State and Federal Health Policy Implications - State Disparities
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Our study also revealed the role of resident state as a separate entity that influences health. The mechanisms for this were not tested, but research has shown that includ- ing cluster level characteristics in MLMs are helpful with identifying the policy or the context that shapes individual behavior. 18 For example, by including the proportion of children covered by private health insurance, model results may reveal policy mechanisms associated with each state’s insurance commission. Or perhaps investigators may find a state-level interaction between the proportion of privately owned pediatric clinics and privately insured children that explains part of the MH-PDS association. In this sense, we may hypothesize the lack of a PDS mandate by private insurers prohibits clinics from providing the screening. Additionally, such cluster-level characteristics may indicate disparities as a function of the wealth of the state. For ex- ample, wemay find that private insurance policy for CSHCN is disproportionately less comprehensive in southern than in northern states, and this may account for a proportion of the individual level variation inMH outcomes according to the child’s health insurance type and race/ethnicity. Strengths and Limitations Discussion of any inferences must be assessed in the context of limitations due to unaccounted biases. Individual level observations are based on respondent knowledge, recollection, and interpretation of survey questions. Hence, information biases may exist (social and recall) and of im- portance, is having a parent respondent versus a medical record abstraction. Compared to doctors, parents report a lower PDS rate. 19 The cross-sectional complex study design limits causal inferences. Analytical methods provided cor- rected standard errors and further enhanced parameter inference precision as we applied appropriate weights for the subsample and improved exactness for estimates by fit- ting standard error adjustment. Despite the relatively low ICC, this type of analysis is deemed appropriate as “even a very weak ICC can substantially deflate standard errors of regression coefficients,” and thus, MLMs are appropriate when data is clustered so that standard errors are accounting for thewithin-cluster dependency of individual observations and hence, the between-cluster variation. 20 CONCLUSION In summary, modeling competing multilevel mecha- nisms that either impede or promote health can aid in the theoretical interpretation of howandwhy healthcaremodels within a given context can influence individual health. State and private sector health programs may then benefit from this research as an aid in developing context relevant policy. REFERENCES
1. American Academy of Pediatrics, Council on Children with
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