Effects of Selected Socio-Demographic Variables on Fertility Among Diabetic Patients in Bangladesh

  • Md. Obaidur Rahman
  • Md. Rafiqul Islam
  • Clyde McNeil
  • M. Korban Ali
Chapter
Part of the Applied Demography Series book series (ADS, volume 8)

Abstract

The purpose of this study is to assess fertility levels by selected socio-demographic variables obtained data from Rajshahi Diabetes Association , Bangladesh by multiple classification analysis (MCA). Results of this study indicate that children ever born (CEB) increase with increasing age, duration of marriage and duration of suffering from diabetes while CEB decrease with increasing household education, age at first marriage, body mass index (BMI) and duration of sleeping. Also, diabetic females 25–34 years of age are more fertile than other ages. It is also identified that the first through tenth strongest influential factors for explaining the variation on CEB are respondent’s education, duration of marriage, age, living house, duration of sleeping, blood pressure, current living place, age at first marriage duration of suffering from diabetic and BMI respectively.

Keywords

Children ever born (CEB) Diabetic patients Multiple classification analysis (MCA) Shrinkage coefficient of the model Bangladesh 

References

  1. Adhikari, R. (2010). Demographic, socio-economic, and cultural factors affecting fertility differentials in Nepal. BMC Pregnancy & Childbirth, 10, 19.CrossRefGoogle Scholar
  2. Ali, M. A. (2003) Fertility patterns and differentials in Bangladesh; M.Sc Thesis, Dept. of Statistics, University of Rajshahi; Bangladesh.Google Scholar
  3. Alo, O. A. (2011). Fertility regimentation of the rural Yoruba women of South-west Nigeria: The case of Ido and Isinbode. Journal of Social Science, 26(1), 57–65.Google Scholar
  4. Anderson, R. L., & Bancraft, T. A. (1952). Statistical theory in research. New York: Mcgraw Hill.Google Scholar
  5. Bongaarts, J. (2008). Fertility transitions in developing countries: progress or stagnation? Studies in Family Planning 39, 105–110.Google Scholar
  6. Casterline, J. B. (2001). The pace of fertility transition: National patterns in the second half of the twentieth century. Population and Development Review, 27(Suppl. Global Fertility Transition), 17–52.Google Scholar
  7. Cochrane, S. H. (1979). Fertility and education. Baltimore: The John Hopkins University Press.Google Scholar
  8. Coombs, P. H., Prosser, C., & Ahmed, M. (1973). New paths to learning for rural children and youth. New York.Google Scholar
  9. Ginneken, J. V., & Razzaque, A. (2003). Supply and demand factors in the fertility decline in Matlab, Bangladesh in 1977–1999. European Journal of Population, 19, 29–45.CrossRefGoogle Scholar
  10. Haupt, A., & Kane, T. T. (2004). Population reference Bureau’s population handbook (5th ed.). Eleventh printing, 2004.Google Scholar
  11. Hossain, M. S., & Islam, M. R. (2013). Age specific participation rates of Curacao in 2011: Modeling approach. American Open Computational and Applied Mathematics Journal, 1(2), 08–21.Google Scholar
  12. Hossain, M. K., Islam, M. R., Khan, M. N., & Ali, M. R. (2015). Contribution of socio-demographic factors on antenatal care in Bangladesh: Modeling approach. Public Health Research, 5(4), 95–102.Google Scholar
  13. Hussain, A., Vaaler, S., Sayeed, M. A., Mahtab, H., Ali, S. M. K., & Khan, A. K. A. (2007). Type 2 diabetes and impaired fasting blood glucose in rural Bangladesh: A population-based study. The European Journal of Public Health, 17(3), 291–296.CrossRefGoogle Scholar
  14. Isiugo-abanibe, U. C. (1997). Fertility preferences and contraceptive practice in Nigeria. Annals of the Social Science Council of Nigeria, 9, 1–20.Google Scholar
  15. Islam, M. R. (2011). Modeling of diabetic patients associated with age: Polynomial model approach. International Journal of Statistics and Applications, 1(1), 1–5.CrossRefGoogle Scholar
  16. Islam, M. R. (2012a). Mathematical modeling of age and of income distribution associated with female marriage migration in Rajshahi, Bangladesh. Research Journal of Applied Sciences, Engineering and Technology, 4(17), 3125–3129.Google Scholar
  17. Islam, M. R. (2012b). Modeling and projecting population for Muslim of urban area in Bangladesh. International Journal of Probability and Statistics, 1(1), 04–10.CrossRefGoogle Scholar
  18. Islam, M. R. (2013). Modeling age structure and ASDRs for human population of both sexes in Bangladesh. International Journal of Anthropology, 28(1), 47–53.Google Scholar
  19. Islam, M. R. (2014). Modeling of ASFRs and study the reproductivity of women of urban area in Bangladesh. Advances in Life Sciences, 4(5), 227–234.Google Scholar
  20. Islam, M. R., Ali, M. K., & Islam, M. N. (2013). Construction of life table and some mathematical models for male population of Bangladesh. American Journal of Computational and Applied Mathematics, 3(6), 269–276.Google Scholar
  21. Islam, M. R., & Hoque, M. N. (2015). Mathematical modeling and projecting population of Bangladesh by age and sex from 2002 to 2031. Emerging Techniques in Applied Demography, Applied Demography Series, 4, 53–60 (Chapter 5).Google Scholar
  22. Islam, M. R., & Hossain, M. S. (2013a). Mathematical modeling of age specific adult literacy rates of rural area in Bangladesh. American Open Demography Journal, 1(1), 01–12.Google Scholar
  23. Islam, M. R., & Hossain, M. S. (2013b). Mathematical modeling of age specific participation rates in bangladesh. International Journal of Scientific and Innovative Mathematical Research, 1(2), 150–159.Google Scholar
  24. Islam, M. R., & Hossain, M. S. (2014a). Some models associated with age specific adult literacy rates of urban area in Bangladesh. International Journal of Ecosystem, 4(2), 66–74.Google Scholar
  25. Islam, M. R., & Hossain, M. S. (2014b). Mathematical modeling of age specific adult literacy rates in Bangladesh. Advances in Life Sciences, 4(3), 106–113.Google Scholar
  26. Islam, M. R., & Hossain, S. (2015). Some standard physical characteristics of students in Seoul: Modeling approach. American Journal of Mathematics and Statistics, 5(5), 230–237.Google Scholar
  27. Islam, M. R., Hossain, M. S., & Faroque, O. (2014). U-Shaped pattern of employees’ job satisfaction: Polynomial model approach. International Journal of Ecosystem, 4(4), 170–175.Google Scholar
  28. Islam, S., & Nesa, M. K. (2009). Fertility transition in Bangladesh: The role of education. Proceedings of Pakistan Academy Science, 46(4), 195–201.Google Scholar
  29. Islam, M. R., & Rahman, M. O. (2012). The risk factors of type 2 diabetic patients attending Rajshahi diabetes association, Rajshahi, Bangladesh and its primary prevention. Food and Public Health, 2(2), 5–11.CrossRefGoogle Scholar
  30. James, W. P., Jackson-Leach, R., & Mhurchu, C. N. (2004). Overweight and obesity. In E. A. D. Lopez, A. Rodgers, C. J. L. Murray, & M. Ezzati (Eds.), Comparative quantification of health risks: Global and regional burden of disease attributable to selected major risk factors (pp. 497–596). Geneva: WHO.Google Scholar
  31. Kasim, K., Amar, M., El Sadek, A. A., & Gawad, S. A. (2010). Peripheral neuropathy in type 2 diabetic patients attending diabetic clinics in Al-Azhar University Hospitals, Egypt. International Journal of Diabetes Mellitus, 2(1), 20–23.CrossRefGoogle Scholar
  32. Khuda and Hossain. (1996). Fertility decline in Bangladesh: Toward an understanding of major causes. Health Transition Review, 6, 155–167.Google Scholar
  33. Kim, S. M., Lee, J. S., Lee, J., Na, J. K., Han, J. H., Yoon, D. K., et al. (2006). Prevalence of diabetes and impaired fasting in Korea. Diabetes Care, 29, 226–232.CrossRefGoogle Scholar
  34. Maheshwari, A., Stofberg, L., & Bhattacharya, S. (2007). Effect of overweight and obesity on assisted reproductive technology—A systematic review. Human Reproduction Update, 13, 433–444.CrossRefGoogle Scholar
  35. Mitra, S. N., Ali, M. N., Islam, S., Cross, A. R., & Saha, T. (1994) Bangladesh demographic and health survey 1993–94, Calverton, Maryland. National Institute of Population Research & Training (NIPORT), Mitra and Associates, and Macro International Inc.Google Scholar
  36. Mitra, S. N., & Associate. (2001). Bangladesh demographic and health survey 1999–2000. Dhaka, Bangladesh: Institute of Population Research and Training (NIPORT).Google Scholar
  37. Mitra, S. N., & Associate. (2007). Bangladesh demographic and health survey. Dhaka, Bangladesh: Institute of Population Research and Training (NIPORT).Google Scholar
  38. Mitra, S. N., & Associate. (2011). Bangladesh demographic and health survey. Dhaka, Bangladesh: Institute of Population Research and Training (NIPORT).Google Scholar
  39. Nasra, M., & Makhdoom, A. (1998). Patterns of desired fertility and contraceptive use in Kuwait. Social–Biology, 37(2), 110–111.Google Scholar
  40. Olalekan, W., Esther, A. O., Olusengun, J., & Olugbenga, A. (2011). A comparative study of socio-demographic determinants and fertility pattern among women in rural and urban communities in southwestern Nigeria. Continental Journal Medical Research, 5(1), 32–40.Google Scholar
  41. Palamuleni, M. E. (2011). Socio-economic determinants of age at marriage in Malawi. International Journal of Sociology and Anthropology, 3(7), 224–235.Google Scholar
  42. Pinborg, A., Gaarslev, C., Hougaard, C. O., Andersen, A. N., Andersen, P. K., Boivin, J., & Schmidt, L. (2011). Influence of female bodyweight on IVF outcome: A longitudinal multicentre cohort study of 487 infertile couples. Reproductive Biomedicine Online, doi: 10.1016/j.rbmo.2011.06.010
  43. Porapakkham, Y., Pattaraarchachai, J., & Aekplakorn, W. (2008). Prevalence, awareness, treatment and control of hypertension and diabetes mellitus among the elderly: The 2004 National Health Examination Survey III. Thailand. Singapore Medical Journal, 49(11), 868–873.Google Scholar
  44. PRB. (2014). World population data sheet.Google Scholar
  45. Rahman, M. O., & Islam, M. R. (2011). Association between fasting of ramadan and risk factors of diabetes: A study from Rajshahi City in Bangladesh. Advance Journal of Food Science and Technology, 3(5), 360–365.Google Scholar
  46. Rahman, M. O., & Islam, M. R. (2012a). Influential determinants of blood glucose level of diabetic patients in Bangladesh. International Journal of Current Biomedical and Pharmaceutical Research, 2(1), 252–256.Google Scholar
  47. Rahman, M. O., & Islam, M. R. (2012b). Socio demographic and health related determinants of over weighted diabetic patients in Bangladesh. Current Research Journal of Biological Sciences, 4(3), 337–344.Google Scholar
  48. Rahman, M. O., & Islam, M. R. (2012c). Socio-demographic and health related determinants of abdominal obesity of male diabetic patients in Bangladesh. Asian Profile, 40(5), 409–420.Google Scholar
  49. Sanchez-Viveros, S., Barquera, S., Medina-Solis, C. E., Velazquez-Alva, M. C., & Valdez, R. (2008). Association between diabetes mellitus and hypertension with anthropometric indicators in older adults: Results of the Mexican health survey, 2000. The Journal of Nutrition, Health & Aging, 12(5), 327–333.CrossRefGoogle Scholar
  50. Sarkar, S. K. (2004). Demand for a child in Bangladesh: A multivariate statistical analysis. Unpublished Ph. D. Thesis, Department of Statistics, University of Rajshahi, Bangladesh.Google Scholar
  51. Sarkar, S. K., Midi, H., & Imon, A. H. M. R. (2009). Binary response model of desire for children in Bangladesh. European Journal of Social Sciences, 10(3), 364–373.Google Scholar
  52. Sayem, A. M., & Nury, T. M. S. (2011). Factors associated with teenage marital pregnancy among Bangladeshi women. Reproductive Health, 8, 16.CrossRefGoogle Scholar
  53. Stevens, J. (1996). Applied multivariate statistics for the social sciences (3rd ed.). Publishers, New Jersey: Lawrence Erlbaum Associates Inc.Google Scholar
  54. UN. (1983). Manual X: Indirect techniques for demographic estimation. Population studies, No. 81. New York.Google Scholar
  55. UNICEF. (2005). Early marriage: A harmful traditional practice. New York: United Nations.Google Scholar
  56. Veghari, G., Sedaghat, M., Joshaghani, H., Hoseini, S. A., Niknezad, F., Angizeh, A., et al. (2010). Association between socio-demographic factors and diabetes mellitus in the north of Iran: A population-based study. International Journal of Diabetes Mellitus, 2, 154–157.CrossRefGoogle Scholar
  57. Yates, F. (1934). The analysis of variance with unequal numbers in the different classes. Journal of American Statistical Association, 29, 51–66.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Md. Obaidur Rahman
    • 1
  • Md. Rafiqul Islam
    • 1
  • Clyde McNeil
    • 2
  • M. Korban Ali
    • 3
  1. 1.Department of Population Science and Human Resource DevelopmentRajshahi UniversityRajshahiBangladesh
  2. 2.Hobby Center for Public PolicyUniversity of HoustonHoustonUSA
  3. 3.Manarat International UniversityDhakaBangladesh

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