The Effect Of Parents’ Insurance Enrollment On Health Care Utilization: Evidence From Ghana

The Effect Of Parents’ Insurance Enrollment

On Health Care Utilization: Evidence

From Ghana

Gissele Gajate-Garrido Clement Ahiadeke IFPRI ISSER-University of Ghana

"Leaders in Development" Seminar Series

February 27, 2013

Research question

What is the impact of parental participation in the National Health Insurance Scheme (NHIS) on health

care utilization in Ghana?

Contributions to the literature This paper is the first attempt to look at the impact

of parental participation in the NHIS on both curative and preventive health care utilization in Ghana using information from the whole country.

It uses exogenous variation in insurance participation to correct for the potential endogeneity

The variation comes from the variability in membership rules of the District Mutual Health Insurance Schemes (DMHIS) located in every district in Ghana

Summary of findings Insurance membership increases the probability of

parents: Seeking higher quality, but not greater quantity of

prenatal services Becoming more active users of child curative care

(covered by the NHIS) Becoming more active users of child preventive care

(not covered by the NHIS)

These findings are consistent with the idea that when curative services become available for free, parents become more proactive towards other kinds of health care utilization practices (sibling hypothesis, access hypothesis, information hypothesis)

Summary of findings IV estimates are larger than OLS ones indicating

underlying heterogeneity in returns to NHIS participation “Compliers” have much higher returns to participating in

the NHIS than the average participant We verify the exogeneity of the instrument using pre

scheme data. We also collect data on barriers to health care access in

districts, as well as challenges to participate in a DMHIS so we can control for district characteristics that determine both membership rules and health care seeking behaviors

Outline Motivation The NHIS and the district membership rules Data Identification Strategy Specification Results and Discussion Instrument Strength and Validity Conclusion

Motivation Access to and utilization of health services continues to

be a main concern in poor countries such as Ghana. Delaying medical treatment or choosing self-treatment

can generate serious health consequences (Hadley 2002). Young children are particularly susceptible to negative

health shocks that can generate nutritional deficits and cognitive disabilities.

Early experiences are particularly important for future skill development and establishment of mental potential (Knudsen, Heckman, Cameron and Shonkoff 2006 and Heckman 2007).

If implemented correctly health insurance systems could provide an effective solution to this challenge.

The NHIS The National Health Insurance Act (Act 560) which

created Ghana’s National Health Insurance Scheme (NHIS) was passed in 2003. Yet, the scheme became operational only in March 2004 (Hsiao & Shaw 2007).

Before this scheme was implemented Ghana had a “cash and carry system” of health delivery. Under this system, patients – even those who had been brought into the hospital on emergencies – were required to pay money at every point of service delivery.

The NHIS The most popular insurance plan is the District-

Wide Mutual Health Insurance Scheme1 which operates in every district in Ghana. This is the insurance scheme analyzed in this paper.

It covers over 95% of disease conditions that afflict Ghanaians and around 40% of our sample is enrolled in it.

People pay an annual fee according to their income, yet the government subsidizes pensioners, the old (70+), the core poor or indigent and the children and dependents below 18 of parents that participate in the system.

1 97% of people with an insurance plan are registered in a DMHIS (DHS 2008)

District Mutual Health Insurance Schemes

The law establishes that the District Mutual Health Insurance Schemes (DMHIS) are autonomous entities.

Each scheme in each district is completely independent of any other in the country with independent boards of directors.

Schemes do not pool risks together in any way. Hence membership rules vary across different

DMHIS. This source of exogenous variation is used for

identification of parental participation in the NHIS

Membership rules The existence of exceptions that allowed

children to benefit from the NHIS without their parents being registered this could increase child participation in the scheme .

The verification methods employed by the DMHIS to ensure parents were registered in the NHIS in order for their children to benefit Easier methods, more participation.

Membership rules The amount of money paid to renew

insurance cards both for adults and children vary across districts Districts with higher renewal fees would probably discourage participation of marginal participants.

The waiting periods for both adults and children when registering Longer wait, less participation.

Data The 2008 Ghana Demographic and Health

Survey (GDHS) is the main source of data for this study. This survey is carried out by the Ghana Statistical Service and the Ghana Health Service.

This is a national survey designed to collect information on housing and household characteristics, education, maternal health and child health, nutrition, family planning and gender.

Data In addition we carried out a census of all the

District Mutual Health Insurance Schemes (DMHIS) in existence in 2008. The census contains information on the membership rules in each of the 145 DMHIS licensed by 2008.

This census also contains district level information on health services delivery in the community during 2008.

Despite the availability of a health insurance program, families might not decide to join if they perceive the services they could access through this system as deficient.

Outcomes analyzed

Curative child health care utilization If mothers chose to deliver at a health

facility If they treat their children with antimalarial

medication when they are sick with either a fever or cough

If they seek advice from a health provider when their children experience a fever or cough

Outcomes analyzed Quantity and quality of maternal health services

Number of prenatal visits sought If pregnant women were told about pregnancy

complications during prenatal visits If they had urine and blood samples taken during

pregnancy

Preventive child health care utilization. Vaccination rates (for yellow fever, BCG, DPT/HepB/

Influenza 2) If the child slept under a mosquito bed net the

previous night.

Descriptive Statistics by insurance participation

Source: DHS 2008

  Not registered in the DMHIS

Registered in the DMHIS    

  N Mean N Mean Diff P-valueChild Gender (male) 1681 0.49 1107 0.53 -0.04 0.04**Child Age (in years) 1681 1.96 1107 1.85 0.11 0.05**Child is a twin 1808 0.04 1178 0.05 -0.01 0.39Mother's educational attainment 1808 1.29 1175 1.90 -0.60 0.00***Mother currently pregnant 1681 0.08 1107 0.08 0.00 0.86Children born in the last 5 years 1808 1.67 1178 1.57 0.11 0.00***Number of living children 1808 1.75 1178 1.61 0.15 0.03**Mother’s Age (in years) 1808 29.84 1178 30.44 -0.60 0.02**Mother currently working 1808 0.86 1178 0.88 -0.02 0.09*Household head is male 1808 0.73 1178 0.75 -0.02 0.20Wealth Index 1808 -45869 1178 -1957 -43912 0.00***Type of place of residence (0 rural, 3 urban) 1808 0.79 1178 1.03 -0.23 0.00***Population in millions (region) 1808 2.20 1178 2.10 0.10 0.00***# of facilities per 100,000 people (region) 1808 7.86 1178 7.92 -0.06 0.61

Distance to doctor (km) 1808 4.48 1178 3.30 1.18 0.03**Distance to private hospital (km) 1808 21.89 1178 17.92 3.97 0.02**Dummy for poor regions 1808 0.33 1178 0.35 -0.02 0.26

Descriptive Statistics by insurance participation

Source: DHS 2003 and 2008, P-values are reported from t-test on the equality of means for each variable. * p<.10; ** p<.05; *** p<.01

  Not registered in the DMHIS

Registered in the DMHIS

  N Mean N Mean Diff P-valueCurative child health careDelivery at health facility 1357 0.45 909 0.74 -0.29 0.00***Anti-malarial medication taken for fever/cough 494 0.27 326 0.37 -0.10 0.00***Seek medical treatment - child has fever/cough 494 0.33 326 0.61 -0.28 0.00***Quantity and quality of maternal health care Number of prenatal visits 992 5.21 707 6.49 -1.28 0.00***Told about pregnancy complications 962 0.65 705 0.74 -0.08 0.00***Urine sample taken during pregnancy 965 0.85 706 0.92 -0.07 0.00***Blood sample taken during pregnancy 965 0.85 706 0.95 -0.10 0.00***Preventive child health care BCG rate 1672 0.90 1101 0.95 -0.05 0.00***DPT/HepB/ Influenza 2 rate 1670 0.83 1098 0.87 -0.04 0.00***Yellow fever vaccine rate 1665 0.71 1103 0.74 -0.02 0.24Child slept under mosquito net 1808 0.45 1178 0.51 -0.06 0.00***

Identification Strategy One of the main difficulties in identifying the effect

of joining a health insurance scheme on child health care utilization is selection bias or omitted variable bias.

Omitted variable bias is an issue because people who participate less in formal health insurance systems may do so because, for example, of their wealth or education level which in turn could affect child health care use. To address this concern we include a wide range of covariates as controls.

Nonetheless, unobserved characteristics that cannot directly be controlled for remain a concern.

Identification Strategy For example the household level of risk aversion

could induce parents to choose to enroll in a health insurance plan and also affect their ability to care for their child.

Moreover, a household could decide to spend more or less in health inputs (such as health insurance) according to the health capital of their children. Parents with healthier children might participate more (favorable selection) in health insurance schemes. Conversely, parents with frailer children might decide to enroll more frequently (adverse selection).

Identification Strategy To address this endogeneity problem we

use an instrumental variable approach. The exogenous variation in the decision to join the National Health Insurance system comes from the variations in the membership rules in each DMHIS.

We would expect parental participation to be higher in districts where membership rules are less stringent or more straightforward.

Testing the IV’s exogeneity Before the creation of these offices, government

officials in the different DMHIS could have chosen to establish their membership rules to make them less stringent in districts that had: Lower child health care utilization rates or worse maternal

health care Higher poverty, malaria or fertility rates

To test this hypothesis we use the 2003 DHS data (the best health indicators available in Ghana at the time the NHIS was created and before the district rules were actually established) and run regressions for each instrument chosen on each of the outcomes examined in the paper, as well as on district level poverty, malaria and fertility rates.

The results show the instruments chosen are indeed exogenous.

Testing the IV’s exogeneity The results could also be biased by the existence of district

characteristics that determine both membership rules and health care seeking behaviors.

Higher barriers to access health care in a district as well as greater challenges to participate in a DMHIS could also imply lower health care utilization rates. E.g. Low quality/quantity health care Poor road network High rate divorce/single parenthood Lack of education/preference for traditional medicine

To discard such a hypothesis we test that the results obtained are not affected after introducing both barriers affecting children's access to health care in 2008, as well as challenges to participation in the DMHIS. The results confirm that the IV coefficients are not biased.

Specification First stage

(1)

Iij is a dummy indicating whether mother i is registered in the DMHIS in district j,

Z1j to Z3j = membership rules in district j Xt

c, XtM, Xt

H, XtR = child, mother, household and community characteristics

Second stage (2)

Cij = child or maternal health care utilization choice ij = unobserved attributes related to both the mother and the child such

as resilience, maternal risk aversion, initiative and determination.

Robust standard errors are used.

543210 ijRj

Hij

Mij

cijijij XXXXIC

76543322110 ijRj

Hij

Mij

cijjjjij XXXXZZZI

ResultsOLS and IV Estimation - Impact of NHIS

participation on child health care practices

Source: DHS 2008 and DMHIS 2008 Census. Note: The estimations include the following controls: child’s gender and age, mother’s age and educational attainment, the number of children born in the last 5 years, the number of living children older than 5, whether the mother is currently pregnant and whether she is currently working, sex of the household head, the household’s wealth level, the place of residence, the population level in each region (in millions), and a dummy if the household belongs to one of the three poorest regions in Ghana, the distance from the district to the nearest doctor and the nearest private hospital (in km), the number of facilities per 100,000 people in each region. The first 2 estimations (delivery at a health facility) also include as a control if the child was a twin, but do not include a control for whether the mother is currently pregnant. Instruments chosen for specification 2: the existence of an exception for children born out of wedlock; the renewal fees for non-exempt adults and the renewal fees for children. Instruments chosen for specification 4 and 6: the existence of an exception for children born out of wedlock; the existence of non-standard verification method and the renewal fees for non-exempt adults. Robust standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%.

Child health care utilization

Delivery at health facility

Anti-malarial medication taken

Seek medical treatment

OLS IV OLS IV OLS IV(1) (2) (3) (4) (5) (6)

NHIS Participation 0.192 0.322 0.044 0.334 0.225 0.423

  (0.020)*** (0.134)** (0.035

)(0.161)*

*(0.037)*

**(0.162)*

**Other Controls YES YES YES YES YES YESN 2133 2133 820 820 820 820R2 0.303 0.288 0.063   0.125 0.092

OLS and IV Estimation-Impact of NHIS participation on maternal health care

practices

Source: DHS 2008 and DMHIS 2008 Census.Note: The estimations include the following controls: if the child was a twin, mother’s age and educational attainment, the number of children born in the last 5 years, the number of living children older than 5, whether the mother is currently working, sex of the household head, the household’s wealth level, the place of residence, the population level in each region (in millions), and a dummy if the household belongs to one of the three poorest regions in Ghana, the distance from the district to the nearest doctor and the nearest private hospital (in km) as well as the number of facilities per 100,000 people in each region. Instruments chosen for all specifications except 3 and 4: the existence of an exception for children born out of wedlock; the renewal fees for non-exempt adults and the renewal fees for children. Instruments chosen for specifications 1 and 2: the existence of a non-standard verification method; the renewal fees for non-exempt adults and the renewal fees for children. Robust standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%.

Maternal health care

Number of prenatal care

visits

Told about pregnancy

complications

Urine sample taken during pregnancy

Blood sample taken during pregnancy

OLS IV OLS IV OLS IV OLS IV(1) (2) (3) (4) (5) (6) (7) (8)

NHIS 0.698 0.059 0.042 0.416 0.053 0.333 0.085 0.346Participation

(0.529)

(0.878)

(0.023)*

(0.136)**

(0.015)***

(0.090)***

(0.019)***

(0.092)***

Other Controls YES YES YES YES YES YES YES YESN 1698 1698 1666 1666 1670 1670 1670 1670R2 0.183 0.175 0.078 0.213 0.052 0.165 0.014

OLS and IV Estimation-Impact of NHIS participation on preventive child health care practices

Preventive child health care practices

Vaccination rates Child slept under a mosquito bed

net BCG DPT/HepB/ Influenza 2

Yellow fever vaccine

OLS IV OLS IV OLS IV OLS IV

(1) (2) (3) (4) (5) (6) (7) (8)

NHIS 0.027 0.153 0.024 0.214 0.015 0.218 0.063 0.371

Participation (0.008) **

(0.074) **

(0.014) *

(0.102) **

(0.015) *

(0.110) **

(0.020) **

(0.121) **

Controls YES YES YES YES YES YES YES YESN 2770 2770 2765 2765 2765 2765 2785 2785R2 0.052 0.006 0.060 0.001 0.280 0.236 0.077  

Source: DHS 2008 and DMHIS 2008 Census. Note: The estimations include the following controls: child’s gender and age, mother’s age and educational attainment, the number of children born in the last 5 years, the number of living children older than 5, whether the mother is currently pregnant and whether she is currently working, sex of the household head, the household’s wealth level, the place of residence, the number of facilities per 100,000 people in each region, the population level in each region (in millions), and a dummy if the household belongs to one of the three poorest regions in Ghana. Instruments chosen for all specifications except 7 and 8: the existence of an exception for children born out of wedlock; the renewal fees for non-exempt adults and the renewal fees for children. Instruments chosen for specifications 7 and 8: the existence of a non-standard verification method; the renewal fees for non-exempt adults and the renewal fees for children.Robust standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%.

Discussion The OLS coefficients are biased downwards: parents

with frailer children could decide to enroll more frequently in the NHIS to provide their children with more curative care.

Adverse selection means underlying heterogeneity in returns to NHIS participation.

IV estimates are substantially larger than OLS estimates because IV calculates local average treatment effects (LATE) in contrast to average treatment effects (ATE).

Membership rules variances do not affect participation decisions of all parents. Parents that to begin with have positive health behaviors are probably not discouraged to join the NHIS by slight variations in membership rules.

Discussion On the other hand, a subgroup of parents did decide to

participate in the NHIS merely because the membership rules in their district were less stringent, but would not have participated otherwise. These are probably parents that are not so keen on health practices.

The IV estimation measures the impact of participating in the NHIS on the health care behaviors of these “compliers”.

If “compliers” have much higher returns to participating in the NHIS, then our IV estimates will be larger than the average marginal return to NHIS membership.

Compliers are more likely to … Have children born out of wedlock they have a

higher cost of becoming members of the NHIS (single income household). Hence, reductions to membership costs and easier access would motivate these parents much more than the average parent to enroll and take advantage of the health services provided by the system.

Have children who are frailer at birth a frailer baby would benefit much more from both preventive and curative care if his/her parents have access to health insurance, which means they would be much more active users of these services.

Compliers are more likely to … Live in a district with lower levels of education and

a higher reliance on traditional medicine they would probably be less eager consumers of professional health services unless prompted by easier access to insurance services.

Live in districts with poor road networks with health insurance parents can afford more expensive health facilities closer to home. Hence parents who live in areas with particularly bad transportation networks might value access to health insurance much more than the average parent and use health services more frequently.

Instrument Strength and Validity The instruments chosen are relevant for all

specifications: They are significant at a 1% level and display a non-

immaterial effect on the probability of being covered by the NHIS.

The signs of the coefficients are in line with expectations.

They are strong according to Stock et al. (2002) The F statistic ensured that the maximal bias of the IV

estimator relative to OLS is no larger than 5%.

The results of over identification tests are consistent with instrument exogeneity.

Dependent variable: Covered by NHIS

Sample defined by the availability of the second stage variableSeek

medical treatment / medicine

Delivery at health facility

Pregnancy complicati

ons

Urine/ Blood

sampleBCG

DPT/H/I 2/ Yellow

feverMosquito

net

Exception for children 0.101 0.110   0.119 0.096 0.094born out of wedlock (0.036)*** (0.023)***   (0.027)*** (0.020)*** (0.020)***

Renewal fees adults -0.008 -0.005 -0.003 -0.006 -0.005 -0.005 -0.003

(0.002)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)***

Renewal fees for -0.029 -0.030 -0.030 -0.028 -0.028 -0.027children (0.008)*** (0.009)*** (0.009)*** (0.007)*** (0.007)*** (0.007)***

Non-standard -0.175 -0.178 -0.168verification method (0.045)*** (0.032)*** (0.025)***

N 820 2133 1666 1670 2770 2765 2785Partial R2 of excluded instrument

0.052 0.023 0.031 0.026 0.020 0.020 0.029

F-test for weak identification 16.21 16.90 20.82 15.13 19.93 19.72 32.60

Hansen J statistic 2 P-value 0.105 0.288 0.134 0.312 0.328 0.148 0.566

First stage estimation - Impact of membership rules on the probability of being covered by the NHIS

Source: DHS 2008 and DMHIS 2008 Census

Conclusion The main objective of this research paper is to look at

the impact of parental participation in the NHIS on health care utilization in Ghana.

This is the first attempt to look into this particular outcome.

This study overcomes the difficulties in identifying the effect of joining a health insurance scheme on child health care utilization by using an instrumental variables approach.

The instrumental variable estimations show positive and statistically significant coefficients which are much bigger than the ones obtained using OLS regressions.

The results are consistent with coefficients biased downwards in the initial estimations.

Conclusion This paper calculates local average treatment effects

(LATE) in contrast to average treatment effects (ATE). Since “compliers” have much higher returns to

participating in the NHIS, IV estimates are larger than the average marginal return to NHIS membership.

This is not a shortcoming of this paper, because, for policy evaluation purposes, it might be more relevant to calculate the average return to NHIS participation for the group who will be impacted by changes in membership rules than to calculate the average marginal return for the whole population.