Subsidies, Information, and the Timing ofChildren’s Health Care in Mali

Anja Sautmann, Samuel Brown(Brown University)

and Mark Dean(Columbia University)

Child Poverty Research Day

November 18, 2016

Introduction

IntroductionI Lack of adequate care for acute illness contributes to continually

high child mortality ratesI Broad policy swing in primary care for children: from “user fees”

(Bamako Initiative) to free access (e.g. Burkina Faso 2016)I New focus on acute care and urban areasI Debate over subsidies for acute care:

I Absent other distortions, subsidies can cause overuse and waste:price is a measure of value

I But subsidies can overcome underuse if there are access barriers orinefficiencies: lack of access to credit, lack of information, benefitsnot taken into account by parents (child welfare, longrun health,infection risk)

I Complementary policy: health education and information toencourage efficient use

Question:Can subsidies, supplemented with information policies, curb underuse ofcare without creating overuse (in urban populations)?

Introduction

I Two common healthpolicies:

I Biweekly healthworker visitsI Subsidies for “5 killers of

children” “Action for Health”NGO Mali Health

I Idea of “integrated care” andinformation as a tool tooptimize healthcare use

I Following guidelines ofIntegrated Management ofChildhood Illness (IMCI) byWHO/Unicef

Introduction

What constitutes overuse and underuse?I Value of care depends on (often unobserved) health statusI In this paper:

I Model and estimate timing of care within an illness spellI As benchmark for overuse/underuse use guidelines of

Integrated Management of Childhood Illness (IMCI) by WHO/Unicef

Supply-side effects of large-scale demand changeI In this paper: randomized control trial, supply-side fixed

A Dynamic Model of Demand forHealthcare

Model: Timing of Care

I Assume a child in an ongoing spell of (given) symptomsI If illness absorptive, or full information about its course: either go to

doctor immediately, or never.

Intuition for delaying a visit:I Initially, child may recover on her own; can save a visitI If symptoms do not abate: probability of not recovering is increasing

over time) Longer illness is more likely “serious”

Model: Timing of Care

Show: optimal “care seeking strategy” specifies after how many days togo see a doctor

I Depends onI seriousness of the illness/symptomsI cost vs. benefit of a visit.

I Parents may disagree on the optimal choice) Parents seek care too early or too late

I Subsidies: reduce the cost threshold and lead parents to seek careearlier

I Information: can teach parents about the optimal action accordingto policy

Predictions

1. Free care leads to earlier care: can reduce underuse, but mayincrease overuse.

2. Better information2.1 can reduce underuse and overuse2.2 but may increase underuse if parents do not agree with the

information

3. Free care and better information may be complements: potential toreduce underuse without creating overuse.

) Motivates policies that combine the two (e.g. IMCI)

Data and Randomized Control Trial

Data: Action for Health RCT

Fall 2012: baseline surveyI Location: Sikoro, peri-urban area of Bamako, MaliI 650 compounds, 1544 children; below local poverty line Attrition

I Two public health clinics provide basic primary care – “CSCom”I Large households (>6 members), USD 63 weekly income, 50%

literate, undernourished children (-0.61 W4H z-score)Typical of the fast-growing population of urban poor in Sub-SaharanAfricaUrban setting means better care, but also a risk for overuse!

Study Area

Data: Action for Health RCTJanuary 2013: independently randomize

1. “Free” care: free services and medications for children under 5 atlocal CSComonly for diarrhea/malnutrition, malaria, vaccinable disease,respiratory disease

2. Healthworker visits: monitor health, teach symptoms, and guideuse of formal care – e.g. accompany to clinic – based on IMCIstandards when care is required

Fall 2013: 10-week follow-up surveyI Formal consultations: 514 CSCom, 67

other (private); average cost of USD 5-10I Symptom records

Symptom DataSymptoms recorded and relative frequency:

Mean SD Mean SD

Number,of,days: 59.94 (9.37) 17.94 (15.89)

Percentage,where,each,symptom,is,present:

Convulsions,,fits,,or,spasms 0.09% 0.34%

Lethargic,or,unconscious 1.39% 3.96%

Unable,to,drink,or,breastfeed 0.34% 1.10%

Vomiting,everything 1.14% 4.79%

Coughing 11.15% 33.05%

Difficulty,breathing 1.63% 4.40%

>,3,loose,stools 2.24% 7.07%

Blood,in,the,stool 0.20% 0.58%

Sunken,eyes 0.62% 1.99%

Unusually,hot,skin 8.42% 31.44%

Other:,rash,,spots,,or,itch 0.89% 2.96%

Other:,cold,symptoms 18.30% 51.52%

Other:,ear,ache 0.30% 1.05%

Other:,wound,,injury,,or,burn 1.27% 3.94%

Other,symptoms 1.42% 5.39%

Total,observed,

days,per,child

Illness,days,per,

child

Data: Spells and Need for Care

Spell: contiguous period of symptoms, ending with doctor visit orrecovery.Policymaker preference:

I Unicef/W.H.O.’s Integrated Management of Childhood Illness(IMCI)Classify symptom days in the spell as “early” for care or “carerequired”

I Example:I Diarrhea < 5 days: home remediesI Diarrhea with blood in the stool: immediate care (dysentery)

Results

Outcomes: Unconditional Utilization

I SubsidiesI decrease CSCOM visit costs by 70% (2964 to 893 CFA on average)I increase formal demand by 317% per child (from 0.18 to 0.57 visits)

I HealthworkersI have little average demand effects.

Outcomes: Over- and Underuse

earlycare

required earlycare

required earlycare

required earlycare

required374 407 327 463 368 430 353 438

%withaconsultation 3% 11% 2% 10% 6%** 31%*** 8%*** 27%***Significancelevels:***1%,**5%,*10%,t-testonmeandifferencefromcontrol.

#spellsthatdid/didnotenter"carerequired"

Control Healthworker Freecare HW&FC

I Control and HW only groups:I Rampant underuse, no overuse

I SubsidiesI remaining underuse of at least 69% of “care required” spells

I Healthworkers have no clear effectsI Proportion of consultations that are overuse constant at about

16%.

Day by day probability of care seeking

0%

2%

4%

6%

8%

10%

12%

14%

1 2 3 4 5 6 7 >7

Prob

abilityofFormalCare

SpellDay

Control

Early

Carerequired

0%

2%

4%

6%

8%

10%

12%

14%

1 2 3 4 5 6 7 >7

Prob

abilityofFormalCare

SpellDay

Healthworkersonly

Early

Carerequired

0%

2%

4%

6%

8%

10%

12%

14%

1 2 3 4 5 6 7 >7

Prob

abilityofFormalCare

SpellDay

Subsidyonly

Early

Carerequired

0%

2%

4%

6%

8%

10%

12%

14%

1 2 3 4 5 6 7 >7

Prob

abilityofFormalCare

SpellDay

SubsidyandHealthworkers

Early

Carerequired

Demand for Care: Interpretation andApplications

Implications for Overuse and Underuse

1. Overuse:I Near zero probability of care-seeking on “early” daysI No additional reduction by healthworkersI Subsidies have only small effect on demand on early days

2. Underuse:I Subsidies increase care-seeking, but to at most 14% daily probabilityI Healthworkers decrease use by 37% (10% significance)

Implications:I Parents can discern “early” daysI Substantial underuse even with subsidiesI Healthworkers increase underuse, as predicted when parents have

high cost thresholdCost/benefit is the binding constraint; information not the main barrier

Predicting Care Seeking For Other Disease EnvironmentsI Care-seeking probabilities based on symptoms: allow out-of-sample

predictionsI Here: use hemorrhagic fever spell descriptions to code set of typical

symptom spellsI Predict proportion without care for each spell day

Model 1, group HWFC

Model 1: each day classified as early/care-required according to C-IMCI.

Model 2, group HWFC

Model 2: early/care-req. classification indicator for each symptom group. Model 3: indicators for C-IMCI classes of diseases.

Model 4: Indicators for disease combinations, using C-IMCI classes of diseases (i.e. generalized fever, malaria, diarrhea, etc.).

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8 9 10

Ebola:pred.proportionwithoutformalcare(Model1:early/care-required)

Model1,groupC Model1,groupFC

Model1,groupHW Model1,groupHWFC

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8 9 10

Ebola:pred.proportionwithoutformalcare(Model3:C-IMCIdiseaseclasses)

Model3,groupC Model3,groupFC

Model3,groupHW Model3,groupHWFC

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8 9 10

Ebola:pred.proportionwithoutformalcare(Model4:day-typeindicators)

Model4,groupC Model4,groupFC

Model4,groupHW Model4,groupHWFC

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8 9 10

Ebola:pred.proportionwithoutformalcare(Model2:symptom-specific early/care-req.)

Model2,groupC Model2,groupFC

Model2,groupHW Model2,groupHWFC

Result: in an (undetected/unexpected) Ebola outbreak, subsidies wouldlead to 20-30% higher use of formal care by day 5.

Other Results

Health Outcome Effects of subsidies:I Average illness spell length reduced by 0.8 days – recall, only 30%

receive a visit!I Mothers self-report significantly less worried about their children;

20% of days instead of 29% of days

Conclusion

Summary of Results

I Open the black box of healthcare demand, estimate timing of careconditional on illness incidence

I Results encouraging for opponents of user fees:I Families recognize need for careI Overuse and moral hazard not a primary concernI Unintended consequences of (only) providing information

Policy Relevance

I Immediate policy impact:I Changes to the programs of our cooperating partner Mali HealthI Focus on subsidies, re-focus health workers onto prevention

I Many open questions:I How were care-seeking guidelines formulated?I Should we trust parents’ observations, but not their decisions? How?I Can we get more data, and how to use it?

I Broader lessons for child poverty and healthcare accessI Urban healthcare is differentI Access 6= use; parents as gatekeepers of children’s use of resourcesI Non-monetary costs of care are important: mutiple dimensions of

scarcity

Thank you!

Treatment Groups: Attrition

Control Healthworker Freecare HW&FC All

OriginalSample 463 433 451 417 1764

NotFoundatBaseline 26 24 19 12 81

MovedPost-Baseline 34 23 35 23 115

DiedPost-Baseline 0 1 3 1 5

RefusedPost-Baseline 0 0 0 1 1

UnexplainedAbsence 4 6 5 3 18

2013Sample 399 379 389 377 1544

TotalAttrition 13.8% 12.5% 13.7% 9.6% 12.5%

AttritionPostBaseline 8.7% 7.3% 10.0% 6.9% 8.3%

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