- Email: [email protected]
Data on Vietnamese patients׳ financial burdens and risk of destitution
Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎
1 Contents lists available at ScienceDirect 2 3 4 5 6 journal homepage: www.elsevier.com/locate/dib 7 8 9 Data Article 10 11 12 13 14 a b 15 Q1 Quan-Hoang Vuong , Trong-Khang Nguyen 16 a 17 Q3 School of Business (FSB), FPT University, VAS-FSB Building, Block C, My Dinh 1, Tu Liem District, Hanoi, Vietnam 18 Q2 b 11th Floor, TTC Building, Duy Tan, Cau Giay District, Hanoi, Vietnam 19 20 21 a r t i c l e i n f o abstract 22 23 Article history: The research process started in the first week of August 10, 2014 24 Received 1 June 2016 and ended early February 2015, obtaining qualified data for 330 25 Received in revised form patients from many hospitals in northern Vietnam. Its expansion 19 September 2016 26 was performed for an enlarged dataset through May 2015, conAccepted 24 September 2016 27 taining 900 records. This article exemplifies the attempt to 28 examine the likelihood of destitution among Vietnamese patients 29 due to insufficient insurance coverage, cost of treatment and patient's status of residency during a curative hospital stay. The 30 result suggests that the patients, who are poor and come from 31 rural areas, face serious obstacles in accessing health care services. 32 This data article presents attributes and values of the data set used 33 in the article provided at DOI: http://dx.doi.org/10.1186/s4006434 015-1279-x Vuong (2015) [4]. 35 & 2016 The Authors. Published by Elsevier Inc. This is an open 36 access article under the CC BY license 37 (http://creativecommons.org/licenses/by/4.0/). 38 39 40 41 Specifications Table 42 43 44 Subject area Medical 45 More specific sub- Patient's financial burdens occurring during a curative hospital stay 46 ject area 47 Type of data Table, graph, figure 48 49 50 E-mail addresses: [email protected] (Q.-H. Vuong), [email protected] (T.-K. Nguyen). 51 52 http://dx.doi.org/10.1016/j.dib.2016.09.040 53 2352-3409/& 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license 54 (http://creativecommons.org/licenses/by/4.0/).
Data in Brief
Data on Vietnamese patients' financial burdens and risk of destitution
Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i
Q.-H. Vuong, T.-K. Nguyen / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎
2
55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 Q4 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
How data was acquired Data format Experimental factors Experimental features Data source locations Data accessibility
Survey Raw, filtered, and partially analyzed Raw data obtained from a survey patients at many hospitals in Northern Vietnam The experiment focuses on examining financial issues, illness, insurance, end result of treatment, health care costs, length of stay, ‘envelope OOP’ and the probability of post-treatment destitution, for different groups of patients Viet Duc, Bach Mai, Vietnam-Japan, Hai Duong Polyclinic, Thai Binh Polyclinic Hospitals, Vietnam (and others) Both original datasets for 330 and 900 survey records are provided with this article, and deposited at DOI: http://doi.org/10.17632/rw28hh58y4.1.
Value of the data
The data potentially offer a specific insight on financial destitution due to costs of treatment. The data can help reveal the likelihood of patient's recovery after treatment. The data help to understand the effect of health insurance on reducing the likelihood of falling into destitution.
The data shed light on the risk of “extra thank-you money” becoming an obligatory part of treatment costs for patients, low-income patients in particular.
The data set structure can be enriched for deeper analysis providing insights and implications for bettering policy making in health insurance and poverty reduction.
1. Data The first data set, which will be used for computation examples provided in this article, contains 900 records obtained from a survey on Vietnamese inpatients concerning family status, patient's income level, patient's extra expenses to doctors and hospital's staff, and their loans to finance treatment. Fig. 1 shows the distribution of patients hospitalization lengths. Continuous (numerical) and discrete (categorical) variables are measured and reported in the survey data set. Table 1 presents numerical variables of the data set. Fig. 1a and b present medical expenditures and average daily costs in relation to health status of patients when hospitalized. They are not significantly different from those provided in [4] (Table 2).
2. Experimental design, materials and methods The dataset was constructed using information from questionnaires in order to enable the modeling of baseline-category logits (BCL). In addition, the computing of empirical probabilities upon events of hypothetical influence is performed. The logic for designing the experiment and thus data sets are similar to what is described in [1], for data groups in J categories of Y as multinomial with corresponding sets of probabilities π 1 ðxÞ; …; π j ðxÞ , and with the multinomial probability mass
function: pðn1 ; n2 ; …; nc Þ ¼
n! n1 !n2 !…nc !
π n11 π n22 …π nc c : The data set has been created to enable an BCL
analysis to simultaneously model effects of x on ðJ 1Þ logits such that the estimating of ðJ 1Þ equations enables the computing of the remaining logits. Therefore, Pearson-type likelihood ratio test statistics X 2 ; G2 or goodness-of-fit, following a multivariate GLM estimations g μi ¼ Xi β become appropriate for hypothesis testing. For practically estimating multinomial logistic models, consult with Refs. [2,3]. Practicality of survey data uses is also provided in Ref. [4]. Some possible questions and hypotheses worth testing of, using the data set analyzed by [4], is in Table 3.
Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i
Q.-H. Vuong, T.-K. Nguyen / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎
109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
3
Fig. 1. Status of health in relation to expenditure and average daily cost. (a) Range of expenses for patients with treatment outcome categories (A, B, C or D). (b) Range of daily cost for patients falling into different treatment outcome groups (A, B, C or D).
Table 1 Numerical variables of the data set. Coded name
Explanation
Unit
Spent
Total money spent during his/her stay in hospital in millions of Vietnamese Dong. According to official exchange rates at the time of survey, VND 1 million was equivalent to $47.2. Average daily cost the patient had to pay during the entire treatment period Annual income
Million of VND
Dcost Income Days Pins; Pinc; Pchar; Ploan Streat, Srel, Senv
Number of days the patient spent in the hospital Portions of finance from sources: insurance reimbursement, income, charity funds from civil organizations or employers, or borrowings 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
Million of VND Million of VND Day Percent
Percent
The following short R commands help create the data set provided in the file named “table1.csv” 4 med ¼read.csv(“E:/…/Med2015/Data/P330.csv,header ¼T) 4 attach(med) 4 table1 ¼xtabs( Res þ InsuredþBurden) 4 ftable(table1) The data set in file name “table1.csv” presents a distribution of patients following by residency status, insurance participation, and extent to which patients fall into destitution due to financial Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i
Q.-H. Vuong, T.-K. Nguyen / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎
4
163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
Table 2 Categorical variables of the data set. Coded name
Explanation
Values
Res
Whether the patient originally resides in the region where the hospital is located A dummy variable to define the patient's hospitalization by length of stay Whether a patient has a valid health insurance Socio-economic status
Yes, no
Stay Insured SES
Less than 10 days (S), 10 days or longer (L)
Yes, no High, medium, low Based on average IncRank of working members in the family who are able and willing to support the patient if so required Illness Severity of illness or injury when hospitalized Emergency, bad, ill, light IncRank Rankings of income of a patient High ( 4180), middle (48–180), low ( o48) Excel: ¼ IF([cell]4180,"Hi",IF([cell] 4 48,"Mid","Lo")) Burden Patient's and family's self-evaluation of their strong, no adverse affect at all (A); affected but not the worfinancial position after paying health care costs rying level (B); seriously affected or destitute/bankrupt (C) End Patient's health status after treatment complete recovery (A), partial recovery, needing post-treatment follow-ups (B), stopped whilst being treated (C), or quit early (D) AvgCost Average daily cost the patient had to pay during r 1.5 (Low), 1.5 to 5.4 (Med), and 45.4 (Hi) treatment period
Table 3 Possible research questions arising from the data set. Do the residency status of patients and insurance coverage determine the probability of patients falling into debts? The specific factor of residency status is important in Vietnam because society has for long been skeptical about provincial healthcare, leading patients to travel to large hospitals in major cities such as Hanoi, Hai Phong, or HCMC. Doing so not only necessitates accompanying and caretaking of family members but also entails travel costs and informational asymmetry on drug prices, treatment schedules, the best hospital to visit and even the ‘right amount’ of “extra thank-you money” (a kind of out-of-pocket expense; or OOP). As for two most important factors to Vietnamese patients/households, i.e. treatment costs and illness, is there evidence to support this view and if yes, whose influence better explains the possibility of end results of treatment, empirically? Can the likelihood of paying too little or too much out-of-pocket “extra thankyou money” be determined by the severity of illness and/or income of patients? This OOP amount may be significant but if a patient appreciates the value of service, he/she would be willing to pay depending on his/her availability of finance, before or after the course of treatment.
hardships after treatment. Its modeling using the BCL method is performed by R commands as follows: 4 4 4 4 4 4
burden1¼ read.csv(“E:/…/Med2015/Data/table1.csv,header ¼T) attach(burden1) contrasts(burden1$Res)¼ contr.treatment(levels(burden1$Res),base ¼2) contrasts(burden1$Insured) ¼ contr.treatment(levels(burden1$Insured),base ¼ 2) fit.burden1 ¼vglm(cbind(C,B,A) Res þ Insured,family¼multinomial,data ¼ burden1) summary(fit.burden1)
Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i
Q.-H. Vuong, T.-K. Nguyen / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎
217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 Q5 266 267 268 269 270
5
Fig. 2. Contrasting financial welfare of patients as a function of status of residency and insurance cover/lack of cover.
Note: NN – non-resident & uninsured; NY – non-resident & insured; YN – Resident & uninsured; YY – Resident & insured. Fig. 3. Changing probabilities of destitution for patients as a function of short versus long hospitalization.
The above estimation yields coefficients and associated statistics that are used in [4] for estimating empirical probabilities, using the 330-observation dataset. In addition, Fig. 2 is drawn for the 900observation dataset. The probabilistic trends for patient's financial burdens are in line with [5]. Examples of R commands for creating a specific dataset and corresponding BCL estimations are provided in Appendixes A and B. In the same vein, Fig. 3 shows declining trends for becoming destitute if a patients is either resident or insured, or both.
Acknowledgements We are grateful for logistic and research supports from Hanoi-based Khang Foundation and Vuong & Associates, especially Dam Thu Ha, Nghiem Phu Kien Cuong, Do Thu Hang and Vuong Thu Trang. Appendix A. R commands help create the data set provided in the file named “table2.csv”
Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i
Q.-H. Vuong, T.-K. Nguyen / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎
6
271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 Q6 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 Q7 311 312
4 med ¼read.csv(“E:/…/P330.csv,header ¼ T) 4 attach(med) 4 table2 ¼xtabs( AvgCostþInsuredþBurden) 4 ftable(table2)
Appendix B. R commands using data in “table2.csv” estimated model follow as: 4 4 4 4 4 4
burden2¼ read.csv(“E:/…/table2.csv,header ¼T) attach(burden2) contrasts(burden2$AvgCost) ¼contr.treatment(levels(burden2$AvgCost),base ¼ 2) contrasts(burden2$Insured) ¼ contr.treatment(levels(burden2$Insured),base ¼ 2) fit.burden2 ¼vglm(cbind(C,B,A) AvgCost þInsured,family¼multinomial,data ¼burden2) summary(fit.burden2)
Transparency document. Supporting information Transparency data associated with this article can be found in the online version at http://dx.doi. org/10.1016/j.dib.2016.09.040.
Appendix C. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi. org/10.1016/j.dib.2016.09.040.
References [1] Q.H. Vuong, Data on Vietnamese patients' behavior in using information sources, perceived data sufficiency and (non) optimal choice of healthcare provider, Data Brief 7 (2016) 1687–1695. http://dx.doi.org/10.1016/j.dib.2016.04.066. [2] A. Agresti, Categorical Data Analysis, Wiley, Hoboken, NJ (2002) 774. [3] PennState Science, Analysis of Discrete Data (STAT 504), 〈https://onlinecourses.science.psu.edu/stat504/〉 (accessed 16.10.15). [4] Q.H. Vuong, Be Rich or Don't be Sick: estimating Vietnamese Patients' Risk Falling into Destitution, SpringerPlus (2015) 4. http://dx.doi.org/10.1186/s40064-015-1279-x, Article #529. [5] G. Bloom, Primary Health Care Meets the Market: Lessons from China and Vietnam. IDS Working Paper 53, University of Sussex, UK, 1997.
Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i
1 Contents lists available at ScienceDirect 2 3 4 5 6 journal homepage: www.elsevier.com/locate/dib 7 8 9 Data Article 10 11 12 13 14 a b 15 Q1 Quan-Hoang Vuong , Trong-Khang Nguyen 16 a 17 Q3 School of Business (FSB), FPT University, VAS-FSB Building, Block C, My Dinh 1, Tu Liem District, Hanoi, Vietnam 18 Q2 b 11th Floor, TTC Building, Duy Tan, Cau Giay District, Hanoi, Vietnam 19 20 21 a r t i c l e i n f o abstract 22 23 Article history: The research process started in the first week of August 10, 2014 24 Received 1 June 2016 and ended early February 2015, obtaining qualified data for 330 25 Received in revised form patients from many hospitals in northern Vietnam. Its expansion 19 September 2016 26 was performed for an enlarged dataset through May 2015, conAccepted 24 September 2016 27 taining 900 records. This article exemplifies the attempt to 28 examine the likelihood of destitution among Vietnamese patients 29 due to insufficient insurance coverage, cost of treatment and patient's status of residency during a curative hospital stay. The 30 result suggests that the patients, who are poor and come from 31 rural areas, face serious obstacles in accessing health care services. 32 This data article presents attributes and values of the data set used 33 in the article provided at DOI: http://dx.doi.org/10.1186/s4006434 015-1279-x Vuong (2015) [4]. 35 & 2016 The Authors. Published by Elsevier Inc. This is an open 36 access article under the CC BY license 37 (http://creativecommons.org/licenses/by/4.0/). 38 39 40 41 Specifications Table 42 43 44 Subject area Medical 45 More specific sub- Patient's financial burdens occurring during a curative hospital stay 46 ject area 47 Type of data Table, graph, figure 48 49 50 E-mail addresses: [email protected] (Q.-H. Vuong), [email protected] (T.-K. Nguyen). 51 52 http://dx.doi.org/10.1016/j.dib.2016.09.040 53 2352-3409/& 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license 54 (http://creativecommons.org/licenses/by/4.0/).
Data in Brief
Data on Vietnamese patients' financial burdens and risk of destitution
Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i
Q.-H. Vuong, T.-K. Nguyen / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎
2
55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 Q4 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
How data was acquired Data format Experimental factors Experimental features Data source locations Data accessibility
Survey Raw, filtered, and partially analyzed Raw data obtained from a survey patients at many hospitals in Northern Vietnam The experiment focuses on examining financial issues, illness, insurance, end result of treatment, health care costs, length of stay, ‘envelope OOP’ and the probability of post-treatment destitution, for different groups of patients Viet Duc, Bach Mai, Vietnam-Japan, Hai Duong Polyclinic, Thai Binh Polyclinic Hospitals, Vietnam (and others) Both original datasets for 330 and 900 survey records are provided with this article, and deposited at DOI: http://doi.org/10.17632/rw28hh58y4.1.
Value of the data
The data potentially offer a specific insight on financial destitution due to costs of treatment. The data can help reveal the likelihood of patient's recovery after treatment. The data help to understand the effect of health insurance on reducing the likelihood of falling into destitution.
The data shed light on the risk of “extra thank-you money” becoming an obligatory part of treatment costs for patients, low-income patients in particular.
The data set structure can be enriched for deeper analysis providing insights and implications for bettering policy making in health insurance and poverty reduction.
1. Data The first data set, which will be used for computation examples provided in this article, contains 900 records obtained from a survey on Vietnamese inpatients concerning family status, patient's income level, patient's extra expenses to doctors and hospital's staff, and their loans to finance treatment. Fig. 1 shows the distribution of patients hospitalization lengths. Continuous (numerical) and discrete (categorical) variables are measured and reported in the survey data set. Table 1 presents numerical variables of the data set. Fig. 1a and b present medical expenditures and average daily costs in relation to health status of patients when hospitalized. They are not significantly different from those provided in [4] (Table 2).
2. Experimental design, materials and methods The dataset was constructed using information from questionnaires in order to enable the modeling of baseline-category logits (BCL). In addition, the computing of empirical probabilities upon events of hypothetical influence is performed. The logic for designing the experiment and thus data sets are similar to what is described in [1], for data groups in J categories of Y as multinomial with corresponding sets of probabilities π 1 ðxÞ; …; π j ðxÞ , and with the multinomial probability mass
function: pðn1 ; n2 ; …; nc Þ ¼
n! n1 !n2 !…nc !
π n11 π n22 …π nc c : The data set has been created to enable an BCL
analysis to simultaneously model effects of x on ðJ 1Þ logits such that the estimating of ðJ 1Þ equations enables the computing of the remaining logits. Therefore, Pearson-type likelihood ratio test statistics X 2 ; G2 or goodness-of-fit, following a multivariate GLM estimations g μi ¼ Xi β become appropriate for hypothesis testing. For practically estimating multinomial logistic models, consult with Refs. [2,3]. Practicality of survey data uses is also provided in Ref. [4]. Some possible questions and hypotheses worth testing of, using the data set analyzed by [4], is in Table 3.
Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i
Q.-H. Vuong, T.-K. Nguyen / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎
109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
3
Fig. 1. Status of health in relation to expenditure and average daily cost. (a) Range of expenses for patients with treatment outcome categories (A, B, C or D). (b) Range of daily cost for patients falling into different treatment outcome groups (A, B, C or D).
Table 1 Numerical variables of the data set. Coded name
Explanation
Unit
Spent
Total money spent during his/her stay in hospital in millions of Vietnamese Dong. According to official exchange rates at the time of survey, VND 1 million was equivalent to $47.2. Average daily cost the patient had to pay during the entire treatment period Annual income
Million of VND
Dcost Income Days Pins; Pinc; Pchar; Ploan Streat, Srel, Senv
Number of days the patient spent in the hospital Portions of finance from sources: insurance reimbursement, income, charity funds from civil organizations or employers, or borrowings 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
Million of VND Million of VND Day Percent
Percent
The following short R commands help create the data set provided in the file named “table1.csv” 4 med ¼read.csv(“E:/…/Med2015/Data/P330.csv,header ¼T) 4 attach(med) 4 table1 ¼xtabs( Res þ InsuredþBurden) 4 ftable(table1) The data set in file name “table1.csv” presents a distribution of patients following by residency status, insurance participation, and extent to which patients fall into destitution due to financial Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i
Q.-H. Vuong, T.-K. Nguyen / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎
4
163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
Table 2 Categorical variables of the data set. Coded name
Explanation
Values
Res
Whether the patient originally resides in the region where the hospital is located A dummy variable to define the patient's hospitalization by length of stay Whether a patient has a valid health insurance Socio-economic status
Yes, no
Stay Insured SES
Less than 10 days (S), 10 days or longer (L)
Yes, no High, medium, low Based on average IncRank of working members in the family who are able and willing to support the patient if so required Illness Severity of illness or injury when hospitalized Emergency, bad, ill, light IncRank Rankings of income of a patient High ( 4180), middle (48–180), low ( o48) Excel: ¼ IF([cell]4180,"Hi",IF([cell] 4 48,"Mid","Lo")) Burden Patient's and family's self-evaluation of their strong, no adverse affect at all (A); affected but not the worfinancial position after paying health care costs rying level (B); seriously affected or destitute/bankrupt (C) End Patient's health status after treatment complete recovery (A), partial recovery, needing post-treatment follow-ups (B), stopped whilst being treated (C), or quit early (D) AvgCost Average daily cost the patient had to pay during r 1.5 (Low), 1.5 to 5.4 (Med), and 45.4 (Hi) treatment period
Table 3 Possible research questions arising from the data set. Do the residency status of patients and insurance coverage determine the probability of patients falling into debts? The specific factor of residency status is important in Vietnam because society has for long been skeptical about provincial healthcare, leading patients to travel to large hospitals in major cities such as Hanoi, Hai Phong, or HCMC. Doing so not only necessitates accompanying and caretaking of family members but also entails travel costs and informational asymmetry on drug prices, treatment schedules, the best hospital to visit and even the ‘right amount’ of “extra thank-you money” (a kind of out-of-pocket expense; or OOP). As for two most important factors to Vietnamese patients/households, i.e. treatment costs and illness, is there evidence to support this view and if yes, whose influence better explains the possibility of end results of treatment, empirically? Can the likelihood of paying too little or too much out-of-pocket “extra thankyou money” be determined by the severity of illness and/or income of patients? This OOP amount may be significant but if a patient appreciates the value of service, he/she would be willing to pay depending on his/her availability of finance, before or after the course of treatment.
hardships after treatment. Its modeling using the BCL method is performed by R commands as follows: 4 4 4 4 4 4
burden1¼ read.csv(“E:/…/Med2015/Data/table1.csv,header ¼T) attach(burden1) contrasts(burden1$Res)¼ contr.treatment(levels(burden1$Res),base ¼2) contrasts(burden1$Insured) ¼ contr.treatment(levels(burden1$Insured),base ¼ 2) fit.burden1 ¼vglm(cbind(C,B,A) Res þ Insured,family¼multinomial,data ¼ burden1) summary(fit.burden1)
Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i
Q.-H. Vuong, T.-K. Nguyen / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎
217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 Q5 266 267 268 269 270
5
Fig. 2. Contrasting financial welfare of patients as a function of status of residency and insurance cover/lack of cover.
Note: NN – non-resident & uninsured; NY – non-resident & insured; YN – Resident & uninsured; YY – Resident & insured. Fig. 3. Changing probabilities of destitution for patients as a function of short versus long hospitalization.
The above estimation yields coefficients and associated statistics that are used in [4] for estimating empirical probabilities, using the 330-observation dataset. In addition, Fig. 2 is drawn for the 900observation dataset. The probabilistic trends for patient's financial burdens are in line with [5]. Examples of R commands for creating a specific dataset and corresponding BCL estimations are provided in Appendixes A and B. In the same vein, Fig. 3 shows declining trends for becoming destitute if a patients is either resident or insured, or both.
Acknowledgements We are grateful for logistic and research supports from Hanoi-based Khang Foundation and Vuong & Associates, especially Dam Thu Ha, Nghiem Phu Kien Cuong, Do Thu Hang and Vuong Thu Trang. Appendix A. R commands help create the data set provided in the file named “table2.csv”
Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i
Q.-H. Vuong, T.-K. Nguyen / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎
6
271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 Q6 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 Q7 311 312
4 med ¼read.csv(“E:/…/P330.csv,header ¼ T) 4 attach(med) 4 table2 ¼xtabs( AvgCostþInsuredþBurden) 4 ftable(table2)
Appendix B. R commands using data in “table2.csv” estimated model follow as: 4 4 4 4 4 4
burden2¼ read.csv(“E:/…/table2.csv,header ¼T) attach(burden2) contrasts(burden2$AvgCost) ¼contr.treatment(levels(burden2$AvgCost),base ¼ 2) contrasts(burden2$Insured) ¼ contr.treatment(levels(burden2$Insured),base ¼ 2) fit.burden2 ¼vglm(cbind(C,B,A) AvgCost þInsured,family¼multinomial,data ¼burden2) summary(fit.burden2)
Transparency document. Supporting information Transparency data associated with this article can be found in the online version at http://dx.doi. org/10.1016/j.dib.2016.09.040.
Appendix C. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi. org/10.1016/j.dib.2016.09.040.
References [1] Q.H. Vuong, Data on Vietnamese patients' behavior in using information sources, perceived data sufficiency and (non) optimal choice of healthcare provider, Data Brief 7 (2016) 1687–1695. http://dx.doi.org/10.1016/j.dib.2016.04.066. [2] A. Agresti, Categorical Data Analysis, Wiley, Hoboken, NJ (2002) 774. [3] PennState Science, Analysis of Discrete Data (STAT 504), 〈https://onlinecourses.science.psu.edu/stat504/〉 (accessed 16.10.15). [4] Q.H. Vuong, Be Rich or Don't be Sick: estimating Vietnamese Patients' Risk Falling into Destitution, SpringerPlus (2015) 4. http://dx.doi.org/10.1186/s40064-015-1279-x, Article #529. [5] G. Bloom, Primary Health Care Meets the Market: Lessons from China and Vietnam. IDS Working Paper 53, University of Sussex, UK, 1997.
Please cite this article as: Q.-H. Vuong, T.-K. Nguyen, Data on Vietnamese patients' financial burdens and risk of destitution, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.09.040i