest that HDHPs may reduce outpatient visits, out-of-pocket (OOP) spending, and emergency department use.

 Others have found that HDHPs do not affect utilization of laboratory tests or acute care visits and that decreases in utilization are temporary as the patient transitions to a new plan with higher levels of cost sharing.

 The evidence on high deductibles and cost sharing suggests that consumers have difficulty differentiating high- and low-value care; this often requires physician judgment.

 Studies also reveal variation in patient adherence to providers’ medical advice and suggest that adherence can be influenced by several patient factors, including age, gender, and patient—provider relationship.

Health maintenance organizations (HMOs) are thought to reduce overuse by requiring patients to select a primary care provider (PCP), from whom a referral is required in order to see a specialist, who must be part of the plan’s network.

 These plans may steer patients away from providers with high rates of low-value service use through limited networks. There may also be financial and nonfinancial mechanisms, such as preauthorization or utilization review, that discourage physicians from ordering low-value services.

Little is known about trends in overuse for adults with private insurance, although evidence is mounting on the prevalence of and variation in overuse in the Medicare population.

 Most studies describing overuse of low-value care among the privately insured have focused on pediatric populations or assessed overuse in a single insurer or state.

 With renewed interest and professional engagement in naming and addressing low-value services, policy makers and payers need evidence on which tools and levers reduce overuse.

We examined cross-sectional data from a large commercially insured population to describe trends in overuse of low-value care between 2009 and 2013 and to capture the association among patient characteristics, health insurance plan type, and the use of 6 low-value services.

We obtained commercial insurance claims data from the Health Care Cost Institute (HCCI) for patients with employer-sponsored health insurance coverage from 2009 to 2013. This dataset includes commercial data from 3 large insurers: Aetna, Humana, and UnitedHealthcare. Across all years, the data reflect the healthcare claims of approximately 45 million adults aged 18 to 64 years. HCCI estimates that these claims data account for about 26% of adults younger than 65 years with employer-sponsored insurance in the United States.

Selecting Choosing Wisely Services for Measurement

We developed claims-based algorithms for 6 services, representing 22 Choosing Wisely recommendations. We selected recommendations for analysis based on: 1) the feasibility of measuring the service and manner of use in claims data only, 2) the expected denominator size, and 3) our desire to study services that vary broadly in cost and represent diverse segments of healthcare. The Choosing Wisely services excluded from analysis were either difficult to measure in claims data or pertained to a very small percentage of the commercially insured population. We studied nonindicated use of the following: imaging early in the course of new back pain, vitamin D deficiency screening for low-risk patients, prescription opioids for migraine, cardiac testing in low-risk patients, short-interval bone densitometry (DEXA), and preoperative cardiac testing in low-risk patients ahead of low-risk (noncardiac) surgery.

Construction of Measure-Specific Cohorts at Risk

International Classification of Diseases, Ninth Revision

) and Current Procedural Terminology codes to construct cohorts at risk for 6 Choosing Wisely services and to identify health service events highlighted by the Choosing Wisely recommendations. In the 

), we describe the measure and event definitions. We used HCCI pharmacy claims data, where applicable, for cohort inclusion/exclusion and to identify Choosing Wisely prescription service events. In all cases, we conservatively excluded beneficiaries not targeted by the Choosing Wisely recommendation. We limited our analysis to nonindicated tests and procedures, excluding services with claims diagnoses that suggested appropriate medical indication. When possible, we drew from published validated claims-based measures and conducted sensitivity analyses to optimize the measure construct. Although we used 2009 to 2013 data for the analyses, some measures were limited to smaller time windows to permit sufficient look-back periods within the data to identify, for example, prevalent disease states.

Multivariate logistic models with hospital referral region (HRR) fixed effects were fitted to analyze the factors associated with overuse at the patient level. To identify the association between overuse of low-value care and each insurance plan type, we created several binary indicator variables. Included in the model were an indicator for enrollment in an HMO plan (compared with all other plan types, of which the majority were point-of-service [POS] plans, exclusive provider organizations [EPOs], or preferred provider organization [PPO] models) and an indicator for an HDHP. We also included an indicator for patients with administrative services only (ASO) plans, which are arrangements where an employer contracts with an organization to deliver administrative services, like claims processing, for its self-funded health insurance program. The indicator for ASO plans may pick up the effects of either or both characteristics of these plans and the patients who are enrolled in them compared with fully insured plans that are not captured by the network type and HDHP indicator. We also included the patient’s age, gender, and health status as explanatory variables. We hypothesized that patients with more contact with the health system overall, which we captured as the number of outpatient visits during the study year, would have more opportunities for overuse. Finally, we included the number of unique PCPs seen during the study period as an indicator of the fragmentation of primary care, which may also be a risk factor for low-value care receipt. We included year indicators in the model to allow for changes over time.

The study was approved by the institutional review board at the Harvard T.H. Chan School of Public Health.

Age and gender differences were consistent with the clinical circumstances of the featured tests and treatments (

). Patients in our sample were enrolled in health insurance products that varied by managed care structure and cost sharing: 8.5% to 15.3% of patients in each measure sample were enrolled in an HMO and 14.1% to 17.2% were enrolled in an HDHP.

Rates of overuse ranged from a low of 6.8% for short-interval DEXA testing to a high of 28.1% for preoperative cardiac testing (Table 1). Two of 6 measures revealed a downward trend in overuse in 2009, 2010, 2011, and 2012: prescription opioids for migraine and preoperative cardiac testing. We observed monotonically increasing overuse of vitamin D deficiency screening from 2009 through 2013. In multivariate analyses controlling for region and individual characteristics for each of the 6 measures, health insurance plan type was associated with overuse (

). HMO enrollment was associated with significantly less overuse of imaging for low back pain (odds ratio [OR], 0.880; 95% CI, 0.870-0.890), vitamin D deficiency screening (OR, 0.606; 95% CI, 0.604-0.607), cardiac testing (OR, 0.962; 95% CI, 0.959-0.965), and preoperative cardiac testing (OR, 0.937; 95% CI, 0.923-0.951). The association between having an HDHP and overuse differed by measure (

); those enrolled in HDHPs had slightly but significantly lower odds of overuse of imaging for low back pain (OR, 0.987; 95% CI, 0.979-0.995), prescription opioids for migraine (OR, 0.960; 95% CI, 0.943-0.980), and short-interval DEXA (OR, 0.946; 95% CI, 0.930-0.963). In contrast, enrollment in HDHPs was significantly associated with greater odds of overuse of vitamin D deficiency screening (OR, 1.036; 95% CI, 1.034-1.038) and preoperative cardiac testing (OR, 1.012; 95% CI, 1.001-1.023). Relative to those who were fully insured, having an ASO plan was associated with slightly but significantly greater odds of imaging for low back pain (OR, 1.010; 95% CI, 1.003-1.021), cardiac testing (OR, 1.020, 95% CI, 1.017-1.022), short-interval DEXA (OR, 1.301; 95% CI, 1.278-1.324), and preoperative cardiac testing (OR, 1.015; 95% CI, 1.005-1.025).

Compared with those with 3 or more PCPs, patients cared for by 1 or 2 PCPs had significantly lower odds of overuse for 4 of 6 measures: vitamin D deficiency screening, prescription opioids for migraine, cardiac testing, and short-interval DEXA.

Our findings suggest that overuse is widespread among commercially insured patients and, despite the availability of utilization management and higher cost sharing, comparable with rates found in

previous studies of the Medicare population.

 For nearly every service we studied, patient characteristics and health insurance plan type were significant predictors of overuse. Fragmentation of primary care, in particular, was associated with significantly more overuse for most measures. This finding is consistent with previous work suggesting that care continuity may play a role in reducing overuse and is perhaps attributable to patient—provider trust and communication.

 Current efforts to redesign primary care delivery, many of which prioritize care continuity, may play a role in reducing overuse.

All of the health insurance plan types studied (HMO, HDHP, or ASO) were associated with significant differences in overuse compared with EPOs, PPOs, or POS plans. However, the direction of the association differed across the set of 6 services studied. Focusing specifically on the role of HMO and HDHP enrollment in informing interventions to reduce waste, we found that these factors were important explanatory variables (based on statistical significance), but their association with overuse was inconsistent and warrants further investigation. Further insights on the causal effects of health insurance plan type could be derived from analyzing an exogenous source of variation in cost sharing or plan structure, such as when an employer switches plan offerings. From the standpoint of intervention, payers might do more to target cost sharing and other policies explicitly to address overuse, rather than put downward pressure on all nonpreventive healthcare utilization as in an HDHP. Although relying on higher cost sharing may achieve reductions in spending, it carries risks, and co-payment amounts and deductible sizes should be set carefully.

 Findings of several studies have shown that high deductibles may encourage individuals, particularly of lower income or with multiple chronic conditions, to forego essential medical care.

As a result, health insurance plan type should be considered alongside other policies and interventions to address overuse of low-value services.

Interpretation of our findings on ASO plans may reflect a complicated mix of both plan and population differences. A 2011 report to Congress indicated that there are small differences in coverage generosity when comparing self-insured and fully insured employer plans, which may affect the use of low-value care. Among HMO plans, for example, OOP payments are higher for self-insured plans relative to fully insured plans. Further, the report found that employers with low-risk employees are more likely to self-insure, resulting in different risk profiles between otherwise identical ASO and fully insured plans.

Our analysis had a number of limitations. We relied on administrative data with limited information on plan benefits and design to identify and describe the use of low-value services. Although we used conservative definitions and exclusion criteria to minimize misclassification risk, claims may not provide enough information or clinical detail to precisely identify low-value care. We were unable to include other measures of benefit design, such as co-payment amounts or OOP maximums. We proxied for these characteristics using insurance plan type. Second, although we aimed to include Choosing Wisely recommendations on lists from multiple specialty societies and services ordered by both specialists and PCPs, the range of services measured was narrow. Lack of variation in demographics or other patient-level factors may have impacted our results. Previous studies have found heterogeneity in nonelderly adults with private insurance; however, we do not think this focus will eliminate variation in our sample.

 Our model did not control for supply-side factors other than by controlling for region using HRR indicators. Finally, our analysis of factors associated with overuse was cross-sectional in nature and cannot be used to make definitive causal inferences. Low pseudo-R

 values are common in cross-sectional analyses with limited explanatory variables.

As clinicians and other experts expand our understanding of low-value health services, payers and policy makers are looking for tactics to influence the use of these services while maintaining access to effective care. For privately insured patients, such as the subjects of our analysis, health insurance plan type might be expected to affect the value of care received. Our work shows that HDHPs do not consistently reduce overuse, nor do HMO plans that typically have closed networks and referral requirements (although HMO plans were never associated with higher degrees of overuse). Contrary to the popular belief that patients need more cost sharing to internalize the value of healthcare services, our study plans with lower cost sharing were not consistently associated with higher use of low-value services. More targeted demand-side mechanisms (eg, value-based cost sharing) or stringent supply-side mechanisms (eg, preauthorization or risk bearing by providers) may be warranted to address the use of low-value care.&ensp;

The authors acknowledge the assistance of the Health Care Cost Institute (HCCI) and its data contributors, Aetna, Humana, and UnitedHealthcare, in providing the claims data analyzed in this study, as well as funding support from The Peterson Center on Healthcare. 

Department of Health Policy and Management, Harvard T.H. Chan School of Public Health (MBR, ZL, KHN), Boston, MA; The Dartmouth Institute for Health Policy and Clinical Practice (CHC, NEM, AJM), Lebanon, NH; Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center (CHC), Lebanon, NH; Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth (NEM), Lebanon, NH; Department of Health Care Policy, Harvard Medical School (TDS), Boston, MA; Division of General Internal Medicine, Brigham and Women’s Hospital (TDS), Boston, MA; Partners HealthCare (TDS), Boston, MA.

 Funding for this study was provided by The Peterson Center on Healthcare.

 The 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 (MBR, CHC, NEM, TDS, AJM, ZL); acquisition of data (MBR, ZL); analysis and interpretation of data (MBR, CHC, NEM, TDS, ZL); drafting of the manuscript (MBR, CHC, NEM, KHN); critical revision of the manuscript for important intellectual content (MBR, CHC, NEM, TDS, AJM, KHN); statistical analysis (MBR, CHC, ZL); obtaining funding (MBR, CHC); administrative, technical, or logistic support (CHC, AJM, KHN); and supervision (MBR).

 Meredith B. Rosenthal, PhD, Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02215. Email: mrosenth@hsph.harvard.edu.

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Health insurance plan type may be an important lever for reducing low-value healthcare use among patients with commercial insurance.

Thomas D. Sequist, MD

Articles

Changes in Hospital Admissions for Urgent Conditions During COVID-19 Pandemic

Overuse and Insurance Plan Type in a Privately Insured Population