# Health Care, Medical Insurance, and Economic Destitution: A Dataset of 1042 Stories

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## Abstract

**:**

**Dataset:**The dataset is submitted as a supplement to this manuscript.

**Dataset License:**CC-BY

## 1. Summary

## 2. Data Description

## 3. Methods

#### 3.1. Data Collection

#### 3.2. Frequentist Analysis

> library(nnet) |

> library(stargazer) |

> data1$Res<-relevel(data1$Res,ref=“Yes”) |

> data1$Insured<-relevel(data1$Insured,ref=“Yes”) |

> logit_burden<-multinom(Burden ~ Res + Insured, data=data1) |

> stargazer(logit_burden,type = “text”, out = “logit_burden.htm”) |

#### 3.3. Bayesian Analysis

# Design the model |

model <- bayesvl() |

model <- bvl_addNode(model, “burden”, “norm”) |

model <- bvl_addNode(model, “res”, “norm”) |

model <- bvl_addNode(model, “insured”, “norm”) |

model <- bvl_addArc(model, “res”, “burden”, “slope”) |

model <- bvl_addArc(model, “insured”, “burden”, “slope”) |

# Generate the stan code for model |

model_string <- bvl_model2Stan(model) |

cat(model_string) |

# Fit the model |

fit <- bvl_modelFit(model, data1, warmup = 2000, iter = 20000, chains = 4, cores = 1) |

data { |

int<lower = 0> Nobs; //number of observations |

vector[Nobs] y; |

vector[Nobs] res; //independent variable 1 |

vector[Nobs] insured; //independent variable 2 |

} |

parameters { |

real alpha; //intercept |

real b_res; //beta for educate, etc |

real b_insured; |

real sigma; |

} |

model { |

alpha ~ normal(0,100); //priors for all betas |

b_res ~ normal(0,100); // |

b_insured ~ normal(0,100); |

y ~ normal(alpha + b_res * res + b_insured * insured, sigma); //model |

} |

generated quantities { |

vector[Nobs] log_lik; |

for(i in 1:Nobs) { |

log_lik[i] = normal_lpdf(y[i] | alpha + b_res * res[i] + b_insured * insured[i], sigma); |

} |

} |

4 chains, each with iter = 5000; warmup = 1000; thin = 10; | ||||||||||

post-warmup draws per chain = 400, total post-warmup draws = 1600. | ||||||||||

mean | se_mean | sd | 2.5% | 25% | 50% | 75% | 97.5% | n_eff | Rhat | |

alpha | 4.08 | 0 | 0.09 | 3.90 | 4.02 | 4.08 | 4.14 | 4.24 | 1485 | 1 |

b_res | −1.03 | 0 | 0.04 | −1.12 | −1.06 | −1.03 | −1.01 | −0.95 | 1502 | 1 |

b_insured | −0.33 | 0 | 0.05 | −0.43 | −0.37 | −0.33 | −0.30 | −0.24 | 1610 | 1 |

sigma | 0.65 | 0 | 0.01 | 0.62 | 0.64 | 0.65 | 0.66 | 0.67 | 1763 | 1 |

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The level of “Income”, “Spent”, and “Dcost” according to the types of “Burden” of the patient.

**Figure 2.**The level of “Income”, “Spent”, and “Dcost” according to the types of “IfHigher” of the patients.

**Figure 4.**The probabilities were computed corresponding to the status of burden outcomes based on the conditions of residency and insurance. Recreated from the idea in [4]. Note: minimally affected (A), adversely affected (B), destitute (C), adversely destitute (D).

**Figure 5.**The probabilities of destitution corresponding to both long-time and short-time hospitalization based on the conditions of residency and insurance. Recreated from the idea in [4]. Note: destitution with long-time hospitalization (DestLong) and destitution with short-time hospitalization (DestShort).

**Figure 6.**The regression model’s posterior distribution of all coefficients. Note: HPDI: Highest Posterior Density Interval.

**Figure 7.**The Hamiltonian Markov chain Monte Carlo (MCMC) technical validations for the simulation model.

Coded Name | Explanation | Items | Total | Male | Female | |||
---|---|---|---|---|---|---|---|---|

Freq | % | Freq | % | Freq | % | |||

Res | Whether the patient lives in the same region as the hospital. | Yes | 578 | 55.5 | 323 | 55.9 | 255 | 44.1 |

No | 464 | 44.5 | 289 | 62.3 | 175 | 37.7 | ||

Stay | How long the patient stays at the hospital: under 10 days (S) or more than 10 days (L). | Long | 289 | 27.7 | 175 | 60.6 | 114 | 39.4 |

Short | 753 | 72.3 | 437 | 58.0 | 316 | 42.0 | ||

Insured | Whether the patient has valid insurance or not. | Yes | 724 | 69.5 | 406 | 56.1 | 318 | 43.9 |

No | 318 | 30.5 | 206 | 64.8 | 112 | 35.2 | ||

Edu | The highest educational level of the patient: junior high school (JHS), high school (HS), university (Uni), or graduate school (Grad). | JHS | 141 | 13.5 | 79 | 56.0 | 62 | 44.0 |

HS | 705 | 67.7 | 426 | 60.4 | 279 | 39.6 | ||

Uni | 194 | 18.6 | 105 | 54.1 | 89 | 45.9 | ||

Grad | 2 | 0.2 | 2 | 100.0 | 0 | 0.0 | ||

SES | The socioeconomic status of the patient. This variable was based on IncRank (the ranking of the patient’s income) or that of the patient’s guardian(s) if required. | Hi | 38 | 3.6 | 20 | 52.6 | 18 | 47.4 |

Med | 908 | 87.1 | 535 | 58.9 | 373 | 41.1 | ||

Low | 96 | 9.2 | 57 | 59.4 | 39 | 40.6 | ||

Illness | The seriousness of the patient’s illness or injury. In the dataset, the variable “Ill2” combined two values “ill” and “light” into one value “light” for analysis. | Emergency | 285 | 27.4 | 204 | 71.6 | 81 | 28.4 |

Bad | 520 | 49.9 | 293 | 56.3 | 227 | 43.7 | ||

Ill | 221 | 21.2 | 105 | 47.5 | 116 | 52.5 | ||

Light | 16 | 1.5 | 10 | 62.5 | 6 | 37.5 | ||

Jcond | The condition of the patient’s employment. | Stable | 513 | 49.2 | 300 | 58.5 | 213 | 41.5 |

Unstable | 335 | 32.1 | 212 | 63.3 | 123 | 36.7 | ||

Unemployed | 99 | 9.5 | 52 | 52.5 | 47 | 47.5 | ||

IncRank | The ranking of the patient’s income. Unit: million VND (Vietnamese Dong). | High (>180) | 8 | 0.8 | 4 | 50.0 | 4 | 50.0 |

Middle (48–180) | 241 | 23.1 | 139 | 57.7 | 102 | 42.3 | ||

Low (<48) | 793 | 76.1 | 469 | 59.1 | 324 | 40.9 | ||

AvgCost | The average cost that the patient spent daily during treatment. Unit: million VND (Vietnamese Dong). | High (>5.4) | 159 | 15.3 | 110 | 69.2 | 49 | 30.8 |

Medium (1.5 to 5.4) | 432 | 41.5 | 255 | 59.0 | 177 | 41.0 | ||

Low (≤1.5) | 451 | 43.3 | 247 | 54.8 | 204 | 45.2 | ||

InsL | The categories of the amount that insurance covered. It is based on the numerical variable “Pins”, which is the portion of fees covered by insurance reimbursement. | A (>0.45) | 546 | 52.4 | 318 | 58.2 | 228 | 41.8 |

B (>0.25 and ≤0.45) | 105 | 10.1 | 45 | 42.9 | 60 | 57.1 | ||

C (≤0.25) | 65 | 6.2 | 35 | 53.8 | 30 | 46.2 | ||

N.E. (=0) | 326 | 31.3 | 214 | 65.6 | 112 | 34.4 | ||

EnvL | The portion of “extra thank-you money” that the patient had to include in the medical fees. | High (>15%) | 108 | 10.4 | 37 | 34.3 | 71 | 65.7 |

Medium (7%–15%) | 158 | 15.2 | 99 | 62.7 | 59 | 37.3 | ||

Low (<7%) | 464 | 44.5 | 294 | 63.4 | 170 | 36.6 | ||

Nil (0) | 312 | 29.9 | 182 | 58.3 | 130 | 41.7 | ||

Burden | The self-reported evaluation of the patient’s and family’s financial situation after paying treatment fees: minimally affected (A), adversely affected (B), destitute (C), adversely destitute (D). | A | 442 | 42.4 | 232 | 52.5 | 210 | 47.5 |

B | 275 | 26.4 | 161 | 58.5 | 114 | 41.5 | ||

C | 312 | 29.9 | 213 | 68.3 | 99 | 31.7 | ||

D | 13 | 1.2 | 6 | 46.2 | 7 | 53.8 | ||

End | The outcome of treatment: recovered (A), need follow-up treatment (B), stopped in the middle (C), and quit early (D). | A | 539 | 51.7 | 273 | 50.6 | 266 | 49.4 |

B | 394 | 37.8 | 259 | 65.7 | 135 | 34.3 | ||

C | 47 | 4.5 | 31 | 66.0 | 16 | 34.0 | ||

D | 62 | 6.0 | 49 | 79.0 | 13 | 21.0 | ||

SatIns | The patient’s satisfaction level regarding health insurance. | Satisfied | 118 | 11.3 | 61 | 51.7 | 57 | 48.3 |

Average | 613 | 58.8 | 344 | 56.1 | 269 | 43.9 | ||

Low | 1 | 0.1 | 1 | 100.0 | 0 | 0.0 | ||

No Comment | 274 | 26.3 | 178 | 65.0 | 96 | 35.0 | ||

IfHigher | The self-reported evaluation of the patient’s and family’s financial situation if the patient continues treatment. The values of this variable are the same as “Burden”. | A | 185 | 17.8 | 80 | 43.2 | 105 | 56.8 |

B | 641 | 61.5 | 391 | 61.0 | 250 | 39.0 | ||

C | 187 | 17.9 | 123 | 65.8 | 64 | 34.2 | ||

D | 29 | 2.8 | 18 | 62.1 | 11 | 37.9 |

Coded Name | Explanation | Unit | Mean | Standard Deviation | Min | Max |
---|---|---|---|---|---|---|

Age | The patient’s age. | Age | 45.43 | 17.96 | 1 | 92 |

Days | The number of days the patient stays in for treatment. | Day | 8.97 | 5.99 | 1 | 60 |

MaxIns | The highest level of insurance coverage. | Percent | 0.60 | 0.42 | 0 | 1.00 |

Saving | The portion of savings. | Percent | 0.18 | 1.99 | 0 | 60.00 |

WkYrs | The number of years the patient has worked. | Year | 20.6 | 15.85 | 0 | 60 |

Income | The annual income of the patient. | Million VND (Vietnamese Dong) | 40.67 | 39.04 | 0 | 550.00 |

Dcost | The cost of staying at the hospital for a day. | 3.07 | 3.76 | 0.03 | 50.33 | |

Spent | The amount of money the patient actually spent. | 27.85 | 42.40 | 0.10 | 665.00 | |

Pins | The portion of fees financed by insurance reimbursement. | Percent | 0.41 | 0.33 | 0 | 0.90 |

Pinc | The portion of fees financed by income. | 0.50 | 0.33 | 0 | 1.00 | |

Pchar | The portion of fees financed by a charity. | 0.02 | 0.09 | 0 | 1.00 | |

Ploan | The portion of fees financed by a loan. | 0.07 | 0.17 | 0 | 1.00 | |

Streat | The portion of funds used for treatment. | Percent | 0.82 | 0.13 | 0.17 | 1.00 |

Srel | The portion of funds used for paying relatives who came to help. | 0.12 | 0.10 | 0 | 0.83 | |

Senv | The portion of funds used for “extra thank-you money” or for bribing doctor/staff. | 0.06 | 0.07 | 0 | 0.60 |

• What are the effects of socio-demographic factors on the probability of being destitute? |

• To what extent are socio-demographic factors the determinants of the degree of illness? |

• What is the impact of hospitalization length on patients’ financial burden? |

• How do the treatment costs and illness explain the end outcome of treatment? |

• How does the amount of out-of-pocket “extra thank-you money” determine the end outcome of treatment? |

Intercept | Resident | Insured | |
---|---|---|---|

No | No | ||

${\mathit{\beta}}_{0}$ | ${\mathit{\beta}}_{1}$ | ${\mathit{\beta}}_{2}$ | |

Logit(B|A) | −1.291*** | 1.784*** | 1.601*** |

Logit(C|A) | −2.599*** | 3.801*** | 1.635*** |

Logit(D|A) | −6.561*** | 4.163*** | 2.401*** |

Residual Deviance = 1777.9, Log-likelihood = −888.96 on 9 df, baseline = “A” |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Ho, M.-T.; La, V.-P.; Nguyen, M.-H.; Vuong, T.-T.; Nghiem, K.-C.P.; Tran, T.; Nguyen, H.-K.T.; Vuong, Q.-H.
Health Care, Medical Insurance, and Economic Destitution: A Dataset of 1042 Stories. *Data* **2019**, *4*, 57.
https://doi.org/10.3390/data4020057

**AMA Style**

Ho M-T, La V-P, Nguyen M-H, Vuong T-T, Nghiem K-CP, Tran T, Nguyen H-KT, Vuong Q-H.
Health Care, Medical Insurance, and Economic Destitution: A Dataset of 1042 Stories. *Data*. 2019; 4(2):57.
https://doi.org/10.3390/data4020057

**Chicago/Turabian Style**

Ho, Manh-Toan, Viet-Phuong La, Minh-Hoang Nguyen, Thu-Trang Vuong, Kien-Cuong P. Nghiem, Trung Tran, Hong-Kong T. Nguyen, and Quan-Hoang Vuong.
2019. "Health Care, Medical Insurance, and Economic Destitution: A Dataset of 1042 Stories" *Data* 4, no. 2: 57.
https://doi.org/10.3390/data4020057