Disparities in medical expenditure and utilization among hypertensive men and women in the U.S.: Cross-section and lifetime analysis

Objectives. To investigate procedural gender equity by assessing predisposing, enabling and need predictors of gender differences in annual medical expenditures and utilization among hypertensive individuals in the U.S. Also, to estimate and compare lifetime medical expenditures among hypertensive men and women in the U.S. ^ Data source. 2001-2004 the Medical Expenditure Panel Survey (MEPS);1986-2000 National Health Interview Survey (NHIS) and National Health Interview Survey linked to mortality in the National Death Index through 2002 (2002 NHIS-NDI). ^ Study design. We estimated total medical expenditure using four equations regression model, specific medical expenditures using two equations regression model and utilization using negative binomial regression model. Procedural equity was assessed by applying the Aday et al. theoretical framework. Expenditures were estimated in 2004 dollars. We estimated hypertension-attributable medical expenditure and utilization among men and women. ^ To estimate lifetime expenditures from ages 20 to 85+, we estimated medical expenditures with cross-sectional data and survival with prospective data. The four equations regression model were used to estimate average annual medical expenditures defined as sum of inpatient stay, emergency room visits, outpatient visits, office based visits, and prescription drugs expenditures. Life tables were used to estimate the distribution of life time medical expenditures for hypertensive men and women at different age and factors such as disease incidence, medical technology and health care cost were assumed to be fixed. Both total and hypertension attributable expenditures among men and women were estimated. ^ Data collection. We used the 2001-2004 MEPS household component and medical condition files; the NHIS person and condition files from 1986-1996 and 1997-2000 sample adult files were used; and the 1986-2000 NHIS that were linked to mortality in the 2002 NHIS-NDI. ^ Principal findings. Hypertensive men had significantly less utilization for most measures after controlling predisposing, enabling and need factors than hypertensive women. Similarly, hypertensive men had less prescription drug (-9.3%), office based (-7.2%) and total medical (-4.5%) expenditures than hypertensive women. However, men had more hypertension-attributable medical expenditures and utilization than women. ^ Expected total lifetime expenditure for average life table individuals at age 20, was $188,300 for hypertensive men and $254,910 for hypertensive women. But the lifetime expenditure that could be attributed to hypertension was $88,033 for men and $40,960 for women. ^ Conclusion. Hypertensive women had more utilization and expenditure for most measures than hypertensive men, possibly indicating procedural inequity. However, relatively higher hypertension-attributable health care of men shows more utilization of resources to treat hypertension related diseases among men than women. Similar results were reported in lifetime analyses.^ Key words: gender, medical expenditures, utilization, hypertension-attributable, lifetime expenditure ^

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

Estradiol and insulin cross -talk in breast cancer

Approximately 33% of clinical breast carcinomas require estrogens to proliferate. Epidemiological data show that insulin resistance and diabetes mellitus is 2–3 times more prevalent in women with breast cancer than those with benign breast lesions, suggesting a clinical link between insulin and estradiol. Insulin and estradiol have a synergistic effect on the growth of MCF7 breast cancer cells, and long-term estradiol treatment upregulates the expression of the key insulin signaling protein IRS-1. The goal of this study was to further define the mechanism(s) of cross-talk between insulin and estradiol in regulating the growth of breast cancer. Using MCF7 cells, acute treatment with insulin or estradiol alone was found to stimulate two activities associated with growth: Erk MAP kinase and PI 3-kinase. However, combined acute treatment had an antagonistic effect on both activities. Acute estradiol treatment inhibited the insulin-stimulated tyrosine phosphorylation of IRS-1 while increasing its serine phosphorylation; the serine phosphorylation was attenuated by the PI 3-kinase inhibitor wortmannin. The acute antagonism observed with combined estradiol and insulin are not consistent with the long-term synergistic effect on growth. In contrast, chronic estradiol treatment enhanced the insulin-sensitivity of breast cancer cells as measured by increases in total cellular insulin-stimulated tyrosine phosphorylation of IRS-1 and activation of PI 3-kinase. Estradiol stimulation of gene transcription was found to require PI 3-kinase activity but not MAP kinase activity. Insulin alone had no effect on ER transcriptional activity, but chronic treatment in combination with estradiol resulted in synergism of ER transcription. The synergistic effect of insulin and estradiol on MCF7 cell growth was also found to require PI 3-kinase but not MAP kinase activity. Therefore, chronic estradiol treatment increases insulin stimulation of PI 3-kinase, and PI 3-kinase is required for estradiol stimulation of gene transcription alone and in combined synergy with insulin. These data demonstrate that PI 3-kinase is the locus for the cross-talk between insulin and estradiol which results in enhanced breast cancer growth with long-term exposure to both hormones. This may have important clinical implications for women with high risk for breast cancer and/or diabetes mellitus. ^

Optimization of a DNSH passive sampling method to measure airborne carbonyls

Various airborne aldehydes and ketones (i.e., airborne carbonyls) present in outdoor, indoor, and personal air pose a risk to human health at present environmental concentrations. To date, there is no adequate, simple-to-use sampler for monitoring carbonyls at parts per billion concentrations in personal air. The Passive Aldehydes and Ketones Sampler (PAKS) originally developed for this purpose has been found to be unreliable in a number of relatively recent field studies. The PAKS method uses dansylhydrazine, DNSH, as the derivatization agent to produce aldehyde derivatives that are analyzed by HPLC with fluorescence detection. The reasons for the poor performance of the PAKS are not known but it is hypothesized that the chemical derivatization conditions and reaction kinetics combined with a relatively low sampling rate may play a role. This study evaluated the effect of absorption and emission wavelengths, pH of the DNSH coating solution, extraction solvent, and time post-extraction for the yield and stability of formaldehyde, acetaldehyde, and acrolein DNSH derivatives. The results suggest that the optimum conditions for the analysis of DNSHydrazones are the following. The excitation and emission wavelengths for HPLC analysis should be at 250nm and 500nm, respectively. The optimal pH of the coating solution appears to be pH 2 because it improves the formation of di-derivatized acrolein DNSHydrazones without affecting the response of the derivatives of the formaldehyde and acetaldehyde derivatives. Acetonitrile is the preferable extraction solvent while the optimal time to analyze the aldehyde derivatives is 72 hours post-extraction. ^