International Journal of Data Envelopment Analysis and *Operations Research*

Current Issue» Volume 1, Number 3 (2014)

Article

Statistical Modeling and Analysis of Mother-To-Child Transmission of HIV: A Case Study in Referral Hospital and Health Center of Hawassa Town, South Nation Nationality People, Ethiopia

1Department of Mathematics and Statistics, Wollo University, Kombolcha, Ethiopia

2Department of Chemical Engineering, Wollo University, Kombolcha, Ethiopia


International Journal of Data Envelopment Analysis and *Operations Research*. 2014, 1(3), 49-52
DOI: 10.12691/ijdeaor-1-3-2
Copyright © 2014 Science and Education Publishing

Cite this paper:
Helen Moges Fentaw, Anteneh Worku, Omprakash Sahu. Statistical Modeling and Analysis of Mother-To-Child Transmission of HIV: A Case Study in Referral Hospital and Health Center of Hawassa Town, South Nation Nationality People, Ethiopia. International Journal of Data Envelopment Analysis and *Operations Research*. 2014; 1(3):49-52. doi: 10.12691/ijdeaor-1-3-2.

Correspondence to: Omprakash  Sahu, Department of Chemical Engineering, Wollo University, Kombolcha, Ethiopia. Email: ops0121@gmail.com

Abstract

Mother-to-Child Transmission of HIV (MTCT) is the major source of HIV infection among children under the age of 15 years. Within the prevention programs, package of services including HIV counseling and testing, provision of prophylactic antiretroviral (ARV) drugs for mothers and babies, safe delivery practices and infant feeding counseling is being given. This study is thus to model mother-to-child transmission of HIV and survival of HIV infected babies. The data were obtained from Health Centre and Referral Hospital at Hawassa town recorded from year 1999-2001. Bayesian logistic regression, Kaplan Meier method & Cox proportional hazards model are applied. The results of the analysis showed that among the 200 children who HIV positive 16% died were. Another finding is that about 97.3% of the total pregnant women were married and 41.3% of them were illiterate. Among 802 mothers, 97 or 12.1% are HIV positive. From the 97 HIV positive pregnant women, a majority (92.8%) had good knowledge about HIV transmission and AIDS disease. However 94.9% of them had very low knowledge about the prophylaxis treatment and ART medication. The variables age, marital status and occupation are the main significant factors that may expose the mothers to HIV infection. Pregnant women who were married and in elementary occupation were more likely to be infected than those who were unmarried and in higher occupation. Moreover, analysis of the children data showed that variables low weight, low CD4 count of children and interaction of both with age decrease the hazard of time to death by 15%, 2% and 0% respectively. Conclusion considering the prevailing high level of HIV infection rate among MTCT clients, re-organization of the PMTCT services especially raising the level of awareness of MTCT/PMTCT among pregnant women is important to prevent the babies from HIV infection.

Keywords

References

[[[[[[[[[[[[[[[[[
[1]  USAID, 2009. Monitoring HIV/AIDS Programs: A Facilitator’s Training Guide, A USAID; Resource for Prevention, Care and Treatment.
 
[2]  HAPCO (2006). Report on Progress towards Implementation of the Declaration of Commitment on HIV/AIDS HAPCO, HIV/AIDS prevention and control.
 
[3]  MHO/HAPCO (2007). Guidelines for Prevention of Mother-to-Child Transmission of HIV In Ethiopia.
 
[4]  Chiang W, Stranix-Chibanda WL, 2007. Routine offer of antenatal HIV testing ("opt-out" approach) to prevent mother-to-child transmission of HIV in Urban Zimbabwe." Bulletin of the World Health Organization 85 (11): 843-50.
 
[5]  Central Statistical Authority (CSA), 2006. The 2006 National statistics; Social statstics, Central Statstical Agency Ethiopia.
 
Show More References
[6]  Central Statistical Authority (CSA), 2007. Ethiopia Demographic and health survey 2005. C. Central Statistical Agency of Ethiopia.
 
[7]  Alemnesh, EC, 2008. Utilization of PMTCT services in Hawassa town, Ethiopia.M.Sc. Centre for International Health Faculty of Medicine and Dentistry University of Bergen, Norway.
 
[8]  CDC 1985. Current Trends Recommendations for Assisting in the Prevention of Perinatal Transmission of Human T-Lymphotropic Virus Type II/Lymphadenopathy- Associated Virus and Acquired Immunodeficiency Syndrome " MMWR 34 (48).
 
[9]  Centre for Disease Control 2005. Revised Recommendations for HIV Testing of Adults, Adolescents, a Pregnant Women in Health-Care Settings. MMWR 50 (RR-19).
 
[10]  Bhat K, Buwn C, 2003. Barriers to the implementation of programs for the prevention of mother-to-child transmission of HIV: a cross-sectional survey in rural and urban Uganda. AIDS research and therapy 2: 10.
 
[11]  Baggley MT, 2002. Ensuring a public health impact of programs to reduce HIV transmission from mothers to infants: the place of voluntary counseling and testing. American journal of public health 92 (3): 347-51.
 
[12]  Essex W, Draper B, Temmerman M, 2002. Implementation of single-dose nevirapine for prevention of MTCT of HIV--lessons from Cape Town. South African medical journal 96 (8): 706-708.
 
[13]  Abashawl A, Lulseged S, Awano T, Adamu R, Kumbi S, Isehak A, Coberly J, Bedri A, Sweat M, Ruff A, 2004. Breastfeeding (BF) practices of HIV seropositive women in PMTCT project. The XV International AIDS Conference. Bangkok.
 
[14]  Berhanu, EC, 2009. Survival Analysis of time to treatment resumption for chronic HIV-1 Patients interrupting Highly Active Antiretroviral Therapy (HAART).
 
[15]  Foster C, Lyall H, 2005. Current guidelines for the management of UK Infants born to HIV-1 infected mothers. Early human development 81 (1): 103-110.
 
[16]  Malyuta R, Newell L, Thorne M, Zhilka NC, 2006. Prevention of mother-to-child transmission of HIV infection: Ukraine experience to date." The European journal of public health 16 (2): 123-127.
 
[17]  Garbus L, 2003. AIDS in Ethiopia,. Country AIDS policy analysis project, University of California San Francisco, AIDS research institute and AIDS policy research center.
 
[18]  John M, 2007. Access to appropriate information on HIV is important in maximizing the acceptance of the antenatal HIV antibody test. AIDS Care 17 (2): 145-146.
 
[19]  Dabis S, 2000. Prevention of mother-to-child transmission of HIV In developing countries: recommendations for practice. The Ghent International Working Group on Mother-To-Child Transmission of HIV." Health policy and Planning 15 (1): 34-42.
 
[20]  Khayelitsha SA, 2002. National program for preventing mother-child HIV transmission in Thailand: successful implementation and lessons learned. AIDS 16 (7): 953-959.
 
[21]  Kourtis P, Lee K, 2006. Mother-to-child transmission of HIV-1: timing and implications for prevention. The Lancet infectious diseases 6 (11): 726-32.
 
[22]  Lewis A, 2001. Determinants of vct uptake among pregnant women attending two ANC clinics in Addis Ababa City: unmatched case control study. Ethiopian Medical Journal 45 (4): 335-42.
 
Show Less References

Article

Global Economic Crisis and Productivity Changes of Banks in India: A DEA-MPI Analysis

1Department of Statistics, Madras Christian College, Chennai, Tamil Nadu, India


International Journal of Data Envelopment Analysis and *Operations Research*. 2014, 1(3), 40-48
DOI: 10.12691/ijdeaor-1-3-1
Copyright © 2014 Science and Education Publishing

Cite this paper:
R. Madhanagopal, R. Chandrasekaran. Global Economic Crisis and Productivity Changes of Banks in India: A DEA-MPI Analysis. International Journal of Data Envelopment Analysis and *Operations Research*. 2014; 1(3):40-48. doi: 10.12691/ijdeaor-1-3-1.

Correspondence to: R.  Madhanagopal, Department of Statistics, Madras Christian College, Chennai, Tamil Nadu, India. Email: madhan.stat@gmail.com

Abstract

The present study explores the relationship between Global economic crisis (GEC) and productivity growth of Indian banking sector using data envelopment analysis based malmquist index (DEA-MI) for the study period 2005 to 2012, which are partition into three different period viz., pre-crisis, crisis and post-crisis. The empirical result showed that total factor productivity (TFP) for pre and crisis regressed by 7 and 0.6% respectively and post by a slight progress of 0.3%. Comparing technical and technological efficiency changes over the study periods, during pre-crisis, improvements in productivity of Indian banking sector was influenced by technological innovation whereas it went down and technical efficiency influenced the productivity in crisis and post-crisis periods. This may be due to effect of economic crisis and banks would have struggled for survival and hard to concentrate on new technological innovations.

Keywords

References

[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[1]  Kumar, R; Vashisht, P., The global economic crisis: Impact on India and policy responses, ADBI working paper series, No. 164, 2009.
 
[2]  Hayami, Y. and V. Ruttan., Agricultural Productivity Differences Among Countries, American Economic Review, 40, 895-911, 1970.
 
[3]  Kawagoe, T. and Y. Hayami., The Production Structure of World Agriculture: An Inter-country Cross-Section Analysis, Developing Economies, 21, 189-206, 1983.
 
[4]  Kawagoe, T., Y. Hayami and V. Ruttan., The Intercountry Agricultural Production Function and Productivity Differences Among Countries, Journal of Development Economics, 19, 113-132, 1985.
 
[5]  Capalbo, Susan M., and J.M. Antle (eds.)., Agricultural Productivity: Measurement and Explanation, Washington D.C. Resources for the Future, 1988.
 
Show More References
[6]  Lau, L. and P. Yotopoulos., The Meta-Production Function Approach to Technological Change in World Agriculture, Journal of Development Economics, 31, 241-269, 1989.
 
[7]  Ahluwalia, I.J., Productivity and Growth in Indian Manufacturing, New Delhi: Oxford University Press, 1991.
 
[8]  Fulginiti, L. and R. Perrin., Prices and Productivity in Agriculture, Review of Economics and Statistics, 75, 471-482, 1993.
 
[9]  Ahluwalia, I.J., India: Industrial Development Review, The Economist Intelligence Unit and UNIDO, 1995.
 
[10]  Rao, D.S.P. and Coelli, T.J., Catch-up and Convergence in Global Agricultural Productivity, 1980-1995, CEPA Working Papers, No. 4/98, Department of Econometrics, University of New England, Armidale, pp. 25, 1998.
 
[11]  Martin, W. and Mitra, D., Productivity Growth and Convergence in Agriculture and Manufacturing, Agriculture Policy Research Working Papers, No. 2171, World Bank, Washington D.C, 1999.
 
[12]  Weber, W.L. and B. R. Domazlicky., Total Factor Productivity Growth in Manufacturing: A Regional Approach Using Linear Programming, Regional Science and Urban Economics, 29: 105-122, 1999.
 
[13]  Ball, V. Eldon, Bureau, J.C., Butault, J.P., Nehring, R., Levels of Farm Sector Productivity: An International Comparison, Journal of Productivity Analysis, 15, 5-29, 2001.
 
[14]  Chavas, J.P., An International Analysis of Agricultural Productivity, in L. Zepeda, ed., Agricultural Investment and Productivity in Developing Countries, FAO, Rome, 2001.
 
[15]  Trueblood, M.A. and Coggins, J., Intercountry Agricultural Efficiency and Productivity: A Malmquist Index Approach, mimeo, World Bank, Washington D.C, 2003.
 
[16]  Unel, B., Productivity Trends in India’s Manufacturing Sectors in the Last Two Decades, IMF Working Paper, WP/03/22, International Monetary Fund, Washington D.C, 2003.
 
[17]  Goldar, B. N. 2004., Productivity Trends in Indian Manufacturing in the Pre and Post Reform Periods, WP/No. 137, Indian Council for Research on International Economic Relations, New Delhi, 2004.
 
[18]  Senturk, S. S., Total Factor Productivity Growth in Turkish Manufacturing Industries: A Malmquist Productivity Index Approach, Master Thesis, KTH Economics of Innovation and Growth, Stockholm, Sweden, 2010.
 
[19]  Shilpa, C., Trend in Total Factor Productivity in Indian Agriculture: State-Level Evidence using Non-Parametric Sequential Malmaquist Index, CDE Working papers, No. 215, Department of Economics, Delhi School of Economics, India, 1-42. 2012.
 
[20]  Subrahmanyam, G., Productivity Growth in India Public Sector Banks: 1970-89, Journal of Quantitative Economics, 9(2) 209-223, 1993.
 
[21]  Bhattacharya. A. Lovell, C. A. K and Sahay, P., The Impact of liberalization on the Productive Efficiency of Indian Commercial Banks, European Journal of operations Research, 98, 332-345, 1997.
 
[22]  Kumbhakar, S.C. and Sarkar, S., Deregulation, ownership, and productivity growth in the banking industry: evidence from India, Journal of Money, Credit, and Banking, 35, 403-424, 2003
 
[23]  Mohan, T.T.R and Ray, S., Productivity Growth and Efficiency in India Banking: A Comparison of Public, Private and Foreign banks, WP/27, Department of Economics, University of Connecticut, 2004.
 
[24]  Galagedera, D.U.A and Edirisuriya, P., Performance of Indian Commercial Banks (1995-2002): An Application of Data Envelopment analysis and Malmquist Productivity Index, 2005. http://ideas.repec.org/p/wpa/wuwpfi/0408006.html.
 
[25]  Wadud, I. K. M., Mokhtarul and S. Paul., Productivity Growth, Efficiency Change and Technical Progress, The Indian Economic Journal, 54(2), 145-165, 2006.
 
[26]  Rezvanian, R., Rao. N and S. M. Mehdian, S.M., Efficiency change, technological progress and productivity growth of private, public and foreign banks in India: evidence from the post liberalization era, Journal of Applied Financial Economics, pp: 1-13, 2007.
 
[27]  Tianshu, Z and Casu, B., Deregulation and Productivity Growth: A Study of Indian Commercial Banking Industry, International Journal of Business Performance Management, 10 (4), 318-343, 2008.
 
[28]  Sharma, Seema, Gupta and Sanjeev., Malmquist Productivity and Efficiency Analysis for Banking Industry in India, International Journal of Business Excellence, 3(1), 65-76, 2010.
 
[29]  Caves, D.W., Christensen, L.R. and Diewert, W. E., Multilateral Comparisons of Output, Input, and Productivity Using Superlative Index Numbers, Economic Journal, Royal Economic Society, 92(365), 73-86, 1982a.
 
[30]  Caves, D. W., Christensen, L. R., & Diewert, W. E. The Economic Theory of Index Numbers and Measurement of Input, Output and Productivity, Econometrica, 50, 1393-1414, 1982b.
 
[31]  Fare, R., Grosskopf, S., Lindgren, B and Roos, P., Productivity changes in Swedish pharmacies 1980-1989: A non-parametric Malmquist approach, Journal of Productivity Analysis, 3, 85-102, 1992.
 
[32]  Farrel, M. J., The measurement of productive efficiency, Journal of royal statistical society, 253-281, 1957.
 
[33]  Mlima, A. P., Four Essays on efficiency and productivity in Swedish banking, Dissertation, Economic Studies, University of Gothenburg, 1999.
 
[34]  Coelli, T., A guide to DEAP version 2.1: a data envelopment analysis (computer program), Working paper 96/08, Centre for Efficiency and Productivity Analysis (CEPA), 1996.
 
[35]  Deliktas and Ertugrul., An analysis of efficiency and total factor productivity growth of the private manufacturing industry in Turkey, METU Studies in Development, 29(3-4), 247-284, 2002.
 
[36]  Coelli Tim J., Rao D.S. Prasada., Total Factor Productivity Growth in Agriculture: A Malmquist Index Analysis of 93 Countries 1980-2000, Centre for Efficiency and Productivity Analysis, Working Paper Series, No. 02/2003, School of Economics, University of Queensland Australia, 2003.
 
[37]  Worthington, A. C., Malmquist Indices of Productivity Change in Australian Financial Services, Journal of International Financial Markets, Institutions and Money, 9(3), 303-320, 1999.
 
[38]  Berger, A. N and Humphrey, D.B., Megamergers in banking and the use of cost efficiency as an antitrust defense, Antitrust Bulletin, 37, 541-600, 1992.
 
[39]  Sealey, C and Lindley J. T., Inputs, outputs and a theory of production and cost at depository financial institution, Journal of finance, 32, 1251-1266, 1977.
 
[40]  Berger, A.N., and Humphrey, D.B., Efficiency of Financial Institutions: International Survey and Directions for Future Research, European Journal of Operational Research, 98, 175-212, 1997.
 
[41]  Galagedera, D.U. A and Silvapulle, P., Experimental Evidence on Robustness of Data Envelopment Analysis, Journal of the Operational Research Society, 54, 654-660, 2003.
 
[42]  Madhanagopal, R and Chandrasekaran, R., Selecting Appropriate Variables for DEA Using Genetic Algorithm (GA) Search Procedure, International Journal of Data Envelopment Analysis and *Operations Research*, 1(2), 28-33, 2014.
 
Show Less References