An evaluation of the Texas "A Woman's Right to Know" booklet: A state mandated patient education document for induced abortion

Many patient educational documents are written at a grade level higher than the level at which most individuals can read. This discrepancy can lead to treatment noncompliance and negative health outcomes. Therefore, it is important that patients receive readable health information. The Texas "A Woman's Right to Know" booklet is a state mandated informational document provided to women seeking abortion services. Given the significance of the abortion procedure, it is imperative that women considering having an abortion receive accurate and readable health materials. However, no published studies were found that evaluated the readability of the "A Woman's Right to Know" booklet. Therefore, the purpose of this study was to assess the readability of the "A Woman's Right to Know" booklet. To assess the readability, the Flesch-Kincaid readability test was used to evaluate the reading grade level of the entire "A Woman's Right to Know" booklet and each of the 7 sections of the booklet. The results showed that the readability of the entire booklet as well as each section of the booklet was written below the 8th grade reading level. Although the booklet was written below the estimated United States reading level (8th grade), the reading level of this booklet may still be too high for people in Texas who read below the 8th grade level. Based on these results, it is recommended that health care professionals involved in the distribution and explanation of the "A Woman's Right to Know" booklet provide their patients with both written and verbal medical information. The patients should be allowed to ask questions about the abortion procedure so that they can make the most informed choice.^

Analyzing left-truncated right-censored data with uncertain onset time with parametric models

Prevalent sampling is an efficient and focused approach to the study of the natural history of disease. Right-censored time-to-event data observed from prospective prevalent cohort studies are often subject to left-truncated sampling. Left-truncated samples are not randomly selected from the population of interest and have a selection bias. Extensive studies have focused on estimating the unbiased distribution given left-truncated samples. However, in many applications, the exact date of disease onset was not observed. For example, in an HIV infection study, the exact HIV infection time is not observable. However, it is known that the HIV infection date occurred between two observable dates. Meeting these challenges motivated our study. We propose parametric models to estimate the unbiased distribution of left-truncated, right-censored time-to-event data with uncertain onset times. We first consider data from a length-biased sampling, a specific case in left-truncated samplings. Then we extend the proposed method to general left-truncated sampling. With a parametric model, we construct the full likelihood, given a biased sample with unobservable onset of disease. The parameters are estimated through the maximization of the constructed likelihood by adjusting the selection bias and unobservable exact onset. Simulations are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to an HIV infection study, estimating the unbiased survival function and covariance coefficients. ^

Left, right and interval bivariate censored data: Evaluating screening mammography in the presence of lead -time bias, length bias and over-detection

Of the large clinical trials evaluating screening mammography efficacy, none included women ages 75 and older. Recommendations on an upper age limit at which to discontinue screening are based on indirect evidence and are not consistent. Screening mammography is evaluated using observational data from the SEER-Medicare linked database. Measuring the benefit of screening mammography is difficult due to the impact of lead-time bias, length bias and over-detection. The underlying conceptual model divides the disease into two stages: pre-clinical (T0) and symptomatic (T1) breast cancer. Treating the time in these phases as a pair of dependent bivariate observations, (t0,t1), estimates are derived to describe the distribution of this random vector. To quantify the effect of screening mammography, statistical inference is made about the mammography parameters that correspond to the marginal distribution of the symptomatic phase duration (T1). This shows the hazard ratio of death from breast cancer comparing women with screen-detected tumors to those detected at their symptom onset is 0.36 (0.30, 0.42), indicating a benefit among the screen-detected cases. ^