What Drives Poor Care for Child Diarrhea: A Standardized Patient Experiment
What Drives Poor Care for Child Diarrhea: A Standardized Patient Experiment
Diarrhea is the second leading cause of death for children around the world, although nearly
 all of these deaths could be prevented with an inexpensive and simple treatment: oral
 rehydration salts (ORS). Many children with diarrhea do not receive ORS when they seek
 treatment and this study uses a field experiment to examine why this occurs. We will use
 anonymous standardized patients combined with a randomized ORS supply intervention to isolate
 the causal effect of several potential reasons for why children do not receive ORS when they
 seek care: 1) caretakers prefer ORS alternatives, 2) providers have a financial incentives to
 prescribe ORS alternatives, and 3) ORS is often out of stock.
Diarrhea is the second leading cause of death for children around the world. This is true
 despite the fact that nearly all such deaths could be prevented with a simple and inexpensive
 solution: oral rehydration salts (ORS). Private health care providers, who treat the majority
 of childhood illness in low- and middle-income countries (LMICs), are particularly unlikely
 to dispense ORS to children with diarrhea. Instead, providers often dispense antibiotics
 inappropriately. Recognizing this significant challenge, several international organizations
 (including USAID) have invested heavily in trying to increase ORS dispensing in the private
 sector. In the absence of evidence on why ORS is so inconsistently dispensed by private
 providers, however, several interventions to promote private sector ORS dispensing have been
 ineffective. Clearly, a critical and urgent next step is to examine the key drivers of
 underprescription of ORS and overprescription of antibiotics in the private sector in order
 to inform efforts to improve diarrhea care. In this study, we examine several leading
 explanations for poor quality of care for child diarrhea in the private sector. First,
 patient preferences for ORS alternatives (e.g., an antibiotic) could be driving
 underprescription of ORS. We will identify the causal effect of patient preferences (Aim 1)
 by having anonymous standardized patients (SPs) pose as caretakers of children with diarrhea
 and express different (randomly assigned) preferences for treatment (ask for ORS, ask for
 antibiotics, or let provider decide). Second, private providers could be responding to
 financial incentives to sell more profitable alternatives to ORS (e.g., an antibiotic). To
 estimate the causal effect of financial incentives (Aim 2), we will instruct a subset of SPs
 to inform providers that they can get discounted treatments at a relative's drug shop. This
 eliminates the provider's financial incentive to recommend a given treatment and allows us to
 estimate the effect of such incentives. Finally, private providers might not directly
 distribute ORS or could have frequent stock-outs. To estimate the causal effect of stock-outs
 (Aim 3), we will randomly assign half of the providers to receive a three-month supply of
 ORS. This generates exogenous variation in stock outs and thus enables us to isolate the
 causal effect of stock outs on ORS and antibiotic prescribing. Combining, (a) causal
 estimates of the impact of each factor on prescribing, and (b) population estimates of the
 prevalence of each factor, will allow us to estimate the population level impact of
 implementing interventions that address each factor (Aim 4). This study will provide the most
 comprehensive evidence to date on why one of the most important health technologies in
 history is often not prescribed. The results will inform the design of interventions aimed at
 increasing ORS dispensing and reducing antibiotic dispensing. If such interventions are
 targeted appropriately, millions of young lives could be saved.
Inclusion Criteria:

 -  Eligible providers will be private providers over 18 years old in our sampled villages
 that treat at least one case of child diarrhea per week

 Exclusion Criteria:

 -  Providers in villages where there are no other providers will be excluded for
 confidentiality concerns