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Leveraging technology to promote women’s health and bargaining power: Evidence from a pilot program
Last registered on May 07, 2020

Pre-Trial

Trial Information
General Information
Title
Leveraging technology to promote women’s health and bargaining power: Evidence from a pilot program
RCT ID
AEARCTR-0005344
Initial registration date
January 27, 2020
Last updated
May 07, 2020 2:02 PM EDT
Location(s)
Region
Primary Investigator
Affiliation
Lahore School of Economics
Other Primary Investigator(s)
PI Affiliation
Lahore School of Economics
PI Affiliation
Lahore School of Economics
Additional Trial Information
Status
On going
Start date
2019-05-01
End date
2020-06-30
Secondary IDs
Abstract
This project uses a randomized control trial to evaluate the impact of telehealth clinic facilities on health and bargaining power of female microfinance borrowers. The project will exploit the variation in timing of introducing this health service to determine the causal impact of the program on our outcome indicators. Our sample comprises of the entire client population from two MFI branches; a treatment branch and a control branch. The overall working sample consists of 2982 clients; 1,356 from the treated branch and 1626 from the control branch. We hope that findings from this study will provide insights on whether technology can be effectively leveraged to improve provision of health services, especially in areas which are marked by low access to health-care.
External Link(s)
Registration Citation
Citation
Ahmad, Hamna , Sadia Hussain and Muhammad Ahmed Nazif. 2020. "Leveraging technology to promote women’s health and bargaining power: Evidence from a pilot program ." AEA RCT Registry. May 07. https://doi.org/10.1257/rct.5344-1.1.
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Experimental Details
Interventions
Intervention(s)
The proposed intervention in our study is provision of a telehealth clinic service by a microfinance institution (MFI). In July 2019, the MFI designed a new feature to serve as an add-on to their existing health micro insurance program currently being offered to borrowers and their family members. The add-on feature comprised of providing telehealth services i.e. virtually connecting patients with a doctor at the MFI branch. This service was available for use by both MFI borrowers as well as non-borrowers. The branch was staffed with a healthcare professional who would welcome the patient, record vital statistics such as weight, blood pressure etc., and note down the patient’s history. Thereafter the patient would be virtually connected with a doctor who would provide a medical consult, and prescribe diagnostic testing and (or) medication (if needed) at a competitive price. At the outset, telehealth services were rolled out in one branch in Lahore with the aim of pilot testing the program. We assess the impact of this pilot program, which provides telehealth clinic facilities on the well-being of women in the context of an urban, developing country setting. We hope that findings from this study will provide insights on whether technology can be effectively leveraged to improve provision of health services, especially in areas which are marked by low access to health-care.
Intervention Start Date
2019-07-01
Intervention End Date
2019-12-01
Primary Outcomes
Primary Outcomes (end points)
a. Mental health status:
We quantify mental health of clients using two measures: (i) the 12 item General Health Questionnaire (GHQ-12) (Goldberg & Williams, 1988) and (ii) the 7 item General Anxiety Disorder (GAD-7) (Spitzer, Kroenke, Williams and Lowe 2006).

(i) General Health Questionnaire: The objective of using GHQ will be to quantify psychiatric well-being of clients. It has been used extensively in the literature as a screening device for common mental disorders. The scale will be used to gauge the overall mental health of the client over the past few weeks for a 12 item list. Clients will be asked to rank each item on a likert scale of 0 (never) to 3 (always). These items are: Over the past few weeks have you: (i) been able to concentrate on what you're doing? (ii) lost much sleep over worry; (iii) Felt you were playing a useful part in things? (iv) felt capable of making decisions about things (v) felt constantly under strain, (vi) felt you couldn't overcome your difficulties; (vii) been able to enjoy your normal day-to day activities, (viii) been able to face up to your problems (ix) been feeling unhappy and depressed (x) been losing confidence in yourself (xi) been thinking of yourself as a worthless person; (xii) been feeling reasonably happy, all things considered. Positive statements will be reverse coded so that for these statements (0) will denote always while (3) will denote never. The overall scale will range between 0-36 with higher scores denoting poor mental health and vice versa. Variable names are: ghq1, ghq2, ghq3, ghq4, ghq5, ghq6, ghq7, ghq8, ghq9, ghq10, ghq11, ghq12 (of which ghq1, ghq3, ghq4, ghq7, ghq8, ghq12 will be reverse coded).

(ii) Generalized Anxiety disorder (GAD-7) scale: This is a standardized self-reported clinical scale used to identify individuals who may suffer or be likely to experience generalized anxiety disorder. Respondents are asked if how often, over the past 2 weeks, they have been bothered by the following problems: (i) Feeling nervous, anxious, or on edge; (ii) not being able to stop or control worrying; (iii) worrying too much about different things (iv) trouble relaxing (v) being so restless that it's hard to sit still (vi) becoming easily annoyed or irritable and (vii) feeling afraid as if something awful might happen. Respondents are asked to rank each of these problems on a likert scale where 0 denotes (not at all) while 3 represents (nearly every day). The overall scale will be a sum of responses to each of these 7 items and will range between 0 and 21, with higher scores implying that the respondent is prone (or experiences) generalized anxiety disorder. The variables to be used for constructing this scale are: (gad1, gad2, gad3, gad4, gad5, gad6, gad7).

b. Physical health status
We will use a series of discrete dummy variables to capture physical health status of the clients. Clients will be asked to report if (i) How they felt on most days of the last week (1 if the client felt tired, weak, sick or extremely sick and 0 if the client felt alright or well); (ii) whether they suffered from any symptoms of illness or injury in the last 30 days (1 if the client reports yes, 0 otherwise) and (iii) whether the illness or injury prevented them from performing daily activities (1 if the client reports yes, 0 otherwise). (variable names: feel, sufferill, preventwork)
c. Empowerment
We will use the following dummy variables as a measure of empowerment:
Whether the woman is allowed to go alone to a relative’s house; How often the husband listens to the woman and respects her opinion; whether the woman has the freedom to make decisions about her own health and the health of her children on her own or in joint consultation with her husband.
(variable names: goalone; husblisten; healthdec)

Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
a. Pecuniary Cost
In order to quantify the financial cost of seeking health services, we will use mean health expenditure (per illness episode) spent by the client for medical treatment in case of sickness, illness or injury. This will include doctor’s consulting fee, additional expenses on diagnostic tests and drugs as well as travel costs to and from the health facility. In order to quantify mean expenditure, we will take the ratio of total health care expenditure by the number of illness episodes involving the client or another member of the client’s nuclear family which took place over the past 6 months, in which the client sought medical treatment from a health-care professional which did not involve an overnight admission in the hospital or another health facility.
(variable names: healthcost, cost_of_conveyance)
b. Non-Pecuniary cost
The objective here will be (i) to measure time taken before the client seeks health services in case of sickness, illness or injury and (ii) estimate the waiting time before the client receives medical services at the health care facility. For (i), we ask the client to report the number of days after the symptoms began did the client first visit a health care provider. For (ii), we ask the client to report how long she had to wait for her turn at the health care facility before receiving a consult from the doctor (less than half an hour, about an hour, between 1 to 2 hours or more than 2 hours). (variable names: seek treatment_days, waiting_time_pref_facility)

c. Switching behaviour
We also ask clients about their enrollment in alternate health insurance programs captured by the following variables: (i). Are you covered by any health insurance, other than Kashf Health Microinsurance Program (ii). If yes, what is the main type of health insurance? (variable names: cover, otherinsur)

Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Basic estimating specification
The project proposes to conduct a randomized control trial to test the impact of telehealth clinic services on women’s well-being. We list down the regressions we anticipate to conduct during the data analysis stage.
Y_ij=αT_j+ω_j+ε_ij (1)
where Y_ij are the outcome variables for individual , i, in branch, j. T_j is the dummy variable equal to 1 if the individual (female) belongs to the treatment branch, otherwise 0. ω_j is the branch unobservable and ε_ij is the standard idiosyncratic error term. To account for any correlation between clients within the treatment or control group, we will cluster standard errors at the branch level.

Hypothesis
The survey data will enable us to test the following hypotheses with respect to the impact of the telehealth clinic services. The hypotheses are stated in Table 1.

A The provision of telehealth clinic service will have a positive impact on women’s mental and reported physical health
B The provision of the telehealth clinic service will result in (some) pecuniary and non-pecuniary savings
C The provision of the telehealth clinic service will improve the status of women within their household.
Table 1: Statement of Hypotheses

Robustness
As a robustness check, we will estimate equation (2) by including a variety of controls. In our heterogeneity analysis, we will account for client-level characteristics such as women’s age, education of the client and her husband, ownership of assets and income, access to alternate health-care services and perceptions about the health microinsurance program. This exercise would ensure that the effect of treatment is still significant after accounting for confounding factors.

Y_ij=αT_j+X_ij+ω_j+ε_ij (2)

3.4 Heterogeneity analysis Further, we account for potential interaction effects between impact of the telehealth clinic service on women’s welfare and perception about the health microinsurance program. It is possible that the treatment has a stronger effect for clients who have a positive perception about the health microinsurance program. We capture this interaction effect as shown in equation below.
Y_ij=βP_ij+αT_j+δP_ij*T_j +X_ij+ω_j+ε_ij (3)
Where 〖 Y〗_ij is a measure of women’s mental and physical health, P_ij is a perception index and P_ij*T_j is the interaction of the treatment dummy and perceptions index. δ captures the differential impact of the treatment for clients with varying degree of perceptions.

We also ask clients about their willingness to avail telehealth clinic services if the MFI were to provide such a service. We may observe heterogeneity in the impact of the treatment depending upon client’s willingness to avail such a service i.e. we may detect a greater impact of the treatment for clients who have a higher proclivity to avail such a service. To test for this, we will regress our outcomes of interest on a variable capturing client’s willingness to pay for a telehealth clinic facility and interacting it with the treatment dummy as shown in equation (4).
Y_ij=βA_ij+αT_j+γA_ij*T_j 〖+X〗_ij+ω_j+ε_ij (4)

Where Y_ij is a measure of women’s mental and physical health, A_ij captures client’s willingness to avail the telehealth clinic service. Throughout our analysis, we will cluster standard errors at the branch-level.
Experimental Design Details
Randomization Method
The choice of the treated branch, where telehealth services were to be pilot tested, was done by the MFI. By design, all borrowers belonging to this branch were allocated to the treatment group. One control branch (of the remaining 24 MFI branches in Lahore) was selected randomly. The primary consideration for selecting the control group was to balance the treatment and control group on branch-level characteristics such as number of clients enrolled, branch age and utilization rate of the health microinsurance program. We surveyed all the clients at the treated and control branch.
Randomization Unit
The unit of randomization was branch-level.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
Planned number of clusters were 2.
Sample size: planned number of observations
In total, our planned sample size is 2,982 clients.
Sample size (or number of clusters) by treatment arms
Within each cluster, we have 1,356 from the treated branch and 1626 from the control branch.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Research Ethics Review Committee , Lahore School of Economics
IRB Approval Date
2019-08-19
IRB Approval Number
RERC 082019-04
Analysis Plan

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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, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS