Technical note: prediction of sex based on five skull traits using decision analysis (CHAID).

Osteologists commonly assess the sex of skeletal remains found in forensic and archaeological contexts based on ordinal scores of subjectively assessed sexually dimorphic traits. Using known-sex samples, logistic regression (LR) discriminant functions have been recently developed, which allow sex probabilities to be determined. A limitation of LR is that it emphasizes main effects and not interactions. Chi-square automatic interaction detection (CHAID) is an alternative classification strategy that emphasizes the information in variable interactions and uses decision trees to maximize the probability of correct sex determinations. We used CHAID to analyze the predictive value of the 31 possible combinations of five sexually dimorphic skull traits that Walker used previously to develop logistic regression sex determination equations. The samples consisted of 304 individuals of known sex of English, African American, and European American origin. Based on practical considerations, selection criteria for the best sex predictive trait combinations (SPTCs) were set at accuracies for both sexes of 75% or greater and sex biases lower than 5%. Although several of the trees meeting these criteria were produced for the English and European American samples, none met them for the African American sample. In the series of out-of-sample tests we performed, the trees from the English and combined sample of all groups predicted best.

Developing a senior healthcare practice using the chronic care model: effect on physical function and health-related quality of life.

An ambulatory senior health clinic was developed using the chronic care model (CCM), with emphasis on an interdisciplinary team approach. To determine the effect of this care model approach in a nonprofit healthcare system, an observational, longitudinal panel study of community-dwelling Medicare beneficiaries was performed to examine the effect on physical function and health-related quality of life (HRQL). Participants in the study were recruited from a community sample of 6,864 eligible Medicare beneficiaries. Informed consent and baseline data were obtained from 1,709 individuals (recruitment response rate=25%) and complete data across 30 months from 1,307 (completion response rate=76%). Participants receiving care in the CCM-based senior healthcare practice (n=318) were compared with patients of primary care physicians supported by care managers (n=598) and a group without care managers (n=391). Self-reported data were collected over the telephone to measure physical function and HRQL at baseline and 6, 18, and 30 months. A multiple group mixture growth model was used to analyze physical function and HRQL across the 30 months. Physical function and HRQL mean scores decreased across time in all participants and were moderately correlated at each wave (correlation coefficient=0.74-0.79). Two latent growth classes were identified. In class 1, physical function decreased, and HRQL remained stable across time. In class 2, physical function and HRQL decreased in parallel. Ninety-seven percent of intervention group patients were in class 1, and 99% of patients in comparison groups 1 and 2 were in class 2. Despite physical function decline, patients in a senior health clinic care model maintained HRQL over time, whereas patients receiving traditional care had physical function and HRQL decline. An interdisciplinary team CCM approach appears to have a positive effect on HRQL in this population.

Do increases in patient activation result in improved self-management behaviors?

OBJECTIVE: The purpose of this study is to determine whether patient activation is a changing or changeable characteristic and to assess whether changes in activation also are accompanied by changes in health behavior. STUDY METHODS: To obtain variability in activation and self-management behavior, a controlled trial with chronic disease patients randomized into either intervention or control conditions was employed. In addition, changes in activation that occurred in the total sample were also examined for the study period. Using Mplus growth models, activation latent growth classes were identified and used in the analysis to predict changes in health behaviors and health outcomes. DATA SOURCES: Survey data from the 479 participants were collected at baseline, 6 weeks, and 6 months. PRINCIPAL FINDINGS: Positive change in activation is related to positive change in a variety of self-management behaviors. This is true even when the behavior in question is not being performed at baseline. When the behavior is already being performed at baseline, an increase in activation is related to maintaining a relatively high level of the behavior over time. The impact of the intervention, however, was less clear, as the increase in activation in the intervention group was matched by nearly equal increases in the control group. CONCLUSIONS: Results suggest that if activation is increased, a variety of improved behaviors will follow. The question still remains, however, as to what interventions will improve activation.

Development and testing of a short form of the patient activation measure.

OBJECTIVE: The Patient Activation Measure (PAM) is a 22-item measure that assesses patient knowledge, skill, and confidence for self-management. The measure was developed using Rasch analyses and is an interval level, unidimensional, Guttman-like measure. The current analysis is aimed at reducing the number of items in the measure while maintaining adequate precision. STUDY METHODS: We relied on an iterative use of Rasch analysis to identify items that could be eliminated without loss of significant precision and reliability. With each item deletion, the item scale locations were recalibrated and the person reliability evaluated to check if and how much of a decline in precision of measurement resulted from the deletion of the item. DATA SOURCES: The data used in the analysis were the same data used in the development of the original 22-item measure. These data were collected in 2003 via a telephone survey of 1,515 randomly selected adults. Principal Findings. The analysis yielded a 13-item measure that has psychometric properties similar to the original 22-item version. The scores for the 13-item measure range in value from 38.6 to 53.0 (on a theoretical 0-100 point scale). The range of values is essentially unchanged from the original 22-item version. Subgroup analysis suggests that there is a slight loss of precision with some subgroups. CONCLUSIONS: The results of the analysis indicate that the shortened 13-item version is both reliable and valid.

Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers.

BACKGROUND: Controlling costs and achieving health care quality improvements require the participation of activated and informed consumers and patients. OBJECTIVES: We describe a process for conceptualizing and operationalizing what it means to be "activated" and delineate the process we used to develop a measure for assessing "activation," and the psychometric properties of that measure. METHODS: We used the convergence of the findings from a national expert consensus panel and patient focus groups to define the concept and identify the domains of activation. These domains were operationalized by constructing a large item pool. Items were pilot-tested and initial psychometric analysis performed using Rasch methodology. The third stage refined and extended the measure. The fourth stage used a national probability sample to assess the measure's psychometric performance overall and within different subpopulations. STUDY SAMPLE: Convenience samples of patients with and without chronic illness, and a national probability sample (N=1,515) are included at different stages in the research. CONCLUSIONS: The Patient Activation Measure is a valid, highly reliable, unidimensional, probabilistic Guttman-like scale that reflects a developmental model of activation. Activation appears to involve four stages: (1) believing the patient role is important, (2) having the confidence and knowledge necessary to take action, (3) actually taking action to maintain and improve one's health, and (4) staying the course even under stress. The measure has good psychometric properties indicating that it can be used at the individual patient level to tailor intervention and assess changes.