The Effects of State Children’s Health Insurance Program on Access to Care for America’s Poor Children

｢지방정부연구｣  제11권 제4호(2007 겨울) : 311～329

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미국 주정부 아동의료보험제도가 저소득층 어린이의 의료서비스

접근성에 미치는 영향

박 경 돈

본 논문은 1997년부터 시행된 미국 주정부 저소득층 아동의료보험(SCHIP)정책이 기존의 공공의료보장제도(Medicaid)와 함께 저소득층 아동의 의료서비스 접근성을 향상시켰는가를 규명하는 데 목적이 있다. 이를 위해 공공보험에 가입한 아동과 사보험에 가입한 아동의 의료서비스 이용 및 지연을 상호 비교·분석하였다. 본 연구의 분석에 사용된 자료는 1997년과 2002년에 미국 전역의 가구를 대상으로 조사한 Kaiser Foundation을 통해 입수하였다. 효과성 측정치는 긍정적 의료서비스 접근성(내과의사 방문 횟수와 치과의사 방문 횟수)과 부정적 의료서비스 접근성(의료서비스 지연, 치과 서비스 지연, 의약품 서비스 지연의 경험유무에 관한 이진수 자료)이다.

의료이용빈도에 관한 회귀분석결과 (incident rate ratio)과 의료지연경험에 관한 로짓회귀분석결과(odds ratio)에 따르면 통계적으로 유의미한 변수들은 있으나, 이런 변수들의 파라미터가 1999년과 2002년 사이에 또는 두 집단간에 별로 차이가 나지 않았다. 이에 따라 SCHIP의 시행이 갖는 공공의료서비스 접근성에 대한 정책효과를 분석하기 위해 정책 시뮬레이션과 비교차이점 비교(difference-in-difference)방법을 이용한 결과, SCHIP의 급진적 확대에도 불구하고 긍정적인 접근성에 있어서는 질적인 서비스 이용의 퇴보 내지 답보를, 또한 부정적인 접근성에 있어서는 치과서비스 이용과 의료서비스 전반적 이용에 있어서 공공보험에 가입한 저소득층 아동이 더 많은 서비스지연을 경험하고 있는 것으로 나타났다.

SCHIP의 시행 및 확대가 무의료보험 아동의 감소와 실제적인 의료서비스의 이용 증가를 동시에 겨냥한 것이라면, 이 프로그램의 전폭적인 확대시행이 실제적인 의료서비스의 접근성을 높이지 못했다는 측면에서는 최소한 긍정적이지 못한 정책효과를 보여준다.

주제어 : 의료의 접근성, 미국의 공공의료보장제도(Medicaid), 미국 주정부 저소득층 아동의료보험제도 (SCHIP)

Ⅰ. Introduction

Do low-income children insured with Medicaid and State Children’s Health Insurance Program (SCHIP) have a lower access to care than privately insured children? In order to reduce the number of uninsured children, the U.S. federal government created SCHIP, which encourages a number of uninsured low-income children to receive state-providing health insurance. It is reported that, responding to the federal government effort, 49 states had the officially authorized plan a year later after it was instituted in 1997 (Urban Institute, 2005). According to the Center for Medicare and Medicaid Services (2005; CMS), all states had an approved SCHIP plan in place on September 30,1999.

In line with this speedy increase in the number of states adopting the program, the states greatly expanded eligibility and spending for SCHIP implementation. The number of eligible children became seven times greater within a year between FY1998 and FY1999 (Ullman, Hill, & Almeida, 2001). Accordingly, the proportion of uninsured low-income children was documented to significantly decrease from 20.1% in 1997 to 16.1% in 2001(Cunningham, 2002).

The substantial drop in the number of uninsured children and a drastic public policy change for children’s insurance (Szilagyi, Holl, Rodewald et al, 2000) are believed to be achieved through three flexible types of SCHIP implementation. The first is that states can use SCHIP funds to "expand"Medicaid eligibility to previously unqualified children. The second way of implementing SCHIP is to create a "separate"children's health insurance program entirely distinctive from Medicaid. Lastly, a state can employ a "combination" approach in which the only difference between SCHIP and Medicaid is eligibility (CMS, 2005).

The main purpose of this paper is to study whether poor children enrolled in Medicaid/SCHIP (including differently named state programs) have a disparity in access to care between 1997 and 2002, as compared to the privately insured poor children. First, the increase in the number of enrolled children does not automatically guarantee better access to care. Since SCHIP is assumed to be well-prepared in 2002, access to care as one of the intended program goals can be investigated. Instead of quantitative rise in publicly insured children, this paper focuses on the "relative change" in access to care among privately and publicly insured poor children.

Second, one time snap shot evaluations are inappropriate for researching SCHIP program impacts on access to care. The study uses difference-in-difference analysis for the short-term outcome, or access to care, with a sufficient time lapse, or 5 year interval. The method of the relative change in access to care has not been a study tool in the previous research. Third,little research has been done with the combined program impact of both Medicaid and SCHIP on low-income children. It is not desirable if one analyzes the separated impact on Medicaid-insured children from SCHIP-insured one, and vice versa. This is in part because SCHIP has been implemented in a very close relationship with Medicaid (Kronebusch & Elbel, 2004) and in part because the both public programs are intended to eliminate the uninsured poor.Success in one program does not necessarily guarantee success in another. Thus, a synthesized evaluation of access to care for the enrolled in the two programs is regarded as appropriate for program evaluation.

Ⅱ. Previous Literature

Much of the priorresearch revealed a positive linkage between access to care and SCHIP/Medicaid coverage (Rosenbach, Irvin, & Coulam, 1999; Szilagyi et al, 2000). The findings in previous studies show a favorable impact on access to care for poor children (Table 1). Research found physician visits increase due to the expansion of health insurance coverage through SCHIP (Rosenbach et al, 1999). Most of the prior studies indicate that public programs, or SCHIP and Medicaid, has positive (Dubay & Kenney, 2004) and partially positive (Fox, Mcmanus, & Limb, 2006) effects on access to care for poor children.

However, studies of access to care for poor children suffer from methodological and theoretical problems. First, it is hardly appropriate for SCHIP to be evaluated independent of Medicaid. Especially from the implementation standpoint, separation of SCHIP from Medicaid is neither proper nor possible, since SCHIP is created to compensate for the weakness in Medicaid. From a public policy standpoint, SCHIP has two purposes: to promote a decline in uninsured children (Halfon, Inkelas, DuPlessis et al, 1999) and to create a provision for appropriate access to care. Medicaid was often criticized for not providing adequate care for poor children with a federal income level between 100 and 200 percent of the Federal Poverty Level (FPL) (Halfon et al, 1999).

Second, previous studies lack a comprehensive evaluation of a public insurance program, SCHIP or Medicaid. Their findings are based on the results either from positive measures on access to care or from negative measures (Rosenbach et al, 1999). However, in essence, access to care should be discussed with both features of promotion and prevention of health service utilization (Halfon et al, 1999). Thus, synthesized evaluations on access to care with both positive measures and negative measures will enhance the validity of partial finding from one of approaches.

Third, other methodological challenges are prominent in previous studies. Some studies drew conclusion from poor children in limited geographic areas (Fox et al, 2006 Rosenbach et al, 1999; Szilagyi et al, 2000). Other research has no sufficient time lapse after initiation of SCHIP in 1997, even though a before-and-after study of SCHIP/Medicaid enrollee group and non-enrollee group has been completed (Fox et al, 2006 Kempe et al, 2005). Thus, this paper tries to overcome thesemethodological challenges by comparing access to care both in a negative way and in a positive way with adequate time lapse after SCHIP initiation and location (a 5-year interval), given SCHIP and Medicaid have the same purposes of promoting access to care for poor children.

<Table 1> SCHIP/Medicaid’s effect on access to care for poor children

Authors	Data	Dependent Variables or primary focus (Analytic Methods)	Findings for SCHIP/Medicaid (+: positive & -: negative effects)
Rosenbach, Irvin, & Coulam. (1999).	2 waves of telephones surveys (n = 4420: n =3504):only Medicaid- expansion program in 3 states	Usual source of care, physician visit, preventative check-up, ER visit, Unmet need (Logistics regression )	(+) beneficial impact on dependent variables
Szilagyi, Holl, Rodewald et al. (2000).	6 counties in NYS (n = 1828 children under 7 year)	SCHIP enrollment, access and utilization of health care, quality of care, health care costs (Logistic regression and Poisson regression)	(+) Use of primary care increased by a great margin
Dubay & Kenney. (2004).	2002 CPS & 1999 National Survey of America’s Families	Uninsured rate for 13 states (descriptive analyses); Usual source of care, Unmet or delayed health needs, confidence, and receipt of care multivariate OLS regression)	(+) Uninsured rate declines after 1998; uninsured parent after SCHIP improve access to care
Kempe, Beaty, Crane et al . (2005)	2 waves of telephone interviews with families enrolled in Colorado’s Child Health Plan Plus (n=711 in 1999; n=480 in 2000 as follow-up)	Access (a usual source of preventive care, unmet medical needs, and utilization) and quality of care (Logistic regression)	(+) beneficial effect on the dependent variables except for specialty care visits (Incident ratio increased)
Stein. & Silver. (2005).	The National Survey of Children with Special Health Care Needs (CSHCN) in FY 2000 in 51 states	Functional limitations of the children (Correlation regression)	(Partial +) Function limitations are higher in a state with a higher un-insurance rate and higher um-med needs.
Fox, Mcmanus, & Limb. (2006).	Meeting with senior staffs and directors in 5 non-Medicaid SCHIP program and Medicaid SCHIP program states (CA, MD, MS, UT, & CT) in 1998 and before	Adolescents’ access to primary care, dental care, family planning, mental health treatment, and prescription drugs	(Partial +)Access to dentist and mental care specialists is problematic due to limited reimbursement rate.

Ⅲ. Study Data and Methods

1. Data

To conduct the analysis, two data sets of children’s access to care from the National Survey of America's Families (NSAF) in 1997 and 2002 are used Park (1994) used three data sets (1980; 1984; 1987) to find the effects of regulatory policy on drinking drive in American state governments. The effects were analyzed in terms of injury rate in a solo traffic accident at night and death rate from traffic accidents at night either during the weekdays or the weekend.. The NSAF contains information on children’s socioeconomic circumstances, health status, and disability that may influence access to care and use of health care. The surveys, as a cross-sectional study In Korea, there are several published papers that use a cross-sectional model to find policy effects (Kim, 2004; Rhee & Lee, 2004)., were conducted as part of the "Assessing the New Federalism Project"at the Urban Institute. Interviews were conducted by the most knowledgeable adult in the child life (MKA; child’s parent or guardian) on child health.

The secondary data set for public use, "Focal Child Data," which was surveyed both in 1997 and in 2002, is utilized and provides a large sample of Medicaid and SCHIP beneficiaries (http://www.urban.org). Data used in the study is limited to children below 300% of the FPL. At the initiation phase of SCHIP, children below 200% of the poverty level are intended to be covered with respect to income level (Fox et al, 2006 Rosenbach et al, 1999). However, a couple of years after SCHIP initiation in 1997, the income level in some states changed to the more than 200% level (American Academy of Pediatrics, 2007; Dubay & Kenny, 2004). To purge high income effects on access to care, households whose income is less than 300% FPL are used in the study. Moreover, the use of data is further limited through eliminating children simultaneously covered by more than two types of insurance (e.g., public and private insurance; Medicaid/SCHIP and CHAMPUS).

2. Methods

1) Dependent Variable: Access to Care

As noted earlier, access to care for Medicaid/SCHIP insured poor or near-poor children is measured both in a positive way and in a negative way. A positive measure of access to care is analyzed in terms of medical visits. In contrast, the negative measure of access to care is estimated from the perspective of postponed medical services.

This study uses the event count dependent variables for positive measures and the dichotomous dependent variables for negative measures. First, as positive measure of access to care, the dependent variables, the number of "visit to doctor (fdoct)"and the number of "visit to dentist (fdent)" areused. Second, the dependent variables for the negative measures are obtained through the questions, which ask respondents if they experienced a postponement of medical service last year. The dependent variables are 1) postponed medical care last year (fwhmed), 2) postponed dental care last year (fwhden), and 3) postponed prescription drugs last year (fwhdrg). Haberer, Garrett, and Baker (2005: 1100) use "delay in care because of cost" and "number of doctor visits," as part of the dependent variable to study the effects of Medicaid Managed Care on access to care.

In the study sample, the average number of doctor visits is 2.08 in 1997 and 2.13 in 2002. The average numbers of the both years are not statistically different. In addition, there is very little difference in the number of dental visits (1.54 in 1997; 1.55 in 2002). The maximum number of the medical visits and dental visits is 12. For the negative measuresfor access to care, about 9% of the sample population experiences delayed dental care. There is an increase in postponed drugs services in the both years (1.93 in 1997; 2.90 in 2002). Nearly 3.5% of the poor children experience postponed medical care, showing no statistically difference in the mean of two survey years. Thus, in terms of average difference during the two survey years, there is no variation in access to care except for experience in postponed dental care.

In short, two event count variables are employed for children who have doctor visits and for those who have dental visits, while three dichotomous dependent variables that are coded 1 for those who experience a postponed medical care, for those who experience a postponed dental care, and for those who experience a postponed prescription drug in the previous year of each survey are used. With these measures, if publicly-insured children experience an increase in medical visits (i.e., positive measure) and a decrease in delayed services (i.e., negative measure) in comparison to the privately insured children, one could safely conclude poor children do not have an access barrier to care. To this end, the relative difference will be provided by comparing the gap across two different years of survey in 1997 and 2002.

2) Independent Variable

This study creates independent variables, by separating three factors out of the following list: predisposing factors, enabling (or impeding) factors, and health status factors (Anderson & Davidson, 1996; 2002; Coughlin, Long, & Shen, 2005). First, this study includes predisposing factors, demographicand social characteristics such as respondents’ education (umhsgrad), child’ race or ethnicity (ubrace4), age (uage), and sex (gender). As one of the predisposing factors, access to care for patients in Managed Care is constrained by personal characteristics (Docteur, Colby, & Gold, 1996; Donabedian, 1985). Shi and Singh (2004:506) summarize that predisposition conditions are socio-demographic characteristics such as sex, education, marital status, family size, race and ethnicity, and religious preference. These factors are referred to as a person’s inclination to use health services and care. Respondents' education level is measured with a dummy variable coded "1" if a respondent received a high school diploma or a GED and otherwise "0". The race or ethnicity variable classifies children into White, Black, and others.

Second, enabling and impeding characteristics are related to family resources such as working status (work) and parent’s immigration status (uimmstat). According to Dubay and Kenney (2004), immigration status has a negative effect on public insurance coverage due to federal regulations. Enabling conditions include income, socioeconomic status, price of medical services, financing of medical services, and occupation. Family immigration status is measured with a dummy variable coded "1" if a husband or/and wife is foreign-born or naturalized and otherwise "0". A binary variable of MKA’s working status is measured "1" if the MKA is working her or his child is in care, MKA’s home, in another’s home, while in school, during after-school care, or during child’s self-care. Plus, mother’s financial contribution (hmfin) and father’s financial contribution (hffin) are modeled.

The third factor to influence access to care is a child’s health needs (bhlthp) and usual source for care (usrc_no). This study measures health need with a response about children’s comparative health status between the previous year and thesurvey year. Long, Coughlin, and King (2005) consider the existence of a usual source of care as one of direct elements for health need, in that a person's need for services can be predicted by health status. A variable named "usual source of care" used in this study is measured, by asking a respondent whether they use the Emergency Room (ER) or have no usual source of care for his or her children. A state dummy indicating 13 states with a higher enrollment rate for Medicaid/SCHIP (stcd13) is incorporated.

As for a controlling indicator, respondents are asked whether a child surveyed was enrolled in Medicaid/SCHIP (upstpub) in the last 12 months. The status of Medicaid/SCHIP enrollment is measured with a dummy variable coded "1" if a child is currently with Medicaid/SCHIP and otherwise is coded "0". In short, the regression equation is the following.

[Negative Binomial regression & Logistic regression]

Medical Visit or Postponed Medical Service = Intercept + Race Dummies (2) +

High School Graduate + Age + Female Child + MKA’s working status + Dummies for Federal Poverty-Line Income (2) + Parent’s Immigration Status + Father’s Financial Contribution + Mother’s Financial Contribution + Comparative Health Status Dummies (3) + Usual Source of Care + State Dummy + error

3) Empirical Approach

For the purpose of measuring access to care for Medicaid /SCHIP-enrolled children with a positive measure and a negative measure, event count and binary dependent variables are analyzed using STATA Version 9.0 (College Station, Texas). To provide a sense of the relative magnitude that the influence of the Medicaid/SCHIP enrollment variable has on the probability of access to care, post-estimates with a command of "mfx or prchange"for the effects of changes in 1997 and in 2002 is used to measure the probability of statistically constructed average child’s access to care. Predicted counts of access to medical services with incident rate ratios (IRR) and predicted probabilities of delayed medical services with an odds ratio (OR) are also calculated.

Ⅳ. Study Result

1. Characteristics of Sample Children

The statistically constructed average person represents the average tendency across the population in Table 2. The selected characteristics of the Medicaid/SCHIP-enrolled and the non-enrolled children in the study samples are listed. About 30% of the children in the sample populations are enrolled in Medicaid and SCHIP in 1997, as compared to approximately 41% of them in 2002. The increased number of the Medicaid/SCHIP-enrolled children partly reflects the positive impact of SCHIP expansion.

Accordingly, the number of the privately-insured poor children in the total sample declines from 70.5% in 1997 to about 59.2% in 2002. Medicaid/SCHIP-insured children between 100% FPL and 200% FPL increased from 29.8% in 1997 to 42.2% in 2002, while privately insured children with the same income range decreased 5.9%% between both years. This finding is partly explained by the crowd-out effect of public insurance on private insurance (Cutler & Gruber, 1997).

In general, more than a half of respondents’ children in both populations are reported to have same or better health status as in the previous year. A little bit more than a half of the children in both groups are male. In both sample populations, at least 64% of children are white. Compared with privately insured children, Medicaid/SCHIP recipient children are more likely to have black ethnicity, to be just a little bit younger, and to have a somewhat more usual source for care in the two years.

In both years’ populations, about 69% of the Medicaid/SCHIP-insured have parents who have a GED or high school degree. Surprisingly, about 85% of the privately insured in 1997 and in 2002 have parents who have a GED or high school degree.

<Table 2> Characteristics of Sample Children

	1997(18,518)				2002(15,801)
	Publicly Insured	Privately Insured	Difference		Publicly Insured	Privately Insured	Difference
Variable	29.5% (5,454)	70.5% (13,064)			40.8% (6,450)	59.2% (9,351)
Predisposing Factors
Race
White	64.36%	81.22%	-16.86%		67.86%	82.99%	-15.13%
Other	4.60%	3.77%	0.83%		4.78%	3.87%	0.91%
Black	31.04%	15.01 %	16.03%		27.36%	13.14%	14.22%
GED or High School Degree	69.45%	85.20%	-15.75%		70.28%	85.20%	-14.92%
Age	8.36	9.79	-1.43		8.45	9.40	-0.95
Female Children	49.03%	48.44%	0.59%		49.26%	49.19%	0.07%
Enabling/ Impeding Factor
Immigrant Parent	3.30%	4.42%	-1.12%		4.40%	6.75 %	-2.35%
Working Status	51.45%	50.25%	1.20%		52.96%	50.69 %	-0.37%
Family Income
FPL 100%*	64.2%	18.5%	45.7%		43.3%	13.8%	29.5%
FPL 200%*	29.8%	43.9%	-14.1%		42.2%	38.0%	4.2%
FPL 300%*	6.1%	37.6%	-31.5%		14.5%	48.2%	-33.7%
Mother’s Financial Contribution	2.11%	2.12%	-0.01%		2.76%	2.94%	-0.18%
Father’s Financial Contribution	22.00%	20.00%	2.00%		22.90%	20.40 %	2.50%
Health Needs
Comparative Health Status
Worse	29.96%	19.39%	10.57%		30.35%	20.40%	10.95%
Same	66.70%	78.24%	-11.54%		65.63%	77.37%	-11.74%
Better	3.34%	2.38%	0.96%		3.03%	2.23%	0.80%
Usual Source of Care	92.56%	90.89%	1.67%		92.48%	90.75%	1.73%
13 State Resident	90.19%	89.82%	0.37%		84.06 %	82.04 %	2.02%
Note: ( ) =The number of sample in the parenthesis FPL 100%* (family income <FPL 100%); FPL 200%* (FPL 100% ≤ family income < FPL 200%); FPL 300%* (FPL 200% ≤ family income < FPL 300%)

This gap of about 15% remains in both years. More than 90% of all respondents report that their child has a usual source of care except for the Emergency Room (ER) or no source. In short, the characteristics of the two sample populations almost do not change across the two different years except for eligibility expansion for higher income families.

2. Negative Binomial Regression

Given alpha is statistically significant at 0.1% level, negative binomial model than Poisson is a more desirable for this study [alpha < 1 or log(alpha)<0: over-dispersion problem]. and Positive Measure

This study compares incidence rate ratios (IRR), which are interpreted as the predicted count number of events in access to care that is affected by each variable. According to the findings from positive measures of access to care, little variation within Medicaid/ SCHIP-insured children in both years is found (i.e., little within-group and between-group variation). Except for dental visit, there is Black ethnicity is significantly associated with more access to care among the privately/publicly insured in the both years of surveys. In most cases, statistically insignificant estimates are associated with the female children insured with Medicaid/SCHIP, whereas the magnitude of the significant estimates for the privately incurred children is very small.

In contrast, age is consistently a predictor for increases in dental visits and decreases with medical visits Dental visit: IRR for publicly insured children = 1.04 in 1997 & 1.06 in 2002; IRR for the privately insured children = 1.05 in 1997 & 1.07 in 2002 Medical visit: IRR for publicly insured children = 0.97 in 1997 & 0.98 in 2002; IRR for the privately insured children = 0.97 in 1997 & 2002.. The age variable plays a significant role in access to care regardless of whether poor children have public or private health insurance in any given year (p<0.01). The statistical significance of child’ age regarding dental visit indicates that, as age increases by one year, the access to dental care rises by more than 4% (IRR = 1.04).

<Table 3> Incident Rate Ratios from Positive Measure from Negative Binomial Regression

	Doctor (Medical) Visit (fdoct)				Dental Visit (fdent)
	1997		2002		1997		2002
Medicaid /SCHIP Enrollment	Yes (5454)	No (13064)	Yes (6450)	No (9351)	Yes (5454)	No (13064)	Yes (6450)	No (9351)
Predisposing Factors
Race
White	1.08	1.13*	1.08	0.98	0.99	0.97	1.01	0.92
Black	0.90	0.94	1.01	0.88	0.95	0.84**	0.91	0.83**
GED or High School Degree	1.12***	1.24***	1.13***	1.37***	1.03	1.20***	1.06	1.29***
Age	0.97***	0.97***	0.98***	0.97***	1.04***	1.05***	1.06***	1.07***
Female Children	1.04	1.02	0.99	1.06*	1.02	1.09***	1.05	1.05*
Enabling/ Impeding Factor
Immigrant Parent	0.92	0.77***	0.69***	0.80***	0.96	0.82***	0.98	0.75***
Working Status	0.93*	0.94*	0.94*	0.99	1.12***	1.12***	1.12***	1.18***
Family Income
FPL 200%*	1.00	1.13***	0.99	1.12**	0.99	1.09**	0.96	1.07*
FPL 300%*	1.04	1.23***	1.03	1.20***	1.02	1.23***	0.97	1.21***
Mother’s Financial Contribution	1.00	0.80**	0.92	0.98	0.82*	1.00	0.93	1.03
Father’s Financial Contribution	0.99	1.16***	1.16***	1.14***	0.95	1.00	0.96	0.96
Health Needs
Comparative Health Status
Worse	2.47***	2.52***	2.17***	2.41***	0.93	0.92	1.14	0.92
Better	1.23***	1.37***	1.21***	1.34***	1.12***	1.00	1.08**	0.99
Usual Source of Care	1.48***	2.36***	1.82***	2.49***	1.15**	1.41***	1.26	1.62***
13 State Resident	0.94	0.98	1.03	0.99	0.98	0.98	0.99	1.03
Log (Alpha)	-0.19***	-0.18***	-0.35***	-0.45***	-1.02***	-0.77***	-0.95***	S-1.06***
Note: * p<0.05 ** p<0.01 *** p<0.001 ( ) =The number of sample in the parenthesis FPL 200%* (FPL 100% ≤ family income < FPL 200%); FPL 300%* (FPL 200% ≤ family income < FPL 300%); Reference dummy for Race (=Other ethnicity ); Reference dummy for Family Income (=family income <FPL 100%); Reference dummy for Comparative Health Status (=Same).

In terms of statistical magnitude, GED or high school degrees are found to be the most significant variable among the predisposing factors except for the publicly insured children in 1997. The access to care for the two groups due to education causes the children to be at least 13% more likely to visit physician or dentist (IRR: 1.13 ~ 1.37, p<0.001). More specifically, the incident rate ratio of care by a doctor shows that the non-enrolled children with parents who have a GED or high school degree in 2002 have 1.37 times more access than those children with parents who do not have a degree. In contrast, the effect of the enabling factors is not in a congruent trend or direction. In most cases, a child being in an immigrant family leads to less medical visits. The higher income families with private health insurance for their children (FPL 200% and FPL 300%) have more access to medical and dental care than those below FPL 100% by over 7% (IRR: 1.07 ~ 1.23).

Different from the fact that dental care is identified as the highest unmet need (Kempe, Beaty, Crane et al, 2005), poor health status is associated with a 217% increase in visit for medical care, as compared to children with the same comparative health status in both years. It is found that poor health status is a single dominant predictor for medical visits, as compared to children with the same comparative health status in both years. In terms of a usual source for care, privatively insured children are more likely to visit physicians and dentists than the publicly insured. Medical visits for the privately insured is 2.36 times higher than the publicly insured in 1997 (p<0.001). Access to dental care for the enrolled children with usual source in 1997 is at least 1.15 times higher than those with no usual source for care.

3. Logistic Regression and Negative Measure

Negative measures of access to care are done with three dependent variables: delayed medical care, delayed dental care, and delayed drug service. All model specifications for delayed medical services in 1997and in 2002 are statistically significant (Table 4). The results from logit regression indicate that little within-group variation for Medicaid/SCHIP-enrolled children is identified in the delayed service area.

<Table 4> Odd Ratios from Negative Measure from Logistics Regression

	Delayed Dental Visit (fwhden)				Delayed Drug Service (fwhdrg)				Delayed Medical Care (fwhmed)
	1997		2002		1997		2002		1997		2002
Medicaid/SCHIP Enrollment*1	Yes (5454)	No (13064)	Yes (6450)	No (9351)	Yes (5454)	No (13064)	Yes (6450)	No (9351)	Yes (5454)	No (13064)	Yes (6450)	No (9351)
Predisposing Factors
Race
White	1.13	1.38	1.18	1.32	1.06	4.86*	0.74	1.08	0.64	1.77	0.99	2.22
Black	0.78	1.01	0.68	1.28	1.06	5.51*	0.60	1.17	0.57	0.98	0.82	2.07
GED or High School Degree	1.55***	1.20*	1.36***	0.93	1.68*	0.94	1.00	1.10	1.49*	1.28	1.06	1.49*
Age	1.07***	1.06***	1.05***	1.07***	1.01	1.07	0.98	1.05	1.09***	1.05***	1.02	1.04**
Female Children	1.09	1.00	1.01	0.89	1.06	1.11	0.96	0.95	1.14	0.87	1.11	1.01
Enabling/ Impeding Factor
Immigrant Parent	0.95	0.83	0.99	0.79	0.27	1.38	1.50	0.75	0.71	0.66	0.86	0.35**
Working Status	1.20	1.13	1.06	1.20	1.28	0.99	0.91	1.10	1.62**	0.81	0.94	1.07
Family Income
FPL 200%*	1.16	1.06	1.20	1.05	1.00	0.90	0.93	0.98	1.35*	0.78*	1.06	1.14
FPL 300%*	1.09	0.76**	1.05	0.85	0.58	0.66*	0.66	0.84	1.25	0.59***	1.14	0.80
Mother’s FIN Contribution	0.70*	0.97	1.42**	1.03	1.21	1.33	1.55*	1.35	0.83	1.42*	1.07	1.22
Father’s FIN Contribution	0.81	0.64	0.78	0.35***	0.71	1.15	0.61	0.32	1.39	0.53	0.50	0.48
Health Needs
Comparative Health Status
Worse	1.64*	1.83***	1.95**	1.90*	2.77*	4.73***	2.05*	2.82	4.08***	3.87***	5.02***	3.62***
Better	0.94	0.93	0.97	0.92	1.02	1.58**	0.90	1.45	1.10	1.07	1.09	1.31
Usual Source of Care	0.64*	0.65***	0.62**	0.56***	0.64	0.69	0.80	0.71	0.69	0.43***	0.68	0.42***
13 State Resident	1.08	1.14	1.22	1.07	0.50*	1.22	0.82	1.00	0.58**	0.86	1.36	1.22
Note: * p<0.05 ** p<0.01 *** p<0.001 ( ) =The number of sample in the parenthesis FPL 200%* (FPL 100% ≤ family income < FPL 200%); FPL 300%* (FPL 200% ≤ family income < FPL 300%) Reference dummy for Race (=Other ethnicity ) Reference dummy for Family Income (=family income <FPL 100%) Reference dummy for Comparative Health Status (=Same).

Given the insignificance of statistical estimation, theprobability of getting delayed care services can be compared through odds ratio. Odds ratio indicates that there is no consistent significance in predisposing factors and enabling and impeding factors, as shown in the positive measure above. The enrolled children whose parents having have a GED and or high school degree or higher more experience, are more likely to higher delayed dental service in both years (OR: 1.55 in 1997 & p<0.01; 1.36 in 2002 & p<0.05).

However, health status factors play a significant role in experiencing delayed medical services in the three areas. Children whose health is worse than the previous year are very likely to have delayed medical services. Children with a worse comparative health status in the past 12 months experience at least 3.6 times medical care delay more than those who feel no different health status from the previous year. Plus, the usual source of care plays a substantial role in decreasing delayed medical services. In short, the inconsistently insignificant odds ratios except for age and parent education show little within-group and between-group variation.

4. Simulation Analysis and Program Impact

A system of two Negative Binomial regression models for positive measures is used to estimate the predicted count of medical visits (fdoct) and dental visit (fdent) between Medicaid/SCHIP-enrolled children and non-enrolled children in a given year (Table 5).Another system of three logistic regression specifications for negative measures is employed to analyze the predicted probability of experiencing delayed medical services (fwhden, fwhdrg, and fwhmed Table 6). By setting the other at mean value, the counted number of medical visits and dental service visits is calculated for a statistically constructed "average" child with public insurance or private insurance.The baseline case, or average case, for all sample populations is a child: white ethnicity, female, 9.5-year old, excellent health condition, a resident in one of 13 states, with parents who have a GED or high school degree, American-born, and with a usual source of care. Through simulations, the direct comparison of the regression results is provided in Table 5 and Table 6.

<Table 5> Difference-In-Difference Analysis of Predicted Number of Visit: Positive Access to Care

DID (Difference-in-Difference) can be used in order to infer policy effect under Nonequivalent Control Group Design. The method will be applied to regression analysis with dummy variable (Kim, 1999: 180-184).

Positive Access	1997			2002			Difference-in- difference (Visit)
	Medicaid/ SCHIP- enrolled	Privately Insured	Difference	Medicaid/ SCHIP- enrolled	Privately Insured	Difference
Medical visit	2.58	1.72	0.86	2.51	1.75	0.76	-0.10
Dental visit	1.50	1.50	0.00	1.51	1.46	0.05	0.05

* (-): fewer visits in a relative term

First, Table 5 displays that more Medicaid/SCHIP-insured children than privately insured children have a greater number of medical visits and dental visits. The finding in this study is consistent with the substantially better access to care for the publicly insured children (Szilagyi et al, 2000). In addition, the result supports the finding that Medicaid insured children are more likely to access physician care than the privately insured children (Rosenbach et al, 1999).

However, difference-in-difference analyses show the consistent result with the argument that the mean numbers of medical visits did not increase significantly due to the initiation of SCHIP (Kempe, Beaty, Crane et al, 2005). In other words, there is no big difference in positive access to care between before and after SCHIP era. Despite of the start and impact of SCHIP during the two years, the medical visits rather decreased between 1997 and 2002 (2.58  2.51). Medicaid/SCHIP seems to be ineffective in promoting access to medical care for poor children. Even though Medicaid/SCHIP-insured children still experienced better access to medical care than privately insured children, the gap is considerably decreasing in 2002.

Given that the state income upper limits in SCHIP increased to almost 300% level, the predicted count of dental visits remains at 1.5 times between the two years. The slightly widening gap for dental visit does not come from the increase in the number of predicted visits by the publicly insured, but from the decline in visits by the privately insured. Thus, the little variation in positive access to care shows no access increase due to SCHIP, while the gap results from the decrease in the predicted number of visits by the privately insured.

<Table 6> Difference-In-Difference Analysis: Probability for Delayed Medical Services

Delayed Service	1997			2002			Difference-in-difference (Service Delay)
	Medicaid/ SCHIP- enrolled	Privately Insured	Difference	Medicaid/ SCHIP- enrolled	Privately Insured	Difference
Dental Visit	8.29%	8.78%	-0.49%	8.42%	7.53%	0.89%	1.38%
Drug Service	2.06%	1.47%	0.59%	2.73%	2.54%	0.19%	-0.40%
Medical Care	3.40%	3.18%	0.22%	3.13%	2.63%	0.50%	0.28%

* (-): less delayed care in a relative term

Second, the difference-in-difference analysis of delayed medical services provides that, in terms of relative values, the probability of experiencing the delayed services between the Medicaid/SCHIP-enrolled and the non-enrolled increases from 1997 to 2002 (Table 6). Surprisingly, the risk of experiencing delayed dental visits and delayed medical care for the publicly insured children had risen (delayed dental visit: -0.49% 0.89%= 1.38 percentage point increase; delayed medical care: 0.22% 0.50% = 0.28 percentage point increase). One could see that the probability of experiencing delayed dental visits slightly increases from 8.29% in 1997 to 8.42% in 2002, while the delay for the privately insured decreased drastically from 8.78% to 7.53%. In the meantime, the likelihood of delayed medical care for Medicaid/SCHIP recipient children somewhat decreased from3.40% to 3.13%, while the delay for privately insured decreased considerably from 3.18% to 2.63%.

Third, from the perspective of difference-in-difference analysis, it is clear that delayed drug service improved because of a significant narrowing of the delay experience gap between publicly insured children and privately insured children. However, the lessening gap results mainly from increased delays experienced by the privately insured. Thus, the considerable expansion of SCHIP with purposes of helping uninsured poor children did not resultin the desirable outcome, or better access to care. In short, the challenge for delayed care for Medicaid/SCHIP-insured children increased despite of the substantial growth of SCHIP since 1997, while positive measures for access to medical and dental care show narrowing advantages or a mix for the publicly insured.

Ⅴ. Discussion and Policy Implications

The study found that poor children insured with Medicaid/SCHIP are more likely to experience access challenges in delayed medical services, while narrowing down advantages of access to dental and medical care. Despite of initially positive effects of SCHIP on access to care for the poor children, the combined effects of the two public insurance programs in a longer policy implementation period reveal negative results or at least mixed results. This is problematic, because Medicaid before the SCHIP was regarded as having a positive effect on access to care in comparison to private insurance (Halfron et al, 1999).

Given little variation of individual characteristics for the two groups in both years, the difference in positive access to care is mainly driven by a change in access to care for the privately insured. From this regard, the drastic coverage expansion of SCHIP in line with the increase in the income limit could not lead to improvement in access to care for medical and dental visits. With respect to negative measures for access to care in terms of delayed services, there exists significant barriers for the publicly insured to use medical services. The rising gap of delayed dental and medical care for the publicly insured poor children is identified in the two negative measures. Thus, the access challenge for the publicly insured poor children between 1997 and 2002 still continues and has not been overcome. If the model incorporates the privately insured children above 300% of the FPL, the access difference between the privately insured and the publicly insured is expected to be more widened. This may hold true, since a higher level of income is found to be substantially associated with the increase in access to care and the decrease in delayed service.

From these perspectives, developing an appropriate way of measuring policy impacts (Halfron et al, 1999) should have the highest priority in efforts to boost and maintain a higher access to care for publicly insured poor children. Based on the results of appropriate measures, better ways of efficiently distributing financial resources and effectively managing the delivery and regulatory mechanisms of two public insurance programs should be considered. The mere expansion of income limit toward the lager enrollment in the public insurance program, or the SCHIP, doesnot automatically guarantee more access to care. Put differently, quantitative decrease in the number of uninsured children should link to qualitative improvement in access to care, or less disparities in access to care. Thus, the policy evaluatorshould pay attention to qualitative changes, or disparities in access to care, and quantitative changes, or decrease in the uninsured children. In summary, this study finds evidence that the combined effects of Medicaid and SCHIP are not successful in improving access to care for the enrolled children. However, the study reveals very little about how medical services are delivered to Medicaid/SCHIP-enrolled children. Future studies may consider a possible influence of cost, quality, care deliver system, and environments on access to care.

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박경돈(朴倞墩): State University of New York (SUNY) at Albany에서 “The Crowding Out Effects of State Medicaid Policy on the Purchase of Private Long Term Care Insurance”라는 제목으로 박사학위를 취득하였으며 현재 한국행정연구원 근무 예정이며. (2008년 3월3일부터) 건국대학교 행정학과에 출강예정이다(2008년 3월3일부터) 그리고 뉴욕주 아동가족청 (the Office of Children &Family Services in New York State) 연구원 (2005년 12월 15일–2008년 2월 15일) 으로 재직한 바 있다.

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