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. Author manuscript; available in PMC: 2013 Nov 1.
Published in final edited form as: Health Place. 2012 Aug 13;18(6):1422–1429. doi: 10.1016/j.healthplace.2012.07.007

Table 3.

Statistical significance of each interaction term in linear regression models of dental utilization for Medicaid-enrolled children with chronic conditions (N=25,908 children; n=99 counties).*

Interaction term β coefficient* Robust standard error Robust z value P-value
Within-level interaction
 Intellectual or developmental disability (IDD) status × chronic condition severity
  IDD (yes) × episodic chronic condition Reference
  IDD (yes) × life-long chronic condition 0.0015 0.0297 0.05 0.9600
  IDD (yes) × complex chronic condition 0.0283 0.0684 0.41 0.6788
Cross-level interactions
 Race/ethnicity × percent population in poverty
  White × percent poverty Reference
  Black × percent poverty −0.0074 0.0036 −2.06 0.0394
  Other × percent poverty −0.0190 0.0078 −2.44 0.0149
  Missing × percent poverty −0.0015 0.0039 −0.39 0.6969
 Race/ethnicity × unemployment
  White × unemployment Reference
  Black × unemployment 0.0076 0.0216 0.35 0.7243
  Other × unemployment −0.0254 0.0387 −0.65 0.5127
  Missing × unemployment 0.0114 0.0207 0.55 0.5815
 Intellectual or developmental disability status × percent population in poverty 0.0044 0.0045 0.97 0.3322
Intellectual or developmental disability status × unemployment 0.0405 0.0186 2.18 0.0296
 Chronic condition severity × percent population in poverty
  Episodic chronic condition × poverty Reference
  Life-long chronic condition × poverty 0.0013 0.0027 0.50 0.6205
  Complex chronic condition × poverty 0.0098 0.0091 1.08 0.2817
 Chronic condition severity × unemployment
  Episodic chronic condition × unemployment Reference
  Life-long chronic condition × unemployment 0.0024 0.0124 0.19 0.8456
  Complex chronic condition × unemployment 0.0173 0.0362 0.48 0.6326
*

Adjusted for child-level predictors variables (age, gender, race/ethnicity, IDD status, chronic condition severity, sibling enrolled in Medicaid, caregiver enrolled in Medicaid, used preventive medical care) and county-level predictor variables (population poverty, unemployment, dental Health Professional Shortage Area designation, rurality).