Structural equation modeling of the medical outcome study: Short Form 36 (SF-36)

The factorial validity of the SF-36 was evaluated using confirmatory factor analysis (CFA) methods, structural equation modeling (SEM), and multigroup structural equation modeling (MSEM). First, the measurement and structural model of the hypothesized SF-36 was explicated. Second, the model was tested for the validity of a second-order factorial structure, upon evidence of model misfit, determined the best-fitting model, and tested the validity of the best-fitting model on a second random sample from the same population. Third, the best-fitting model was tested for invariance of the factorial structure across race, age, and educational subgroups using MSEM.^ The findings support the second-order factorial structure of the SF-36 as proposed by Ware and Sherbourne (1992). However, the results suggest that: (a) Mental Health and Physical Health covary; (b) general mental health cross-loads onto Physical Health; (c) general health perception loads onto Mental Health instead of Physical Health; (d) many of the error terms are correlated; and (e) the physical function scale is not reliable across these two samples. This hierarchical factor pattern was replicated across both samples of health care workers, suggesting that the post hoc model fitting was not data specific. Subgroup analysis suggests that the physical function scale is not reliable across the "age" or "education" subgroups and that the general mental health scale path from Mental Health is not reliable across the "white/nonwhite" or "education" subgroups.^ The importance of this study is in the use of SEM and MSEM in evaluating sample data from the use of the SF-36. These methods are uniquely suited to the analysis of latent variable structures and are widely used in other fields. The use of latent variable models for self reported outcome measures has become widespread, and should now be applied to medical outcomes research. Invariance testing is superior to mean scores or summary scores when evaluating differences between groups. From a practical, as well as, psychometric perspective, it seems imperative that construct validity research related to the SF-36 establish whether this same hierarchical structure and invariance holds for other populations.^ This project is presented as three articles to be submitted for publication. ^

Changes in blood lipids and their associations with changes in measures of height, weight and body mass index from age 8 to 18 years: Project Heartbeat!

A variety of studies indicate that the process of athrosclerosis begins in childhood. There was limited information on the association of the changes in anthropometric variables to blood lipids in school age children and adolescents. Previous longitudinal studies of children typically with insufficient frequency of observation could not provide sound inference on the dynamics of change in blood lipids. The aims of this analysis are (1) to document the sex- and ethnic-specific trajectory and velocity curves of blood lipids (TC, LDL-C, HDL-C and TG); (2) to evaluate the relationship of changes in anthropometric variables, such as height, weight and BMI, to blood lipids from age 8 to 18 years. ^ Project HeartBeat! is a longitudinal study designed to examine the patterns of serial change in major cardiovascular risk factors. Cohort of three different age levels, 8, 11 and 14 years at baseline, with a total of 678 participants were enrolled. Each member of these cohorts was examined three times per year for up to four years. ^ Sex- and ethnic-specific trajectory and velocity curves of blood lipids; demonstrated the complex and polyphasic changes in TC, LDL-C, HDL-C and TG longitudinally. The trajectory curves of TC, LDL-C and HDL-C with age showed curvilinear patterns of change. The velocity change in TC, HDL-C and LDL-C showed U-shaped curves for non-Blacks, and nearly linear lines in velocity of TG for both Blacks and non-Blacks. ^ The relationship of changes in anthropometric variables to blood lipids was evaulated by adding height, weight, or BMI and associated interaction terms separately to the basic age-sex models. Height or height gain had a significant negative association with changes in TC, LDL-C and HDL-C. Weight or BMI gain showed positive associations with TC, LDL-C and TC, and a negative relationship with HDL-C. ^ Dynamic changes of blood lipids in school age children and adolescents observed from this analysis suggested that using fixed screening criteria under the current NCEP guidelines for all ages 2–19 may not be appropriate for this age group. The association of increasing BMI or weight to an adverse blood lipid profile found in this analysis also indicated that weight or BMI monitoring could be a future intervention to be implemented in the pediatric population. ^

Malaria prevention in Kenyan women of childbearing age

The objective of this program is to reduce malaria incidence in Kenya. Malaria poses a large public health challenge in Kenya, and although public health efforts have traditionally been focused on treatment of infected patients, due to increased drug resistance and lack of drug-adherence, prevention strategies are needed. This program targets Kenyan women, the likely caretakers in the home, and promotes malaria prevention behaviors through health education. ^ A planning group will be assembled and a needs assessment will be performed, verifying risk factors and conditions associated with malaria, as well as personal and external determinants. Behavioral and environmental outcomes will be determined, and performance objectives for each outcome will be established. Matrices of change objectives will be created, and detailed methods and strategies will be linked to each change objective. Program elements include media, education, and incentives. All materials used in this program will be subjected to pre-test to ensure cultural relevance and fidelity. Matrices of change objectives will be created for program adopters and implementers, as well as correlating methods and strategies associated with each change objective. Performance objectives will also be compiled for program maintainers. A program evaluation plan will follow "Pre-Post Comparison Group" design. Outcome evaluation and process evaluation will be conducted. The sample population will be screened based on age and gender so as to maintain comparability to the target population. Measurements will be taken before the program to establish baseline, directly following the program to determine short-term effects, and three months after the program is completed to determine long-term effects. ^ One limitation of this program is selection bias, due to the nature of quasi-experimental studies. Thorough screening prior to sample selection will minimize selection bias and ensure group homogeneity. Another limitation is attrition, and this will be minimized where possible through the use of incentives. In cases where loss to follow-up is not avoidable, such as death or natural disasters, the attrition effect will be estimated using structural equation modeling after reviewing the sample size, differential attrition and total attrition. ^ This intervention is based heavily on health promotion theories, but it is important to remember that in the field, the program plan will likely include only the necessary practical strategies. The target population, Kenyan women of childbearing age, will be significant in decreasing the malaria disease burden in Kenya.^