Leveraging Patients' Social Networks to Overcome Tuberculosis Under-detection in India
Last registered on June 18, 2018

Pre-Trial

Trial Information
General Information
Title
Leveraging Patients' Social Networks to Overcome Tuberculosis Under-detection in India
RCT ID
AEARCTR-0000773
Initial registration date
November 12, 2015
Last updated
June 18, 2018 5:21 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
University of Maryland
Other Primary Investigator(s)
PI Affiliation
University of Maryland
PI Affiliation
University of Chicago
PI Affiliation
Johns Hopkins University
Additional Trial Information
Status
In development
Start date
2015-09-01
End date
2018-12-31
Secondary IDs
Abstract
In India alone, it is estimated that 3.5 million people suffer from Tuberculosis. The under-detection of TB represents a key challenge for health officials in developing countries because the success of any treatment program rests crucially on identifying those who have the disease. We propose to evaluate an approach with the potential to augment and strengthen the WHO’s global strategy for community engagement in the fight against TB. We will explore the use of financial incentives to encourage referrals, and we will focus on a specific community group with unique potential to generate referrals; current patients under treatment for TB. We will conduct a randomized controlled trial to compare the effects of different types of financial incentives to encourage TB patients to refer people from within their social networks for TB screening and testing. We will vary the conditionality of the incentives (“unconditional” and “conditional” incentives), the method of outreach ("peer-to-peer outreach" or "provider promotional visits") and whether the new suspects know who named them (“known referrer” and “anonymous referrer” conditions).
External Link(s)
Registration Citation
Citation
Chintagunta, Pradeep et al. 2018. " Leveraging Patients' Social Networks to Overcome Tuberculosis Under-detection in India." AEA RCT Registry. June 18. https://www.socialscienceregistry.org/trials/773/history/30881
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Experimental Details
Interventions
Intervention(s)
In collaboration with Operation ASHA (OpASHA), an Indian NGO that operates 200 community-based DOTS (“Directly Observed Treatment Short Course”) centers in India, we propose to conduct a randomized controlled trial to compare the effectiveness of different types of financial incentives to encourage referrals from current TB patients to new suspects. We will vary (1) the conditionality of the incentives (“unconditional” and “conditional” incentives), (2) whether outreach is led by patients or OpASHA’s health workers (“patient outreach” and “patient provides names” conditions), and (3) whether the new suspects know who named them (“known referrer” and “anonymous referrer” conditions).

The “unconditional” incentive will consist of Rupees (Rs.) 150 offered to current patients for each new suspect they induce to come to the center and get tested for TB. When the reward is fixed, each current patient has an incentive to refer any person who may be willing to be tested, irrespective of whether they know or believe they have TB. The “conditional” incentive treatment will consist of Rs. 100 for each new suspect who comes to the center and gets tested, plus a Rs. 150 bonus if the new suspect tests positive for TB. Comparison of the effect of conditional and unconditional incentives will allow us to determine whether the subjects take advantage of incentives that do not depend on test results, and whether they have concrete information about their contacts’ health.

To separate the effect of current patients’ networks from their ability to deliver information and persuade new suspects to get tested, we will vary whether outreach is led by patients or OpASHA’s health workers. In the “patient outreach” treatment, current patients will be given a set of referral cards and will be told that they will receive the reward if new suspects present at the center with the card and get tested for TB; in the “patient provides names” treatment, current patients will be invited to provide names and contact information of people whom they believe should get tested, and will receive the reward if the new suspect, who will be approached by OpASHA’s health workers, comes to the center and gets tested.

Finally, there will be two versions of the “patient provides names” treatment. In the “known referrer” version, the health workers will reveal to the new suspects who named them, whereas in the “anonymous referrer” version, the name of the referrer will not be revealed to the new suspects. This will allow us to determine the extent to which social stigma is a barrier to referrals. In fact, if social costs are the reason why current patients are reluctant to approach others, anonymity should remove them resulting in more and perhaps more effective referrals.

Intervention Start Date
2015-09-01
Intervention End Date
2017-11-30
Primary Outcomes
Primary Outcomes (end points)
The key outcomes of interest in this experiment are:

1. How many new patients enter treatment under each incentive scheme? Do financial incentives result in more new patients?
2. Which is more effective in enrolling new suspects, outreach by peers (current patients) or outreach by health workers?
3. What are the characteristics of new patients who enter treatment as a result of each treatment condition?
4. Which incentive scheme is most effective in causing new patients to complete treatment?
5. What is the role of stigma and other social cost in preventing current patients from referring new suspects? Do current patients refer more new suspects if they can remain anonymous?
6. Do current patients behave strategically by bringing in false suspects as a result of financial incentives?
7. Do new patients who have received financial incentives for entering treatment behave differently when seeking care for other illnesses in the future?

We will collect data through the following sources:

1. Data recorded by Op ASHA's health counselors (including TB test results obtained from the DMCs)
2. Existing Patient surveys
3. New Patient surveys
4. Extra cards offer and cards buy-back
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
All of Opertaion ASHA's DOTs centers will be divided into one of the following 9 treatment conditions and a pure control group. All patients attending a given center who have been in treatment for at least two weeks, plus those who recently completed treatment, will be asked to participate in a TB detection scheme under one of the following 9 conditions:

T0: Pure control group: No treatment whatsoever. Current patients will be simply surveyed at baseline. New patients will also be surveyed and asked whether current or previous Operation ASHA patient induced them to get tested and start treatment.

T1: Encouragement to refer new suspects (“CP-gives-out-cards”): Each current patient at these centers will receive ten referral cards and will be invited to give the cards to five different people whom they think should be tested for TB.

T2: Encouragement to provide names of new suspects (“CP-provides-names – referrer revealed”): Each current patient at these centers will be invited to provide names and contact information of people whom they think should be tested for TB. The patients will be told that these individuals will be approached by OpASHA’s health workers, and that their names will be mentioned to the new suspects.

T3: Encouragement to provide names of new suspects (“CP-provides-names – referrer anonymous”): This is the same as T2, except that the current patient will be told that their name will NOT be mentioned to the new suspects.

T4: CP-gives-out-cards + Unconditional Incentives: Current patients in these centers will be offered Rupees (Rs.) 150 for each new patient who brings one of their cards to the center and gets tested for TB.

T5: CP-provides-names/referrer revealed + Unconditional Incentive: Current patients in these centers will be told they will receive Rupees (Rs.) 150 for each new patient of those whose names they provided comes to the center and gets tested for TB.

T6: CP-provides-names/referrer anonymous + Unconditional Incentive: This is the same as T5, except that the current patient will be told that their name will NOT be mentioned to the new suspects.

T7: CP-gives-out-cards + Unconditional Incentive + Conditional Bonus: Current patients in these centers will be offered Rs. 100 for each new patient who brings one of their cards to the center and gets tested; in addition, they will receive a Rs. 150 bonus if the new suspect tests positive for TB.

T8: CP-provides-names/referrer revealed + Unconditional Incentive + Conditional Bonus: Current patients in these centers will be offered Rs. 100 for each new patient of those whose names they provided comes to the center and gets tested for TB; in addition, they will receive a Rs. 150 bonus if the new suspect tests positive for TB.

T9: CP-provides-names/referrer anonymous + Unconditional Incentive + Conditional Bonus: This is the same as T8, except that the current patient will be told that their name will NOT be mentioned to the new suspects.


Experimental Design Details
Below we explain how our experimental design will allow us to answer our primary research questions. This will also clarify the marginal contribution of each treatment arm. The experimental variation will allow us to test for the effectiveness of a variety of incentives, in order to identify the mechanisms through which barriers to referrals and treatment are alleviated. To measure the effects of our interventions, we will estimate versions (e.g., probit) of the following econometric model: yij= α + βE ecnourage + βR reward + βB bonus + βN names +βA anonym + γXi +Tt + εijt where i denotes the individual patients, j the randomly assigned treatment condition, and t the month of the intervention. yij represents the outcome variable of interest (e.g., yi = 1 if patient i made a referral, and 0 otherwise; or yi = 1 if patient i referred someone who tested positive for TB, and 0 otherwise). The key parameters of interest on the right-hand side of model (1) are the treatment effects βk, with the constant α representing, all else equal, the response in the pure control condition T0. First of all, the estimated coefficient βE will reveal whether encouragement to refer new suspects has an effect, even in the absence of incentives. In fact, an explicit appeal accompanied by referral cards might increase the salience of the request, thereby increasing referrals. Second, comparing βR and βE will determine whether financial rewards are effective at inducing current patients to find and refer new suspects (Q1). Third, comparing βB vs. βR will allow us to assess the extent to which unconditional incentives induce strategic behavior on the part of patients to refer individuals who are not sick only to obtain the reward (Q2a) and whether the current patients have concrete information on the health status of their social contacts (Q2b). Specifically, if patients in the unconditional incentive arm behave opportunistically by disproportionally referring people who are not sick, the number of referrals made will be higher and the share that test positive for TB smaller compared to those for patients in the bonus condition. Such a result will also indicate that patients do have information about the health condition of their social contacts (or an obtain it by exerting more effort). Fourth, comparison between βN and βE will reveal whether outreach by peers (current patients) or outreach by health workers is more effective in enrolling new suspects (Q3). Since current patients may also contact new suspects when they receive incentives based on intake of those new suspects, we can also estimate differences-in-differences specifications that include the interaction between eligibility for a reward, and outreach by peers vs. health workers. Finally, the estimated coefficient βA will inform us about the role of stigma and other “social costs” in making current patients reluctant to make referrals (Q4). For example, if we found that the share of referrals who are TB positive is higher when the referrer remains anonymous, that would indicate that stigma and related social costs are indeed a barrier that is making patients reluctant to approach others in their social circles who are likely also sick. Tt are month fixed effects, included to control for seasonality effects and other unobservable factors associated with the different intervention periods. Individual characteristics (including demographics such as age and gender, and other characteristics such as personality traits, experience with the disease and the clinic, etc.) will be reported in the vector of controls Xi. εijt is the error term. Because the randomization was done at the center level, we adjust the standard errors by allowing for clustering by center.
Randomization Method
Randomization will be done in the office by a computer using STATA programming
Randomization Unit
Our unit of randomization is the Op ASHA DOTS (Directly Observable Treatment Short-Course) centers. We will roll out the intervention across all of Op ASHA’s 200 DOTs centers in a period of 18 months. DOTS centers will be divided into four groups or data collection cycles. Each cycle will include anywhere between 18 and 40 centers, based on the spread of centers across geographic areas. For each cycle, the project randomizes the outreach type and the incentives at the center- level, with stratification based on geographic location. The centers will then be randomized into one of nine treatment arms and a control arm before the start of a data collection cycle. With one pure control and nine treatment conditions, there will be 20 centers and 400 subjects (current patients) in each treatment arm.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
We expect to run the experiment in 150 DOTS Centers across India. We are however constrained by the fact that the number of Op ASHA centers and the number of patients in each center is constantly evolving.
Sample size: planned number of observations
5,000 patients
Sample size (or number of clusters) by treatment arms
Patients in Operation ASHA’s centers will be randomly assigned to a pure control condition and nine treatment conditions as follows:

Treatment 1: 20 patients in 20 DOTS centers will receive ten referral cards and will be invited to give the cards to ten different people whom they think should be tested for TB

Treatment 2: 20 patients in 20 centers will be invited to provide names and contact information of people whom they think should be tested for TB. The patients will be told that these individuals will be approached by OpASHA’s health workers, and that their names will be mentioned to the new suspects.

Treatment 3: 20 patients in 20 centers will be invited to provide names and contact information of people whom they think should be tested for TB. The patients will be told that these individuals will be approached by OpASHA’s health workers, and that their names will not be mentioned to the new suspects.
Treatment 4: 20 patients in 20 centers will be offered Rupees (Rs.) 150 for each new patient who brings one of their cards to the center and gets tested for TB.

Treatment 5: 20 patients in 20 centers will be invited to provide names and contact information of people whom they think should be tested for TB. The patients will be told that these individuals will be approached by OpASHA’s health workers, and that their names will be mentioned to the new suspects. Patients will be offered Rupees (Rs.) 150 for each new patient who brings one of their cards to the center and gets tested for TB.

Treatment 6: 20 patients in 20 centers will be invited to provide names and contact information of people whom they think should be tested for TB. The patients will be told that these individuals will be approached by OpASHA’s health workers, and that their names will not be mentioned to the new suspects. Patients will be offered Rupees (Rs.) 150 for each new patient who brings one of their cards to the center and gets tested for TB.

Treatment 7: 20 patients in 20 centers will be offered Rs. 100 for each new patient who brings one of their cards to the center and gets tested; in addition, they will receive a Rs. 150 bonus if the new suspect tests positive for TB

Treatment 8: Same as T5, except 20 patients in 20 centers will be offered Rs. 100 for each new patient who brings one of their cards to the center and gets tested; in addition, they will receive a Rs. 150 bonus if the new suspect tests positive for TB

Treatment 9: Same as T6, except 20 patients in 20 centers will be offered Rs. 100 for each new patient who brings one of their cards to the center and gets tested; in addition, they will receive a Rs. 150 bonus if the new suspect tests positive for TB

Control: 400 patients in 20 centers will be given no treatment whatsoever. Patients in these centers will be simply surveyed at baseline and at endline
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We use the results of a pilot study to determine whether our study is sufficiently powered to detect economically meaningful effects. Since we are proposing multiple treatments, we make hypothetical comparisons between different combinations of pairs. Specifically, we compare the “encouragement” condition (T1) with the “Rs. 150 unconditional incentive for referrers” treatment condition (T4). Treatment is assigned at the center level. Because we are comparing pairs of treatments, and because each treatment condition will be assigned to 20 centers, the number of clusters in our calculations below is 40. We focus on three key outcomes of interest: Y1 = 1 if the patient made at least one referral, 0 otherwise; Y2 = N. of referrals made by the patient; Y3 = Number of TB positive referrals made by the patient. Given the standard deviation of the outcome variables, the effects found in a pilot study imply the following effect sizes (effect/standard deviation): 0.45 for Y1, 0.58 for Y2, and 0.59 for Y3. The intra-class correlations within centers from the pilot are as follows: Y1 ICC = 0.07; Y2 ICC = 0.13; Y3 ICC = 0.14. The minimum detectable effect sizes are 0.31 for Y1, 0.38 for Y2, and 0.39 for Y3 assuming 0.80 power; and 0.36 for Y1, 0.44 for Y2, and 0.45 for Y3 assuming 0.90 power. Given that the effects we obtained in the pilot are substantially larger than those computed in these power calculations, we conclude that our proposed sample sizes of 20 sites for T0-T9 are appropriate to detect effects of meaningful size. Note that in the pilot, the incentives conditional on TB test results delivered effects similar in magnitude to those of the encouragement condition. In the full-scale experiment, we have increased the expected value of the conditional reward to match that of the unconditional one, which we anticipate will increase its size effect.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of Maryland
IRB Approval Date
2013-07-09
IRB Approval Number
419706-2
IRB Name
Institute of Financial Management and Research
IRB Approval Date
2013-05-29
IRB Approval Number
IRB00007107
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers