A Randomized Controlled Trial of Concentrated Investment in Black Neighborhoods to Address Structural Racism as a Fundamental Cause of Poor Health
Phase: NA
Conditions: Financial Stress, Economic Problems, Environmental Exposure, Health Behavior, Health, Subjective, Mental Health Wellness 1
Sponsor: University of Pennsylvania

IGNITE  
A Randomized Controlled Trial of Concentrated Investment in Black 
Neighborhoods to Address Structural Racism as a Fundamental Cause of Poor 
Health 
  
  
  
  
Study Protocol 
  
April 2023 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 

 
Outline 
1. Abstract 
2. Overall objectives 
3. Aims 
3.1 Primary outcome 
3.2 Secondary outcomes 
4. Background 
5. Study design 
5.1 Design 
5.2 Study duration 
5.3 Target population 
5.4 Accrual 
5.5 Key inclusion criteria 
5.6 Key exclusion criteria 
6. Subject recruitment 
7. Subject compensation 
8. Study procedures 
8.1 Consent 
8.2 Procedures 
9. Analysis plan 
10. Investigators 
11. Human research protection 
11.1 Data confidentiality 
11.2 Subject confidentiality 
11.3 Subject privacy 
11.4 Data disclosure 
11.5 Data safety and monitoring 
11.6 Risk/benefit  
11.6.1 Potential study risks 
11.6.2 Potential study benefits 
11.6.3 Risk/benefit assessment 

12. Bibliography  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
1. Abstract 
Black Americans in the US fare worse across nearly every health indicator compared to White individuals. In 
Philadelphia, the location of this study, these health disparities culminate in a stark longevity gap, with average 
life expectancies in poor, predominantly Black neighborhoods being 20 years lower than in nearby affluent, 
predominantly White neighborhoods. We will conduct a cluster randomized controlled trial (RCT) of a suite of 
place-based and financial-wellbeing interventions at the community, organization, and individual/household 
levels that address the social determinants of racial health disparities. At the community level, we address 
underinvestment in Black neighborhoods by implementing vacant lot greening, abandoned house remediation, 
tree planting, and trash cleanup. At the organization level, we partner with community-based financial 
empowerment providers to develop cross-organizational infrastructure to increase reach and maximize efficiency. 
At the individual/household levels, we increase access to public benefits, financial counseling and tax preparation 
services, and emergency cash assistance. We will test this “big push” intervention in 60 Black neighborhood 
microclusters, with a total of at least 480 adults and 480 children across up to 720 households. We hypothesize 
that this “big push” intervention will have significant impact on overall health and wellbeing.  
 
2. Overall objectives 
The objective is to develop and test a concentrated suite of place-based and financial well-being interventions at 
the community, organization, and individual levels to address structurally-mediated racial health disparities.  
3. Aims 
3.1 Primary outcomes 
Overall health and psychological distress of participants.  
3.2 Secondary outcomes 
The secondary outcomes are described in detail in Table 1 below.  
4. Background 
Black individuals in the United States fare worse than White individuals across almost every social, economic, 
and health indicator. The Black health disadvantage starts at birth, reflecting the cumulative toll of racialized 
social stressors and healthcare discrimination on maternal health and resulting in higher rates of preterm birth 
and low birth weight. Black youth are disproportionately exposed to environmental toxins such as lead and 
adverse childhood events such as financial hardship and neighborhood violence. Black adults have higher rates 
of chronic disease, including diabetes, hypertension, as well as many cancers.  These and other forces culminate 
in a stark racial longevity gap: in Philadelphia, the location of this study, life expectancy for people living in a 
poor, predominantly Black neighborhood is 20 years lower than for people living in a nearby affluent, 
predominantly White neighborhood.  
The fundamental cause of these striking and pervasive disparities is structural racism – the confluence of deep 
historical, institutional, cultural, and ideological forces that unequally distribute resources and risks across 
racialized groups. Structural racism patterns health by affecting a range of interconnected, mutually reinforcing 
social determinants of health at the national, neighborhood, household, and individual levels. Most notably, 
longstanding, systematic disinvestment has resulted in highly segregated Black neighborhoods with dilapidated 

environmental conditions and severe economic insecurity within Black households, leading to a “feedback loop 
of concentrated racial disadvantage,” all of which have been strongly tied to poor health.  
Most interventions seeking to address racial health disparities focus on individual-level behaviors and 
outcomes, or individual channels by which structural racism harms health. However, by failing to address 
upstream social determinants, these interventions have had limited population level impact. A multi-level, 
multi-component intervention package focused on a range of social determinants of health is necessary to 
meaningfully address structural racism as a fundamental cause of racial health disparities. 
 
5. Study design 
5.1 Design 
This is a cluster randomized controlled trial. We will enroll 60 neighborhood microclusters in the study. Half of 
the study clusters will be randomized to the intervention and the other half to the control group.  
5.2 Study duration 
The study is expected to begin in September 2022 and participants will continue in the study for 24 months. The 
study will last for 5 years. Year 1 will include protocol development and cluster selection. Participant selection 
and baseline data collection will start in Year 2 and last 9 months. The place-based and financial well-being 
interventions will begin quarter 3 of Year 2 and last 18 months, followed by follow-up data collection.    
Qualitative interviews will be conducted in Year 3. Data analysis occurs from Year 2 into Year 5. Year 5 will 
involve final data analysis, manuscript preparation and submission, and dissemination of study findings. 
5.3 Target population 
Our population will include adults living in our target Philadelphia neighborhood microclusters. 
5.4 Accrual 
We will enroll at least 480, and up to 720, adults from 60 neighborhood microclusters in the study, half of 
which will be randomized to the intervention and half to the control. If more than one household resident is 
interested and eligible in the study, the person with the most recent birthday will be enrolled. One participant 
per household is eligible.  
If there are one or more children ages 3-17 living in the household, this child’s caregiver will also be given the 
option of completing an additional survey regarding their child’s health. We will enroll adult caregivers of 480 
children ages 3-17, including the 480 adults described above and up to 240 additional participants.  
5.5 Key inclusion criteria 
To be eligible patients must:  
1. Be at least 18 years of age 
2. Have the ability to communicate via text messaging  
3. Be comfortable communicating in English 
4. Be a permanent resident of the home where they are to be enrolled 
5. Have knowledge of their household finances 
 
5.6 Key exclusion criteria 

1. Individuals who plan to move out of the study microcluster within 6 months 
2. Individuals who are unable to fully consent and participate based on CC team assessment 
 
5.7 Key inclusion criteria for IGNITE Kids Caregivers  
To be eligible caregivers must:  
1. Be at least 18 years of age 
2. Be the primary caregiver of one or more children ages 3-17 years  
3. Have the ability to communicate via text messaging  
4. Be comfortable communicating in English 
5. Be a permanent resident of the home where they are to be enrolled 
6. Have knowledge of their household finances 
 
5.6 Key exclusion criteria 
1. Individuals who plan to move out of the study microcluster within 6 months 
2. Individuals who are unable to fully consent and participate based on CC team assessment 
3. Individuals who are not the primary caregivers of children ages 3-17 years  
 
6. Subject recruitment 
Participant recruitment will commence in September 2022 and will run through March 2024. Recruitment in 
control and intervention microclusters will occur simultaneously. The data analyst who performed the 
randomization will randomize microcluster order (stratified by intervention and control status and city section) 
to determine the order in which recruitment occurs.  
Several Community Coordinator (CC) teams will be recruiting participants. A given team of two will remain in 
a microcluster until a total of 12 participants are fully enrolled in that microcluster. They will then move to the 
next microcluster on the randomly generated list. Multiple microclusters will be undergoing enrollment 
simultaneously. The CC team will be blinded to what arm of the trial the microcluster is in until all participants 
are enrolled. Participants will also be blinded until after everyone in that cluster is enrolled.  
When starting recruitment in a new microcluster, the CC team will first place door knockers on all homes 
within the microcluster boundaries to alert neighbors to the presence of the study team in the neighborhood. 
They will then begin door-to-door recruitment at a pre-determined house that is closest to the geographic center 
of the study microcluster. They will follow a pre-determined route throughout the neighborhood that includes 
walking up one side of the street and back down the other side of the street before moving on to an adjacent 
block, and the next adjacent block, etc. The block order will be pre-determined by the data analyst performing 
randomization to where blocks will be ordered as closet to concentrically in a clockwise manner from the first 
block. 
If more than one household resident is interested and eligible in the study, the person with the most recent 
birthday will be enrolled. One participant per household is eligible. The CC team will conduct an informed 
consent with all participants. The CC team will then administer a demographic and baseline survey and directly 
enter data into REDCap. If the resident is interested in the study but is not available or able to complete the 
baseline survey at the time of the first encounter, CC team will conduct the Informed Consent Process (resident 
will sign the Informed Consent Form) and then identify a time to reschedule completion of the survey. If 
necessary, the CC team can schedule to meet with the resident virtually to complete the baseline survey. 

Completion of the baseline survey indicates resident is fully enrolled in the study and is to be considered the 
official date of enrollment.  
If there are one or more children ages 3-17 living in the household, the child or children’s primary caregiver 
will be given the option of completing an additional follow-up survey regarding their child’s health and well-
being. One participant per household is eligible to complete the children’s health and well-being survey for up 
to three children residing in the household. If more than three children ages 3-17 reside in the household, the 
enrolled caregiver will be asked to complete the survey for the three children with the most recent birthdays. 
We will enroll adult caregivers of approximately 480 children ages 3-17. To meet this goal, approximately up to 
240 additional caregivers will be enrolled. Towards this end, after enrollment of adult subjects is complete 
within a microcluster, CC teams will continue to enroll caregivers from homes within a microcluster with which 
contact has not already been made. Enrollment will end when either enough caregivers are enrolled for up to 10 
children per microcluster or two weeks after adult enrollment closes within the microcluster.   
 
Subject compensation 
Participants will be compensated $40 after the baseline survey using ClinCards, $15 after a 6 month check in, 
$15 after a 12 month check in, $15 after an 18 month check in, and $40 after a follow up survey. Participants 
with children who agree to complete a supplemental follow-up survey regarding their children’s health will 
receive an additional $20 per child (up to 3 children per household). Follow up data collection will occur 
between September 2024 and March 2025, with each participant surveyed 24 months after their initial 
enrollment date. If participants are selected to participate in an additional qualitative feedback interview, they 
will receive $40 compensation. 
8. Study procedures 
8.1 Consent 
Informed consent will be obtained in person at the participant’s home using REDCap, a HIPAA compliant web-
based platform. The potential participant will receive information explaining the purpose of the study and what 
would be required of the participant, and that participation is voluntary.  Any questions the potential participant 
may have will be addressed. The community coordinator team will convey important and relevant information 
about the study, disclose all risks and benefits, fully describe the study procedures relevant to participants, 
answer all questions, and enable each participant to make a competent, informed decision about volunteering 
for the study, free from undue influence.  
In response to a global or regional epidemic (e.g., COVID-19) or other extreme circumstances (e.g., natural 
disaster, medical emergency), the informed consent process may be conducted remotely to minimize potential 
risks to participants. The informed consent process may also be conducted remotely if participants are interested 
in the study but are not available during our normal recruitment hours. To aid in this process, a copy of the 
informed consent form can be provided to the participant in advance of the consent discussion, either in person 
when they are approached at their residence or via email. The informed consent discussion will be conducted 
via telephone or a video-conferencing platform (e.g., Zoom). To ensure the privacy of the participant, the 
community coordinator will conduct the remote informed consent process from a private location, while the 
participant is expected to be in their home. The participant will have ample time to consider their participation 
and consult with others (e.g., family, friends) prior to signing the informed consent. Once the consent discussion 
is complete, REDCap will be used to document the participant’s consent and capture a hand signature using a 

finger, stylus, or mouse. The community coordinator will review the REDCap form to make sure the signature 
has saved.  
8.2 Procedures 
Site selection 
Census tract selection  
We created a list of all census tracts (n=384) in Philadelphia, PA using the 2019 American Community Survey 
(ACS). Using 5-year population estimates, we determined the total number of Black residents for each tract by 
combining the number of residents who identified as Black only and Black in addition to one or more races. We 
then sorted the census tracts based on percent of Black residents from high to low. To ensure that our study 
focused on predominantly Black neighborhoods, census tracts with a high proportion of Black residents (80% or 
more of the population; n=99) were deemed eligible for inclusion in the study.  
Another goal of the trial is to reach the most economically distressed neighborhoods and households. To 
achieve this, we included only those census tracts where at least 39% of households had an income at least 
200% below Federal Poverty Level (FPL). This threshold mirrors the citywide proportion of families living at 
200% or below of FPL. Seventy five of the 99 predominantly Black census tracts met this inclusion criteria (one 
census tract met this criteria but was excluded as it was in the South Philadelphia section of Philadelphia which 
is geographically dissimilar from the rest of the tracts). 
Block random sampling  
Neighborhood microclusters (hereafter, microcluster) served as the intervention unit for the study. To form 
these microclusters, a master list of every block, or street segment from one intersection to the next intersection, 
in the 75 eligible census tracts was created. A total of 8,462 blocks were included in the master list. A shapefile 
of selected blocks was imported into R. A random block was chosen as an index block and a 0.125 (one eighth) 
mile diameter buffer was created around that block. All other blocks that fell within this radius were used to 
form a microcluster of geographically proximal blocks. A block was included in the microcluster if at least half 
of the street segment fell within the microcluster boundaries. This microcluster formed a study area. All blocks 
that were included in the microcluster were removed from the master list to ensure no overlap of microclusters. 
Next, an additional 0.125-mile exclusion zone buffer was created on all sides of each microcluster. The purpose 
of the exclusion zone was to create an additional buffer between microclusters to minimize potential spillover 
effects from the interventions. This process was then repeated until 60 total non-overlapping microclusters were 
created, stratified by section of the city (West and North), so that approximately 30 microclusters were in each 
city section. 
Our goal was to have a final set of microclusters that minimized overlap of microcluster exclusion zones to 
reduce potential spillover and contamination effects across trial arms. To do this in a random manner, the 
process to create 60 study microclusters with 0.125-miles exclusionary zones was repeated 1,000 times to create 
1,000 unique maps. An analysis was run to determine the total area of exclusion zone buffer overlap for each of 
the 1,000 microcluster map iterations, which was then ordered from lowest to highest. We then selected the 
bottom microcluster iterations with the lowest exclusion zone overlap. From this list, one iteration was selected 
at random to determine final study sites.  
Randomization  
We randomly assigned the 60 microclusters to treatment (intervention, n=30 microclusters) and control (non-
intervention, n=30 microclusters) groups. Prior to randomization, microclusters were stratified by city section 

(West and North). The land area and number of streets in each section was near-equal, allowing for 
stratification pre-constrained randomization.1  
Within the city section strata, we conducted a covariate-constrained randomization process in order to ensure 
balance between microclusters randomized to intervention and those randomized to control based on relevant 
sociodemographic variables. Covariate-constrained randomization quantifies baseline imbalance of 
microcluster-level covariates (both categorical and continuous) across all microclusters using a balance metric 
and then randomly selects one randomization schema from those with acceptable balance. Furthermore, the 
intraclass correlation coefficient calculation – a measure of reliability – remains unaffected under a constrained 
randomization model.2  
We chose the following covariates to balance on: age (categorized by shares under age 19, 19-49, 50-64, and 65 
and above); gender; median income; unemployment rate; amount of vacancy; and tree canopy. We did not 
balance on race/ethnicity (e.g., % Black) or poverty rates, since initial criteria for study site selection focused on 
census tracts whose populations were at least 80% Black and with elevated poverty rates. The covariates were 
measured based on their distribution of means by each microcluster using block group data from the US Census 
Bureau’s 2020 ACS. Each microcluster’s mean is the weighted mean for the covariates based on percent of land 
area in each census block group in which the microcluster sits. A microcluster could be in one census block 
group or could cross over 2 or 3 census block groups. Following the literature, we used a threshold of 0.2 for 
standardized mean differences in the covariates.  
We used the -cvcrand- package, specifically the -cvrall- function, in R to calculate the final covariate balance 
metric and selection schema at random after covariate balance by cluster was achieved.2  
Enrollment 
Participant recruitment will commence in September 2023 and will run through May 2023 (or until we enroll at 
least 480, but no more than 720, in the main trial). Recruitment in control and intervention microclusters will 
occur simultaneously. The data analyst who performed the randomization will randomize microcluster order 
(stratified by intervention and control status and city section) to determine the order in which recruitment 
occurs.  
Several Community Coordinator (CC) teams will be recruiting participants. A given team of two will remain in 
a microcluster until at least 8participants are fully enrolled in that microcluster. They will then move to the next 
microcluster on the randomly generated list. Multiple microclusters will be undergoing enrollment 
simultaneously. The CC team will be blinded to what arm of the trial the microcluster is in until all participants 
are enrolled. Participants will also be blinded until after everyone in that cluster is enrolled.  
When starting recruitment in a new microcluster, the CC team will first place door knockers on all homes 
within the microcluster boundaries to alert neighbors to the presence of the study team in the neighborhood. 
They will then begin door-to-door recruitment at a pre-determined house that is closest to the geographic center 
of the study microcluster. They will follow a pre-determined route throughout the neighborhood that includes 
walking up one side of the street and back down the other side of the street before moving on to an adjacent 
block, and the next adjacent block, etc. The block order will be pre-determined by the data analyst performing 
randomization to where blocks will be ordered as closet to concentrically in a clockwise manner from the first 
block.  
For a given house, a potential participant will be screened by the CC team to determine eligibility: 
Key Inclusion Criteria: 
• At least 18 years of age 
• Have the ability to communicate via text messaging  

• Individuals comfortable communicating in English 
• A permanent resident of the home where they are to be enrolled 
• Have knowledge of their household finances 
 
Key Exclusion Criteria:   
• Individuals who plan to move out of the study microcluster within 6 months 
• Individuals who are unable to fully consent and participate based on CC team assessment 
 
If more than one household resident is interested and eligible in the study, the person with the most recent 
birthday will be enrolled. One participant per household is eligible. The CC team will conduct an informed 
consent with all participants. The CC team will then administer a demographic and baseline survey and directly 
enter data into REDCap. Participants will be compensated $40 after the baseline survey using ClinCards, $15 
after a 6 month check in, $15 after a 12 month check in, $15 after an 18 month check in, and $40 after a follow 
up survey. Follow up data collection will occur between September 2024 and March 2025, with each participant 
surveyed roughly 24 months after their initial enrollment date.  
For all households including one or more children ages 3-17 living in the household, the child or children’s 
primary caregiver will be asked to complete an additional follow-up survey regarding their child’s health and 
well-being, for up to three children per household. If more than three children ages 3-17 reside in the household, 
the enrolled caregiver will be asked to complete the survey for the three children with the most recent birthdays. 
Participants who complete the follow-up survey regarding children’s health will be compensated $20 after 
survey completion. 
To support recruitment efforts, we will partner with community organizations to widen the reach of study 
promotion and to improve dissemination of recruitment materials, particularly in communities where 
recruitment is found to be difficult. We will also use a community-facing website to promote the study and 
increase screening for enrollment. 
Respondent Driven Sampling 
Respondent driven sampling will be implemented to drive enrollment when Community Coordinators identify 
that completing enrollment (i.e., reaching at least 8 participants) is difficult in a particular microcluster. This 
strategy will be implemented only as a contingency  
Participants will then be invited to refer individuals who they think may be willing to participate in the study. If 
those referrals meet eligibility criteria and are successfully enrolled, the participant who made the initial referral 
will receive a payment of $15 via their ClinCard for that successful referral.  
Participants can make an unlimited number of referrals, but only up to 2 referrals will be enrolled per 
participant (to limit referral networks at a max of four networks per cluster.) 
 
Intervention 
Place-Based Interventions 
Our suite of place-based interventions consists of four separate interventions which will all be performed 
simultaneously in all of the intervention microclusters. The four interventions are: vacant lot greening, tree 
planting, weekly trash pickup, and abandoned house remediation. The first three will be performed through the 
Pennsylvania Horticultural Society (PHS), while the last will be performed through the City of Philadelphia.  

After microclusters have been selected and randomized to the intervention arm (n=30), we will conduct an 
inventory of the locations of existing vacant lots, abandoned houses, and tree canopy. All place-based 
interventions will start in April 2023. See below for protocol for each intervention including the intervention 
timeframe for each.  
Vacant lot greening. We will green a total of 150 vacant lots in the intervention microclusters (roughly 3-5 lots 
totally up to 5000 square feet per microcluster). Vacant lot greening is a standard, reproducible intervention 
involving (a) the removal of trash and debris, (b) grading of the land, (c) planting new grass and for some lots a 
small number of trees, and (d) installing a low wooden perimeter fence.3 Vacant lots will be greened between 
April and June 2023. During the growing season (April-October), bi-monthly maintenance is performed which 
involves mowing and picking up trash between July 2023 and August 2024. In order to determine which vacant 
lots will be greened within each microcluster, we will first use a master list of vacant lots provided by PHS and 
overlay this list with our microcluster boundaries. Vacant lots are eligible for greening if they are in violation of 
city ordinance (eg. over 10 inches of vegetation) and if they show no signs of active maintenance or ownership 
(eg. the presence of a locked chain fence). Within each microcluster we will green up to 5 vacant lots totalling 
up to 5000 square feet of vacant space. We will first eliminate vacant lots over 5000 square feet. If lots in a 
given microcluster total more than 5000 square feet we will randomly sort the list of vacant lots and select the 
top 3-5 to total up to 5000 square feet. We will give priority to lots that are contiguous (eg. If a given parcel of 
land is selected that has 2 other vacant parcels connected to it, we will also include those adjacent parcels before 
moving down the list to the next vacant lot). If lots in a given microcluster total less than 5000 square feet, we 
will then inventory vacant lots that are in PHS Community LandCare program, which are vacant lots that 
receive mowing and trash cleanup, but previously did not receive the full vacant lot treatment described above. 
We will randomly select vacant lots from this pool to receive the full vacant lot greening treatment to get up to 
5000 square feet of vacant lots total in the microcluster. Lots determined eligible by PHS are sent to the City of 
Philadelphia for permission to green. Some lots sent to the City may ultimately be deemed ineligible and would 
not receive the greening intervention. 
Tree planting. We will plant a total of 300 trees in the intervention microclusters during this study, or roughly 
10 trees per cluster. Trees will be planted between September and October 2023, with monthly watering 
occurring between planting and August 2024 during the growing season. We anticipate that some clusters may 
receive less than 10 trees and other clusters will receive more than 10 trees based on availability of planting 
opportunities. Trees will be planted and maintained through PHS. In Philadelphia, permission is needed from 
the owner of a property to plant a tree. After all participants in an intervention microcluster are enrolled and the 
microcluster randomization is revealed to the community coordinator team, all participants who indicated they 
were interested in a tree will have a tree planted in front of their residence based on the following criteria: (a) 
there is an open space for a tree, (b) there are no physical barriers to tree planting (eg fire hydrant), and (c) the 
owner of the property lives in the home and is able to give permission to plant a tree. If the participants rents the 
property, we will determine if they would like to seek permission from the landlord. After we determine which 
study participants are eligible for a tree, we will then canvas the neighbourhood microcluster to find other 
residents, businesses, places of worship, etc in the cluster who are not participants in the study who want a tree 
planted, for a total of 12-15 potential tree planting opportunities. We know that some tree planting sites will 
ultimately not be eligible for planting which is why we will initially select 12-15 sites with the aim of planting 
10 trees per cluster. This list will then be sent to the PHS who will work with the City of Philadelphia to 
determine which physical locations are eligible for a tree. For example, sometimes there are water lines too 
close to the side-walk surface that make a location ineligible.  
Trash cleanup. We will provide weekly trash cleanup in all public spaces in the blocks included in our 
intervention clusters between April 2023 and August 2024. Public space includes on streets and sidewalks, but 
not into private front yard space. Although the city performs weekly trash pickup, this is limited to trash that is 

in garbage cans, leaving neighborhoods with a significant trash burden that does not fall under the purview of 
regular sanitation. Trash cleanup will occur through PHS.  
Abandoned house remediation. We will remediate 90 abandoned houses in the intervention clusters during this 
study, or approximately 3 houses per cluster. Remediation will take place between April and August 2023.  
Abandoned house remediation follows a standard protocol and includes (1) replacement of plywood boards or 
missing/broken doors and windows on the front façade of the house with new, standard, exterior, front 
entryway, wooden doors and standard, double-hung, wooden windows; (2) removal or replacement of 
deteriorated structures on front building façade; and (3) cleaning and graffiti removal on building façade. A 
master list of publicly owned abandoned houses in our study intervention clusters will be compiled using 
publicly available data from the City of Philadelphia. We will not include privately owned abandoned houses in 
the trial. If there are more than 3 eligible abandoned houses in a cluster, we will randomly sort the list and select 
the top 3 houses to remediate. Remediation will be conducted through the City of Philadelphia.  
Individual and Household-Level Financial Interventions 
Participants in the intervention group will be directly connected to three Philadelphia-based community-based 
partners who specialize in improving access to financial services and public benefits among low-income 
individuals. The partners and the services provided are: 
• Clarifi – provides free financial counseling, starting with a detailed intake process that includes 
elicitation of financial goals and development of an action plan over multiple sessions.  
• Benefits Data Trust (BDT) – provides free integrated services that screen participants for eligibility for 
public sector benefit programs (e.g., Medicaid, SNAP, WIC, and nearly 20 other local, state, and 
national programs) as well as facilitating applications for those benefits. 
• Campaign for Working Families (CFW) – provides free tax preparation services for families and 
individuals earning less than $55,000/year. 
 
In addition to these services, participants living in intervention microclusters will also be eligible to receive a 
one-time $400 cash grant, which will be administered by Clarifi. The cash grant will address both potential 
direct and opportunity costs of working closely with partners (e.g., potential travel costs or lost wages) as well 
as general financial insecurity the participant and their household may be facing at baseline. 
The rationale for providing these services as part of this study follows from a large literature linking economic 
and financial security to health and well-being.4–6 While these services are generally available to all 
Philadelphians, informational and time constraints (current models of delivery require individuals to reach out 
to ask for services) and budgetary constraints on the partner side remain barriers to engagement. The goal of the 
financial intervention in this study is to increase access to these services by increasing financial capacity among 
the partner organizations and reducing barriers among potential beneficiaries.  
The intervention group will be provided the above package of financial services using the following general 
approach. Within one month of enrollment and completing the baseline survey, the research team will contact 
intervention group participants, provide information on each of the financial partners, as well as a checklist for 
financial documents needed to have on hand to work with each partner. At that time, intervention group 
participants will select a pre-filled appointment slot with the Clarifi team for their initial intake. This 
appointment can either be in-person or virtual, depending on the participant’s preference. Intervention group 
participants will receive text-message reminders in advance of their initial appointment, as well as a follow up 
text to assess whether they attended the appointment after the intended date and time. In the event participants 
were unable to participate, the research team will reschedule the initial session and again confirm participation 
via text-message. Participants will be eligible to receive their one-time $400 at this initial session, which will be 
delivered in the form of a check or direct deposit based on participant preference. After this initial meeting, the 

research team will help participants schedule a second appointment (and any subsequent appointments, as 
needed), following the same strategy.    
After connecting participants with the Clarifi team, participants will then be directly connected to BDT and 
CFW using a similar approach. BDT provides a screening tool that determines which benefits individuals are 
eligible for. The CC team will set up a time based on the participants’ availability to complete this screening 
process with them in person. the participants have completed the screening process, they will call the phone line 
that BDT created for our study where a BDT representative will then walk the participants through the 
application process for benefits. 
CFW provides both virtual and in-person (at several dedicated locations in the city) tax preparation services. 
Participation in these services will be confirmed in the same manner as the Clarifi services with the CC 
providing pre-selected appointment slots to the participants. 
By the end of the intervention period in August 2024, the goal of the financial intervention will be for 
intervention group participants to have worked with a Clarifi counsellor at least 2 times and to have received a 
one time $400 cash grant, completed the benefits screening, application and follow-up process with BDT one 
time, and filed income taxes with CFW.  
At the end of the intervention period, participants in the control group will receive a list comprising of each of 
the financial partners and their contact information, but will not receive the further engagement and support to 
enroll in these services; nor will they receive the $400 cash grant. Provision of information to this group goes 
one step beyond current outreach efforts by the financial partners, which include direct mailers and advertising. 
 
9. Analysis plan 
Outcome Measures 
The primary and secondary outcomes for this study are described in detail Table 1. Primary outcomes will be 
drawn from our two-wave survey of study participants (one baseline wave and one endline wave); our 
secondary outcomes will be drawn from the same survey as well as from key administrative data sources 
(detailed in Table 1).  
Our two primary outcomes will focus on (1) overall health and (2) psychological distress. Our measure of 
overall health comes from an index based on responses to three survey questions: a self-reported rating  of the 
participant’s general health (on a 5 point Likert scale, ranging from poor to excellent); the self-reported number 
of days in the last 30 in which poor physical or mental health precluded engagement in the participant’s usual 
activities; and a self-reported rating change in overall health outcomes over the last 12 months (three point scale 
denoting worse, same, or better). We will use the method of Anderson (2008) to create a single (standardized) 
index of overall health.7 This method uses the inverse covariance matrix of the variables to create the index and 
allows for missing observations.  
Our measure of psychological distress is the Kessler-6 scale, a validated measure of non-specific psychological 
distress represented by the summed score of six questions querying the frequency of symptoms of feeling 
nervous, hopeless, depressed, restless, depressed, that everything was an effort, and worthless over the past 30 
days.8 The Kessler-6 ranges from 0-24. 
Secondary child health outcomes 
In addition to the outcomes listed above, we plan to evaluate a series of secondary outcomes focused on child 
health and well-being, derived from a follow-up survey administered to participants with children ages 3 to 17 

at the time of 24-month follow-up. These will include caregiver-reported child health, health care utilization, 
and several outcomes related to child well-being, neighborhood safety for children, and parenting, as listed in 
Table 2. 
We will also examine additional child health outcomes for all children residing in IGNITE study clusters using 
secondary analysis of existing data sets. Specifically, we will assess school attendance and performance for all 
cluster residents using Philadelphia school district data, birth outcomes for cluster residents using Pennsylvania 
birth certificate data, and health care utilization outcomes for all cluster residents using electronic health record 
data from the Children’s Hospital of Philadelphia. These outcomes are also included in Table 2. We will obtain 
separate approval from the Children's Hospital of Philadelphia Institutional Review Board prior to abstraction 
of electronic health record data for these child health outcomes. 
Power Calculations 
For this cluster randomized trial, we assume a Type I error rate of 0.05 and a desired power of 0.8 for the 
primary outcomes. Our study will include 60 microclusters (30 intervention, 30 control) with at least 8 
participants in each cluster (n=480 across all clusters). We will have no fewer than 480 and can enroll up to 720. 
The number of participants enrolled will depend on our dynamic enrollment strategy that we've discussed 
already. We assume an intraclass correlation (ICC) of 0.05. For our primary outcomes (and all survey-based 
outcomes), we will fit a marginal model using generalized estimating equations (GEE) to estimate the effect of 
the intervention relative to control. Under these assumptions, we will be able to detect an intervention effect of 
0.33 s.d at 80% power with 30microclusters per arm (60 total) and 8 participants per microcluster.9 This is a 
conservative estimate of power since we will adjust for the baseline value of the outcome and other covariates 
associated with the outcome. If we are only able to sample from 50 microclusters (25 per arm; n=400 across all 
clusters), we will be able to detect a moderate effect size of 0.37 s.d. at 80% power. Assuming up to 25% 
attrition between baseline and endline, we would still be able to detect a treatment effect of 0.31 s.d. 
 
Statistical Analysis 
Individual-level primary and secondary outcomes 
For all primary and secondary outcomes drawn from our two-wave survey of 720 trial participants, we will 
assess differences in outcomes between the intervention and comparison groups using Generalized Estimating 
Equation models (GEE) that account for clustering at the study microcluster level. We will use canonical link 
functions for all outcomes. All models will adjust for the baseline level of the outcome, all microcluster-level 
characteristics used in the covariate constrained randomization (see randomization protocol),10 any individual 
characteristics found to be imbalanced between the intervention and control groups at baseline.  
We will conduct an intention-to-treat analysis: participants that move out of (or across) study clusters, 
separately engage with community partner services while in the control group, or do not engage with these 
services while in the treatment group will all be considered exposed to their original treatment assignment.  
Given that our study includes two primary outcomes, we will address for simultaneous inference using a 
stacked GEE approach.11 This approach adequately covers family-wise Type I errors in a manner that is more 
efficient than overly conservative approaches such as Bonferroni-Holm.  
We will use a similar set of approaches for the survey-based secondary outcomes. For these outcomes we will 
report both unadjusted p-values as well as p-values adjusted for multiple inference (again based on models 
using a stacked GEE approach). 

Will conduct four sensitivity analyses. First, we will assess for non-random attrition by regressing a binary 
indicator for each participant equal to 1 if the participant did not complete a follow-up survey on the treatment 
indicator, microcluster level attributes used in the covariate constrained randomization procedure, and any 
individual-level covariates that were not balanced at baseline (adjusting standard errors for clustering at the 
microcluster level). In the event of non-random attrition (denoted by a statistically significant coefficient on the 
treatment indicator from the above regression), we will use inverse probability weighing approaches (IPW) to 
address potential biases. We will also use an alternate strategy that calculates the lower bound of treatment 
effects by assigning the “best” (i.e., most positive) sample value of an outcome to attritors in the control group 
and “worst” sample value of an outcome to attritors in the treatment group.12 
Second, we will estimate models that additionally include binary indicators denoting any CC who conducted 
interviews and fostered linkages with financial partners for each participant. Third, we will estimate a 
permutation inference model in which we will run our GEE models without covariates in 1,000 permutations, 
each of which represents a model in which treatment assignment is randomly assigned (or shuffled) 1:1 at the 
microcluster level (95% CIs for this method, which does not assume a specific error structure, will be 
constructed a the interval of estimates ranging from the 2.55th to the 97.5th percentile of the distribution of 1,000 
point estimates).13 We will compare estimates from our main models to estimates from these uncontrolled 
models. Differences would suggest potential unobserved confounding. In the event of substantive differences, 
we will estimate bounds on the ITT effects.14 Fourth in the event of prevalent (e.g., greater than 20%) non-
adherence to the intervention, we will estimate average treatment on the treated (ATT) parameters to recover 
causal effects of full participation in the intervention on the outcomes.15 (Given that the place-based 
interventions will occur without direct involvement of trial participants, non-adherence in this case would most 
likely to apply to the financial interventions.) 
Microcluster level secondary outcomes 
For secondary outcomes based on microcluster-level administrative data, we will use a difference-in-differences 
strategy, which leverages additional power from repeat (quarterly) observations that are available in 
administrative data.16 Specifically, we will estimate a Poisson-fixed effects model for each secondary outcome 
on the interaction between binary indicators for calendar time (quarter-year) and binary indicator of intervention 
status, adjusting for cluster and calendar time (quarter-year) fixed effects (the cluster fixed effect subsumes the 
main effect for treatment status); microcluster populations at baseline will be used as the exposure terms. This 
approach is known as an event study specification.17 The time period of analysis will comprise the 8 quarters 
prior to intervention and all quarters over the course of the intervention period, which comprises the duration 
between the end of enrollment and baseline survey data collection to the beginning of follow-up data collection, 
and the post-intervention period, which all quarters between the beginning and end of follow-up survey data 
collection. (As data becomes available, we will, in the future, also conduct separate analysis that includes 
periods 8 quarters after the end of survey data collection, once these data become available. This analysis will 
be viewed as an extension of our primary difference-in-differences model to assess whether treatment effects 
were sustained after intervention period ended.)  
The coefficients on the terms interacting treatment status with time periods prior to the intervention serve as 
visual and statistical checks of the parallel trends assumption, which we expect to hold given the randomized 
selection of intervention and control groups. However, to address potential failures of the parallel trends 
assumption, we will conduct a specification test in which we will estimate and trend out of the post-intervention 
period any differential pre-intervention trends in outcomes between the two groups.17 The coefficients on the 
terms interacting treatment status with time periods after the intervention period will recover the overall 
(dynamic) intervention effect. 

In addition to this “event study specification,” we will also estimate a standard difference-in-differences model 
in which the calendar time × × treatment interactions are replaced by intervention period ××treatment 
interaction and a post-intervention period ××treatment interaction. These two terms will capture average 
intervention impacts during intervention implementation and immediately thereafter, with the latter being the 
treatment effect of interest. 
For all models, we will adjust standard errors for clustering at the study microcluster level. We will report both 
unadjusted and multiple-comparisons adjusted18 p-values for all outcomes.  
Sub-group analyses 
For our primary outcomes and survey-derived secondary outcomes, we will estimate the above GEE models 
separately for the following subgroups: 
• Gender (persons identifying as men vs. persons identifying as women; if the sample size permits, we 
will also consider separate models for trans- and non-binary individuals) 
• Level of education (split by the sample median years of schooling) 
• Age (over 50 versus under 50) 
 
We will assess statistical differences in treatment effects between categories within each subgroup by estimating 
versions of our main GEE models that fully interact all terms with the binary subgroup indicator, and assessing 
the standard error and 95% CI on the interaction between treatment and the subgroup indicator. 
Evaluating Mechanisms 
In addition to evaluating treatment effects on secondary outcomes, we will assess potential mechanisms 
underlying intervention effects on the primary outcomes using three descriptive (and exploratory) approaches.  
First, we will use our survey data to examine the extent to which the non-health secondary outcomes (e.g., food 
security, financial security, social cohesion, stress, experiences of racism, exposure to crime, engagement with 
greenspaces, health care utilization) mediate the relationship between the intervention and any impacts on the 
primary outcomes. Specifically, we will conduct a causal mediation analysis to decompose intervention effects 
by each set of potential mediators. Our method will follow the strategy of Heckman et al (2013), who use an 
ordinary least squares based approach.19  
Second, we will assess changes in key process measures, namely aggregate information on participation in 
specific social programs, tax returns and refunds, and credit scores. Per agreement with the community financial 
organizations partnering on this study, this information would only be available at the aggregate level for the 
treatment group. However, the degree of engagement in different financial services can help assess which 
services may have been more important in driving intervention effects. For example, if participants in the 
treatment group on average received large tax refunds, but did not sign up for new benefit programs, that would 
implicate the former as a potential mechanism (though without information counterfactual changes in the 
outcome in the control group, this cannot be proven).  
Third, we will assess whether estimated intervention effects were larger among individuals who were most 
likely to benefit from different program components. For example, individuals who at baseline were not 
accessing major public benefits or who had not filed tax returns prior would be more likely to benefit from 
financial interventions than those already engaged with these activities. Similarly, individuals living in study 
clusters with relatively larger numbers of abandoned lots, fewer trees, or more abandoned homes would stand to 
benefit more from the environmental interventions than those who did not. Formally, we will estimate versions 
of our main GEE models for the primary outcomes in which we will include interactions between the treatment 
indicator and binary indicators for individuals participating in fewer than sample median number of public 

benefit programs; not filing (or had someone file on their behalf) a tax return in the previous year; being below 
the median of the household financial security scale; living in a microcluster below the median in terms of tree 
canopy; living in a microcluster that is above the median in terms of the number of abandoned lots; and living in 
a microcluster that is above the median in terms of the number of houses. Positive and statistically significant 
coefficients on these interaction terms would provide suggestive evidence that the specific intervention that 
would address the pre-intervention attribute in question helped drive treatment effects. For example, a positive 
and significant coefficient on the interaction between treatment and living in a cluster with above the median 
numbers of abandoned lots would suggest that abandoned lot remediation played a role in driving overall 
treatment effects. Interpretation of these coefficients requires knowledge of actual exposure to treatments; e.g., 
it would be more credible to conclude that provision of tax preparation services helped drive health outcomes if 
large numbers of intervention group participants reported using these services. 
Table 1. Primary and Secondary Outcome Measures 
Primary Outcomes 
Outcome 
Measure 
Data Source 
Type of Data 
Timeframe 
Overall Health Index 
Composite index using method of 
Anderson (2008) based on three 
questions: rating of overall health (5-pt 
Likert ranging from poor to excellent); 
rating of how health has changed in last 
6 months (better, same, worse); and 
number of days in the last 30 where 
physical or mental health precluded 
engagement in usual activities (self-care, 
work, recreation); (Oregon HIE) 
Survey 
Continuous 
(index) 
Baseline, 24 
months 
Psychological distress 
Kessler-6 
Survey 
Continuous 
(scale) 
Baseline, 24 
months 
Secondary Outcomes 
Outcome (by domain) 
Measure 
Data Source 
Type of Data 
Timeframe 
Health 
 
 
 
 
Overall health 
Rating of overall health (5-pt Likert 
ranging from poor to excellent) (Oregon 
HIE) 
Survey 
Ordinal (poor, 
fair, good, very 
good, excellent) 
Baseline, 24 
months 
Poor health 
Whether individual reported either poor 
or fair health to overall health question 
(Oregon HIE) 
Survey 
Binary (1 = poor 
or fair health) 
Baseline, 24 
months 
Change in overall 
health  
Rating of how health has changed in last 
6 months (better, same, worse) 
Survey 
Ordinal (better, 
same, worse) 
Baseline, 24 
months 
Healthy days 
number of days in the last 30 where 
physical or mental health precluded 
engagement in usual activities (self-care, 
work, recreation) 
Survey 
Continuous 
(number of 
days) 
Baseline, 24 
months 
Sleep duration 
Number of hours of usual sleep (BRFSS) 
Survey 
Continuous 
(number of 
houses) 
Baseline, 24 
months 

Short sleep 
Less than seven hours of usual night 
sleep (BRFSS) 
Survey 
Binary (1 = 
short sleep) 
 
Health care access 
and utilization 
 
 
 
 
Healthcare access 
Received all needed care in the last 6 
months (BRFSS) 
Survey 
Binary (1 = 
received all 
needed care) 
Baseline, 24 
months 
Finances and Benefit 
Program 
Participation 
 
 
 
 
Financial well-being 
Consumer Financial Protection Bureau, 
Abbreviated Financial Well-being Survey 
Survey 
Continuous 
(scale) 
Baseline, 24 
months 
Food insecurity 
Current Population Survey Food Security 
Supplement Screener 
Survey 
Continuous 
(scale) 
Baseline, 24 
months 
Income tax filing 
Whether or not individual (or someone in 
household on behalf of individual) filed 
previous years income tax (yes, planning 
to file late, no) (internally developed) 
Survey 
Binary (1 = yes) 
Baseline, 24 
months 
Home ownership 
Whether or not individual owns house, 
condo, or mobile home (Add Health) 
Survey 
Binary (1 = yes) 
Baseline, 24 
months 
     Owing on mortgage 
Whether or not individual has remaining 
mortgage payments (internally 
developed)  
Survey 
Binary (1 = yes) 
Baseline, 24 
months 
Total debt 
Amount of debt added altogether, not 
including mortgage. (Add Health) 
Survey 
Continuous 
(scale) 
Baseline, 24 
months 
Participation in public 
medical benefit 
programs 
Participation of a household member 
(including respondent) in Medicaid, 
Medicare, Medicare savings, LIS, CHIP, 
Qualified Health Plans, SelectPlan, other, 
or none (internally developed) 
Survey 
Binary 
indicators for 
participating in 
any program 
(=1) and 
separate 
indicators for 
participating in 
each program 
(=1) 
Baseline, 24 
months 
Participation in public 
food benefit programs 
Participation of a household member 
(including respondent) in SNAP, WIC, 
Senior Food Box, other, or none  
(internally developed) 
Survey 
Binary 
indicators for 
participating in 
any program 
(=1) and 
separate 
indicators for 
participating in 
each program 
(=1) 
Baseline, 24 
months 

Participation in public 
income support or 
cash benefit programs 
Participation of a household member 
(including respondent) in TANF, LIHEAP, 
SSI/SSDI, UI, PA General Assistance, PA 
Emergency Rental Assistance, EITC, CTC 
Refugee Cash Assistance, CCIS, PA Child 
Care Tax Credit, other, or none  
(internally developed) 
Survey 
Binary 
indicators for 
participating in 
any program 
(=1) and 
separate 
indicators for 
participating in 
each program 
(=1) 
Baseline, 24 
months 
Participation in public 
home ownership 
benefit programs 
Participation of a household member 
(including respondent) in PTRR, 
Homestead Exemption, LOOP, Basic 
Systems Repair Program, PA Homeowner 
Assistance, Phily First Home Program, 
Philadelphia Home Repair Assistance, 
other or none  (internally developed) 
Survey 
Binary 
indicators for 
participating in 
any program 
(=1) and 
separate 
indicators for 
participating in 
each program 
(=1) 
Baseline, 24 
months 
Greenspaces and 
Trees 
 
 
 
 
Frequency of 
greenspace 
engagement 
Frequency with which individual visits a 
greenspace (such as a park, garden, 
greened vacant lot, trail, or any other 
outdoor space with vegetation) (adapted 
from Evenson et al 2013 Environment 
and Behavior) 
Survey 
Ordinal (never, 
rarely, once a 
month, few 
times a month, 
once a week, 
few times a 
week, every 
day) 
Baseline, 24 
months 
Time spent in 
greenspace 
Time spent in a greenspace on a typical 
day (adapted from Evenson et al 2013 
Environment and Behavior)) 
Survey 
Ordinal (30 min 
or less, 31-60 
min, 1-2 hrs, 2+ 
hrs) 
Baseline, 24 
months 
Reasons for not 
spending time in 
greenspace 
Things that stop an individual from 
spending time in greenspace (adapted 
from Evenson et al 2013 Environment 
and Behavior) 
Survey 
Categorical 
(weather (too 
cold or too 
hot), safety 
concerns, no 
time, too tired, 
I don’t like 
spending time 
outside, other, 
nothing stops 
me from 
spending time 
outside in 
greenspace) 
Baseline, 24 
months 
Perception of tree 
cover 
Beliefs about number of trees in the 
neighborhood (internally developed) 
Survey 
Categorical 
(need more 
trees, enough 
trees, need less 
ttrees, unsure) 
Baseline, 24 
months 

Tree planting 
concerns 
Whether or not individual has concerns 
about planting more trees in 
neighborhood (internally developed) 
Survey 
Binary 
indicators for 
any concerns 
(=1) 
Baseline, 24 
months 
Perceived tree health 
benefits 
Whether or not individual believes trees 
confer health benefits (e.g., safety, 
mental health benefits, physical health 
benefits, social benefits, environmental 
benefits, aesthetic benefits) 
Survey 
Binary 
indicators 
denoting belief 
of any health 
benefits (=1) 
and separate 
indicators for 
each type of 
benefit 
Baseline, 24 
months 
Stress and Agency 
 
 
 
 
Perceived stress  
Perceived Stress Scale 
Survey 
Continuous 
(scale) 
Baseline, 24 
months 
Neighborhood 
Perceptions 
 
 
 
 
Time spent in 
neighborhood 
If individual endorses spending time 
relaxing, socializing, or hanging out in 
porches, stoops, and front yards of 
neighborhoods (adapted from Kahneman 
et al 2004 Science) 
Survey 
Continuous 
(scale) 
Baseline, 24 
months 
Neighborhood social 
capital 
Neighborhood Social Cohesion & 
Exchange and Social & Physical Disorder 
Scales 
Survey 
Continuous 
(scales) 
Baseline, 24 
months 
Physical disorder 
Whether or not participant reports a lot 
of abandoned buildings in their 
neighborhood (Ross and Mirowksi) 
Survey 
Binary (1 = yes) 
Baseline, 24 
months 
Microcluster-Level 
Outcomes 
 
 
 
 
Neighborhood crime 
rates 
Number of violent crimes, serious crimes   Philadelphia 
Police Dept. 
Crime Data - 
open access 
Continuous 
(rate) 
Quarterly data 
from 8 quarters 
prior to 
enrollment and 4 
quarters after 
intervention 
period complete 
Nuisance calls 
Number of 311 calls  
City of 
Philadelphia - 
open access data 
Continuous 
(rate) 
Quarterly data 
from 8 quarters 
prior to 
enrollment and 4 
quarters after 
intervention 
period complete 
 
Table 2. Child Health Outcome Measures 
Secondary Child Health Outcomes 

Outcome  
(by domain)  
Measure  
Data Source  
Type of Data  
Timeframe  
Health  
 
 
 
 
Caregiver-Reported 
Child Health Composite 
Score  
Child Health Questionnaire-28 (CHQ-28) 
Composite Score (Includes a series of 
questions focused on overall child health, 
activity limitation, emotional/behavioral 
difficulties, mood, relationships, and  
family cohesion)  
Survey  
Continuous 
(composite  
score)  
24 months  
Caregiver-Reported 
Child Health  
Rating of child health on a 5-point Likert 
scale, ranking from poor to excellent  
(CHQ-28)  
Survey  
Ordinal (poor,  
fair, good, very 
good, excellent)  
24 months  
Health Care  
Utilization  
 
 
 
 
Well child care use  
Caregiver report of number of well child 
visits in the preceding 12 months  
(National Survey of Children’s Health)  
Survey  
Continuous 
(number of  
visits)  
24 months  
Sick clinic visits  
Caregiver report of number of sick clinic 
visits in the preceding 12 months  
(National Survey of Children’s Health)  
Survey  
Continuous 
(number of  
visits)  
24 months  
Emergency room visits  Caregiver report of number of  
emergency room visits in the preceding 
12 months (National Survey of Children’s 
Health)  
Survey  
Continuous 
(number of  
visits)  
24 months  
Hospitalizations  
Caregiver report of number of 
hospitalizations in the preceding 12  
months (National Survey of Children’s 
Health)  
Survey  
Continuous 
(number of  
visits)  
24 months  
Usual source of care  
Caregiver report of whether the child has  
a usual source of care (National Survey of 
Children’s Health)  
Survey  
Binary (1 = yes)  
24 months  
Forgone health care  
Caregiver report of whether child needed 
but did not receive medical, mental 
health, dental, or vision care in the 
previous 12 months (National Survey of 
Children’s Health)  
Survey  
Binary (1 = yes)  
24 months  
Difficulty paying  
medical bills  
Caregiver report of difficulty paying  
medical bills in the previous 12 months 
(National Survey of Children’s Health)  
Survey  
Binary (1 = yes)  
24 months  
Uninsurance or gaps  
in insurance in  
previous 12 months  
Caregiver report of uninsurance or gaps  
in insurance in the previous 12 months 
(National Survey of Children’s Health)  
Survey  
Binary (1 = yes)  
24 months  
Child Well-Being  
 
 
 
 
Average sleep  
duration  
Caregiver report of child’s average sleep 
duration in the previous week (National 
Survey of Children’s Health)  
Survey  
Categorical  
(Less than 7  
hours, 7 hours,  
8 hours, 9  
hours, 10 hours, 
11 hours, 12 or 
more hours)  
24 months  
Time spent playing 
outdoors on  
weekdays  
Caregiver report of child’s time spent 
playing outdoors on weekdays (National 
Survey of Children’s Health)  
 
Survey  
Categorical  
(Less than 1  
hour, 1 hour, 2 
hours, 3 hours,  
4 or more hours)  
24 months  

Time spent playing 
outdoors on  
weekends  
Caregiver report of child’s time spent 
playing outdoors on weekends (National 
Survey of Children’s Health)  
 
Survey  
Categorical  
(Less than 1  
hour, 1 hour, 2 
hours, 3 hours,  
4 or more  
hours)  
24 months  
Screen time  
Caregiver report of child’s time spent in 
front of a TV, computer, or other  
electronic device (National Survey of 
Children’s Health)  
Survey  
Categorical  
(Less than 1  
hour, 1 hour, 2 
hours, 3 hours,  
4 or more  
hours)  
24 months  
Neighborhood safety  
for children  
 
 
 
 
Neighbors watch out  
for children  
Caregiver agreement with the statement, 
“We watch out for each other’s children  
in this neighborhood” (National Survey of 
Children’s Health)  
Survey  
Categorical 
(Definitely  
agree,  
somewhat 
agree,  
somewhat 
disagree, 
definitely 
disagree)  
24 months  
Child safety in 
neighborhood  
Caregiver agreement with the statement, 
“This child is safe in our neighborhood” 
(National Survey of Children’s Health)  
Survey  
Categorical 
(Definitely agree, 
somewhat agree, 
somewhat 
disagree, 
definitely 
disagree)  
24 months  
Parenting  
 
 
 
 
Difficulty caring for  
child  
Caregiver report of how often they feel 
their child is difficult to care for (National 
Survey of Children’s Health)  
Survey  
Categorical 
(never, rarely, 
sometimes, 
usually, always)  
24 months  
Bothered by child  
Caregiver report of how often they feel 
their child does things that bother them a 
lot (National Survey of Children’s Health)  
Survey  
Categorical 
(never, rarely, 
sometimes, 
usually, always)  
24 months  
Angry with child  
Caregiver report of how often they feel 
angry with their child (National Survey of 
Children’s Health)  
Survey  
Categorical 
(never, rarely, 
sometimes, 
usually, always)  
24 months  
Coping with child  
raising  
Caregiver-self report of how they are 
handling the day-to-day demands of  
raising children (National Survey of 
Children’s Health)  
Survey  
Categorical  
(Very well, 
somewhat well, 
not very well,  
not well at all)  
24 months  
Microcluster-level  
child health outcomes  
 
 
 
 
School attendance  
Proportion of school days attended  
Philadelphia  
School District 
Data  
Continuous  
Annual data from  
2 years prior to 
enrollment through 
1 year after 

intervention period 
complete  
School performance  
Grade point average  
Philadelphia  
School District 
Data  
Continuous  
Annual data from 2 
years prior to 
enrollment through 
1 year after 
intervention period 
complete  
Preterm birth  
Rate of preterm birth (birth < 37 weeks 
gestational age)  
Pennsylvania  
birth certificate 
data  
Continuous  
(rate)  
Quarterly data from 
8 quarters prior to 
enrollment and 4 
quarters after 
intervention period 
complete  
Low birth weight  
Rate of low birthweight (birthweight  
< 2000 grams)  
Pennsylvania  
birth certificate 
data  
Continuous  
(rate)  
Quarterly data from 
8 quarters prior to 
enrollment and 4 
quarters after 
intervention period 
complete  
Emergency room 
utilization  
Emergency room visits in the previous  
12 months  
Children’s  
Hospital of 
Philadelphia 
Electronic Health 
Record  
Continuous  
(rate)  
Annual data from 2 
years prior to 
enrollment through 
1 year after 
intervention period 
complete  
Hospitalization  
Number of hospitalizations in the  
previous 12 months  
Children’s  
Hospital of 
Philadelphia 
Electronic Health 
Record  
Continuous  
(rate)  
Annual data from 2 
years prior to 
enrollment through 
1 year after 
intervention period 
complete  
 
Qualitative Analysis 
We will use an applied thematic analytic approach to qualitative data analysis. Data analysis will coincide with 
data collection, enabling us to adjust data collection procedures to capture emerging and unexpected 
phenomena. Starting with the first 3-5 interviews, qualitative data will be coded in cycles by the coding team, 
led by Dr. Lane-Fall and performed by the Penn Mixed Methods Research Laboratory. The first cycle of coding 
will enable generation of a codebook grounded in the data (de novo codes) and in a priori codes reflecting 
CFIR, the Health Equity Implementation Framework, and our knowledge about the interventions. During 
successive coding cycles, we will refine the codebook to reflect emerging themes and recode data as needed 
using a constant comparative approach. Although we will use consensus coding to ensure consistency in the 
analytic process, we will double code a subset of the transcripts to enable calculation of interrater reliability. We 
will use interrater reliability, in turn, to recalibrate the coding process as needed. We will use NVivo qualitative 
analysis software (QSR International, Doncaster, Victoria) to manage transcripts and to undertake analysis. 
Although the interview analysis is primarily qualitative, we will seek out ways to mix qualitative and 
quantitative data in meaningful ways, e.g., interview findings may prompt additional quantitative analyses to 
elucidate relationships between measures thought to be unrelated. Finally, because qualitative interviews will 
begin during the intervention period and analyzed in real time, we will specifically evaluate for perceived 
worsening of disparities or inequities to ensure we are not causing harm. 
 

10. Investigators 
Eugenia South, MD and Atheendar Venkataramani, MD, PhD are lead Principal Investigators   
11. Human research protection 
11.1 Data confidentiality 
Paper-based records will be kept in a secure location and only be accessible to personnel involved in the study. 
Computer-based files will only be made available to personnel involved in the study through the use of access 
privileges and passwords. Wherever feasible, identifiers will be removed from study-related information.  
Precautions are in place to ensure the data are secure by using passwords and encryption, because the research 
involves web-based surveys.  
11.2 Subject confidentiality 
 All participants will provide informed consent for access to the data they generate as part of this research study. 
All study staff will be reminded to appreciate the confidential nature of the data collected and contained in these 
databases. The Penn Medicine Academic Computing Services (PMACS) will be the hub for the hardware and 
database infrastructure that will support the project. The PMACS is a joint effort of the University of 
Pennsylvania's Abramson Cancer Center, the Cardiovascular Institute, the Department of Pathology, and the 
Leonard Davis Institute. The PMACS provides a secure computing environment for a large volume of highly 
sensitive data, including clinical, genetic, socioeconomic, and financial information. Among the IT projects 
currently managed by PMACS are: (1) the capture and organization of complex, longitudinal clinical data via 
web and clinical applications portals from cancer patients enrolled in clinical trials; (2) the integration of genetic 
array databases and clinical data obtained from patients with cardiovascular disease; (3) computational biology 
and cytometry database management and analyses; (4) economic and health policy research using Medicare 
claims from over 40 million Medicare beneficiaries. PMACS requires all users of data or applications on 
PMACS servers to complete a PMACS-hosted cybersecurity awareness course annually, which stresses federal 
data security policies under data use agreements with the university. The curriculum includes Health Insurance 
Portability and Accountability Act (HIPAA) training and covers secure data transfer, passwords, computer 
security habits and knowledge of what constitutes misuse or inappropriate use of the server. We will implement 
multiple, redundant protective measures to guarantee the privacy and security of the participant data. All 
investigators and research staff with direct access to the identifiable data will be required to undergo annual 
responsible conduct of research, cybersecurity, and HIPAA certification in accordance with University of 
Pennsylvania regulations.  
Data will be stored, managed, and analyzed on a secure, encrypted server behind the University of Pennsylvania 
Health System (UPHS) firewall. This server was created for projects conducted by the Urban Health Lab. All 
study personnel that will use this data are listed on the IRB application and have completed training in HIPAA 
standards and the CITI human subjects research. Data access will be password protected. Whenever possible, 
data will be de-identified for analysis. 
11.3 Subject privacy 
Participation will be kept confidential. Data are being collected specifically for research purposes and will be 
managed by the PIs and study team. Subject identifier paperwork includes contact list with name, street address, 
telephone number, possibly email (depending on participants preferred method of contact), up to 2 contact 
people, and full consent forms. We will inform participants that we are collecting this information in order to 

contact participants. No people outside the study team will have access to this information. This information 
will be linked to a study ID.  
 
Data will be transferred via a firewall-protected, secure, electronic file-transfer to computer server space 
dedicated to the proposed study. Only MPIs Drs. South and Venkataramani will have master access to this 
server. Co-investigators, project managers, research coordinators, and research assistants will be granted data 
access at the discretion of the MPIs. Any paper-based records (such as the informed consent and HIPAA 
authorization form), will be kept in a locked cabinet, inside a locked office, within a Blockley access only 
building on Penn’s campus. All identifying information will be destroyed at the end of the study period. 
 
This research is covered by a Certificate of Confidentiality from the National Institutes of Health. This means 
that the researchers cannot release or use information, documents, or samples that may identify participants in 
any action or suit unless they say it is okay. They also cannot provide them as evidence unless participants have 
agreed.  This protection includes federal, state, or local civil, criminal, administrative, legislative, or other 
proceedings. An example would be a court subpoena. 
 
11.4 Data disclosure 
The following entities, besides the members of the research team, may receive protected health information 
(PHI) for this research study:  The Office of Human Research Protections at the University of Pennsylvania -
Federal and state agencies (for example, the Department of Health and Human Services, the National Institutes 
of Health, and/or the Office for Human Research Protections), or other domestic or foreign government bodies 
if required by law and/or necessary for oversight purposes.  Information will also be shared with our study 
partners providing services for participants: 
• Clarifi  
• Campaign for Working Families  
• Benefits Data Trust  
• Pennsylvania Horticultural Society 
 
11.5 Data safety and monitoring 
The PIs will be responsible for monitoring data and safety and reporting all serious adverse events, protocol 
deviations/violations and unanticipated events to the IRB. A written research protocol will go through an IRB 
review at the University of Pennsylvania to ensure protection of the rights and welfare of human research 
subjects. The research team, led by the investigators, will be aware of and will monitor possible areas of risk to 
the research participants. Weekly team meetings will include discussion of any safety and data issues that are 
observed.  
  
11.6 Risk/benefit  
11.6.1 Potential study risks 

There are almost no risks to from participation in this study. We will make every effort to keep personal 
information confidential. Participants may find it uncomfortable to answer some of the questions. They will be 
able to skip any question they do not feel comfortable answering or end the interview at any time. 
A final risk involves the safety of our community coordinator team. Their safety is of the utmost importance so 
various measures are in place to minimize their risk. The community coordinators will be able to end a home 
visit at any time if they feel the safety of the subject or themselves is at risk, or if the subject is experiencing 
psychological distress. 
 
11.6.2 Potential study benefits 
Participants may possibly benefit by increasing their financial education, receiving money in their tax return, 
and accessing a range of benefits they were eligible for but did not previously receive. They may also benefit 
from an emergency cash grant. Participation could also help us better understand the impact of neighborhood 
environments and economic inequalities on health outcomes. Participants may get satisfaction knowing they are 
contributing to our understanding of racial health disparities. 
 
11.6.3 Risk/benefit assessment 
There is minimal risk of breach of data and appropriate measures have been taken. Therefore, we believe the 
risk/benefit assessment is favorable given the potential insights that could be yielded from the findings of this 
study.   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
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