892 resultados para Coverage bias


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Objective. In June 2006, the first vaccine for human papillomavirus (HPV) was approved by the FDA and shortly after approval, the Advisory Committee on Immunization Practices (ACIP) voted to recommend the HPV vaccine for young girls. As a result of ACIP recommendations, state legislators introduced bills to mandate the vaccine. Policies related to public health issues, such as vaccination mandates, are often influenced by news coverage of these issues. News media, particularly in times of controversies, reinforce specific messages and plays an essential role in framing issues for the public. The objective of this study is to examine the quality, content, and scope of policies for the HPV vaccine before and after Texas Governor Rick Perry issued an executive order mandating the vaccine for middle school girls.^ Methods. The Lexis-Nexis database was used to identify 335 articles on HPV vaccination mandate policies that were published in U.S. newspapers from February 1, 2006 to February 2, 2008. The coding instrument captured information about article type, main news story concern, general information about HPV, HPV vaccine mandate policies, arguments for and against HPV vaccination mandates, arguments for and against the HPV vaccine, and sources of information.^ Results. Most news articles (82.4%) occurred after Governor Rick Perry issued an executive order mandating the HPV vaccine. Most articles mentioned that HPV is sexually transmitted (90.7%) and linked HPV infection to cervical cancer (96.1%). Only 63.9% of the articles reported that the HPV vaccine protects against types of HPV that cause cervical cancer and 18.8% of the articles reported that the vaccine protects against genital warts. Only 18.2% of the news articles presented a balanced argument regarding mandatory HPV vaccinations, and only 39.4% of the news articles presented a balanced argument for the HPV vaccine.^ Conclusions. Our study revealed that news coverage regarding mandating the HPV vaccine and issues related to the vaccine itself is biased, unbalanced, and incomplete. Since the public pays a great deal of attention to health in the media, it is essential that news stories are balanced, complete, and accurate. In order to ensure that future vaccination mandates are not covered in the same way the HPV vaccination was, public health officials, health care providers and scientists should work effectively with the media to ensure that balanced information is provided.^

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Health departments, research institutions, policy-makers, and healthcare providers are often interested in knowing the health status of their clients/constituents. Without the resources, financially or administratively, to go out into the community and conduct health assessments directly, these entities frequently rely on data from population-based surveys to supply the information they need. Unfortunately, these surveys are ill-equipped for the job due to sample size and privacy concerns. Small area estimation (SAE) techniques have excellent potential in such circumstances, but have been underutilized in public health due to lack of awareness and confidence in applying its methods. The goal of this research is to make model-based SAE accessible to a broad readership using clear, example-based learning. Specifically, we applied the principles of multilevel, unit-level SAE to describe the geographic distribution of HPV vaccine coverage among females aged 11-26 in Texas.^ Multilevel (3 level: individual, county, public health region) random-intercept logit models of HPV vaccination (receipt of ≥ 1 dose Gardasil® ) were fit to data from the 2008 Behavioral Risk Factor Surveillance System (outcome and level 1 covariates) and a number of secondary sources (group-level covariates). Sampling weights were scaled (level 1) or constructed (levels 2 & 3), and incorporated at every level. Using the regression coefficients (and standard errors) from the final models, I simulated 10,000 datasets for each regression coefficient from the normal distribution and applied them to the logit model to estimate HPV vaccine coverage in each county and respective demographic subgroup. For simplicity, I only provide coverage estimates (and 95% confidence intervals) for counties.^ County-level coverage among females aged 11-17 varied from 6.8-29.0%. For females aged 18-26, coverage varied from 1.9%-23.8%. Aggregated to the state level, these values translate to indirect state estimates of 15.5% and 11.4%, respectively; both of which fall within the confidence intervals for the direct estimates of HPV vaccine coverage in Texas (Females 11-17: 17.7%, 95% CI: 13.6, 21.9; Females 18-26: 12.0%, 95% CI: 6.2, 17.7).^ Small area estimation has great potential for informing policy, program development and evaluation, and the provision of health services. Harnessing the flexibility of multilevel, unit-level SAE to estimate HPV vaccine coverage among females aged 11-26 in Texas counties, I have provided (1) practical guidance on how to conceptualize and conduct modelbased SAE, (2) a robust framework that can be applied to other health outcomes or geographic levels of aggregation, and (3) HPV vaccine coverage data that may inform the development of health education programs, the provision of health services, the planning of additional research studies, and the creation of local health policies.^

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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. ^

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Additive and multiplicative models of relative risk were used to measure the effect of cancer misclassification and DS86 random errors on lifetime risk projections in the Life Span Study (LSS) of Hiroshima and Nagasaki atomic bomb survivors. The true number of cancer deaths in each stratum of the cancer mortality cross-classification was estimated using sufficient statistics from the EM algorithm. Average survivor doses in the strata were corrected for DS86 random error ($\sigma$ = 0.45) by use of reduction factors. Poisson regression was used to model the corrected and uncorrected mortality rates with covariates for age at-time-of-bombing, age at-time-of-death and gender. Excess risks were in good agreement with risks in RERF Report 11 (Part 2) and the BEIR-V report. Bias due to DS86 random error typically ranged from $-$15% to $-$30% for both sexes, and all sites and models. The total bias, including diagnostic misclassification, of excess risk of nonleukemia for exposure to 1 Sv from age 18 to 65 under the non-constant relative projection model was $-$37.1% for males and $-$23.3% for females. Total excess risks of leukemia under the relative projection model were biased $-$27.1% for males and $-$43.4% for females. Thus, nonleukemia risks for 1 Sv from ages 18 to 85 (DRREF = 2) increased from 1.91%/Sv to 2.68%/Sv among males and from 3.23%/Sv to 4.02%/Sv among females. Leukemia excess risks increased from 0.87%/Sv to 1.10%/Sv among males and from 0.73%/Sv to 1.04%/Sv among females. Bias was dependent on the gender, site, correction method, exposure profile and projection model considered. Future studies that use LSS data for U.S. nuclear workers may be downwardly biased if lifetime risk projections are not adjusted for random and systematic errors. (Supported by U.S. NRC Grant NRC-04-091-02.) ^

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This study establishes the extent and relevance of bias of population estimates of prevalence, incidence, and intensity of infection with Schistosoma mansoni caused by the relative sensitivity of stool examination techniques. The population studied was Parcelas de Boqueron in Las Piedras, Puerto Rico, where the Centers for Disease Control, had undertaken a prospective community-based study of infection with S. mansoni in 1972. During each January of the succeeding years stool specimens from this population were processed according to the modified Ritchie concentration (MRC) technique. During January 1979 additional stool specimens were collected from 30 individuals selected on the basis of their mean S. mansoni egg output during previous years. Each specimen was divided into ten 1-gm aliquots and three 42-mg aliquots. The relationship of egg counts obtained with the Kato-Katz (KK) thick smear technique as a function of the mean of ten counts obtained with the MRC technique was established by means of regression analysis. Additionally, the effect of fecal sample size and egg excretion level on technique sensitivity was evaluated during a blind assessment of single stool specimen samples, using both examination methods, from 125 residents with documented S. mansoni infections. The regression equation was: Ln KK = 2.3324 + 0.6319 Ln MRC, and the coefficient of determination (r('2)) was 0.73. The regression equation was then utilized to correct the term "m" for sample size in the expression P ((GREATERTHEQ) 1 egg) = 1 - e('-ms), which estimates the probability P of finding at least one egg as a function of the mean S. mansoni egg output "m" of the population and the effective stool sample size "s" utilized by the coprological technique. This algorithm closely approximated the observed sensitivity of the KK and MRC tests when these were utilized to blindly screen a population of known parasitologic status for infection with S. mansoni. In addition, the algorithm was utilized to adjust the apparent prevalence of infection for the degree of functional sensitivity exhibited by the diagnostic test. This permitted the estimation of true prevalence of infection and, hence, a means for correcting estimates of incidence of infection. ^

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Preventable Hospitalizations (PHs) are hospitalizations that can be avoided with appropriate and timely care in the ambulatory setting and hence are closely associated with primary care access in a community. Increased primary care availability and health insurance coverage may increase primary care access, and consequently may be significantly associated with risks and costs of PHs. Objective. To estimate the risk and cost of preventable hospitalizations (PHs); to determine the association of primary care availability and health insurance coverage with the risk and costs of PHs, first alone and then simultaneously; and finally, to estimate the impact of expansions in primary care availability and health insurance coverage on the burden of PHs among non-elderly adult residents of Harris County. Methods. The study population was residents of Harris County, age 18 to 64, who had at least one hospital discharge in a Texas hospital in 2008. The primary independent variables were availability of primary care physicians, availability of primary care safety net clinics and health insurance coverage. The primary dependent variables were PHs and associated hospitalization costs. The Texas Health Care Information Collection (THCIC) Inpatient Discharge data was used to obtain information on the number and costs of PHs in the study population. Risk of PHs in the study population, as well as average and total costs of PHs were calculated. Multivariable logistic regression models and two-step Heckman regression models with log-transformed costs were used to determine the association of primary care availability and health insurance coverage with the risk and costs of PHs respectively, while controlling for individual predisposing, enabling and need characteristics. Predicted PH risk and cost were used to calculate the predicted burden of PHs in the study population and the impact of expansions in primary care availability and health insurance coverage on the predicted burden. Results. In 2008, hospitalized non-elderly adults in Harris County had 11,313 PHs and a corresponding PH risk of 8.02%. Congestive heart failure was the most common PH. PHs imposed a total economic burden of $84 billion at an average of $7,449 per PH. Higher primary care safety net availability was significantly associated with the lower risk of PHs in the final risk model, but only in the uninsured. A unit increase in safety net availability led to a 23% decline in PH odds in the uninsured, compared to only a 4% decline in the insured. Higher primary care physician availability was associated with increased PH costs in the final cost model (β=0.0020; p<0.05). Lack of health insurance coverage increased the risk of PH, with the uninsured having 30% higher odds of PHs (OR=1.299; p<0.05), but reduced the cost of a PH by 7% (β=-0.0668; p<0.05). Expansions in primary care availability and health insurance coverage were associated with a reduction of about $1.6 million in PH burden at the highest level of expansion. Conclusions. Availability of primary care resources and health insurance coverage in hospitalized non-elderly adults in Harris County are significantly associated with the risk and costs of PHs. Expansions in these primary care access factors can be expected to produce significant reductions in the burden of PHs in Harris County.^

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Cardiovascular disease (CVD) is highly preventable, yet it is a leading cause of death among women in Texas. The primary goals of this research were to examine past and current trends of CVD, as well as identify whether there is an association between the insurance coverage and mortality from CVD among women aged 60–65 in Texas between 2000 and 2011. ^ The systematic review of the research is based on the guidelines and recommendations set by the Centre for Reviews and Dissemination for conducting reviews in health care. Over 47 citations of peer-reviewed articles from Ovid MEDLINE and PubMed databases and five websites were identified, of which 7 studies met inclusion criteria for the first systematic review to examine the trends of CVD in Texas. Ten citations of peer-reviewed articles from Ovid MEDLINE and PubMed databases and five web sites were reviewed for the second systematic review (to study the association between insurance coverage and cardiovascular health among Texas women 60–64 years of age), of which 3 studies met inclusion criteria and were included in the research. The results of the study highlighted key gaps in the existing literature and important areas for the further research, as well as determined directions for future public health CVD prevention programs in Texas. ^ Based on the conducted research, the major determinants of premature mortality among women attributed to cardiovascular disease are based on individual level characteristics, more specifically sex, age, race/ethnicity, and education. The results indicate that African American and non-Hispanic white women are more likely to have higher CVD mortality rates than Hispanic women due to higher prevalence of cardiac risk factors. The data also shows higher levels of mortality from CVD in the southeastern United States, with Texas ranking as the third state with the highest prevalence of CVD among women. According to the Texas Department of State Health Services, there are approximately 56,000 deaths caused by CVD annually in Texas, which represents about one death every ten minutes. Coronary artery disease and stroke were the causes of 31.2 percent of all female deaths in Texas in 2009, meaning that approximately 68 women die from any form of cardiac disease in Texas each day. ^ The data of the reviewed studies indicate that women' lack of health insurance was significantly associated with a higher prevalence of cardiovascular disease. The uninsured women were more likely to be unaware of their risk factors and more likely to have undiagnosed diabetes—a co-morbidity factor of CVD. One of the studies also reports strong correlation between state rates of uninsured and lower rates of preventive care. Given these strong correlations, those who were chronically uninsured were at a higher risk of mortality than the insured, due to prolonged periods of time without basic access to preventive and medical care. ^ Suggested recommendations to decrease CVD mortality rates in Texas are consistent with the existing literature and include state policy development that addresses elimination of health disparities, consideration of potential benefits of universal health coverage by the legislative policymakers, and maintenance of solid partnerships between public health agencies and hospitals to educate on, diagnose, and treat CVD among the female population in Texas. ^

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The problem of analyzing data with updated measurements in the time-dependent proportional hazards model arises frequently in practice. One available option is to reduce the number of intervals (or updated measurements) to be included in the Cox regression model. We empirically investigated the bias of the estimator of the time-dependent covariate while varying the effect of failure rate, sample size, true values of the parameters and the number of intervals. We also evaluated how often a time-dependent covariate needs to be collected and assessed the effect of sample size and failure rate on the power of testing a time-dependent effect.^ A time-dependent proportional hazards model with two binary covariates was considered. The time axis was partitioned into k intervals. The baseline hazard was assumed to be 1 so that the failure times were exponentially distributed in the ith interval. A type II censoring model was adopted to characterize the failure rate. The factors of interest were sample size (500, 1000), type II censoring with failure rates of 0.05, 0.10, and 0.20, and three values for each of the non-time-dependent and time-dependent covariates (1/4,1/2,3/4).^ The mean of the bias of the estimator of the coefficient of the time-dependent covariate decreased as sample size and number of intervals increased whereas the mean of the bias increased as failure rate and true values of the covariates increased. The mean of the bias of the estimator of the coefficient was smallest when all of the updated measurements were used in the model compared with two models that used selected measurements of the time-dependent covariate. For the model that included all the measurements, the coverage rates of the estimator of the coefficient of the time-dependent covariate was in most cases 90% or more except when the failure rate was high (0.20). The power associated with testing a time-dependent effect was highest when all of the measurements of the time-dependent covariate were used. An example from the Systolic Hypertension in the Elderly Program Cooperative Research Group is presented. ^

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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. ^

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The dataset contains a cropland percent coverage map for Africa created through the combination of five existing land cover products: GLC-2000, MODIS Land Cover, GlobCover, MODIS Crop Likelihood and AfriCover. A synergy map was created in which the products are ranked by experts, which reflects the likelihood or probability that a given pixel is cropland. The cropland map was calibrated with national and sub-national crop statistics using a novel approach. Preliminary validation of the map was undertaken. The resulting cropland map has an accuracy of 83%, which is higher than the accuracy of any of the individual maps. The cropland percent coverage map for Africa is available for overlay on Google Earth or for download at http://agriculture.geo-wiki.org.

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In this paper we present a deuterium excess (d) record from an ice core drilled on a small ice cap in Svalbard in 1997. The core site is located at Lomonosovfonna at 1255 m asl, and the analyzed time series spans the period 1400-1990 A.D. The record shows pronounced multidecadal to centennial-scale variations coherent with sea surface temperature changes registered in the subtropical to southern middle-latitude North Atlantic during the instrumental period. We interpret the negative trend in the deuterium excess during the 1400s and 1500s as an indication of cooling in the North Atlantic associated with the onset of the Little Ice Age. Consistently positive anomalies of d after 1900, peaking at about 1950, correspond with well-documented contemporary warming. Yet the maximum values of deuterium excess during 1900-1990 are not as high as in the early part of the record (pre-1550). This suggests that the sea surface temperatures during this earlier period of time in the North Atlantic to the south of approximately 45°N were at least comparable with those registered in the 20th century before the end of the 1980s. We examine the potential for a cold bias to exist in the deuterium excess record due to increased evaporation from the local colder sources of moisture having isotopically cold signature. It is argued that despite a recent oceanic warming, the contribution from this local moisture to the Lomonosovfonna precipitation budget is still insufficient to interfere with the isotopic signal from the primary moisture region in the midlatitude North Atlantic.

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Este trabajo comparó la cobertura de revistas procedentes de América Latina y el Caribe incluidas en SciELO, RedALyC y SCOPUS, por país y por tema. Calculó el porcentaje de revistas en estas fuentes en relación con las registradas en el catálogo de LATINDEX. Estimó el volumen de la producción científica que registra visibilidad en las tres fuentes y su evolución en el período 2005-2009. Los resultados indicaron que las tres fuentes son complementarias. En promedio, el porcentaje de solapamiento de títulos es bajo y desigual la distribución de revistas por países. Ningún país registró en las fuentes estudiadas todas las revistas incluidas en LATINDEX. SCOPUS y SciELO están más equilibradas temáticamente que RedALyC, que mostró un marcado sesgo hacia las ciencias sociales. El volumen de producción científica visible en SCOPUS es muy superior al de SciELO y RedALyC, aunque su distribución por países es muy desigual. Las tres fuentes registran tendencias de crecimiento de la producción en el período analizado