kong_photoEdward Kong is a 5th-year MD-PhD student in the Harvard Department of Economics and Harvard Medical School. His research fields are in Public Economics and Industrial Organization.

He is currently working on projects relating to health insurance market stability, the effects of copay coupons on prescription drug use and pricing, and the labor market impacts of shutdown policies implemented in response to the COVID-19 pandemic

Prior to the MD-PhD, Ed obtained a Bachelor of Science from Yale where he double-majored in Economics and Biomedical Engineering. Afterward, he worked as a researcher at the National Bureau of Economic Research for two years. 

Resources

  1. Compliance with the CMS Hospital Price Transparency Rule (as of December 2021) for U.S. acute-care hospitals
    • Data (CSV)
    • ReadMe (PDF) 
    • Download Paper (PDF)
    • Citation: Ji, Yunan, and Edward Kong (2022) "US Hospital Characteristics Associated With Price Transparency Regulation Compliance." JAMA Health Forum. Vol. 3. No. 6.

Recent Publications

Leemore Dafny, Kate Ho, and Edward Kong. Working Paper. “How Do Copayment Coupons Affect Branded Drug Prices and Quantities Purchased?” Revised and Resubmitted, American Economic Journal: Economic Policy.Abstract
Drug copayment coupons to reduce patient cost-sharing have become nearly ubiquitous for
high-priced brand-name prescription drugs. Medicare bans such coupons on the grounds
that they are kickbacks that induce utilization, but they are commonly used by commercially-
insured enrollees. We estimate the causal effects of coupons for branded drugs without
bioequivalent generics using variation in coupon introductions over time and comparing
differential responses across enrollees in commercial and Medicare Advantage plans. Using
data on net-of-rebate prices and quantities from a large Pharmacy Benefits Manager, we
find that coupons increase quantity sold by 21-23% for the commercial segment relative
to Medicare Advantage in the year after introduction, but do not differentially impact
net-of-rebate prices, at least in the short-run. To quantify the equilibrium price effects of
coupons, we employ individual-level data to estimate a discrete choice model of demand
for multiple sclerosis drugs. We use our demand estimates to parameterize a model of
drug price negotiations. For this category of drugs, we estimate that coupons raise negoti-
ated prices by 8% and result in just under $1 billion in increased U.S. spending annually.
Combined, the results suggest copayment coupons increase spending on couponed drugs
without bioequivalent generics by up to 30%.
Edward Kong, Mark Shepard, and Adrianna McIntyre. 4/22/2022. “Turnover in Zero-Premium Status Among Health Insurance Marketplace Plans Available to Low-Income Enrollees.” JAMA Health Forum, 3, 4, Pp. e220674. Download PaperAbstract

Importance  Recent subsidy enhancements in Affordable Care Act (ACA) Marketplaces made many low-income enrolles (below 150% of the federal poverty level [FPL]) eligible for 2 free silver-tier plans. eligible for 2 free silver-tier plans. However, an unintended consequence of this structure is that the identity of which silver plans are free will often “turn over” between years, requiring that enrollees actively initiate premium payment (or lose coverage). The prevalence of this free-plan turnover is not known.

Objective  To measure the prevalence of free-plan turnover in ACA Marketplaces and to estimate how many enrollees below 150% of FPL are likely to be affected.

Design, Setting, and Participants  This observational cross-sectional study used data on plan offerings and premiums in 33 state ACA Marketplaces using HealthCare.gov in 2021 and 2022, along with estimates of county-level enrollee characteristics and plan selection patterns. The enrollment-weighted share of county markets affected by free-plan turnover was quantified, along with the association of turnover with enrollee and market characteristics. Estimates of the number of affected low-income enrollees were calculated using the data plus statistics reported in past research. Data were analyzed from November 21, 2021, to February 28, 2022.

Results  This study found that turnover of zero-premium plans was quite common, with 93% of HealthCare.gov counties (weighted by enrollment) experiencing at least 1 zero-premium plan in 2021 turning over to nonfree in 2022; 84% of counties experienced turnover of all $0 silver plans from 2021 to 2022. This turnover affected an estimated 1.36 million people with incomes below 150% of FPL. Turnover was more common in counties with a higher share of non-White enrollees, in Medicaid nonexpansion states, in counties with more carriers, and in counties with changes in the number of offered plans.

Conclusions and Relevance  The findings of this cross-sectional study suggest that owing to the prevalence of zero-premium plan turnover, many low-income ACA enrollees faced elevated risk of disenrollment at the start of 2022. Outreach to affected enrollees and other actions to encourage coverage retention and midyear reenrollment could help mitigate coverage losses.

Yunan Ji and Edward Kong. 6/24/2022. “US Hospital Characteristics Associated With Price Transparency Regulation Compliance.” JAMA Health Forum, 3, 6, Pp. e221702. Publisher's VersionAbstract

Introduction: As of January 1, 2021, the Centers for Medicare & Medicaid Services (CMS) required all US acute care hospitals to release the prices they negotiate with insurance plans to make price comparison across hospitals easier for consumers. We report data on compliance with this requirement for all 4484 acute care hospitals in the US as of December 2021 and explore the association between hospital characteristics and compliance.

Results: The final sample included 2892 hospital systems representing 4484 hospitals in 306 HRRs. The Table shows descriptive statistics at the hospital system level for 2892 hospital systems; mean (SD) compliance was 68% (46%). We found a negative association between compliance and market competitiveness; compliance was higher in less competitive HRRs (scaled effect size, 0.07; 95% CI, 0.03-0.10) and for hospital systems with greater market shares (scaled effect size, 0.08; 95% CI, 0.05-0.11) (Figure). Both associations remained significant when controlling for number of beds. Multihospital systems (effect size, 0.13; 95% CI, 0.09-0.16), for-profit hospitals (effect size, 0.05; 95% CI, 0.01-0.10), and teaching hospitals (effect size, 0.11; 95% CI, 0.03-0.20) had higher compliance. Government hospitals had lower compliance (effect size, −0.06; 95% CI, −0.10 to −0.02), but the association did not remain after controlling for integration into multihospital systems. Hospital systems with more beds had higher compliance (scaled effect size, 0.04; 95% CI, 0.02-0.07), whereas critical access hospitals (effect size, −0.03; 95% CI, −0.07 to 0.01) and those lacking intensive care units (effect size, −0.13; 95% CI, −0.16 to −0.09) had lower compliance.

Discussion: As of December 2021, 68% of hospitals had released payer-specific negotiated prices, more than 50% higher than rates reported earlier in the year.3-5 The higher compliance rate may be attributable to reporting delays from technical difficulties or pressure from the media and CMS.

The findings suggest that competition and hospital resources may have a role in determining compliance. Hospitals in the least competitive markets and those with greater market shares had higher compliance rates, consistent with safeguarding of negotiated prices in the presence of greater competition. A prior study5 found similar results as of June 1, 2021. In our study, the associations were found through the end of 2021, despite a significant increase in overall compliance.

Our results highlight factors associated with hospital compliance with price transparency regulation. A limitation is that we defined compliance based on availability of payer-specific negotiated prices and omitted other CMS requirements such as availability of a shoppable service tool. A more stringent definition would likely lead to lower measured compliance rates.

 

Edward Kong, John Beshears, David Laibson, Brigitte Madrian, Kevin Volpp, George Loewenstein, Jonathan Kolstad, and James J.Choi. 2020. “Do physician incentives increase patient medication adherence?” Health Services Research, 55, 4, Pp. 503-511. Publisher's VersionAbstract

Objective

To test the effectiveness of physician incentives for increasing patient medication adherence in three drug classes: diabetes medication, antihypertensives, and statins.

Data Sources

Pharmacy and medical claims from a large Medicare Advantage Prescription Drug Plan from January 2011 to December 2012.

Study Design

We conducted a randomized experiment (911 primary care practices and 8,935 nonadherent patients) to test the effect of paying physicians for increasing patient medication adherence in three drug classes: diabetes medication, antihypertensives, and statins. We measured patients’ medication adherence for 18 (6) months before (after) the intervention.

Data collection/extraction methods

We obtained data directly from the health insurer.

Principal Findings

We found no evidence that physician incentives increased adherence in any drug class. Our results rule out increases in the proportion of days covered by medication larger than 4.2 percentage points.

Conclusions

Physician incentives of $50 per patient per drug class are not effective for increasing patient medication adherence among the drug classes and primary care practices studied. Such incentives may be more likely to improve measures under physicians’ direct control rather than those that predominantly reflect patient behaviors. Additional research is warranted to disentangle whether physician effort is not responsive to these types of incentives, or medication adherence is not responsive to physician effort. Our results suggest that significant changes in the incentive amount or program design may be necessary to produce responses from physicians or patients.

Edward Kong and Daniel Prinz. 9/2020. “Disentangling Policy Effects Using Proxy Data: Which Shutdown Policies Affected Unemployment During the COVID-19 Pandemic?” Journal of Public Economics, 189, Pp. 104257. Publisher's VersionAbstract
We use high-frequency Google search data, combined with data on the announcement dates of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic in U.S. states, to disentangle the short-run direct impacts of multiple different state-level NPIs in an event study framework. Exploiting differential timing in the announcements of restaurant and bar limitations, non-essential business closures, stay-at-home orders, large-gatherings bans, school closures, and emergency declarations, we leverage the high-frequency search data to separately identify the effects of multiple NPIs that were introduced around the same time. We then describe a set of assumptions under which proxy outcomes can be used to estimate a causal parameter of interest when data on the outcome of interest are limited. Using this method, we quantify the share of overall growth in unemployment during the COVID-19 pandemic that was directly due to each of these state-level NPIs. We find that between March 14 and 28, restaurant and bar limitations and non-essential business closures can explain 6.0% and 6.4% of UI claims respectively, while the other NPIs did not directly increase own-state UI claims. This suggests that most of the short-run increase in UI claims during the pandemic was likely due to other factors, including declines in consumer demand, local policies, and policies implemented by private firms and institutions.
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