From: Be rich or don’t be sick: estimating Vietnamese patients’ risk of falling into destitution

Factor | Basic description | Characteristics | Remark for later use |
---|---|---|---|

ID | Coded number for each patient | Each patient has a unique code | This will only be used to produced graphs where needed |

Name | Name of patient | Those who refuse to allow the use of their true name will be replaced by N.A. Some patients may have identical names, and they will be distinguished by ID | This will not be used |

Res | This answers if the patient is resident of the region where the hospital is located | This takes binary value of yes or no | This has a potential value for implying transports and related costs for patient’s relatives |

Days | Number of days the patient spent in the hospital | Quantitative variable | This is for charting the frequency distribution and transforming into long or short stay |

Stay | A dummy variable to define if length of stay in hospital by a patient is short or longer | This takes value of S if one’s stay is less than 10 days, and L (longer) if 10 days or longer | This is potentially a good candidate for a binary predictor variable |

Insured | Whether a patient has a valid health insurance policy | This variable is binary and takes value of “Yes” or “No” | This is potentially a good candidate for a binary predictor variable |

MaxIns | The maximum level of coverage by insurance policy if the patient has one | Quantitative factor, measured by percentage of total “eligible costs” according to regulations | This is supplementary information only |

Edu | Highest level of education |
Values: JHS: junior high HS: high school Uni: college/university Grad: graduate school | This is supplementary information only |

SES | Socio-economic status | This is a multicategory variable, taking values of: high, medium, low | This is potentially a good candidate for a multi-categorical predictor variable |

Illness | Degree of severity of illness or injury when hospitalized | This is a multicategory variable, taking values of: emergency, bad, ill and light | This is potentially a good candidate for a multi-categorical predictor variable. Ill and Light categories can also be grouped into one single category, in which case we have a transformed factor of Ill2 (also in the data set) |

Jcond | Status of job | This takes value of good, stable, unstable, unemployed or N.A. (i.e. others: retired, high school students) | This is supplementary information only |

Income | Annual income in millions of Vietnamese Dong. Current exchange rate: $1 = VND21,200 (as of Dec 1, 2014) | Quantitative factor. The factor can be continuous in theory, but is mostly categorical in practice | This will be used to derive the IncRank variable |

IncRank | Rankings of income of a patient | This takes value of high, middle, or low | This is potentially a good candidate for a multi-categorical predictor variable |

Spent | Total money spent during his/her stay in hospital in millions of Vietnamese Dong. Using official exchange rate, VND 1 million is equivalent to $47.2 | Quantitative variable | |

Dcost | Average daily cost the patient had to pay during treatment period | Quantitative variable | |

Pins; Pinc; Pchar; Ploan | Portions of finance from sources: insurance reimbursement, income, charity funds from civil organizations or employers, or borrowings | Quantitative variables. They are measure in percentage of total costs the patient had to cover | |

InsL2 | Ranked levels of actual insurance reimbursement for patients who have policies | It takes value of high, medium, or low if a patient has an insurance policy. It takes nil if the patient is not insured at all | For actual transformation when necessary both low and nil can be grouped into one category of Neg (i.e., negligible) |

Streat, Srel, Senv | Percentage of funds used for the purpose of main treatments, for covering costs of relatives coming to help the patient, or paying “extra thank-you money” or bribing doctors/staff | These are quantitative variables, taking value of percentage. For instance, patient ID001 has {89;4;7 %} = {0.89; 0.04; 0.07}. Streat + Srel + Senv = 100 % | Only ‘Senv’ can be a potential candidate for modeling as dependent categorical variable, after being transformed into levels of “extra fees”, taking value of high, medium, or low. that newly derived categorical variable is called EnvL and also appears in the data set |

Burden | Patient’s and family’s self-evaluation of their financial position after paying health care costs | This is a multi-category variable, taking value of A (strong; no adverse affect at all); B (affected but not the worrying level); C (seriously affected) and D (destitute/“bankrupt” | This represents a group of critically important categories that the study employs to learn about what factors likely affect the probabilities of falling into each category of financial burden |

End | The health status after treatment | This is a group of multi-categorical variables, taking values of A (complete recovery), B (partial recovery, needing post-treatment follow-ups), C (stopped whilst being treated), or D (quit early) | This represents a group of critically important categories that the study wants to model to know what factors affect the probabilities of falling into each category of treatment completion after patients’ stay in hospital |

IfHigher | Self-evaluation of patient and family about financial status if the patient continues to be hospitalised again | This takes the same values as “Burden” factor | This can potentially be a candidate for future examination, especially when a larger sample is available. It is not used for this analysis |