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Order Num ber 9420158

Coping in a highly seasonal environment: A household study of changing nutritional statu s, health, and diet am ong 'women and children from highland Lesotho Himmelgreen, David Allen, Ph.D. State University of New York at Buffalo, 1994

Copyright © 1994 by H inm ielgreen, D avid A llen. A ll rights reserved.

UMI 300 N. ZeebRd.

Ann Aibor, MI 48106

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COPING IN A HIGHLY SEASONAL ENVIRONMENT: A HOUSEHOLD STUDY OF CHANGING NUTRITIONAL STATUS, HEALTH, AND DIET AMONG WOMEN AND CHILDREN FROM HIGHLAND LESOTHO

by David Allen Himmelgreen

A dissertation submitted to the Faculty of the Graduate School of State University of New York at Buffalo in partial fulfillment of the requirements for the degree of Doctor of Philosophy


ACKNOWLEDGEMENTS

There were many people along the way who in one way or another contributed to this dissertation.

I owe them my

heart-felt thanks for their help and support, especially during the times when the task seemed unsurmountable. I would like to thank those individuals at the State University of New York who contributed to my development as a researcher, teacher, and as a person concerned with the human condition.

First and foremost, I want to thank Dr.

A.T. Steegmann, Jr., my advisor, mentor, and friend,

who

provided intensive guidance and training in human biology, but at the same time gave me enough latitude to explore those anthropological questions that most stimulated me.

I

would like to thank my committee members Drs. Christine Duggleby, Richard V. Lee, Ann P. McElroy, Elizabeth Randall, and Rebecca Huss-Ashmmore who contributed in a variety of ways to my intellectual and professional development.

My sincere thanks to Jean Grela who with her

expert knowledge guided me through the ins-and-outs of the anthropology program.

To Bobby Benedito, Debi Crooks, Kiko

Datar, Tim Sullivan, and David Turkon my sincere thanks for their friendship, humor, and intellectual stimulation. Ultimately the success of this dissertation is due to the help I received from many people in Lesotho.

First I

want to thank my dear friend Michael Matsumunyane who first introduced me to Lesotho and to the kindness of the Basotho i


people while he was a graduate student at SUNY Buffalo. Without Mike's encouragement this project would have never gotten off the ground.

Thanks to Douglas Ebner (former

director) and his staff at the United States Information Service (USIS) for all their help while I was in Lesotho and for their administration of my Fulbright Grant.

My thanks

to David Gittleman from the CDC for his expert knowledge on Lesotho and his extended hand of friendship.

My sincere

thanks to the many people within the Lesotho Government and various NGO's who graciously permitted me to conduct this research , offered invaluable criticism, and provided assistance.

These people include M.M. Mpeta (director of

FNCO), M.M. Ntsike (deputy director of FNCO), N. Kolane (nutritionist at FHP in the Ministry of Health),

M. Thato

(from the nutrition section of MOA), M. Pakose (nutritionist at FHP), Dr. M. Metsing (former head, FHP), and T. Khumalo (project officer at UNICEF). My sincere thanks to the following people from Mokhotlong:

Paramount Chief Mathealira Seeiso (Thabang),

Military District Officer Joseph Tsela, District Secretary Khoasi Letsisa, District Agricultural Officer Masheane Alotsi, Farmer Training Centre Coordinator Mamoswati Thobothobo, Chiefs Nkhasi Matete (Nthlolohetsane), Mojakisane Tsita

(Ha Mojakisane), Steven Molefi Phakisi

(Bfali), and Esau Majara Majara (Salang). Thanks also to Drs. Evert Kamphorst and Anton Wohlfart

ii


at the Government Hospital in Mokhotlong for their help, insights, and their friendship. I also want to thank our field assistants Ntsepeng Monese and Mohlolo Lertholi for their fantastic work and their friendship; without their skills this research could not have be done. My greatest thanks and love to my wife Nancy RomeroDaza who, as a colleague and friend, provided me with the support and insight to make this research successful beyond my expectations; additionally, I thank her for her invaluable help in editing this manuscript.

My sincere

thanks and love to my parents Arnold and Barbara and my sisters Carolyn and Laura for encouraging me to do what I loved most. Finally, I want to thank the women and children of Mokhotlong who enlightened me and taught me about a world I only read about.

There love of life in the face of

adversity is certainly a triumph of the human spirit.

To all of you, my sincere gratitude.

David

iii


This research was supported by a Fulbright Grant from The Board of Foreign Scholarships and the United States Information Agency; a dissertation improvement grant from the National Science Foundation (BNS-9100128); a grant-inaid of research from Sigma Xi, The Scientific Research Society; a grant from UNICEF-Lesotho, and a grant from the Mark Diamond Research Fund of the Graduate Student Association, SUNY Buffalo.


TABLE OF CONTENTS

Page Acknowdgements Table of Contents List of Tables List of Figures List of Photos Abstract

i v ix xiii xvi xii

A VIEW FROM THE FIELD: ENTRY

2

CHAPTER ONE: INTRODUCTION

3

I. II. III. IV. V. VI. A. B. C.

Statement of Problem Aims of the Research Specific Objectives Hypotheses Definition of Terms Significance of the Research Recognizing Seasonality An At-Risk Population and Under-Studied Area Adding to the literature

3 4 6 8 13 14 15 15 16

A VIEW FROM THE FIELD: THE LESSONS

17

CHAPTER TWO: REVIEW OF THE LITERATURE

18

I. II. A. B. III. A.

19 24 24 24 27 27

1. 2. B. 1. 2. C. 1. 2. 3. 4.

Synergism: Nutrition, Disease, and the Environment Malnutrition, Function, and Adaptation Reproduction and Work Capacity The Adaptive Significance of Small Body Size An Overview of Seasonality Seasonal Constraints and Bio-behavioral Coping Strategies Seasonality and Energy Balance Seasonality and Coping Strategies The Complexity of Studying Seasonality Recognition of Seasonal Hunger Seasonality as Multiple Phenomena Seasonality, Weight, Body Composition, and Growth The "Bad Season(s)" Seasonal Changes in Weight and Body Composition Seasonality, Maternal Work, and Birth Outcome Seasonality and Fertility

A VIEW FROM THE FIELD: AN UNCERTAIN FUTURE FOR LESOTHO

v


27 28 32 32 36 37 37 38 42 48 50

CHAPTER THREE: AN OVERVIEW OF LESOTHO: THE ORIGINS OF POVERTY

51

I. Geography, Ecology, and Climate II. The Basotho People A. The Population and its Demographic Make-Up B. The People III. A Brief History of Lesotho A. Early Human Habitation, The Bakoena and Bafokeng B. Moshoeshoe and the Basotho Nation C. The Wars with the Boers and British Annexation D. Cape Rule: From Prosperity to Conflict and Poverty E. Into the 20th Century and Independence IV. A Summary of the Origins ofPoverty in Lesotho V. The Recent Economic Situation A. General trends B. Migrant Labor C. Agriculture D. Economic Infrastructure E. Poverty in Lesotho V I . Education VII. Health and Nutrition A. Health Care Services B. Child Mortality C. Child Morbidity D. Child Nutritional Status E. Nutritional Deficiencies F. Maternal Mortality and Morbidity

50 57 57 62 67 67 68 70 72 74 80 83 83 87 88 91 95 97 99 99 100 106 106 109 110

A TEMPEST IN A TEAPOT: VIOLENCE ERUPTS IN LESOTHO

113

CHAPTER FOUR: METHODOLOGY I. Introduction II. Research Design A. Human Adaptability and Food Systems Models B. Variable Classification III. Field Assistants IV. Sampling Procedure and Sample V. Helping Those in Need VI. Timetable for Data Collection VII. Types of Data Collected A. Household Demography Bl. Socioeconomic Indicators I: Employment and Income Generating Activities B2. Socioeconomic Indicators II: Agriculture and Livestock B3. Socioeconomic Indicators III: Household Resources, Water, and Sanitation Cl. Anthropometries C2. Measurements and Procedures C 3 . Assessment of Nutritional Status D1 Dietary Consumption Patterns and Food Habits

115 115 115 115 116 117 117 123 124 126 127 127

vi


128 128 129 130 132 136

D2. D3. E. F. G. VII.

Dietary Data Dietary Instruments and Analysis Health Data Seasonal Work Climatic Data Statistical Analysis of the Data

138 138 140 141 142 143

INTO THE LESOTHO HIGHLANDS

147

CHAPTER FIVE: MOKHOTLONG AND ITS PEOPLE

149

I. II. A. B. III. A. B. IV. A. B.

149 157 157 162 167 167 173 176 176 179

Mokhotlong District: The People, Land, and Climate Demographic Characteristics of the Sample Demographic Characteristics Patterns of Marriage and Family Life The Household Economic Indicators of Households Household Sanitation Education, Wage Labor, Agriculture, and Livestock Education and Wage Labor Agriculture, Fuel Foraging, and Livestock

THE WORK BEGINS

194

CHAPTER SIX: NUTRITIONAL STATUS I. Seasonal Body Weight and Composition for Caretakers A. Descriptive Statistics for Body Weight and Composition B. Seasonal Nutritional Status for Caretakers by Household Type and Village Grouping C. The Relationship between Caretaker Nutritional Status, SES, and Agricultural Activities II. Child growth and Nutritional Status A. Descriptive Statistics for Child Growth B. Child Nutritional Status Differences by Age Groups, Seasons, and Sex C. Anthropometric Indices of Malnutrition D. Differences in Seasonal Nutritional Status in Children by Household Type and Village Grouping E. Correlations Between Seasonal Nutritional Status for Children and Socioeconomic Indicators III. Caretaker-Child Weight Change between Seasons IV. Comparisons with Other Populations

195 197

THE ROLE OF THE RESEARCHER

273

CHAPTER SEVEN: RESULTS III. HEALTH AND SEASONAL ILLNESS

274

I.

274

II.

Common Diseases Among Women and Children in Lesotho Helping Those who were Sick vii


197 204 213 222 222 236 245 247 253 260 268

276

III. The Most Common Illnesses Reported to Us IV. Analysis of Illness Episodes

278 281

THE INITIATION

295

CHAPTER EIGHT: RESULTS IV. DIETARY PATTERNS

296

I. A. B. C. D. II. III. A. B. C.

The Diet: General and Specific Considerations 296 Traditional and Changing Diet 296 Food Storage, Processing, Preservation, and 299 Preparation Usual Diet, meal Patterns, and Specific Preparation 302 Composition of the Diet 312 Breast-Feeding and Weaning 313 Seasonal Changes in Diet 315 Diet Variability Across Seasons 315 Diet Variability within Seasons by Household Type 320 and Village Grouping Associations between Diet Variability and Other 326 Variables

CHAPTER NINE: SUMMARY, DISCUSSION, AND CONCLUSION

330

I. Main Findings of Study A. Socioeconomic Status,Agriculture, and Livestock B. Seasonal Nutritional Status C. Seasonal Illness D. Seasonal Diet II. Testing the Hypotheses III. Biological Adaptation: Shifting Nutrient Stores IV. Coping with Seasonal Constraints: Wider Considerations V. Further Analysis and Follow-Ups. VI. The Importance of Seasonal Research

330 330 331 334 334 324 338 344

BIBLIOGRAPHY

352


348 349

L I S T OF TABLES Page

Table 3.1.

Description of Seasons

58

Table 3.2. Table 3.3. Table 3.4.

Historical summary of Lesotho's population Population Pyramid for Lesotho Aggregate indicators for the Lesotho Economy, 1980-1988

60 63 86

Table 3.5.

Government revenue from the Customs Union in millions of Maloti

89

Table 3.6.

Production of Main Crops in Lesotho 1977/8 - 1987/8 (metric tonnes)

90

Table 3.7.

Household by sex and main source of income

94

Table 3.8.

Average income per household by sex of head, presence of migrant worker and by education

94

Table 3.9.

Peri-natal mortality in hospitals 19841988

102

Table 3.10. Maternal mortality rate in Lesotho Hospitals

102

Table 4.1. Table 4.2.

Timetable for data collection Lesotho five food group scheme

126 141


Population size for study villages Sample size by household type and village grouping

152 158

Table Table Table Table Table

Household demography for the total sample Sex distribution for children in the sample Demographic statistics by household type Demographic statistics by village grouping Household economic indicators for total sample

160 161 162 163 167

Table 5.8.

Household economic indicators by household type

169

Table 5.9,

Household economic indicators by village grouping

171

5.3. 5.4. 5.5. 5.6. 5.7.

ix


Table 5.10. Household involvement in wage labor by household type and village grouping

179

Table 5.11. Agricultural and fuel foraging activities for the total sample

183

Table 5.12. Agricultural and fuel foraging activities by household type

183

Table 5.13. Agricultural and fuel foraging activities by village grouping

184

Table 5.14. Access to fields for agriculture by house hold type and village grouping

184

Table 5.15. Types of access to fields for agriculture Table 5.16. Agriculture and livestock for the total sample

186 186

Table 5.17. Agriculture and livestock by household type

188

Table 5.18. Agriculture and livestock by village grouping

188

Table 5.19. Intercorrelations between economic indic ators, agriculture, and fuel foraging

190

Table 5.20. Intercorrelations between agricultural and livestock indicators

191

Table 5.21. Results of regression analysis of independ ent variables on household location

192


Body weight for caretakers by age groups Body Mass Index (BMI) for caretakers by age groups

199 199

Table 6.3.

Seasonal upper-arm circumference (AC) for caretakers by age group

201

Table 6.4.

Triceps skinfolds for caretakers by age groups

202

Table 6.5.

Subscapular skinfolds for caretakers by age group

204

Table 6.6.

Seasonal anthropometric indices (reported as Z-scores) and body mass index for total sample.

208

x


Table 6.7.

Correlations between seasonal caretaker 221 nutritional status (reported as Z-scores) and socioeconomic and agricultural indicators

Table 6.8.

Results of regression analysis of independent variables of selected caretaker anthropometric measures in Season-3

222

Table Table Table Table Table

Seasonal body weight for boys by age group Seasonal body weight for girls by age group Seasonal stature for boys by age group Seasonal stature for girls by age group Child weight-for-stature a percentage of the median

236 236 239 239 240

6.9. 6.10. 6.11. 6.12. 6.13.

Table 6.14. Seasonal Z-scores for child nutritional status

241

Table 6.15. Seasonal nutritional status indices for children as Z-scores and by age groups

245

Table 6.16. Child nutritional status as Z-scoes by sex in Season 3

245

Table 6.17. Anthropometric indices of malnutrition in children

247

Table 6.18. Seasonal child nutritional status indices as 248 Z-scores by household type and village group Table 6.19. Correlations between seasonal Z-scores 254 for child weight-for-age (WAZ) and socioeconomic indicators Table 6.20. Correlations between seasonal Z-scores for child stature (SAZ) and socioeconomic indicators

254

Table 6.21. Correlations between seasonal Z-scores for weight-for-stature (WHZ) for children and socioeconomic indicators

254

Table 6.22. Results of regression analysis of independent variables on seasonal child weight

258

Table 6.23. Results of regression analysis of independent variables on child stature

259

Table 6.24. Results of regression analysis of independent variables on seasonal stature percentiles.

260

xi


Table 6.25. Caretaker and child weight change between 268 seasons as a percentage of total body weight Table 7.1.

Number and percentage of caretakers reporting illness during previous month

282

Table 7.2.

Number and percentage of caretakers reporting illness during the previous month by household type and village grouping

285

Table 7.3.

Number and percentage of children reported to be ill during previous month

288

Table 7.4.

Mean number of child illness episodes during previous month

288

Table 7.5.

Mean number of child illness episodes in 289 previous month by household type and village grouping

Table 7.6.

Mean number of child illness episodes in previous month by age groups

291

Table 7.7.

Correlations between child illness and nutritional status indices as Z-scores

292

Table 8.1.

Measures of diet variability for care takers across seasons

317

Table 8.2.

Measures of dietary variability for children across seasons

317

Table 8.3.

Measures of dietary variability for care takers within seasons by household type

322

Table 8.4.

Measures of dietary variability for children within seasons by household type

322

Table 8.5.

Correlations between ranked variety for caretakers and socioeconomic indicators

327

Table 8.6.

Correlations between caretakers dietary 328 indicators and nutritional status (Z-scores) in Season-1

Table 8.7.

Correlations between caretaker and child dietary indicators in each season

xii

R eproduced with perm ission of the copyright owner. Further reproduction prohibited without perm ission.

329

L I S T OF FIG U R ES

Page Figure 1.1.

General research model

5

Figure 2.1. Figure 2.2

Nutrition-infection complex Seasonal model showing linkages between SES, diet, nutrition behaviors, and Nutritional and health status

22 23

Figure Figure Figure Figure

General map of Lesotho Map of ecological zones Map of poverty in Lesotho Infant and child mortality in Lesotho Hospitals Estimated immunization coverage 1985-89 Age 8 952

1970/80

i

105 610

40 386

105 674

1

1980/1

!

Peas

Total

4 42“

502 050

5 350

6 S5b

242 045

Beans

1

:0 783

1

33 629

J

2S 104

j

J585

4 562

201 24f

47 720

16 993

j

3 3P

3 198

1"? Ill

4 525

133 0“ !

3 3o7

12i' 665

3 630

135 256

1981/2

j

S3 028

j

26 158

14 462

j

4 S98

1OS2/3

|

76 180

]

'oi87

14 810

1

4 624

19S3/4

|

79 384

j

35 76S

17 127

1084/5

j

92 350

54 823

18 434

1985/6

j

86 4SS

53 440

04 912

3 i 232

1086/7 1087 As

158 638

j

j

1 j

1 538

j

I

2 47ft

3 277

171 3o2

11 009

i i |

2 779

I 502

13o 21S

18 520

j

7 383

2 564

241) 957

7 883

2 5f>4

240 O.v

!

|

1!

;

I

55 135

19 237 i

j !

Source: Lesotho Govermenl. Ministry of Agriculture. 198s

90

R e p ro du ced with perm ission of the copyright owner. Further reproduction prohibited without perm ission.

This represents a 15% drop in a ten year period (Bureau of Statistics 1988). This decreasing agricultural base suggests serious problems for household incomes in the not too distant future. The negative agricultural situation is also reflected in the declining yields of main crops such as maize, sorghum, wheat, and beans (Ministry of Planning 1991: 21). In a 10 year period from 1977/78 to 1986/87

total yield for

all crops combined dropped by 20% (Bureau of Statistics 1990), with only maize production increasing during this period (Ministry of Planning 1991: 21).

Table 3.6. shows

the production of main crops during this ten year period.

D. Economic Infrastructure. For most of Lesotho/s history, there has been little or no industrial development.

In 1967, a year after

independence, the Lesotho National Development Corporation was established with the goal of building economic and physical infrastructure so as to create employment opportunities within the country (Ministry of Planning 1991: 23).

Today there are several large enterprises including

the Maluti Mountain Brewery (a subsidiary of South African Breweries), the flour mill in Maseru, and the multinational Highlands Water Project.

There is also a flourishing

textile industry with many small manufacturing companies, a majority of them foreign-owned.

However, in many of these

91


textile mills working conditions are quite poor and wages are very low, causing increasing discontent among workers (Ministry of Planning 1991: 24).

There are also locally

owned light industry and commercial business promoted by the Basotho Enterprises Development Corporation (BEDCO). There is an increasing demand for finished products because of the spending power of migrant laborers, civil servants, and employees of the many aid projects in the country.

This

demand has resulted in the expansion of retail and wholesale trade, especially in Maseru (Ministry of Planning 1991: 24). The growth of Maseru has also lent to the increase in service oriented business such as taxi services. Table 3.7 shows the main source of income by household type.

As expected, female-headed households (households

where there is no male contribution of economic resources) depend more on subsistence farming for income, while male headed households depend more on migrant remittances.

As

seen in Table 3.8., average income is similar between male headed and female-headed households where there is a household member with higher education (i.e., technical degree).

On the other hand, male-headed households with no

education earn higher incomes than female-managed households with no education.

These statistics support the notion of

limited access to resources in female-headed households where there is no male contribution at all.

Interestingly

enough, my data do not show significant differences in SES 92


between male- and female-headed households in Mokhotlong. This is probably due to the fact that the majority of female-headed households in my sample (called female-managed households) have males who are migrant laborers, and many of these males do contribute resources- in cash and kind- to the household. The Lesotho Highlands Water Project is an ongoing engineering scheme with the aim of exporting water to South Africa and generating hydroelectric power for Lesotho.

It

is the largest single project undertaken in Lesotho and is the largest engineering enterprise currently in Africa (Ministry of Planning: 1991: 24).

A series of dams and

tunnels are currently being constructed to channel water from the highlands to the Vaal catchment area.

The economic

impact will be great through the sale of water and from employment during the construction phase.

However, there

has been some concern that the initial estimates for employment were too high.

People have been flocking to the

project sites, located in Thaba Tseka and Katse in central Lesotho, in search of work.

Unfortunately, many have not

been able to gain employment. During my stay in Lesotho there was some unrest in the area while we were in Lesotho over the inability of the government to employ more Basotho.

93


TABLE 3 .7 .

Household by Sex and Main Source of Income S ource of Incom e

H o usehold w ith M ale H ead %

H o u seh o ld w ith F e m ale H ead

Subsistence Farming

20.1

28.1

Cash Cropping

11.7

10.8

Business income

3.0

4.3

Wages and Salaries

17.9

14.7

Migrant Remittances

39.7

22.8

Other Sources

0.1

0.1

%

Source: Lesotho Government. Bureau of Statistics. 1988

TABLE 3.8.

Average Income per Household by Sex of Head, Presence of Migrant Worker and by Education E ducation

No Education

M ale H ead o f H o u seh o ld

F em ale H ead of H ousehold

N o M igrant

1+ M igrant

N o M ig ra n t

1+ M igrant

146

346

74

118

380

102

237

Standard 1-6 Standard 7

265

614

236

a

JC Equivalent

843

760

296

600

COSC

720

a

399

a

Post COSC

1 009

a

a

a

Technical

1 420

a

1 406

a

Source: Lesotho Government. Bureau of Statistics. 1988

Reproduced with perm ission of the copyright owner. Further reproduction prohibited without perm ission.

E. Poverty in Lesotho. Recently Gay et al. (1991) conducted a nationwide study of absolute and relative poverty in Lesotho. Poverty was measured based on criteria defined by the Basotho themselves (Gay et al. 1991).

These criteria include the lack of the

following basic needs: 1) an adequate food supply; 2) clean and abundant water; 3) good roads and transport; 4) money to buy consumer goods; 5) wage employment; 6) medical facilities; 7) clean sanitation and toilets; 8) household furnishings and clothing; 9) fuel and firewood; 10) home and community goods; 11) secure and warm housing; 12) relevant and affordable schools; 13) livestock; and 14) fields. For purposes of analysis, the country was divided into 60 areas (with roughly equal population in each).

Poverty measures

were derived as a percentage deviation for each area and then compared to Central Maseru, the most prosperous area in Lesotho.

Figure 3.3 shows the distribution of relative

poverty in each area of Lesotho as compared to Central Maseru. While the results of this study suggest that poverty in Lesotho is in no way comparable to the situation in places like Somalia or the Sudan (there are few people in Lesotho who are starving or homeless), there is a fair degree of relative poverty which is increasing and which is unequally distributed.

Although there is relative poverty throughout

the country, the situation is worse in the mountains and 95


FIGURE 3.3.

Map of Poverty in Lesotho Matsooinc T s im a

Oslo Lipalanen* Malibomatso Pela-Tsoeu Mphosorue Nqachone Qoqolosinc LikJtaken* Likhetlan* Mankfl XoLonvnma Koancn* Mosalemane

Kjwiftjni; '•t.tlimofi* T*»yateynn*»n* S2*»ra« y' .lifM-r\ubu ‘T'innunyane ‘•l«»s**ru North

y«s*»r»i t> n c rn i '••n^oru ;'o»jth 'kcapi ?JSt ‘♦alela M^nmn noro-rioro Hvibnn«-N tsorwana UnkhAlen* U>» letsuivyane Tcomn* Thaba-rhecne ia 25 Qalobane 36 Eoleka 37 Ualiepetsane 33 Unsemouse 33 Thabana-Morena 40 Mafeten* Taurt* 42 Mpharane 13 Mohsle's Koek 14 Mekalin* 15 .

CG T3 M—

o 0

o

c

o o

_C 03 *

o.

£

co

O 4 —•

co "O

0

Months

(Morning, noon, and night)

Fig. 5.1. Monthly tem peratu re

E i-

O) C 'c

T3 i_ O a 0

03

Q.

03 O CM

LO

155

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

Months

(recorded in Camp)

Fig. 5.2. Monthly relative humidity

Q_

o

o

o

o

o

o

05 J D

o

None

85% -

4%

4%

j}—G o v e r n m e n t

prohibited without permission.

O ther

G overnm ent 7%

2%

5% O ther

16%

None 37%

Caretakers

Biological fathers

TABLE 5.10. Household involvement in full-time wage labor by household type and village grouping. FMH

MPH

DHH

CHH (%)

Wages

N

Caretaker

10 (18)

Father

33 (58)

103*** (74)

82 (73)

54* (65)

Migrant

29 (50)

19*** (14)

23 (21)

25* (30)

*p=/< 0.05;

**p=

N

(%)

(%)

17 (12.3)

N

N

(%)

5** (6)

22 (20)

0.005? ***p= 0.001

FMH= female-managed households? MPH= multiple-parent households CHH= close households; DHH= distant households

B. Agriculture, Fuel Foraging, and Livestock. In sub-Saharan Africa, women are the main producers of food with 70% sharing in production, 50% in storage, 100% in processing, 60% in marketing, and 100% in cooking (Saenz de Tejada 1989; Blumberg 1981).

The role of women in agriculture

in Lesotho is that much greater because of male migrant labor (Saenz de Tejada 1989).

This places an increased burden on

women who are already looking after the children and managing the household.

Moreover, while women have a great role in

agricultural production, they do not have the same access to agricultural land, training, and credit as men do (Ministry of Planning (1991: 51). Women participate in all different aspects of agriculture 179


including planting, plowing, cultivation, weeding, harvesting, and processing.

At village meetings flipitso). where

agricultural matters are discussed, it is usually women who are in the majority; they are the ones who obtain the seeds, decide who will plow, and determine whether sharecropping will take place (Ministry of Planning 1991: 151; Gay 1982). Plowing is generally done in October after the coming of the rains (for the summer crops) and in March (for the winter crops).

This work is usually done by men and boys using ox-

drawn plows; however, during this time women prepare food which they then bring out to the fields for the workers.

This

activity is time consuming since the fields that are allocated to a household are often located at a considerable distance from the house compound.

In our sample, we did come across a

very small group of women who did the plowing themselves (N=3); they told us that there were no male members in their household to do this work or that they did not have enough money to hire men to plow their fields.

After plowing, the

seeds are planted, usually by men; once again women bring food and drink to the men during this time.

After the first

sprouts appear, women spend more and more time (from October through March for maize) weeding and protecting the crops from attack by grazing livestock, birds, rodents, and from theft.

Weeding is especially laborious and time consuming and

is done mostly by women.

On several occasions I had the

chance to observe women weeding between the rows of maize; 180


they would be bending over for long periods of time, and used either hoes or their bare hands.

During the months when these

activities were going on, we had some difficulty in conducting our interviews as many caretakers were out working in the fields. After the crops are ready (around March or April), harvesting begins; there are two types of harvesting in Lesotho: green cutting, which is done throughout the ripening period, and mature harvest done when the crops are mature and dry.

Harvesting involves several activities including

"cutting, pulling, threshing, winnowing, bagging, and transporting the crop to the place of storage" (Ministry of Planning, 1991: 151). activities.

Women participate in all of these

For example they may thresh the wheat or sorghum

themselves, sort or stack them for men to thresh, or prepare food for men who are threshing; women also shell the maize and legumes, and winnow and class/sort them (Gay 1982).

After the

harvest women return to the fields to gather the dry stalks to use for winter fodder and fuel (Gay 1982).

We observed many

of these activities while collecting data, and in all cases we saw only women doing this work.

In a study of lowlanders, Gay

(1982) found that women spend nearly 15% of their time involved in agricultural activities.

No doubt, the time

devoted to agriculture increases or decreases depending on the time of year. In addition to agriculture, women spend a lot of time 181


foraging for wild vegetables (see chapter 8) and fuel.

Since

fuels such as kerosine and propane are too expensive for many rural Basotho to buy, they depend upon gathered fuels such as dung, wood, crop residue, and dried maize cobs.

The preferred

fuel in Mokhotlong is lisu which is dried and compacted cattle dung.

This fuel is slow burning and provides an even heat

(Huss-Ashmore 1982).

Lisu is easily collected from Kraals,

which are usually located in the resident compound; therefore it does not require too much time investment.

Unfortunately,

households that lack cattle usually do not have access to lisu.

The availability of this type of fuel is seasonal as

cattle are kept far-away in the cattle-posts during the summer months and in the Kraals during the winter months.

Other

types of fuel include khaoane (uncompacted cattle dung) and kholuba

(horse dung), both of which require more time to

gather as women must search for them by bridle-paths, roads, and open fields.

All of these fuels require kindling, which

is a scarce resource in Lesotho.

Women often have to walk

for hours to collect patsi (wood and dried bushes) to use as kindling or as fuel itself.

Those who have fields can collect

dried stalks and maize cobs to burn; while this is time consuming, there is some guarantee of fuel after the harvest. Finally, for those who can afford it, there is a plentiful supply of kerosine and propane in the Camp, as well as kerosine in some of the villages. We asked caretakers what types of agricultural (i.e., 182


plowing, planting, cultivation and weeding, and harvesting) and fuel foraging (i.e., the number of types of fuels they gathered) activities they participated in. Table 5.11. shows these data for the entire sample.

TABLE 5.11 Agricultural and fuel foraging activities for the total sample Variable

Mean

SD

# fuel foraging activities

2.9

1.2

# agricultural activities

2.2

1.7

Table 5.12. shows agricultural and fuel foraging activities by household types.

There are significant

differences with caretakers from FMHs participating in fewer agricultural activities and collecting fewer types of fuel than their MPH counterparts.

These differences are related to

the fact that FMHs have less access to agriculture (not statistically significant) and that they tend to buy fuel (e.g., kerosine) from the market instead of foraging.

TABLE 5.12. Agricultural and fuel foraging activities by household type. Variables

FMH Mean (SD)

MPH Mean (SD)

Sig P

# fuel types

2.6 (1.2)

3.0 (1.2)

0.045

# agricultural acts

1.7 (1.6)

2.3 (1.6)

0.018

FMH= female-managed households MPH= multiple-parent households 183


Table 5.13. shows these same data by village grouping. Again there are significant differences, with caretakers from DHHs foraging for more fuel types and participating in more agricultural activities than caretakers from CHHs.

Distant

households (DHH) have greater access to fields (p= 0.000) than CHHs.

They tend not to purchase fuel from the markets.

Table 5.14. shows access to fields for agriculture by household type and village grouping.

TABLE 5.13. Agricultural and fuel foraging activities by village grouping. CHH Mean (SD)

DHH Mean (SD)

Sig

2.5 (1.2)

3.4 (1.0)

0.000

# agricultural activities 1.3 (1.5)

3.3 (1.1)

0.000

Variables # fuel types

P

CHH= close households DHH= distant households

TABLE 5.14.

Access to fields for agriculture by household type and village grouping

Household type

N

%

Sig P

Total sample

137/195

70.3

-

FMH MPH

35/57 102/138

61.4 73.4

NS

CHH DHH

60/112 77/83

53.6 92.8

0.000

184


Table 5.15. shows the types of access to fields that households have for agricultural production.

As can be seen

44.1% of the households have their own land, while 12.8% of the households work the land for non-family members usually in return for a portion of the harvest.

Another 7.8% work on the

land of extended kin for food, while 4.1% share fields with other households, and 1.5% work on cooperative fields. 30% of the sample have no access to land.

Nearly

The majority of

these landless are located within or nearby the Camp. Table 5.16. shows the average number of fields, gardens, and crops, as well as the types of animals owned by households.

Some of the different types of crops that

households grow in Mokhotlong include maize, sorghum, peas, potatoes, beans, and pumpkin.

Garden crops include cabbage,

turnip, carrot, spinach, beet root, and pumpkin. Households may also have fruit trees such as peach and apricot trees. Finally, depending upon their affluence, households may own cattle, horses, donkeys, sheep, goats, chickens, and to a lesser extent pigs.

185


TABLE 5.15. Types of Access to fields for agriculture. Variable

N

Have land

86

44.1

Don't have land

58

29.7

Work land/non-family

25

12.8

Work land/family or friend

15

7.8

Share fields

8

4.1

Cooperative fields

3

1.5

195

100.0

Total

%

TABLE 5.16. Agriculture and livestock for the total sample. Variable

Mean

SD

# fields

1.0

1.1

# gardens

1.4

1.7

# crops

2.8

2.1

# animal types

2.3

1.8

Tables 5.17. and 5.18. show agriculture and livestock by household type and village grouping, respectively.

FMHs have

significantly fewer fields, gardens, and types of animals than MPHs.

The same is true for CHHs as compared to DHHs.

So,

it appears that both MPHs and DHHs tend to more involved in agriculture and animal husbandry than FMHs and CHHs. Our ethnographic observations support these data. 186


We

found clear distinctions between households that center their activities around agriculture as opposed to those whose labor is focused on earning an income.

More often than not these

differences were based on whether or not the household was close or far from the Camp.

For example, when a caretaker

from a CHH was not home, she was often in Camp selling goods at the market or taking a child to the clinic.

On the other

hand, caretakers from DHHs were always out either collecting fuel or working in the fields.

People from DHHs spoke more

about the land and the crops; their concerns about the persistent drought was revealed to us more often, and with more urgency, than for example, by someone who lived in Camp and worked for the government.

This is not to say that there

were not CHHs that participated in subsistence agriculture or DHHs where household members worked as policeman, bank clerks, or nurses, who had little interest in agriculture. The data and our ethnographic observations only suggest a trend in decreasing dependence upon the land as we get closer to the Camp.

187


TABLE 5.17. Agriculture and livestock by household type. Sig. P

Variable

FMH Mean (S D )

MPH Mean (SD)

# fields

0.5 (1.0)

1.2 (1.2)

0.02

# crops

2.3 (2.1)

3.0 (2.0)

0.038

# animal types

1.6 (1.6)

2.6 (1.9)

0.001

FMH= female-managed households MPH= multiple-parent households TABLE 5.18. Agriculture and livestock by village grouping. Variable

CHH Mean (SD)

DHH Mean (SD)

Sig. P

# fields

0.5 (1.0)

1.2 (1.2)

0.000

# crops

2.2 (2.1)

3.6 (1.6)

0.000

# animal types

1.8 (1.6)

3.0 (1.9)

0.000

CHH= close households; DHH= distant household

In Table 5.19. correlations between household economic indicators, agriculture, and fuel foraging activities are presented.

House index is negatively correlated with the

number of buildings in the resident compound, number of fuel foraging activities, number of fields, and number of crops. All of these latter variables tend to be positively associated with DHHs.

On the other hand, house index is positively

correlated (r= .42) with the number of household possessions; higher scores for house index and household possessions have 188


been shown previously to be associated with CHHs. The relationship between agricultural and livestock indicators is shown in Table 5.20.

There are moderately

high correlations between of the number of fields with the number of crops (r= .5012) and with the number of animal types (r= .5444).

Interestingly, there were no significant

correlations between the number of gardens and other agricultural or livestock variables.

We found that gardens

(individual and communal) tended to be found in households in and close to the Camp. Moreover, many of the gardens in CHHs were constantly being weeded and watered; in addition, all the space in these gardens was utilized.

Conversely, we saw a

fair number of gardens in DHHs that were dry and under utilized.

189


TABLE 5.19. Intercorrelations between economic indicators, agriculture, and fuel foraging HH index

# build,

# posess.

*

HH index

-.1755

# fuel

**

.4194

# fields **

-.2838

**

-.0756

**

# build.

.1755

# posess.

.4194

.1178

# crops -.2122

**

**

.2226

.4486

.2724

-.1201

.1020

-.0843

** .1179 **

# fuels

**

-.2838

*

.2226 -.1201

**

.1914

.3594

**

f fields

-.0756

# crops

-.2122

.4486 **

**

.1028

.1914

**

.2724

_ **

-.0843

.3594

.5012 **

.5012

_

*p= 0.01; **p= 0.001

In order to examine the contribution of several economic and agricultural independent variables to the differences that appeared to be correlated with village grouping (i.e., CHH and DHH), multiple regression analysis was performed using dummy variables (CHH= 0 and DHH= l).

The use of dummy variables is

justifiable since each of the categories for the dependent variable (village grouping) contained more than 20% of the cases for the total sample (CHH= 57.4% and DHH= 42.6) (Hedderson 1991: 120).

Table 5.21. shows the results of the

regression analysis. The variables in the equation below (based on an exclusion set at p= 0.05). account for nearly 24% of the variance.

Both the number of possessions and house 190


index have negative beta scores, while all the other independent variables maintain positive beta scores.

Again

this suggests differences by location based on how the dummy variables were set up. TABLE 5.20. Intercorrelations between agricultural and livestock indicators

# fields

# animal types

# crops

**

** # fields

-.0634

.5444

.5012

-

# gardens

**

** .4190

# crops

.5012

# animal types

.5444

.4190

# gardens -.0634

-.0379

-.0379

**

**

—

.0217

.0217

‘p= 0.01? **p= 0.001

191


TABLE 5.21. Results of regression analysis of independent variables on household location (using dummy variables). B

Independent variable

Sig. T.

# fuel type

.2550

0.0002

# stoves

.2144

0.0036

-.1634

0.0224

# fields

.2026

0.0047

# buildings

.1427

0.0512

house index

-.1168

0.0136

# possessions

R2= .237, Adj. R*= .237, F— 13.05, Sig. F= 0.000

192


Ch i ldren in Thabang

193


THE WORK BEGINS From the start we established good relations with the medical officers at the Government Hospital (in Mokhotlong) and among the traditional healers in the region. Mokhotlong is the only district in Lesotho where healers and hospitrl staff meet every three months to exchange information; where traditional healers are encouraged to use their skills and to direct their patients to the hospital. The first village we worked in was Thabang, a relatively large and bustling place with three distinct settlements built alongside the mountains at different altitudes. In Thabang, as in all the villages, we had to get permission to work from the village chief, who sent along one of his assistants to make the proper introductions. Six days a week we would drive out to Thabang and along the way, pile in up to 10 people looking for lifts. We would spend all day collecting data, visiting different households and getting a feel for how the people lived. As time went on we learned more and our data became richer as people got to know us and our interviewing improved. We began to learn about illnesses caused by the breaking of taboos, witchcraft and Tokolosi- an evil spirit who comes in many different forms. We saw how medicinal herbs are used for treating ailments ranging from headaches to problems with the womb. We heard about the rare practice of ritual murder for medicine and the problem of wife beating. We obtained firsthand information on the unpredictable food supply, how diet is dictated by beliefs and life stages, and how seasons and the closeness to the land affect the lives of the people. Of course, we looked at the children's seasonal growth and recorded changes in the women's weight and body composition associated with vigorous agricultural work. Excerpt from the Reporter. 24,9: 9 by D.H.


CHAPTER S IX

NUTRITIONAL STATUS In this chapter I first examine the biological impact of seasonality on caretaker body weight and body composition by describing the patterns of anthropometric change across seasons for the total population and within seasons by household type and village grouping. Particular attention will be given to the relationship among fluctuating nutritional status, seasonal work, and SES. Seasonal changes in adult nutritional status can be interpreted as evidence of stress or as an appropriate response to an uncertain environment Goodman 1988; Richards 1983).

(Huss-Ashmore and

According to this latter

interpretation, it is advantageous to put on weight when food is available so as to prepare for those times when it becomes scarce; Richards (1983) suggests that this response is a reasonable alternative to storing food in the granary. Changes in weight as well as body composition may be adaptive in environments where work is highly seasonal (Dugdale and Payne 1977).

As Huss-Ashmore and Goodman

(1989) state: Seasonal storage of fat may provide energy reserves necessary to survive the "hungry season" without significant loss of muscle and working capacity. The degree and timing of fluctuation in fat and muscle mass may therefore be seen as evidence of adjustment to the seasonality of work requirements, (pg. 30).

195


In the second part of this chapter, I present the data on growth and nutritional status for children; again the total sample will be examined across seasons, while differences by household type and village grouping will be examined within seasons.

The children will also be compared

with their U.S. counterparts by sex and age-groups using Ztransformations and reporting data as a percentage of the median.

The focus here is not so much to make comparisons

between the U.S and Lesotho sample, but rather to look at relative change in nutritional status through time for different groups of Lesotho children. The patterns of growth for Lesotho children are similar to children throughout the developing world where mild-tomoderate undernutrition is common.

Generally, low stature-

for-age is found with adequate weight-for-stature;

this

pattern reflects the cumulative effects of undernutrition through critical periods of growth (Martorell and Habicht 1986).

In Lesotho however, this pattern of stunting may be

amplified because of the strong

seasonality

which maynot

permit adequate catch-up growth

between the

"good" andubad"

seasons.

a less than

adequate

The end result may be

attainment of adult size which may or may not affect adaptive functioning in the environment. In the final section of this chapter, I will present the data on intra-household weight-loss and show that maternal and child nutritional status are linked within the 196


household.

After the data for caretakers and children have

been described, they will be compared with other data on rural producers from Lesotho and other countries.

I. SEASONAL BODY WEIGHT AND COMPOSITION FOR CARETAKERS. A. Descriptive statistics for body weight and composition. Descriptive statistics for body weight and composition for the caretakers by age-group are found in Tables 6.1. through 6.5.

Table 6.1. shows that body weight for the

total sample is lowest during Season 1, from late-winter through early-spring, followed by Season 3, covering a period from mid-summer through early-to-mid fall.

These

seasons coincide with the end of the post-harvest period when food becomes more scarce and the time-period when agricultural labor is most intensive, respectively.

Body

weight, body mass index (BMI), triceps and subscapular skinfolds were all highest during Season 2, a period covering from mid-spring through early summer. One-way ANOVAS were used for the entire sample in order to determine whether or not there were significant differences in seasonal weight by age group. A posteriori tests (Student-Newman-Kuels? SNK) were then employed to determine which age groups, if any, showed significant differences (at p= 0.05).

Significant weight differences by

age group were found in Season 1 (p= 0.02) and Season 3 (p= 0.05), and a marginally significant difference was found in 197


Season 2 (p= 0.06).

In Season 1, caretakers between the

ages of 30 and 39 and those older than 50 years are significantly heavier than caretakers less than 30 years old (p= 0.05)

This pattern is also found in Season 2 and

Season 3 when the SNK test is employed.

The differences may

be related to age-specific patterns of nutrient utilization and to differences in work patters.

Gay (1982) has shown

differential work tasks for Basotho women, with younger women doing more of the heavy work, while older women perform less demanding work. When compared with the NCHS sample (Frisancho 1988), caretakers less than 20 years old (N= 10) have weights between the 5th and 50th percentiles in all three seasons, while all other age groups are between the 50th and 75th percentiles for weight. In Table 6.2., BMI (weight/height2) is presented in each season for the total sample and by age group.

The

highest BMI is found in Season 2, while the lowest BMI is found in Season 3.

Again One-way ANOVAS show highly

significant differences between age groups within each season (p= 0.001, 0.002, 0.001; for Seasons 1-3, respectively).

The SNK test reveals that caretakers over 50

years old (N=14) have higher BMI's than all other age groups throughout all three seasons (p= 0.05); also caretakers between 30 and 39.99 years have significantly higher BMI's than those less than 30 years old (p= 0.05). 198


In Seasons 1 and 3, caretakers have BMI's between the 50th and 75th percentiles when compared with the NCHS sample; these indices increase to the 75th percentile in Season 2.

Caretakers between 30 and 39.99 and over 50 years

maintain BMI's over the 75th percentile in all three seasons.

TABLE 6.1

Body weight for caretakers by age groups. Weight (kg) Season 3 Mean SD

Weight (kg) Season 1 Mean SD

Weight (kg) Season 2 SD Mean

10 88 58 25 14

54.99 59.94 64.70 63.79 69.31

6.95 10.67 14.81 11.99 14.59

55.13 60.47 65.53 65.25 69.61

6.7 11.56 14.95 12.26 16.17

53.02 59.66 64.57 63.31 68.88

6.64 10.76 14.98 12.06 16.41

Total 195

62.27

12.68

62.97

13.25

62.04

12.99

Age (yrs)

N

-

0.1

2

1

3

S eason s Household type ° Total sample

( Female-managed HH's " 0

Based on NCHS data. No significant differences within sea so n s.

-parent HH's

FIG. 6.9. Seasonal triceps/age Z-scores for caretakers Village grouping (CHH vs. DHH) (Compared with U.S. sample) Triceps skinfold-for-age Z-score

0.3 0.2 0.1 0.1 - 0.2 -

-0.3 -0.4

1

2

3

S eason s Village grouping ° Total sample

( C lose households ^ Distant households

Based on NCHS data. S eason s 1-2 (p= 0.001) and Season 2-3 (p= 0.001) Season-1 (p=0.05) and Season-3 (p= 0.08)

FMHs (70%) are located in or near the Camp. Table 6.8. shows the results of regression analysis of various independent variables on dependent anthropometric measures in Season 3 (the most seasonally stressful period). Variables such as the number of household fields, number of illness episodes, number of household members, number of children less than 10 years old, and the number of agricultural activities that caretakers perform are included in the various regression equations.

Their negative

association suggests that as each one of these independent variables increases, anthropometric measures decrease (see Beta values).

These regression equations however, only

explain between 5.4 and 8.8% of the total variance, depending upon the anthropometric measure; thus their explanatory power is limited. TABLE 6.7. Correlations between seasonal caretaker nutritional status (reported as Z-scores) and socioeconomic and agricultural indicators # possess.

House index

# agric. acts

Weight Z-l Weight Z-2 Weight Z-3

.1007 .1515* .1361*

.1354* .1463* .1413*

-.1451* -.1194* -.1573*

Triceps Z-l Triceps Z-2 Triceps Z-3

.1831“ .1880“ .1617*

.1992“ .1815“ .1691*

-.1798“ -.1903“ -.1903“

Subscap Z-l Subscap Z-2 Subscap Z-3

.1417* .1782* .1525*

.2026“ .2051“ .1802*

-.1860“ -.1575* -.1481*

*p < 0.05;

*p < 0.001. 221


TABLE 6.8. Results of regression analysis of independent variables on selected maternal anthropometric measures in season 3. Independent variable B

Weight Sig. T

-.1750 -.1951

§ HH fields # illness episodes

0.05 0. 008

Upper-arm circ. (AC) Sig. T B -.1819 -.2058 -.1683

# illness episodes # HH members # children < 1 0 yrs

0.01 0.015 0.046

Triceps skinfolds Sig. T B # illness episodes # Agricultural activities # HH members

-.1699 -.1852 .1882

0.02 0.045 0.025

Subscapular skinfolds B # Illness episodes # HH wage earners # HH members

-.2028 -.1683 -.2123

Sig. T 0.005 0.028 0.01

Weight: R2= 0.1149, Adj. R2= 0.062, F= 2. 16, p= 0.018 AC: R2= 0.1082, Adj. R2= 0.054, F= 2. 01, p= 0.03 Triceps: Ra= 0.1101, Adj. R2= 0.056, F= 2 .06, p= 0.025 Subscap: R2= 0.1402, Adj. R2= 0.088, F= 2 .71, p= 0.003

. CHILD GROWTH AND NUTRITIONAL STATUS. A. Descriptive Statistics for Child Growth. Figures 6.10. through 6.21. show weight and stature for Lesotho boys and girls plotted relative to U.S. reference 222


data by age (i.e., Fels Research Institute and NCHS growth curves).

In general, for the majority of boys and girls (45

to 55% of the total sample), absolute measures of weight (Figs. 6.10- 6.15.) fall between the 5th and 50th percentiles when compared with the NCHS sample in each of the three seasons.

Between 30 and 35% of both sexes have

weights lower than the 5th percentile, while the remainder have weights higher than the 50th percentile when compared with the U.S. sample.

This same pattern is shown when

examining absolute measures of stature (Figs. 6.16-6.21.); however, the percentage of girls falling below the NCHS 5th percentile is somewhat lower than that of boys in all three seasons.

Although there appears to be some catch-up growth

for stature between Season 1 and Season 2 for both boys and girls, by the time Season 3 comes around, more children of both sexes, are below the 5th percentile as compared to Season 2. This finding supports the pattern of stunting for children described in other populations from developing countries (Martorell and Habicht 1986).

In addition, it

reaffirms the notion that highly seasonal ecologies, which are intense and frequently dispersed, do not allow for adequate catch-up growth during the "good season(s)11 when food is in relative abundance.

These findings will be

discussed in the last section of this chapter.

223


FIG. 6.10. W eight-for-age-boys: S easo n 1 (Compared with U.S. Sample) Weight (Kg)

30 25

20 15 10

5 0

0

10

20

30

40

50

60

A ge (m onths) — U.S. 5th + U.S. 50th -*■ U.S. 95th B ased on U.S. data from Fels R esearch Institute and NCHS.

■ L esotho B oys

70

Reproduced with permission

FIG. 6.11. Weight-age-girls: Season 1 (Compared with U.S. sample)

of the copyright owner.

Weight (kg)

Further reproduction

— U.S. 5th all other age groups Season2: p= 0.11; SNK= 12 months > all other age groups Season3: p= 0.002; SNK= 23-35 months > 36+ months Correlations between various illness variables and seasonal child nutritional status are presented in Table 7.7. While the mean number of illness episodes shows a weak inverse correlation with child nutritional status (Z-scores for weight-for-age, stature-for-age, and weight-for-stature ) in Season 3, the correlations are stronger when examining variables such as the mean number of diarrhea symptoms (e.g., fever, stomach pain, and vomiting) and the mean number of stool symptoms during diarrhea (e.g., loose stool, blood in stool, mucus in stool, large volume, very bad odor, green color) in all three seasons.

291


TABLE 7.7

Correlations between child illness and nutritional status indices as Z-scores

# illness episodes # diarrhea symptoms # stool symptoms

# illness episodes # diarrhea symptoms § stool symptoms

# illness episodes # diarrhea symptoms # stool symptoms

WAZ-1

WAZ-2

WAZ-3

.0532 -.4723 -.3469*

.0603 -.3822** -.3369*

-.1305* -.4274* -.4218“

SAZ-1

SAZ-2

SAZ-3

.0698 -.5646** -.3369*

.0483 -.5090** -.4878“

-.1323* -.1323“ -.4260“

WSZ-1

WSZ-2

WSZ-3

.0545 -.1829 -.1613

.0708 -.1852 -.0480

-.1424* -.2184 -.0984

*p= 0.01 **p= 0.001

292


Q.

with permission

FIG. 7.2. Children with diarrhea by season and age group (Reported as a percentage)

of the copyright owner.

% of children with diarrhea

70 f

Further reproduction

to

prohibited

60

Child age-groups

50

B