Maternal health care in vietnam demand for antenatal care and choice of delivery care services

UNIVERSITY OF ECONOMICS ERAMUS UNIVERSITY ROTTERDAM HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS MATERNAL HEALTH CARE IN VIETNAM: DEMAND FOR ANTENATAL CARE AND CHOICE OF DELIVERY CARE SERVICES By Nguyen Thi Hoai Trang A Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Art in Development Economics Academic Supervisor: Dr Truong Dang Thuy HO CHI MINH CITY, June 2016 DECLARATION “This is to certify that this thesis entitled “MATERNAL HEALTH CARE IN VIETNAM: DEMAND FOR ANTENATAL CARE AND CHOICE OF DELIVERY CARE SERVICES”, which is submitted by me in fulfillment of the requirements for the degree of Master of Art in Development Economics to the Vietnam – The Netherlands Programme (VNP) The thesis constitutes only my original work and due supervision and acknowledgement have been made in the text to all materials used HCMC, June 06th, 2016 Nguyen Thi Hoai Trang i ACKNOWLEDGEMENT I would like to acknowledge my supervisor, Dr Truong Dang Thuy for his great contribution to my thesis Without his support, my thesis would be not possible By his large knowledge and experiences, he gave me the informative comments and enabled me to understand my work better I would like to express my sincere gratitude to his guidance and encouragement, which make me stronger to overcome the challenges and fulfill my work completely By this chance, I would like to express my appreciation toward all lecturers of the Vietnam – Netherlands Program who have provided with valuable economic knowledge during my study in this program Next, I wish to thank to all my friends here at VNP- MDE 19, who share unforgettable memories in studying together Finally, I would like to express my deep gratitude to my family for their support and endurance when I pursue my postgraduate studies ii ABSTRACT This thesis research aims to analyze the impact of individual characteristics, household characteristic and communities in utilization of maternal health care services in Vietnam Using the latest data of Vietnam’s Multiple Indicator Cluster Survey 2013-2014, it employs the Negative Nominal Model for demand of prenatal care visits and Multinomial Logistic Model for the choice of delivery facility With respect to the demand of prenatal care visits, the result shows that higher education, higher age, exposure to mass media and no religion increase the number of prenatal care visits while higher birth order, unmarried or separated status, ethnicity group and lower household wealth index decrease the number of prenatal care Moreover, living in rural, disadvantaged areas and the community with higher illiteracy rate decrease the demand of prenatal care visits while living in the community with higher proportion of women giving birth at health facilities increase the demand Concerning the choice of delivery facility, more prenatal care visits and exposure to mass media are positively associated with the choice of giving birth at public hospital In contrast, suffering the burden of taking care more children, lower household wealth index, living in rural and the community with higher illiteracy ratio adversely affect the choice of public hospital delivery The results suggest the improvement of maternal health program in rural and underdeveloped areas as well as universal education over the country, especially for the ethnic minority group Keywords: prenatal care visits, the place of childbirth, individual characteristics, household characteristics, community characteristics, Vietnam Contents DECLARATION i ACKNOWLEDGEMENT .ii ABSTRACT iii LIST of TABLES and FIGURES vii ABBREVIATION .viii CHAPTER I INTRODUCTION 1.1 Problem statement 1.2 Research objectives 1.3 Research questions .4 1.4 Structure CHAPTER II LITERATURE REVIEW .5 2.1 The role of maternity health care 2.2 Overview of maternal health and health care in Vietnam 2.2.1 The culture 2.2.2 The two-child policy 2.2.3 Maternal mortality ratio and maternal health care in Vietnam .7 2.3 The demand for health care 11 2.3.1 Theoretical background 11 2.3.2 Empirical Literature Review 13 2.4 The choice of health care provider 19 2.4.1 Theoretical background 19 2.4.2 Empirical literature review 20 CHAPTER III 23 METHODOLOGY AND DATA DESCRIPTION 23 3.1 Conceptual framework 24 3.2 Empirical framework 25 3.2.1 Demand for Prenatal care 26 3.2.2 Choice of birth delivery facility 27 3.3 Data 28 3.4 Variables definition 28 3.4.1 Dependent variables 28 3.4.2 Independent variables .29 RESULTS AND DISCUSSIONS 31 4.1 Descriptive Results 32 4.2 Analysis of Demand for prenatal care 34 4.2.1 Bivariate analysis 34 4.2.2 Analysis of Negative Binomial Model 37 4.3 Analysis of Choice in the delivery care providers 41 4.3.1 Bivariate analysis 41 4.3.2 Analysis of Multinomial Logistic Model 44 CHAPTER V 47 CONCLUSION, RECOMMENDATION and LIMITATION 48 5.1 Main findings .48 5.2 Policy Recommendation 49 5.3 Limitation and Further Research 50 REFERENCE .51 APPENDIX 56 STATA RESULTS 71 LIST of TABLES and FIGURES List of Tables Table 1: Description of Variables 30 Table 2:Descriptive Results – Numeric Variables 33 Table : Descriptive Results - Dummy Variables 33 Table 4: Bivariate analysis in the demand of prenatal care visits 35 Table 5: Negative binomial regression for the demand of prenatal care visits 40 Table : Bivariate analysis in the choice of delivery care providers - numeric independent variables 41 Table 7:Bivariate analysis in the choice of delivery care provider – dummy independent variables 43 Table 8: Multinomial Logistic Regression for the choice of delivery care provider 46 Table 9: Marginal effects for the choice of delivery care provider 47 List of Figures Figure 1: MMR in Vietnam in the period of 2000 – 2015 Figure 2: MMR of the Asian countries in the period of 2000 – 2015 Figure 3: Percentage of women having at least visit and at least visits during pregnancy Figure 4: The percentage of the women taking antenatal care visits by residence in 2011 and 2014 10 Figure 5: The percentage of the women taking antenatal care visits by ethnicity in 2011 and 2014 10 Figure The association between individual level, household level and community level characteristics with the utilization of maternal health care services 25 Figure 7: The association between the demand of maternal care visits and numerical independent variables .37 vii ABBREVIATION ANC Antenatal Care CSDH Commission on Social Determinants on Heath GSO General Statistics Office IMR Infant Mortality Ratio MDGs Millennium Development Goals MICS Multiple indicator cluster survey MMR Maternal Mortality Ratio WHO World Health Organization viii CHAPTER I INTRODUCTION 1.1 Problem statement There is a growing concern about the maternal health care globally, especially in low income countries World Health Organization (WHO 2014) reported that the global maternal mortality ratio (MMR) in 2013 was 210 maternal deaths per 100 000 live births, decreasing from 380 maternal deaths per 100 000 live births in 1990 However, the ratio in developing regions was 14 times higher than in developed regions Even though maternal death is generally decreasing worldwide, it has yet to achieve the target of Millennium Development Goal by reducing the MMR by three quarters between 1990 and 2015 (WHO 2014) The maternal death has direct causes and indirect causes The direct cause results from arising complications during pregnancy, delivery and postpartum, or improper treatment such as hemorrhage, infection, obstructed labor, unsafe abortion, ectopic pregnancy and anesthesiarelated deaths while the indirect cause results from the disease which previously exists or be not due to indirect obstetric causes like hepatitis anemia, malaria, heart disease and tetanus (WHO 2005) It was reported that direct causes made up the higher number of maternal death than indirect causes with 80% of the total MMR (WHO 2005) These complications could be preventable thanks to the intervention of health care such as antenatal care and delivery care, which was introduced by WHO in the safe motherhood package in 1994 (Tran 2012) Antenatal cares provide the opportunities to pregnancy women and their family to be informed of their health and the growth status of unborn baby Low birth weights could be prevented if the pregnant women are well acknowledged about their unborn baby’s weight and height during the antenatal care and then improve their diet In addition, antenatal check-ups detect the danger signs and risks of pregnancy and delivery and make timely interventions For example, tetanus immunization in the antenatal care period is vital to save the life of the women and their baby The management of high blood pressure during pregnancy ensures the maternal health and increase the infant survival (WHO and UNICEF 2003) Furthermore, delivery care also plays an important role in reducing maternal deaths WHO recommended the child birth at health facility or attended by skilled health staffs to ensure to the safe delivery and give birth to healthy baby With good hygiene and adequate medical equipment, the delivery at facility could decrease the complications arising from the RECODE of MN18 (Place of delivery) 129 1,060 48 1,237 % 94.85 82.81 76.19 83.64 220 15 242 % 5.15 17.19 23.81 16.36 Total 136 1,280 63 1,479 % 100 100 100 100 SE Total RECODE of MN18 (Place of delivery) MD Total 135 1,097 45 1,277 % 99.26 85.7 71.43 86.34 1 183 18 202 % 0.74 14.3 28.57 13.66 Total 136 1,280 63 1,479 % 100 100 100 100 Appendix 4: The association between the demand of maternal care visits and independent variables Variable Obs Mean Std Dev Min Max NOEDU = ANC 1390 5.981295 3.655771 36 NOEDU = ANC 89 1.629213 2.186643 10 PRIMARY =0 ANC 1280 5.958594 3.773784 36 PRIMARY =1 ANC 199 4.180905 3.023049 16 LOWSECOND = ANC 969 5.932921 3.750019 30 LOWSECOND = ANC 510 5.313725 3.662534 36 UPSECOND = ANC 1133 5.57105 3.877033 36 UPSECOND = ANC 346 6.205202 3.160641 20 TERTIARY = ANC 1144 5.09965 3.523529 36 TERTIARY = ANC 335 7.835821 3.64518 30 MARITAL = ANC 1439 5.772064 3.738053 36 MARITAL = ANC 40 3.825 2.899049 10 UNWANTED = ANC 1216 5.915296 3.804727 36 UNWANTED = ANC 263 4.813688 3.2208 20 WORKING = ANC 259 5.826255 3.758804 36 WORKING = ANC 1220 5.696721 3.725595 30 MOBIPHONE = ANC 1075 5.11814 3.645614 36 MOBIPHONE = ANC 404 7.319307 3.476903 28 NEWSPAPER = ANC 1194 5.161642 3.590502 36 NEWSPAPER = ANC 285 8.05614 3.388621 28 RADIO = ANC 1299 5.665897 3.768841 36 RADIO = ANC 180 6.105556 3.425989 28 95 Appendix 4: The association between the demand of maternal care visits and independent variables (continued) Variable Obs Mean Std Dev Min Max TV=0 ANC 245 4.285714 4.08014 28 TV=1 ANC 1234 6.004052 3.59135 36 POOR = ANC 856 7.211449 3.694904 36 POOR = ANC 623 3.669342 2.65118 18 ETHNIC = ANC 1129 6.582817 3.594286 36 ETHNIC = ANC 350 2.934286 2.643849 20 NORELI=0 ANC 377 5.339523 3.613119 25 NORELI=1 ANC 1102 5.849365 3.762596 36 RURAL = ANC 556 7.106115 3.757692 30 RURAL = ANC 923 4.884074 3.456986 36 RRD = ANC 1253 5.496409 3.666753 36 RRD = ANC 226 6.955752 3.845666 30 NM = ANC 1199 6.030859 3.648571 36 NM = ANC 280 4.385714 3.789413 30 NC = ANC 1258 5.767886 3.869204 36 NC = ANC 221 5.443439 2.804658 18 CH = ANC 1171 6.098207 3.761899 36 CH = ANC 308 4.279221 3.230548 20 SE = ANC 1237 5.318513 3.668561 36 SE = ANC 242 7.768595 3.358918 20 MD = ANC 1277 5.638998 3.702767 30 MD = ANC 202 6.227723 3.872036 36 Appendix 5: The association between the demand of maternal care visits and numerical independent variables 0 10 Number of HH members 15 0 10 20 30 Age of woman 40 50 0 Children ever born 10 0 20 40 POVERTY 60 80 100 0 20 40 ILLITERACY 60 80 0 20 40 hospdeliratio 60 80 100 STATA RESULTS Appendix 6: Negative Binomial Regression with test for alpha nbreg ANC AGE NOEDU PRIMARY LOWSECOND UPSECOND TERTIARY MOBIPHONE NEWSPAPER RADIO TV MARITAL UNWANTED WORKING CEB HHSIZ > E POOR ETHNIC NORELI RURAL POVERTY ILLITERACY hospdeliratio RRD NM NC CH SE MD note: TERTIARY omitted because of collinearity note: MD omitted because of collinearity Fitting Poisson model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood = -3448.3122 = -3444.684 = -3444.6797 = -3444.6797 Fitting constant-only model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood = = = = -4180.5339 -3888.5482 -3888.4566 -3888.4566 likelihood likelihood likelihood likelihood likelihood = = = = = -3535.4462 -3424.8311 -3401.3536 -3400.8474 -3400.8471 Fitting full model: Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log Negative binomial regression Number of obs LR chi2(26) Prob > chi2 Pseudo R2 Dispersion = mean Log likelihood = -3400.8471 ANC Coef AGE NOEDU PRIMARY LOWSECOND UPSECOND TERTIARY MOBIPHONE NEWSPAPER RADIO TV MARITAL UNWANTED WORKING CEB HHSIZE POOR ETHNIC NORELI RURAL POVERTY ILLITERACY hospdeliratio RRD NM NC CH SE MD _cons 0160117 -.3900604 -.1601858 -.0984853 -.0542757 0468756 0745407 -.062405 -.0139186 -.2601115 -.0616706 -.0220105 -.1043196 -.0024364 -.2171986 -.1165967 0612898 -.048201 -.0006953 -.009919 0060119 -.1063645 -.1484127 -.2265449 -.1575368 0545024 1.267123 Std Err .0031144 1086435 0548148 0395761 0377538 (omitted) 0312068 0358181 0384391 0394659 0923302 0377641 0345044 0213732 0066826 0417474 0504541 031688 0318918 0007952 0021059 001244 0505048 052316 0486083 0488774 0478909 (omitted) 1726739 z P>|z| = = = = 1479 975.22 0.0000 0.1254 [95% Conf Interval] 5.14 -3.59 -2.92 -2.49 -1.44 0.000 0.000 0.003 0.013 0.151 0099076 -.6029978 -.2676209 -.176053 -.1282719 0221157 -.1771231 -.0527507 -.0209175 0197204 1.50 2.08 -1.62 -0.35 -2.82 -1.63 -0.64 -4.88 -0.36 -5.20 -2.31 1.93 -1.51 -0.87 -4.71 4.83 -2.11 -2.84 -4.66 -3.22 1.14 0.133 0.037 0.104 0.724 0.005 0.102 0.524 0.000 0.715 0.000 0.021 0.053 0.131 0.382 0.000 0.000 0.035 0.005 0.000 0.001 0.255 -.0142886 0043386 -.1377443 -.0912703 -.4410754 -.1356869 -.089638 -.1462104 -.0155341 -.299022 -.2154848 -.0008176 -.1107078 -.002254 -.0140465 0035737 -.2053521 -.2509501 -.3218155 -.2533348 -.0393621 1080399 1447429 0129343 0634332 -.0791476 0123457 045617 -.0624289 0106613 -.1353753 -.0177085 1233972 0143059 0008633 -.0057915 00845 -.0073769 -.0458752 -.1312744 -.0617388 1483669 7.34 0.000 9286879 1.605557 /lnalpha -2.88871 1393498 -3.161831 -2.61559 alpha 0556479 0077545 0423481 0731246 Likelihood-ratio test of alpha=0: chibar2(01) = 87.67 Prob>=chibar2 = 0.000 Negative Binomial Regression with robust nbreg ANC AGE NOEDU PRIMARY LOWSECOND UPSECOND TERTIARY MOBIPHONE NEWSPAPER RADIO TV MARITAL UNWANTED WORKING CEB HHSIZ > E POOR ETHNIC NORELI RURAL POVERTY ILLITERACY hospdeliratio RRD NM NC CH SE MD, robust note: TERTIARY omitted because of collinearity note: MD omitted because of collinearity Fitting Poisson model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood = -3448.3122 = -3444.684 = -3444.6797 = -3444.6797 Fitting constant-only model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood = = = = -4180.5339 -3888.5482 -3888.4566 -3888.4566 Fitting full model: Iteration 0: log pseudolikelihood = -3535.4462 Iteration Iteration Iteration Iteration log log log log 1: 2: 3: 4: pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood = = = = -3424.8311 -3401.3536 -3400.8474 -3400.8471 Negative binomial regression Dispersion = mean Number of obs Wald chi2(26) = = 1479 883.37 Log pseudolikelihood = -3400.8471 Prob > chi2 = 0.0000 Robust Std Err ANC Coef z P>|z| [95% Conf Interval] AGE NOEDU PRIMARY 0160117 -.3900604 -.1601858 0035153 1215797 0534887 4.55 -3.21 -2.99 0.000 0.001 0.003 0091217 -.6283523 -.2650217 0229016 -.1517685 -.0553498 LOWSECOND -.0984853 0418866 -2.35 0.019 -.1805815 -.016389 UPSECOND TERTIARY -.0542757 0366175 (omitted) -1.48 0.138 -.1260447 0174932 MOBIPHONE NEWSPAPER RADIO 0468756 0745407 -.062405 0303556 034722 0393799 1.54 2.15 -1.58 0.123 0.032 0.113 -.0126202 0064869 -.1395882 1063715 1425946 0147781 TV MARITAL -.0139186 -.2601115 0463241 1057703 -0.30 -2.46 0.764 0.014 -.1047122 -.4674175 0768751 -.0528056 UNWANTED WORKING -.0616706 -.0220105 0380513 0385014 -1.62 -0.57 0.105 0.568 -.1362497 -.0974718 0129085 0534508 CEB -.1043196 0216745 -4.81 0.000 -.1468009 -.0618384 HHSIZE POOR ETHNIC -.0024364 -.2171986 -.1165967 0064648 0469517 0521578 -0.38 -4.63 -2.24 0.706 0.000 0.025 -.0151072 -.3092222 -.218824 0102344 -.1251751 -.0143693 NORELI RURAL 0612898 -.048201 0326458 0331721 1.88 -1.45 0.060 0.146 -.0026948 -.113217 1252744 0168151 POVERTY ILLITERACY -.0006953 -.009919 0008107 0023879 -0.86 -4.15 0.391 0.000 -.0022843 -.0145992 0008936 -.0052388 hospdeliratio 0060119 0014543 4.13 0.000 0031615 0088623 RRD NM -.1063645 -.1484127 0542513 0586204 -1.96 -2.53 0.050 0.011 -.212695 -.2633065 -.000034 -.0335188 NC CH SE -.2265449 -.1575368 0545024 0494113 0531675 0496652 -4.58 -2.96 1.10 0.000 0.003 0.272 -.3233892 -.2617433 -.0428396 -.1297006 -.0533303 1518444 6.52 0.000 885958 1.648287 MD (omitted) _cons 1.267123 1944753 /lnalpha -2.88871 2242637 -3.328259 -2.449162 alpha 0556479 0124798 0358555 086366 10 Marginal effect mfx Marginal effects after nbreg y = Predicted number of events (predict) = 5.0515799 variable AGE NOEDU* PRIMARY* LOWSEC~D* UPSECOND* MOBIPH~E* NEWSPA~R* RADIO* TV* MARITAL* UNWANTED* WORKING* CEB HHSIZE POOR* ETHNIC* NORELI* RURAL* POVERTY ILLITE~Y hospde~o RRD* NM* NC* CH* SE* dy/dx 0808842 -1.670329 -.7639974 -.490159 -.2702806 2393492 3853626 -.307939 -.0706393 -1.165153 -.3054536 -.111988 -.526979 -.0123077 -1.080706 -.5715099 3050419 -.2449753 -.0035125 -.0501067 0303695 -.5180725 -.7165916 -1.059297 -.7608472 2804522 Std Err .01791 43445 24053 20498 17973 15673 18374 1898 23625 41725 18475 19738 10892 03265 23102 2478 16018 16931 0041 01199 00729 25484 26996 21455 24568 26002 z 4.52 -3.84 -3.18 -2.39 -1.50 1.53 2.10 -1.62 -0.30 -2.79 -1.65 -0.57 -4.84 -0.38 -4.68 -2.31 1.90 -1.45 -0.86 -4.18 4.16 -2.03 -2.65 -4.94 -3.10 1.08 P>|z| [ 95% C.I 0.000 0.000 0.001 0.017 0.133 0.127 0.036 0.105 0.765 0.005 0.098 0.570 0.000 0.706 0.000 0.021 0.057 0.148 0.392 0.000 0.000 0.042 0.008 0.000 0.002 0.281 045774 -2.52184 -1.23543 -.891903 -.622539 -.067832 025243 -.679945 -.533684 -1.98295 -.667562 -.49885 -.740463 -.076309 -1.5335 -1.05719 -.008902 -.576808 -.011548 -.073606 016073 -1.01755 -1.2457 -1.47981 -1.24237 -.229187 ] 115994 -.818817 -.292565 -.088415 081978 546531 745482 064067 392405 -.347358 056655 274874 -.313495 051694 -.62791 -.085831 618986 086857 004523 -.026608 044666 -.018598 -.187479 -.638784 -.279329 790091 (*) dy/dx is for discrete change of dummy variable from to X 27.5842 060176 13455 344828 233942 273158 192698 121704 834348 027045 177823 824882 1.84314 5.75321 421231 236646 745098 62407 39.9499 7.60748 90.8046 152806 189317 149425 208249 163624 Appendix 7: Multinominal Logistics Reression mlogit DELIVERY ANC AGE NOEDU PRIMARY LOWSECOND UPSECOND TERTIARY MOBIPHONE NEWSPAPER RADIO TV MARITAL UNWANTED WORKING > CEB HHSIZE POOR ETHNIC NORELI RURAL POVERTY ILLITERACY RRD NM NC CH SE MD, robust note: TERTIARY omitted because of collinearity note: MD omitted because of collinearity Iteration 0: log pseudolikelihood = -708.35443 Iteration 1: log pseudolikelihood = -528.53357 Iteration 2: log pseudolikelihood = -432.99207 Iteration 3: log pseudolikelihood = -392.62785 Iteration 4: log pseudolikelihood = -387.30586 Iteration 5: log pseudolikelihood = -386.64721 Iteration 6: log pseudolikelihood = -386.51174 Iteration 7: log pseudolikelihood = -386.48639 Iteration 8: log pseudolikelihood = -386.48093 Iteration 9: log pseudolikelihood = -386.47959 Iteration 10: log pseudolikelihood = -386.47931 Iteration 11: log pseudolikelihood = -386.47925 Iteration 12: log pseudolikelihood = -386.47924 Multinomial logistic regression Number of obs Wald chi2(52) Prob > chi2 Pseudo R2 Log pseudolikelihood = -386.47924 DELIVERY Coef ANC AGE NOEDU PRIMARY LOWSECOND UPSECOND TERTIARY MOBIPHONE NEWSPAPER RADIO TV MARITAL UNWANTED WORKING CEB HHSIZE POOR ETHNIC NORELI RURAL POVERTY ILLITERACY RRD NM NC CH SE MD _cons -.5126496 -.0133922 87926 4332457 -.5065291 -.6681007 -1.357971 -12.20788 -1.21934 -.4421614 -.576598 1253515 5073354 3736658 -.0792065 1.953048 1.356612 3058496 8734962 -.0149591 0260589 1.524815 1.826784 1.868156 1.941388 1.428129 -4.958635 Robust Std Err z P>|z| = = = = 1479 17850.06 0.0000 0.4544 [95% Conf Interval] 1055184 0312854 835032 7668838 7948502 8944219 (omitted) 1.10213 6549611 6885448 3146755 6680064 4122238 4417912 1739643 0568956 8851309 5212304 3584633 4848745 0122396 009765 1.132244 9758699 1.075765 9550212 9987133 (omitted) 1.939714 (base outcome) -4.86 -0.43 1.05 0.56 -0.64 -0.75 0.000 0.669 0.292 0.572 0.524 0.455 -.7194618 -.0747105 -.7573726 -1.069819 -2.064407 -2.421135 -.3058374 047926 2.515893 1.93631 1.051349 1.084934 -1.23 -18.64 -1.77 -1.41 -0.86 0.30 1.15 2.15 -1.39 2.21 2.60 0.85 1.80 -1.22 2.67 1.35 1.87 1.74 2.03 1.43 0.218 0.000 0.077 0.160 0.388 0.761 0.251 0.032 0.164 0.027 0.009 0.394 0.072 0.222 0.008 0.178 0.061 0.082 0.042 0.153 -3.518106 -13.49158 -2.568863 -1.058914 -1.885867 -.6825923 -.3585595 0327021 -.1907197 2182228 335019 -.3967256 -.0768403 -.0389482 0069199 -.6943431 -.0858863 -.2403044 0695805 -.5293126 8021644 -10.92418 1301825 1745913 7326705 9332953 1.37323 7146296 0323068 3.687872 2.378205 1.008425 1.823833 00903 045198 3.743973 3.739453 3.976616 3.813195 3.385571 -2.56 0.011 -8.760404 -1.156866 Multinominal Logistics Reression (continued) ANC AGE NOEDU PRIMARY LOWSECOND UPSECOND TERTIARY MOBIPHONE NEWSPAPER RADIO TV MARITAL UNWANTED WORKING CEB HHSIZE POOR ETHNIC NORELI RURAL POVERTY ILLITERACY RRD NM NC CH SE MD _cons 0435657 -.032046 5487294 -.5984313 -.7018067 -.269463 1971021 0331622 0936408 -.3248382 -.474224 -.0265002 -.5619982 2349459 -.0421632 -.9170955 -.1205223 4736594 -.6398772 007296 0046362 -2.097738 -17.7778 -.9678047 -.4488791 -.7927254 -.7252088 0329205 0336321 8578957 5340849 3990452 358309 (omitted) 3263564 3419306 3829845 4126861 1.093957 3924757 3171955 2036672 0719171 4792335 4615013 3281441 2979514 0083815 0187234 5949775 3461364 425129 3843864 4301356 (omitted) 1.326519 1.32 -0.95 0.64 -1.12 -1.76 -0.75 0.186 0.341 0.522 0.263 0.079 0.452 -.0209573 -.0979637 -1.132715 -1.645218 -1.483921 -.9717358 1080886 0338716 2.230174 4483558 0803074 4328098 0.60 0.10 0.24 -0.79 -0.43 -0.07 -1.77 1.15 -0.59 -1.91 -0.26 1.44 -2.15 0.87 0.25 -3.53 -51.36 -2.28 -1.17 -1.84 0.546 0.923 0.807 0.431 0.665 0.946 0.076 0.249 0.558 0.056 0.794 0.149 0.032 0.384 0.804 0.000 0.000 0.023 0.243 0.065 -.4425446 -.6370095 -.656995 -1.133688 -2.61834 -.7957384 -1.18369 -.1642344 -.1831181 -1.856376 -1.025048 -.1694912 -1.223851 -.0091314 -.0320609 -3.263873 -18.45622 -1.801042 -1.202263 -1.635776 8367488 7033339 8442766 4840116 1.669892 742738 0596934 6341262 0987916 022185 7840037 1.11681 -.0559031 0237235 0413334 -.9316039 -17.09939 -.1345672 3045045 0503249 -0.55 0.585 -3.325139 1.874721 Multinominal Logistics Reression – Marginal effect mfx, predict (p outcome(1)) Marginal effects after mlogit y = Pr(DELIVERY==1) (predict, p outcome(1)) = 00031878 variable ANC AGE NOEDU* PRIMARY* LOWSEC~D* UPSECOND* MOBIPH~E* NEWSPA~R* RADIO* TV* MARITAL* UNWANTED* WORKING* CEB HHSIZE POOR* ETHNIC* NORELI* RURAL* POVERTY ILLITE~Y RRD* NM* NC* CH* SE* dy/dx -.0001634 -4.25e-06 0004249 0001634 -.0001505 -.0001815 -.0003431 -.0033406 -.0002605 -.0001644 -.0001417 0000416 0001389 000119 -.0000252 0008473 0006663 0000906 0002581 -4.77e-06 8.30e-06 0009084 001187 0013201 0012689 0008003 Std Err .00006 00001 00062 00035 00023 00021 00022 00174 00014 00015 00014 00014 00011 00008 00002 00058 00042 0001 00018 00000 00001 00113 00124 0015 00129 00096 z -2.61 -0.41 0.68 0.47 -0.65 -0.86 -1.54 -1.92 -1.81 -1.10 -1.01 0.29 1.26 1.57 -1.20 1.47 1.58 0.88 1.45 -1.03 1.65 0.80 0.96 0.88 0.98 0.84 P>|z| [ 95% C.I 0.009 0.684 0.494 0.637 0.514 0.388 0.123 0.054 0.070 0.271 0.311 0.769 0.208 0.117 0.230 0.141 0.114 0.378 0.146 0.304 0.098 0.423 0.337 0.378 0.327 0.402 -.000286 -.000041 -.000025 000016 -.000791 001641 -.000514 000841 -.000602 000301 -.000594 000231 -.000779 000093 -.006742 000061 -.000542 000021 -.000457 000128 -.000416 000133 -.000236 000319 -.000077 000355 -.00003 000268 -.000066 000016 -.000281 001975 -.00016 001492 -.000111 000292 -.00009 000606 -.000014 4.3e-06 -1.5e-06 000018 -.001316 003132 -.001237 003611 -.001612 004253 -.001268 003806 -.001072 002672 (*) dy/dx is for discrete change of dummy variable from to mfx, predict (p outcome(2)) ] X 5.71941 27.5842 060176 13455 344828 233942 273158 192698 121704 834348 027045 177823 824882 1.84314 5.75321 421231 236646 745098 62407 39.9499 7.60748 152806 189317 149425 208249 163624 mfx, predict (p outcome(2)) Marginal effects after mlogit y = Pr(DELIVERY==2) (predict, p outcome(2)) = 99800811 variable ANC AGE NOEDU* PRIMARY* LOWSEC~D* UPSECOND* MOBIPH~E* NEWSPA~R* RADIO* TV* MARITAL* UNWANTED* WORKING* CEB HHSIZE POOR* ETHNIC* NORELI* RURAL* POVERTY ILLITE~Y RRD* NM* NC* CH* SE* dy/dx 0000904 0000578 -.0016039 0006525 0012234 0006015 -2.26e-06 0032791 000098 0007713 0007809 2.32e-06 0010022 -.0005112 0000956 0006292 -.0004701 -.0008017 0009188 -7.42e-06 -.000016 0011125 0450928 -.0001213 -.0006039 000242 Std Err .00008 00006 00244 00069 00063 00057 00063 00183 0007 00088 00118 00067 00079 00036 00012 00095 00085 00047 00062 00001 00003 0012 00761 00155 0014 00107 z 1.10 1.01 -0.66 0.94 1.94 1.06 -0.00 1.79 0.14 0.88 0.66 0.00 1.27 -1.42 0.78 0.66 -0.56 -1.71 1.47 -0.50 -0.50 0.93 5.92 -0.08 -0.43 0.23 P>|z| [ 95% C.I 0.272 0.313 0.510 0.347 0.052 0.287 0.997 0.073 0.889 0.381 0.508 0.997 0.202 0.156 0.433 0.510 0.578 0.087 0.141 0.614 0.614 0.355 0.000 0.938 0.666 0.821 -.000071 -.000054 -.006377 -.000708 -.000013 -.000507 -.001242 -.000306 -.00128 -.000956 -.001532 -.001303 -.000539 -.001217 -.000144 -.001242 -.002128 -.001721 -.000304 -.000036 -.000078 -.001244 030173 -.003159 -.003343 -.001854 ] 000251 00017 00317 002013 00246 00171 001238 006864 001476 002498 003094 001308 002543 000194 000335 0025 001188 000117 002142 000021 000046 003469 060013 002916 002135 002338 (*) dy/dx is for discrete change of dummy variable from to X 5.71941 27.5842 060176 13455 344828 233942 273158 192698 121704 834348 027045 177823 824882 1.84314 5.75321 421231 236646 745098 62407 39.9499 7.60748 152806 189317 149425 208249 163624 mfx, predict (p outcome(3)) Marginal effects after mlogit y = Pr(DELIVERY==3) (predict, p outcome(3)) = 00167311 variable ANC AGE NOEDU* PRIMARY* LOWSEC~D* UPSECOND* MOBIPH~E* NEWSPA~R* RADIO* TV* MARITAL* UNWANTED* WORKING* CEB HHSIZE POOR* ETHNIC* NORELI* RURAL* POVERTY ILLITE~Y RRD* NM* NC* CH* SE* dy/dx 000073 -.0000535 001179 -.0008159 -.0010729 -.00042 0003454 0000615 0001626 -.0006069 -.0006391 -.000044 -.001141 0003922 -.0000704 -.0014765 -.0001962 0007111 -.0011769 0000122 7.73e-06 -.0020209 -.0462798 -.0011988 -.0006651 -.0010423 Std Err .00005 00006 00235 00059 00058 00052 00059 00058 00069 00086 00116 00065 00077 00035 00012 00076 00072 00046 00059 00001 00003 00038 00749 00038 0005 00045 z 1.37 -0.96 0.50 -1.38 -1.84 -0.81 0.58 0.11 0.24 -0.70 -0.55 -0.07 -1.47 1.13 -0.59 -1.93 -0.27 1.56 -1.99 0.88 0.25 -5.32 -6.17 -3.12 -1.33 -2.30 P>|z| [ 95% C.I 0.171 0.338 0.615 0.169 0.065 0.421 0.561 0.916 0.813 0.481 0.582 0.946 0.141 0.259 0.556 0.054 0.786 0.118 0.047 0.381 0.805 0.000 0.000 0.002 0.183 0.022 -.000032 -.000163 -.003418 -.001978 -.002213 -.001442 -.000819 -.00108 -.001183 -.002296 -.002918 -.001309 -.00266 -.000288 -.000305 -.002976 -.00161 -.000181 -.002336 -.000015 -.000054 -.002765 -.060969 -.001951 -.001644 -.001932 000178 000056 005776 000346 000067 000602 00151 001203 001508 001082 001639 001221 000378 001073 000164 000023 001217 001603 -.000017 000039 000069 -.001277 -.03159 -.000446 000314 -.000153 (*) dy/dx is for discrete change of dummy variable from to ] X 5.71941 27.5842 060176 13455 344828 233942 273158 192698 121704 834348 027045 177823 824882 1.84314 5.75321 421231 236646 745098 62407 39.9499 7.60748 152806 189317 149425 208249 163624 ... ? ?MATERNAL HEALTH CARE IN VIETNAM: DEMAND FOR ANTENATAL CARE AND CHOICE OF DELIVERY CARE SERVICES? ??, which is submitted by me in fulfillment of the requirements for the degree of Master of Art in. .. maternal health care in Vietnam The next part is to present the theoretical background for the demand for health care services, and the choice of health care facility and their determinants The final... review and empirical review regarding the demand for prenatal care visits and the choice of facility for delivery The first part is to provide the role of maternal health care and the overview of maternal