Missing Data Graham, John W
2012, 2012-06-10
eBook, Book
Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences. Good missing data analysis solutions are available, but practical information about ...implementation of these solutions has been lacking. The objective of this book is to enable investigators who are non-statisticians to implement modern missing data procedures properly in their research, and reap the benefits in terms of improved accuracy and statistical power. The contains essential information for both beginners and advanced readers. For researchers with limited missing data analysis experience, this book offers an easy-to-read introduction to the theoretical underpinnings of analysis of missing data .... For more advanced readers, unique discussions of attrition, non-Monte-Carlo techniques for simulations involving missing data, evaluation of the benefits of auxiliary variables, and highly cost-effective planned missing data designs are provided. ... Most analyses described in the book are conducted using the well-known statistical software packages SAS and SPSS, supplemented by Norm 2.03 and associated Java-based automation utilities. (DIPF/Orig.).
IMPORTANCE: The culture of academic medicine may foster mistreatment that disproportionately affects individuals who have been marginalized within a given society (minoritized groups) and compromises ...workforce vitality. Existing research has been limited by a lack of comprehensive, validated measures, low response rates, and narrow samples as well as comparisons limited to the binary gender categories of male or female assigned at birth (cisgender). OBJECTIVE: To evaluate academic medical culture, faculty mental health, and their relationship. DESIGN, SETTING, AND PARTICIPANTS: A total of 830 faculty members in the US received National Institutes of Health career development awards from 2006-2009, remained in academia, and responded to a 2021 survey that had a response rate of 64%. Experiences were compared by gender, race and ethnicity (using the categories of Asian, underrepresented in medicine defined as race and ethnicity other than Asian or non-Hispanic White, and White), and lesbian, gay, bisexual, transgender, queer (LGBTQ+) status. Multivariable models were used to explore associations between experiences of culture (climate, sexual harassment, and cyber incivility) with mental health. EXPOSURES: Minoritized identity based on gender, race and ethnicity, and LGBTQ+ status. MAIN OUTCOMES AND MEASURES: Three aspects of culture were measured as the primary outcomes: organizational climate, sexual harassment, and cyber incivility using previously developed instruments. The 5-item Mental Health Inventory (scored from 0 to 100 points with higher values indicating better mental health) was used to evaluate the secondary outcome of mental health. RESULTS: Of the 830 faculty members, there were 422 men, 385 women, 2 in nonbinary gender category, and 21 who did not identify gender; there were 169 Asian respondents, 66 respondents underrepresented in medicine, 572 White respondents, and 23 respondents who did not report their race and ethnicity; and there were 774 respondents who identified as cisgender and heterosexual, 31 as having LGBTQ+ status, and 25 who did not identify status. Women rated general climate (5-point scale) more negatively than men (mean, 3.68 95% CI, 3.59-3.77 vs 3.96 95% CI, 3.88-4.04, respectively, P < .001). Diversity climate ratings differed significantly by gender (mean, 3.72 95% CI, 3.64-3.80 for women vs 4.16 95% CI, 4.09-4.23 for men, P < .001) and by race and ethnicity (mean, 4.0 95% CI, 3.88-4.12 for Asian respondents, 3.71 95% CI, 3.50-3.92 for respondents underrepresented in medicine, and 3.96 95% CI, 3.90-4.02 for White respondents, P = .04). Women were more likely than men to report experiencing gender harassment (sexist remarks and crude behaviors) (71.9% 95% CI, 67.1%-76.4% vs 44.9% 95% CI, 40.1%-49.8%, respectively, P < .001). Respondents with LGBTQ+ status were more likely to report experiencing sexual harassment than cisgender and heterosexual respondents when using social media professionally (13.3% 95% CI, 1.7%-40.5% vs 2.5% 95% CI, 1.2%-4.6%, respectively, P = .01). Each of the 3 aspects of culture and gender were significantly associated with the secondary outcome of mental health in the multivariable analysis. CONCLUSIONS AND RELEVANCE: High rates of sexual harassment, cyber incivility, and negative organizational climate exist in academic medicine, disproportionately affecting minoritized groups and affecting mental health. Ongoing efforts to transform culture are necessary.
Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a ...selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.
IMPORTANCE: Clinical prediction models estimated with health records data may perpetuate inequities. OBJECTIVE: To evaluate racial/ethnic differences in the performance of statistical models that ...predict suicide. DESIGN, SETTING, AND PARTICIPANTS: In this diagnostic/prognostic study, performed from January 1, 2009, to September 30, 2017, with follow-up through December 31, 2017, all outpatient mental health visits to 7 large integrated health care systems by patients 13 years or older were evaluated. Prediction models were estimated using logistic regression with LASSO variable selection and random forest in a training set that contained all visits from a 50% random sample of patients (6 984 184 visits). Performance was evaluated in the remaining 6 996 386 visits, including visits from White (4 031 135 visits), Hispanic (1 664 166 visits), Black (578 508 visits), Asian (313 011 visits), and American Indian/Alaskan Native (48 025 visits) patients and patients without race/ethnicity recorded (274 702 visits). Data analysis was performed from January 1, 2019, to February 1, 2021. EXPOSURES: Demographic, diagnosis, prescription, and utilization variables and Patient Health Questionnaire 9 responses. MAIN OUTCOMES AND MEASURES: Suicide death in the 90 days after a visit. RESULTS: This study included 13 980 570 visits by 1 433 543 patients (64% female; mean SD age, 42 18 years. A total of 768 suicide deaths were observed within 90 days after 3143 visits. Suicide rates were highest for visits by patients with no race/ethnicity recorded (n = 313 visits followed by suicide within 90 days, rate = 5.71 per 10 000 visits), followed by visits by Asian (n = 187 visits followed by suicide within 90 days, rate = 2.99 per 10 000 visits), White (n = 2134 visits followed by suicide within 90 days, rate = 2.65 per 10 000 visits), American Indian/Alaskan Native (n = 21 visits followed by suicide within 90 days, rate = 2.18 per 10 000 visits), Hispanic (n = 392 visits followed by suicide within 90 days, rate = 1.18 per 10 000 visits), and Black (n = 65 visits followed by suicide within 90 days, rate = 0.56 per 10 000 visits) patients. The area under the curve (AUC) and sensitivity of both models were high for White, Hispanic, and Asian patients and poor for Black and American Indian/Alaskan Native patients and patients without race/ethnicity recorded. For example, the AUC for the logistic regression model was 0.828 (95% CI, 0.815-0.840) for White patients compared with 0.640 (95% CI, 0.598-0.681) for patients with unrecorded race/ethnicity and 0.599 (95% CI, 0.513-0.686) for American Indian/Alaskan Native patients. Sensitivity at the 90th percentile was 62.2% (95% CI, 59.2%-65.0%) for White patients compared with 27.5% (95% CI, 21.0%-34.7%) for patients with unrecorded race/ethnicity and 10.0% (95% CI, 0%-23.0%) for Black patients. Results were similar for random forest models, with an AUC of 0.812 (95% CI, 0.800-0.826) for White patients compared with 0.676 (95% CI, 0.638-0.714) for patients with unrecorded race/ethnicity and 0.642 (95% CI, 0.579-0.710) for American Indian/Alaskan Native patients and sensitivities at the 90th percentile of 52.8% (95% CI, 50.0%-55.8%) for White patients, 29.3% (95% CI, 22.8%-36.5%) for patients with unrecorded race/ethnicity, and 6.7% (95% CI, 0%-16.7%) for Black patients. CONCLUSIONS AND RELEVANCE: These suicide prediction models may provide fewer benefits and more potential harms to American Indian/Alaskan Native or Black patients or those with undrecorded race/ethnicity compared with White, Hispanic, and Asian patients. Improving predictive performance in disadvantaged populations should be prioritized to improve, rather than exacerbate, health disparities.
This lively and engaging book explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models ...of your own. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modelling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.
To compare racial and ethnic differences between obstetrician-gynecologists (ob-gyns) and other large groups of adult medical specialists who provide the predominant care of women. Whether physician ...diversity influences their practice locations in underserved areas was also sought.
This cross-sectional study reports an analysis of U.S. national data about racial and ethnic characteristics, gender, and specialty (obstetrics and gynecology, general internal medicine, family medicine, emergency medicine) of 190,379 physicians who came from three resources (Association of American Medical Colleges Student Records System, Association of American Medical Colleges Minority Physicians Database, American Medical Association Physician Masterfile). Underserved locations were identified as being rural, having 20% or more of the population living in poverty or being federally designated as areas of professional shortages or underserved populations. Bivariate measures of associations were performed to study the association between physician race and ethnicity and their practice location.
Female physicians in all specialties were more likely than males to be nonwhite, and ob-gyns were most likely to be female (61.9%). Compared with other studied specialists, ob-gyns had the highest proportion of underrepresented minorities (combined, 18.4%), especially black (11.1%) and Hispanic (6.7%) physicians. Underrepresented minority ob-gyns were more likely than white or Asians to practice in federally funded underserved areas or where poverty levels were high. Native Americans, Alaska Natives, and Pacific Islanders were the ob-gyn group with the highest proportion practicing in rural areas.
Compared with other adult medical specialists, ob-gyns have a relatively high proportion of black and Hispanic physicians. A higher proportion of underrepresented minority ob-gyns practiced at medically underserved areas.
BACKGROUND: Hypotension during surgery is frequent in the elderly population and is associated with acute kidney and myocardial injury, which are, themselves, associated with increased 30-day ...mortality. The present study compared the hemodynamic effects of hypobaric unilateral spinal anesthesia (HUSA) to general anesthesia (GA) in patients ≥70 years of age undergoing hip fracture surgery. METHODS: We conducted a single-center, prospective, randomized study. In the HUSA group, patients were positioned with the operated hip above, and the hypobaric anesthetic solution was composed of 9 mg ropivacaine, 5 µg sufentanil, and 1 mL of sterile water. Anesthesia was adjusted for the GA group. Mean arterial pressure (MAP) was measured with a noninvasive blood pressure upper arm cuff every 3 minutes. Hypotension was treated with a bolus of ephedrine and then a continuous intravenous of norepinephrine to obtain a MAP ≥65 mm Hg. Primary outcome was the occurrence of severe hypotension, defined as a MAP <65 mm Hg for >12 consecutive minutes. RESULTS: A total of 154 patients were included. Severe hypotension was more frequent in the GA group compared to the HUSA group (odds ratio, 5.6; 95% confidence interval, 2.7–11.7; P < .001). There was no significant difference regarding the short-term outcomes between the HUSA and GA groups: acute kidney injury (respectively, 5.1% vs 11.3%; P = .22), myocardial injury (18.0% vs 14.0%; P = .63), and 30-day mortality (2.4% vs 4.7%; P = .65). CONCLUSIONS: HUSA leads to fewer episodes of severe intraoperative hypotension compared to GA in an elderly population undergoing hip fracture surgery.
Disparities in Access to Oral Health Care Northridge, Mary E; Kumar, Anjali; Kaur, Raghbir
Annual review of public health,
04/2020, Letnik:
41, Številka:
1
Journal Article
Recenzirano
Odprti dostop
In the United States, people are more likely to have poor oral health if they are low-income, uninsured, and or members of racial ethnic minority, immigrant, or rural populations who have suboptimal ...access to quality oral health care. As a result, poor oral health serves as the national symbol of social inequality. There is increasing recognition among those in public health that oral diseases such as dental caries and periodontal disease and general health conditions such as obesity and diabetes are closely linked by sharing common risk factors, including excess sugar consumption and tobacco use, as well as underlying infection and inflammatory pathways. Hence, efforts to integrate oral health and primary health care, incorporate interventions at multiple levels to improve access to and quality of services, and create health care teams that provide patient-centered care in both safety net clinics and community settings may narrow the gaps in access to oral health care across the life course.