Understanding the effects of financial and social interventions in support of the ‘indigents’ in four health zones of South Kivu, DR Congo
Last registered on September 12, 2018


Trial Information
General Information
Understanding the effects of financial and social interventions in support of the ‘indigents’ in four health zones of South Kivu, DR Congo
Initial registration date
September 11, 2018
Last updated
September 12, 2018 2:57 AM EDT
Primary Investigator
University of Edinburgh
Other Primary Investigator(s)
PI Affiliation
Tufts University
Additional Trial Information
On going
Start date
End date
Secondary IDs
This research looks at how the well-being and access to health-care of the indigents, a legally defined category of vulnerable people in DR Congo, is affected by (1) an external intervention seeking to durably alleviate the financial barrier the indigents may face when trying to seek care, (2) an intervention seeking to ease social relationships between the indigents and the community they live in (social barriers), and (3) the combination of both interventions. The study of each case is key to advance our understanding of the mechanisms that protect the poorest, as most research to date has focused either on the identification of those indigents or on short-term financial support. The interventions take place in the province of South Kivu, DRC, and covers 80 primary health care centres across 4 health zones close to Bukavu, the provincial capital. The interventions are implemented by the Dutch NGO Cordaid, which has been working in those areas for decades. Access to health care is evaluated through simple questions to the indigents while well-being relies on international survey instruments: WHOSDAS and PHQ. Qualitative work and surveys at the level of the health facilities help pinpoint the mechanisms (potentially) at play and ensure that meso-level invariants are accounted for.
External Link(s)
Registration Citation
Falisse, Jean-Benoît and Patrick Mirindi. 2018. "Understanding the effects of financial and social interventions in support of the ‘indigents’ in four health zones of South Kivu, DR Congo." AEA RCT Registry. September 12. https://doi.org/10.1257/rct.3314-1.0.
Former Citation
Falisse, Jean-Benoît, Jean-Benoît Falisse and Patrick Mirindi. 2018. "Understanding the effects of financial and social interventions in support of the ‘indigents’ in four health zones of South Kivu, DR Congo." AEA RCT Registry. September 12. http://www.socialscienceregistry.org/trials/3314/history/34128.
Sponsors & Partners

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information
Experimental Details
I. A financial intervention seeks to lead the health centres to behave differently towards the indigents, and to incentivize the indigents to attend the health centre when required. It consists in matching grants for health centres to organise income generating activities that will cover the health expenses of the indigents.

II. A social intervention that brings the indigent closer to the community and Health Facility Committee (CODESA) can lead to a change in the behaviour of the indigent but also of the community. It mainly consists in facilitating indigent - CODESA meetings.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
• Primary outcome: general wellbeing – through the WHODAS module
• Primary outcome: general wellbeing – through the PHQ 9 module
• Primary outcome: access to care for the indigent (personal level)
Primary Outcomes (explanation)
indexes (mean values) will be calculated for primary outcomes 1 and 2
Secondary Outcomes
Secondary Outcomes (end points)
• Secondary outcome: satisfaction with care and access to care (short version of the PSQ)
• Secondary outcome: participation in social life
• Secondary outcome: aspirations and self-perception
Secondary Outcomes (explanation)
indexes (mean values) will be calculated for secondary outcomes 1 and 2
Experimental Design
Experimental Design
The treatment assignment is factorial, with two groups of intervention and the unit of assignment at the health centre level. 80 health centres are in the sample (all health centres in the four zones) and any indigent of a health centre catchment area is a potential beneficiary. We then have four groups:

A. no intervention - control group: 20 health centres,
B. intervention 1: financial barrier. control group: 20 health centres,
C. intervention 2: social barrier: 20 health centres.
D. intervention 1: financial barrier + intervention 2: social barrier. 20 health centres.

The big advantage of this design is that it allows to understand the effect of each intervention as well as the effect of the two interventions combined. It is possible that the effect of the two interventions combined, because a positive interaction is created, is ultimately stronger than the sum of the effects of each of the two interventions.

The partner NGO is intending to scale up the intervention to all health centres (if the results are conclusive).
Experimental Design Details
Randomization Method
Randomization done in office by a computer
Randomization Unit
Health facility catchment area
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
20 health facilities for each group (4 groups, factorial design)
Sample size: planned number of observations
27 per health facility
Sample size (or number of clusters) by treatment arms
Each of the 3 treatment arms (and the control group) is 20 health facility * 27 individuals = 540 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
It is hard to do properly evaluate the sample required for the evaluation. What we used is the variable "was not at the health centre in the year when he / she felt sick and had no means of being treated at home", which is an indicator closely related to our primary outcome and was present in data Cordaid collected in a household survey in 2012-2014. Back then the figure was, in the general population: 70% (standard deviation: 0.42, n = 4162) and 82% for the indigents (standard deviation: 0.38, n = 372). We therefore calculated our sample looking at the probability of detecting indigents falling back to the same level as the population (alpha 0.05, using the ICC found, which is 0.027) - comparing groups with each other and thus neglecting the benefits of factorial design, we found: • With 20 health centres, 19 indigents per health centre: power = 0.86 • With 20 health centres, 26 indigents per health centre: power = 0.92 Taking into account attrition (expected at around 25%) and a willingness to get our power level at around 0.90, we decided to target 27 indigents per health centre.
IRB Name
University of Edinburgh
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)