A Bayesian shared components modeling approach to develop small area indicators of social determinants of health with measures of uncertainty.

OBJECTIVES: Existing Canadian social determinants of health (SDOH) indicators do not quantify uncertainty to identify priority areas. The objectives of this methodologic study were: (1) to estimate and map small area (dissemination area) shared and variable-specific SDOH indicators with measures of uncertainty using a Bayesian model that accounts for spatial dependence; (2) to quantify geographic variation in the SDOH indicators and their contribution to a shared indicator; and (3) to assess the SDOH indicators' associations with behavioural risk factors and their consistency with the Ontario Marginalization Index (ON-Marg). METHODS: Lower education-, income-, unemployment-, living alone- and visible minority-related variables used in existing Canadian SDOH indices were fit as dependent variables to a Bayesian model to produce area-based SDOH indicators that were mapped with measures of uncertainty in two study areas. The fractions of spatial variation explained by the model components were computed. Bayesian analysis of variance was used to examine the SDOH indicator associations with behavioural risk factors and their consistency with ON-Marg examined using Pearson's correlation coefficient. RESULTS: The shared component was strongly associated with material deprivation (i.e., income) in each study area; however, variable-specific SDOH indicators were important too. The SDOH indicators were associated with behavioural risk factors for chronic disease, particularly alcohol consumption and smoking, and the shared component estimates were consistent with the ON-Marg material deprivation. CONCLUSIONS: The Bayesian approach to produce SDOH indicators met the three study objectives and as such provides a new approach to prioritize areas that may experience health inequalities.

Reliability of Radiographic Assessments of the Hip in Cerebral Palsy.

INTRODUCTION: Children with cerebral palsy are at risk for progressive hip displacement. Since surveillance for hip displacement uses specific radiographic measurements to guide decision making, it is important to establish the reliability of these measurements, which include Reimer's migration percentage (MP), acetabular index or acetabular angle (AI or AA), and pelvic obliquity (PO). The purpose of this study was to determine the intraobserver and interobserver reliability of these radiographic measures among an international group of pediatric orthopaedic surgeons participating in the prospective international multicenter Cerebral Palsy Hip Outcomes Project (CHOP) currently underway to evaluate the outcomes of hip interventions in cerebral palsy. METHODS: Two compact discs (CDs) containing the same 25 anteroposterior pelvis radiographs in Digital Imaging and Communications in Medicine (DICOM) format were provided to participating surgeons at least 2 weeks apart. To reduce the likelihood of recall or any effects of learning or fatigue, the order of the radiographs varied on the 2 CD versions, and participating surgeons received the 2 CDs in random order. The intraclass correlation coefficients (ICCs) were calculated to assess interobserver and intraobserver reliability. Mean absolute differences of hip measurements obtained at 2 time points were also calculated. RESULTS: The MP had the highest reliability followed by PO, AI, and AA with a mean intrarater ICC (SD; range) of 0.95 (0.04; 0.84 to 0.98); 0.92 (0.03; 0.85 to 0.97); 0.84 (0.05; 0.75 to 0.92); and 0.82 (0.14; 0.51 to 0.98); respectively. The mean interrater ICC (SD; range) for MP, PO, AI, and AA were 0.94 (0.05; 0.78 to 0.99); 0.90 (0.04; 0.76 to 0.99); 0.79 (0.08; 0.52 to 0.93); and 0.69 (0.23; 0.42 to 0.98) for MP, PO, AI, and AA, respectively. The mean (SD; 95% confidence interval) for the absolute difference between the 2 measurements for the raters was 4.9% (2.9%; 3.4%-6.4%); 3. 8 degrees (1.2 degrees; 3.1-4.5 degrees); 2.6 degrees (1.5 degrees; 1.7-3.5 degrees); and 1.3 degrees (0.3 degrees; 1.29-1.31 degrees) for MP, AI, AA, and PO, respectively. CONCLUSIONS: MP is a reproducible measure with excellent intrarater and interrater reliability. However, differences in MP of <7% should be treated with caution as these might be a consequence of measurement error. Although we found a high level of intrarater and interrater reliability of the AI, AA, and PO, these measurements are more variable and not ideal for use as discrete outcome measures. Instead, these parameters might be useful for prognostication and decision making when consistent trends are observed longitudinally over time which might be better indications of true change.