Asia-Pacific Journal of Public Health 1988 - Vol 2 No.1

Breast-Feeding, Dietary Intake and Weight-For-Age of Children in

Rural Burma

RR Frerichs: DVM, Dr PH KT Tar,t MD, MPH +School of Public Health University of California, Los Angeles (UCLA)Los Angeles, Ca 90024, USA tDepartrnent of Health Rangoon, Burma

Abstract

We conducted a rapid survey of 396 children aged o to 35 months in rural Burma using the following methods: cluster sampling; a portable, battery­powered microcomputer; and recently-developed software. Among the children in the survey, the percentage consuming breast milk remained high until they reached 15 to 17 months of age, by which time most of the children were already eating pro­tein-rich solids (eggs, fish or meat). During the first year of life, 11% were undernourished as deter­mined by weight-for-age less than the first percen· tile of the reference standard. This percentage in­creased to 38% and 35% for years two and three, respectively. Reduced weig ht-for-age was found to be associated with eating solid foods, even after adjusting for either age and breast-feeding behav­Iour or breast-feeding alone. This finding Is proba­bly due to Insufficient calorIc consumption or con­current Infections. Rapid, computer-assisted sur­veys such as this are intended to provide quick descriptive information for planning, implementing or evaluating community-based intervention or prevention programmes.

Keywords: Anthropometric, Breast-feeding, Com­puters, Surveys.

Introduction

Undernutrition is widely believed to contribute to high levels of morbidity and mortality among children in the developing world. Maternal and child health pro­grammes commonly measure the nutritional status of young children and then use the information to plan, modify or evaluate programme activities. Included as measurements are anthropometric and dietary assess­ments. In rural Burma, as in other less developed areas of the world, midwives and other health professionals are expected to gather data on growth patterns, dietary intake and the number of children measured. These provider-based data are then combined into reports for submission to government officials. Since infonnation gathering is often not considered a high priority activ­ity, under- or selective reporting limits the usefulness of the data. In Burma, we have developed and tested an alternative method to help policy-makers identify quickly the nutrition and immunisation status of chil­dren. The approach, tenned "rapid survey methodol­ogy" (RSM), uses portable, battery-powered micro­computers and recently-developed software to conduct quick sample surveys in rural communities. Using

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Asia-Pacific Journal of Public Health 1988 - Vol2 No.1

RSM, we were able during four days to sample scien­tifically the young children in a rural region of Burma and one day later, to present the study findings to the local medical officer. Within a period offive more days, we used the same computer and software to prepare a 50-page report with graphs and extensive tables docu­menting the sun.rey findings.

Elsewhere we have described RSM and shown how the method can be used to assess quickly immunisation coverage in a community.I.3 Here we will present our findings on the weight-far-age and early dietary intake of the sun.reyed population. Some of the findings were included in our original 50-page report while others were derived using additional anthropometric and sta­tistical software. All analyses presented here and in our earlier reports were done using a portable microcom­puter. Thus, the frndings sen.re as an illustration of analyses and graphs that could theoretically have been completed in many remote areas of the world.

Materials and Methods

Survey Population

From 4th to 7th May, 1987, a two-stage cluster sample was conducted in Hlegu Township, Rangoon Division, Burma, of all births within the last three years. The sample population was estimated at 5,192 births during the past three years to residents of 79 different commu­nities. Using a spreadsheet program, l 30 clusters were selected with probability proportionate to size (PPS) from the 79 villages in the sampled population. Within each cluster, approximately 14 births during the past three years were selected using the Standard Expanded Programme on ImmunizationIWorld Health Organiza­tion (WHO) sampling meLhod.4 The final study popula­tion was comprised of417 births: 396 children were still

alive while 21 had died. Only the living children are included in this analysis. Participation of the eligible respondents was 100%.

InterviewlExamination Procedures

All inten.riews and examinations were done by seven physicians and two statisticians as part of a programme to establish a Rapid Survey Unit in the Ministry of Health. Twenty-six variables were gathered for each subject; only the dietary and anthropometric items will be described here. A local or auxiliary midwife accom­panied the inten.riewer. The persons being inten.riewed were not told that the inten.riewer was a physician or a statistician. The mother or other respondent was asked if the child was now breast-feeding and if the child was being fed protein-rich solid foods - specifically eggs, meat or frsh. Each child was weighed using a Salter spring balance scale with a hanging cloth container. The scale was set to zero before each weighing.

Data Entry, Transformation and Analysis

All analyses were done on a Toshiba TI100 portable, battery-powered computer (IBM compatible, 5] 2K RAM, single nOK disk drive). Data were entered into the computer at the end of each field day using Sun.rey Mate].5 (Henry Elkins and Associates, Inc., 15 Willow Circle, Bronxville, NY 10708, USA), a software pro­gram for entry, editing and analysis of sun.rey data. The weight-far-age percentile score was derived for each child using CASP 3.0 (CDC/Center for Health Promo­tion and Education, Division of Nutrition/Statistics Branch, 1600 Clifton Rd, Atlanta, Ga 30330, USA), an anthropometric analysis program using the United States National Center for Health Statistics (NCHS) growth curves for children, from birth to 18 years old,

Table 1. - Age and Sex of Surveyed Children.

Age in years

<1 1-2 2-3 Total

Sex No. % No. % No. % No. %

Male 68 17.2 68 17.2 70 17.7 206 52.0 Female 82 20.7 56 14.1 52 13.1 190 48.0

Total 150 37.9 124 31.3 122 30.8 396 100.0

- - -

Asia-Pacific Journal of Public Health 1988 • Vol 2 No.1

- ...... C'l ~ ~ .....-~ ~ lI) - ­~ "? N .;, 00 r- 0 M 0 M v:; d-. ...... C'l ~ C'l ..... .....

Age group (months)

Fig 1. - Percentage of children currently breast-feeding and eating protein-rich solid foods, by age.

--f'Or- Solids

. ­ -+­ - Breast

~ . ~

'\ \

\

\ \

\

\ 't

\ \

\

\ \

\

~-~'" •,.

~

••

"

............... ..................... - ...

0 ~ C'l~ r­ 0 lI)- -

20

80

40

60

100

%

100

\I \

/ \

I

I

- - ... - - < 5th P I~

"I o < lstP

I \

80

60 %

40

20

0 '<t r- 0 M v:; 0\ C'l lI) ..... ..... ('I M M

('I 00 C";l C";l , C";l

v;> - .;, 0N 00 r- M0 M ..0 d-. - ('I ~ ('I M M -Age !,JfOUp (months)

Fig 2. - Percentage of children with weight-for-age less than the first and fifth percentile of the CDC reference standard, by age.

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Asia-Pacific Journal of Public Health 19BB - Vol 2 No.1

as the reference. This same population has been adopted by the WHO as a comparative reference standard.~

Graphs by age group were generated and printed using Supercalc4 (Computer Associates International, Inc., San Jose, Ca 95131, USA), a spreadsheet and graphics program. Cross-tabulation analyses and regression analyses were done using both Survey Mate and Systat 3.0 (Systat Inc., 2902 Central St., Evanston, II 60201, USA), a general purpose statistical analysis program.

Results

Mothers were the respondents for 86.9% of the 396 children; other respondents were fathers (4.3%), older siblings (2.0%), other relatives (6.6%), and unrelated persons (0.3%). The age and sex of the children are shown in Table 1. While the sampled children were evenly distributed among the second and third year of life, there was a moderate excess of infants. Males comprised 52% of the sampled population.

The respondent was asked if the child was now breast-feeding and/or eating eggs, fish or meat. Rather than asking about the consumption of solids, some of which have low nutritional value, we decided to limit the question to the early consumption of protein-rich foods. Among the children in the sample, the percent­age consuming breast milk remained high until they reached 15 to 17 months ofage (Figure 1). By this time, most children were already consuming either eggs, fish or meat (termed "solids" in Figure 1).

Each child's weight-for-age was compared, with the help of the computer, with the sex-specific weight distribution at the same age in the reference pop~ation.

A percentile score was then determined. Thus a 2.4­month-old boy weighing as much as the median boy of the same age in the reference population would have a percentile score of 50%. In Burma, children tend to weigh much less than the reference population. Yet the pattern of growth should not differ from the reference standard once the lower weight has been taken into account. Undernutrition is usually defined as being below agiven cut-offvalue for one or more anthropom­etric measurements. To observe the trend with age, we selected two low percentiles: less than the fifth percen­tile and less than the first percentile. If the Burmese population experienced the same growth trend as the reference United States population, we would expect 1% of the children at every age to be below the first percentile and 5% to be below the fifth percentile. Figure 2 shows that this was clearly not the case. The percentage of the Burmese children who were below the first and fifth percentiles increased dramatically through the first year of life and then remained at 20%

to 50% for those below the first percentile and 60% to 90% for those below the fifth percentile.

When comparing Figures 1 and 2, the consumption of solid (i.e., protein-rich) foods exhibits a pattern similar to that of the level of undernutrition. Table 2 presents this comparison in greater detail. While the numbers in the cells are too small for definite state­ments, some patterns are observable. Among children less than one year of age, the level of undernutrition is least among those consuming only breast milk. By the second yearoflife the pattern is reversed, with children consuming only breast milk exhibiting the higher level of undernutrition. By the third year of life, those con­suming both breast milk and protein-rich solids exhib­ited slightly higher levels of undernutrition than those consuming solids butno breastmilk. Only four children in the study wcre rcported to be receiving neither breast milk nor solids. All four weighed less than the first percentile of the reference standard.

To assess more completely the relative contributions of the two dietary components, we did two linear regression analyses with the weight-for-age percentile score as the dependent (or outcome) variable and either age and the two dietary components or the two dietary components alone as the independent (or predictor) variables. Table 3 shows that knowledge of the three independent variables of age, breast milk, and solids explains 23% of the variance of the weight-for-age percentile score (multiple R2 =0.23). During this age span, all three of the variables exhibit a negative, independent association with weight-for-age. For ex­ample, with every additional one month of age, the weight-for-age percentile score independently de­creases by 0.7%. For those reporting the consumption of protein-rich solids, the weight-for-age percentile score is 6.2% lower than those not consuming solids. The regression coefficients for both age and solids are deemed different from zero using the standard statisti­cal criteria ofp < 0.01. Excluding agefrom the analysis, we observe in the lower part of Table 3 that knowledge of the two dietary components now explains only 16% of the variance in the weight-for-age percentile score. While less predictive, this second equation permits us to assess more clearly the comparative effect of breast milk and solids consumption. Here the consumption of solids exhibits a more decisive negative effect on the weight-for-age percentile score independent of the reported value for breast milk.

Discussion

Using RSM, we were able to describe quickly health and dietary patterns among children at the community

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Asia-Pacific Journal of Public Health 1988 - Vol 2 No.1

Table 2. - Weight-for-Age Less Than First Percentile by Age and Dietary Intake Among Children Aged 0-35 Months.

Weight-for-age less than

1st percentile* Number of

Age and dietary intake children No. %

Age 0-1 year Breast milk only Breast milk and solidst Solids only Neither

Age 1-2 years Breast milk only Breast milk and solids Solids only Neither

Age 2-3 years Breast milk only Breast milk and solids Solids only Neither

115 31

2 2

18 89 17 0

2 44 72

4

Total 396

9 5 1 2

9 35

3

0 17 22

4

107

7.8 16.1 50.0

100.0

50.0 39.3 17.6

0.0 38.6 30.6

100.0

27.0

*CDC standard deviation-derived growth reference curves tSolids: eggs, fish or meat

Table 3. - Linear Regression of Weight-for-Age Percentile* by Dietary Intake and Age Among Chil­dren Aged 0-35 Months (n=396).

Level of Standard error statistical

Regression of regression significance Variable coefficient coefficient (2 tail)

Constant Age of child (months) Breast milk (yes= 1, no=O) Solids (yes=1, no=O) t

Constant Breast milk (yes=l, no=O) Solids (yes= 1, no=O)

29.98 -0.70 -3.84 -6.22

Multiple R2 = 0.23

18.28 3.29

-14.44 Multiple R2 = 0.16

3.19 0.13 2.42 2.36

2.49 2.13 1.91

0.00 0.00 0.11 0.01

0.00 0.12 0.00

*CDC standard deviation-derived growth reference curves tSolids: eggs, fish or meat

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level. Besides receiving descriptive information, ad­ministrators in Burma can also use the method to monitor objectively and independently the effects of various programmatic activities. For example, there has been some concern that children are weaned from the breast too early. Based on our survey results, this does not appear to be a problem in Hlegu Township. There has also been concern that protein-rich foods such as fish, eggs or meat are not being offered to children in rural communities. Again, we see from our data that this does not seem to be the case in this single township. Finally, there is concern that nutrition pro­grammes in Burma are not as beneficial as they might be in reducing the level of undernutrition. Our rapid analysis confirms that the level of undernutrition in­creases with age, at least through the first year of life. Thus the findings suggest that efforts to reduce the level of undernutrition should be intensified during infancy.

The consumption of solid foods being associated with a lowerweight-for-age percentile deserves further comment. At least two other factors could account for this association. Firstly, we did not assess the amount of food consumed. Thus the children may be eating less solids than are necessary for active growth. Secondly, infectious agents may find their way into the mouths of the young children when eating solid foods or when exploring their physical surroundings. Both infections and intestinal parasites are known to affect growth patterns, although neither were measured in our brief survey.

Other anthropometric surveys have been done in Burma and reported to the WHO as part of its global surveillance of the prevalence of undernutrition. The cut-off value for undernutrition is established by WHO as two standard deviations below the reference median for children of the same age and sex. As in the present study, the reference population used by WHO is based on the United States sample. Approximately 2.3% of all children in the reference population are below the two standard deviations cut-off value. Thus using this cut­off value, we would expect the prevalence of undernu­trition to lie somewhere between our first and fifth percentiles. In the most recent survey for rural Burma carned out between 1983 and 1985,66.8% of all chil­dren were below the cut-off value during the first six

Asia-Pacific Journal of Public Health 1988 - Vol 2 NO.1

months ofHfe, while 29.2% fell below the same cut-off value in the second six months of life. During the second and third yearoflife, 53.1 % and 51.2%, respec­tively, were below the stated cut-off value. This pattern is similar to that shown in Figure 2 but intermediate in level between the lines for the first and fifth percentiles.

While surveys are not meant to replace the existing health information system as sources of programmatic data, they do provide administrators with the means to determine independently and objectively local patterns of disease or health-related behaviour. With the recent reductions in cost, microcomputers will soon be readily available in many developing countries. By using the sampling procedure and software described here, 1-3

health professionals should have another method at their disposal for gathering information to deliver pri­mary efficient primary health care services in rural regions of their country.

Acknowledgements

This work was supported by the United States Agency for International Development, Project 482-004, Ran­goon, Burma. We gratefully acknowledge the assis­tance of Drs Tin Tin Win, Myint Htwe, Kyaw Thein, Khin Swe Min, Thein Swe, Than Tun Sein, and U Than Lwin and Daw Htay Htay Aye. We would also like to thank Virginia Hansen for her editorial assistance.

References

1. Frerichs RR, Tar Tar K: Computer-assisted rapid surveys in developing countries. Pub Hlth Rep (submitted).

2. Frerichs RR: Rapid analysis by portable microcomputers of WHO/EPI cluster sample surveys. Int J Epidemiol (SUbmit­ted).

3. Frerichs RR, Tar Tar K: Use of rapid survey methodology to determine immunization coverage in rural Burma. J Trop Pe­dialr (submitted).

4. Lemeshow S, Robinson D: Surveys to measure program coverage and impact: a review of the methodology used by the Expanded Program on Immunization. Wid Hllh Statist Otly 1985;38:65-75.

5. Measuring Change in Nutrition Status. Geneva: WHO, 1983. 6. World Health Organization. Global surveillance through

anthropometric measurements, Part IV - Prevalence of wasting and stunting in the Soulh-Easl Asia Region. Wk/y Epidemiol Rec 1987;10:64-6.

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