Cash Benefits and Reproductive/Perinatal Health
Phase: NA
Conditions: Maternal Care Patterns, Pregnancy Related, Miscarriage
Sponsor: Harvard Medical School (HMS and HSDM)

1 
 
 
 
 
 
 
Cash Benefits and Reproductive/Perinatal Health 
Protocol and Analysis Plan 
 
March 23, 2023 
 
ClinicalTrials.gov Identifier: NCT05782660 
 
Study Team 
Sumit Agarwal, MD, MPH, Brigham and Women’s Hospital, Harvard Medical School 
Jeffrey Liebman, PhD, Harvard Kennedy School 
 
 

2 
 
Table of Contents 
 
Section A. Introduction …………………………………………………………………… 
3  
 
 
Section B. Treatment ……………………………………………………………………… 
3  
 
 
Section C. Randomization and Participant Eligibility …………………………………… 
4  
 
 
Section D. Data, Outcomes and Other Variables ………………………………………… 
5  
 
 
Section E. Statistical Analysis ……………………………………………….…………… 
6  
 
 
 
 
 
 
 
 
 

3 
 
A. Introduction 
During the first two years of the COVID-19 pandemic, the City of Chelsea, Massachusetts held a 
lottery to allocate cash benefits to its residents for ten months. In one of the original analyses of 
the Chelsea Eats program, the survey results showed that there was a higher rate of pregnancy in 
the treatment group compared to control. The underlying mechanism, however, remained 
unclear; the cash benefit could theoretically have induced changes in family planning or in 
maternal health, leading to the observed changes in pregnancy rates. Using data from the Chelsea 
Eats program, we propose to study the impact of the cash benefit on reproductive and perinatal 
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. 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. 
 
 

4 
 
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 
payments for the first and second months – so the total payments received by waitlist households 
are the same as initial lottery winners.  
 

5 
 
D. Data, Outcomes, and Other Variables 
D.1. Data 
Female study participants of childbearing age (typically defined as age 15-44) will be matched to 
their electronic health record at Cambridge Health Alliance (CHA), Mass General Brigham 
(MGB), and East Boston Neighborhood Health Center (EBNHC) using a probabilistic algorithm 
based on name, date of birth, gender, address, and phone number.  
 
D.2. Planned Outcomes 
Outcomes will be assessed on a per-participant basis and measured using the electronic health 
record data.  
1. Primary Outcome 
a. Pregnancy 
i. 
We will first confirm the result from the survey data by assessing for 
pregnancies using the electronic health record data. 
ii. 
Pregnancies will be identified in the electronic health record as a 
positive urine or blood pregnancy test, ultrasound, and/or diagnosis 
codes over the 10 months of the trial.  
iii. 
We will then use the electronic health record identify live child births 
delivered vaginally or via Cesarean section. These can occur after the 
10-month duration of trial.  
iv. 
Any pregnancy without an eventual delivery (not carried to term), the 
primary outcome for our main analysis, could be indicative of 
miscarriage, abortion, or fetal death, which will be assessed further with 
the secondary outcomes below. 
 
2. Secondary Outcomes 
a. Miscarriage 
i. 
We will use diagnosis codes and documentation within clinical notes to 
subsequently identify miscarriages that present to the health care setting. 
b. Abortion 
i. 
We will also identify procedural abortions and prescriptions for medical 
abortions. Using documentation within clinical notes to distinguish 
between miscarriage-related abortions (i.e., those performed after early 
pregnancy loss, incomplete abortions, and ectopic pregnancies) and 
induced abortions, this outcome will focus specifically on induced 
abortions. 
c. Prenatal vitamin prescriptions prior to pregnancy 
i. 
To characterize family planning among participants, we will assess new 
use of prenatal vitamins, including multivitamins. Physicians can 
prescribe these vitamins or document over-the-counter use of them in 

6 
 
the electronic health record. We will exclude those that were prescribed 
or documented during pregnancy and focus specifically on those that 
occurred prior to any pregnancy over the 10 months of the trial. 
d. Contraception 
i. 
To further understand family planning among participants, we will 
assess utilization of long-acting reversible contraceptive methods 
(intrauterine devices or implants) and prescriptions for hormonal birth 
control methods (pill, patch, ring). 
e. Number and timing of prenatal visits before delivery 
i. 
In addition to quantity of visits, we will also assess for initiation during 
the first trimester and calculate the Adequacy of Prenatal Care 
Utilization (APNCU) Index, which characterizes the adequacy of a 
patient’s prenatal care as inadequate, intermediate, adequate, or adequate 
plus based on the timing of their prenatal care initiation and the number 
of prenatal visits after initiation. 
f. Composite of birth outcomes 
i. 
Following McConnell et al. (2022), we will construct a composite of at 
least one of: low birth weight, preterm birth, small for gestational age, or 
perinatal mortality. These are described in further detail below. 
ii. 
Birth weight is a continuous measure of the infant’s birth weight in 
grams. Low birth weight is defined as a birth weight less than 2500 
grams.  
iii. 
Gestational age at birth is a continuous measure of the infant’s 
gestational age at birth. Preterm birth is defined as an infant born before 
a gestational age of 37 weeks. 
iv. 
Birth weight for gestational age is a continuous measure of the infant’s 
birth weight relative to the average in the population for a given 
gestational age at birth. Small for gestational age is defined as a birth 
weight below the 10th percentile for infants of the same gestational age. 
v. 
Perinatal mortality is a binary indicator for whether there was a fetal 
death at or after 20 weeks of gestation or mortality within the first 7 days 
of life. 
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 

7 
 
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.  
 
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) financial distress at baseline, and (3) poor self-
reported health at baseline, or (4) any of the prior. 

8 
 
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 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, 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 one of 
the clinical sites of care. 
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.