Welfare Reform/Child Well-Being Administrative Data Linking

By South Carolina Department of Social Services

The South Carolina Department of Social Services (SCDSS), along with its partners, have been pleased with the success of the CHILD LINK Project and will continue in the future to build upon its efforts.

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Capability of the Linked Databases

CHILD LINK enabled SCDSS and its partners to link the following databases:

CHIPS

Client History and Information Profile System. This database contains administrative and payment information regarding both AFDC/TANF and Food Stamps cases;

CIS

Client Information System. This system contains administrative information regarding client eligibility for Medicaid services;

Medicaid Services Payment System

This database actually resides with another state agency, the SC Department of Health and Human Services, and contains Medicaid payment data;

Work Support (or WNAT)

This database contains client data about work support eligibility, participation, and services;

TitleXX

This system contains information about child and adult protective services and foster care services;

CPS

Child Protective Services. The CPS system contains investigative information on Child Abuse & Neglect cases;

Foster Care Tracking

This system contains information about foster children and their placements;

State of South Carolina Masterfile

This file contains all services for one calendar year from several health and human service type agencies such as the Department of Juvenile Justice, Department of Alcohol and Other Drug Abuse Services, and the Department of Health and Environmental Control;

Employment Security Commission (ESC) Wage Match

This file contains quarterly wage information for all South Carolina employers required to report wage data.

All of these files are statistical SAS databases that are individually stripped off of the agencies’ legacy systems and linked statistically. Each file is updated either monthly or quarterly (Medicaid Eligibility, Medicaid Payment System, and the ESC information are updated quarterly) with the exception of the state’s Masterfile. The information in the CHILD LINK warehouse is only for those CHIPS clients who matched to this enormous population file. The statistical warehouse stores historic information on all clients from each of these systems so that SCDSS can measure change. In many ways, the CHIPS system served as the primary "linker" file to which the other files were linked. There are a couple exceptions. Because of the uniqueness and the lack of identifiers in the Child Protective Services, SCDSS relied heavily on the TitleXX number that caseworkers added to the legacy system. Thus a more natural linkage was to link Child Protective Services first to TitleXX. Likewise the Foster Care Tracking was first linked with the TitleXX system. The TitleXX system was then linked to CHIPS. For the other data systems, CHIPS linked directly.

CHILD LINK enabled SC to build software to extract identified data elements, create update programs, and build software to link the files statistically. Most importantly CHILD LINK helped SC to build up expertise in these data systems fostering relationships between program specialists and researchers.

This linked database has enormously increased the capability in South Carolina to examine a variety of issues. For example:

• By including employment information, we can examine employment patterns and the job retention of our clients and changes in their quarterly wages;
• Because this information is also linked to the Medicaid files, we can explore changes in Medicaid utilization after becoming employed. Because all clients are tracked including children, we can further investigate the Medicaid utilization for children after a parent becomes employed;
• By linking to the state’s Masterfile, we can review what other agencies SCDSS clients access. As these clients leave the welfare rolls and the state’s Masterfile builds historic information, we can check if utilization patterns change. For example, as a client leaves welfare, do they access other agencies more to help them through the transition period?
• Because all this information also is linked to the human services systems, additional questions on child welfare can be asked. Do rates of abuse and neglect increase as a client leaves welfare? Other questions regarding the cycle of poverty can also be asked. Do former foster care clients become TANF clients?
• Even more questions can be asked regarding impacts on other agencies. Are children who are abused and/or neglected more likely to end up in the Department of Juvenile Justice system?
• Additionally, CHILD LINK enabled us to geocode or address match our founded child abuse and neglect cases to the Census Block Group. This allowed us to add a further dimension by helping us to locate potential "high-risk" neighborhoods.

As welfare reform continues to evolve and more clients leave the rolls, these questions and many more are important. South Carolina believes it is critical to ensure that there are few (if any) negative consequences to clients, especially children.

Implementation Issues

SC is pleased with the implementation process. There are several structural issues that contributed to the success of the project.

• For CHILD LINK, a Management Team was created which met regularly. Members included key SCDSS researchers and upper management, program specialists in the human services systems, our partners with the Department of Health and Human Services and our partners with the Budget and Control Board’s Office of Research and Statistics (ORS). The management team served several functions such as:
  • Educating team members regarding the capability of a linked database;
  • Providing the primary lead in brainstorming on possible analyses;
  • Reviewing progress reports on the implementation issues;
  • Utilizing the expertise in the management team to check for any biases; and
  • Tracking progress and overcoming any obstacles.
• Our partnership with the SC Budget and Control Board’s Office of Research and Statistics (ORS) also contributed to the success of the project. ORS functions as a service and research agency to state government. As part of their on-going work with the SCDSS, they had already created a statistical warehouse using the CHIPS system and had gained experience in dealing with those administrative files. In addition, they also had experience in analyzing and linking a number of administrative files to the Medicaid system and the state Masterfile.
• As members of the Governor’s Cabinet, the SCDSS has a solid relationship with the SC Department of Health and Human Services (SCDHHS). While a confidentiality agreement was necessary to link the CHIP system to the Medicaid system, that agreement was sketched out in a brief meeting with the CHILD LINK lead coordinator, ORS, and SCDHHS. As noted before, SCDHHS was invited to join the CHILD LINK Management Team. Use of the state Masterfile was similarly permitted since the Governor’s Office controls its release. However a confidentiality agreement had to be negotiated with the SC Employment Security Commission. Because of ORS’s position as a service agency, it was agreed that the data should reside there.
• In the course of the CHILD LINK grant, a number of technical issues arose. One particular issue was the conscious decision to link the files using various combinations of known identifiers as opposed to probabilistic matching which is extensively used in the research world. To check the validity of the matches, multiple random samples were pulled and the matches reviewed. Because of the lack of identifiers especially on the Child Protective Services database and the lack of experience in using the Human Service files, it was agreed that this was the proper course for SC. ORS, however in another project unrelated to CHILD LINK, will be doing a comparison study of the more traditional approach to probabilistic matching. It is expected that this comparison will provide valuable insights.

Summary of Completed Research and Analysis

A number of research products were initiated. The following lists some of the issues under examination, a description of the process, and the results of the research.

1. Research Question: Do clients who leave TANF have a higher usage rate of TitleXX and child welfare services?

   Description of the Process: Using the linkage established between the CHIPS system and the TitleXX system, ORS staff refined the information to primarily focus on the CHILD population (children 0-17 years old). Staff proceeded first by sub-setting the data set to children only. After that step, children were separated into two further sub-populations: children who have received AFDC/TANF and children who are in the Food Stamps only population. Two populations were created: "Before TANF" and an "After TANF". These two populations were followed for twelve months (controlling for seasonality) in a longitudinal study to determine by comparison if the "After TANF" population was more likely to incur higher rates of TitleXX services and particularly those related to child abuse and neglect. In order to control bias, both starting populations were controlled for previous TitleXX services experience.

   Results: While initial analysis suggests that the rate for the TitleXX services did not appear to increase for the "After TANF" population, that population needs to be continually studied and refined. The results of some of this work were presented at The National Association for Welfare Research and Statistics held in Chicago from August 2-5, 1998.

2. Research Question: Are there particular neighborhoods with higher than normal rates of abuse and neglect?

   Description of the Process: From the Child Protective Services database, a 3-4 year trend database of founded cases only was extracted. This trend database was geocoded to the neighborhood level in order to locate "high rates/numbers of abuse and neglect". Maps were produced statewide by zip code showing absolute numbers and rates of children (rates were calculated using a 1996 estimate of children by zip code). In addition, maps at the neighborhood (census block groups) level were produced for Lexington, Richland, and Newberry. For these neighborhood maps, both absolute numbers and rates using census information were included.

   Results: Results do indicate that there are neighborhoods with higher than usual rates of abuse and neglect. This information was also shared with another project "Safe Futures" which is a coalition of state and local agencies in the Midlands area (four-county geographic region) concerned with youth violence. The "Safe Futures" project used the abused and neglected maps along with Department of Juvenile Justice information to target high-risk neighborhoods. In additional several counties have requested to have their TANF caseloads as well as their CPS founded cases be mapped. CHILD LINK has enabled us to respond to those requests.

3. Research Question: Has Medicaid utilization changed for any member of a TANF household where at least 1 adult member has become employed?

   Description of the Process: In this study, we began with all AFDC/TANF cases that had closed for earned income. In order to provide adequate time frames to detect differences in utilization and to control for seasonality, the study population required 1-year of pre-employment activity and 1-year of post employment activity. Therefore each of these cases must have been an active AFDC/TANF case for at least one year prior to the closure date. In order to be retained, the case must have had at least one adult earning wages in the first and last quarters of the post closure year.

   Because varying Medicaid Eligibility coverage can affect results, the Medicaid Eligibility coverage was next examined for any biases. This study used a working definition of Medicaid Eligibility coverage of 700 or more days of Medicaid eligibility (2 years less 1 month). Claims essentially were divided into three types: HIC (physician, clinic, and laboratory claims), Outpatient (which includes both emergency and non-emergency room visits) and Inpatient hospitalization. For HIC claims, only physician visits were included. Outpatient claims were further sub-categorized as emergency and non-emergency visits. In addition, outpatient and HIC claims were summed thereby providing an index for ambulatory care utilization. For each claim type and combination of claims, the number of paid claims for the pre-employment and post-employment periods were summed. One way Analysis of Variance (ANOVA) with repeated-measures factor was the tool selected to use for the analysis. This tool allowed for the analysis of the number of Medicaid claims per individual filed one year prior to employment versus the number of claims filed one year after employment. In an attempt to control for natural changes in medical care utilization as individuals age, the analysis was stratified by age.

   Results: Our analysis finds virtually no support for employment effects on Medicaid utilization by former TANF recipients. The evidence is weak and inconsistent. The substantive differences among pre- and post – employment means is typically .2 claims per person and never exceeds .45 claims when all claim categories are combined. Few of these differences are statistically significant at the .05 level.

4. Additional Uses of the Linked Databases: SC conducts a quarterly random sample survey of its TANF Leavers. While the response rate to the survey is excellent (for the interviews conducted during June, July, and August 1998, the response rate was 76%); there is of course concern on the well being of the remaining percentage of clients. To locate these "unavailable" clients, ORS linked all adult household members of the TANF Leavers survey to the ESC Wage match. For Waves 1 and 2 of the TANF Leavers survey, ORS linked overall 67% of the clients to the ESC files where the client had wages after the survey’s interview date. Of those clients who completed the survey, the match rate was 71% (for not completed: the match rate was 55%). The "not completed" category breaks down into three sub-types: no answer, unavailable, and refused. Their corresponding match rates were 69% (no answer), 44% (unavailable), and 81% (refused). In addition to looking at match rates by the various interview result categories, ORS also calculated average number of quarters with wages greater than $0, average wages earned in the last quarter and the average number of jobs in the last quarter. To examine if there were any significant differences, ORS performed several T-Tests on differences of the means. The results showed no significant differences in average wages earned last quarter among the various categories of clients.

   Using the statistical warehouse, SCDSS plans to study two particularly important populations in the near future. One such population is all sanctioned clients. Another population would be the abuse and neglected "founded" cases. In the latter study, we would link to the emergency room and hospital databases to determine if we could have predicted in advance these abuse and neglected cases.

Description of Products

A number of products were produced as a result of the CHILD LINK project.

• Details of the linkage, methodology, and preliminary results were presented at The National Association for Welfare Research and Statistics held in Chicago from August 2-5, 1998.
• While not formally presented or published, a paper was written on the results of analysis on Medicaid utilization before and after employment. (See attached)
• A workshop on the capabilities of the CHILD LINK statistical warehouse is being prepared for all SCDSS upper management, research, and county director level staff. It is envisioned that through this workshop, new ideas on the potential used of this warehouse will be brainstormed.
• The success of CHILD LINK in creating a statistical system helped position SCDSS in furthering its research goals in the TANF and Food Stamp Leavers grants.
• A number of maps and printouts were distributed to SCDSS management, policy and program specialists and County front-line staff. Some examples include:
• An analysis on Foster Care children who left Foster Care (where their case closed but had not aged out), who later reappear as Foster Care cases and/or Child Protective Services cases.
• An analysis on children who aged out of the Foster Care system to determine how many later reappeared on CHIPS as a TANF or Food Stamps case and/or were tracked in the ESC Wage Match file.
• Maps (and printouts) showing TANF cases with Founded Abused and Neglected Cases at the Neighborhood level (Census Block Group) for several counties.
• Maps showing Founded Abused and Neglected Children and Violent Juvenile Justice children at the Neighborhood Level (Census Block Group) for several counties.

Future Expectations

SCDSS is planning to continue to maintain and expand the linked database in the future.

• In addition to its continually relationship with ORS, SCDSS has been notified of receipt of two additional grants focusing on TANF and Food Stamp Leavers. In the TANF Leavers project, we plan to expand and enrich our current survey on clients who have left the welfare rolls and have not returned. We will also create a longitudinal database by revisiting former clients thereby enriching our knowledge on their well being. This longitudinal database and the results of the expanded survey will be linked to the rest of the statistical warehouse.
• In addition to the TANF Leavers project, we will be expanding our survey instrument to include Food Stamp Leavers. Again the results of this effort will be linked to the full statistical warehouse. In addition as part of the Food Stamp Leavers project, we anticipate using the statistical warehouse to complete an administrative study.
• During the CHILD LINK period, SCDSS was building a new human services system (known as SACWIS). While too late to be built into the CHILD LINK work, SCDSS has plans to incorporate the new database into the statistical warehouse.

Appendix: Medicaid Utilization: A Comparison between Pre- and Post-Employment

Introduction

With the onset of Welfare Reform, numerous research questions have arisen. One overall prevailing question that continues to haunt Welfare Reform is whether members of a household (particularly children) have been hurt in the implementation of Welfare Reform and the onset of employment. There are indeed many questions on what happens inside the household now that a parent or head of the household must work. While this paper does not offer conclusive answers to these and other questions, it does however offer a brief examination into a household's utilization of Medicaid medical services before and after employment. While utilization of Medicaid medical services does not ensure good health and the lack of utilization does not necessarily indicate "poor" health, changes in the utilization patterns could serve as a warning signal to policy makers - giving administrators the opportunity to investigate. This paper examines the research question "Has Medicaid utilization changed for any member of a TANF household where at least 1 adult member has become employed?" While the findings of this paper do not answer that question conclusively, it does provide some preliminary information using several key administrative files.

Background

The SC Department of Social Services (SCDSS), with the assistance of the Budget and Control Board's Office of Research (ORS), has developed a statistical data warehouse. In this statistical data warehouse, information from several key administrative files are stripped off (either on a monthly or a quarterly basis) and are linked together to answer key research questions. This statistical data warehouse has in part become available through the result of the CHILD LINK federal grant that supported the development of Child Well-Being indicators. Key administrative files have included information from SCDSS's automated CHIPS systems (TANF and Food Stamps only populations); Child Welfare systems like Title XX, Foster Care Tracking, and Child Protective Services; Employment Security System's (ESC) WAGE match which provides quarterly wages on employed SCDSS clients; and the Department of Health and Human Services Medicaid Payment (and Eligibility) systems. For this study, information on the TANF population off of the SCDSS CHIPS system, from ESC's Wage Match, and from the Medicaid Eligibility and Payment systems was utilized.

Study Population

Because the intent of this study was to examine Medicaid Utilization before and after employment, selection of the study population was critical. The initial population of SCDSS AFDC/TANF clients was selected based upon a number of criteria.

1. First, all cases must have been coded as an AFDC/TANF case, though not every client associated with a particular case had to be coded as an AFDC/TANF participant.
2. Second, the case had to be closed for earned income (closure code of 'EI') within a time frame that allowed for adequate tracking using quarterly ESC Wage Match data. Given the availability of ESC data at the time of the study, these cases closed in June 1996, September 1996, or December 1996. No restriction on subsequent returns and closures was imposed.
3. In order to provide adequate time frames to detect differences in utilization and to control for seasonality, the study population required l-year of pre-employment activity (and 1-year of post employment activity - see next step). Therefore third, each of these cases must have been an active AFDC/TANF case for at least one year prior to the closure date.
4. The resulting population from step 3 was then matched to the quarterly ESC Wage Match information. In order to be retained, the case must have had at lease one adult earning wages in the first and last quarters of the post closure year. Continuous employment however was not a requirement.
5. After controlling for one-year pre-employment and one-year post employment and the other above criteria, the resulting population was 1,635 clients. These clients then underwent cleaning to insure that any possible changes in name, etc. were captured for linking purposes. Date constants were also added to mark the beginning of the "pre-" period, the closure date, and the ending of the "post-" period.
6. The 1,635 starting population was next linked to the Medicaid Eligibility files. The matching criteria used varying combinations of a number of key identifiers: SSN, full name, and date of birth. Numerous quality control checks were performed to ensure that clients were matched correctly. 1,557 of the 1,635 SCDSS clients identified linked to the Medicaid Eligibility files resulting in a 95.2% match rate.
7. Because varying Medicaid Eligibility coverage can affect results, the Medicaid Eligibility coverage was next examined for any biases. This study used a working definition of Medicaid Eligibility coverage of 700 or more days of Medicaid eligibility (2 years less 1 month). One thousand and thirty one clients (1,031) met that definition.
8. One result of using Medicaid coverage definition of 700+ days is that it eliminated most of the newborns. While there, is a great deal of interest, of course, about newborns, it was felt that within the confines of this study that an adequate comparison could not be done and that it would bias the study. All pregnancy related and post-partum claims were also excluded. Again, the purpose was to eliminate any bias introduced by a roughly nine (9) month gestation period within the context of a two-year study. For example, impact of conception in the post-period would fall outside the study's time frame, while pregnancies from the pre-period might result in deliveries in the post period. Differences in the lengths of individual pregnancies confounded our inability to determine dates of conceptions necessary to account for these claims. Therefore all claims of these types were excluded to eliminate their confounding effects. In addition, mothers who are pregnant and newborns require higher numbers of doctor's visits and other types of claims.. Over a two year time period that could again bias the results by inflating the number of claims. Because of the natural "aging" process and the 2-year time period, pregnancy related and post partum claims may not be "evenly distributed" across the period. For example, mothers who had a baby in the first year (pre-employment) period were less likely to have a baby in the next period (or post-employment) period.
9. Those clients who met the 700 or more day requirement were next linked to the Medicaid Services files. Claims essentially were divided into three types: HIC (physician, clinic, and laboratory claims), Outpatient (which includes both emergency and non-emergency room visits) and Inpatient hospitalization. For HIC claims, only-physician visits were included. Outpatient claims were further sub-categorized as emergency and non-emergency visits. In addition, outpatient and HIC claims were summed thereby providing an index for ambulatory care utilization.
10. For each claim type and combination of claims, the number of paid claims for the pre-employment and post-employment periods were summed.

Design

1. One way Analysis of Variance (ANOVA) with repeated-measures factor was the tool elected to use for the analysis. This tool allowed for the analysis of the number of Medicaid claims per individual filed one year prior to employment versus the number of claims filed one year after employment.
2. In an attempt to control for natural changes in medical care utilization as individuals age, the analysis was stratified by age. The design is affected by natural changes in people that may occur over time. Because the same population is included in both periods of time - the population quite literally is aging. This aging could bias the results. For example, a child from two years old to three years old may go to the doctor more often for illnesses than that same child who ages from three years old to four years old. Elderly adults also have much higher rates of utilization. The data allowed for three general age classifications to maintain sufficient sample sizes to detect meaningful differences in Medicaid utilization. Each age group had approximately 30% of the population. These age groups were defined as 0-5 years, 6-17 years, and 18 years and over. The pre-employment. and post-employment time frames covered the same months for any given individual thereby controlling for seasonal variations in medical care utilization.
3. In order to partially test for this aging bias, a similar analysis was repeated on the entire 0-5 year old Medicaid population.

Limitations

1. Analysis of variance with repeated measures is subject to some statistical assumptions involving the sample distributions. Repeated measures ANOVA assumes normal distributions with homogeneous variance in the pre- and post- groups. Unlike two-way and r-way ANOVA and least squares regression, repeated measures ANOVA is not particularly robust when these assumptions are violated. Violations tend to result in inflated F-ratios and greater likelihood of falsely rejecting the null hypotheses. In the case of this study, plots did not uncover dramatic departures from normality and the distributions will converge towards normality as sample size increases (i.e. they are asymmetrically normal). Plots also indicate rough homogeneity of variance though formal tests were not performed given the weakness of the substantive findings.
2. The primary limitation of this design is the lack of a control group. Since all subjects in the study had at least one person in the household who became employed, no comparison can be made that can evaluate whether any potential differences in Medicaid utilization are due to changes in employment of persons in the household. In addition, finally, there may be factors other than employment, which explain any potential differences observed in Medicaid utilization. Examples of potential confounding factors in this study could be age or obtaining private medical insurance with employment.

Conclusions

Our analysis finds virtually no support for employment effects on Medicaid utilization by former TANF recipients. The evidence is weak and inconsistent. The substantive differences among pre- and post - employment means is typically .2 claims per person and never exceeds .45 claims when all claim categories are combined. Not surprisedly, few of these differences are statistically significant at the .05 level. Furthermore these insignificant results occur using a technique where undiagnosed violations of underlying statistical assumptions make finding a significant relationship more, rather than less, likely.

More importantly only one specific finding, a decrease in outpatient claims among 18+ year olds is supportive of the employment hypothesis. The magnitude of this difference is not large enough to effect overall claims. Total claims, as well as HIC claims, are instead attributable to aging and differences in the health care needs of 0-5 year olds. It is clear from our analysis of the Medicaid population as a whole, that Medicaid utilization declines as children move from infancy into childhood. This pattern is also found in the SCDSS linked population. In this case, over-all differences in the number of claims are attributable to decrease among 0-5 year olds, particularly through reductions in the number of HIC claims by this group. Thus overall differences in pre- and post- employment Medicaid utilization are an artifact of aging by 0-5 year olds during the time frame of this study.

While the findings for employment effects on Medicaid Utilization are, at best, very weak, it should not be inferred that these relationships do not merit further investigation. This initial study should be supplanted and refined with additional data, and by alternative research designs. A longer time frame might reveal relationships missed by this study. Continued expansion of the Employment Security Commission data set should allow for 18 month pre- and post- periods by early 1999. The results presented here also suggest that a more detailed comparison of the pre- and post- employment SCDSS populations to the general Medicaid populations be in order if only to determine how the SCDSS population differs.

Likewise other analytic strategies should prove useful. For example, the current study focused on controlling for the individual's state of health pre- and post- by examining the same person at each point in time (i.e. matched pairs). This strength brought certain trade-offs such as the inability to control for continuous variables effectively, the inability to use multiple controls, and a lost of statistical robustness. Future studies might use more powerful multivariate techniques coupled with a proxy to control for the health status of the individual.

Table 1.
SCDSS-Medicaid Linked Population
Pre-Employment versus Post-Employment Medicaid Utilization

Claim Type	N	Mean	Std Dev.	ANOVA. Results	P < 0.05?
HIC  All ages:     Pre-employment                    Post-employment  0-5 years:   Pre-employment                    Post-employment  6-17 years: Pre-employment                    Post-employment  18 + years: Pre-employment                    Post-employment	1031 1031 337 337 412 412 282 282	1.42 1.25 1.27 1.02 1.14 1.04 2.01 1.84	3.52 2.85 2.70 2.46 3.21 2.68 4.60 3.40	F value = 3.51, p=0.061 F value = 4.26, p=0.040 F value = 0.48, p=0.487 F value = 0.63, p=0.428	No Yes No No
Outpatient - All  All ages:     Pre-employment                    Post-employment  0-5 years:   Pre-employment                    Post-employment  6-17 years: Pre-employment                    Post-employment  18 + years: Pre-employment                    Post-employment Outpatient - Emergency Room  All ages:     Pre-employment                    Post-employment  0-5 years:   Pre-employment                    Post-employment  6-17 years: Pre-employment                    Post-employment  18 + years: Pre-employment                    Post-employment Non-Emergency Room  All ages:     Pre-employment                    Post-employment  0-5 years:   Pre-employment                    Post-employment  6-17 years: Pre-employment                    Post-employment  18 + years: Pre-employment                    Post-employment	1031 1031 337 337 412 412 282 282 1031 1031 337 337 412 412 282 282 1031 1031 337 337 412 412 282 282	1.39 1.19 1.33 1.18 0.94 0.81 2.12 1.75 0.77 0.66 0.83 0.69 0.51 0.44 1.07 0.93 0.62 0.53 0.50 0.49 0.43 0.37 1.06 0.82	2.59 2.05 2.03 2.05 1.65 1.52 3.85 2.55 1.82 1.27 1.35 1.13 1.00 0.87 2.88 1.77 1.56 1.41 1.37 1.64 1.20 1.02 2.07 1.55	F value = 7.53, p=0.006 F value = 1.28, p=0.259 F value = 2.50, p=0.114 F value = 4.13, p=0.043 F value = 5.55, p=0.019 F value = 3.36, p=0.068 F value = 1.18, p=0.179 F value = 1.27, p=0.261 F value = 3.10, p=0.079 F value = 0.01, p=0.907 F value = 1.03, p=0.310 F value = 3.81, p=0.052	Yes No No Yes Yes No No No No No No No
HIC + All Outpatient  All ages:     Pre-employment                    Post-employment  0-5 years:   Pre-employment                    Post-employment  6-17 years: Pre-employment                    Post-employment  18 + years: Pre-employment                    Post-employment	1031 1031 337 337 412 412 282 282	2.81 2.44 2.60 2.20 2.07 1.86 4.13 3.58	4.79 3.91 3.68 3.48 3.86 3.48 6.59 4.69	F value = 9.30, p=0.002 F value = 4.45, p=0.036 F value = 1.97, p=0.161 F value = 3.28, p=0.071	Yes Yes No No

Table 2.
Medicaid - Eligible Children (Ages 0-5 Years)
Care Utilization

Claim Type	N	Mean	Std Dev.	ANOVA Results	P < 0.05?
HIC  0-5 years: 1/1/96-12/31/96                  1/1/97-12/31/97	54644 54644	1.71 1.62	4.37 3.70	F value = 27.56, p=0.0001	Yes
Outpatient - All  0-5 years: 1/1/96-12/31/96                  1/1/97-12/31/97 Outpatient - Emergency Room  0-5 years: 1/1/96-12/31/96                  1/1/97-12/31/97 Outpatient-Non-Emergency Room  0-5 years: 1/1/96-12/31/96                  1/1/97-12/31/97	54644 54644 54644 54644 54644 54644	1.52 1.26 0.85 0.71 0.67 0.56	2.77 2.47 1.37 1.20 2.20 1.98	F value = 652.48, p=0.0001 F value = 602.82, p=0.0001 F value = 213.61, p=0.0001	Yes Yes Yes
HIC + All Outpatient  0-5 years: 1/1/96-12/31/96                  1/1/97-12/31/97	54644 54644	3.23 2.90	5.61 4.89	F value = 295.14, p=0.0001	Yes