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correct for self-reporting bias and to estimate state- specific and demographic subgroup–specific trends and projections of the preva- lence of categories of body-mass index (BMI)

T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e

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From the Center for Health Decision Sci- ence (Z.J.W.) and the Departments of Health Policy and Management (S.N.B.) and Social and Behavioral Sciences (A.L.C., J.L.B., C.M.G., C.F., S.L.G.), Harvard T.H. Chan School of Public Health, Boston; and the Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington, D.C. (M.W.L.). Address reprint requests to Mr. Ward at the Center for Health Decision Science, Harvard T.H. Chan School of Public Health, 718 Huntington Ave., Boston, MA, 02115, or at zward@ hsph . harvard . edu.

BACKGROUND Although the national obesity epidemic has been well documented, less is known about obesity at the U.S. state level. Current estimates are based on body measures reported by persons themselves that underestimate the prevalence of obesity, es- pecially severe obesity.

METHODS We developed methods to correct for self-reporting bias and to estimate state- specific and demographic subgroup–specific trends and projections of the preva- lence of categories of body-mass index (BMI). BMI data reported by 6,264,226 adults (18 years of age or older) who participated in the Behavioral Risk Factor Surveillance System Survey (1993–1994 and 1999–2016) were obtained and cor- rected for quantile-specific self-reporting bias with the use of measured data from 57,131 adults who participated in the National Health and Nutrition Examination Survey. We fitted multinomial regressions for each state and subgroup to estimate the prevalence of four BMI categories from 1990 through 2030: underweight or normal weight (BMI [the weight in kilograms divided by the square of the height in meters], <25), overweight (25 to <30), moderate obesity (30 to <35), and severe obesity (≥35). We evaluated the accuracy of our approach using data from 1990 through 2010 to predict 2016 outcomes.

RESULTS The findings from our approach suggest with high predictive accuracy that by 2030 nearly 1 in 2 adults will have obesity (48.9%; 95% confidence interval [CI], 47.7 to 50.1), and the prevalence will be higher than 50% in 29 states and not below 35% in any state. Nearly 1 in 4 adults is projected to have severe obesity by 2030 (24.2%; 95% CI, 22.9 to 25.5), and the prevalence will be higher than 25% in 25 states. We predict that, nationally, severe obesity is likely to become the most common BMI category among women (27.6%; 95% CI, 26.1 to 29.2), non- Hispanic black adults (31.7%; 95% CI, 29.9 to 33.4), and low-income adults (31.7%; 95% CI, 30.2 to 33.2).

CONCLUSIONS Our analysis indicates that the prevalence of adult obesity and severe obesity will continue to increase nationwide, with large disparities across states and demo- graphic subgroups. (Funded by the JPB Foundation.)

A B S T R A C T

Projected U.S. State-Level Prevalence of Adult Obesity and Severe Obesity

Zachary J. Ward, M.P.H., Sara N. Bleich, Ph.D., Angie L. Cradock, Sc.D., Jessica L. Barrett, M.P.H., Catherine M. Giles, M.P.H., Chasmine Flax, M.P.H.,

Michael W. Long, Sc.D., and Steven L. Gortmaker, Ph.D.

Special Article

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Although the growing obesity epi-demic in the United States has been well documented,1-4 less is known about long- term trends and the future of obesity prevalence. Although national projections of obesity have been made previously,5-7 state-specific analyses are limited. State-specific projections of the bur- den of obesity are important for policymakers, given the considerable variation in the prevalence of obesity across states,8 the substantial state- level financial implications,9 and the opportunity for obesity-prevention interventions to be imple- mented at a local level.10-13

However, a barrier to accurate state-level pro- jections is the paucity of objectively measured body-mass index (BMI) data according to state. The Behavioral Risk Factor Surveillance System (BRFSS), a nationally representative telephone survey of more than 400,000 adults each year,14 provides participants’ estimates of height and weight according to state. These data have been used to track obesity prevalence and are the basis of maps that have illustrated the growth of the obesity epidemic.1 Although the BRFSS pro- vides valuable state-level estimates over time, the reliance on subjective body measures reported by participants substantially underestimates the prev- alence of obesity owing to the well-documented self-reporting bias.8,15,16 We developed a method of bias correction to adjust the entire distribu- tion of BMI in the BRFSS surveys from 1993 through 2016 and estimated state-level historical trends and projections of the prevalence of BMI categories from 1990 through 2030 according to demographic subgroup.

M e t h o d s

Overview

We adjusted reported BMI data from the BRFSS to align the data with objectively measured BMI distributions from the National Health and Nu- trition Examination Survey (NHANES), a nation- ally representative survey in which measured data on height and weight are collected with the use of standardized examination procedures.17 We estimated trends in the prevalence of BMI categories according to subgroup in each state and made projections through 2030. The first author designed the study, gathered and analyzed

the data, and vouches for the accuracy and com- pleteness of the data. All the authors critically revised the manuscript and made the decision to submit the manuscript for publication.

Data

We obtained BRFSS data from 1993 through 1994 and 1999 through 2016, periods during which annual data were collected for all 50 states and Washington, D.C. (except for Wyoming in 1993, Rhode Island in 1994, and Hawaii in 2004). We obtained nationally representative NHANES data from 1991 through 1994 (phase 2 of NHANES III) and from 1999 through 2016 (con- tinuous NHANES). Data from pre-1999 BRFSS surveys were restricted to 1993 and 1994 to co- incide with phase 2 of NHANES III. (Before 1993, not all states were included in the BRFSS.) We cleaned each data set to ensure that the vari- ables of interest were not missing and ensured that reported height and weight in the BRFSS were biologically plausible. Our final BRFSS data set included 6,264,226 adults (18 years of age or older), and our NHANES data set included 57,131 adults. (Exclusion criteria and respondent characteristics are provided in Section 1 in the Supplementary Appendix, available with the full text of this article at NEJM.org.)

Adjustment for Self-Reporting Bias

We adjusted reported BMI data from the BRFSS so that the distribution was similar to measured BMI from NHANES. Because both the BRFSS and NHANES are designed to be nationally repre- sentative surveys, data from NHANES can be used to adjust participant-reported body measures in the BRFSS. By adjusting the entire distribution of reported BMI to be consistent with measured BMI in NHANES, we adjusted for self-reporting bias while preserving the relative position of each person’s BMI.8 Specifically, we estimated the dif- ference between participant-reported BMI and measured BMI according to quantile and then fit cubic splines to smoothly estimate self-reporting bias across the entire BMI distribution. Each per- son’s BMI was then adjusted for this bias given his or her BMI quantile. We adjusted BMI dis- tributions separately according to sex and time period (1993–1994, 1999–2004, 2005–2010, and 2011–2016) to control for potential time trends

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in self-reporting bias and composition of demo- graphic subgroups. (Additional details are provid- ed in Section 2 in the Supplementary Appendix.)

State-Specific Trends and Projections

BMI categories were defined according to the Centers for Disease Control and Prevention (CDC) guidelines: underweight or normal weight (BMI [the weight in kilograms divided by the square of the height in meters], <25), overweight (25 to <30), moderate obesity (30 to <35), and severe obesity (≥35).18 We used multinomial (renormal- ized logistic) regressions to predict the preva- lence of each BMI category as a function of time. This method ensures that the prevalence of all categories sums to 100% in each year and allows estimation of nonlinear trends in the prevalence of BMI categories. Our reduced covariate model (i.e., with year as the independent variable) im- plicitly accounts for trends in the composition of demographic subgroups (e.g., age distribution and composition of race or ethnic group catego- ries) within each state, since the relative contri- butions of these various factors (and their po- tential changing effect over time) are already reflected in the prevalence estimates. Such an ap- proach also implicitly controls for trends in other variables that may affect BMI, such as smoking or illness. Although it is important to explicitly con- trol for these variables when estimating the ef- fect of BMI on related health outcomes, because our outcome of interest was the prevalence of BMI categories over time, it was not necessary to control for these variables because their effect was already reflected in the observed prevalence estimates used to fit the models. (Additional de- tails and a discussion of previous approaches are provided in Sections 3.1 and 3.2 in the Supple- mentary Appendix.)

Regressions were performed nationally and for each state independently, while taking the complex survey structure of the BRFSS into ac- count. We estimated historical trends and pro- jections of the prevalence of each BMI category from 1990 through 2030, as well as the preva- lence of overall obesity (BMI, ≥30). We also made projections for demographic subgroups to examine trends and explore the effect of geogra- phy (i.e., state of residence) on obesity trends within subgroups. We estimated trends accord- ing to sex (male or female), race or ethnic group

(non-Hispanic white, non-Hispanic black, His- panic, or non-Hispanic other), annual house- hold income (<$20,000, $20,000 to <$50,000, or ≥$50,000), education (less than high-school grad- uate, high-school graduate to some college, or college graduate), and age group (18 to 39, 40 to 64, or ≥65 years) (Section 3.3 in the Supplemen- tary Appendix). Because of the small sample sizes and changing BRFSS categories of race or ethnic group over time, we combined five groups (“American Indian or Alaskan Native,” “Asian,” “Native Hawaiian or Pacific Islander,” “other,” and “multiracial”) into one “non-Hispanic other” category.

In accordance with the CDC guidelines that consider BRFSS estimates unreliable if they are based on a sample of fewer than 50 people,19 we suppressed state-level estimates from subgroups with fewer than 1000 respondents; given our data set of 20 rounds of BRFSS surveys, we sup- pressed estimates from subgroups with fewer than 50 respondents on average per year in a state. Thus, estimates for the following sub- groups were suppressed: non-Hispanic black adults in 12 states (Alaska, Hawaii, Idaho, Maine, Montana, New Hampshire, North Dakota, Ore- gon, South Dakota, Utah, Vermont, and Wyo- ming) and Hispanic adults in 2 states (North Dakota and West Virginia).

To account for uncertainty, we bootstrapped both data sets (NHANES and BRFSS) 1000 times, considering the complex structure of each survey (Section 3.4 in the Supplementary Ap- pendix) and repeated all analyses (i.e., adjustment for self-reporting bias and state-specific projec- tions). We report the mean and 95% confidence interval (calculated as the 2.5 and 97.5 percen- tiles of the bootstrapped values) for all esti- mates.

Assessment of Predictive Accuracy and Sensitivity Analyses

To evaluate the accuracy of our approach, we restricted our data sets (NHANES and BRFSS) to include only data from 1999 through 2010. We then repeated our analyses with this subset of data and predicted the prevalence of each BMI category in 2016 (i.e., 6 years after the last ob- served year in our truncated data). We compared our predictions with the observed prevalence (corrected for self-reporting bias) in 2016. This

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exercise allowed us to evaluate the accuracy of our approach in predicting future values and allowed us to assess the potential effect of the change in the BRFSS sample design in 2011 to include cell-phone interviews on our estimation of trends. For our predictions, we calculated the coverage probability (i.e., the percentage of ob- served estimates that fell within our 95% confi- dence intervals), the percentage of our mean predictions that fell within a certain distance (e.g., 10% relative error) of the observed esti- mate, and the mean absolute error.

In a sensitivity analysis, we also made projec- tions based on self-reported body measures (i.e., no adjustment for self-reporting bias). Statistical analyses were performed with the use of R soft- ware, version 3.2.5 (R Foundation for Statistical Computing), with BRFSS bootstrapping per- formed in Java for computational efficiency.

R e s u l t s

Bias-Corrected BMI Data

After we corrected for self-reporting bias, our adjusted BMI distributions in the BRFSS data set did not differ significantly (P>0.05) from those in the NHANES data set for each sex and time period. Adjustment of the entire BMI distribu- tion also ensured that the prevalence of each BMI category in the BRFSS data set was similar to that in the NHANES data set. BMI values for men and women were adjusted on average by 0.71 and 1.76 units, respectively, with differential (increasing) adjustment according to reported BMI. (Additional details are provided in Sec- tion 2 in the Supplementary Appendix.)

Predictive Accuracy

Our coverage probability (i.e., the percentage of time that our 95% confidence intervals con- tained the observed estimate) for state-level prev- alence in 2016 was 94.6% across the four BMI categories. Subgroup-specific coverage probabil- ities were 92.5% on average (Section 4 in the Supplementary Appendix). Our mean predictions for states were within 10% (relative error) of the reported estimate 95.6% of the time, with a mean absolute error of 0.85 percentage points. Although our coverage probabilities are high, our mean predictions are less accurate for subgroups with smaller sample sizes.

Trends and Projections

Our projections show that the national preva- lence of adult obesity and severe obesity will rise to 48.9% (95% confidence interval [CI], 47.7 to 50.1) and 24.2% (95% CI, 22.9 to 25.5), respec- tively, by 2030, with large variation across states. Maps of state-level prevalence of obesity and severe obesity over time are provided in Figure 1. Based on current trends, our projections show that the prevalence of overall obesity (BMI, ≥30) will rise above 50% in 29 states by 2030 and will not be below 35% in any state. We also project that the prevalence of severe obesity (BMI, ≥35) will rise above 25% in 25 states (Table 1). State- level trends in the prevalence of each BMI cate- gory are presented according to subgroup in Section 5 in the Supplementary Appendix. These trends show that the prevalence of overweight is declining as obesity develops in more people.

Our sensitivity analyses, which did not cor- rect for self-reporting bias, revealed similar trends over time but with an overall projected obesity prevalence that was on average 5.3 percentage points lower than the bias-corrected obesity prevalence (relative error of approximately 10%) and similar underestimates according to sub- group (Section 6 in the Supplementary Appendix).

Our projections also revealed large disparities in obesity prevalence across subgroups. We project that by 2030 severe obesity will be the most com- mon BMI category nationwide among women, black non-Hispanic adults, and low-income adults (i.e., household income <$50,000) (Fig. 2).

In addition, we found large geographic dis- parities within subgroups (Fig. 3). (State-level maps and tables are provided in Sections 7 and 8 in the Supplementary Appendix.) In general, we found a higher prevalence of obesity among non- Hispanic black and Hispanic adults than among non-Hispanic white adults, and the heterogene- ity in the composition of the non-Hispanic other category of race or ethnic group across states was ref lected by the variation in obesity preva- lence across states for this group.

We also found a large gradient in the preva- lence of obesity according to income. For exam- ple, our projections show that severe obesity will be the most common BMI category in 44 states among adults with an annual household income of less than $20,000, as compared with only 1 state among adults with an annual household income

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B Prevalence of Severe Obesity (BMI, ≥35)A Prevalence of Overall Obesity (BMI, ≥30) 1990 1990

2000 2000

2010 2010

2020 2020

2030 2030

0 10 20 30 40 50 60

Prevalence (%)

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of greater than $50,000 (Fig. 3). State-specific analyses according to subgroup are provided in Sections 7 through 9 in the Supplementary Ap- pendix, including the results for education and age subgroups, as well as suppressed estimates for race or ethnic groups.

D i s c u s s i o n

In this study, we used more than 20 years of data from more than 6 million adults and applied an analytical approach that provided more accurate state-level estimates of BMI trends, corrected for self-reporting bias. Our method differentially ad- justed the entire BMI distribution, an approach that preserves heterogeneity, in contrast to regres- sion-based approaches that adjust mean values.6,15 Adjustment of the entire BMI distribution has been shown to better capture the tails of the BMI distribution, resulting in more accurate es- timates of obesity prevalence, especially for severe obesity.8

Although analyses of trends in adult obesity in the United States have been performed previ- ously,1-6,15,20-23 a strength of our analysis is that we provided both national and state-level, sub- group-specific estimates (i.e., 832 demographic subgroups) based on bias-corrected data from more than 6 million adults over many years. Although previous criticisms of obesity projec- tions — often based on small samples over short periods — argue that changes in obesity preva- lence have not followed a predictable pattern,24 we observed remarkably stable and predictable trends across a wide range of states and demo- graphic subgroups. Moreover, we provided em- pirical evidence of the predictive validity of our approach, showing that our model has a high degree of accuracy. Our coverage probabilities of approximately 95% indicate that our 95% confi-

dence intervals appropriately reflect the uncer- tainty around our estimates.

Our sensitivity analyses, which did not adjust for self-reporting bias, revealed similar trends to those in our main analysis but with a lower prevalence, as expected. For example, our unad- justed projections of the prevalence of obesity among women in 2030 were on average 13% (6.4 percentage points) lower than our bias- corrected projections, a finding that highlights the importance of correcting for self-reporting bias to obtain accurate prevalence estimates.

We found that nearly 1 in 2 adults nationwide will probably have obesity by 2030, with large disparities across states and demographic sub- groups. Using our model, we projected that by 2030 the majority of adults in 29 states will have obesity and that the prevalence of obesity will approach 60% in some states and not be below 35% in any state. These results are similar to previous estimates showing that 57% of children 2 to 19 years of age in 2016 are projected to have obesity by the age of 35 years.7

We noted that as more adults cross the threshold to obesity, the prevalence of overweight is declining, a finding that highlights the impor- tance of assessing changes in weight across the entire BMI distribution rather than focusing on only one category. Especially worrisome is the projected rise in the prevalence of severe obesity, which is associated with even higher mortality and morbidity25 and health care costs.9 Using our model, we projected that by 2030 nearly 1 in 4 U.S. adults will have severe obesity, and the prevalence will be higher than 25% in 25 states. Severe obesity is thus poised to become as preva- lent as overall obesity was in the 1990s. Indeed, our projections suggest that severe obesity may become the most common BMI category among adults in 10 states by 2030 and even more common in some subgroups, especially among women, non-Hispanic black adults, and low-income adults; these findings highlight persistent disparities according to sex, race or ethnic group, and in- come. The high projected prevalence of severe obesity among low-income adults and the high medical costs of severe obesity have substantial implications for future health care costs,9 espe- cially as states expand access to obesity-related services for adult Medicaid beneficiaries.26

Although severe obesity was once a rare con-

Figure 1 (facing page). Estimated Prevalence of Overall Obesity and Severe Obesity in Each State, from 1990 through 2030.

Shown is the estimated prevalence of overall obesity (Panel A) and severe obesity (Panel B) among adults in each U.S. state from 1990 through 2030. Overall obesity includes the BMI (body-mass index) categories of moderate obesity (BMI, 30 to <35) and severe obesity (BMI, ≥35).

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T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e

State Overall Obesity (BMI, ≥30)* Severe Obesity (BMI, ≥35)

Overall Men Women Overall Men Women

percentage (95% confidence interval)

U.S. overall 48.9 (47.7–50.1) 48.2 (46.8–49.6) 49.9 (48.5–51.4) 24.2 (22.9–25.5) 21.1 (19.6–22.6) 27.6 (26.1–29.2)

Alabama 58.2 (56.2–60.2) 56.7 (53.8–59.4) 59.7 (57.3–62.3) 30.6 (28.5–32.8) 25.6 (22.6–28.5) 35.7 (33.2–38.3)

Alaska 49.3 (46.3–52.2) 48.9 (45.0–53.1) 50.0 (46.1–54.1) 24.2 (21.4–26.8) 21.7 (17.5–25.7) 27.6 (24.1–31.4)

Arizona 51.4 (48.9–53.9) 49.3 (45.7–53.0) 53.6 (50.5–56.6) 24.4 (22.1–26.7) 20.8 (17.5–24.2) 28.3 (25.3–31.2)

Arkansas 58.2 (55.7–60.4) 56.7 (53.2–59.9) 59.9 (57.0–62.8) 32.6 (30.1–35.1) 29.6 (26.2–33.1) 36.1 (33.0–39.1)

California 41.5 (39.9–43.3) 41.1 (39.0–43.4) 42.1 (40.0–44.3) 18.3 (16.8–19.8) 16.1 (14.1–18.1) 20.9 (19.0–22.8)

Colorado 38.2 (36.3–40.3) 37.5 (34.8–40.0) 39.2 (36.7–42.0) 16.8 (15.2–18.6) 14.3 (12.1–16.6) 19.8 (17.6–22.2)

Connecticut 46.6 (44.4–48.9) 46.5 (43.5–49.4) 46.9 (44.3–49.6) 22.5 (20.6–24.6) 19.8 (17.2–22.7) 25.3 (22.9–27.9)

Delaware 53.2 (51.0–55.7) 51.4 (48.2–55.0) 55.0 (51.9–58.1) 27.1 (24.8–29.6) 22.2 (19.0–25.6) 31.7 (28.7–34.8)

District of Columbia 35.3 (33.0–37.8) 32.3 (29.1–36.3) 39.0 (35.9–42.2) 17.3 (15.2–19.3) 11.3 (8.9–13.9) 23.1 (20.3–26.1)

Florida 47.0 (45.0–48.9) 47.9 (45.5–50.2) 46.3 (43.9–48.8) 21.3 (19.7–23.1) 19.0 (16.7–21.1) 24.0 (22.0–26.3)

Georgia 51.9 (49.9–54.2) 49.6 (46.6–52.7) 54.5 (51.8–57.2) 26.6 (24.3–28.8) 21.2 (18.3–24.2) 32.1 (29.6–34.7)

Hawaii 41.3 (39.2–43.4) 43.3 (40.3–46.1) 39.1 (36.4–41.9) 18.2 (16.4–20.2) 17.5 (14.9–20.1) 19.1 (17.0–21.7)

Idaho 47.7 (45.4–50.0) 48.0 (44.5–51.3) 47.7 (44.6–50.6) 23.0 (20.8–25.2) 20.8 (17.9–23.8) 26.0 (23.3–28.7)

Illinois 50.0 (47.8–52.1) 48.6 (45.3–51.3) 51.6 (48.9–54.5) 25.5 (23.5–27.7) 20.7 (17.8–23.5) 30.4 (27.5–33.0)

Indiana 51.6 (49.7–53.6) 50.7 (48.1–53.5) 52.9 (50.3–55.4) 26.9 (24.8–29.0) 24.1 (21.2–26.9) 30.3 (27.8–32.8)

Iowa 52.0 (50.0–54.0) 52.6 (49.8–55.2) 51.9 (49.2–54.4) 26.4 (24.4–28.5) 24.8 (22.0–27.7) 28.8 (26.1–31.5)

Kansas 55.6 (53.8–57.5) 54.3 (51.8–56.9) 57.0 (54.7–59.5) 30.6 (28.7–32.5) 26.7 (24.3–29.3) 34.8 (32.6–37.2)

Kentucky 54.8 (52.9–56.8) 54.5 (51.8–57.2) 55.4 (53.0–57.9) 29.4 (27.4–31.4) 26.0 (23.3–28.8) 33.1 (30.5–35.7)

Louisiana 57.2 (55.1–59.2) 56.3 (53.2–59.3) 58.3 (55.6–61.0) 31.2 (28.9–33.5) 26.8 (23.5–29.9) 36.0 (33.2–38.9)

Maine 50.3 (48.1–52.6) 49.4 (46.3–52.5) 51.3 (48.5–54.0) 24.2 (22.1–26.4) 20.9 (18.2–23.7) 27.7 (25.0–30.3)

Maryland 50.0 (48.1–52.0) 48.0 (45.4–50.8) 52.1 (49.7–54.5) 24.6 (22.8–26.6) 19.7 (17.5–22.1) 29.4 (27.0–31.9)

Massachusetts 42.3 (40.2–44.3) 43.1 (40.4–45.7) 41.7 (39.1–44.2) 20.0 (18.2–22.1) 18.7 (16.3–21.4) 21.5 (19.3–24.0)

Michigan 51.9 (50.2–53.7) 51.2 (48.8–53.6) 53.0 (50.8–55.2) 27.2 (25.5–29.1) 24.4 (21.9–26.9) 30.7 (28.3–33.1)

Minnesota 46.1 (44.3–48.0) 48.2 (46.0–50.4) 44.3 (41.9–46.6) 20.4 (18.7–22.2) 20.0 (17.7–22.3) 21.6 (19.5–23.6)

Mississippi 58.2 (56.0–60.2) 54.3 (51.1–57.2) 62.0 (59.3–64.6) 31.7 (29.5–33.9) 24.6 (21.4–28.0) 38.6 (35.9–41.2)

Missouri 52.4 (50.2–54.6) 51.0 (47.8–54.1) 53.9 (51.0–56.5) 28.3 (26.1–30.5) 24.4 (21.5–27.5) 32.4 (29.6–35.1)

Montana 44.2 (41.8–46.6) 44.5 (41.4–47.6) 44.3 (41.3–47.5) 21.4 (19.3–23.5) 19.6 (16.7–22.6) 23.9 (21.2–26.8)

Nebraska 51.3 (49.3–53.3) 51.0 (48.3–53.7) 51.7 (49.2–54.1) 25.4 (23.4–27.4) 21.5 (18.9–24.1) 29.6 (27.0–32.2)

Nevada 45.5 (42.7–48.3) 45.3 (41.5–49.0) 45.8 (42.1–49.6) 20.6 (18.1–23.4) 18.1 (14.7–22.1) 23.4 (20.0–26.8)

New Hampshire 48.8 (46.6–51.1) 50.5 (47.3–53.5) 47.1 (44.1–50.0) 24.1 (21.9–26.5) 21.9 (18.8–25.2) 26.6 (23.7–29.6)

New Jersey 46.6 (44.4–48.6) 48.6 (45.6–51.6) 44.8 (42.0–47.4) 21.7 (19.8–23.5) 19.9 (17.2–22.7) 23.8 (21.4–26.2)

New Mexico 51.8 (49.5–54.1) 49.5 (46.0–52.6) 54.6 (51.8–57.3) 24.8 (22.6–27.0) 22.7 (19.6–26.0) 27.5 (24.9–30.3)

New York 42.8 (41.0–44.8) 42.0 (39.5–44.7) 43.9 (41.4–46.3) 19.8 (18.2–21.6) 17.5 (15.2–19.9) 22.5 (20.4–24.8)

North Carolina 50.3 (48.3–52.2) 47.3 (44.8–49.9) 53.4 (50.8–55.7) 25.7 (23.6–27.5) 21.0 (18.3–23.6) 30.6 (28.0–33.0)

North Dakota 53.9 (51.6–56.1) 56.5 (53.4–59.4) 51.3 (48.5–54.0) 26.9 (24.7–29.0) 26.6 (23.4–29.6) 27.9 (24.9–30.7)

Ohio 53.2 (51.0–55.3) 52.4 (49.5–55.3) 54.1 (51.3–56.9) 26.8 (24.8–28.8) 23.8 (21.1–26.6) 30.0 (27.2–32.7)

Oklahoma 58.4 (56.4–60.2) 59.5 (56.9–61.9) 57.5 (54.9–59.8) 31.7 (29.7–33.9) 29.0 (26.1–32.0) 34.9 (32.6–37.6)

Oregon 47.5 (45.5–49.5) 47.9 (45.1–50.8) 47.3 (44.7–49.8) 24.1 (22.0–26.1) 21.6 (18.7–24.5) 27.1 (24.5–29.7)

Pennsylvania 50.2 (48.2–52.1) 50.8 (48.1–53.2) 50.0 (47.7–52.5) 24.8 (22.7–26.8) 23.3 (20.7–25.8) 27.0 (24.5–29.6)

Table 1. Projected State-Specific Prevalence of Adult Obesity and Severe Obesity in 2030.

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Projec ted Pr e va lence of Obesit y a nd Se v er e Obesit y

State Overall Obesity (BMI, ≥30)* Severe Obesity (BMI, ≥35)

Overall Men Women Overall Men Women

percentage (95% confidence interval)

Rhode Island 47.3 (45.0–49.9) 48.8 (45.3–52.3) 46.3 (42.8–49.7) 22.9 (20.6–25.4) 21.9 (18.7–25.3) 24.5 (21.6–27.6)

South Carolina 52.8 (51.0–54.6) 49.6 (47.0–52.3) 56.0 (53.6–58.3) 27.2 (25.3–29.1) 21.2 (18.8–23.8) 33.0 (30.7–35.4)

South Dakota 50.6 (48.1–52.9) 53.0 (49.6–56.1) 48.2 (45.1–51.4) 25.2 (22.9–27.7) 24.1 (20.8–27.3) 26.9 (24.1–29.9)

Tennessee 55.8 (53.9–57.8) 55.0 (52.1–57.8) 56.9 (54.4–59.5) 29.9 (27.8–32.1) 26.5 (23.5–29.7) 33.7 (31.2–36.5)

Texas 52.9 (50.9–54.7) 50.1 (47.3–52.5) 55.9 (53.5–58.5) 26.6 (24.6–28.5) 22.5 (20.0–25.2) 31.1 (28.5–33.8)

Utah 43.2 (41.3–45.1) 43.9 (41.5–46.3) 42.7 (40.2–45.2) 20.6 (18.9–22.6) 18.8 (16.7–21.3) 23.0 (20.6–25.5)

Vermont 43.6 (41.5–45.8) 43.1 (40.2–46.1) 44.2 (41.7–47.0) 20.7 (18.9–22.7) 17.8 (15.4–20.2) 23.9 (21.5–26.4)

Virginia 48.9 (46.7–50.9) 46.0 (43.0–48.9) 51.8 (48.9–54.7) 25.3 (23.3–27.5) 20.7 (18.0–23.4) 30.0 (27.4–32.4)

Washington 47.4 (45.6–49.2) 48.0 (45.7–50.3) 47.2 (44.9–49.5) 22.6 (20.9–24.4) 20.9 (18.6–23.2) 25.0 (23.0–27.2)

West Virginia 57.5 (55.6–59.4) 57.0 (54.2–59.6) 58.3 (55.8–61.0) 30.8 (28.7–32.8) 27.0 (24.1–29.9) 35.2 (32.5–37.9)

Wisconsin 50.3 (48.0–52.7) 50.3 (47.0–53.2) 50.7 (47.6–53.7) 25.5 (23.4–27.8) 23.1 (20.2–26.1) 28.6 (25.7–31.7)

Wyoming 48.2 (45.6–50.9) 45.5 (41.6–49.3) 51.3 (47.7–54.8) 22.4 (19.8–25.0) 19.2 (16.0–22.4) 26.1 (22.7–29.8)

* “Overall obesity” includes the body-mass index (BMI) categories of moderate obesity (BMI, 30 to <35) and severe obesity (BMI, ≥35).

Table 1. (Continued.)

Figure 2. Projected National Prevalence of BMI Categories in 2030, According to Demographic Subgroup.

Shown is the projected national prevalence of BMI categories in 2030, according to sex, race or ethnic group, and annual household income.

0 10 20 30 40 50 60 70 80 90 100

Prevalence (%)

Underweight or normal weight (BMI, <25)

Overweight (BMI, 25 to <30)

Moderate obesity (BMI, 30 to <35)

Severe obesity (BMI, ≥35)

Overall

Male

Female

Non-Hispanic white

Non-Hispanic black

Hispanic

Non-Hispanic other

<$20,000

$20,000 to <$50,000

≥$50,000

Annual Household Income

Race or Ethnic Group

Sex

21.5 (20.5−22.6)

17.9 (17.1−18.8)

19.8 (18.9−20.7)

37.9 (35.9−39.8)

17.1 (16.0−18.2)

17.5 (16.6−18.6)

21.7 (20.8−22.6)

23.5 (22.4−24.6)

19.4 (18.5−20.3)

21.4 (20.6−22.3)

31.4 (30.2−32.6)

27.7 (26.7−28.8)

24.6 (23.6−25.7)

31.7 (30.0−33.6)

30.5 (29.0−32.0)

25.6 (24.3−26.9)

30.2 (29.1−31.2)

26.6 (25.7−27.5)

32.5 (31.2−33.8)

29.7 (28.6−30.7)

25.6 (24.6−26.6)

25.8 (24.8−26.7)

23.9 (22.8−24.9)

16.8 (15.5−18.1)

27.9 (26.4−29.4)

25.2 (24.0−26.5)

24.7 (23.8−25.5)

22.3 (21.6−23.0)

27.1 (25.7−28.5)

24.8 (23.9−25.6)

21.5 (20.2−22.9)

28.6 (27.1−30.0)

31.7 (30.2−33.2)

13.7 (12.4−15.0)

24.5 (22.8−26.2)

31.7 (29.9−33.4)

23.4 (22.1−24.8)

27.6 (26.1−29.2)

21.1 (19.6−22.6)

24.2 (22.9−25.5)

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T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e

A Sex

B Race or Ethnic Group

C Annual Household Income

Male Female

Non-Hispanic White Non-Hispanic Black

Hispanic Non-Hispanic Other

<$20,000 $20,000 to <$50,000

≥$50,000 Overall

Underweight or normal weight (BMI, <25)

Overweight (BMI, 25 to <30)

Moderate obesity (BMI, 30 to <35)

Severe obesity (BMI, ≥35)

Suppressed estimate

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Projec ted Pr e va lence of Obesit y a nd Se v er e Obesit y

dition, our findings suggest that it will soon be the most common BMI category in the patient populations of many health care providers. Given that health professionals are often poorly pre- pared to treat obesity,27 this impending burden of severe obesity and associated medical compli- cations has implications for medical practice and education. In addition to the profound health effects, such as increased rates of chronic dis- ease and negative consequences on life expec- tancy,25,28 the effect of weight stigma29 may have far-reaching implications for socioeconomic dis- parities as severe obesity becomes the most common BMI category among low-income adults in nearly every state.

Given the difficulty in achieving and main- taining meaningful weight loss,30,31 these find- ings highlight the importance of prevention ef- forts. Although some cost-effective prevention interventions have been identified,10 a range of sustained approaches to maintain a healthy weight over the life course, including policy and envi- ronmental interventions at the community level that address upstream social and cultural deter- minants of obesity,32 will probably be needed to prevent further weight gain across the BMI dis- tribution.

Our analysis has certain limitations. Although we found that our model predictions are accu- rate for states overall, our point estimates (i.e., mean predictions) may be less accurate for sub- groups with smaller sample sizes. However, our high coverage probabilities for all subgroups

indicate that we appropriately accounted for the uncertainty around our estimates, which high- lights the importance of considering the 95% confidence intervals of our projections as well. In addition, our assessment of predictive accu- racy reveals that our projections are robust to the change in the BRFSS sample design in 2011 to include cell-phone interviews. Although our predictive validity checks from 2010 through 2016 help build confidence in our approach, projec- tions through 2030 involve a much longer period, so the uncertainty around our projections may be larger than estimated because we assumed that current trends will continue.

Because of data limitations, we could not ex- plore trends in obesity according to all race or ethnic groups included in our “non-Hispanic other” category. We found large differences in the prevalence of obesity across states for this category, a finding that is consistent with the well-known differences in obesity prevalence among Native American, Native Hawaiian, and Asian populations that are included in this hetero- geneous category, which differs in composition from state to state. Also, because the BRFSS re- ports categories of annual household income (as opposed to actual dollar values), we were unable to adjust the household income of respondents for inflation over time.

Finally, because of the small sample size, we combined underweight (BMI, <18.5) and normal weight into one category. (Underweight com- prises only 2% of respondents in our NHANES data set.) Although this grouping may be prob- lematic when used as the reference category for estimating BMI-related health risks, it should not present any problems for estimating the prevalence of BMI categories.

We project that given current trends, nearly 1 in 2 U.S. adults will have obesity by 2030, and the prevalence will be higher than 50% in 29 states and not below 35% in any state — a level currently considered high. Furthermore, our pro- jections show that severe obesity will affect nearly 1 in 4 adults by 2030 and become the most common BMI category among women, black non- Hispanic adults, and low-income adults.

Supported by the JPB Foundation. Disclosure forms provided by the authors are available with

the full text of this article at NEJM.org.

Figure 3 (facing page). Projected Most Common BMI Category in 2030 in Each State, According to Demo- graphic Subgroup.

Shown is the projected most common BMI category (underweight or normal weight, overweight, moderate obesity, or severe obesity) in 2030 in each U.S. state, according to sex (Panel A), race or ethnic group (Panel B), and annual household income (Panel C). In accordance with the Centers for Disease Control and Prevention guidelines that consider Behavioral Risk Factor Surveil- lance System (BRFSS) survey estimates unreliable if they are based on a sample of fewer than 50 respon- dents,19 we suppressed state-level estimates from sub- groups with fewer than 1000 respondents; given our data set of 20 rounds of BRFSS surveys, we suppressed estimates from subgroups with fewer than 50 respon- dents on average per year in a state.

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Projec ted Pr e va lence of Obesit y a nd Se v er e Obesit y

References 1. Mokdad AH, Serdula MK, Dietz WH, Bowman BA, Marks JS, Koplan JP. The spread of the obesity epidemic in the United States, 1991-1998. JAMA 1999; 282: 1519-22. 2. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014; 311: 806-14. 3. Flegal KM, Kruszon-Moran D, Carroll MD, Fryar CD, Ogden CL. Trends in obe- sity among adults in the United States, 2005 to 2014. JAMA 2016; 315: 2284-91. 4. Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007-2008 to 2015-2016. JAMA 2018; 319: 1723-5. 5. Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M. Health and eco- nomic burden of the projected obesity trends in the USA and the UK. Lancet 2011; 378: 815-25. 6. Finkelstein EA, Khavjou OA, Thomp- son H, et al. Obesity and severe obesity forecasts through 2030. Am J Prev Med 2012; 42: 563-70. 7. Ward ZJ, Long MW, Resch SC, Giles CM, Cradock AL, Gortmaker SL. Simula- tion of growth trajectories of childhood obesity into adulthood. N Engl J Med 2017; 377: 2145-53. 8. Ward ZJ, Long MW, Resch SC, et al. Redrawing the US obesity landscape: bias- corrected estimates of state-specific adult obesity prevalence. PLoS One 2016; 11(3): e0150735. 9. Wang YC, Pamplin J, Long MW, Ward ZJ, Gortmaker SL, Andreyeva T. Severe obesity in adults cost state Medicaid pro- grams nearly $8 billion in 2013. Health Aff (Millwood) 2015; 34: 1923-31. 10. Gortmaker SL, Wang YC, Long MW, et al. Three interventions that reduce child- hood obesity are projected to save more than they cost to implement. Health Aff (Millwood) 2015; 34: 1932-9. 11. Roberto CA, Lawman HG, LeVasseur MT, et al. Association of a beverage tax on sugar-sweetened and artificially sweetened beverages with changes in beverage prices

and sales at chain retailers in a large ur- ban setting. JAMA 2019; 321: 1799-810. 12. Silver LD, Ng SW, Ryan-Ibarra S, et al. Changes in prices, sales, consumer spend- ing, and beverage consumption one year after a tax on sugar-sweetened beverages in Berkeley, California, US: a before-and- after study. PLoS Med 2017; 14(4): e1002283. 13. State policies to prevent obesity. Prince- ton, NJ: Robert Wood Johnson Founda- tion (https://www .stateofobesity .org/ state – policy/ ). 14. Centers for Disease Control and Pre- vention. Behavioral Risk Factor Surveil- lance System: about BRFSS (https://www .cdc .gov/ brfss/ about/ index .htm). 15. Ezzati M, Martin H, Skjold S, Vander Hoorn S, Murray CJ. Trends in national and state-level obesity in the USA after correction for self-report bias: analysis of health surveys. J R Soc Med 2006; 99: 250-7. 16. Connor Gorber S, Tremblay M, Moher D, Gorber B. A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review. Obes Rev 2007; 8: 307-26. 17. Centers for Disease Control and Pre- vention. National Health and Nutrition Ex- amination Survey: about NHANES (http:// www .cdc .gov/ nchs/ nhanes/ about_nhanes .htm). 18. Centers for Disease Control and Pre- vention. Overweight & obesity: defining adult obesity (https://www .cdc .gov/ obesity/ adult/ defining .html). 19. Klein RK, Proctor SE, Boudreault MA, Turczyn KM. Healthy People 2010 criteria for data suppression. Healthy People 2020 Stat Notes 2002; 24: 1-12 20. Wang Y, Beydoun MA, Liang L, Cabal- lero B, Kumanyika SK. Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity (Silver Spring) 2008; 16: 2323-30. 21. Sturm R, Hattori A. Morbid obesity rates continue to rise rapidly in the United States. Int J Obes (Lond) 2013; 37: 889-91. 22. Preston SH, Stokes A, Mehta NK, Cao B. Projecting the effect of changes in smok- ing and obesity on future life expectancy

in the United States. Demography 2014; 51: 27-49. 23. Hales CM, Fryar CD, Carroll MD, Freedman DS, Aoki Y, Ogden CL. Differ- ences in obesity prevalence by demo- graphic characteristics and urbanization level among adults in the United States, 2013-2016. JAMA 2018; 319: 2419-29. 24. Flegal KM, Ogden CL. Use of projec- tion analyses and obesity trends — reply. JAMA 2016; 316: 1317. 25. The Global BMI Mortality Collabora- tion. Body-mass index and all-cause mor- tality: individual-participant-data meta- analysis of 239 prospective studies in four continents. Lancet 2016; 388: 776-86. 26. Jannah N, Hild J, Gallagher C, Dietz W. Coverage for obesity prevention and treatment services: analysis of Medicaid and state employee health insurance pro- grams. Obesity (Silver Spring) 2018; 26: 1834-40. 27. Dietz WH, Baur LA, Hall K, et al. Management of obesity: improvement of health-care training and systems for pre- vention and care. Lancet 2015; 385: 2521- 33. 28. The GBD 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med 2017; 377: 13-27. 29. Puhl RM, Heuer CA. The stigma of obesity: a review and update. Obesity (Sil- ver Spring) 2009; 17: 941-64. 30. Barte JC, ter Bogt NC, Bogers RP, et al. Maintenance of weight loss after lifestyle interventions for overweight and obesity, a systematic review. Obes Rev 2010; 11: 899-906. 31. LeBlanc ES, Patnode CD, Webber EM, Redmond N, Rushkin M, O’Connor EA. Behavioral and pharmacotherapy weight loss interventions to prevent obesity-related morbidity and mortality in adults: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA 2018; 320: 1172-91. 32. Katan MB. Weight-loss diets for the prevention and treatment of obesity. N Engl J Med 2009; 360: 923-5. Copyright © 2019 Massachusetts Medical Society.

specialties and topics at nejm.org Specialty pages at the Journal’s website (NEJM.org) feature articles in cardiology, endocrinology, genetics, infectious disease, nephrology,

pediatrics, and many other medical specialties.

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Judges of the State Supreme Court and Court of Appeals serve a six year term while judges of the Superior Court, Probate Court, and State Court serve four year terms

1. The two major ways that judges are selected are popular elections and appointment. The electorate chooses the judge in a popular election while the executive or legislative branch decides the matter in appointment. Popular elections allow for accountability to the people but also require the judge to be a political candidate which can erode trust in the judiciary due to campaign financing. Appointment can produce distrust in the judicial system due to the potential for loyalty to a particular party or person who is instrumental in the appointment.

The state of Georgia elects judges based on a nonpartisan election and does not utilize retention elections. Judges of the State Supreme Court and Court of Appeals serve a six year term while judges of the Superior Court, Probate Court, and State Court serve four year terms. It has been my experience that the local judiciary elections are most contentious and partisan with large fundraising efforts that easily show the electorate the party the candidate is affiliated with. I am ashamed to admit that I couldn’t name a single member of the Supreme Court or Court of Appeals for the state of Georgia and I have lived here my entire life. I do believe this system works with the opportunity to elect judges and hold them accountable to the job.

(Please respond with 200 words or more)

2. Having lived in the south for most of my life, but having worked for an electric company based out of New England for the past three years, and having in Iowa for two years a decade ago, I feel that I have gathered a feel for the catch 22 in the major variables in local government structures.In South Carolina, there are many large areas with no city or township incorporation, thus, even in high population density areas, much of South Carolina leans on the County governments as the primary local government agency. While the cities have a fair amount of automy, they often border on redundancy. For instance, in Aiken County, where I live, there are ten municipal governments within it. However, only North Augusta’s Public Saftey takes responsibility for police patrol. All other city police departments only suppliment the Aiken County Sheriff’s Department’s patrol division.My biggest grievance with this structure is that the uniformity often comes at the price of property taxes that do not render their worth to the tax payer.In New England, the exact opposite happens. Their elimination or minimization of county goverments is modeled after the townships in the United Kingdom. In both cases, the benefit of minimizing the local tax burden subsequently adds the burden to the state. This is not entirely negative; every one of these states have a more developed state police force, as well as other state level services that many southern states are prone to delegating to the counties. Midwestern state seem to strike a balance with these two polar opposites. The municipal goverments are developed comprehensively to meet the needs of the community, while county governments never fully abandon the responsibilities of supporting the larger geographic area. The further west you move, the more intensive both get, and once you get to the west coast, you have the outlandishly high cost of living because of egregiously redundant government facilities on the state, county, and city levels. (Please respond with 200 words or more)

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Design an organizational assessment to establish the current state of shared governance

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Post date November 30, 2021
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Topic: Evidence-Based Leadership Practices

You are considering accepting a new position as a leader. However, prior to accepting the position, you are going to perform an organizational assessment. How would you design an organizational assessment to establish the current state of:

shared governance,
culture of the organization,
and safety of patient care?

What do you expect to see in each of these areas if they are functioning well?

What will you do to assess if each these areas are functioning well?

Assume you are given permission to view meeting minutes, to view reports, to view policies and procedures, to interview staff and leaders, to observe staff working with patients and interacting with each other, etc.

This course utilizes the Post-First feature in all Discussion Board Forums. This means you will only be able to read and interact with your classmates’ threads after you have submitted your thread in response to the provided prompt. For additional information on Post-First, click here for a tutorial

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d and the United States (state which distance is larger and by how much

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Post date October 16, 2021
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GEOG 1710 Map Projections Lab 3 Part 2

Use this document in accordance with the handout to record your answers.

Screenshot of Greenland outline with measurements:

	from Google My Maps	from online search
distance* around Greenland
area* of Greenland

*include the units of measurement

Screenshot of the United States (“Lower 48” – excluding Alaska and Hawaii) outline with measurements:

	from Google My Maps	from online search
distance* around United States
area* of United States

*include the units of measurement

Difference between distances around Greenland and the United States (state which distance is larger and by how much):

Difference between areas of Greenland and the United States (state which area is larger and by how much):

Screenshot of shape drawn around Greenland with measurements:

Screenshot of “Greenland” shape covering the United States:

Screenshot of shape drawn around the United States with measurements:

Screenshot of “United States” shape covering Greenland:

When you drag the shape of a country around the map, what happens to the size of the country as it gets closer to the equator? What happens when it gets farther away?

Use the “Draw a line” tool to outline five different countries (excluding Greenland and the United States). Screenshot that shows all five shapes around your selected countries:

Rank chosen countries from smallest to largest by distance around border, including values and units of measurement:

Rank chosen countries from smallest to largest by area of country, including values and units of measurement:

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Steps you are required to take that are consistent with state statutes, your district’s school board policies, faculty handbook, and the student handboo

A student notifies you that she has been subjected to bullying through a classmate’s Facebook page. In 500- words, address the following:

Steps you are required to take that are consistent with state statutes, your district’s school board policies, faculty handbook, and the student handbook;
Any First Amendment arguments you think the student with the Facebook page may raise; and
Responses you could make to the First Amendment arguments that are consistent with the cases in the assigned readings.

Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

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You are Chris North, VP of Human Resources for Sunshine State Hospital in Orlando Florida and have been asked by the President of the Hospital to consider mandatory flu shots for all employees of the Hospital.

Please read chapters 1 and 3 along with the articles located in the reading material section and then address the discussion board questions for the week both chapters are uploded and articles copy and pasted

You are Chris North, VP of Human Resources for Sunshine State Hospital in Orlando Florida and have been asked by the President of the Hospital to consider mandatory flu shots for all employees of the Hospital. What would you do?
How would you advise the President on how to move forward? Is it a good idea or not?
Things to consider: Should it be for all employees? Should it be for health care workers only?
Would you terminate the employment of those who do not comply?

Sunshine State Hospital is unionized, and represented by Local 2001 – Nurse Professional Association.

How, if at all, does this change your response to question 1.
What are your personal thoughts in response to the article?
Do you agree with the hospital’s stance?

1st article

ABC News – Nurses Fired for Refusing Flu Shot (ABC News) (January 4, 2013)

An Indiana hospital (Links to an external site.) has fired eight employees, including at least three veteran nurses, after they refused mandatory flu (Links to an external site.) shots, stirring up controversy over which should come first: employee rights or patient safety. The hospital imposed mandatory vaccines, responding to rising concerns about the spread of influenza.

Ethel Hoover wore all black on her last day of work as a nurse in the critical care unit at Indiana University Health Goshen Hospital. She said she was in “mourning” because she would have been at the hospital 22 years in February, and she’s only called out of work four or five times in her whole career , she said.

“This is my body. I have a right to refuse the flu vaccine,” Hoover, 61, told ABCNews.com. “For 21 years, I have religiously not taken the flu vaccine, and now you’re telling me that I believe in it.”

More than 15,100 flu cases have been reported (Links to an external site.) to the Centers for Disease Control and Prevention since Sept. 30, including 16 pediatric deaths. Indiana’s flu activity level is considered high, according to the CDC (Links to an external site.), which last month announced that the flu season came a month.

When Hoover first heard about the mandate, she said she didn’t realize officials would take it so seriously. She said she filed two medical exemptions, a religious exemption and two appeals, but they were all denied. The Dec. 15 flu shot deadline came and went. Hoover’s last day of employment was Dec. 21.

Fellow nurse Kacy Davis said she and her colleagues were “horrified” over Hoover’s firing, calling her their “go-to” nurse and a “preceptor.”

“It was a good place to work,” Hoover said. “We’ve worked together all these years. We’re like a family.”

The hospital said in a statement that it implemented the mandate to promote patient safety based on recommendations from the American Medical Association, the American Nurses Association, and the Centers for Disease Control and Prevention. It announced the mandate in September. Of the hospital’s 26,000 employees statewide, 95 percent complied. That means 1,300 employees did not comply, but only eight were fired.

“IU Health’s top priority is the health and wellbeing of our patients,” said hospital spokeswoman Whitney Ertel. “Participation in the annual Influenza Patient Safety Program is a condition of employment with IU Health for the health and safety of the patients that we serve, and is therefore required.”

The CDC recommends (Links to an external site.) flu shots for everyone older than six months of age. Dr. William Schaffner, chair of preventive medicine at Vanderbilt University Medical Center in Nashville, Tenn., said hospital patients are especially vulnerable to flu complications because their bodies are already weakened.

“I cannot think of a reason for any health care professional to decline influenza immunization that’s valid,” said Schaffner, a former president of the National Foundation for Infectious Diseases, adding that people with egg allergies may have to avoid the flu shot to prevent anaphylactic shock, but even that hurdle has been remedied. The Food and Drug Administration approved an egg-free vaccine (Links to an external site.) in November.

Schaffner said invalid excuses to avoid the shot include being afraid of needles and simply promising to stay home when they’re sick. Patients now have the option of a vaccine nasal spray if they want to avoid needles. And since flu victims become contagious before they start to feel sick, they can get patients sick even if they stay home when they have symptoms.

Over the last several years, hospitals have been moving toward mandatory vaccinations because many only have 60 percent vaccination rates, Schaffner said. He is leading an effort for a similar mandate at Vanderbilt University Medical Center.

Nurses in particular tend to be the most reluctant to get vaccinated among health care workers, Schaffner said, citing his opinion.

“There seems to be a persistent myth that you can get flu from a flu vaccine among nurses,” he said. “They subject themselves to more influenza by not being immunized, and they certainly do not participate in putting patient safety first.”

In October 2011, Vanderbilt broke the world record for number of vaccines administered in an eight-hour period in an event called Flulapalooza. (Links to an external site.) From 6:50 a.m. to 2:50 p.m., they vaccinated 12,647 people. By that evening, more than 14,000 people had been vaccinated, and there were no severe adverse reactions, he said.

But still, Hoover’s lawyer, Alan Phillips, says his client had the right to refuse her flu shot under Title VII of the Civil Rights Act of 1964 (Links to an external site.), which prohibits religious discrimination of employees. Religion is legally broad under the First Amendment, so it could include any strongly held belief, he said, adding that the belief flu shots are bad should suffice.

“If your personal beliefs are religious in nature, then they are a protected belief,” Phillips said.

Phillips, who is based out of North Carolina, has made a name for himself fighting for employees’ rights to get out of mandated flu shots, but he has never needed to go to court. Although he usually handles a couple dozen health care workers per year, he had 150 this fall in 25 states.

Dr. Damon Raskin, an internist with his own practice in the Pacific Palisades in Los Angeles, said hospitals should mandate flu vaccines as a matter of public safety. The flu can lead to complications like pneumonia and death, said Raskin, who is also affiliated with the Cliffside Malibu Addiction Rehabilitation Center.

“I think if the health care worker has some problem with religious faith then perhaps during flu season, they shouldn’t do that job,” Raskin said, suggesting that the worker do something administrative instead during flu season. “It’s not fair to the patient. The people who are most at risk are in the hospital.”

2nd article

Talks with Jim CollinsJim Collins speaks at the SHRM 2012 Annual Conference. Photo by Steven E. Purcell

ATLANTA—“The single most important strategic pillar of any great enterprise is people,” best-selling author Jim Collins said in his Tuesday keynote session at the SHRM 2012 Annual Conference.

After spending nine years studying why some companies thrive in uncertainty or even chaos, while others do not, for his latest book Great by Choice: Uncertainty, Chaos, and Luck—Why Some Thrive Despite Them All (HarperBusiness, 2011), Collins concluded that “it all begins with people.”

The most important executive skills for building a great organization are “the ability to pick the right people, to make disciplined people decisions and to make sure all key seats are filled with the right people,” Collins told attendees.

Collins, whose previous works include Good to Great, How the Mighty Fall and Built to Last, has spent nearly a quarter of a century studying great companies that endure—how they grow, how they attain superior performance, and how good companies can become great companies.

“It’s very dangerous to study success … so we don’t,” he said. “We study the contrast between success and failure … between great and good.

“Greatness is not primarily a function of circumstance … it is a matter of conscious choice and discipline,” he noted.

Level 5 Leaders

Collins described five levels of leadership competence. The first level includes highly capable individuals, followed by contributing team members at level two, competent managers at level three, effective leaders at level four and executives at level five.

According to Collins’ research, the greatest leaders share a common trait: they are level five leaders. “Level five leaders have an ‘X factor’ that is different than level four leaders,” Collins explained: humility.

Though Collins mentioned a few great leaders with “very healthy confidence,” such as Bill Gates and Steve Jobs, he said that the critical difference is that for level four leaders, “it really is about them.” By contrast, level five leaders’ “ego and ambition and confidence and drive are channeled outward into a cause, into a purpose, into an organization or into a quest that is not about them,” he said.

“Success coupled with arrogance inevitably leads to failure,” he added. “It is outrageous arrogance to neglect people and simultaneously expect them to deliver their best.

“No single leader by himself or herself can make a great company,” Collins added. “Level five leaders understand this; they have to build an entire team to make a company great.”

Triad of Behaviors

Level five leaders possess other key behaviors, Collins has found. These include:

Fanatic discipline. Such leaders are “disciplined people who engage in disciplined thought and who then take disciplined action.” But he warned attendees not to confuse discipline with bureaucracy. “The purpose of bureaucracy is to make up for undisciplined people,” he said.

As an example, Collins compared Roald Amundsen’s successful 1910-12 South Pole expedition and Robert F. Scott’s ultimately fatal Antarctic expedition during that same time to describe the kind of discipline level five leaders use to pursue results. “Discipline also means not going too far,” he said.

Empirical creativity. “Creativity is the natural human state … discipline is not,” Collins noted. “The really rare combination is finding out how to marry the two so we amplify creativity rather than destroy it.

Productive paranoia. “The only mistakes you can learn from are the ones you survive,” Collins noted. This means preparing before bad stuff happens, he said. The ultimate hedge against uncertainty, he said, is who you have on the other end of the rope.

Right People for Key Seats

When looking for people to hold key seats, Collins said level five leaders seek those who:

• Share core values.

• Don’t need to be tightly managed.

• Understand they do not have a job; they have responsibilities.

• Do what they say they will do 100 percent of the time.

In addition, such individuals tend to look outward when good things happen, and give credit to others. However, when bad things happen they look in the mirror and take responsibility.

“It all starts and ends with people,” he noted.

Collins ended the session with a “to do list” for attendees that reiterated some of the points he made throughout the session. Among his suggestions:

• Banish the word “job” and replace it with “responsibilities.”

• Start a “stop doing” list. “Work is infinite; time is finite,” he said. “If you have more than three priorities, you have none.”

• Commit to challenging all young leaders to become level five leaders. “We need legions,” he said. “We need a level five generation.”

Rebecca R. Hastings, SPHR, is an online editor/manager for SHRM

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large municipal police department such as Chicago, New York, Los Angeles, or your state police department

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Post date September 10, 2021
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Choose a large municipal police department such as Chicago, New York, Los Angeles, or your state police department, and discuss the following: 400-600 words

Summarize its history of the use of data analysis.
How are the statistical variables such as mean, mode, and median utilized in analyzing criminal data?
Explain how crime rates are calculated and utilized to address specific issues or problems with the data sets.

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Lone Wolf Terror Threats from Islamic State

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Post date July 18, 2021
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Instructions

Write a background assessment using the PMESII framework for a specific national security issue you select from among a list of options upon which to base a main national security policy recommendation prepared for the same issue.National Security Issue Options:· Iran WMD Programs· Lone Wolf Terror Threats from Islamic State· China Cyberthreats· Russian exploration of the Arctic Ocean.The format should be as follows:· Introduction – introduces topic selected by describing it briefly and explaining why it is a critical US national security concern — 1 page· Political assessment – 1 page· Military assessment – 1 page· Economic assessment – 1 page· Social assessment – 1 page· Infrastructure assessment – 1 page· Information assessment – 1 page· Conclusion – summarizes main points of PMESII assessments and identifies the major ways in which it qualifies as a critical US national security concern — 1 pageRefer to the PMESII & DIMEFIL Primer to ensure you address all relevant aspects of the PMESII construct.Technical Requirements· Your paper must be at a minimum of 8-12 pages (the Title and Reference pages do not count towards the minimum limit).· Use the PMESII elements as subheadings in your paper.· Information and assessments must be based upon scholarly and credible references.· Type in Times New Roman, 12 point and double space.· Students will follow the current Turabian Style as the sole citation and reference style used in written work submitted as part of coursework. · All submissions will be graded using the assignment rubric below

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State Health Improvement Program- Obesity and Tobacco Prevention Program

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Post date July 16, 2021
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Clinical: State Health Improvement Program- Obesity and Tobacco Prevention Program Purpose: Students will be able to describe population health issues such as obesity and tobacco use. Students will identify strategies at the individual/family, community and systems level for a comprehensive community health plan.Directions: As part of your clinical experience you will explore the Statewide Health Improvement Program that focuses on policy, systems, and environmental changes as an evidenced-based approach to the population-based issues of obesity and tobacco use.1.) Explore the MN Department of Health Website about the State Health Improvement Partnership. Read the SHIP fact sheets and explore 2-3 science-based strategies. Minnesota Department of Health State Health Improvement Program 2.) Review examples in the MN State Health Improvement Partnership 2018- 2019 Report to the Legislature, beginning on page 29. Select one local community site participating in this program and identify an evidence-informed policy, program, or system’s change that is being used. 3.) Then submit 300-600 words with the following:

Describe in a paragraph one of the strategies that you identified at the community level of practice.
Then, describe a different strategy at the system’s level of practice, and explain in one paragraph why this strategy is a more effective way to impact population health than requiring change from every single individual in the community

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