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

Pre-Trial

General Information

Title

RCT ID

AEARCTR-0003314

Initial registration date

September 11, 2018

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

September 12, 2018, 2:57 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

Congo (Democratic Republic of the)

Region

South Kivu

Primary Investigator

Name

Jean-Benoît Falisse

Affiliation

University of Edinburgh

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Patrick Mirindi

PI Affiliation

Tufts University

Contact Investigator

Additional Trial Information

Status

On going

Start date

2017-04-01

End date

2018-11-30

Keywords

Governance, Health, Post-conflict, Welfare

Additional Keywords

DR Congo, indigent, community participation

JEL code(s)

I12, I14, D71

Secondary IDs

Abstract

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

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

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Experimental Details

Interventions

Intervention(s)

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

2017-05-01

Intervention End Date

2018-11-30

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

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?

Yes

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.

Supporting Documents and Materials

IRB