Smooth individual level covariates adjustment in disease mapping.

Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non-log-linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth-indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two-step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth-indiCAR through simulation. Our results indicate that the smooth-indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia.

Development and validation of the Psychological Adaptation Scale (PAS): use in six studies of adaptation to a health condition or risk.

OBJECTIVE: We introduce The Psychological Adaptation Scale (PAS) for assessing adaptation to a chronic condition or risk and present validity data from six studies of genetic conditions. METHODS: Informed by theory, we identified four domains of adaptation: effective coping, self-esteem, social integration, and spiritual/existential meaning. Items were selected from the PROMIS "positive illness impact" item bank and adapted from the Rosenberg self-esteem scale to create a 20-item scale. Each domain included five items, with four sub-scale scores. Data from studies of six populations: adults affected with or at risk for genetic conditions (N=3) and caregivers of children with genetic conditions (N=3) were analyzed using confirmatory factor analyses (CFA). RESULTS: CFA suggested that all but five posited items converge on the domains as designed. Invariance of the PAS amongst the studies further suggested it is a valid and reliable tool to facilitate comparisons of adaptation across conditions. CONCLUSION: Use of the PAS will standardize assessments of adaptation and foster understanding of the relationships among related health outcomes, such as quality of life and psychological well-being. PRACTICE IMPLICATIONS: Clinical interventions can be designed based on PAS data to enhance dimensions of psychological adaptation to a chronic health condition or risk.

Process, cost, and clinical quality: the initial oral contraceptive visit.

OBJECTIVES: To demonstrate how the analysis of clinical process, cost, and outcomes can identify healthcare improvements that reduce cost without sacrificing quality, using the example of the initial visit associated with oral contraceptive pill use. STUDY DESIGN: Cross-sectional study using data collected by HealthMETRICS between 1996 and 2009. METHODS: Using data collected from 106 sites in 24 states, the unintended pregnancy (UIP) rate, effectiveness of patient education, and unit visit cost were calculated. Staff type providing education and placement of education were recorded. Two-way analysis of variance models were created and tested for significance to identify differences between groups. RESULTS: Sites using nonclinical staff to provide education outside the exam were associated with lower cost, higher education scores, and a UIP rate no different from that of sites using clinical staff. Sites also providing patient education during the physical examination were associated with higher cost, lower education scores, and a UIP rate no lower than that of sites providing education outside of the exam. CONCLUSIONS: Through analyzing process, cost, and quality, lower-cost processes that did not reduce clinical quality were identified. This methodology is applicable to other clinical services for identifying low-cost processes that do not result in lower clinical quality. By using nonclinical staff educators to provide education outside of the physical examination, sites could save an average of 32% of the total cost of the visit.

Modelling decisions to undergo genetic testing for susceptibility to common health conditions: an ancillary study of the Multiplex Initiative.

New genetic tests reveal risks for multiple conditions simultaneously, although little is understood about the psychological factors that affect testing uptake. We assessed a conceptual model called the multiplex genetic testing model (MGTM) using structural equation modelling. The MGTM delineates worry, perceived severity, perceived risk, response efficacy and attitudes towards testing as predictors of intentions and behaviour. Participants were 270 healthy insured adults aged 25-40 from the Multiplex Initiative conducted within a health care system in Detroit, MI, USA. Participants were offered a genetic test that assessed risk for eight common health conditions. Confirmatory factor analysis revealed that worry, perceived risk and severity clustered into two disease domains: cancer or metabolic conditions. Only perceived severity of metabolic conditions was correlated with general response efficacy (ß = 0.13, p<0.05), which predicted general attitudes towards testing (ß = 0.24, p<0.01). Consistent with our hypothesised model, attitudes towards testing were the strongest predictors of intentions to undergo testing (ß = 0.49, p<0.01), which in turn predicted testing uptake (OR 17.7, ß = 0.97, p<0.01). The MGTM explained a striking 48% of the variance in intentions and 94% of the variation in uptake. These findings support use of the MGTM to explain psychological predictors of testing for multiple health conditions.

Clinical process variation: effect on quality and cost of care.

OBJECTIVE: To identify optimal transferable practice processes for provision of a clinical service by studying the relationships among unit cost, clinical outcome, patient satisfaction, and staff satisfaction observed for a discreet service performed at multiple sites in a well-defined patient population. STUDY DESIGN: Cross-sectional study using data collected by HealthMETRICS from 1996 to 2007. METHODS: Data from 165 US clinics in 29 states, totaling 8835 patients and 1583 clinic staff, were reviewed. Four parameters of the initial visit for oral contraceptives (OCs) were measured: unit visit cost, patient satisfaction, staff satisfaction, and clinical indicators, including patient education effectiveness and occurrence of unintended pregnancies within 6 months of the initial OC visit. Patient population and visit type were narrowly defined to ensure intersite comparability. Data collection tools included surveys, time logs, financial work sheets, and on-site visits to document process. RESULTS: Clinical process variation was widespread. The unit cost of an initial OC visit varied from $42 to $206 (mean: $90, coefficient of variation: 38%). Staff satisfaction varied more than patient satisfaction. Clinical indicators, including unintended pregnancies, varied little. The sites achieving lower unit costs demonstrated no apparent decrease in clinical quality. CONCLUSIONS: Clinical processes used to provide initial visits for OC services varied demonstrably across study sites, generating variation in cost with little impact on clinical quality or patient satisfaction. By adopting appropriate components of the optimal practice process, clinical sites could lower the care cost by more than 20% while maintaining or increasing care quality.