Does coupling of 4Ps and PhilHealth Sponsored … 4Ps Those located in selected municipalities and...

Does coupling of 4Ps and PhilHealth Sponsored Program

induce those with TB symptoms to seek treatment?

Mel Lorenzo M. Accad Graduate Student

PROGRAM OVERVIEW: Pantawid Pamilyang Pilipino Program (4Ps)

– Cash transfer to the poor

– Started in 2008 and still running

– Administered by Department of Social Welfare and Development (DSWD)

– Funded by taxes and development assistance

ELIGIBILITY: 4Ps

Those located in selected municipalities and barangays (based on Proxy Means Test, also known as National Household Targeting System for Poverty Reduction (NHTS-PR))

Household (per capita) income below provincial poverty line

With children aged 0-14 years old (6-14 years old must be in school) or have pregnant woman at the time of registration into the program

Commitment to follow the conditions

Those who passed the Eligibility Check Routine 2, which delineates those with regular income and subjects them to further validation.

BENEFITS & CONDITIONS: 4Ps

Health Grant: PhP 500 (around US$ 11.50) per household per month

Conditions:

(i) Children under five years old should visit the health center or rural health unit regularly;

(ii) Pregnant women attend the health center or rural health unit according to DOH protocol

(iii) All school-aged children (6-14 years old) comply with the de-worming protocol at schools

(iv) For households with children 0-14 years old, the mother or father should attend Family Development Sessions at least once a month.

BENEFITS & CONDITIONS: 4Ps

Education Grant: PhP 300 (about US$ 6.89) per child per month (for a period of 10 months/year), for up to a maximum of three children

Condition: School-aged children (6-14 years old) enrolled in primary or secondary school and attend 85% of the school days every month

DELIVERY SYSTEM: 4Ps

Compliance is monitored using Compliance Verification System (CVS) bimonthly CVS forms are distributed to schools and health facilities School teachers and health facility workers verify

compliance

As of July 2011, from the impact evaluation survey done by DSWD: 43% received grants through ATM cash cards 30% though Globe G-Remit merchants 18% through Rural Bank 9% through over-the-counter at Land Bank branches.

PROGRAM OVERVIEW: PhilHealth Sponsored Program (SP)

– Health insurance targeted for the poor

– Started in 1996 and still running

– Administered by National Health Insurance Corporation (a.k.a. PhilHealth)

– Funded by Local Government Units (LGUs) and National Government (NG) and sometimes by nongovernment organizations

ELIGIBILITY: PhilHealth Sponsored Program (SP)

Original 1996 Rules:

Means test is administered by the Social Welfare Development officer at the Local Government Unit level

Target beneficiaries are the lowest 25 percent of the local population

Identified through the Community Based Information System-Minimum Basic Needs Indicators and a uniform family threshold, which is then verified by the PhilHealth family data survey.

The list is submitted to the LGU for verification and decision making

(Suffers from inclusion and exclusion errors due to loopholes in means test. (Chakraborty, 2013))

ELIGIBILITY: PhilHealth Sponsored Program (SP)

Beginning 2009:

National Government would pay only for NHTS-PR identified households.

BENEFITS: PhilHealth SP

TYPE OF BENEFIT

DETAILS OF COVERAGE ONLY FOR POOR

OR ALL NHIP?

Inpatient Acute Care

First-peso coverage for hospitalization. Payment is on a fee-for-service basis, up to a limit identified by PhilHealth. PhilHealth pays for (a) room and board (b) services of health care professionals (c) diagnostic, laboratory, and other medical

examination services; (d) use of surgical or medical equipment and

facilities; (e) prescription drugs, subject to limitations; (f) inpatient education packages. Hospitals are

allowed to balance bill over and above the PhilHealth payments. Prices are not regulated by PhilHealth and are not transparent.

For all members

Table from (Chakraborty, 2013)


TYPE OF BENEFIT

DETAILS OF COVERAGE ONLY FOR POOR

OR ALL NHIP?

Inpatient Acute Care

No Balance Billing package where public hospitals are paid a fixed rate per case. Initially, 23 cases are included PhilHealth hopes to cover all cases in 2012.

Only for Sponsored Program using services in public hospitals. For private hospitals, case rates apply but private



TYPE OF BENEFIT DETAILS OF COVERAGE ONLY FOR

POOR OR ALL NHIP?

Special packages TB DOTS (Tuberculosis Directly Observed Therapy, Short-course), Maternal and Neonatal Care

Provides fixed rates with no balance billing. Now, largely integrated into No Balance Billing scheme.

All members



TYPE OF BENEFIT

DETAILS OF COVERAGE ONLY FOR

POOR OR ALL NHIP?

Outpatient benefits

The outpatient benefits cover primary consultation with general physicians, as well as lab tests such as complete X-rays, fecal and urine analysis, complete blood count, and sputum microscopy.

Only SP

Table copied from (Chakraborty, 2013)

DATA

Family Health Survey 2011

- Done by National Statistics Office

- HH respondents: 49,374 (226,392 HH members)

- Survey round: August 2011

DATA STRUCTURE

All Household Members

N=226,392

No TB Symptoms

n=223,072

With TB Symptoms

n=3,320

Did not seek treatment (Y=0)

n=887

Why?

Sought treatment (Y=1)

n=2,433

Where did (hh member) seek

treatment?

Survey question: Has any member of your household ever had symptoms of tuberculosis like coughing for two weeks or longer, recurring fever for two weeks or longer, or coughing up of blood?

OUTCOME VARIABLE

873

436

254

77 224

31

3

Non SP-PH Insurance

n=984

4Ps n=558

SP-PH n=694

With TB Symptoms (n=3,320)

No insurance & no 4Ps n=1422

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0.00 0.20 0.40 0.60 0.80 1.00

Cum

ula

tive

% o

f outc

om

e var

iable

Cumulative % of population, ranked from poorest to richest

Line of equality Did not seek treatment for TB

HH Member with TB sought treatment HH Member has TB Symptom

TB ACROSS WEALTH QUINTILES

Tuberculosis is concentrated among the poor Tuberculosis treatment is concentrated among the rich

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

Did not seek treatment for TB

HH Member with TB sought in a health care facility

AMONG WITH TB SYMPTOMS

WHERE DO THEY SEEK TREATMENT? (non-exclusive responses)

The poor’s first choice of facility is the Public Health Center

0%

10%

20%

30%

40%

50%

60%

Lowestquintile

2 3 4 Highestquintile

Total

Public Health Center Public Hospital Private Hospital or Clinic

WHY MANY DON’T SEEK TREATMENT? (non-exclusive responses)

Many would resort to self-medication.

0.00%2.00%4.00%6.00%8.00%

10.00%12.00%14.00%16.00%18.00%20.00%

1 2 3 4 5 Total

Self-Medication Cost DistanceSymptoms Harmless Embarassed Others

WEALTH QUINTILE

METHODOLOGY

Propensity Score Matching

- Observation data (non-randomized/quasi-experimental)

- Solves curse of dimensionality

Ignorability Assumption for ATT: Weaker Unconfoundedness: 𝑌0 ⊥ 𝑇|𝑃(𝒙) Overlap: 0 < 𝑃 𝑇 = 1 𝒙 < 1

PSM Estimator:

𝜏𝐴𝑇𝑇𝑃𝑆𝑀 = 𝐸𝑃 𝑋 |𝑇=1{𝐸 𝑌 1 𝑇 = 1, 𝑃 𝑋 − 𝐸 𝑌 0 𝑇 = 0, 𝑃 𝑋 }

BALANCING TEST

Variables in PSM

Nearest Neighbor N=1, common, ties

Nearest Neighbor N=2, cal(0.01), common, ties

Mean (4Ps=1)

Mean (4Ps=0)

SB (%) p>t Mean

(4Ps=1) Mean

(4Ps=0) SB (%) p>t

1 Female HH Member 43.518 33.466 42.1 0 43.179 34.135 37.9 0

2 Number of HH Members 0.43244 0.3722 12.3 0.081 0.41288 0.36486 9.8 0.163

3 Number of HH Members aged 0 to 1 5.1714 6.6233 -59.1 0 5.3076 6.5586 -51 0

4 Number of HH Members aged 02 to 17 0.13168 0.30045 -34.3 0 0.13145 0.29054 -32.3 0

5 HH Head 2.1889 3.4081 -68.2 0 2.1819 3.3604 -65.9 0

6 Age of HH Head 0.51985 0.35874 33.7 0 0.50172 0.37387 26.7 0

7 Male Head of Household 49.998 45.874 30.7 0 50.116 45.712 32.7 0

8 HGC of HH Head: some elementary 0.89504 0.95067 -18.6 0.008 0.90772 0.92793 -6.8 0.314

9 HGC of HH Head: elementary graduate 0.25725 0.43946 -38.4 0 0.27321 0.42117 -31.2 0

10 HGC of HH Head: some high school 0.14198 0.12556 -56511 0.498 0.14215 0.15541 -45601 0.588

11 HGC of HH Head: high school graduate 0.17595 0.18834 -3.8 0.642 0.18647 0.16667 6.1 0.466

12 HGC of HH Head: some college 0.18168 0.11211 20.2 0.009 0.17788 0.12613 15 0.051

13 HGC of HH Head: college graduate 0.02328 0.02242 0.4 0.935 0.06649 0.02027 20.8 0.006

14 Rural 0.09847 0.03139 33.3 0.001 0.05369 0.02027 16.6 0.03

15 II - Cagayan Valley 0.6855 0.81166 -28.7 0 0.67386 0.81081 -31.1 0

16 III - Central Luzon 0.02328 0.03139 -4.6 0.447 0.02331 0.03604 -7.2 0.237

BALANCING TEST

Variables in PSM

Nearest Neighbor N=1, common, ties

Nearest Neighbor N=2, cal(0.01), common, ties

Mean (4Ps=1)

Mean (4Ps=0)

SB (%) p>t Mean

(4Ps=1) Mean

(4Ps=0) SB (%) p>t

17 V - Bicol 0.01069 0 7.5 0.121 0.01509 0 10.6 0.065

18 VI - Western Visayas 0.03092 0.12108 -33.5 0 0.03515 0.12162 -32.1 0

19 VII - Central Visayas 0.06412 0.08969 -9.9 0.14 0.05923 0.07883 -7.6 0.241

20 VIII - Eastern Visayas 0.05305 0.04484 4.3 0.598 0.06057 0.03829 11.7 0.176

21 IX - Zamboanga Peninsula 0.04084 0.0852 -19.1 0.002 0.04547 0.08108 -15.3 0.017

22 X - Northern Mindanao 0.03893 0.08969 -21.2 0 0.04968 0.08784 -16 0.015

23 XI - Davao 0.06298 0.10762 -16.7 0.01 0.04987 0.12613 -28.6 0

24 XII - SOCCSKSARGEN 0.03664 0.01794 11.2 0.146 0.0277 0.00901 11.2 0.094

25 NCR 0.0542 0.06726 -5.5 0.412 0.06362 0.06306 0.2 0.974

26 CAR 0.1874 0.03587 52.8 0 0.18284 0.04279 48.8 0

27 ARMM 0.0187 0.00897 5.1 0.293 0.02159 0.01577 3.1 0.563

28 XIII - CARAGA 0.16145 0.11211 15.5 0.052 0.19201 0.10811 26.3 0.002

29 IVA - CALABARZON 0.02137 0.02691 -2.8 0.587 0.02102 0.03378 -6.5 0.213

30 IVB - MIMAROPA 0.01031 0.00448 2.6 0.398 0.01165 0.01351 -0.8 0.806

31 Wealth asset index: 2 0.07061 0.1435 -24.1 0 0.06171 0.13288 -23.5 0

32 Wealth asset index: 3 0.16908 0.24215 -18.7 0.006 0.14043 0.23874 -25.1 0

33 Wealth asset index: 4 0.1145 0.13901 -6.9 0.273 0.16603 0.1509 4.2 0.56

34 Wealth asset index: 5 0.05229 0.09865 -16.1 0.004 0.06649 0.08333 -5.9 0.338

35 Wealth asset index: 6 0.14504 0.0852 18.5 0.014 0.1219 0.12613 -1.3 0.853


37 Wealth asset index: 8 0.14198 0.0583 33.5 0 0.15075 0.0473 41.4 0

38 Wealth asset index: 9 0.09504 0 47.1 0 0.05044 0.00225 23.9 0.001

39 Wealth asset index: 10 0 0 . . 0 0 . .

BALANCING TEST

Variables in PSM

Radius cal(0.001), common, ties

Kernel biweight, bw=0.01, common

Mean (4Ps=1)

Mean (4Ps=0)

SB (%) p>t Mean

(4Ps=1) Mean

(4Ps=0) SB (%) p>t

1 Female HH Member 40.8 32.573 34.5 0 42.366 33.287 38 0

2 Number of HH Members 0.39517 0.422 -5.5 0.461 0.38087 0.4069 -5.3 0.444

3 Number of HH Members aged 0 to 1 5.3196 6.1741 -34.8 0 5.4466 6.5262 -44 0

4 Number of HH Members aged 02 to 17 0.17097 0.26221 -18.5 0.003 0.16326 0.28551 -24.8 0

5 HH Head 2.3219 3.0552 -41 0 2.2779 3.3604 -60.6 0

6 Age of HH Head 0.51022 0.33175 37.3 0 0.50978 0.34405 34.6 0

7 Male Head of Household 49.015 45.646 25.1 0.001 49.682 45.893 28.2 0

8 HGC of HH Head: some elementary 0.89614 0.92442 -9.5 0.206 0.91346 0.92263 -3.1 0.64

9 HGC of HH Head: elementary graduate 0.27074 0.43063 -33.7 0 0.24582 0.44997 -43.1 0

10 HGC of HH Head: some high school 0.16368 0.17328 -32979 0.728 0.17287 0.17666 -12957 0.886

11 HGC of HH Head: high school graduate 0.15046 0.14782 0.8 0.921 0.15798 0.14473 4.1 0.602

12 HGC of HH Head: some college 0.20658 0.11397 26.9 0.002 0.17536 0.10774 19.6 0.01

13 HGC of HH Head: college graduate 0.04342 0.0216 9.8 0.139 0.08247 0.0171 29.4 0

14 Rural 0.04955 0.02608 11.6 0.136 0.06375 0.0217 20.8 0.012

15 II - Cagayan Valley 0.6982 0.8265 -29.2 0 0.66328 0.8382 -39.8 0

16 III - Central Luzon 0.02989 0.02911 0.4 0.95 0.02272 0.02668 -2.2 0.706

BALANCING TEST

Variables in PSM

Radius cal(0.001), common, ties

Kernel biweight, bw=0.01, common

Mean (4Ps=1)

Mean (4Ps=0)

SB (%) p>t Mean

(4Ps=1) Mean

(4Ps=0) SB (%) p>t

17 V - Bicol 0.01582 0.00362 8.5 0.17 0.01412 0.00411 7 0.212

18 VI - Western Visayas 0.03585 0.08662 -18.8 0.001 0.04175 0.10885 -24.9 0

19 VII - Central Visayas 0.06279 0.09 -10.6 0.139 0.04846 0.07821 -11.6 0.053

20 VIII - Eastern Visayas 0.05193 0.0345 9.2 0.282 0.04807 0.03733 5.7 0.469

21 IX - Zamboanga Peninsula 0.04322 0.08506 -18 0.008 0.04887 0.08516 -15.6 0.019

22 X - Northern Mindanao 0.06235 0.09069 -11.9 0.123 0.04972 0.08107 -13.1 0.044

23 XI - Davao 0.06801 0.10444 -13.7 0.058 0.05012 0.10945 -22.2 0

24 XII - SOCCSKSARGEN 0.03219 0.01295 11.6 0.13 0.03042 0.01187 11.1 0.114

25 NCR 0.06651 0.05743 3.8 0.621 0.10331 0.06559 15.8 0.073

26 CAR 0.13897 0.03706 35.5 0 0.14969 0.03298 40.6 0

27 ARMM 0.02377 0.02283 0.5 0.933 0.01852 0.03054 -6.3 0.214

28 XIII - CARAGA 0.17204 0.13049 13 0.134 0.19047 0.12812 19.6 0.022

29 IVA - CALABARZON 0.02891 0.03672 -4 0.536 0.02689 0.03721 -5.3 0.369

30 IVB - MIMAROPA 0.02009 0.01688 1.4 0.756 0.02256 0.01582 3 0.512

31 Wealth asset index: 2 0.06834 0.14068 -23.9 0 0.05077 0.12825 -25.6 0

32 Wealth asset index: 3 0.16141 0.24306 -20.9 0.003 0.14201 0.23116 -22.8 0



35 Wealth asset index: 6 0.13026 0.12766 0.8 0.917 0.10715 0.10908 -0.6 0.929

36 Wealth asset index: 7 0.05713 0.06843 -4.2 0.517 0.09168 0.07403 6.5 0.378

37 Wealth asset index: 8 0.09135 0.04843 17.2 0.04 0.12107 0.04554 30.3 0.001

38 Wealth asset index: 9 0.04422 0.00343 20.2 0.005 0.05627 0.00337 26.2 0.001

39 Wealth asset index: 10 0 0 . . 0 0 . .

BALANCING TEST

Sample Pseudo R2 LR chi2 p>chi2 Mean Bias

Med Bias

(NN1 ties) Raw 0.192 312.69 0 21.3 20

Matched 0.19 287.76 0 21.2 18.5

(NN2 caliper .01) Raw 0.192 312.69 0 21.3 20

Matched 0.18 279.81 0 19.4 15.3

(Radius caliper .001) Raw 0.192 312.69 0 21.3 20

Matched 0.149 192.64 0 15.1 11.9

(Kernel biweight bw .01) Raw 0.192 312.69 0 21.3 20

Matched 0.164 255.8 0 18.7 15.8

BALANCING TEST

-100 -50 0 50 100Standardized % bias across covariates

Unmatched

Matched

NN1 ties

BALANCING TEST

-100 -50 0 50 100Standardized % bias across covariates

Unmatched

Matched

NN2 caliper .01

BALANCING TEST

-50 0 50 100Standardized % bias across covariates

Unmatched

Matched

Radius caliper .001

BALANCING TEST

-50 0 50 100Standardized % bias across covariates

Unmatched

Matched

Kernel biweight bw .01

PSM Model VARIABLE Odds Ratio SE P-value

Age of HH Member 0.0015104 0.004818 0.754

Female HH Member -0.0210438 0.17863 0.906

Number of HH Members 0.0400057 0.061233 0.514

Number of HH Members aged 0 to 1 0.1733811 0.178011 0.33

Number of HH Members aged 02 to 17 0.2951637 0.077344 0

HH Head -0.2545795 0.222817 0.253

Age of HH Head -0.0177732 0.007463 0.017

Male Head of Household -0.0130309 0.284457 0.963

Highest Grade Completed of HH Head

No Grade Completed (BASE)

some elementary 0.371862 0.294319 0.206

elementary graduate -0.1514243 0.330928 0.647

some high school -0.1947114 0.358036 0.587

high school graduate -0.4117733 0.376241 0.274

some college -1.223636 0.595218 0.04

college graduate -0.1621539 0.589137 0.783

Rural 0.73974 0.218979 0.001

Region

I - Ilocos (BASE)

II - Cagayan Valley -0.0267645 0.678676 0.969

III - Central Luzon -1.251356 1.147592 0.276

V - Bicol 1.040549 0.571664 0.069

Observations 3112

LR Chi2 309.08

Prob > chi2 0

Psuedo R2 0.1901

Log likelihood -658.27552

VI - Western Visayas 0.6837197 0.585911 0.243

VII - Central Visayas -0.0937214 0.660308 0.887

VIII - Eastern Visayas 0.6417174 0.595797 0.281

IX - Zamboanga Peninsula 0.8615423 0.592642 0.146

X - Northern Mindanao 1.113782 0.57985 0.055

XI - Davao -0.7059965 0.791642 0.372

XII - SOCCSKSARGEN 0.3140553 0.602941 0.602

NCR -0.2408728 0.659442 0.715

CAR 0.1318876 0.654468 0.84

ARMM -0.2585838 0.574716 0.653

XIII - CARAGA 1.012777 0.624084 0.105

IVA - CALABARZON -0.8274877 0.735275 0.26

IVB - MIMAROPA 0.8991848 0.5679 0.113

Wealth-asset decile

1 (BASE)

2 0.2898254 0.214544 0.177

3 0.2141455 0.240349 0.373

4 0.1872435 0.292533 0.522

5 0.2503782 0.272232 0.358

6 -0.3721308 0.342124 0.277

7 -0.1649599 0.383582 0.667

8 -2.312744 1.035179 0.025

9 0 (empty)

10 -1.18896 1.088415 0.275

Constant -3.541959 0.822363 0

PSM Model

Average Treatment Effects on the Treated

Coupled SP PhilHealth and 4Ps

ATT Bootstrap Std. Err.

z P>z [95% Conf. Interval]

(NN1 ties) 0.0807175 0.056962 1.42 0.156 -0.030926 0.192361

(NN2 caliper .01) 0.045045 0.0570076 0.79 0.429 -0.0666878 0.1567779

(Radius caliper .001) 0.073419 0.0403816 1.82 0.069 -0.0057274 0.1525655

(Kernel biweight bw .01) 0.0674947 0.0380736 1.77 0.076 -0.0071281 0.1421175

We cannot use PSM because of imbalance

ALTERNATIVE METHODOLOGY

Inverse Probability Weighted Regression Adjustment (IPWRA)

- Also known as “Wooldridge’s double-robust estimators”

- Uses observation data (non-randomized/quasi-experimental)


Inverse Probability Weighted Regression Adjustment (IPWRA)

- Allows multivalued treatments - Has potential efficiency gains in estimation compared

to binary treatment effect models. (Cattaneo, 2010)

- Double robustness feature - Either the outcome model or treatment model should

be correctly specified. (Brookhart, 2006) - Offers protection against mismodelling

- Available in Stata® 13 (teffects ipwra)


Inverse weighting intuition:

-Imitating conditional independence


Adjustment intuition (double robustness):

𝜇1,𝐷𝑅 = 𝐸𝑍𝑌

Pr(𝑍 = 1|𝑿)−

𝑍 − Pr 𝑍 = 1 𝑿

Pr(𝑍 = 1|𝑿)𝑚1 𝑿,𝜶1

= 𝐸 𝑌1 + 𝐸𝑍 − Pr 𝑍 = 1 𝑿

Pr(𝑍 = 1|𝑿)𝑌1 −𝑚1 𝑿,𝜶1

Where 𝑚1 𝑿,𝜶1 is the regression 𝐸(𝑌|𝑍 = 1, 𝑿)

(Bang, 2005)


Inverse Probability Weighted Regression Adjustment (IPWRA):

-Combination of regression adjustment and inverse weighting

- Double robustness feature - Either the outcome model or treatment model should

be correctly specified. (Brookhart, 2006) - Offers protection against mismodelling


3 steps to estimate treatment effects:

• “Estimate the treatment model to get the inverse-probability weights (aka Generalized Propensity Scores)

• Estimate the weighted regression models of outcome (using the previously estimated GPS) for each treatment level.

• Compute the means of treatment-specific potential outcomes. The contrasts of the means of treated subjects provide the ATTs.” (Stata, 2013)

TREATMENT MODEL (MULTINOMIAL LOGIT)

Treatment Model (T=0)





Number of children aged 2 to 17 living in HH

-0.012489 (0.047855)

0.1838172*** (0.0516904)

0.3044296*** (0.0658761)

0.292223*** (0.09654)

0.3778432*** (0.0701369)

HH Head's Age 0.0091881** (0.0036771)

0.0111403*** (0.0040898)

-0.0007894 (0.0060695)

-0.0046528 (0.0104303)

-0.0125862** (0.0060625)

Male Head of Household 0.616897*** (0.150317)

0.6098425*** (0.1843992)

0.4618084* (0.2678443)

0.7386561 (0.4791027)

0.2984181 (0.2824919)

HH Head's Highest Grade Completed

No grade completed (BASE)

at least some elementary 0.6842175** (0.3279966)

0.3376265 (0.2279713)

-0.5872239*** (0.2228497)

-0.213521 (0.4610485)

0.3606929 (0.2927288)

at least some high school 1.407035***

(0.3342) 0.4778156*

(0.25056) -0.366451

(0.2653387) 0.0671689

(0.5197611) 0.0770937

(0.3314792)

at least some college 1.505852*** (0.3535502)

0.2581254 (0.3163145)

-0.9523691** (0.4484758)

-0.7003886 (0.7257197)

-0.9774304 (0.6219924)

college graduate 2.184502*** (0.3703521)

0.2201995 (0.4207218)

-0.7406798 (0.6894242)

0.3301044 (0.6959221)

0.5091834 (0.5793221)

Number of Household Members

-0.1114026*** (0.0322794)

-0.0931156*** (0.0351521)

0.054403 (0.0459974)

-0.0011161 (0.0678519)

0.0210511 (0.0534132)

(CONTINUATION)

Treatment Model

(T=0) Treatment Model




(T=4)

Wealth Index

1 (BASE)

2 0.7442759*** (0.1640282)

0.1959019 (0.1460216)

-0.0712745 (0.1793485)

-0.2040703 (0.3466407)

-0.0127866 (0.1911281)

3 1.117592*** (0.1595397)

0.2087959 (0.1512296)

-0.7827049*** (0.2296348)

0.0508582 (0.3545736)

-0.1433208 (0.2136563)

4 1.520714*** (0.1658739)

0.1718862 (0.181409)

-1.304789*** (0.3340113)

0.3418545 (0.3854693)

-1.121936*** (0.3600767)

5 1.889767*** (0.1960036)

-0.396145 (0.3210582)

-2.824213*** (1.068307)

0.1695125 (0.5605466)

-1.738435** (0.7870722)

Rural -0.3436139***

(0.1018561) 0.6616151***

(0.123753) 0.8522036*** (0.1808688)

0.1440584 (0.241365)

1.195756*** (0.1953974)

Constant -2.934321*** (0.4300598)

-2.961293*** (0.3950455)

-2.935156*** (0.5358131)

-4.040772*** (0.8590472)

-3.463316*** (0.6067832)

OUTCOME MODEL (LOGIT)

Outcome Model

(T=0) Outcome Model

(T=1) Outcome Model

(T=2) Outcome Model

(T=3) Outcome Model

(T=4) Outcome Model

(T=5)

Age 0.0192353*** (0.0071983)

0.0247301 (0.0203436)

0.0136066 (0.0109159)

0.0201107** (0.010043)

0.0514428 (0.0339421)

0.0135043 (0.0134297)

Female -0.0648056 (0.2481515)

-0.2955831 (0.6110685)

-0.0309395 (0.3991441)

0.0239585 (0.4032797)

5.512328*** (2.013524)

-0.0801108 (0.3897616)

Highest Grade Completed of HH Member

Not yet school age (BASE)

No grade completed -0.4773613 (0.560275)

0.0446051 (1.541784)

-0.131107 (0.9640467)

-1.138272 (0.7871484)

1.105168 (2.021812)

-1.170832 (0.9994926)

At least some elementary -0.1269738 (0.3888676)

-1.628605 (1.065309)

0.3392404 (0.6004436)

-1.325904** (0.5492978)

-0.3465372 (2.718918)

-0.0568484 (0.5012806)

At least some high school -0.2056754 (0.4282061)

-1.097272 (1.079913)

-0.6109118 (0.6465161)

-1.181632* (0.6539884)

1.378843 (3.859887)

-0.0306106 (0.6018776)

At least some college -0.5951501 (0.6971721)

0.2476261 (1.257129)

0.7989235 (1.206587)

-0.5942801 (1.836507)

-0.8141898 (2.592809)

-18.77459*** (1.481858)

College graduate -0.9807096 (0.8999754)

-2.013056 (1.268801)

-1.810974 (1.844401)

-1.423002 (1.863799)

1.458275 (3.430481)

5.252616*** (1.253486)

Head of the Household -0.3788761 (0.3095795)

-0.9948049 (0.7707503)

-0.0380339 (0.5013057)

-0.4897258 (0.4537511)

-0.1056166 (2.441602)

0.3961802 (0.5803989)

HH Head's Age -0.0049965 (0.0093221)

0.0049575 (0.0299257)

0.0079638 (0.0163582)

-0.0026664 (0.0142646)

0.0857218 (0.0941047)

0.0040508 (0.0194835)

Male Head of Household 0.1808545 (0.2839463)

-0.0659903 (0.686284)

0.6749348 (0.4813386)

-0.8583068 (0.6757677)

4.090644 (3.246541)

0.8299323 (0.5660229)

Outcome Model

(T=0) Outcome Model

(T=1) Outcome Model

(T=2) Outcome Model

(T=3) Outcome Model

(T=4) Outcome Model

(T=5)

HH Head's Highest Grade Completed

No grade completed (BASE) (BASE) (BASE) (BASE) (BASE) (BASE) (BASE)

At least some elementary -0.1060688 (0.4462312)

2.123962 (1.691245)

-0.2441283 (0.6431428)

0.259715 (0.5739496)

2.205355 (4.00942)

-1.107434 (1.002119)

At least some high school -0.0724399 (0.4769381)

1.344151 (1.701901)

0.9172019 (0.7742388)

0.3598357 (0.6082596)

1.000282 (5.486754)

-0.1915232 (1.019125)

At least some college 0.2765999 (0.8996348)

2.128769 (1.977162)

0.480573 (0.9936923)

0.400014 (1.331291)

3.041897 (1.914576)

5.293221*** (1.266897)

College graduate 1.387625** (0.6848532)

1.753046 (1.785631)

0.3105161 (1.638518)

-0.724328 (1.687012)

6.525433 (5.102996)

-2.294781* (1.383192)

Number of Household Members

0.0480507 (0.0390328)

0.0949761 (0.1147484)

-0.0230386 (0.0707381)

0.0200825 (0.0640782)

-0.2782699 (0.5105001)

0.0605397 (0.0764978)

Wealth Index

1 (BASE) (BASE) (BASE) (BASE) (BASE) (BASE) (BASE)

2 0.6091919** (0.2809825)

1.081429* (0.5843136)

-0.5328735 (0.3811472)

0.452077 (0.3574231)

5.617542 (6.851733)

-0.3125396 (0.3819857)

3 0.8418145*** (0.2698421)

0.8392642 (0.5526513)

0.2649891 (0.4652471)

-0.0853981 (0.4671406)

0.5010141 (1.284563)

0.1151205 (0.4933197)

4 0.6538538** (0.3200278)

0.6508139 (0.7638519)

-0.0000742 (0.5182148)

3.166639* (1.879283)

-3.510464** (1.756466)

-1.224161 (0.9246503)

5 0.1877545 (0.5067382)

0.8097338 (0.7849491)

1.86209 (1.406387)

6.163549*** (1.028592)

-0.2010186 (1.942437)

5.381495*** (1.267593)

Rural -0.5637146*** (0.1935034)

-0.9628246* (0.5266025)

-0.2837467 (0.3778574)

-1.012326** (0.4349515)

-2.639913 (1.780055)

-1.307626** (0.5105286)

Constant 0.3166354 (0.7520693)

-0.4150566 (2.398635)

-0.1762123 (1.397285)

2.028701 (1.281933)

-7.742066 (8.335906)

1.148707 (1.554198)

(CONTINUATION)

AVE. TREATMENT EFFECTS ON THE TREATED SET AS CONTROL GROUP

w/o 4Ps

w/o Any

Insurance

T=0

w/o 4Ps

w/ insurance - NOT

SP PhilHealth

T=1

w/o 4Ps

w/ insurance -SP

PhilHealth

T=2

w/ 4Ps

w/o insurance

T=3

w/ 4Ps

w/ insurance -

NOTSP PhilHealth

T=4

w/o 4Ps

w/o Any Insurance

-0.045323 (0.0680229)

-0.0318995 (0.0384487)

0.0993387** (0.0402397)

-0.0805385 (0.0966056)

w/o 4Ps

w/ insurance - NOT SP

PhilHealth

0.045323 (0.0680229)

0.0134235

(0.0725404) 0.1446617** (0.0722104)

-0.0352155 (0.1150766)

w/o 4Ps

w/ insurance -SP PhilHealth

0.0318995 (0.0384487)

-0.0134235 (0.0725404)

0.1312382*** (0.0470622)

-0.048639 (0.1001861)

w/ 4Ps

w/o insurance

-0.0993387** (0.0402397)

-0.1446617** (0.0722104)

-0.1312382*** (0.0470622)

-0.1798772* (0.1003406)

w/ 4Ps

w/ insurance - NOT SP

PhilHealth

0.0805385 (0.0966056)

0.0352155 (0.1150766)

0.048639 (0.1001861)

0.1798772* (0.1003406)

w/ 4Ps

w/ insurance -SP PhilHealth

0.0275072 (0.0374382)

-0.0178158 (0.0717761)

-0.0043923 (0.0431914)

0.1268458*** (0.0457448)

-0.0530313 (0.0995538)

Potential Outcome Mean

of Control Group

0.6509069*** (0.0227064)

0.6962299*** (0.064788)

0.6828064*** (0.0321329)

0.5515682*** (0.0346432)

0.7314454*** (0.0954248)

SUMMARY OF MAJOR FINDINGS

• Sponsored Program of PhilHealth alone does not significantly induce those with TB symptoms to seek treatment.

• 4Ps alone (without any type of insurance or SP PhilHealth) will not induce those with TB symptoms to seek treatment.

• Coupling of 4Ps and SP PhilHealth significantly induces those with TB symptoms to seek treatment by 12.68 percentage point higher chance compared to those with 4Ps alone.

POLICY IMPLICATIONS

•Package/complement 4Ps with health insurance to effectively protect the poorest and vulnerable from health shocks.

Thank you for listening!

REFERENCES Bang, H. and Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference

models. Biometrics 61, 962–972.

Brookhart, M. A. and van der Laan, M. J. (2006). A semiparametric model selection criterion with

applications to the marginal structural model. Computational Statistics and Data Analysis 50,

475–498

Cattaneo, M. D. (2010). Efficient semiparametric estimation of multi-valued treatment effects under

ignorability. Journal of Econometrics, 138-154.

Chakraborty, S. (2013). Philippines' Government Sponsored Health Coverage Program for Poor

Households. Washington DC: Universal Health Coverage Studies Series (UNICO). Retrieved

from

https://openknowledge.worldbank.org/bitstream/handle/10986/13295/75010.pdf?sequence=1

Stata (2013). Stata Treatment-Effects Reference Manual: Potential Outcomes/Counterfactual Outcomes.

Texas: Stata Press.

The World Bank. (2013). Philippines Conditional Cash Transfer Program Impact Evaluation 2012. The

World Bank. Retrieved from https://openknowledge.worldbank.org/handle/10986/13244

Vianzon, R. (2013). The Tuberculosis Profile of the Philippines, 2003-2011: Advancing DOTS and

beyond. Western Pacific Surveillance and Response Journal.

doi:10.5365/wpsar.2012.3.4.022

Wooldridge, J. (2010). Econometric Analysis of Cross Section and Panel Data

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