Journal of
Public Health and Epidemiology

  • Abbreviation: J. Public Health Epidemiol.
  • Language: English
  • ISSN: 2141-2316
  • DOI: 10.5897/JPHE
  • Start Year: 2009
  • Published Articles: 666

Full Length Research Paper

Modeling health status using the logarithmic biophysical modulator

Azizur Rahman
  • Azizur Rahman
  • School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, New South Wales, Australia
  • Search for this author on:
  • Google Scholar
Md. Abdul Hakim
  • Md. Abdul Hakim
  • Department of Food Technology and Nutritional Science, Mawlana Bhashani Science and Technology University, Santosh, Tangail, Bangladesh
  • Search for this author on:
  • Google Scholar


  •  Received: 16 August 2016
  •  Accepted: 30 September 2016
  •  Published: 30 May 2017

 ABSTRACT

There are different methods to measure health and nutritional status of samples at different sociodemographic settlements in different communities. Of them, one method can be preferable at a certain topographic condition for specific group of people. Typically, it can then become a common method due to widespread uses by other groups, and hence many researchers often are in doubt to choose the apt method for their studies in dynamic environment of health science. This study aims to design a new modulator of health status for examining the ongoing health and nutritional concerns to the communities including malnutrition, health demography and possible socioeconomic and environmental changes of heath. It uses a wide range of instruments and theories ranging from the Quetelet’s index in health science to the Albert Einstein’s theory of relativity in physical science. Findings reveal that our proposed logarithmic biophysical modulator of health status [equation (ix)] is a modern and simple tool for health assessment of individuals through statistical modeling. It could be applicable to the study on the worldwide health and nutritional research, geospatial and community health and biostatistics and public health. Also it can be a uniquely functional biophysical model to the discipline of health pedagogy in nutritional epidemiology.

 

Key words: Health pedagogy, statistical modeling, health status, malnutrition, biophysical modulator


 INTRODUCTION

About 2 billion people in the world suffer from various forms of malnutrition (Rahman and Biswas, 2009). Malnutrition is an underlying cause of death of 2.6 million children each year - a third of child deaths globally (Black et al., 2008; Rahman et al., 2008). One in every four of the world’s children is stunted, and in developing countries this is as high as one in three (de Onis et al., 2011). This means their bodies fail to develop fully as a result of malnutrition. Undernutrition accounts for 11% of the global burden of disease and is considered the number one risk to health worldwide (Black et al., 2008). Childhood malnutrition leads to stunted growth and influence mortality and morbidity (Rahman and Hakim, 2016a; Rahman, 2006; Megabiaw and Rahman, 2013; Hakim and Kamruzzaman, 2015), which lower the survival opportunities of adults in their later  life  (Rahman and Chowdhury, 2007). About 4 of each 5 malnourished children live in South-East-Asia, and about 83% of childhood deaths are linked to mild to moderate forms of malnutrition (UNICEF, 1997; Rahman et al., 2009). Malnutrition in developing countries happened due to poverty, household food insecurity, gender bias, population pressure, food taboos, health, hygiene and nutritional negligence, famine and man-made disasters (Hakim et al., 2015; Rahman and Harding, 2013; Ghosh and Shah, 2004; Rahman and Kuddus, 2014a; Fakir and Rahman, 2015; Kuddus et al., 2014; Hakim, 2015). Malnutrition is also caused by intra-family feud, poor socioeconomic status, child abuse, deprivation of schooling opportunity and faulty family planning practices (Rahman and Sapkota, 2014; Kuddus and Rahman, 2015; Kamruzzaman and Hakim, 2016; Rahman, 2004; Kamruzzaman and Hakim, 2015; Hakim and Kamruzzaman, 2016; Hakim, 2016). These nutritional issues are affecting the world’s population day by day (Hasan et al., 2016). As a result, typical health issues are in need of identification to overcome such poor health conditions with a view to meet healthy samples of population in different communities.
 
Some common methods for measuring population health and nutritional status are outlined below. The BMI (Body Mass Index) method is the measure of body fat based on body weight (mass) and height applied to individuals, i.e. BMI = Body weight in kg/(Body height in m)2. Classification of BMI ranges are: i) Underweight: if BMI < 18 kgm-2; ii) Normal weight: if BMI from 18 to 25 kgm-2; iii) Overweight: if BMI from 25 to 30 kgm-2; and iv) Obese: if BMI > 30 kgm-2. This method is widely used for measuring health status of adult people in the community (Rahman et al., 2008). Another method is called MAC or MUAC or MUAC for age (Mid-Upper Arm Circumference). MAC is the circumference of the left upper arm, measured at the mid-point between the tip of the shoulder and the tip of the elbow. MAC is useful to assess the nutritional status of younger children and it is mostly used in emergency situations to identify nutritional treatment requirements of younger patients. Table 1 provides the typical classification of MAC measurements to assess the moderate and severe form of under-nutrition. An additional measurement based on MAC is known as MUAA (Mid-upper Arm Area) which is estimated as: MUAA = MAC2/ 4π (Kamruzzaman and Hakim, 2016). Besides this, there are three indices called Height for age (HAZ), Weight for age (WAZ), and Weight for height (WHZ) that are based on the standard ratio measures of  the  height,  weight  and  age  following  the WHO’s growth charts (Rahman and Chowdhury, 2007). These indices are further standardized using statistical concepts of the Z-score. Topically, a Z-score measure tells us whether a particular value for observation is equal to the mean, below the mean or above the mean of a bunch of scores. In the Z-scores approach: if the measure of a Z-score with the population mean µ has a value of 0, then it is equal to the group mean; if µ is positive then, it is above the group mean; if µ is negative, then it is below the group mean; if µ = +1, then it is 1 standard deviation (SD) above the mean; if µ = +2, then it is 2 SD above the mean, if µ = -1, then it is 1 SD below the mean; and if µ = -2, then it is 2 SD below the mean. However, none of these universal methods can precisely obtain the health and nutritional status at small area communities. 
 
 
This study aims to design a simple modulator of health status for examining the ongoing health and nutritional concerns in communities including the malnutrition. 


 METHODOLOGY

This is a methodological review study to design an appropriate technique for modelling health status of samples. A range of instruments have been collected from the Quetelet’s index in health science, unit bracket method, SI unit of body mass (Bandini and Dietz, 1987), SI and CGS units of body height in health physics, logarithmic gesture in algebra (Poliyanin and Manzhirov, 2007), ratio and percentages in statistics and Albert Einstein’s theory of relativity in the branch of physics (Freedman et al., 2005; Einstein, 1916). These instruments have then used for examining all mathematical and/or statistical modeling in search of a quicker  health status modulator in order to measure the health status of the population in the community.


 RESULTS

The BMI is the measure of body fat relying on the body mass and body height (Ganong, 2001; Garrow and Webster, 1985; Strain and Zumoff, 1992), derived from the body mass divided by the square of the body height and is universally manifested in unit of kgm-2, resulting from body mass in kilogram (kg) and body height in metre (m) of a sample in a population. So the statistical indicator to assess the BMI yielding to the equation as follows:
 
BMI = mkg / hm2                                                              (1)
 
Where,
mkg = Body mass in kg, hm = Body height in m and BMI = Body mass index in kgm-2
 
Lambert Alolphe Jacques Quetelet (Eknoyan, 2008), a Belgian astronomer, mathematician, statistician and sociologist invented the BMI philosophy during the period of his pioneering social physics. The BMI concept was named the Quetelet index pointing at the BMI invertor’s surname  Quetelet.  So  the  BMI   is   equivalent   to   the Quelelet index and the mathematical music in the connection is as follows:
 
BMI = QI                                                                      (2)
 
Where,
QI = Quetelet index
 
Inserting the Equation 2 value in the Equation 1.
 
QI = mkg / hm2                                                                (3)
 
Body mass measuring in the SI base unit kg is the easiest trick in biophysics while body height measuring in the SI base unit m is harder and so the biophysics laureates are in vast use of the CGS base unit centimeter (cm) in order to measure body height of a sample in a population which is then converted into the SI base unit m with a view to bring to light the QI value in the SI derived unit kgm-2.  The following rule is applicable glancing at the unity bracket method (Bela, 2013) to convert the CGS unit cm into the SI unit m,
 
100 cm = 1 m
Or, 102  cm = 1 m
Or, 1 cm = 1 / 102  m
Or, 1 cm = 10-2  m                                                         (4)
 
Inserting the Equation 4 value in the Equation 3,
 
QI =  mkg / (10-2   hcm)2
     =  mkg / 10-4 hcm2
       = 104 mkg / hcm2                                                                         (5)
 
QI is a parameter used to determine health status of a sample in a population (Norgan and Ferro-Luzzi, 1982; Freedman, 2005; Davies and Lucas, 1989) and health status is imposed to the symbol H as rule as the myth of considering the initial alphabet of a word having massive influx in the scientific branch of physics.
 
So,   QI = H;                                                                (6)
 
Where,
H = Modulator of health status
 
The Equation 5 turned into the following icon as the Equation 6 stealing into the Equation 5,
 
H = 104 mkg / hcm2                                                                           (7)
 
Taking log (Shirali, 2002) in both side of the Equation 7,
 
log H =  log (104 mkg / hcm2 )
          =  log (104 mkg ) – log hcm2 
   =  log 104 + log mkg – log hcm2 
    =  4log10+ log mkg – 2 log hcm
     =  4+ log mkg – 2 log hcm          [As log10=1 in common logarithm]                                                        (8)
 
The health status is replied to the equation below calculating the Equation (8),
 
H = log-1 (4 + logmkg – 2 log hcm)                                     (9)
 
The picked up H value within the range of 18 to 25 kgm-2  found putting the measured  body mass in kg and body height in cm in the  Equation 9 is the indicator of healthy (H+) and 18 to 25 kgm-2  deviated value the indicator of non-healthy (H-)  sample in the population (WHO, 2006a).
 
The number of healthy samples (H+)n  and non-healthy samples (H-)n are found  using the Equation 9 within the population of N in a community and then the following five postulates are with distinctive features in statistical modeling (Freeman, 2005; Schervish, 1995; Robertson, 1949):
 
(i) The ratio of healthy and non-healthy samples = (H+)n  :   (H-)n,
(ii) % of (H+)n  = 100 (H+)n / N,
(iii) % of (H-)n    =  100 (H-)n    / N,
(iv) The population is relatively healthy38 if (H+)n > (H-)n  and
(v) The population is relatively non-healthy39 if (H+)n<(H-)n.


 DISCUSSION

Health is the level of function or metabolic capacity of living organisms. In humans it is the ability of individuals or communities to adapt and self-manage when facing physical, mental and social challenges (Hube, 2011). The WHO defined health in its broader sense in its 1948 constitution as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity” (WHO, 2006b). To determine health and nutritional difficulties of people is generally challenging because different risk factors are associated with health and nutritional state (Rahman, 2016). The people are widely deprived of their country’s constitutional basic health, hygiene nutritional care and shelter. The BMI can measure health and nutritional condition in these circumstances defining as a value derived from mass (weight) and height of an individual in the unit of kgm-2 giving a big confliction on using mass and/or weight. The mass is a scalar quantity of unit metre (m) while the weight is a vector quantity containing the unit newton (N). The unit of BMI is kgm-2 and/or kgN-2 which is totally a false concept as well as unit of mass is a base unit but unit of weight is a derived unit.
 
According to the BMI definition, mass (weight) is used and hence the researchers are at bay over choosing the surer or surest quantity and its corresponding unit. So a simple apt modulator is essential to determine the prevailing  health  status  in  the  communities  aiming   to resist malnutrition influencing factors and so the Equation 9 can be the easier option to choose in assessing the overall health status. The German chemist Friedrich Wohler, the pioneer of organic chemistry disapproves the vitalism inventing Wohler synthesis in 1828 (Brinkman et al., 2012; Wohler, 1828). The current study findings similarly can overlap the place of the BMI (Wohler, 1828) in health science. The BMI or other methods (Hakim and Rahman, 2016; Alam et al., 2011; Hakim and Talukder, 2016; Rahman and Kuddus, 2014b; Ahmed and Islam, 2009; Chumlea et al., 2007;  Petitt and Sidney, 1988) are using to measure the health and nutritional condition of people fighting shy of taking initiatives to invent a new method (Garrow et al., 1979) to explore the health status of population in communities and the present study derived biophysical modulator that is, logarithmic Equation 9 can take place in the path of the prior existing methods. This biophysical modulator can be a microsimulation modeling (Rahman et al., 2010;  Rahman et al., 2013; Islam et al., 2015; Rahman and Harding, 2014) constructive to design effective policies and see the governments and NGOs, environmental and spatial effects across different countries (Rahman, 2016b; Rahman  and Upadhyay, 2015; Rahman and Harding, 2016; Phill, 2011; Rahman, 2009) to bear up against the health and nutritional perils to make sure for ending malnutrition by 2020: an agenda for change in the millennium (Rahman, 2016a).
 
This health microsimulation modeling (Equation 9) is for exploring health status in bio-statistical rhythm as rule as five postulates derived from statistical analyzing of the respective logarithmic modulator. The current study can be a super active tool in health pedagogy (Poole et al., 2016; Karsten, 2012; Glickman and Mitchell, 1948) to resist nutritional victimization through using nutrition counseling with the help of modified mass energy equivalence in nutritional epidemiology in the branch of health science and biostatistics (Rahman and Hakim, 2016b; Chowdhury et al., 2016; Rahman and Hakim, 2016c).


 CONCLUSION

Malnutrition is one of the splendid public health panics in both the developing and developed countries in the world. The current study findings are the five galore popular bio-statistical postulates on existing health status based on the logarithmic biophysical Equation 9. This equation can help to take immediate bid to shirk the malnutrition intensity identifying the health confounding trails and therefore the equation H = log-1(4+ log mkg – 2 log hcm) is the logarithmic biophysical modulator of health status in statistical modeling. So the national and international think tank should pave the way to make pinch of salt on this modulator to reduce malnutrition bulk as degree as possible at national as well as international level.  Future  research  should  adopt  this  modulator   in designing microsimulation modeling on health pedagogy and nutritional epidemiology. This modulator should also be explored in further study for policy designing, analysis and checking spatial effects for childhood, adulthood and geriatric health status for health and nutritional upgradation.


 CONFLICT OF INTERESTS

The authors have not declared any conflict of interests.



 REFERENCES

Ahmed F, Islam MA (2009). Dietary pattern and nutritional status of Bangladeshi manual workers (rickshaw pullers). Int. J. Food Sci. Nutr. 48(5):285-291.
Crossref
 

Alam MA, Hakim MA, Rouf MA, Haque MO, Ali ME, Zaidul ISM (2011). Nutritional status of urban slum children below five years: Assessment by anthropometric measurements with special reference to socioeconomic status. J. Food Agric. Environ. 9(2):85-90.

  
 

Bandini LG, Dietz WH Jr (1987). Assessment of body fatness in childhood obesity: evaluation of laboratory and anthropometric techniques. J. Am. Diet Assoc. 87(10):1344-1348.

  
 

Bela BCJ (2013). Introduction to Soil Mechanics. John Wiley and Sons. P 9.

  
 

Black RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, Ezzati M, Mathers C, Rivera J (2008). Maternal and child undernutrition: global and regional exposures and health consequences. Lancet 371(9608):243-260.
Crossref

  
 

Brinkman SA, Gialamas A, Rahman A, Mittinty MN, Gregory TA, Silburn S, Goldfeld S, Zubrick SR, Carr V, Janus M, Hertzman C, Lynch JW (2012). Jurisdictional, socioeconomic and gender inequalities in child health and development: Analysis of a national census of 5 year olds in Australia. BMJ Open 2(5):1-15.
Crossref

  
 

Chowdhury MR, Rahman MS, Khan MM, Mondal MN, Rahman MM, Billah B (2016). Risk factors for child malnutrition in Bangladesh: A multilevel analysis of a nationwide population-based survey. J. Pediatr. 172:194-201.
Crossref

  
 

Chumlea WC, Schubert CM, Sun SS, Demerath E, Towne B, Siervogein RM (2007). A review of body water status and the effects of age and body fatness in children and adults. J. Nutr. Health Aging 11(2):111-118.

  
 

Davies PS, Lucas A (1989). Quetelet's index as a measure of body fatness in young infants. Early Hum. Dev. 20(2):153-141.
Crossref

  
 

de Onis M, Blossne M, Eorghi E (2011). Prevalence of stunting among pre-school children 1990-2020', Growth Assessment and Surveillance Unit. Publ. Health Nutr. 14:1-7.

  
 

Einstein A (1916). Relativity: the Special and General Theory. New York: H. Holt and Company.
Crossref

  
 

Eknoyan G (2008). Adolphe Quetelet (1796-1874) - the average man and indices of obesity. Nephrol Dial Transplant 23(1):47-51.
Crossref

  
 

Fakir AMS, Rahman MW (2015). Determinants of malnutrition among urban slum children in Bangladesh. Health Educ. Rev. 5:22.
Crossref

  
 

Freedman DS, Ogden CL, Berenson GS, Horlick M (2005). Body mass index and body fatness in childhood. Curr. Opin. Clin. Nutr. Metab. Care 8(6):618-23.
Crossref

  
 

Freeman DA (2005). Statistical Models: Theory and Practice, Cambridge University Press.
Crossref

  
 

Ganong WF (2001). Review of Medical Physiology (12th edn.). P 302.

  
 

Garrow JS, Stalley S, Diethelm R, Pittet P, Hesp R, Halliday D (1979). A new method for measuring the body density of obese adults. Br J Nutr. 42(2):173-183.
Crossref

  
 

Garrow JS, Webster J (1985). Quetelet's index (W/H2) as measure of fatness. Int. J. Obes. 9(2):147-53.

  
 

Ghosh S, Shah D (2004). Nutritional problems in urban slum children. Indian Pediatr. 41(7):682-696.

  
 

Glickman N, Mitchell HH (1948). The total specific dynamic action of high-protein and high-carbohydrate diets of human subjects. J. Nutr. 36(1):41-57.

  
 

Hakim MA (2015). Nutritional Status and Hygiene Practices of Primary School Goers in Gateway to the North Bengal. Int. J. Publ. Health Res. 3(5):271-275.

  
 

Hakim MA (2016). Malnutrition prevalence and nutrition counseling in developing countries: A case study. Int. J. Nurs. Health Sci. 3(3):19-22.

  
 

Hakim MA, Kamruzzaman M (2015). Nutritional status of preschoolers in four selected fisher communities. Am. J. Life Sci. 3(4):332-336.
Crossref

  
 

Hakim MA, Kamruzzaman M (2016). The dance of poverty and education for childhood nutritional victimization in Bangladesh. J. Biol. Environ. Eng. 1(1):6-9.

  
 

Hakim MA, Rahman A (2016). Health and Nutritional Condition of Street Children of Dhaka City: An Empirical Study in Bangladesh. Sci. J. Publ. Health 4(1-1):6-9.

  
 

Hakim MA, Talukder MJ (2016). An Assessment of Health Status of Street Children in Tangail, Bangladesh. Sci. J. Publ. Health 4(1-1):1-5.

  
 

Hakim MA, Talukder MJ, Islam MS (2015). Nutritional status and hygiene behavior of government primary school kids in central Bangladesh. Sci. J. Publ. Health 3(5):638-642.
Crossref

  
 

Hasan N, Ashraf M, Rahman A (2016). Effects of Information Communication Technology (ICT) in the Childhood Developments and Lives of Disabilities: A Multivariate Analysis. Sci. J. Publ. Health 4(1-1):28-37.

  
 

Huber M, Knottnerus JA, Green L, van der Horst H, Jadad AR, Kromhout D, Schnabel P (2011). How should we define health? BMJ 343 (d4163).
Crossref

  
 

Islam D, Ashraf M, Rahman A, Hasan R (2015). Quantitative Analysis of Amartya Sen's Theory: An ICT4D Perspective. Int. J. Inform. Commun. Technol. Human Dev. 7(3):13-26.
Crossref

  
 

Kamruzzaman M, Hakim MA (2015). Socio-economic Status of Child Beggars in Dhaka City. J. Social Sci. Humanities 1(5):516-520.

  
 

Kamruzzaman M, Hakim MA (2016). Basic Rights on the Wane, Human Rights on Brown Study: A Case Study on Thrown Away Children in Bangladesh. Int. J. Environ. Plan. Manage. 2(4):29-35.

  
 

Karsten K (2012). Educational theory as tropical rheotic: The concepts of pedagogy of Johann Freidrich Herbert and Freidrich Schleiermacher. Stud. Phil. Educ. 31(1):265-273.

  
 

Kuddus A, Rahman A (2015). Affect of Urbanization on Health and Nutrition. Int. J. Stat. Syst. 10(2):165-174.

  
 

Kuddus A, Rahman A, Talukder MR, Hoque A (2014). A modified SIR model to study on physical behaviour among smallpox infective population in Bangladesh. Am. J. Maths. Stat. 4(5):231-239.

  
 

Megabiaw B, Rahman A (2013). Prevalence and determinants of chronic malnutrition among under-5 children in Ethiopia. Int. J. Child Health Nutr. 2(3):230-236.
Crossref

  
 

Norgan NG, Ferro-Luzzi A (1982). Weight-height indices as estimators of fatness in men. Hum Nutr. Clin. Nutr. 36(5):363-372.

  
 

Petitt DB, Sidney S (1988). Obesity and cholecystectomy among women: implications for prevention. Am. J. Prev. Med. 4(6):326-30.

  
 

Phill M (2011). Small area housing stress estimation in Australia: Microsimulation modeling and statistical reliability, University of Canberra, Australia.

  
 

Poliyanin AD, Manzhirov AV (2007). Handbook of mathematics for engineers and scientists. CRC Press. P 9.

  
 

Poole SA, Hart CN, Jelalian E, Raynor HA (2016). Relationship between dietary energy and dietary quality in overweight young children: a cross-sectional analysis. Pediatr. Obesity 11(2):128-135.
Crossref

  
 

Rahman A (2004). A review of child malnutrition and maternal health in developing countries, Discussion Paper - 04, Department of Statistics, University of Chittagong, pp. 1-37.

  
 

Rahman A (2006). Risk Factors for Malnutrition in Bangladeshi Children, Paper presentation at the Showcasing Toowoomba Area Health Research conference (STAHR-06), Centre for Rural and Remote Area Health, University of Southern Queensland, Toowoomba.

  
 

Rahman A (2009). Small area estimation through spatial microsimulation models: Some methodological issues, Paper presented at the 2nd General Conference of the International Microsimulation Association, The National Conference Centre Ottawa, Canada, (June 8 to 10) 2009. pp. 1- 45.

  
 

Rahman A (2016a). Significant risk factors for childhood malnutrition: Evidence from an Asian developing country. Sci. J. Publ. Health 4(1-1):16-27.

  
 

Rahman A (2016b). Small area housing stress estimation in Australia: Calculating confidence intervals for a spatial microsimulation model. Communications in Statistics - Simulation and Computation (forthcoming; 
Crossref

  
 

Rahman A, Biswas SC (2009). Nutritional status of under-5 children in Bangladesh. South Asian Journal of Population and Health 2(1): 1-11.

  
 

Rahman A, Chowdhury S (2007). Determinants of chronic malnutrition among preschool children in Bangladesh. J. Biosoc. Sci. 39(2):161-173.
Crossref

  
 

Rahman A, Chowdhury S, Hossain D (2009). Acute malnutrition in Bangladeshi children: levels and determinants. Asia Pacific J. Publ. Health 21(1):294-302.
Crossref

  
 

Rahman A, Chowdhury S, Karim A, Ahmed S (2008). Factors associated with nutritional status of children in Bangladesh: A multivariate analysis. Demogr. India 37(1):95-109.

  
 

Rahman A, Hakim MA (2016a). An epidemiological study on hygiene practice and malnutrition prevalence of beggars children in Bangladesh. Int. J. Nutr. Diet. 4(1):29-46.
Crossref

  
 

Rahman A, Hakim MA (2016b). Malnutrition prevalence and health practices of homeless children: A cross-sectional study in Bangladesh. Science J. Publ. Health 4(1-1):10-15.

  
 

Rahman A, Hakim MA (2016c). Measuring modified mass energy equivalence in nutritional epidemiology: A proposal to adapt the biophysical modelling approach. Int. J. Stat. Med. Res. 5(3):219-223.
Crossref

  
 

Rahman A, Harding A (2010). Some health related issues in Australia and methodologies for estimating small area health related characteristics. Online Working Paper Series 2010; WP-15, NATSEM, University of Canberra. pp. 1-59.

  
 

Rahman A, Harding A (2013). Prevalence of overweight and obesity epidemic in Australia: some causes and consequences. JP J. Biostat. 10(1):31-48.

  
 

Rahman A, Harding A (2014). Spatial analysis of housing stress estimation in Australia with statistical validation. Austr. J. Reg. Stud. 20(3):452-486.

  
 

Rahman A, Harding A (2016). Small area estimation and microsimulation modeling. Chapman and Hall/CRC Press, Florida.
Crossref

  
 

Rahman A, Harding A, Tanton R, Liu S (2010). Methodological issues in spatial microsimulation modelling for small area estimations. Int. J. Microsimul. 3(2):3-22.

  
 

Rahman A, Harding A, Tanton R, Liu S (2013). Simulating the characteristics of populations at the small area level: New validation techniques for a spatial microsimulation model in Australia. Comput. Stat. Data Anal. 57(1):149-165.
Crossref

  
 

Rahman A, Kuddus A (2014a). A new model to study on physical behaviour among susceptible infective removal population. Far East J. Theo. Stat. 46(2):115-135.

  
 

Rahman A, Kuddus A (2014b). Effects of some sociological factors on the outbreak of chickenpox disease. JP J. Biostat. 11(1):37-53.

  
 

Rahman A, Sapkota M (2014). Knowledge on vitamin A rich foods among mothers of preschool children in Nepal: impacts on public health and policy concerns. Sci. J. Publ. Health 2(4):316-322.
Crossref

  
 

Rahman A, Upadhyay S (2015). A Bayesian reweighting technique for small area estimation. Current Trends in Bayesian Methodology with Applications, CRC Press, London pp. 503-519.
Crossref

  
 

Robertson HP (1949). Postulate versus Observation in the Special Theory of Relativity. Rev. Modern Phys. 21(3):378-382.
Crossref

  
 

Schervish MJ (1995). Theory of statistics. New York: Springer.
Crossref

  
 

Shirali S (2002). A primer on Logarithms. Hyderabad: University Press, esp. section 2.

  
 

Strain GW, Zumoff B (1992). The relationship of weight-height indices of obesity to body fat content. J. Am. Coll. Nutr. 11(6):715-718.
Crossref

  
 

UNICEF (United Nations International Children's Emergency Fund) (1997). Malnutrition in South Asia; A Regional Profile, UNICEF report, P 8.

  
 

Wohler F (1828). Ueber kunstliche Bildung des Harnstoffs. Annalen der Physik und Chemie 88(2):253-256.
Crossref

  
 

WHO (World Health Organization) (2006a). BMI Classification. Global Database on Body Mass Index.

  
 

WHO (World Health Organization) (2006b). Constitution of the World Health. WHO, Geneva.

  

 




Copyright © 2025 Author(s) retain the copyright of this article.

This article is published under the terms of the Creative Commons Attribution License 4.0

Back to Vol. 9 No. 5
Back to articles
SUBSCRIBE TO RSS
SUBMIT MANUSCRIPT
CONFERENCES
          */?>