Volume 6, Issue 1, June 2018, Page: 25-30
Simulating the Nutritional Traits of Populations at the Small Area Levels Using Spatial Microsimulation Modelling Approach
Md. Abdul Hakim, Department of Food Technology and Nutritional Science, Mawlana Bhashani Science and Technology University, Santosh, Bangladesh
Azizur Rahman, School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, Australia
Received: Feb. 12, 2018;       Accepted: Apr. 3, 2018;       Published: May 5, 2018
DOI: 10.11648/j.cbb.20180601.13      View  955      Downloads  66
Nutritional traits simulating at an awesome local geographic level is vital for effective nutritional promotion programs, provision of better nutritional services and population-specific nutritional planning and management. Deficient in micro-dataset readily available for attributes of individuals at small areas affects the local and national agencies on the route ahead of their smooth managing of the serious nutritional issues and related risks in the community. A solution of this ongoing challenge would be to form a method to simulate reliable small area statistics. This paper provides a dashing appraisal of the methodologies for simulating nutritional traits of populations at geographical limited areas. Findings reveal that microsimulation-based spatial models have the significant robustness over the other methods stated in this study representing a more precise means of simulatingn utrition-related traits of population at the small area levels.
Nutritional Traits, Microsimulation Modelling, SWOT Analysis, Multilevel Models, Small Area Estimates
To cite this article
Md. Abdul Hakim, Azizur Rahman, Simulating the Nutritional Traits of Populations at the Small Area Levels Using Spatial Microsimulation Modelling Approach, Computational Biology and Bioinformatics. Vol. 6, No. 1, 2018, pp. 25-30. doi: 10.11648/j.cbb.20180601.13
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Rahman A and Chowdhury S. Determinants of chronic malnutrition among preschool children in Bangladesh. J Biosoc Sci 2007; 39 (2): 161-173.
Macharia PM, Ouma PO, Gogo EG, Snow RW and Noor AM. Spatial accessibility to basic public health services in South Sudan. Geospat Health 2017; 12: 106-113.
Rahman A and Hakim MA. An epidemiological study on hygiene practices and malnutrition prevalence of beggars children in Bangladesh. Int J Nutr Diet 2016; 4 (1): 29-46.
Seliske L, Norwood TA, McLaughlin JR, Wang S, Palleschi C and Holowaty E. Estimating micro area behavioural risk factor prevalence from large population-based surveys: a full Bayesian approach. BMC Public Health 2016; 16: 478.
Bubliy OA, Loeschcke V and Imasheva AG. Genetic variation of morphological traits in Drosophila melanogaster under poor nutrition: isofemale lines and offspring-parent regression. Heredity 2001; 86: 363-369.
Banerjee A and Chaudhury S. Statistics without tears: populations and samples. Ind Psychiatry J 2010; 19 (1): 60-65.
Rahman A and Hakim MA. Modelling health status using the logarithmic biophysical modulator. J Public Health Epidemiol 2017; 9 (5): 145-150.
Hakim MA. Modelling food energy in bioenergetics. J Adv Med Pharaceut Sci 2017; 14 (4): 1-7.
Hakim MA. Simulating the ideal body weight in human populations. Int J Biochem Biophys 2017; 5 (4): 79-82.
Rahman A and Hakim MA. Malnutrition prevalence and health practices of homeless children: a cross-sectional study in Bangladesh. Sci J Public Health 2016; 4 (1-1): 10-15.
Rahman A and Hakim MA. Measuing modified mass energy equivalence in nutritional epidemiology: a proposal to adapt the biophysical modelling approach. Int J Stat Med Res 2016; 5 (3): 219-223.
Rahman A. Significant risk factors for childhood malnutrition: evidence from an Asian developing country. Sci J Public Health 2016; 4 (1-1): 16-27.
Charlton J. Use of the census sample of anonymised records (SARs) and survey data in combination to obtain estimates at local authority level. Env Plan Anal 1998; 30: 775-84.
Schaible W. Indirect estimators in U.S. federal programs. Springer, New York, NY, USA, 1996.
Rao JNK. Small area estimation. John Wiley & Sons, Inc., New York, NY, USA, 2003.
Ghosh M and Rao JNK. Small area estimation – an appraisal. Stat Sci 1994; 9: 55-76.
Rahman A, Harding A, Tanon R and Liu S. Methodological issues in spatial microsimulation modelling for small area estimation. Int J Microsimul 2010; 3 (2): 3-22.
Rao JNK and Molina I. Small area estimation. John Wiley & Sons, Inc., New York, NY, USA, 2015.
Zhang X, Onufrak S, Holt JB and Croft JB. A multilevel approach for estimating small area childhood obesity prevalence at the sensus block-group level. Prev Chronic Dis 2013; 10: E68.
Pfeffermann D. Small area estimation – new developments and directions. Int Stat Rev 2002; 70: 125-43.
Rahman A and Harding A. Small area estimation and microsimulation modelling. Chapman and Hall/CRC, London, UK, 2016.
Edwards KL and Clarke GP. The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity. Soc Sci Med 2009; 69: 1127-34.
Rahman A and Harding A. Spatial analysis of housing stress estimation in Australia with statistical validation. Aust J Reg Stud 2014; 20:452-86.
Rahman A and Harding A. Tenure specific small area estimation of housing stress in Australia. Paper presented at the 2013 International Indian Statistical Association (IISA) Conference in Chennai, India (January 2-5), 2013.
Rahman A and Upadhyay S. A Bayesian reweighting technique for small area estimation. In: S. K. Upadhyay, U. Singh, and D. K. Dey (eds.). Current trends in bayesian methodology with applications. Chapman and Hall/CRC, London, UK, 2015; pp. 503- 19.
Brown L, Yap M, Lymer S, Chin SF, Leicester S, Blake M and Harding A. Spatial microsimulation modelling of care needs, costs and capacity for self-provision: detailed regional projection for older Australians to 2020. Paper presented at the Australian Population Association Conference, Canberra, Australia, 2004.
Rahman A. A review of small area estimation problems and methodological developments. University of Canberra, Canberra, Australia, 2008.
Mena C, Fuentes E, Ormazabal Y and Polomo I. Geographic clustering of elderly people with above-norm anthropometric measurements and blood chemistry. Geospat Health 2017; 12: 90-95.
Chin SF and Harding A. Regional dimensions: Creating synthetic small-area microdata and spatial microsimulation models. NATSEM, University of Canberra, Australia, 2006.
Hakim MA. Biophysical modelling of dietary energy in biochemical modelling. Eur J Biophys 2017; 5 (3): 57-61.
Bajekal M, Scholes S, Pickering K and Purdon S. Synthetic estimation of healthy lifestyles indicators: stage 1 report. National Centre for Social Research, London, UK, 2004.
Rahman A, Hakim MA, Hanif MA, Islam MR and Kamruzzaman M. Dietary practices, health status and hygiene observance of slum kids: a pilot study in an Asian developing country. JP J Biostat 2016; 13 (2): 195-208.
Rahman A. Small area housing stress estimation in Australia: calculating confidence intervals for a spatial microsimulation model. Commun Stat 2017; 84 (9):1-19.
Hakim MA. Mathematical modelling of energy balancing for diet plannning in nutritional physics. Int J Nutr Diet 2017; 5 (1): 29-41.
Rahman A and Hasan N. Modelling effects of KM and HRM processes to the organizational performance and employee’s job satisfaction. Int J Business Manage 2017; 12 (7): 35.
Hakim MA and Rahman A. Health and nutritional condition of street children of Dhaka city: an empirical study in Bangladesh. Sci J Public Health 2016; 4(1-1): 6-9.
Islam J, Rahman A and Boland G. Nexus of learning style with satisfaction and success of accounting students: a cultural study at an Australian university. Int J Learn Change 2011; 5 (3-4): 288-304.
Rahman A. Small area estimation through spatial microsimulation models: Some methodological issues. Paper presented at the 2nd General Conference of the International Microsimulation Association in Ottawa, Canada, 2009; p. 1- 45 (June 8-10).
Heady P, Clarke P, Brown G, Ellis K, Heasman D, Hennell S, Longhurst J and Mitchell B. Model-based small area estimation series no. 2: small area estimation project report. Office for National Statistics, London, UK, 2003.
Rahman A and Harding A. Social and health costs of tobacco smoking in Australia: level, trend and determinants. Int J Stat Syst 2011; 6:375-87.
Kamruzzaman , Hakim MA, Hanif MA, Rahman MH, Islam MA, Talukder MJ and Islam MR. Patterns of behavioral changes among adolescent smokers: an empirical study. Front Biomed Sci 2016; 1 (1): 1-6.
Kamruzzaman M and Hakim MA. Basic rights on the wane, human rights on brown study: a case study on thrown away children in Bangladesh. Int J Environ Plan Manage 2016; 2 (4): 29-35.
Hakim MA and Kamruzzaman M. The dance of poverty and education for childhood nutritional victimization in Bangladesh. J Biol Environ Eng 2016; 1 (1): 6-9.
Hakim MA, Talukder MJ and Islam MS. Nutritional status and hygiene behavior of government primary school kids in central Bangladesh. Sci J Public Health 2015; 3(5): 638-642.
Rahman A. Estimating small area health-related characteristics of populations: a methodological review. Geospat Health 2017; 12: 3-14.
Burden S and Steel D. Constraint choice for spatial microsimulation. Popul Space Place 2016; 22:568-83.
Brown L and Harding A. The new frontier of health and aged care: using microsimulation to assess policy options: tools for microeconomic policy analysis. Productivity Commission of Australia, Canberra, Australia, 2005.
Hakim MA. Biophysical modelling of cellular energy in human dietetics: an appraisal in nutritional physics and cell biology. Am J Food Sci Nutr Res 2017; 4 (4): 125-129.
Procter KL, Clarke GP, Ransley JK and Cade J. Micro-level analysis of childhood obesity, diet, physical activity, residential socio-economic and social capital variables: where are the obesogenic environments in Leeds? Area 2008; 40:323-40.
Tranmer M, Pickles A, Fieldhouse E, Elliot M, Dale A, Brown M, Martin D, Steel D and Gardiner C. Microdata for small areas. The Cathie Marsh Centre for Census and Survey Research (CCSR), University of Manchester, Manchester, UK, 2001.
Williamson P. Community health care policies for the elderly: a microsimulation approach. School of Geography, University of Leeds, Leeds, UK, 1992.
Norman P. Putting iterative proportional fitting on the researcher’s desk. School of Geography, University of Leeds, Leeds, UK, 1999.
Bell P, 2000. GREGWT and TABLE macros user guide (unpublished). Australian Bureau of Statistics, Canberra, Australia, 2000.
Huang Z and Williamson P. A comparison of synthetic reconstruction and combinatorial optimisation approaches to the creation of small-area microdata. Population Microdata Unit, Department of Geography, University of Liverpool, Liverpool, UK, 2001.
Singh AC and Mohl CA. Understanding calibration estimators in survey sampling. Survey Methodol 1996; 22:107-15.
http://mathworld. wolfram.com/NewtonsMethod.html.
Rahman A, Chowdhury S and Hossain D. Acute malnutrition in Bangladeshi children: levels and determinants. Asia Pac J Public Health 2009; 21 (1): 294-302.
Hakim MA. Simulating the ideal body mass in adult human samples. Int J Sport Sci Phys Educ 2017; 2 (4): 57-60.
Rahman A, Harding A, Tanton R and Liu S. Simulating the characteristics of populations at the small area level: New validation techniques for a spatial microsimulation model in Australia. Comput Stat Data Analysis 2013; 57 (1): 149-165.
Whitworth A, Carter E, Ballas D and Moon G. Estimating uncertainty in spatial microsimulation approaches to small area estimation: A new approach to solving an old problem. Comput Environ Urban Syst 2017; 63: 50-57.
Tanon R, Vidyattama Y, Nepal B and McNamara J. Small area estimation using a reweighting algorithm. J Royal Stat Society 2011; 174 (4): 931-951.
Hakim MA and Kamruzzaman M. Nutritional status of preschoolers in four selected fisher communities. Am J Life Sci 2015; 3(4): 332-336.
Sakboonyarat B, Chokcharoensap K, Meesaeng M, Jaisue N, Janthayanont D and Srisawat P. Prevalence and associated factors of Low Back Pain (LBP) among adolescents in Central, Thailand. Glob J Health Sci 2018; 10 (2): 49-59.
Hakim MA. Malnutrition prevalence and nutrition counseling in developing countries: A case study. Int J Nurs Health Sci 2016; 3(3):19-22.
[65] Kuddus A, Rahman A, Talukder MR and Hoque A. A modified SIR model to study on physical behaviour among smallpox infective population in Bangladesh. Am J Math Stat 2014; 4(5): 231-239.
Kamruzzaman M and Hakim MA. Socio-economic status of child beggars in Dhaka City. J Soc Sci Human 2015; 1(5): 516-520.
Rahman A, Gao J and Ahmed S. An assessment of the effects of prior distribution on the Bayesian predictive inference. Int J Stat Prob 2016; 5 (5): 31-42.
Poole SA, Hart CN, Jelalian E and Raynor HA. Relationship between dietary energy and dietary quality in overweight young children: A cross-sectional analysis. Pediatr Obesity 2016; 11(2):128-135.
Armstrong M. A handbook of human resource management practice (10th ed). Kogan Page, London, 2006.
Osita C, Onyebuchi I and Justina N. Organization’s stability and productivity: the role of SWOT analysis. Int J Innov Appl Res 2014; 2 (9): 23-32.
Birkenmaier J. The practice of generalist social work. New York, NY: Routledge, 2001.
Kamruzzaman M and Hakim MA. A review on child labour criticism in Bangladesh: an analysis. Int J Sport Sci Phys Educ 2018; 3 (1): 1-8.
Linkon MR, Prodhan UK et al. Comperative analysis of the physico-chemical and antioxidant properties of honey available in Tangail, Bangladesh. Int J Res Eng Technol 2015; 4 (3): 89-92.
Browse journals by subject