Using Remote Tracking to Detect and Deter Medications Theft in Malawi
Last registered on December 28, 2018


Trial Information
General Information
Using Remote Tracking to Detect and Deter Medications Theft in Malawi
Initial registration date
December 19, 2018
Last updated
December 28, 2018 10:58 AM EST

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Primary Investigator
University of California, San Diego
Other Primary Investigator(s)
PI Affiliation
University of California, San Diego
PI Affiliation
University of North Carolina-Chapel HIll
PI Affiliation
London School of Economics and Political Science
Additional Trial Information
In development
Start date
End date
Secondary IDs
This project seeks to identify how bottom-up community interventions and top-down audits change spatial patterns of medicinal drug diversion, impact access to public health aid, and perceptions of clinic accountability using a two-arm factorial randomized control trial. While firm estimates are lacking, medication theft is likely one of the leading causes of preventable disease in low income countries. According to Malawi Ministry of Health officials, 29% of spending on drugs and medical supplies disappears due to theft alone. Malawi is a particularly extreme outlier, but these estimates are not far off from estimates in other countries. We introduce a novel measurement protocol that allows to more precisely estimate these theft rates, and the impact of these interventions on drug theft across the supply chain.
External Link(s)
Registration Citation
Carvalho, Mariana et al. 2018. "Using Remote Tracking to Detect and Deter Medications Theft in Malawi." AEA RCT Registry. December 28.
Sponsors & Partners

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Experimental Details
Studies of drug theft confront significant measurement difficulties that can impede appropriate policy designs: (1) identifying theft and distinguishing it from mismanagement or poor accounting is extremely difficult due to incentives for obfuscation; (2) identifying where in the supply chain theft occurs is not possible using most existing measurement protocols; and (3) the precise mechanisms linking policy interventions to outcomes are difficult to determine. We have designed our project to resolve these challenges. Our intervention involves two treatment arms:

1) The first treatment arm is a top-down government-led intervention involving the provision of information to clinic officials via posters. The information in the posters will convey that technology is being used to monitor drug deliveries in that specific clinic and the penalties for being discovered with stolen drugs.

2) The second intervention is a bottom-up community-led intervention that will improve the capacity of communities to monitor and hold officials accountable for drug theft. It will involve workshops with Health Centre Advisory Committees (HCACs) providing information regarding how to learn of drug delivery dates, drug availability, clinic responsibilities, and a reporting hotline. Committees will also receive a stipend for transport and communications costs for committee members to conduct site visits at the clinics.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Share of drug cartons per clinic delivery that fail to reach their destination.
Share of cartons stolen upstream or downstream from a clinic
Primary Outcomes (explanation)
We will use GPS and Bluetooth tracking devices to identify the point at which a drug leaves a warehouse, truck or clinic. Drug cartons that fail to reach a clinic (upstream theft) or disappear from a clinic after delivery (downstream theft) will be coded as having been stolen.
Secondary Outcomes
Secondary Outcomes (end points)
Patient health outcomes, including access to drugs, cost of drugs, perceptions and knowledge of corruption, and perceived efficacy of anti-corruption efforts (by both citizens and government).
Secondary Outcomes (explanation)
We will investigate if medications theft affects community welfare outcomes – e.g., access to healthcare, the cost of healthcare, and health outcomes. One of our research questions is to evaluate if interventions designed to reduce theft improve these welfare outcomes or if it is the case that they perversely affect healthcare by increasing the price or limiting the supply of medications in surrounding markets (i.e., open-air markets or private pharmacies).
Experimental Design
Experimental Design
We will randomly sample clinics within the Southern region of Malawi from a pre-existing list produced by the Ministry of Health. Two hundred (200) clinics will receive tracking medications measurement at endline. Our goal is to identify at what point in the medications supply chain is theft most common: in transit to clinics; at clinics at time of delivery; at clinics while in inventory; or after dissemination to patients. We propose two interventions to evaluate how the government can reduce the level of theft.

These 200 clinics will also receive baseline and endline surveys of government officials, Health Centre Advisory Committee members, and citizens in the area surrounding the clinic. At these same 200 clinics, we will collect baseline, midline, and endline data on the cost and availability of medications in private pharmacies and markets surrounding the clinics to investigate the effects of monitoring on welfare outcomes.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
200 Clinics.
Sample size: planned number of observations
We will monitor 30% of the drug cartons for deliveries to 200 clinics. We anticipate this will involve tracking 2400 cartons. For the patient health outcomes, our proposed sample size is 7,000 subjects (200 clinics x 35 subjects per clinic).
Sample size (or number of clusters) by treatment arms
50 clinics "top-down" treatment
50 clinics "bottom-up" treatment
50 clinics "top-down and bottom-up" treatment
50 clinics pure control (no intervention)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For the primary outcomes: We assume 200 clusters, 40 cartons per cluster and 30% of cartons being tracked. We also assume an overall population theft rate of 10%. At a power of 0.8, we have the power to identify treatment effects above 0.055 in a linear probability model with no covariates or blocking (conservative). At power of 0.9, we estimate a minimum detectable effect of 0.07. For the secondary outcomes: If we consider a power of 0.80, 200 clusters (with size 30), standard deviation of 0.4, the minimum detectable effect is 0.05. If we consider a power of 0.90, 200 clusters (with size 30), standard deviation of 0.4, the minimum detectable effect is 0.06.
Supporting Documents and Materials

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IRB Name
IRB Approval Date
IRB Approval Number