Effect of Cash Benefits on Health Care Utilization
Phase: NA
Conditions: Health Care Utilization
Sponsor: Harvard Medical School (HMS and HSDM)

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Cash Benefits, Health Care Utilization, and Health 
Protocol and Analysis Plan 
 
November 14, 2022 
 
ClinicalTrials.gov Identifier: NCT05622903 
 
Study Team 
Sumit Agarwal, MD, MPH, Brigham and Women’s Hospital, Harvard Medical School 
Benjamin Lê Cook, PhD, MPH, Cambridge Health Alliance, Harvard Medical School 
Jeffrey Liebman, PhD, Harvard Kennedy School 
 
 

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Table of Contents 
 
Section A. Introduction …………………………………………………………………… 
3  
 
 
Section B. Treatment ……………………………………………………………………… 
3  
 
 
Section C. Randomization and Participant Eligibility …………………………………… 
4  
 
 
Section D. Data, Study Sample, and Outcomes …………………………...……………… 
5  
 
 
Section E. Statistical Analysis ……………………………………………….…………… 
6  
 
 
Section F. References ……………………………………………………………………… 
9 
 
 
 
 
 
 
 

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A. Introduction 
Unrestricted and unconditional cash benefits, sometimes referred to as a basic income, have 
emerged as a potential solution to several social issues, including poverty, food insecurity, 
income volatility, and mental illness, with small pilot studies suggesting improvements in these 
and other outcomes.1–3 It is an old idea for which there has been renewed interest.4–7 In this 
study, we investigate the effect of basic income on health care utilization, for which there is less 
evidence and little to none from a randomized study design.8–11 Theoretically, a cash benefit 
could increase health care utilization by improving access, or it could decrease health care 
utilization by improving health. The effect of a cash benefit on health care utilization is therefore 
an empirical question.  
Using data from the Chelsea Eats program, we propose to study a randomized controlled trial in 
which the City of Chelsea, Massachusetts held a lottery to allocate cash benefits to its residents 
for ten months during the first two years of the COVID-19 pandemic.12 We will analyze the 
impact of the cash benefit on health care utilization and health. This proposal and analysis plan 
detail the intervention, randomization, and data source. Importantly, while this analysis plan is 
specified after the conduct of the trial, it does pre-specify the outcomes and statistical analysis 
prior to analysis of the data. No analyses of post-intervention outcomes were conducted prior to 
gaining access to the data. 
 
B. Treatment 
Chelsea, Massachusetts, a city of 40,000 people just north of Boston, was among the places in 
the country hardest hit by COVID-19, both from a health and an economic perspective. Its 
heavily Latino population is concentrated in sectors of the economy that were shut down when 
the pandemic hit, and Chelsea residents are also disproportionately likely to be front-line service 
workers exposed to infection risk.13 In April 2020, local community organizations and the City 
of Chelsea responded to the economic crisis facing jobless Chelsea residents by mounting an 
unprecedented food distribution effort. 
In September 2020, after five months of running its food distribution sites, the City redirected its 
efforts toward distributing financial support so that residents could purchase their own food 
through a program called Chelsea Eats. By combining city general revenue funds, state aid, and 
philanthropic contributions, the City assembled enough resources to distribute Chelsea Eats debit 
cards to approximately 2,000 households and to replenish the cards on a monthly basis for a total 
of six months, that was later extended to ten months. The card amounts vary with household size. 
Most households received $400 per month, but one- and two-person households received $200 
and $300, respectively. Spending from the cards was not restricted to food but could be spent on 
anything and anywhere Visa was accepted. In total, 3,615 households applied for the cards, and 
2,074 were chosen to receive the cash assistance cards via a lottery. The debit cards were 
credited with the first payment on November 18th, 2020 and the second payment on December 
18th, 2020. The program continued with monthly credits through August 2021. 

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C. Randomization and Participant Eligibility 
Between July 27, 2020 and August 17, 2020, the City accepted applications for the Chelsea Eats 
cash assistance cards. Multilingual information about the cards was distributed to individuals 
using the city-run food distribution sites. Additionally, information and applications were 
disseminated to community-based organizations, food pantries, faith groups, health care 
organizations, and low- and moderate-income housing complexes. Direct outreach to residents 
by city staff occurred in multiple locations, including food pantry lines, COVID-19 testing lines, 
and social service agencies. Applications could be submitted online via the city’s website or on 
paper by dropping them off or mailing them to City Hall. In practice, most applications were 
submitted by residents attending one of the various city or community partner food pantries, 
where city staff, equipped with tablets, assisted residents with the application.  
Eligibility criteria included: 
• Resident of Chelsea, Massachusetts 
• Household income at or below 30% of the U.S. Department of Housing and Urban 
Development’s Area Median Income 
The lottery was a weighted lottery. Specifically, a household could receive additional lottery 
tickets by meeting any of the following criteria:  
• No one in the household was currently working 
• The household was not receiving unemployment insurance 
• The household was not receiving food assistance (e.g., SNAP benefits) 
• There was a disabled household member 
• There was a household member over 65 years of age 
• There was a household member who was a veteran 
• There was a household member who was under 6 years of age 
• There was a household member between the ages of 6 and 17 
The total number of lottery tickets per application ranged from 1 to 8. Although it was the city’s 
intention to restrict households to a single application, some households managed to enter the 
lottery with more than one application. In these cases, the household still received only a single 
Chelsea Eats card. Records for the duplicate applicants will be combined into a single record, 
summing the lottery tickets across the duplicate records to determine the household’s overall 
probability of winning the lottery. We account for the differential probability of winning the 
lottery across households in the statistical analysis (see section E.2). 
Of note, some households never picked up their Chelsea Eats cards. The city then gave these 
unused cards to households from a randomly generated waitlist that was created at the time of 
random assignment. All waitlisted households ultimately received Chelsea Eats cards and are 
therefore part of the treatment group. The waitlist households received their cards approximately 
one month later than the primary lottery winners, but the cards were credited with both the 

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payments for the first and second months – so the total payments received by waitlist households 
are the same as initial lottery winners.  
 
D. Data, Study Sample, Outcomes, and Other Variables 
D.1. Data and Study Samples 
The two closest emergency departments to the City of Chelsea are Cambridge Health Alliance’s 
Everett Hospital and Massachusetts General Hospital. Participants in the study will be matched 
to their electronic health record at Cambridge Health Alliance (CHA) and Mass General 
Brigham (MGB) using a probabilistic algorithm based on name, year of birth, gender, address, 
and phone number.  
There are two additional/secondary study samples. The first will be a subsample of study 
participants who received their primary care at CHA or MGB at baseline. The second will be 
children of the participants, identified using address and phone number of the participants as well 
as dependency relationships available through the electronic health record data. 
 
D.2. Planned Outcomes 
Outcomes will be assessed on a per-household basis and measured using the electronic health 
record data over the ten months of the trial.  
1. Primary Outcome 
a. Emergency department use 
i. 
We will use the electronic health record data to count visits to the 
emergency department. Our primary outcome will be assessed on a 
monthly basis over the duration of the cash benefit and is therefore 
specified as a frequency variable.  
ii. 
We will also characterize the nature of these visits including by pre-
specified diagnosis categories and whether it resulted in a hospital 
admission, as secondary outcomes (see below). 
iii. 
Our primary outcome is count-based and not binary (i.e., any versus 
none) in order to maximize power. To understand general access to 
emergency department care versus intensity in emergency department 
use, we will separately assess a dichotomized version of the outcome. 
 
2. Secondary Outcomes 
a. Emergency department use by pre-specified categories: 
i. Behavioral health-related ED visit 
ii. Substance use disorder-related ED visit 
iii. Potentially avoidable vs non-avoidable (categorized using the previously 
validated New York University algorithm) 

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iv. Resulting in admission to the hospital 
v. As above, we will separately assess count-based and binary versions of 
these outcomes. 
b. Outpatient service use 
i. Outpatient service use includes office visits and other types of outpatient 
care such as imaging and procedures. We will also assess outpatient 
service use disaggregated according to type of service (i.e., office visits to 
primary care, cancer screening tests). 
ii. As above, we will separately assess count-based and binary versions of 
these outcomes. 
c. COVID vaccination 
i. The first COVID vaccine received emergency use authorization on 
December 11, 2020. Vaccination status will be a binary variable for 
having received at least one dose of any COVID vaccine by the end of the 
trial period. 
d. Clinical measures: hemoglobin A1c, blood pressure, weight, and cholesterol 
i. These are continuous measures available in the electronic health record. 
We will use the latest available reading or result during trial period.  
ii. Given potential changes in access to care (see outcomes above) that could 
affect the set of participants for whom we have data for these measures, 
we have also specified a sensitivity analysis below using the subsample 
for patients who received their primary care at CHA or MGB at baseline. 
 
D.3. Patient Characteristics 
The data includes information on participants’ household size, disability status, veteran status, 
work status, income, receipt of other benefits or assistance, and utilization/spending prior to 
randomization. These characteristics come from the lottery application form or electronic health 
records and are assessed prior to randomization. In the event that there are high levels of 
missingness for any key covariate (i.e., >2%), multiple imputation methods will be used. 
 
E. Statistical Analysis 
E.1. Evaluation of randomization, balance, and attrition 
We will test for balance between treatment and control based upon observable baseline 
characteristics for the overall study population.  
Because differential attrition correlated with treatment, for example due to death, could introduce 
bias into our results, we will also evaluate the attrition rate and assess for balance between 
treatment and control based upon both baseline characteristics for the final analytic sample (and 
attritors) and potential causes of attrition.  

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E.2. Statistical specification 
Our primary analytic approach is an analysis based on the intent-to-treat principle that compares 
outcomes for those randomized into the treatment group to those who were randomized into the 
control group. Specifically, we will estimate the following linear regression model: 
𝑦! = 𝛽" + 𝛽#CASH! + 𝛽$𝐗! + 𝜖!, 
where 𝑦! is the utilization or clinical outcome for individual or household 𝑖. See section D.2. for 
a list of our primary and secondary outcomes. “𝐶𝐴𝑆𝐻!” is an indicator for whether individual 𝑖 
won the lottery and was thus randomized into the treatment group. 𝑿𝒊 is a vector of covariates, 
specifically patient characteristics, including baseline (pre-randomization) values of the outcome 
variable, which are not explicitly necessary since they should be unrelated to treatment status, 
but they may increase the precision of our estimates to the extent that they explain some of the 
variance in the outcome. 
The coefficient on CASH!, 𝛽#, is our main coefficient of interest; it provides the difference in 
means between the treatment groups and the control group. In addition to our primary 
specification above that corresponds to testing for a level shift, we will also include an 
interaction term between an indicator for time and treatment status to test for changes in slope. 
As described in section C, a household could receive additional lottery tickets by meeting 
specific criteria. Observations will thus be weighted by the inverse probability of winning the 
lottery so that 𝛽# is an unbiased estimate of the relationship between winning the lottery and the 
outcome. An alternative specification that includes indicators for number of lottery tickets is 
discussed below. Standard errors will be clustered at the household level and adjusted for 
heteroskedasticity.  
 
E.3. Subgroup analyses 
To examine heterogenous effects of the cash benefits, we will repeat our analyses for four pre-
specified subgroups, each defined based on data from the pre-randomization period. The 
subgroups are: (1) chronic disease at baseline, (2) history of acute care utilization, (3) financial 
distress at baseline, and (4) poor self-reported health at baseline.  
Specifically, we use electronic health record data to define participants by whether they have a 
known chronic disease (e.g., diabetes, hypertension, depression, anxiety) for which she/he/they is 
prescribed a regular medication. We will also use the electronic health record data to define 
participants by whether the participant had any acute care utilization in the year prior to 
randomization.  
For a subset of the participants, we have baseline survey data that enables subgroup analyses by 
level of financial distress and self-reported health. Financial distress is defined as an affirmative 
answer on a survey question asking the participant whether she/he/they had any bills, expenses, 

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or needs that they were unable to pay. Poor self-reported health is defined as a positive screening 
(score≥3) on the 2-question version of the Patient Health Questionnaire, a positive screening 
(score≥3) on the 2-question version of the Generalized Anxiety Disorder questionnaire, or a 
fair/poor response on the 5-point scale of self-rated health. 
 
E.4. Alternative specifications and sensitivity analyses 
Our primary specification includes patient characteristics and baseline values of the outcome in 
the model to improve power as well as any chance imbalance between the study arms after 
randomization. As described in section E.1., we will compare covariates between the treatment 
and control groups, and we will explore whether our results are sensitive to inclusion of these 
covariates in the model. Importantly, our primary specification accounts for differential 
probability of winning the lottery using inverse probability weights. As an alternative approach, 
we will include the set of covariates that are correlated with treatment probability into the model, 
specifically indicators for number of lottery tickets. Finally, as a sensitivity check, we will also 
conduct our analyses on the subpopulation of participants with established primary care at MGB 
or CHA at baseline. 
To ensure our estimates are robust to method of estimation, we will also estimate generalized 
linear models assuming a negative binomial distribution for the count utilization outcomes and a 
generalized linear model with a Bournoulli distribution and logit link function for the binary 
outcomes. In a final robustness check, we will Winsorize the utilization measures to ensure that 
our estimates are not sensitive to outliers. 
 
E.5. Statistical significance and adjustments for multiple comparisons 
Statistical significance was defined as two-sided P<0.05 for the primary outcome. Because we 
have one prespecified primary outcome, we will not make any adjustments for multiple 
inference. For our secondary outcomes, we will the Benjamini-Hochberg procedure or similar to 
calculate adjusted p-values that account for testing of multiple outcomes. 
 
 

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F. References  
1. 
West S, Baker AC, Samra S, Coltrera E. Preliminary Analysis: SEED’s First Year.; 
2021:25. Accessed March 10, 2021. https://www.stocktondemonstration.org/ 
2. 
Gennetian LA, Duncan G, Fox NA, et al. Unconditional Cash and Family Investments in 
Infants: Evidence from a Large-Scale Cash Transfer Experiment in the U.S. Published 
online August 2022. doi:10.3386/w30379 
3. 
Troller-Renfree SV, Costanzo MA, Duncan GJ, et al. The impact of a poverty reduction 
intervention on infant brain activity. Proc Natl Acad Sci U S A. 2022;119(5):e2115649119. 
doi:10.1073/pnas.2115649119 
4. 
Lessons from the Income Maintenance Experiments. Federal Reserve Bank of Boston and 
The Brookings Institution; 1986:263. Accessed September 1, 2022. 
https://eml.berkeley.edu/~saez/course/Munnell(1986)book.pdf 
5. 
King ML. Where Do We Go from Here: Chaos or Community? Illustrated edition. Beacon 
Press; 1967. 
6. 
Bregman R. Utopia for Realists : How We Can Build the Ideal World. First Back Bay trade 
paperback edition. Back Bay Books; 2018. 
7. 
Yang A. The War on Normal People : The Truth about America’s Disappearing Jobs and 
Why Universal Basic Income Is Our Future. First edition. Hachette Books; 2018. 
8. 
Silver D, Zhang J. Impacts of Basic Income on Health and Economic Well-Being: Evidence 
from the VA’s Disability Compensation Program. National Bureau of Economic Research; 
2022. doi:10.3386/w29877 
9. 
Downes H, Phillips DC, Sullivan JX. The effect of emergency financial assistance on 
healthcare use. Journal of Public Economics. 2022;208:104626. 
doi:10.1016/j.jpubeco.2022.104626 
10. Berman J. Can Income Buy Health? Evidence from Social Security Beneﬁt Discontinuities 
and Medicare Claims. :79. 
11. Forget EL. The Town with No Poverty: The Health Effects of a Canadian Guaranteed 
Annual Income Field Experiment. Canadian Public Policy. 2011;37(3):283-305. 
doi:10.1353/cpp.2011.0036 
12. City of Chelsea. Chelsea Eats: Chelsea Food Debit Card Program. Accessed September 1, 
2022. https://www.chelseama.gov/ChelseaEats 
13. Figueroa JF, Wadhera RK, Lee D, Yeh RW, Sommers BD. Community-Level Factors 
Associated With Racial And Ethnic Disparities In COVID-19 Rates In Massachusetts. 
Health Aff (Millwood). 2020;39(11):1984-1992. doi:10.1377/hlthaff.2020.01040