The bill will work on itself

1. Estimating the effect of Tobacco on cost of Medical Insurance
Name
Institution

2. 1. Introduction
Annually, more than 7 million adults die from smoking-related diseases, the annual death
rates from smoking-related complications resulting from smoking are expected to be at 6 billion by
2025 (Braun, Hanoch & Barnes, 2017). More than 80% are in the middle and low-income groups
(Braun, Hanoch & Barnes, 2017). In America, the financial consequences associated to smoking are
considerably high, the other consequences of smoking, such as the utilization of health services,
direct costs, and indirect costs, other costs such as absenteeism, are high in developing countries.
Evidence for the study show that there are high rates of smoking to be 12.7%, and according to the
country’s national statistics, individuals in Iran smoke more than 13.8 sticks every day.
Meat&ChikenCo has various branches across the United states and the medical insurance figures
are increasing ver the years, the study is to determine if the high medical insurance is related to
smoking. The study therefore aims to decide whether the Arkansas premium be brought into the
group, or should the company absorb all the premiums and reward the people who may have
different lifestyles.
2. The problem statement
Meat&ChieknCo is pondering about their unexpected result in the insurance health plan;
since the move to outsource operations to third party suppliers, the company has endured heavy
health plan, the company seeks to decide which factor leads to the high premiums and how to deal
with it. The costs of Hospitalization in Arkansas are high, although the data analysis will just help
confirm this. The reason for the increased smoking costs in Arkansas could be due to increased
hospitalization time and healthcare utilization on conditions related to smoking. The company,
therefore, needs to reduce the prevalence of smoking habits in all its branches with a particular
focus on Arkansas.
a) Age and weight are factors in explaining medical bills

3. The variable of interest is how gender, weight, smoking and having children are correlated
to high health premiums. The result of the multiple regression is shown in Appendix 1. The
regressions result shows that gender has an intercept of -971, showing that gender is a significant
factor in predicting medical bills; other minor predictors are age (intercept of -70), Children
(intercept of 236). The major predictor is smoke (intercept of 13986.93), therefore the institution
should check out how to reduce the health insurance burden.
b) Number one factor that contributes to smoking
From the regression performed above, smoking is the single-most-important factor
contributing to increased health insurance. This variable is followed by gender, which negatively
affects the insurance premium (Nugrahaeni & Usman, 2014). All the relationships are linear, as
shown with the above intercepts. The surprising outcome that goes against the general belief is
weight, weight is always a factor when considering increased medical insurance, and in this case,
Smoking, Number of Children, and gender variables play more critical roles than weight. Weight,
therefore, remains a surprising factor. The value was a surprise because of the low coefficient
compared to other factors such as gender. The model has an R-Squared value that is close to Zero
(0.4273), this shows that the model fits the data. When other variables like Height are included in
the model, the coefficient is reduced to 2796 from 5512; this shows that the other models help to
explain the increase in Hospital bills.
c) Prevalence of smoking across the regions
When comparing the hospital Bills across the locations, for both Non-smokers and smokers,
Arkansas leads by a large margin, for example, for non-smokers, Arkansas has bills of 7560.726,
only second to California, but for Smokers, the bill is 20983.40 as shown below. This confirms the
hypothesis that Arkansas has high medical bills across the region.

4. d) Medical cost is highest in Arkansas
The data above can also prove that Arkansas Plant has the highest smoking across the
regions, having average medical cost of 20, 983.384, this figure is higher than all other branches,
leading to Smokers; the state also leads in smokers at 98. This explains the high bills in the region.
The data echoes that of the Obama Care (Béland, Rocco & Waddan, 2016). The provision requires
health insurance providers to categorize their plans according to tobacco use. Many health
insurance providers use tobacco smoking to increase insurance ratings for smokers, the practice is
called tobacco ratings, ACA allows the insurance providers to charge tobacco smokers 50% more
than 50% when compared to non-smokers, through a tool called tobacco surcharge, the policy has
been implemented across the United States, and it means that all states have decided to implement
this, state of Arkansas charges 20% more premiums for smokers, hence the high cost of smoking
(Béland, Rocco & Waddan, 2016).

5. Therefore, the Arkansas Plant is leading on medical insurance expenditure because of the
high number of smokers compared to other regions.
3. Discussion
The study investigates how smoking affects medical insurance costs particularly in
its Arkansas branch. The study found that insurance expenses on health have increased in Arkansas
in the past few years, and Smokers in Arkansas were found to be higher in number compared to
other regions. The never smoker categories have lower medical costs when compared to smoker
categories. The results are consistent with the studies that have shown that smoking is directly
correlated to increased cost of medicals for former smokers compared to never smokers. One study
in Germany studies an association between the history of their smoking habit, the costs associated
with the habit (Béland, Rocco & Waddan, 2016). The study also shows that the annual cost of
insurance was high for former or current smokers, this value is high when compared to the numbers
for never smokers. Similarly, another study (Braun, Robert, Yaniv and Andrew J. Barnes)
supported this hypothesis by finding that medical costs for former and current smokers are higher
than never smokers. From the research, the hospitalization costs for current smokers were almost
$500 higher than costs of non-smokers, the costs of former smokers were also higher than never
smokers by $200. In the study, the total difference between the current smokers and the non-
smokers was 18.8 million, whereas the difference between the never smokers was lesser.
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6. Another factor that increased medical bill for smokers is that they were more likely to visit
hospital more than non-smokers. This directly contributed toward the high costs of insurance
premiums. The study also showed that outpatient visits was four-time more frequent than never
smokers. The study above found little correlation between age, sex, and increased cost of
hospitalization. However, the study found a significant correlation between weight and increased
cost of hospitalization, with an increased cost by 15.3% for patients weighing more than 80 Kgs
(McCurdy & Ross, 2018).
The insurance providers have also realized that smoking increases the likelihood of an
individual having high medical costs, insurers consider an individual a smoker when they used
tobacco at least four times a week, in the last six months, regular smokers also face steep premium
in case they don’t tell the truth, the providers are even harder on the people who lie about their
tobacco habits since anyone who lies about it can be penalized with insurance fraud (Béland, Rocco
& Waddan, 2016). In some states in the United States, soft fraud is considered a misdemeanor and
can result to a sentence of probation or community service. People who lie about their smoking
lives are also likely to be banned from insurance benefits . Casual smokers may avoid tobacco
surcharges, the tobacco use needs to each a certain threshold to be considered during medical
insurance rating. Many insurance providers need to verify that the users use tobacco more than four
times a week during the past six months. However, there is an exception when the users use tobacco
for religious or ceremonial uses, especially by Arkansas natives.
Not all states have the same ratings; some states prohibit insurers from overcharging
smokers, for example, in California, New York, Vermont, New York, and Washington DC. Other
states charge moderately, such as Kentucky 40%, Arkansas at 20%, Colorado, 15%. In States in the
US, smokers are charged over 50% more monthly premium when compared to individuals who
don’t smoke. The smoking surcharges cannot be categorized under ACA premium subsidies
(Yawson et al, 2013). The insurance providers can also charge tobacco users 50% more, other states

7. in the United States, even Obama Care (Béland, Rocco & Waddan, 2016) allows insurance brokers
to boost their premiums by up to 50%. However, the plan does not allow the providers to raise your
coinsurance or copayments.
4. Conclusion
The study showed that the costs of hospitalization across the country is attributed to
smoking. The research was particularly in Arkansas, where the hospitalization premiums were high,
the study found that the leading cause of such costs are related to smoking, followed by weight of
individual and lastly, location where individual stays. The advice to the board of directors is to take
measures to curb smoking habits in the company, engage human resources to reduce intake of
current or former smokers. Since the premium in Arkansas is averagely equal to the other areas, the
company can absorb the premium and look for ways of reducing smoking among its workforce like
rewarding those who may have different lifestyles.

8. 1. Works cited
Braun, R. T., Hanoch, Y., & Barnes, A. J. (2017). Tobacco use and health insurance literacy among
vulnerable populations: implications for health reform. BMC health services
research, 17(1), 1-10.
Yawson, A. E., Baddoo, A., Hagan-Seneadza, N. A., Calys-Tagoe, B., Hewlett, S., Dako-Gyeke, P.,
... & Biritwum, R. (2013). Tobacco use in older adults in Ghana: sociodemographic
characteristics, health risks and subjective wellbeing. BMC Public Health, 13(1), 1-8.
Nugrahaeni, W. P., & Usman, Y. (2014). Tobacco consumption by health insurance participants:
BPJS risk?[riskesdas 2013 data sources]. Buletin Penelitian Sistem Kesehatan, 17(4), 20922.
McCurdy, S. A., & Ross, M. W. (2018). Qualitative data are not just quantitative data with text but
data with context: On the dangers of sharing some qualitative data: Comment on Dubois et
al.(2018).
Béland, D., Rocco, P., & Waddan, A. (2016). Obamacare and the politics of universal health
insurance coverage in the United States. Social Policy & Administration, 50(4), 428-451.

9. Appendix 1: Regressionanalysis In Rstudio code
##
## Call:
## lm(formula = Ann_expenses ~ Gender_num + Weight + Height + Age +
## Children + Smoke_num, data = HR_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18784 -2944 -1073 1301 43701
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2796.127 6155.831 0.454 0.6499
## Gender_num -971.530 643.430 -1.510 0.1318
## Weight 13.610 3.402 4.000 7.47e-05 ***
## Height -70.308 36.495 -1.927 0.0547 .
## Age 356.834 44.469 8.024 1.04e-14 ***
## Children 236.099 340.004 0.694 0.4878
## Smoke_num 13986.935 793.164 17.634 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6291 on 419 degrees of freedom
## Multiple R-squared: 0.506, Adjusted R-squared: 0.4989
## F-statistic: 71.53 on 6 and 419 DF, p-value: < 2.2e-16
p<- HR_data %>%
group_by(Location, Smoke_num) %>%
summarise(mean = mean(Ann_expenses), sd= sd(Ann_expenses ), n = n())
## `summarise()` has grouped output by 'Location'. You can override using the `.groups`
argument.
p %>% drop_na()

10. ## # A tibble: 2 x 5
## # Groups: Location [1]
## Location Smoke_num mean sd n
## <chr> <dbl> <dbl> <dbl> <int>
## 1 Arkansas Plant 0 7561. 4967. 328
## 2 Arkansas Plant 1 20983. 11081. 98
lmHeight = lm(Ann_expenses~Gender_num+Weight+Children+Smoke_num, data = HR_data) #Creat
e the linear regression
summary(lmHeight) #Review the results
##
## Call:
## lm(formula = Ann_expenses ~ Gender_num + Weight + Children +
## Smoke_num, data = HR_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18789 -3442 -934 2253 43405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5512.802 991.407 5.561 4.78e-08 ***
## Gender_num -1156.410 688.990 -1.678 0.094008 .
## Weight 12.883 3.577 3.602 0.000354 ***
## Children 276.809 365.167 0.758 0.448855
## Smoke_num 13207.240 782.613 16.876 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6758 on 421 degrees of freedom
## Multiple R-squared: 0.4273, Adjusted R-squared: 0.4219
## F-statistic: 78.54 on 4 and 421 DF, p-value: < 2.2e-16