Learning Style Characteristics

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Post date January 29, 2022
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Topic: Learning Style Characteristics

Using this week’s readings, complete the following:

Based on what you have read and your experiences, which learning style (visual, kinesthetic, or auditory) do you think is the hardest for teachers to accommodate? Why?
Now, take what you have learned through the readings and any personal experiences you may have had to respond to the following scenario:

Three new students have been assigned to your fourth-grade math class. Each student has a particular learning style. How do you ensure you are providing them with effective instructional strategies based on their learning styles?

-Peggy requires structure and has visual preferences.
-George requires a quiet learning environment, is teacher-motivated, prefers learning alone, is a factual and kinesthetic learner, and requires mobility.
-Moses lacks persistence, is a peer-oriented learner, has auditory preferences, requires food while he is working, and learns best in the morning.

For each of the students, complete a description under each of the following headings:

Teaching Strategy
Evaluation/Assessment Strategy
Part 2

Finally, review the responses from at least two peers’ posts and respond to the following:

You are a kinesthetic and tactile learner; however, you are taking an online course with no real tactile component. How do you deal with the fact that almost everything in your online course is visual? What modifications would you make to your learning style to be successful in class?

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First, watch the video below for an introduction to the gender spectrum, a second framework in which gender in comprised of a dynamic line between two ever-changing ideals

Gender as Spectrum From EDUC 251

Session Slides

(Links to an external site.)

1) First, watch the video below for an introduction to the gender spectrum, a second framework in which gender in comprised of a dynamic line between two ever-changing ideals.

2) Next, take two minutes to reflect and journal about the ways in which you have been taught that gender is a spectrum going from masculine to feminine.

· Where have you seen this line or fragments of it? What places you more toward one end of the spectrum or the other? What places you more toward the middle? For example, when have you have been told “As a boy/girl you should … more.” or “Because you’re a boy/girl you need to do … less.” Please note that these messages also apply to people who are genderqueer in the middle of the spectrum except that they are given messages to be or do more or less to fit into either masculinity or femininity rather than being more genderqueer.

· What rewards or opportunities will you receive if you stay where you are or move in one direction or another?

· What costs do you incur or risks do you take if you stay where you are or move in one direction or another?

3) Then, watch Makkai (2002) below for a shelf resource to balance with your self.

4) Now take two more minutes to reflect and journal about balancing your own story of your self with Makkai’s (2002) story from the shelf. What windows and mirrors came up for you?

5) Post your reflection on the discussion board below. Read and respectfully respond to other people’s posts.

References

Makkai, K. (2002). Pretty. National Poetry Slam

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specific alteration in health

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Find a dietary assessment tool that can be used either generally or for a specific alteration in health.

When you have found your assessment tool, answer the following questions:

· What is the purpose of this tool?

· Do you believe that the purpose is fulfilled based on the questions being asked? Why?

· In what ways does the tool account for the individual perceptions and needs of the client?

· Is there a nutritional history included? What does it cover?

· Is the tool easy to use? Why or why not?

· Does the tool provide enough information to determine next steps or interventions? Explain.

The writing assignment should be no more than 2 pages and APA Editorial Format must be used for citations and references used. Attach a copy of the assessment tool

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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,

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Explain multicultural communication and its origins

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Multicultural Patient Education, Illiteracy, and Effective Communication

Write a 650-1300 word response to the following questions:

Explain multicultural communication and its origins.
Compare and contrast culture, ethnicity, and acculturation.
Explain how cultural and religious differences affect the health care professional and the issues that can arise in cross-cultural communications.
Discuss family culture and its effect on patient education.
List some approaches the health care professional can use to address religious and cultural diversity.
List the types of illiteracy.
Discuss illiteracy as a disability.
Give examples of some myths about illiteracy.
Explain how to assess literacy skills and evaluate written material for readability.
Identify ways a health care professional may establish effective communication.
Suggest ways the health care professional can help a patient remember instructions.

This assignment is to be submitted as a Microsoft Word document

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Setting the Stage for Community Health Nursing

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Case Study
Setting the Stage for Community Health Nursing

At the community health care agency, the assigned nurse reviews with the assigned student the conceptual foundations and core functions of community health practice that are integrated into the various roles and settings of community health nursing. After working at the agency for the day, the student has to prepare an oral report to present to the class the next day.

What are the three core public health functions that are basic to community health nursing?
There are seven different roles of the community health nurse. What are the seven different roles of the community health nurse?
The role of manager is a critical role for the community health nurse. What is involved in the role of manager within the framework of public health nursing functions?
There are seven settings in which community health nurses practice. What are the seven settings and provide a brief description of the settings in which community health nurses practice?

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Continue shipping exclusively by rail

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Alabama Atlantic is a lumber company that has three sources of wood and five markets to be supplied. The annual availability of wood at sources 1, 2, and 3 is 15, 20, and 15 million board feet, respectively. The amount that can be sold annually at markets 1, 2, 3, 4, and 5 is 11, 12, 9, 10, and 8 million board feet, respectively.

In the past, the company has shipped the wood by train. However, because shipping costs have been increasing, the alternative of using ships to make some of the deliveries is being investigated. This alternative would require the company to invest in some ships. Except for these investment costs, the shipping costs in thousands of dollars per million board feet by rail and by water (when feasible) is given by the above table for each route.

Data attached below.

Questions and solution is attached below as well.

The capital investment (in thousands of dollars) in ships required for each million board feet to be transported annually by ship along each route is given next.

Considering the expected useful life of the ships and the time value of money, the equivalent uniform annual cost of these investments is one-tenth the amount given in the table. The objective is to determine the overall shipping plan that minimizes the total equivalent uniform annual cost (including shipping costs).

You are the head of the management science team that has been assigned the task of determining this shipping plan for each of the three options listed next.

Option 1: Continue shipping exclusively by rail.

Option 2: Switch to shipping exclusively by water (except where only rail is feasible).

Option 3: Ship by either rail or water, depending on which is less expensive for the particular route.

Present your results for each option. Compare.

Finally, consider the fact that these results are based on current shipping and investment costs, so that the decision on the option to adopt now should take into account management’s projection of how these costs are likely to change in the future. For each option, describe a scenario of future cost changes that would justify adopting that option now

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Hospitals turn bad debt into charity care

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Hospitals turn bad debt into charity care

Pittsburgh Post-Gazette (Pittsburgh, PA)

Byline: Sean D. Hamill

Sept. 25–Quietly over the last decade, a growing number of hospitals in Pennsylvania and across the country have been redefining how they award much, if not most, of their charity care. Typically called by the arcane name of “presumptive eligibility,” this new way of identifying people eligible for charity care uses credit-score-like technology — plus the addition of demographic and even social media data — to evaluate whether people qualify for free care at a hospital, instead of having their bills labeled “bad debt” and possibly sent to a collection agency. Among the health systems in Pennsylvania using the tool are Allegheny Health Network, St. Clair Hospital in Mt. Lebanon and UPMC, as well as Wellspan Health and Geisinger Health System in central Pennsylvania, and several dozen others. So many hospitals in Pennsylvania — particularly large health systems — are using presumptive eligibility now that it appears to be a major reason why the average percentage of charity care that hospitals provide in the state has nearly doubled in the last eight years, according to data from the Pennsylvania Health Care Cost Containment Council (PHC4), a state agency. What has patient advocates upset about the way this new technology is being used is that many hospitals using presumptive eligibility — including UPMC, Allegheny Health Network and St. Clair Hospital — don’t tell patients they are qualified for charity care if they are approved this way. Instead, they leave patients in the dark about their financial situation. Though the hospital takes credit for providing charity care, the patients leave the hospital thinking they still owe money. “There really shouldn’t be a secret financial assistance policy for some patients,” said Julie Trocchio, senior director of community benefit at the Catholic Health Association, and an expert on charity care. But hospitals praise the new technological tool because it accomplishes a long-sought goal of finding a way to qualify people for charity care who would not, or could not, fill out a traditional charity care application. “The industry has always thought that it is people who are uninsured who need financial assistance. But there are always some people who won’t complete the [charity care] form. This helps us get them assistance,” said Rich Chesnos, the chief financial officer for St. Clair Hospital, which began using a presumptive eligibility model in 2010. Growing use Why don’t patients cooperate with hospitals and fill out the charity care applications if they need financial help? Advocates and hospitals say the individual reasons are as numerous as there are uninsured patients, from illiteracy, to a language barrier, or they just don’t know it exists. Barbara Tapscott, vice president of revenue management at Geisinger Health System, which has five hospitals in central and eastern Pennsylvania, said: “Some people are just plain embarrassed and don’t apply. Or they may think on their own, ‘That doesn’t apply to me.’ ” But the biggest reason,https://bi.gale.com/global/search?u=ashford#displayGroup=help&sort=articleTitle https://link.gale.com/apps/menu?userGroupName=ashford https://bi.gale.com/global/article/GALE%7CA464550649?u=ashford https://bi.gale.com/global/publication/0QFE?u=ashford

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many interviewed for this series said, is something the hospitals themselves have heard over and over again. “They fear they’re going to have to give up a state benefit” from some other program that they have already qualified for, such as public housing or food stamps, said Liz Allen, the former CFO for Allegheny Health Network, who retired late last year. Hospitals say one of the benefits to them of presumptive eligibility is that it allows them to focus their collection efforts on people who do have assets but have not paid their bill. That potentially provides them with more revenue at a time when many hospital budgets are strained. PARO Decision Support, a Miami-based company that is the industry leader, said it has sold its presumptive eligibility tool to 350 hospitals nationally. The Advisory Board Co. of Washington, D.C., said it has sold its tool to more than 200 hospitals. At least another dozen companies sell similar tools, including Experian, which provided it to St. Clair Hospital. The number of hospitals using presumptive eligibility is sure to grow dramatically in coming years, consultants and the companies said. That is because of a new Affordable Care Act-related regulation that went into effect this year. The regulation was enacted by the IRS and is known as “501r.” It includes a provision that requires hospitals, starting in 2016, to “make reasonable efforts to determine whether an individual is eligible for assistance under the hospital’s financial assistance policy before engaging in extraordinary collections against the individual.” “Extraordinary collections” are efforts to recover the money patients owe, such as sending the debt to a collection agency, reporting it to a credit bureau, suing in court, placing liens against the person’s home, and other actions. “We’re seeing a market interest in demand and interest in this across the country. And not just because of 501r,” said Jim Lazarus, managing director of strategy and innovation at The Advisory Board. “But also because of the ‘patient centered revenue cycle’ that hospitals are adopting. As that happened hospitals realized they needed intelligence like this.” It also allows hospitals to increase what they classify as charity care without actually providing more care to the poor, but, rather, reclassifying accounts that would have been considered bad debt. “We were providing the care, but it was just getting written off as bad debt,” said Ms. Allen, who was Allegheny Health Network’s chief financial officer when the network began using presumptive eligibility in 2014. Presumptive history Some hospitals had long used the phrase “presumptive eligibility” to describe the way they would award charity care to patients without making them fill out the application. Instead, if a patient was already approved for some other means-tested government program that had a similar financial threshold, like food stamps or public housing, they would simply okay them for charity care, too. Many hospitals continue to use such programs, including the University of Pennsylvania Hospital. The difference is those hospitals tell the patient that they have been approved for charity care. This new kind of presumptive eligibility started to be offered to hospitals about a decade ago. Mark Rukavina, an expert on medical debt who consults for hospitals, said some of the early versions of the presumptive eligibility tools were the result of a series of lawsuits filed against non-profit hospitals in 2004 across the country — including one filed against UPMC. The lawsuits alleged the hospitals were not doing enough to justify their tax-exempt status. The lawsuits — begun by Richard Scruggs, the attorney who filed the lawsuit against the big tobacco companies that won states billions of dollars in damages — contended that hospitals were over- charging patients, were overly aggressive in pursuing collections and were not providing enough charity care. Jeff Suher, the Monroeville attorney who filed the lawsuit against UPMC, said, like the other lawsuits elsewhere, the one against UPMC “went nowhere.” Still, they led to concerns by hospitals. Mr. Lazarus of the Advisory Board said several credit score companies responded by creating presumptive eligibility algorithms for hospitals that were similar to ones they created for other

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types of business. “It is financial profiling patients, something other businesses have been doing for 15 to 20 years, such as banks, auto dealers, retailers,” he said. Mr. Rukavina said that while the public might understand a for-profit car dealer or bank suing someone who defaulted on a loan, hospitals have other considerations. Presumptive eligibility was designed “to save themselves from the embarrassing story of taking action against someone with limited means,” he said. UPMC enters One of the earliest adopters of the new financial tool was UPMC. It began using presumptive eligibility at least as far back as 2009, UPMC spokeswoman Susan Manko said in an email response to questions. The financial tool is “part of our whole assessment process to assist patients and their families. … The PARO score identifies patients who are eligible for charity care, ensuring we reduce or eliminate the financial burden for those individuals,” Ms. Manko wrote. In 2007, at UPMC’s 11 hospitals, on average, 35.8 percent of its uncompensated care was charity care, and 64.2 percent was bad debt. By 2008, that jumped to 46.5 percent charity care and 53.5 percent bad debt. By 2014, UPMC had more than flipped the ratio from 2007, to 74.9 percent charity care and just 25.1 percent bad debt. That switch more than tripled the percentage of charity care that UPMC hospitals reported to the state, jumping from .8 percent of net patient revenue, system-wide, in 2007, to 2.52 percent in 2014. Ms. Manko said UPMC began using presumptive eligibility because “there are many patients who do not follow up on or return applications or necessary documentation. … Rather than using money and resources to chase after them and pursue collection, we first assess their ability to pay using presumptive eligibility; if they qualify, we immediately write-off the account and never initiate any collection efforts.” St. Clair Hospital, a non-profit hospital, generated a $25 million surplus on $296 million in revenue in 2014, according to its IRS 990 tax form. But for years it provided among the least amount of charity care of any hospital in the region, far less than 1 percent. In 2010, though, it began to use “Passport Health,” a presumptive eligibility tool provided by Experian. Charity care spending at the hospital jumped from just $620,000 or .3 percent in 2009 to $3.9 million or 1.63 percent in 2014. (These figures were provided by St. Clair, which says that it has been incorrectly reporting its data to PHC4 for the last decade. Figures from PHC4 in the Pittsburgh Post-Gazette’s database with this story are different for St. Clair.) Like UPMC, it also saw the ratio of charity care to bad debt change dramatically. It rose steadily from 13.4 percent charity care and 86.6 percent bad debt in 2009, to 48.9 percent charity care and 51.1 percent bad debt in 2014. “We didn’t alter our policies just because we didn’t provide enough charity care,” said Mr. Chesnos, St. Clair’s chief financial officer, “but because we wanted to be at the forefront of best practices.” Allegheny Health Network says there are two basic reasons why it began using PARO’s presumptive eligibility tool in the later half of 2014. “It’s a safety net to ensure that patients are screened prior to any collection actions and as a way to help AHN comply with the new [IRS 501r] rules,” said AHN’s spokesman Dan Laurent in an email answer to questions. Because it only started using presumptive eligibility in 2014, there is no state data available that would demonstrate its impact on the hospital system. (AHN switched to a calendar year reporting in 2014, so it only reported data from the last six months of 2013 for the state’s 2014 fiscal year.) But Mr. Laurent said internal, calendar year 2014 data from AHN shows the same dramatic impact on charity care that other hospitals have seen. He said Allegheny General Hospital jumped from just .23 percent charity care in calendar year 2013 to 1.41 percent in calendar year 2014; West Penn Hospital went from .04 percent to 1.52 percent; and Forbes Regional Hospital went from .19 percent to 1.24 percent. “If a patient meets that [presumptive eligibility] scoring we can give it without making them go through that laborious process” to get traditional charity care, said Ms. Allen, the former CFO. “That’s probably the

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biggest reason for the switch” from bad debt to charity care. Why not tell the patient? Not many people outside of the health care industry know about presumptive eligibility. That is, in part, because many of the hospitals that use it — like St. Clair, AHN and UPMC — don’t tell the patient they qualified for charity care using the new method. The hospital simply reclassifies an unpaid “bad debt” bill to charity care and the patient just stops getting phone calls or paper bills in the mail seeking payment. So, while the existing bill is taken care of as charity care at some hospitals, patients qualified under presumptive eligibility are not told they can come back to receive more free care for the next six months or a year until having to re-qualify. Advocates say that being told you can come back for more free care for the next six months or so is an important advantage that traditional charity care patients typically receive that encourages them to get follow-up care. Charity care experts and patient advocates say that if hospitals are not informing the patients that they qualified for charity care under presumptive eligibility, it may be a violation of the same federal regulation — 501r — that they were hoping to adhere to when they started using the new tool. “If they’re not telling them, it certainly doesn’t seem that they’re following the letter or the spirit of what the ACA [and 501r] requires,” said Gayle Nelson, senior policy analyst at The Hilltop Institute in Baltimore, which has a program that evaluates each state’s charity care policies. Ms. Nelson said part of the letter and spirit of the ACA and the 501r regulation is to get hospitals to be more transparent and not just provide more charity care, but get patients to come back for regular hospital care. Heather Klusaritz, senior fellow at the University of Pennsylvania’s Center for Public Health Initiatives, said it is particularly odd to be a growing tactic in the Affordable Care Act era, because a big aim of the act is “trying to drive down readmission rates and provide complete care; this does not do that.” “What rubs me the wrong way about this is that it’s not in the true nature of how we conceptualize charity care,” she said. “The goal of charity care is providing the comprehensive care that a patient needs, not just a single point-in-time encounter.” Hospitals counter, though, that they believe federal regulations do allow them to qualify someone for charity care without telling them. “Treasury [IRS] rules relating to presumptive charity qualification do not require us to notify the patient of the free care decision as long as the discount is at our most generous level,” said Allegheny Health Network’s Mr. Lauren in an emailed answer to questions. Ms. Manko, UPMC’s spokeswoman, gave a similar explanation: “We do whatever the law requires.” She later added that “Until further clarification by CMS regarding the use of presumptive charity, it has been the prevailing opinion of CMS auditors that presumptive charity should not be used to create an ongoing state of charity care. … Also, UPMC wants to avoid the cost and burden of trying to notify patients that have not engaged in the final assistance process.” Both the federal — Centers for Medicare and Medicaid Services — and state — PHC4 — entities that collect data on charity care, were unaware that hospitals were reporting charity care to them using this new form of presumptive eligibility. Joe Martin, PHC4’s executive director, said that his agency was unaware of this but “we’ll look at it.” CMS has no regulations concerning presumptive eligibility, but said in a statement to the Post-Gazette: “CMS does not dictate a provider’s charity care policy, but whatever that policy is, it should be applied universally to all patients.” But not all hospitals avoid telling the patients that they were qualified for charity care with presumptive eligibility. Geisinger has been using the PARO’s system for six years and, like UPMC and other hospitals, greatly increased the percentage of charity care compared to bad debt. But it tells its patients when they’re qualified. Ms. Tapscott said the reason seemed obvious to Geisinger: “I’d imagine it might be very stressful for someone who is ill to be burdened by unpaid bills. Their getting

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charity care can help relieve that stress.” Sean D. Hamill: shamill@post-gazette.com or 412-263-2579 or Twitter: @SeanDHamill

___

Visit the Pittsburgh Post-Gazette at www.post-gazette.com

Distributed by Tribune Content Agency, LLC.

By Sean D. Hamill

Source Citation:

“Hospitals turn bad debt into charity care.” Pittsburgh Post-Gazette [Pittsburgh, PA] 25 Sept. 2016. Business Insights: Global. Web. 27 Jan. 2022.

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Compare the types of entrepreneurship. 1.1 Contrast different types of entrepreneurship related to a level of risk.

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Entrepreneurship and Innovative Business Development 1

Course Learning Outcomes for Unit I Upon completion of this unit, students should be able to:

1. Compare the types of entrepreneurship. 1.1 Contrast different types of entrepreneurship related to a level of risk. 1.2 Analyze a type of entrepreneurship.

4. Examine business models.

4.1 Apply a business model. 4.2 Explain a company’s use of business models.

Course/Unit Learning Outcomes

Learning Activity

1.1 Chapter 1 Student Resource: Frugal Innovation Unit I Case Study

1.2, 4.1 Unit Lesson Chapter 1 Unit I Case Study

4.2

Unit Lesson Chapter 1 Student Resource: Challenges of a Family Business Unit I Case Study

Required Unit Resources Chapter 1: Practicing Entrepreneurship In order to access the following resource, click the link below. Navigate to the Video and Multimedia area in Student Resources for Chapter 1 of the eTextbook to view the items listed below.

• Challenges of a Family Business

• Frugal Innovation

Unit Lesson

Characteristics of an Entrepreneur How do you define an entrepreneur? Are you surprised to learn the truth about entrepreneurs rather than the myths that are falsely attributed to entrepreneurs? In the article by Goyette (2019), it is stated that more managers are building an entrepreneurial mindset in their employees with a growing preference to hire people who can contribute within an organization as an intrapreneur, a person who acts and thinks in an entrepreneurial manner. In this course, entrepreneurship is explained in depth to help you understand who entrepreneurs are, the characteristics of entrepreneurs, and why these characteristics support the success of the entrepreneurial venture. In later units, tools and resources will be presented as mechanisms to enhance your creativity and build your own entrepreneurial skills. Characteristics connected to entrepreneurship include creativity,

UNIT I STUDY GUIDE

Exploring Entrepreneurship https://edge.sagepub.com/neckentrepreneurship2e/student-resources/1-practicing-entrepreneurship/video-and-multimedia

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resourcefulness, perseverance, passion, motivation, being future-oriented, optimism, an adventurous spirit, flexibility, ethical behavior, comfort with ambiguity and risk, emotional and social intelligence, and humility. Each of these characteristics associated with successful entrepreneurs comes from research on the qualities displayed by entrepreneurs through a variety of studies that attempted to find out why some people become entrepreneurs, while other people are content to work for others.

Entrepreneurship and Ethics A common theme within the field of entrepreneurship is the importance of being ethical and honest. Since there are many risks associated with the progression from identifying a problem, finding a solution, and building the venture, a variety of people are needed along this path. Entrepreneurs focus more on collaborating with others than on competing against other people or organizations. Within each of these relationships, each person is dependent on the other for building a successful venture. In these relationships, each person must speak the truth, even when the information is detrimental. The earlier any negative information is identified and communicated, the quicker the problem can be addressed. Building teams and networks is another important characteristic of entrepreneurs. An entrepreneurial team is a significant part of a venture’s success.

Some derailers result from childhood experiences where learned messages, such as a fear of failure, hold us back in life. Surprisingly, some people even have a fear of being successful, as the message heard in childhood could have been that the person was inadequate or incapable of being successful.

Social Entrepreneurship

In fitting with this emphasis on ethical behavior, a subdivision within the field of entrepreneurship is about creating non-profit ventures and for-profit ventures that solve a societal need. Social entrepreneurs identify challenges faced by social and environmental problems, such as how to add more protein to a population’s diet in a tropical climate. One solution to this problem could be the creation of shitake mushroom farming to add a new source of protein into the local populations’ diet. Another idea is farming oyster mushrooms to teach people how to earn extra income by selling their mushrooms. Once you start developing your

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entrepreneurial mindset, you will start to notice more examples of social entrepreneurship and the creation of new products to solve unique challenges.

Intrapreneurship In this course, preconditioned restrictive messages about entrepreneurship will be diminished as your knowledge of the field of entrepreneurship is expanded and as you learn how to develop your entrepreneurial skills to become alert in continuously viewing the world through a lens that notices all of the possibilities for creating new ventures to solve problems. Not only is the topic of entrepreneurship relevant to the idea of starting a venture, it’s also relevant from the perspective of intrapreneurship, the focus on creating new products within an organization. An example is 3M, which was originally named Minnesota Mining and Manufacturing Company. It has a history of success, failure, success, innovation, and perseverance (3M, n.d.). In addition to its history of innovation and perseverance, 3M has a focus on collaboration and trust, essential components that propelled 3M into the Fortune 500 list with over 60,000 products and a presence in over 70 countries. One-third of those products were invented within the last 5 years (3M, n.d.). To support intrapreneurship, 3M is known for encouraging employees to spend 15% of their time working on new ideas. Even ideas that do not result in success count as work toward new inventions (Goetz, 2011). 3M’s mission includes the importance of protecting employees and the environment, creating products with a high value to the customer, incubating and protecting disruptive technologies and processes, and developing and engaging employees (3M, n.d.). This type of supportive environment is intentionally designed to encourage entrepreneurial activities that are, in the case of working within an established corporation, examples of intrapreneurship.

Business Models and Entrepreneurship Along with growing entrepreneurial behaviors through intrapreneurship, other forms of entrepreneurship can occur through understanding business models. Business models describe the method for providing a value that results in revenue. Some organizations like YouTube avoided creating a traditional business model around selling a service. Instead, YouTube was created with the intent of harvesting, or selling the company. Their innovative business model was designed to grow the volume of video data, rather than create a business model based on revenue related to uploading video. Another innovative type of business model includes subscription services, the idea of the customer purchasing a regular predetermined quantity of a product over a set timeline, a growing trend in start-up companies. The topic of business models and entrepreneurial venture success has not been studied extensively. An article by McDonald and Eisenhardt (2020) explores how business models position the new venture for success in noting the value in adjusting traditional business models to align with the value proposition. The example from this article points to the difference between Blockbuster and Netflix. Blockbuster’s business model was time-locked; whereas, Netflix’s business model offered ease of use and flexibility while simultaneously tracking individuals’ streaming history and preferences. These key alterations to the business model used by Blockbuster differentiated Netflix as an entertainment provider that offered key values to their users. The openness to reconsidering the business model from a creative value-added perspective locked Blockbuster out of the video rental industry. Even redefining how the industry is described provides new insights into defining the business model. The redefined industry move from video rental to entertainment provider has reshaped this industry with Netflix advancing to creating their content, solidifying their business model even further to block out competing players. The topic of business models and revenue models is covered later in the course in more depth.

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Manager vs. Entrepreneur There is a difference between being a manager and being an entrepreneur. Entrepreneurial ventures are not small versions of large corporations. There is a difference between following a method rather than a process. Entrepreneurs focus on taking action, experimenting, and seeking out the best business model that fits their venture and target market. There is even the idea of failing until success is reached. Failure is an opportunity to learn, rather than accepting a result without considering the various components or variables that were part of the action or experiment.

Behaviors of Entrepreneurs Keep in mind the importance of enjoying this course as part of the entrepreneurial behaviors of playfulness and experimentation. During this course, take time to apply the topics covered in the chapter readings. For example, Table 1.7 in Chapter 1 of the eTextbook shows how to use deliberate practice, which is a concept similar to practicing mindfulness. Consider practicing these activities at least once a day. Being deliberate, being consciously aware, and being in the moment are activities that you could build into your life. The benefit is improved perception, memory, intuition, and metacognition (Neck et al., 2021). These are documented behaviors of entrepreneurs proven to be effective and worth your time in practicing the activities and tools covered throughout this course.

Interactive Activity In each unit, you will find an interactive knowledge check activity, where you follow Claire’s story and help her develop a business idea. This is a nongraded activity.

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In order to check your understanding of concepts from this unit, complete the Unit I Knowledge Check activity. Unit I Knowledge Check PDF version of the Unit I Knowledge Check Note: Be sure to maximize your internet browser so that you can view each individual lesson on a full screen, ensuring that all content is made visible.

Conclusion

In today’s world of uncertainty, stepping out of traditional career paths and stepping into the world of entrepreneurship can provide greater control over your life. Although this sounds contradictory given the unknowns in starting a new venture where even the industry might not have existed before the startup opened, the average employment time within one company has now dropped to 3.2 years for employees between the ages of 25 and 34 (Doyle, 2019). As an entrepreneur, your efforts are directly related to advancing the development of your venture. Growing your idea into a successful venture is a rewarding manifestation of your dream, your idea.

References Doyle, A. (2019, November 8). How long should an employee stay at a job? The Balance Careers.

https://www.thebalancecareers.com/how-long-should-an-employee-stay-at-a-job-2059796 Goetz, K. (2011, February 1). How 3M gave everyone days off and created an innovation dynamo. Fast

Company. https://www.fastcompany.com/1663137/how-3m-gave-everyone-days-off-and-created-an- innovation-dynamo https://online.columbiasouthern.edu/bbcswebdav/xid-136931866_1 https://online.columbiasouthern.edu/bbcswebdav/xid-138335266_1 https://online.columbiasouthern.edu/bbcswebdav/xid-136931866_1

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Goyette, K. (2019). 5 things leaders do that stifle innovation. Harvard Business Review. https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire ct=true&db=bsu&AN=139155074&site=eds-live&scope=site

McDonald, R. M., & Eisenhardt, K. M. (2020). Parallel play: Startups, nascent markets, and effective

business- model design. Administrative Science Quarterly, 65(2), 483–523. https://doi- org.libraryresources.columbiasouthern.edu/10.1177/0001839219852349

Neck, H. M., Neck, C. P., & Murray, E. L. (2021). Entrepreneurship: The practice and mindset. SAGE. 3M. (n.d.). The history of 3M: From humble beginnings to Fortune 500.

https://www.3m.com/3M/en_US/company-us/about-3m/history/

Learning Activities (Nongraded) Nongraded Learning Activities are provided to aid students in their course of study. You do not have to submit them. If you have questions, contact your instructor for further guidance and information. In order to access the following resource, click the link below. Utilize the following Chapter 1 Flashcards to review terminology from the eTextbook. https://edge.sagepub.com/neckentrepreneurship2e/student-resources/chapter-1/flashcards

Course Learning Outcomes for Unit I
Required Unit Resources
Unit Lesson
- Characteristics of an Entrepreneur
- Entrepreneurship and Ethics
- Social Entrepreneurship
- Intrapreneurship
- Business Models and Entrepreneurship
- Manager vs. Entrepreneur
- Behaviors of Entrepreneurs
- Interactive Activity
- Conclusion
- References
Learning Activities (Nongraded)

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Describe Gyan’s business model

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Book Reference: Neck, H. M., Neck, C. P., & Murray, E. L. (2021). Entrepreneurship: The practice and mindset (2nd ed). SAGE. https://online.vitalsource.com/#/books/9781544354644

For this assignment, read the case study on pages 27 and 28 of the eTextbook. Once you have read and reviewed the case scenario, Instead,

Compare the different types of entrepreneurship concerning the amount of risk associated with starting a technology services company. Think about the ramifications of starting a company, where the core product (technology) is changing at a rapid pace.

Describe Gyan’s business model. Why does this business model fit or not fit this organization? Apply one other business model to this case, and then explain whether you think your selected business model would contribute to the company’s success and growth or not.

Analyze the type of entrepreneurship Gyan exhibits. As you think about doing something entrepreneurial, what is your motivation for entering an entrepreneurial activity?

Your case study should be at least two pages in length. References should include your eTextbook and a minimum of one additional credible source. Adhere to APA Style when constructing this assignment, including in-text citations and references for all sources that are used. Please note that no abstract is needed.

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