| Title: | Obesity Cost Database | 
| Version: | 0.1.0 | 
| Description: | This database contains necessary data relevant to medical costs on obesity throughout the United States. This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses. This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis". Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic (2022) "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" <doi:10.3390/risks10100197>. The database is composed of the following main tables: 1. Relative_Risks: (constant) Relative risks for a given disease group with a risk factor of obesity; 2. Disease_Cost: (obesity_cost_disease) Supplementary output with all variables related to individual disease groups in a given state and year; 3. Full_Cost: (obesity_cost_full) Complete output with all variables used to make cost calculations, as well as cost calculations in a given state and year; 4. National_Summary: (obesity_cost_national_summary) National summary cost calculations in a given year. Three functions are included to assist users in calling and adjusting the mentioned tables and they are data_load(), data_produce(), and rel_risk_fun(). | 
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] | 
| Depends: | R (≥ 2.10) | 
| Imports: | dplyr,tidyr, stats | 
| Encoding: | UTF-8 | 
| LazyData: | true | 
| RoxygenNote: | 7.1.2 | 
| NeedsCompilation: | no | 
| Packaged: | 2022-12-24 06:48:12 UTC; etsu | 
| Author: | Tianyue Zang [aut, cre, cph], Thomas Woods [aut], Tatjana Miljkovic [aut] | 
| Maintainer: | Tianyue Zang <zangt2@miamioh.edu> | 
| Repository: | CRAN | 
| Date/Publication: | 2023-01-06 11:30:02 UTC | 
Obesity Cost Database
Description
This database contains necessary data relevant to medical costs on obesity throughout the United States. This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses.
So far the functions included in the package are:
-  data_loadgenerate the essential four tables that concerns obesity
-  data_produceload all critical values in a returned list format
-  rel_risk_funupdate the relative risks (or the constants) when crucial data needs updating
The database is composed of the following main tables:
-  constantRelative risks for a given disease group with a risk factor of obesity.
-  obesity_cost_diseaseSupplementary output with all variables related to individual disease groups in a given state and year.
-  obesity_cost_fullComplete output with all variables used to make cost calculations, as well as cost calculations in a given state and year.
-  obesity_cost_national_summaryNational summary cost calculations in a given year.
-  full_dataNecessary raw data for generating new tables with user input
This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic. 2022. Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis. Risks 10: 197. <doi:10.3390/risks10100197>
| Package: | obcost | 
| Type: | Package | 
| Version: | 0.1.0 | 
| Date: | 2022-10-23 | 
| License: | public | 
| LazyData: | no | 
Note
Please make sure that packages of dplyr and tidyr is applied
Author(s)
Tianyue Zang (zangt2@miamioh.edu)(zangtianyue.312@163.com) Thomas Woods, Tatjana Miljkovic
Maintainer: Tianyue Zang (zangt2@miamioh.edu)(zangtianyue.312@163.com)
References
State Population Totals. 2020. State Population Totals and Components of Change: 2010–2019. Suitland: U.S. Census Bureau. Available online: www.census.gov (accessed on 20 July 2021).
Current Population Survey Annual Social and Economic Supplements. 2020. Income and Poverty in the United States: 2019. Available online: www.census.gov (accessed on 20 July 2021).
Disability Characteristics. 2020. American Community Survey (acs). Available online: www.census.gov (accessed on 20 July 2021).
Characteristics of the Employed. 2020. Current Population Survey (cps). Available online: www.bls.gov (accessed on 20 July 2021).
Employer Costs for Employee Compensation. 2021. Archived News Releases. Available online: www.bls.gov (accessed on 20 July 2021).
Table of Overweight and Obesity (BMI). 2020. Behavioral Risk Factor Surveillance System. Available online: www.cdc.gov (accessed on 21 July 2021).
Premium, Schedule T., and Annuity Considerations. 2020. Total Health Industry Schedule T Allocated by States and Territories. Available online: www.spglobal.com (accessed on 29 October 2021).
Murray, Christopher J. L., Aleksandr Y. Aravkin, Peng Zheng, Cristiana Abbafati, Kaja M. Abbas, Mohsen Abbasi-Kangevari, Foad Abd-Allah, Ahmed Abdelalim, Mohammad Abdollahi, Ibrahim Abdollahpour, and et al. 2020. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the global burden of disease study 2019. The Lancet 396: 1223–249. [CrossRef]
Consumer Price Index. 2021. All Urban Consumers (Current Series). Available online: www.bls.gov (accessed on 21 July 2021).
Default Input of Relative Risk
Description
This dataset gives default input of Relative Risk, however could be updated latter by the user
- cvd
- Relative Risk for cardiovascular disease 
- diabetes
- Relative Risk for diabetes 
- cancer
- Relative Risk for cancer 
- copd_asthma
- Relative Risk for chronic obstructive pulmonary disease or asthma 
- osteoarthritis
- Relative Risk for osteoarthritis 
- hypertension
- Relative Risk for hypertension 
- kidney
- Relative Risk for kidney diseases 
- g_p_l
- Relative Risk for gallbladder, liver, and pancreatic diseases 
- stroke
- Relative Risk for strokes 
Usage
constant
Format
An object of class tbl_df (inherits from tbl, data.frame) with 9 rows and 2 columns.
data_load function
Description
The function "data_load" would load all critical values in a returned list format
Usage
data_load()
Value
a list(dataframe) of pop (population), gdp (global gdp), mi (median income), bmi (body mass index), disab (disability rate), employ (employment rate), med_cost (medical conditions cost), med_prev (medical conditions prevalence cost), natl_med_prev (national medical conditions prevalence), rel_risk (relative risk), benefits, and insurance
Examples
raw_data <- data_load()
population <- raw_data$pop
data_produce function
Description
The data_produce function would generate the essential four tables that concerns obesity including 1. Relative Risks (constant): Relative risks for a given disease group with a risk factor of obesity. 2. Disease Cost (obesity_cost_disease): Supplementary output with all variables related to individual disease groups in a given state and year. 3. Full Cost (obesity_cost_full): Complete output with all variables used to make cost calculations, as well as cost calculations in a given state and year. 4. National Summary (obesity_cost_national_summary): National summary cost calculations in a given year.
Usage
data_produce(rr = c())
Arguments
| rr | the relative risks of diseases – Cardiovascular disease, diabetes, cancer, Chronic obstructive pulmonary disease or asthma, osteoarthritis, hypertension, kidney diseases, (Gallbladder, Liver, Pancreatic) diseases, and strokes. | 
Value
a list (dataframe) of constant, obesity_cost_disease, obesity_cost_full, and obesity_cost_national_summary
Examples
new_data <- data_produce(rr = c(1,2,3,4,5,6,7,8,9.1))
cnst <- new_data$constant
Necessary Raw Data for Generating New Tables With User Input
Description
This dataset gives users opportunities to update the outputs with there own input of relative risks
- pop
- Population 
- gdp
- GDP 1963-2020 in millions of current dollars 
- mi
- Median Income 1967-2019 
- bmi
- BMI 1996-2019 
- disab
- Disability 1981-2019 
- employ
- Employment Rate 1950-2020 
- med_cost
- Medical Conditions Cost 1996-2018 
- med_prev
- Medical Conditions Prevalence 1996-2019 
- natl_med_prev
- Medical Conditions National Prevalence 2996-2019 
- rel_risk
- Relative Risks 
- benefits
- Employee Benefits 1996-2018 
- insurance
- insurance_data 
Usage
full_data
Format
An object of class list of length 12.
Relevant Data for Obesity, Cost, and Diseases
Description
This dataset gives supplementary output with all variables related to individual disease groups in a given state and year.
- State
- state of interest 
- Year
- year of interest 
- pi_it
- obesity prevalence in state i and year t 
- cause
- disease group 
- rr_j
- relative risk of disease group j on obesity 
- psi_jt
- national cost of disease group j in year t 
- rho_jit
- population-attributable risk percent of disease group j in state i and year t 
- DC_jit
- direct cost for disease group j in state i and year t 
Usage
obesity_cost_disease
Format
An object of class data.frame with 10350 rows and 8 columns.
Relevant Data for Obesity, Cost, and Diseases
Description
Complete output with all variables used to make cost calculations, as well as cost calculations in a given state and year.
- State
- state of interest 
- Year
- year of interest 
- m_t
- median income in year t 
- d_t
- work-impacting disability prevalence in year t 
- e_t
- employment average ration in year t 
- b_t
- employment benefit in year t 
- p_it
- population in state i and year t 
- pi_it
- obesity prevalence in state i and year t 
- tau_t
- total employee benefits in year t 
- varphi_it
- gross domestic product of state i in year t 
- DC_it
- direct cost of state i in year t 
- M_it
- excess mortality cost of state i in year t 
- A_it
- absenteeism cost of state i in year t 
- D_it
- disability cost of state i in year t 
- IC_it
- indirect cost of state i in year t 
- TC_it
- total cost of state i in year t 
Usage
obesity_cost_full
Format
An object of class data.frame with 1150 rows and 16 columns.
National summary cost calculations in a given year
Description
National summary cost calculations in a given year
- Year
- year of interest 
- DC_t
- direct cost in year t 
- M_t
- excess mortality cost in year t 
- A_t
- absenteeism cost in year t 
- D_t
- disability cost in year t 
- IC_t
- indirect cost in year t 
- TC_t
- total cost in year t 
- p_t
- total population in year t 
- pi_t
- average obesity rate in year t 
Usage
obesity_cost_national_summary
Format
An object of class data.frame with 23 rows and 9 columns.
rel_risk_fun function
Description
The "rel_risk_fun" could update the relative risks (or the constants) when crucial data needs updating
Usage
rel_risk_fun(rr)
Arguments
| rr | the relative risks of diseases – Cardiovascular disease, diabetes, cancer, Chronic obstructive pulmonary disease or asthma, osteoarthritis, hypertension, kidney diseases, (Gallbladder, Liver, Pancreatic) diseases, and strokes. | 
Value
a list (dataframe) of relative risks
Examples
key <- rel_risk_fun(rr = c(1,2,3,4,5,6,7,8,1.2))
key$rr