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Indian Journal of Community Medicine

Multifactorial Analysis of Blood Pressure
Variations in a Rural Community of West Bengal

Author(s): S. K. Sadhukhan1, A. Dan2

Vol. 30, No. 2 (2005-04 - 2005-06)


Research question: How is the variations of blood pressure in relation to different risk factors of hypertension in rural community. Objective: To study the blood pressure variations in relation to different risk factors of hypertension in a rural community of West Bengal. Study design: Cross-sectional. Setting: A village (Dearah) of Singur block in Hoogly District of West Bengal. Participants: All adult persons in the village (>18 years). Study variables: Independent: Age; Education; Occupation; Family assets; Additional salt intake with diet; Body mass index. Dependent: Systolic blood pressure (SBP); Diastolic blood pressure (DBP). Statistical analysis: Correlation and Multiple linear regression. Results: Multifactorial analyses revealed that 4 important factors i.e. age, occupation, BMI and additional salt intake with diet had significant role in SBP and DBP variations among study subjects (p< 0.001). Together they accounted for about 39% of SBP and 23.5% of DBP variations leaving a large part (about 61% of SBP and 76.5% of DBP) unnexplained. Individual role of these factors were analyzed (after controlling all Other Independent variables and interactions between themselves) and observed that age was most important explaining about 16.57% of SBP and 7.90% of DBP variations alone. Similar figures for others were BMI (3.10% % 7.78%), occupation (3.18% & 0.66%) and additional salt intake (1.55% & 1.53%). Conclusions: More and more similar rural community based studies need to be done to identify the role of known and unknown factors in blood pressure variations. Maintenance of an ideal body weight, non-sedentary lifestyle and avoiding extra salt with diet need to be promoted.

Key Words: Multifactorial Analysis; Blood Pressure; Variations; Rural Community.


Arterial blood pressure is a continuously distributed variable with no natural dividing line to distinguish normal and high blood pressure. But for day to day clinical and epidemiological work, certain cut-off points have been suggested and accepted worldwide for adults1-4. These cut-off points are accepted levels of blood pressure at which action is warranted considering risks and benefits in a specific population group, may be regarded as an operational definition5.

Hypertension is a classical disorder with multifactorial causation. Moreover, for majority of the persons, no definite cause is found, labeling them as essential hypertension. The search for various factors continues to explain the etiology of hypertension. As the dividing line is an arbitrary one, the factors influencing hypertension will also influence the normal variations of blood pressure in a community. Here is an experience of blood pressure variations in a rural community of West Bengal.

Matrials and Methods

A cross-sectional study on hypertension was carried out in the year 2001 among all adult persons (> 18 years) of a village (Dearah) in Singur block of Hoogly district of West Bengal. It was perfomed by house to house visit to contact each and every adult person of the village (Total 1,201; Male 597 and Female 604), using a pre-tested semi structured schedule. The following important factors were studied for their association with hypetension: age, educational level, occuptional physical activity (i.e. type/category of worker- ICMR), family asset score [an indirect assessment of socioeconomic status, used in National Family Health Survey 1992 (NFHS)], additional salt intake with diet, BMI [ = Weight (Kg) Height (Mt)2-measure of obesity], tobacco and alcohol consumption, family history of hypertension and use of oral contraceptive pills among females. SBP and DBP of every adult were measured by a sphygmomanometer, a well calibrated regularly checked mercury manometer standard guidelines were achieved to, while measuring the blood pressure of individuals.

After unifactorial analysis, 6 factors (i.e. Age, Educational level, Family asset score, Occupational physical activity, additional salt intake with diet and BMI) were found to have significant association with hypertension among all study subjects. Then a multiple linear regression analysis was done for SBP and DBP variation with an assumption that the factors responsible for hypertension will also be responsible for SBP and DBP variations and the final blood pressure of a person, as there is no natural dividing line between normal and high blood pressure, the so called cut-off points are arbitrary.

The method of correlation and multiple linear regression was used with the help of SPSS software package using least square techniques. To perform this analysis, variables were assigned uniform numerical scores7 (Table-I)


The correlation matrix obtained between 8 variables (6 independent variables and 2 dependent variables SBP & DBP) are depicted in table II. This table shows that all the six independent variables had significant correlation with both SBP and DBP except between asset score and DBP. This corelation was found to be much less for education vs. DBP and asset score vs. SBP (r2< 0.1). This table also shows that there were significant corelation between many of these independent (regressor) variables themselves. It suggests that it was possible to have interactions between the independent variables in the prediction of final SBP and DBP of an individual through multiple regression.

Table I - Score assigned to different factors for multifactorial analysis

  Score assigned
Factor 1 2 3 4 5 6
Age (Years) 18-27 28-37 38-47 48-57 58-67 68+
Education Illiterate Class I-IV Class V-VIII Class IX-XII >Class XII  
Asset Score <5 6-15 >16      
Occupation Sedentary Non sedentary        
Addl. salt intake Yes No        
BMI <18.5 18.5-25 >25      
SBP (mm Hg) up to 109 110-129 130-149 150-169 170-189 >190
1 2 3 4 5 6 DBP (mm Hg) up to 64 65-74 75-84 85-94 95-104 >105


Table II - The 8 Variable Correlation Matrix

Variable Age Edn. Asset Occu-
Age 1.0 -0.32 0.06 -0.36 -0.06 -0.07 0.54 0.34
  ** * * ** * ** **
Edn.   1.0 0.33 -0.15 0.22 0.23 -0.12 -0.07
    ** ** ** ** ** *
Asset     1.0 -0.19 0.12 0.18 0.06 0.05
      ** ** ** * NS
Occupn.       1.0 0.01 -0.06 -0.37 -0.22
        NS ** **  
Added salt         1.0 0.01 -0.15 -0.14
          NS ** **
BMI           1.0 0.15 0.26
            ** **
SBP             1.0 0.74
DBP               1.0
* P<0.05, ** p<0.001, NS - Not significant


Table III - Independent Contribution (after Controlling Other Variables) by each Regressor Variable to the Total Variations of SBP and DBP (N=1201)
Variable Systolic Blood Pressure Diastolic Blood Pressure
(R2 Change)
F Change p value Variation
(R2 Change)
F Change **
Age 16.57 324.07 ** 7.90 123.28 **
Occupation 3.18 62.13 ** 0.66 10.34 **
BMI 3.10 60.38 ** 7.78 121.41 **
Added salt 1.55 30.30 ** 1.53 23.81 NS*
Education 0.21 3.97 + 0.02 0.37 NS*
Asset 0.00 0.22 NS* 0.00 0.11 NS*
Total 24.61     17.89    
[email protected] 14.32     5.61    
Total variations explained 38.93     23.5    
* Not significant, + P.< 0.05,
** P < 0.01
@ Explains the total variations of SBP & DBP explained by the interaction between all the six independent variables.

Since almost all the variables were found to have significant corelation with both SBP and DBP, all of them were included in the multiple regression anjalyses and the results were as follows:

Ys = 126.44+6.511 x1 - 0.0011x2- 1.034x3-8.846x4-5.411x 5 + 6.135 x 6

Yd = 76.087+2.051x1-0.0496x2-0.284x3-1.843x4-2.450x 5 + 4.433 x 6

Where x1 x2 x3 x4 x5 and x6 are the respective scores for age, educational level, asset score, occupational physical activity, additional salt intake and BMI of an individual respectively and Ys and Yd are the predicted SBP and DBP of an individual respectively in original unit (mm Hg). (not on table)

These results showed that all the factors were significant except Education and Asset score (a likely confounding effect). Regression of SBP yields a multiple correlation coefficient (R) of 0.624 which was highly significant (p< 0.0001) and explained about 39% of total variations of SBP among study subjects. Similar figure for DBP was 0.480 (R), which was also highly significant (p< 0.000)1 and explained about 23.5% of the total variations of DBP. It was also noted that a large part of variations of SBP and DBP remained unexplained, probably signifying the role of many other factors which were unknown and not considered in the present study.

The role of each of the above mentioned factors in the variations in of SBP and DBP are detailed below in this tables shows the independent role of the regressor variables when all other variables are controlled and the interaction effect between independent variables themselves were taken into account. For SBP, age alone explained 16.57% of the total variations followed by occupatioonal physical activity (3.18%) and BMI (3.10%). For DBP also, age explained maximum variations (7.90%) followed by BMI (7.78%) and additional salt intake (1.53%). Independetly, educational level and asset score contributed very little to the total variations of SBP and DBP, which for the most part were statistically insignificant. It also shows that occupational physical activity alone explained more SBP variation than DBP (3.18%) vs. (0.66%) whereas BMI alone explained more DBP variation than SBP (7.78% vs. 3.10%).


Multiple linear regressions revealed that out of 6 factors found to have significant association with hypertension, 4 factors i.e. Age, BMI, Occupational physical activity and Additional salt intake were found to have significant role in variations of SBP and DBP. All these factors together explained about 39% and 23.5% of total variations of SBP and DBP respectively leaving large variations unexplained, suggesting the role of many other unknown factors. Bagchi SC8 observed these figures to be 21.60% and 15.28% respectively in Allahabad urban community with 4 factors i.e. Age, Body built, Social class and Diet. Shrivastava RN9 observed these figures to be 5.19% and 6.19% respectively for male and 9.60% and 2.24% respectively for female in a rural population of Jhansi with 4 factors i.e. Age, Weight, Height, and Body built. Regression analyses by many other workers also revealed the importance of similar factors for variations and final predictions of SBP and DBP i.e. Age10; Age, BMI, Central obesity and higher socio-economic status11; Age and BMI12; Age, BMI and daily alcohol intake13.

Conclusions and Recommendations

After multiple linear regression, the present study revealed 4 important factors to significantly contribute for the SBP and DBP variations i.e. Age, BMI, Occupational phyiscal activity and additional salt intake. Nevertheless about 61% and 76.5% of total SBP and DBP variations respectively were found to be unexplained indicating that we have miles to go before revealing all the factors responsible for blood pressure variations and hypertension.

The individual role of each factor (after controlling for other variables) revealed that age was the most important explaining 16.57% of SBP and 7.90% of DBP variations alone. Similar figures for other factors were -3.10% and 7.78% for BMI, 3.18% and 0.66% for Occupational physical activity and 1.55% and 1.53% for additional salt intake.


I express my sincere gratitude and thanks to Dr. Kaushik Sankar Bose, Ex-Lecturer, Dept. of Anthropology, University of Calcutta and Mr. Ashis Mukhopadhyay, Junior Research Fellow, Anthropological Survey of India (ASI-HQ), Kolkata-16 for their wholehearted effort to perform this multifactorial analyses. Without their help, this study would not have been possible.


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  12. Rywik SL, Davis CE, Pajak A, Broda G, Folsom AR and Kawalee E et. al: Pravalence, awareness, treatment and control of hypertension in the Pol-MONICA project and the US atherosclerosis risk in communities study: Ann. Epidemiol: 1998: 8 (1); 3-13.
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1: Deptt. of Community Medicine, North Bengal Medical College, Sushrutanagar, Darjeeling-734432
2: Deptt. of Community Medicine, Medinipur Medical College, Medinipur

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