Leveraging Patients’ Social Networks to Overcome ..._Pilot_Chintagunta.pdfThe potential of...

Conceptual frameworkExperimental design

Lessons from operational pilot

Leveraging Patients’ Social Networks toOvercome Tuberculosis Under-detection in India:

A Field Experiment

Jessica Goldberg, Mario Macis, Pradeep Chintagunta

July 2014

Goldberg, Macis, Chintagunta Social networks and the detection of TB



Motivation

I Tuberculosis (TB) is a major health challenge in thedeveloping world

I 8.7 million people affected worldwide; 3.5 million in India(WHO 2012)

I causes more than 1 million deaths/year – second only toHIV/AIDS for deaths due to a single infectious agent

I debilitating even when not fatal – $300 million in lost wagesdue to TB per year




TB Estimates




Motivation





I TB is under detected and under treatedI 1

3 of those with TB are neither diagnosed nor in treatmentI treatment has big benefits to patients: $13,935 in economic

wellbeing, 4.6 DALYs (RNTCP 2013)




Motivation





I TB is under detected and under treatedI 1

3 of those with TB are neither diagnosed nor in treatmentI treatment has big benefits to patients: $13,935 in economic

wellbeing, 4.6 DALYs (RNTCP 2013)I TB is the subject of major public health campaigns

I combatting TB is part of the 6th MDGI WHO recently released guidelines for community-based

outreach to combat TB (ENGAGE-TB)




Community involvement




The potential of referrals

I Identifying new patients is costlyI TB patients marginalized and hard to reachI ENGAGE-TB strategy emphasizes role of referrals by

community health workers

I Prospective patients tend to underestimate the benefits oftreatment

I Current patients can help address both sides of thisasymmetric information problem

I likely to know others infected with TB (correlated risk factors,contagious)

I can credibly vouch for the benefits of treatmentI may be able to identify different types of patients than paid

health workers




Proposed intervention

I Despite potential, patient-to-patient referrals are uncommonI making a referral can be socially costly – TB is stigmatizedI revealing one’s own TB statusI suggesting an acquaintance has TB

I Proposed solution: provide incentives to current patients tomake referrals

I Test various incentive schemesI encouragement vs. financial incentiveI conditional on new patient’s TB status vs. unconditional

I Role of interactions and anonymityI outreach by current patients vs. health workers (“contact

tracing”)I anonymous vs. revealed contact tracing




Specific research questions (1 of 2)

1. What is the effect of financial incentives on the number ofnew patients referred for TB treatment?

I do financial incentives induce strategic behavior (i.e. referringasymptomatic individuals)?

I how does the cost of detection using financial incentivescompare to other outreach strategies? (Bossuroy et al)

2. Which is more effective, outreach by current patients or byhealth workers?

I are current patients more effective for identifying newsymptomatics or providing information?

I who is referred when current patients’ identities are protected?




Specific research questions (2 of 2)

1. What is the importance of interaction with peers compared toinformation from peers?

I how do referrals change when current patient anonymity isprotected?

2. How do the characteristics of new patients compare acrossincentive schemes and to those identified under otheroutreach strategies?

I which outreach strategy has the most potential to reach at-riskpatients?

I how do incentives affect long-term outcomes, such acompletion of treatment?




Related literature

I Financial incentives can affect health and productivityI reduce risk of contracting HIV and other STDs in Malawi (Thornton 2008, Baird et al. 2012,

Kohler and Thornton 2013) and Malawi (deWalque et al. 2012)I reduce childhood anemia in China (Miller et al. 2012)I increase productivity (Lazear 2000 Moretti and Perloff 2002, Shearer 1996 and 2004)I attract workers with different characteristics than flat wages (Lazear 2000)

I Referrals are used by firms to attract and screen workers(Bryan et al. 2010, Heath 2011, Kugler 2003)

I experiments on incentivized referrals to attract candidates with desired characteristics in India(Beaman and Magruder 2012) and Malawi (Beaman et al. 2012)

I Our contribution: unify literature about incentives for healthbehaviors and the role of networks by studying the role ofincentives for referrals in a health context




Conceptual framework – based on Beaman et al. 2012

I Current patient (CP) i undergoing TB treatment has a set ofcontacts j = 1...n who are potential symptomatics

I CPi receives “social reward” sij for referring contact jI social reward can be positive (“warm glow,” financial

exchange, reduced financial support)I or negative (due to stigma, time costs, etc.)

I NGO makes a fixed payment fi for each new symptomaticreferred by patient i

I NGO makes a contingent payment pi for each newsymptomatic who tests positive for TB

I Contact j tests positive for TB with probability λjI CPi makes a referral if her net expected benefit is positive:

sij + fi + λjpi > 0




Experimental design

I We design an experiment to manipulate fi and piI Collaboration with Operation ASHA

I Indian NGO operating 185 community-based DOTS centersI works in 17 cities in six states (Chattisgarh, Delhi NCR,

Madhya Pradesh, Punjab, Rajasthan, Uttar Pradesh)I employs health counselors who are responsible for outreach,

screening, and administering treatment

I We will randomly allocate centers to one of nine incentiveschemes

I Key outcomes: administrative data about new symptomaticsreferred by each current patient

I Sample: current adult patients at all Operation ASHA clinics




Operation ASHA




Data

I Administrative data from Operation ASHAI roster of new symptomatics (collected as part of normal

enrollment procedures)I ID number for current patient who referred each new

symptomaticI treatment adherence for all patients

I Surveys of current patientsI baseline survey (socio-economic characteristics, physical and

psychological health, risk- and information-sharing networks,attitudes towards Operation ASHA and TB treatment)

I endline survey (same as above, plus satisfaction withOperation ASHA and self-reported referrals)

I Surveys of new symptomaticsI intake survey (current patient baseline + relationship to

referrer)I endline survey (current patient endline)




Intervention

I Enumerators will meet patients at clinics and arrange a timeand place for baseline interview

I After baseline survey, enumerator will explain the referralsscheme

I describe treatment (incentives for which current patients areeligible)

I patients in the encouragement-only group will be asked toparticipate in a health promotion scheme

I patients in all other conditions will be asked to participate in ahealth promotion scheme, and told that they are eligible forcash payments based on the number of new patients theyrecruit

I distribute “referral cards”I all groups including encouragement-only group get cardsI cards valid for 60 days




Referrals

I “Referral cards” will enable us to match new symptomatics tothe current patients who referred them

I the cards will have a unique ID number for the current patientI these cards can be given to new symptomatics the current

patient refers to Operation ASHAI Operation ASHA counselor will record the ID number when a

new symptomatic comes to the clinic with a card

I Initially, we will give patients 10 cards; they can request moreI we can later construct incentive-compatible measures of

outreach effort details




Referral card (front)




Referral cards (back)




Treatments

I Three incentive typesI encouragement to refer new symptomatic (no financial

incentive)I Rs. 150 for each new symptomatic who is tested

(unconditional – fi = 150)I Rs. 100 for each new symptomatic who is tested (fi = 100)

plus Rs. 150 if the symptomatic tests positive for TB(conditional – pi = 150)

I calibrated such that the expected value of the unconditionaltreatment equals the expected value of the conditionaltreatment

I Cross cutting randomization varies engagement of currentpatient: provide names vs. reach out to new symptomatics

I In total, 9 treatment arms with 20 centers each

I First phase: 6 treatment arms with 4 centers each




Peer-to-peer vs. contact tracing

I “Contact tracing” is the standard of care in the USI Peer-to-peer outreach incorporates two types of information

from current patientsI who to target (information CPs can share with health care

providers)I benefits of treatment (information CPs can share with new

symptomatics)

I We want to separate current patients’ information about whoto target from their effectiveness in conducting outreach

I To do that, include conditions where CPs provide names buthealth workers hand out referral cards

I isolate the value of interaction between the CP and newsymptomatic




Anonymity

I Peer-to-peer outreach is not anonymous by definitionI Direct test of the impact of peer interaction (relative to

interaction with a health worker) is a comparison tonon-anonymous contact tracing

I holds constant the sample of new symptomatics in order totest effectiveness of outreach

I (unless there are people who CPs don’t mind sending healthworkers to but will not speak to personally)

I option: solicit list of new symptomatics before revealingwhether CP or health worker will distribute cards?

I Additional treatment arm is anonymous contact tracingI compare the characteristics of patients referred under

anonymous and non-anonymous conditions




Treatment groups: nine cell design

Outreach type

Ince

nti

vety

pe

Contact tracing Contact tracing Peer-to-peer(anonymous) (identified) outreach

Encouragement T1 T4 T5

Rs. 150 T2 T6 T8unconditional

Rs. 100 + Rs. 150 T3 T9 T7if TB positive




Treatment groups: six cell design

Outreach type

Ince

nti

vety

pe

Contact tracing Peer-to-peer(identified) outreach

Encouragement T4 T5

Rs. 150 T6 T8unconditional

Rs. 100 + Rs. 150 T9 T7if TB positive

I Preserves ability to test persuasive value of patient-to-patientoutreach against the status quo

I Does not explicitly test the value of current patients’knowledge of who to test




Heterogeneous effects and mechanisms

I Do individuals with different baseline characteristics responddifferently to incentives? (interaction between treatmentcondition and current patients’ characteristics)

I extroversion, degree of social connectionI treatment stage at the time of the intervention

I Do different incentive schemes identify different types of newsymptomatics? (new symptomatics’ characteristics asoutcomes)

I which schemes are more successful in attracting particularlyat-risk populations (women, extremely poor, notwell-connected)?

I are some schemes more likely to identify patients in the earlystage of the disease?

I tests of means and distributions of new symptomatics’characteristics across the various treatment conditions




Incentive compatible measures of referral effort

I We observe the number of new patients who come toOperation ASHA based on referrals from each CPi , but notthe number approached unsuccessfully

I We expect over-reporting of effort if we rely on surveyquestions

I Instead, we will use a card buy-back schemeI we want to know how many were given out but not redeemed

I cards not redeemed = total cards - cards redeemed - cards not given out

I after endline, we will unexpectedly offer to buy any cards thecurrent patient has on hand for Rs. 10/card

I After endline, patients can buy more cards for Rs. 50/cardI in order to identify patients who believe they can reach more

potential symptomaticsI new cards will be for the same incentive type as initial

assignmentI (could randomize the price of new cards)

backGoldberg, Macis, Chintagunta Social networks and the detection of TB



Operational pilot

I Small operational pilot to validate the field protocol, calibrateincentives, and develop data collection system

I Five centers in South Delhi, Summer 2012I Four conditions

I encouragement onlyI Rs. 50, unconditionalI Rs. 150, unconditionalI Rs. 150, conditional on having TB symptoms

I Baseline surveys with current patients, and administrativedata

I Referrals by current patients are extremely uncommonI Health information networks extend beyond the household




Lessons from the pilot

I Rs. 150 unconditional generated 0.6 extra referrals/currentpatient compared to encouragement

I Rs. 50 incentive seemed to be too small; we have droppedthis arm in the full scale study

I Conditionality may have been too strict; combination of fixedand conditional bonus in full scale study

I Cost of detection: Rs. 500 ($9 USD) per TB-positivesymptomatic detected (including cost of incentives for all newsymptomatics and printing referral cards)

I Informed power calculations




Next Steps

I June 2014 - August 2014: Larger pilot interventionI Subset of experimental conditions; three cities: Jaipur

(Rajasthan), Gwalior (MP), Sagar (MP)I validate and refine research protocolI work with OpASHA to refine procedures and infrastructure to

implement full scale study

I January 2015: begin full-scale intervention


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