Medicaid Managed Care Structures
and Care Coordination
Douglas H. Gilchrist-Scott, MD, MPH, a James A. Feinstein, MD, MPH, b Rishi Agrawal, MD, MPHc
BACKGROUND: Child enrollment in Medicaid managed care (MMC) has expanded dramatically, primarily through state mandates. Care coordination is a key metric in MMC evaluation because it drives much of the proposed cost savings and may be associated with improved health outcomes and utilization. We evaluated the relationships between enrollment in 2 MMC structures, primary care case management (PCCM) and health maintenance organization (HMO) and access to and receipt of care coordination by children.
METHODS: Using data from the 2011/2012 National Survey of Children’s Health and the Medicaid Statistical Information System state data mart, we conducted a retrospective, cross-sectional analysis of the relationships between fee-for-service, PCCM or HMO enrollment, and access to and receipt of care coordination. State-level univariate analyses and individual and state multilevel multivariable analyses evaluated correlations between MMC enrollment and care coordination, controlling for demographic characteristics and state financing levels.
RESULTS: In univariate and multilevel multivariable analyses, the PCCM penetration rate was significantly associated with increased access to care coordination (adjusted odds ratio: 1.23, P = .034) and receipt of care coordination (adjusted odds ratio: 1.37, P = .02). The HMO penetration rate was significantly associated with lower access to care coordination (adjusted odds ratio: 0.85, P = .05) and receipt of care coordination (adjusted odds ratio: 0.71, P < .001). Fee-for-service served as the referent.
CONCLUSIONS: State utilization of MMC varied widely. These data suggest that care
coordination may be more effective in PCCM than HMO structures. States should consider care coordination outcomes when structuring their Medicaid programs.
abstract
aDepartment of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago,
Illinois; bAdult and Child Consortium for Health Outcomes Research and Delivery Science (ACCORDS), and
Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado; and cDepartment of
Pediatrics, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
Dr Gilchrist-Scott conceptualized and designed the study, acquired the data, conducted the initial analyses, analyzed and interpreted the data, and drafted the initial manuscript; Drs Feinstein and Agrawal conceptualized and designed the study, analyzed and interpreted the data, and drafted the initial manuscript; and all authors approved the final manuscript as written.
DOI: https:// doi. org/ 10. 1542/ peds. 2016- 3820 Accepted for publication Jun 6, 2017
Address correspondence to Rishi Agrawal, MD, MPH, Pediatrics, Ann and Robert H. Lurie
Children’s Hospital of Chicago, 225 E Chicago Ave, Chicago, IL 60610. E-mail: ragrawal@
luriechildrens.org
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
Copyright © 2017 by the American Academy of Pediatrics
What’s KnOWn On thIs subject: Medicaid health maintenance organizations and primary care case management models are commonly used by states to improve care coordination. However, it is unknown whether such models are associated with receipt of care coordination.
What thIs stuDy aDDs: Primary care case management adoption is associated with increased receipt of care coordination, whereas health maintenance organization usage is associated with decreased receipt of care coordination.
to cite: Gilchrist-Scott DH, Feinstein JA, Agrawal R. Medicaid
Managed Care Structures and Care Coordination. Pediatrics.
To improve value, state Medicaid organizations are shifting children in Medicaid from pure fee-for-service (FFS) arrangements to those that seek to incentivize care coordination.1
Over the past 3 decades, mandatory enrollment in Medicaid managed care (MMC) has expanded dramatically, 2, 3
with states using 2 primary structures: contracts with capitated health maintenance organizations (HMOs) and primary care case management (PCCM) programs, which add care coordination incentives to FFS payment to providers. These organizations now encompass >3 quarters of all Medicaid enrollment.2
One of the primary goals of such organizations (and a justification for their implementation given by state policymakers) is to achieve cost savings and improve care outcomes through coordination of services for chronically ill individuals and others requiring services from multiple providers.1 For pediatric populations,
the American Academy of Pediatrics (AAP) has established guidelines for the family-centered medical home, of which care coordination is a key component.4, 5 However,
there is limited research in which researchers evaluate the association of these financing structures with the receipt of care coordination or other downstream outcomes, particularly within the pediatric population.6–10
Our objective was to determine the association between state enrollment of children in MMC and access to and receipt of effective care coordination among the pediatric population. Our findings may help to inform policy makers about the influence of financial and organizational structures on the receipt of care coordination, which may have downstream effects on cost and quality of care.
MethODs
study Design and Population
In this retrospective, cross-sectional analysis, we examine the relationship between state-level MMC variables
and individual-level indicators of care coordination within those states. We included all states in the state-level analysis and all children who received public insurance in the individual-level analysis.
Data sources
State-level HMO and PCCM penetration, defined as the
percentage of children on Medicaid who are enrolled in a given plan type, were drawn from the Medicaid Statistical Information System (MSIS) state data mart.11 These publicly
available data describe the eligibility criteria and plan enrollment for all children enrolled in Medicaid (including children enrolled in Medicaid-expanded Children’s Health Insurance Programs [CHIPs]), as reported to the Centers for Medicare and Medicaid Services (CMS) by each state. MSIS data are routinely reviewed to validate data elements against other reported data and ensure consistency between different data elements and within data elements across time. Discrepancies are publicly documented on the CMS Web site.12 For these analyses, the
MSIS state data mart was queried to determine the number of unique individuals eligible for Medicaid who qualified for the program as children, children of unemployed parents, or foster children. States that operate a separate CHIP program apart from their Medicaid program are not required to submit data in which individuals enrolled in the separate program are described, although voluntary submission of these data are accepted and included in the MSIS.13 Limited demographic
data in which race, sex, and age were described were collected to inform sensitivity analyses. All data were reported at the state aggregate level. Individual-level care coordination and demographic measures were drawn from the National Survey of Children’s Health (NSCH). The Data Resource Center for Child and Adolescent Health reports a series of validated
medical home metrics, which assess individuals’ need for, access to, and satisfaction with various components of the AAP Medical Home model, including access to care coordination (NSCH Indicator 4.9d) and receipt of care coordination when needed (NSCH variable “carehelp”). No modifications to these data were performed by the research team; the derivation of these subcomponents is described in more detail in the NSCH data guide.14
The 2010 Federal Medical Assistance Percentage (FMAP), which is the federal contribution to each state Medicaid program, was obtained from CMS.
Data analysis
The key outcome variables were (1) access to care coordination, which represents whether the individual’s care meets the AAP requirements of care coordination (NSCH Indicator 4.9d); and (2) receipt of care coordination when needed, which represents those individuals who, in their own approximation, received adequate care coordination services when they needed them (NSCH element “carehelp”).
The analysis was performed in 3 steps. First, by using the provided NSCH weights, we calculated the estimated percentage of publicly insured children (as defined by the NSCH to include any publicly financed and administered health insurance) who received each of the care coordination target outcomes within each state. Publicly insured children include children enrolled in Medicaid (81.43%), children enrolled in Medicaid-expanded CHIPs (10.39%), children enrolled in separate CHIPs (8.15%) and children enrolled in Medicare.15 Variance among
between HMO and PCCM penetration and care coordination outcomes along with demographic chara cteristics found to be relevant in the literature.16, 17
Selected demographic characteristics included the respondents’ race, household education (less than a high school education, general education development (GED) or high school diploma, or any education beyond a GED or high school diploma), ethnicity, primary language, and health status, all of which were drawn from the NSCH data. Third, we performed 4 multilevel logistic regressions to estimate the relationship between the state-level HMO or PCCM penetration rates and the odds of having access to care coordination or receiving care coordination when needed compared with those enrolled in traditional Medicaid FFS payment structures. Individual- and state-level controls were introduced into the model to control for all individual sociodemographic characteristics and FMAP, estimated from previous research and our own unadjusted state-level correlations to be associated with medical home outcomes and the probability of enrollment in MMC.16
Fixed effects were used to estimate all predictor coefficients, allowing for a random intercept at the state level. No interaction or random slope coefficients were introduced because the structure of the data did not allow for a full mixed model. Multinomial logistic regression was used for responses with 3 possible outcomes.
Given the complex nature of the NSCH data and its structure, we reweighted the data to isolate publicly insured children, which is not a sampling stratum of the NSCH. The NSCH weights14 were rescaled to account for
the individual sampling probability, rather than the total population weight reported in the NSCH, as required by our model.18 We used near-0
weighting of non-Medicaid enrolled participants to generate Medicaid-specific subpopulation estimates of all predictors while maintaining the full set of data for variance estimation.19
At the state level (level 2), uniform weights were applied in all model iterations to generate a national estimate of the fixed effects of HMO and PCCM enrollment, independent of state population.
Data cleaning, preparation, design weight scaling, and univariate state-level analyses were performed in SAS 9.3 (SAS Institute, Cary, NC). Multilevel logistic regression was performed by using the Stata add-on GLLAMM.20 sensitivity analysis
Although the NSCH is widely used to generate national and state-level estimates of health outcomes among children, it is not explicitly stratified to sample children enrolled in Medicaid and Medicaid-expanded CHIP. To assess the comparability of the MSIS and NSCH data sources, we compared demographic characteristics at the state level. We used MSIS as the true population value (because it is not sampled data) for our analysis. We then evaluated whether each MSIS demographic variable fell within the 95% confidence interval (CI) generated by the NSCH sample. Age and sex showed no significant discrepancies between the 2 data sets. Mild differences were observed in Hispanic ethnicity and white race. However, different classifications of race and ethnicity between the 2 data sets made direct comparison difficult. Furthermore, errors in race documentation and translation from state-specific to standardized MSIS measures have been noted by CMS.12 No significant differences were
observed when race was collapsed into an African American and non-African American measure. To ensure that these race and ethnicity differences did not influence the conclusions of the analysis, we performed linear analyses, modeling the medical home outcomes by using only the common demographic measures within the 2 data sets. These analyses revealed no statistically significant difference between the model estimates of the 2 data sets,
as measured by a dummy variable indicating the data source.
Results
A total of 95677 individuals were surveyed within the NSCH, of which 27381 were publicly insured. Among the publicly insured respondents, 19671 were eligible for care coordination services based on their care utilization rate. Nationally, 46.7% of children enrolled in Medicaid were nonwhite, 26.5% came from a non-English‒speaking household, and 48.6% came from a household falling <100% of the federal poverty line; 96.2% fell <300% (Table 1). Children with special health care needs (CSHCN) comprised 23.4% of weighted survey responses.
In Figs 1 and 2, we present a geographic representation of access to and receipt of care coordination when needed superimposed onto HMO and PCCM penetration rates by state for 2011. A tabular form of these data are provided in Supplemental Table 2. In unadjusted state-level analyses, higher levels of PCCM penetration were significantly associated with greater availability of care coordination (β: 5.25, P = .01) and receipt of care coordination when needed (β: 9.48, P = .0007), whereas higher HMO penetration was significantly associated with lower availability of care coordination (β: −3.98, P = .03) and receipt of care coordination when needed (β: −10.18, P ≤ .0001). P values were calculated from standard scores.
table
1
Univariate and Multilevel Multivariable Associations Between Care Coordination Outcomes and Individual- and State-level Variabl
es, 2011 Data From the NSCH, MSIS, and CMM
Variables
Access to Care Coordination
Receipt of Care Coordination When Needed
Unadjusted
Multivariable PCCM Model
Multivariable HMO Model
Unadjusted
Multivariable PCCM Model
Multivariable HMO Model
Individual demographics Sex
Boy 0.86 (0.78 – 0.96) 0.92 (0.82 – 1.03) 0.92 (0.81 – 1.03) 0.87 (0.76 – 1.01) 0.92 (0.79 – 1.06) 0.92 (0.79 – 1.06) Age <1 y 1.19 (0.90 – 1.56) 0.98 (0.74 – 1.30) 0.98 (0.74 – 1.30) 1.55 (0.99 – 2.41) 1.50 (0.94 – 2.39) 1.49 (0.93 – 2.38) 1 – 5 y 1.1 (0.99 – 1.21) 0.99 (0.30 – 1.11) 1.00 (0.90 – 1.10) 1.09 (0.96 – 1.24) 1.04 (0.90 – 1.19) 1.04 (0.90 – 1.20) 6 – 18 y — — — — — — — — — — — — Race White — — — — — — — — — — — — African American 0.87 (0.76 – 1.01) 0.84 (0.73 – 0.98) 0.84 (0.73 – 0.98) 0.67 (0.53 – 0.85) 0.64 (0.50 – 0.83) 0.65 (0.51 – 0.83) Other 0.85 (0.75 – 0.96) 0.85 (0.74 – 0.97) 0.85 (0.74 – 0.97) 0.76 (0.64 – 0.90) 0.80 (0.67 – 0.95) 0.80 (0.67 – 0.95) Non-Hispanic 0.97 (0.85 – 1.12) 0.98 (0.80 – 1.19) 0.98 (0.80 – 1.19) 0.88 (0.76 – 1.02) 0.80 (0.60 – 1.07) 0.80 (0.61 – 1.07) Pover ty level <100% FPL — — — — — — — — — — — — 100% – 199% FPL 1.07 (0.95 – 1.21) 1.04 (0.91 – 1.18) 1.04 (0.91 – 1.17) 1.3 (1.11 – 1.51) 1.23 (1.04 – 1.45) 1.23 (1.04 – 1.45) 200% – 399% FPL 1.08 (0.93 – 1.26) 1.06 (0.90 – 1.24) 1.06 (0.90 – 1.24) 1.53 (1.24 – 1.89) 1.40 (1.14 – 1.73) 1.40 (1.14 – 1.72) ≥ 400% FPL 0.94 (0.77 – 1.15) 0.93 (0.75 – 1.15) 0.93 (0.75 – 1.15) 1.53 (1.07 – 2.20) 1.50 (1.01 – 2.21) 1.49 (1.01 – 2.20) Education <HS education 1.03 (0.90 – 1.18) 1.07 (0.92 – 1.25) 1.07 (0.92 – 1.25) 0.81 (0.68 – 0.95) 0.84 (0.69 – 1.01) 0.84 (0.69 – 1.01)
GED or HS diploma
1.13 (0.99 – 1.28) 1.14 (0.99 – 1.31) 1.15 (1.00 – 1.31) 0.91 (0.77 – 1.07) 0.90 (0.76 – 1.06) 0.90 (0.77 – 1.06)
>GED or HS diploma
— — — — — — — — — — — — Non-English speaking 1.06 (0.92 – 1.22) 1.28 (1.02 – 1.58) 1.27 (1.03 – 1.58) 1.05 (0.89 – 1.24) 0.95 (0.69 – 1.32) 0.94 (0.68 – 1.30) CSHCN 0.53 (0.47 – 0.59) 0.50 (0.44 – 0.57) 0.50 (0.44 – 0.57) 0.62 (0.53 – 0.73) 0.60 (0.50 – 0.72) 0.60 (0.50 – 0.72)
State-level characteristics FMAP
1.01 (1.01 – 1.02) 1.00 (0.99 – 1.01) 1.01 (0.99 – 1.02) 1.02 (1.01 – 1.04) 1.02 (1.01 – 1.03) 1.02 (1.01 – 1.03)
HMO penetration rate
a 0.98 (0.97 – 0.99) — — 0.98 (0.97 – 0.99) 0.96 (0.94 – 0.98) — — 0.97 (0.95 – 0.98)
PCCM penetration rate
a 1.02 (1.01 – 1.04) 1.02 (1.01 – 1.04) — — 1.04 (1.01 – 1.07) 1.03 (1.01 – 1.06) — —
All data presented as odds ratio (CI) and adjusted odds ratio (CI). FPL, federal pover
ty level; HS, high school;
—
, not applicable.
having access to care coordination (CI, 1.02–1.50; P = .034) and a 37% increase in receiving care coordination when needed (CI, 1.05–1.78; P = .018). States’ utilization of HMO MMC was significantly associated with a 15% reduction in an individual’s odds of having access to care coordination (CI, 0.73–1.00; P = .05) and a 29% reduction in the odds of receipt of care coordination when needed (CI, 0.58–
0.86; P < .001). P values were calculated from standard scores. Among the individual-level predictors, race and CSHCN status were also significantly associated with both care coordination measures in unadjusted and adjusted models. Increased family income level and FMAP were significantly associated
with increased likelihood of receipt of care coordination when needed, but not access to care coordination, in all models.
DIscussIOn
Our results demonstrate a significant, national-level correlation between the form of MMC used by states and the odds that a child living within that state will have access to adequate care coordination (as defined by the AAP) and that he or she will receive care coordination when needed. Notably, among states that use a PCCM model to implement managed care, there is an associated increase in access to care
coordination and receipt of care coordination when needed, whereas HMO utilization is associated with decreased odds of both measures.
The reasons underlying the observed differences in care coordination outcomes are unclear. There are limited data describing the effects of MMC on quality as well as patient satisfaction, 8, 10, 21 and to our
knowledge, no researchers have evaluated care coordination within the pediatric age group. Although this observational analysis cannot specify causality within this relationship, the significant discrepancy in care coordination outcomes between
FIGuRe 1
PCCM and HMO financing structures is notable in and of itself.
MMC mandates have been implemented primarily with the goals of reducing state Medicaid expenditures and increasing access to high-quality care; care coordination is a fundamental underpinning of the managed care model for cost savings. It assumes that financially incentivized proper care coordination will reduce unnecessary care and shift care to more cost-effective forms while also reducing redundancies. However, there has been little evidence that MMC produces cost savings, particularly in national-level studies. Moreover, children have not been
isolated as a subset in these analyses, and most adult evidence points to MMC programs being cost neutral or cost increasing.6, 7 As such, the MMC
structure that states use may be better evaluated by its effects on care access, quality, and
satisfaction.
Evidence revealing the effects of care coordination within a family-centered medical home is limited, but care coordination has been associated with improved health access, timeliness of care, and utilization of health-promoting resources.22–24
Moreover, effective care coordination is associated with reduced family financial burden25 and may account
for documented disparities in
care that cannot be accounted for by reimbursement rates and demographic risk factors.17, 26–28
Given the structures of PCCM and HMO programs, both models would be expected to be associated with increased care coordination outcomes when compared with traditional FFS models, which lack any financial incentives for activities beyond the standard of care. Yet, our findings in this study challenge that assumption by using analyses of a large national-level data set, and further investigation is needed. The interpretation of our results must be considered within the context of our model. Individuals were clustered within states and the overall state
FIGuRe 2
utilization of MMC enrollment was used; this precludes evaluation of individual-level effects of MMC enrollment. As an example, the HMO penetration measurement odds ratio of 0.85 can be interpreted as follows: any child enrolled in Medicaid residing within a state is predicted to have a 15% reduced odds of having access to care coordination if the state has 100% HMO penetration, compared with an individual living in a state with 0% HMO penetration, with all other variables being equal. Although few states entirely switch from 1 form of MMC to another at any given time, these results give us a robust, national-level estimate of the overarching associations between financing structure and care coordination outcomes within states. The magnitude of this association was modest but robust. Multiple factors affect the likelihood that any 1 individual will receive care coordination, ranging from individual patient and provider characteristics to regional variations in practice patterns and political influences. Moreover, the pooled state-level effects of HMO or PCCM penetration likely introduces a negative bias, diminishing the effect of the MMC program on an individual’s likelihood of obtaining effective care coordination. Notably, however, only 2 individual-level variables were significantly associated with care coordination outcomes in all models. Race and CSHCN status are known nonmodifiable risk factors linked to disparities in a variety of care outcomes.6, 22, 24, 25, 27–30 Although
the magnitudes of these individual-factor associations were greater, the structure of our model makes comparison of odds ratios between individual- and state-level factors difficult. More evaluation is required to isolate the individual relationship between MMC enrollment and care coordination outcomes, particularly among the different groups affected by MMC including CSHCN, adults, and individuals who are eligible for dual Medicare and Medicaid coverage.
Given the nature of the MSIS data, no further subsetting to isolate particular subpopulations (such as CSHCN or rural versus urban enrollees) was possible within this analysis.
Additionally, the cross-sectional, observational design cannot assign causality and may be biased by unmeasured factors. Choosing to implement PCCM may reflect unmeasured preferences within the state Medicaid system and among providers for an independent care coordination system rather than a capitated structure. Specific unmeasured factors within this study include the availability of high-quality managed care within a state or region, specialist physician acceptance rates for Medicaid patients, political influences of insurance and physician interest groups, and preexisting care distribution and coordination networks that may not be easily incorporated into an HMO program. It is unknown whether these factors would subsequently affect access to and receipt of care coordination, but the inability to measure these factors limits the interpretation of the findings. Nevertheless, through the use of multilevel multivariable analyses, we attempted to limit the potential biases that could be introduced when incorporating multiple different data sets drawn at different clustered levels and controlled for common individual- and state-level determinants of care coordination and care quality previously identified in the literature as significant contributors to care outcomes. Therefore, our models generated robust, national level estimates of state MMC enrollment associations. We also performed multiple sensitivity analyses to ensure that no significant biases were introduced by merging multiple independent data sets.
cOnclusIOns
Financial incentives continue to serve as an important tool for
influencing health care delivery, and new models have been developed that focus on comprehensive pediatric primary care delivery. We found wide variation in states’
utilization of PCCM and HMO models and associated variations in care coordination outcomes for publicly insured children. Our findings in this study challenge the assumption that MMC uniformly increases care coordination because we found significantly lower care coordination outcomes in states utilizing HMO models and significantly higher outcomes in states utilizing PCCM. Further study is needed to fully elucidate these relationships and should be considered during policy discussions of further expansions or shifts in MMC.
acKnOWleDGMent
We thank Stacey C. Tobin, PhD, for critical review of this manuscript.
abbRevIatIOns
AAP: American Academy of Pediatrics
CHIP: Children’s Health Insurance Program CI: confidence interval
CMS: Centers for Medicare and Medicaid Services CSHCN: children with special
health care needs FFS: fee-for-service FMAP: Federal Medical
Assistance Percentage GED: general education
development HMO: health maintenance
organization
MMC: Medicaid managed care MSIS: Medicaid Statistical
Information System NSCH: National Survey of Children’s Health PCCM: primary care case
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FInancIal DIsclOsuRe: The authors have indicated they have no financial relationships relevant to this article to disclose. FunDInG: No external funding.
POtentIal cOnFlIct OF InteRest: The authors have indicated they have no potential conflicts of interest to disclose. Dr Agrawal had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
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DOI: 10.1542/peds.2016-3820 originally published online August 24, 2017;
2017;140;
Pediatrics
Douglas H. Gilchrist-Scott, James A. Feinstein and Rishi Agrawal
Medicaid Managed Care Structures and Care Coordination
Services
Updated Information &
http://pediatrics.aappublications.org/content/140/3/e20163820
including high resolution figures, can be found at:
References
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DOI: 10.1542/peds.2016-3820 originally published online August 24, 2017;
2017;140;
Pediatrics
Douglas H. Gilchrist-Scott, James A. Feinstein and Rishi Agrawal
Medicaid Managed Care Structures and Care Coordination
http://pediatrics.aappublications.org/content/140/3/e20163820
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by the American Academy of Pediatrics. All rights reserved. Print ISSN: 1073-0397.