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Improving diabetes care through low cost private one stop clinics
Last registered on November 21, 2018

Pre-Trial

Trial Information
General Information
Title
Improving diabetes care through low cost private one stop clinics
RCT ID
AEARCTR-0003589
Initial registration date
November 21, 2018
Last updated
November 21, 2018 12:54 PM EST
Location(s)

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Primary Investigator
Affiliation
ITAM
Other Primary Investigator(s)
PI Affiliation
MIT
PI Affiliation
MIT
Additional Trial Information
Status
In development
Start date
2018-11-26
End date
2020-03-31
Secondary IDs
Abstract
Diabetes is a serious problem worldwide and it is growing rapidly. We work with a for profit medical institution (Clinicas del Azucar) to test
whether their ‘one-stop shop’ model of care delivery to diabetic patients decreases health expenditure and improves health outcomes using an RCT methodology. We estimate heterogeneous effects by whether patients have access to public health insurance. Finally, we estimate predictive models pf patient retention and treatment compliance.

External Link(s)
Registration Citation
Citation
Bronsoler, Ari, Jonathan Gruber and Enrique Seira Bejarano. 2018. "Improving diabetes care through low cost private one stop clinics ." AEA RCT Registry. November 21. https://doi.org/10.1257/rct.3589-1.0.
Former Citation
Bronsoler, Ari et al. 2018. "Improving diabetes care through low cost private one stop clinics ." AEA RCT Registry. November 21. https://www.socialscienceregistry.org/trials/3589/history/37772.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
Clinicas del Azucar (CdA) offers free diabetes screening. We will work on their 12 branches with all clients coming for this first screening and implement a baseline survey. After the screeining CdA offers service for a monthly fee. We will work with people that did not buy into the service the first time. We will call people that rejected the service and offer of a discount to a random subset of them. Given enough take up, this encouragement design will enable us to estimate the effect of CdA health services on the population that did not enroll in the service at normal prices.

The treatment: CdA directly supports the individual’s quality of life by reducing the prevalence of poorly controlled diabetes and the associated chronic illnesses. Through a ‘one-stop shop’ model, it delivers a comprehensive treatment to its patients, including: nutrition advice, check up of kidney, heart, feet, and eyes, blood tests, and psychological support. Its objective is to stabilize sugar levels, and prevent diabetes related complications.
Intervention Start Date
2018-11-26
Intervention End Date
2020-03-31
Primary Outcomes
Primary Outcomes (end points)
Health outcomes:
1) A1c levels (Glycosylated hemoglobin -- sugar in blood)
2) Weight and BMI
3) Waist measures
Utilization of health care services
4) Number of Hospitalizations
5) Number of visits to hospital on Seguro popular and IMSS (public health services)
Expenditure:
6) out-of-pocket expenditure in medicine, doctors and hospitalizations
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
1) Self-perception of health condition
2) Self-perception of well-being
3) Diet habits, in particular number of times drank soda and/or ate tortillas per period
4) Type of diabetes treatment being used
5) Adherence: number of visits to Clinicas del Azucar, dummy for took medicine and for complied with Clinicas recommendation.
6) Probabilistic Expectations of catastrophic event (like problem with limbs or eyes)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We use an encouragement design. We will work with those that people who are diabetic, did the screening test but ended up not buying the CdA membership in the next 2 weeks. Within that population, we will randomly call by phone a random half of them and offer the CdA membership for at least half the price (we may increase the subsidy if take up is low-- we want to have a 30% take up rate). Those who accept will be given membership to CdA and therefore access to their treatment for diabetics.
Experimental Design Details
Not available
Randomization Method
Computer randomization in office.
Randomization Unit
Randomization is done at the person level
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
We will not cluster.
Sample size: planned number of observations
3000 total
Sample size (or number of clusters) by treatment arms
1500 for the control arm and 1500 for the treatment arms, spread in 12 clinic branches
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
MIT Committee On the Use of Humans as Experimental Subjects
IRB Approval Date
2018-05-17
IRB Approval Number
1804343739