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  • JAMA Internal Medicine October 1, 2017

    Figure 1: Unmet Need for Prescription Drugs Among Adults Insured Throughout Year 1

    Estimates from multivariable linear probability model controlling for the effects of age, sex, and race/ethnicity in year 1 and time-varying measures of household income as a percentage of the federal poverty line, self-reported health, and number of chronic conditions (coronary heart disease, angina, myocardial infarction, other heart disease, stroke, emphysema, high cholesterol, diabetes, arthritis, and asthma). Increase in unmet need for those losing health insurance in Year 2 was significantly greater than for the continuously insured.
  • JAMA Internal Medicine October 1, 2017

    Figure 2: Unmet Need for Prescription Drugs Among Adults Uninsured Throughout Year 1

    Estimates from multivariable linear probability model controlling for the effects of age, sex, and race/ethnicity in year 1 and time-varying measures of household income as a percentage of the federal poverty line, self-reported health, and number chronic conditions (coronary heart disease, angina, myocardial infarction, other heart disease, stroke, emphysema, high cholesterol, diabetes, arthritis, and asthma). Decrease in unmet need for those gaining health insurance in Year 2 was significantly greater than for the continuously uninsured.
  • JAMA Internal Medicine September 18, 2017

    Figure: Age- and Sex-Adjusted Rate of Healthy Older Adults, 2000-2014

    The age- and sex-adjusted rate of healthy older adults by race/ethnicity (A),level of education (B), and level of family annual income (C). Family annual income measured as a percentage of the federal poverty line as poor or near poor (<125%), low family income (125% to <200%), middle family income (200% to <400%) and high family income (≥400%).
  • JAMA Internal Medicine August 1, 2017

    Figure 2: Values Reflect Mean Advance Care Planning (ACP) Engagement Process Scores From Repeated Measures, Mixed-Effects Linear Regression Models Adjusted for Race, Literacy, Baseline ACP Documentation, and Clustering by Physician

    P values reflect significance for overall group × time interactions. AD indicates advance directive; PREPARE, patient-centered, advance care planning website.
  • JAMA Internal Medicine August 1, 2017

    Figure 3: Values Reflect Total Advance Care Planning (ACP) Engagement Action Scores From Repeated Measures, Mixed-Effects Linear Regression Models Adjusted for Race, Literacy, Baseline ACP Documentation, and Clustering by Physician

    P values reflect significance for overall group × time interactions. AD indicates advance directive; PREPARE, patient-centered, advance care planning website.
  • Inequalities in Life Expectancy Among US Counties, 1980 to 2014: Temporal Trends and Key Drivers

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    JAMA Intern Med. 2017; 177(7):1003-1011. doi: 10.1001/jamainternmed.2017.0918

    This data analysis estimates trends in geographic inequalities in life expectancy and age-specific risk of death by US county from 1980 to 2014.

  • Racial Disparities in Medical Student Membership in the Alpha Omega Alpha Honor Society

    Abstract Full Text
    JAMA Intern Med. 2017; 177(5):659-665. doi: 10.1001/jamainternmed.2016.9623

    This study examines the association between medical student race/ethnicity and induction into the Alpha Omega Alpha honor society.

  • Implicit Bias in Academic Medicine: #WhatADoctorLooksLike

    Abstract Full Text
    JAMA Intern Med. 2017; 177(5):657-658. doi: 10.1001/jamainternmed.2016.9643
  • JAMA Internal Medicine April 1, 2017

    Figure: Experiences of Sponsorship by Sex

    This graph depicts self-reported experiences of sponsorship by K08 and K23 award recipients for men with male mentors (n = 442), men with female mentors (n = 89), women with male mentors (n = 323), and women with female mentors (n = 131). Unadjusted percentages are depicted for each of 4 individual sponsorship experiences and for a composite binary measure of having reported at least 1 of the 4 individual experiences. aP values evaluate the presence of a difference between men and women holding National Institutes of Health (NIH) Mentored Career Development (K) awards in regression models that adjust for other demographic characteristics (age, race), job characteristics (grant type, year of grant award, medical specialty), level of funding for the NIH institute that granted the K award, and level of NIH funding received by the individual’s institution of employment.
  • Adherence to Newly Prescribed Diabetes Medications Among Insured Latino and White Patients With Diabetes

    Abstract Full Text
    JAMA Intern Med. 2017; 177(3):371-379. doi: 10.1001/jamainternmed.2016.8653

    This study examines the association of patient race/ethnicity, preferred language, and physician language concordance with patient adherence to newly prescribed diabetes medications.

  • Adult Utilization of Psychiatric Drugs and Differences by Sex, Age, and Race

    Abstract Full Text
    JAMA Intern Med. 2017; 177(2):274-275. doi: 10.1001/jamainternmed.2016.7507

    This population-based study uses survey data from the Agency for Healthcare Research and Quality to compare the demographic characteristics of US adults who report use of psychiatric drugs.

  • JAMA Internal Medicine August 1, 2016

    Figure 1: Change in Total Mortality Associated With Increases in the Percentage of Energy From Specific Types of Fat

    Multivariable hazard ratios of total mortality associated with replacing the percentage of energy from total carbohydrates by the same energy from specific types of fat (P < .001 for trend for all) were used. The model was adjusted for age (in months), white race (yes vs no), marital status (with spouse, yes or no), body mass index (<23.0, 23.0-24.9, 25.0-29.9, 30.0-34.9, or ≥35.0 [calculated as weight in kilograms divided by height in meters squared]), physical activity (<3.0, 3.0-8.9, 9.0-17.9, 18.0-26.9, or ≥27.0 h of metabolic equivalent tasks per week), smoking status (never, past, current 1-14 cigarettes/d, current 15-24 cigarettes/d, or current ≥25 cigarettes/d), alcohol consumption (women: 0, 0.1-4.9, 5.0-14.9, or ≥15.0 g/d; men: 0, 0.1-4.9, 5.0-29.9, or ≥30.0 g/d), multivitamin use (yes vs no), vitamin E supplement use (yes vs no), current aspirin use (yes vs no), family history of myocardial infarction (yes vs no), family history of diabetes (yes vs no), family history of cancer (yes vs no), history of hypertension (yes vs no), history of hypercholesterolemia (yes vs no), intakes of total energy and dietary cholesterol (quintiles), percentage of energy intake from dietary protein (quintiles), menopausal status and hormone use in women (premenopausal, postmenopausal never users, postmenopausal past users, or postmenopausal current users), and percentage of energy from the remaining specific types of fat (saturated fatty acids, polyunsaturated fatty acids, monounsaturated fatty acids, and trans-fatty acids, all modeled as continuous variables). Results for the Nurses’ Health Study and Health Professional Follow-up Study from the multivariable model were combined using the fixed-effects model.
  • JAMA Internal Medicine August 1, 2016

    Figure 2: Multivariable Hazard Ratios (HRs) of Mortality by Isocaloric Substitution of Specific Types of Fatty Acid for Saturated Fatty Acids

    The model was adjusted for age (in months), white race (yes vs no), marital status (with spouse, yes or no), body mass index (<23.0, 23.0-24.9, 25.0-29.9, 30.0-34.9, or ≥35.0 [calculated as weight in kilograms divided by height in meters squared]), physical activity (<3.0, 3.0-8.9, 9.0-17.9, 18.0-26.9, or ≥27.0 h of metabolic equivalent tasks per week), smoking status (never, past, current 1-14 cigarettes/d, current 15-24 cigarettes/d, or current ≥25 cigarettes/d), alcohol consumption (women: 0, 0.1-4.9, 5.0-14.9, or ≥15.0 g/d; men: 0, 0.1-4.9, 5.0-29.9, or ≥30.0 g/d), multivitamin use (yes vs no), vitamin E supplement use (yes vs no), current aspirin use (yes vs no), family history of myocardial infarction (yes vs no), family history of diabetes (yes vs no), family history of cancer (yes vs no), history of hypertension (yes vs no), history of hypercholesterolemia (yes vs no), intakes of total energy and dietary cholesterol (quintiles), percentage of energy intake from dietary protein (quintiles), menopausal status and hormone use in women (premenopausal, postmenopausal never users, postmenopausal past users, or postmenopausal current users), and percentage of energy from remaining fatty acids (saturated fatty acids, polyunsaturated fatty acids [PUFAs], monounsaturated fatty acids [MUFAs], trans-fatty acids, ω-6 PUFAs, ω-3 PUFAs, linoleic acid, arachidonic acid, α-linolenic acid, and marine ω-3 fats, all modeled as continuous variables). Results for the Nurses’ Health Study and Health Professional Follow-up Study from the multivariable model were combined using the fixed-effects model. UFA indicates unsaturated fatty acid; and error bars, 95% CI.
  • JAMA Internal Medicine January 1, 2016

    Figure 1: Distribution of Preexposure Prophylaxis Engagement by Visit Week

    Engagement is a 5-level ordinal measure, with missing the visit as the lowest level of engagement and increasing levels of engagement based on estimated dosing frequency based on tenofovir diphosphate concentrations. Numbers indicate number of participants contributing data at each time point. Engagement varied by site and by race or ethnicity (P < .001). BLQ indicates below the limit of quantitation.
  • JAMA Internal Medicine October 1, 2015

    Figure 1: Distribution in the 2010 US Population, 2012 Medical School Graduates, 2012 Practicing Physicians, and the 2012 Graduate Medical Education (GME) Trainee Pool

    When comparing the total GME percentage representation for each demographic with the other groups, representation was significantly different for all groups (P <.001 for all comparisons, except for the Hispanic medical school graduates and trainees [P = .85]). Not shown are the male sex, non-Hispanic ethnicity, “other” race, and white race categories. AI indicates American Indian; AN, Alaska Native, NH, Native Hawaiian; PI, Pacific Islander; URM, underrepresented minorities in medicine (non-URM category is not shown).
  • Diversity in Graduate Medical Education in the United States by Race, Ethnicity, and Sex, 2012

    Abstract Full Text
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    JAMA Intern Med. 2015; 175(10):1706-1708. doi: 10.1001/jamainternmed.2015.4324

    In this data analysis, graduate medical education diversity was assessed by race, ethnicity, and sex in 2012.

  • JAMA Internal Medicine April 1, 2015

    Figure 2: Kaplan-Meier Estimates of Total VMS Duration of Frequent VMS by Menopausal Transition Stage at First VMS Report (A) and by Race/Ethnicity (B)

    A, By menopausal transition stage at first VMS report. B, By race/ethnicity. VMS indicates vasomotor symptoms. Menopausal transition stage at first VMS report is missing for 9 participants. Median duration for each group is calculated as the value on the x-axis corresponding to the intersection of the dashed horizontal line (50%) with the group’s survival curve.
  • JAMA Internal Medicine April 1, 2015

    Figure 3: Kaplan-Meier Estimates of Post-FMP Persistence of Frequent VMS by Menopausal Transition Stage at First VMS Report and by Race/Ethnicity

    A, By menopausal transition stage at first VMS report. B, By race/ethnicity. FMP indicates final menstrual period; VMS, vasomotor symptoms. Menopausal transition stage at first VMS report is missing for 2 participants. Median duration for each group is calculated as the value on the x-axis corresponding to the intersection of the dashed horizontal line (50%) with the group’s survival curve.
  • JAMA Internal Medicine November 1, 2014

    Figure: Reporting of Sex and Race/Ethnicity and Percentage of Female Patients and Racial Groups in RCTs and Guidelines

    A, Reporting of patients’ sex and racial groups over time. B, Percentage of female patients in the RCTs of AF, HF and ACS guidelines. C, Percentage of different racial groups in the AF, HF, and ACS guidelines. ACS indicates acute coronary syndromes; AF, atrial fibrillation; HF, heart failure; and RCTs, randomized controlled trials.
  • JAMA Internal Medicine November 1, 2014

    Figure: Predicted Probabilities of Generic Drug Discount Program Use

    A, Age groups, determined as younger adults (aged 18-45 years), older adults (aged 46-64 years), and seniors (aged ≥65 years). B, Presence of health insurance. C, Drug therapy complexity, as measured by the number of drugs in use. D, Comorbidities, as measured by the Charlson Comorbidity Index. E, Race/ethnicity. F, Family income in relationship to the FPL. Probabilities were predicted for each group with adjustments for variables according to Andersen’s behavioral model at their mean value. FPL indicates federal poverty level; HSP, Hispanic; NHB, non-Hispanic black; and NHW, non-Hispanic white. Bars indicate means; limit lines, 95% CIs.