Open Access

Temporal changes and determinants of childhood nutritional status in Kenya and Zambia

Journal of Health, Population and Nutrition201736:27

https://doi.org/10.1186/s41043-017-0095-z

Received: 11 January 2016

Accepted: 11 May 2017

Published: 5 June 2017

Abstract

Background

The prevalence of undernutrition is decreasing in many parts of the developing world, but challenges remain in many countries. The objective of this study was to determine factors influencing childhood nutrition status in Kenya and Zambia. The objective of this study is to determine factors associated with temporal changes in childhood nutritional status in two countries in sub-Saharan Africa.

Methods

Data from national demographic and health surveys from the World Bank for Kenya (1998–2009) and Zambia (1996–2014) were used to select the youngest child of each household with complete data for all variables studied. Multiple linear regression analyses were used for data from 2902 and 11,335 children from Kenya and Zambia, respectively, in each year to determine the relationship between social and economic factors and measures of nutritional status, including wasting, stunting, and overweight.

Results

There was a decreased prevalence of stunting (35% in Kenya and 40% in Zambia), while the prevalence of wasting was unchanged (6–8% in both countries). From 1998 to 2009, there was a protective effect against stunting for wealthier families and households with electricity, for both countries. Finally, better educated mothers were less likely to have stunted children and girls were less likely to be stunted than boys.

Conclusions

Based on the data analyzed, there was a higher risk of stunting in both Kenya and Zambia, for those with lower literacy, less education, no electricity, living in rural areas, no formal toilet, no car ownership, and those with an overall lower wealth index. Improving the education of mothers was also a significant determinant in improving the nutritional status of children in Kenya and Zambia.

More broad-based efforts to reduce the prevalence of undernutrition need to focus on reducing the prevalence of undernutrition without promoting excess weight gain. Future economic advances need to consider integrated approaches to improving economic standings of households without increasing the risk for overnutrition.

Keywords

Stunting Wasting Nutritional status Children Kenya Zambia Dietary diversity

Background

The prevalence of obesity is a major public health problem in most developed and many transitional countries and has also contributed to the “double burden of disease”, the coexistence of both obesity and underweight [15]. The double burden is not necessarily a reflection of competing problems [6] and the fact that excess weight gain is now co-existent with low-weight underscores the need to better understand how chronic nutrition problems change over time and within specific socio-geographic regions. To that end, it is of interest to better understand what factors influence temporal changes in nutritional status, especially in less developed regions that have yet to experience the double burden of disease, such as Sub-Saharan Africa. Therefore, the focus of this paper is to describe the current state of childhood nutrition in Kenya and Zambia and determine factors associated with temporal changes in childhood nutritional status.

In terms of childhood nutrition, a high prevalence of chronic malnutrition has been reported in both urban and rural settings throughout Africa [713]. For example, a high percentage of children are stunted, underweight, or wasted, in Nairobi, Kenya, but most especially within informal urban settlements [11, 14]. For example, in the Dagoretti Division of Nairobi, 24.5% of children aged 4–11 were stunted, 14.9% were underweight, and 9.7% were wasted [14]. At the same time, more boys than girls are undernourished; however, there are some conflicting results with different trends emerging between separate regions [11, 1416]. Specifically, a higher percentage of boys were stunted compared to girls [14]. In Kibera, an informal settlement in Nairobi, one survey of 1310 children aged 6–59 months found that 47.0% of children were stunted (severe stunting in 23.4% of the children), 11.8% were underweight (severe underweight in 3.1%), and 2.6% were wasted (severe wasting in 0.6%) [11]. Moreover, girls were more likely than boys to be wasted at 3 years of age compared to boys. Such gender differences in these data may arise from a number of cultural or economic factors that favor one gender over the other. These studies highlight the fact that poor nutrition is most prevalent in impoverished and marginalized areas, but the cross-sectional nature of the research does not allow for broader conclusions regarding how social determinants of nutritional status may change with time.

With regards to undernutrition, the prevalence of stunting or wasting varies across Sub-Saharan Africa and even within rural regions of Kenya. One recent study reported that fetal growth restriction and poor sanitation are the primary predictors of stunting in many parts of the world, but most particular for Sub-Saharan Africa [7]. Within country data are more illustrative, such that in the rural Bondo district of Kenya, 30% of children under the age of 5 were stunted (12% severely stunted), 4% were wasted (1% severely wasted), and 20% were underweight (5% severely underweight) with height and weight deficits most prevalent for children aged 18–23 months [10]. Meanwhile, in the Suba district, children between the ages of 11 and 16 years had the highest percentages of undernourished subjects and the most severe undernutrition with boys more likely to be stunted and underweight compared to girls. One study of malnutrition rural Kenya found that among children under the age of 60 months, there was a higher prevalence of malnutrition among girls compared to boys. In addition, girls also tended to have lower overall energy intake compared to boys. In particular, of the 629 subjects surveyed in the Mwingi and Makueni districts, stunting, underweight, and wasting were all more prevalent among girls [16]. Thus, gender differences may confound other determinants of nutritional status, emphasizing the need for more comprehensive research on factors that influence childhood nutritional status.

In Zambia, one study of children from the Samfya district found that food intake of infants and toddlers was insufficient such that total energy, calcium, iron, and vitamin A were below recommended daily intake for both infants and toddlers, while infants were also below the recommended intake for protein [9]. Moreover, weaning foods consumed by toddlers were found to be inadequate as well, increasing the risk for continued nutritional deficits during childhood [9]. A study from the Chroma district reported poor nutritional status in a sample of 388 children aged 24-59 months and among children aged 12-23 months, only 40% were adequately nourished [13]. Finally, one study in Zambia focused on adults in the Katete district and reported that lower self-perceived socioeconomic status was related to a lower adult BMI in the sample of 254 men and women [17].

In summary, it is clear that undernutrition continues to be a serious problem that persists in these two countries of Eastern Africa [1820]. However, while these studies have consistently reported a high prevalence of childhood undernutrition, they often do not extend the research to determine how nutritional status is affected by other factors, such as urbanization, education, and maternal autonomy. Therefore, additional research is need to better understand how various social and economic conditions can be modified to promote better nutritional status of children and adults in both countries. The objective of this paper is to determine socio-economic factors that influence childhood nutritional status in a temporal setting using nationally representative data from Kenya and Zambia.

Methods

Using data from the Health Nutrition and Population Statistics of national demographic and health surveys (DHS) at the World Bank [20], the prevalence of stunting and wasting was calculated using available years for both Kenya and Zambia. The sampling framework for DHS is fully covered in the manual for DHS data collection [21]. Briefly, the ministry responsible for DHS can submit data collected only if the survey follows key principles explained in detail in the DHS manual. Such principles include the use of an existing sampling frame that provides full coverage of the target population (such as households with children) and is conducted using a random design with a sample size consistent with the manual. In addition, households sampled must conform to the selection criteria and strict confidentiality is maintained. Datasets were extracted from the World Bank website for each country and year studied. Statistical analyses were conducted on the datasets after the deletion of missing values, implausible values, and only respondents with all available data for each variable studied were included. After data cleaning, the final dataset studied for each country included more than 2500 children (ages birth to 4 years) for each year in both Kenya and Zambia. For each outcome of interest, social and economic factors that may influence each was analyzed using stepwise linear regression to best determine how such factors are modified by year of each survey. Using this method allowed for us to determine how specific factors that are associated with nutritional status vary as time progresses, especially in light of the fact that each country has experienced consistent economic growth of 5% of greater since the mid-1990s 20, 21. All data were analyzed using SPSS version 22 (IBM SPSS Statistics, NY, USA) and statistical significance was set a p < 0.05.

Nutritional status

The prevalence of stunting and wasting in Kenya and Zambia was calculated according to the WHO guidelines [22] in which stunting was defined as a height-for-age Z-score (HAZ) < −2.00 and wasting was defined as a weight-for-height Z-score (WHZ) < −2.00. Overweight was defined as WHZ < 2.00 and BMI percentile for age above 85%. According to the conceptual framework of poverty proposed by UNICEF [23], nutritional status is the outcome of a complex hierarchy of factors that begins with direct exposure to quality diet and health care and extends to more indirect interactions with social and economic infrastructure that contribute to a myriad of socio-environmental factors that ultimately contribute to a child’s nutritional status.

Multivariate logistic regression analyses were used to determine how social and economic factors contribute to risk of stunting and wasting, as well as potential changes across time. Specifically, the main outcomes of stunting and wasting were entered as the dependent variables in two models for each country. Known risk factors for these conditions were entered as independent variables, including wealth index, number of household members, rural or urban setting, type of toilet, maternal age, maternal educational status, and age and sex of the child. Backward stepwise analyses were conducted and only the statistically significant independent variables were included in each year analyzed for each country. This was the preferred method to determine if specific variables differed in terms of influencing the nutritional status of the child over the time period studied.

Results

A summary of the temporal changes in childhood nutritional status is presented in Table 1. The prevalence of stunting in Kenya averaged 35% for the years analyzed while the prevalence in Zambia decreased from 50% in 1996 to 40% in 2014. Wasting remained a less prevalent condition with an average of 7% of Kenyan and 6% of Zambian children suffering from wasting. At the same time, approximately 6% of Kenyan and Zambian children are classified as overweight (Table 2 and Table 3).
Table 1

Nutritional status (%) of children in Kenya and Zambia in selected years of available data

 

Kenya

Zambia

Year

1998

2003

2009

1996

2002

2007

2014

N

5478

5150

5088

5478

5150

5088

11335

Wasted (WHZ < −2.0)

37

34.7

34.5

50.3

53.7

44.3

39.9

Stunted (HAZ < −2.0)

7.5

6.8

8.1

5.4

5.9

5.9

6.3

Wasted (BMI percentile)

7.7

5.6

5.2

6.1

5.3

8.3

5.8

Overweight (WHZ > 2.0)

9

7

6.4

8.1

7.7

10.7

7.1

Table 2

Demographic characteristics of households surveyed for Kenya between 1998 and 2009

 

1998

2003

2009

Characteristics

N

% of total

% Stunted

% Wasted

% OW

N

% of total

% Stunted

% Wasted

% OW

N

% of total

% Stunted

% Wasted

% OW

Type of place of residence

 Urban

418

14.3

29.2

6.2

8.6

1076

23.7

27.9

5.8

8.2

1182

23.2

27.3

6.3

5.4

 Rural

2503

85.7

38.4

7.8

7.6

3462

76.3

36.8

7.2

4.7

3914

76.8

36.6

8.7

5.1

Source of drinking water

 Piped

747

25.6

32.9

6.7

9.8

1266

27.9

27.4

4.7

7.8

1392

27.3

29.8

7.4

5.5

 Well

731

25.0

36.7

7.4

8.1

859

18.9

33.4

9.1

4.5

1372

26.9

34.5

8.1

5.0

 Surface water

1360

46.6

39.9

8.2

6.4

2125

46.8

40.2

6.8

4.6

2143

42.1

37.8

8.7

4.8

 Rainwater

33

1.1

36.4

3.0

6.1

288

6.3

30.2

9.4

5.9

189

3.7

30.2

7.4

9.0

Has electricity

 No

2668

91.3

38.7

7.6

7.1

3948

87.0

36.8

7.3

4.6

4302

84.4

36.6

8.7

5.0

 Yes

244

8.4

19.3

6.6

14.3

589

13.0

20.7

3.7

11.7

792

15.5

23.0

4.8

6.6

Toilet type

 Flush toilet

171

5.9

24.0

7.0

12.9

421

9.3

19.2

4.0

11.9

484

9.5

23.3

5.0

8.1

 Pit toilet

2219

76.0

36.0

7.2

8.2

3062

67.5

34.8

4.7

5.2

3320

65.1

33.6

5.9

5.1

 None

517

17.7

46.0

9.5

4.1

1031

22.7

40.6

14.1

4.1

1255

24.6

40.9

14.8

4.5

Has car

 No

2826

96.7

37.6

7.7

7.4

4342

95.7

35.6

7.0

5.3

4924

96.6

35.0

8.2

5.1

 Yes

87

3.0

20.7

3.4

16.1

187

4.1

14.4

2.7

11.2

166

3.3

19.9

4.2

7.8

Has television

 No

2603

89.1

39.0

7.8

7.2

3773

83.1

37.5

7.4

4.8

3961

77.7

37.8

9.0

5.1

 Yes

306

10.5

20.6

5.6

12.1

759

16.7

21.1

3.8

9.5

1129

22.2

22.5

5.2

5.6

Literacy

 Reads easily

1746

59.8

33.3

6.0

8.5

2942

64.8

31.9

4.3

6.4

3061

60.1

31.5

5.1

5.5

 Reads with difficulty

671

23.0

39.0

10.1

6.9

359

7.9

39.8

3.3

4.2

672

13.2

40.0

8.6

6.0

 Cannot read

496

17.0

48.2

9.7

6.3

1228

27.1

40.1

13.9

4.0

1317

25.8

38.6

14.5

4.3

Highest educational level

 No education

335

11.5

47.2

9.0

5.4

826

18.2

39.1

17.1

3.9

1042

20.4

38.4

16.7

4.5

 Primary

1863

63.8

39.8

8.3

7.8

2691

59.3

38.4

5.1

5.4

2908

57.1

37.0

6.3

4.8

 Secondary

672

23.0

26.3

5.2

8.2

825

18.2

23.6

3.3

6.8

878

17.2

26.1

4.7

6.8

 Higher

51

1.7

11.8

2.0

13.7

196

4.3

12.8

3.1

10.2

268

5.3

19.0

5.6

7.1

Head of household

 Male

2148

73.5

37.2

7.3

8.2

3435

75.7

35.0

6.9

5.6

3627

71.2

33.9

8.0

5.2

 Female

773

26.5

36.7

8.3

6.2

1103

24.3

33.9

6.5

5.3

1469

28.8

35.9

8.4

5.3

Child age

 0

1801

37.0

20.0

9.6

11.7

1145

25.2

29.0

6.1

7.7

1161

22.8

20.1

11.4

10.2

 1

1020

34.9

45.1

8.0

5.2

971

21.4

39.1

6.8

4.0

1002

19.7

45.0

7.4

5.7

 2

820

28.1

49.6

4.1

5.6

886

19.5

37.8

6.3

5.8

1058

20.8

43.2

6.8

3.8

 3

 

0.0

   

842

18.6

34.8

8.4

5.0

984

19.3

34.0

5.7

2.4

 4

 

0.0

   

694

15.3

33.9

6.8

4.6

891

17.5

31.4

9.0

2.8

Age of mother

 Under 20

414

14.2

36.7

7.2

9.2

503

11.1

36.2

7.6

7.0

531

10.4

32.8

11.3

6.0

 20–29

1549

53.0

37.5

7.4

7.7

2296

50.6

34.0

6.6

5.1

2557

50.2

33.7

7.5

5.5

 30+

958

32.8

36.5

7.8

7.1

1739

38.3

35.2

6.9

5.8

2008

39.4

35.9

8.1

4.6

Employmenta

 Not currently employed

1414

48.4

37.8

7.3

7.9

1816

40.0

33.6

8.3

6.2

2283

44.8

35.1

10.6

5.5

 Currently employed

1506

51.6

36.5

7.8

7.6

2718

59.9

35.5

5.8

5.2

2812

55.2

33.9

6.1

5.0

Household members

 2–6 members

1756

60.1

37.2

7.9

8.3

2880

63.5

34.2

6.1

6.1

3281

64.4

33.0

7.5

5.9

 7+ members

1165

39.9

36.9

7.0

6.9

1658

36.5

35.6

8.0

4.6

1815

35.6

37.1

9.2

4.0

Wealth index

 Poorest (1)

702

24.0

46.6

8.8

5.0

1124

24.8

43.4

11.7

3.8

1479

29.0

42.1

13.5

5.0

 Poorer (2)

647

22.1

41.0

10.0

7.4

898

19.8

37.5

6.9

3.8

931

18.3

37.7

6.7

4.4

 Middle (3)

567

19.4

35.1

5.8

9.3

843

18.6

33.9

4.5

5.6

850

16.7

32.7

6.1

4.9

 Richer (4)

555

19.0

33.5

5.6

8.1

736

16.2

32.3

5.7

5.6

844

16.6

30.0

6.4

5.1

 Richest (5)

450

15.4

23.6

6.4

9.8

937

20.6

24.1

3.9

9.3

992

19.5

25.3

4.7

6.6

Sex of child

 Male

1453

49.7

42.3

8.0

8.0

2273

50.1

36.0

7.3

5.3

2598

51.0

37.0

8.9

5.3

 Female

1468

50.3

31.9

7.1

7.4

2265

49.9

33.4

6.4

5.8

2498

49.0

31.8

7.3

5.1

Stunted defined by HAZ score, wasted and overweight by WHZ

aEmployment of mother

Table 3

Demographic characteristics of households surveyed for Zambia between 1996 and 2014

 

1996

2001–02

2007

2014

Characteristics

N

% of total

% Stunted

% Wasted

% OW

N

% of total

% Stunted

% Wasted

% OW

N

% of total

% Stunted

% Wasted

% OW

N

% of total

% Stunted

% Wasted

% OW

Type of place of residence

 Urban

1820

33.0

41.1

3.7

5.6

1295

15.1

42.7

6.3

5.6

1602

31.3

38.3

5.7

7.7

4129

36.2

35.9

6.4

6.2

 Rural

3697

67.0

54.8

6.2

6.4

3870

74.9

57.4

5.8

5.2

3519

68.7

47.0

6.0

8.5

7278

63.8

42.1

6.3

5.6

Source of drinking water

 Piped

1640

29.7

40.4

3.9

5.5

1241

24.0

42.5

6.0

5.6

1235

24.1

36.5

5.7

7.5

2646

23.2

32.2

6.7

6.7

 Well

2910

52.7

54.1

5.9

6.5

2816

54.5

55.9

5.6

4.5

2649

51.7

45.4

6.5

7.5

6609

57.9

40.9

6.4

5.4

 Surface water

925

16.8

55.8

6.3

6.4

1095

21.2

60.6

6.7

6.9

1079

21.1

50.4

4.6

11.7

2032

17.8

46.3

5.7

6.0

 Rainwater

     

13

0.3

53.8

0.0

0.0

158

3.1

43.7

5.7

3.2

120

1.1

46.7

5.8

5.8

Has electricity

 No

4636

84.0

53.6

5.8

6.1

4529

87.7

56.3

5.9

5.2

4418

86.3

46.3

6.0

8.4

9338

81.9

42.0

6.4

5.6

 Yes

871

15.8

33.1

3.0

5.9

633

12.3

34.6

6.3

5.8

703

13.7

31.6

5.5

7.1

2044

17.9

30.0

6.0

6.7

Toilet type

 Flush toilet

841

15.2

34.2

4.0

5.5

506

9.8

37.5

7.1

6.1

439

8.6

31.7

4.6

6.4

964

8.5

26.1

6.7

6.8

 Pit toilet

2958

53.6

52.9

5.3

6.3

3071

59.5

53.7

5.9

5.2

3293

64.3

45.3

5.4

8.4

8454

74.1

40.8

6.5

5.7

 None

1689

30.6

53.4

6.0

6.1

1578

30.6

58.9

5.6

5.2

1361

26.6

45.3

7.3

8.2

1949

17.1

42.6

5.4

5.8

Has car

 No

5390

97.7

50.9

5.4

6.1

5062

98.0

54.2

5.9

5.3

5030

98.2

44.7

5.9

8.3

10803

94.7

40.6

6.4

5.7

 Yes

113

2.0

21.2

6.2

8.0

89

1.7

28.1

5.6

4.5

91

1.8

23.1

7.7

6.6

591

5.2

25.7

5.8

6.8

Has television

 No

4575

82.9

53.8

5.8

6.2

4412

85.4

56.3

6.1

5.2

3996

78.0

47.0

6.2

8.7

7985

70.0

42.6

6.8

5.5

 Yes

930

16.9

33.2

3.4

5.6

751

14.5

38.1

5.3

5.6

1122

21.9

34.4

5.0

6.8

3417

30.0

33.5

5.2

6.5

Literacy

 Reads easily

2248

40.7

45.0

42.0

5.3

2215

42.9

47.6

5.4

4.2

2165

42.3

41.1

5.0

8.1

5265

46.2

35.7

5.8

5.9

 Reads with difficulty

1141

20.7

50.5

5.4

7.1

482

9.3

55.4

4.6

5.8

597

11.7

50.3

7.2

9.7

1078

9.5

42.2

7.9

5.7

 Cannot read

2122

38.5

55.7

6.6

6.5

2416

46.8

59.0

6.7

6.3

2239

43.7

45.9

6.3

8.2

4990

43.7

43.8

6.6

5.7

Highest educational level

 No education

832

15.1

57.8

6.5

7.2

775

15.0

59.0

6.3

5.9

677

13.2

44.0

7.4

6.6

1289

11.3

43.8

7.3

5.7

 Primary

3578

64.9

52.1

5.7

5.6

3324

64.4

56.3

6.0

5.4

3214

62.8

47.1

5.7

8.9

6404

56.1

42.4

6.2

5.5

 Secondary

1014

18.4

41.1

3.5

6.9

988

19.1

43.3

5.8

3.9

1119

21.9

38.2

5.5

7.7

3314

29.1

36.1

6.3

6.0

 Higher

91

1.6

12.1

4.4

6.6

78

1.5

23.1

3.8

9.0

111

2.2

24.3

6.3

6.3

393

3.4

17.0

5.9

8.9

Head of household

 Male

4646

84.2

49.5

5.6

6.2

4394

85.1

53.7

5.8

5.6

4240

82.8

44.2

5.7

8.3

9306

81.6

39.7

6.1

5.7

 Female

871

15.8

54.3

4.4

5.9

771

14.9

53.4

6.2

3.5

881

17.2

44.6

6.9

7.9

2101

18.4

40.6

7.2

6.1

Child age

 0

1376

24.9

26.7

9.9

6.9

1275

24.7

50.1

5.9

7.4

1176

23.0

25.3

9.4

14.5

2423

21.2

23.7

8.9

12.4

 1

1241

22.5

54.5

7.7

3.6

1179

22.8

59.1

6.7

4.2

1137

22.2

51.5

6.2

6.5

2384

20.9

48.7

6.5

5.2

 2

1130

20.5

65.0

2.6

6.7

1031

20.0

57.1

3.5

5.6

1015

19.8

52.7

5.4

6.4

2289

20.1

50.3

5.5

3.9

 3

950

17.2

60.3

2.0

7.8

896

17.3

48.5

8.7

4.9

975

19.0

48.7

3.0

7.1

2192

19.2

41.7

5.2

3.9

 4

820

14.9

51.7

2.0

5.7

784

15.2

52.7

5.0

3.3

818

16.0

45.8

4.6

5.5

2119

18.6

35.3

5.2

3.0

Age of mother

 Under 20

690

12.5

50.1

7.0

5.2

692

13.4

54.3

8.7

6.1

491

9.6

43.4

6.7

6.9

1257

11.0

43.0

6.0

7.8

 20-29

2725

49.4

50.3

5.3

6.0

2544

49.3

53.5

5.6

5.4

2562

50.0

45.7

5.8

8.4

5178

45.4

40.0

6.3

5.7

 30+

2102

38.1

50.2

4.9

6.6

1929

37.3

53.7

5.4

4.8

2068

40.4

42.7

5.9

8.4

4972

43.6

39.0

6.4

5.4

Employmenta

 

0.0

0.0

0.0

0.0

               

 Not currently employed

2665

48.3

48.6

5.5

6.4

2059

39.9

51.4

6.5

5.7

2440

47.6

44.6

5.7

9.3

4808

42.1

38.6

5.9

5.9

 Currently employed

2851

51.7

51.9

5.2

5.8

3104

60.1

55.2

5.6

5.0

2676

52.3

43.9

6.1

7.3

6599

57.9

40.8

6.6

5.7

Household members

 2–6 members

2587

46.9

53.7

5.5

5.5

2827

54.7

54.4

6.7

5.6

3054

59.6

46.2

5.8

8.6

6130

53.7

41.1

6.2

6.1

 7+ members

2930

53.1

47.3

5.3

6.6

2338

45.3

52.8

5.0

4.9

2067

40.4

41.5

6.0

7.7

5277

46.3

38.5

6.5

5.5

Wealth Index

 

0.0

0.0

0.0

0.0

               

 Poorest (1)

1643

29.8

58.1

7.2

5.9

1277

24.7

60.5

5.9

4.7

1140

22.3

47.4

7.1

9.1

2757

24.2

47.2

7.1

5.3

 Poorer (2)

993

18.0

55.1

5.8

6.4

1181

22.9

59.1

5.8

5.3

1131

22.1

49.6

5.7

7.9

2755

24.2

42.4

6.5

4.8

 Middle (3)

1112

20.2

52.0

4.7

7.0

1227

23.8

54.1

6.4

5.6

1188

23.2

46.9

5.3

9.8

2605

22.8

38.9

5.6

6.8

 Richer (4)

954

17.3

44.8

4.2

4.8

870

16.8

48.6

5.5

4.8

1030

20.1

39.2

6.0

6.8

1905

16.7

36.6

6.2

5.9

 Richest (5)

815

14.8

32.8

3.4

6.0

610

11.8

35.2

6.2

6.2

632

12.3

32.4

4.9

6.8

1385

12.1

26.6

5.9

6.7

Sex of child

 Male

2688

48.7

51.6

6.2

6.2

2585

50.0

54.6

6.2

5.3

2512

49.1

47.4

6.7

8.6

5721

50.2

42.2

6.6

6.3

 Female

2829

51.3

49.1

4.6

6.0

2580

50.0

47.2

5.7

5.3

2609

50.9

41.3

5.1

8.0

5686

49.8

37.5

6.0

5.3

Stunted defined by HAZ score, wasted and overweight by WHZ

aEmployment of mother

Results of the multivariate logistic regression analyses are summarized in Tables 4 and 5 for stunting and wasting, respectively. With regards to socio-economic factors in Kenya, the higher the wealth index of a family, the lower the risk of having a stunted child for all years analyzed. For 1998 and 2008 only, those households that reported having electricity were less likely to have a stunted child compared to those without electricity. There were no significant results for the number of family members, household setting, or type of toilet for any of the years analyzed.
Table 4

Multivariate logistic regression model for risk factors in relation to stunting in children in Kenya (1998–2009) and Zambia (1996–2014)

  

Kenya

Zambia

 
  

1998

2003

2008–09

1996

2001–02

2007

2013–14

Household characteristics

OR (CI)

P

OR (CI)

P

OR (CI)

P

OR (CI)

P

OR (CI)

P

OR (CI)

P

OR (CI)

P

Wealth index quintile

Poorest

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Poor

0.82

(0.65 -1.03)

0.09

0.81

(0.69 - 0.97)

0.02

0.82

(0.68 - 0.98)

0.03

0.91

(0.77 - 1.07)

0.27

0.95

(0.81 - 1.12)

0.54

1.09

(0.93 - 1.30)

0.29

0.84

(0.75 - 0.93)

0.00

Middle

0.68

(0.53 - 0.86)

0.00

0.70

(0.58 - 0.85)

0.00

0.68

(0.56 - 0.82)

0.00

0.72

(0.61 - 0.86)

0.00

0.79

(0.67 - 0.93)

0.00

0.98

(0.83 - 1.16)

0.83

0.72

(0.64 - 0.81)

0.00

Rich

0.64

(0.50 - 0.83)

0.00

0.69

(0.57 - 0.85)

0.00

0.63

(0.52 - 0.77)

0.00

0.56

(0.46 - 0.68)

0.00

0.72

(0.60 - 0.87)

0.00

0.74

(0.62 - 0.89)

0.00

0.67

(0.58 - 0.78)

0.00

Richest

0.54

(0.38 - 0.76)

0.00

0.53

(0.43 - 0.65)

0.00

0.66

(0.51 - 0.85)

0.00

0.50

(0.31 - 0.81)

0.00

0.73

(0.46 - 1.15)

0.17

0.84

(0.59 - 1.20)

0.33

0.50

(0.41 - 0.61)

0.00

Household members

2-6

.

.

.

.

.

.

.

.

.

.

.

.

.

.

7+

.

.

.

.

.

.

0.87

(0.78 - 0.98)

0.03

.

.

.

.

.

.

Setting

Urban vs Rural

.

.

.

.

.

.

.

.

.

.

.

.

0.84

(0.75 - 0.94)

0.00

Toilet Type

Flush

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Pit

.

.

.

.

.

.

1.01

(0.72 - 1.42)

0.95

.

.

.

.

.

.

None

.

.

.

.

.

.

0.80

(0.55 -1.15)

0.23

.

.

.

.

.

.

Electricity

No vs Yes

0.63

(0.41 - 0.99)

0.04

.

.

0.75

(0.58 - 0.97)

0.03

0.69

(0.50 - 0.95)

0.02

0.58

(0.38 - 0.88)

0.01

0.69

(0.51 - 0.95)

0.02

.

.

Family Characteristics

              

Mother Age

Years

0.98

(0.97 - 0.99)

0.01

.

.

.

.

0.99

(0.98 - 1.00)

0.04

.

.

0.98

(0.98 - 0.99)

0.00

0.99

(0.98 - 0.99)

0.00

Educational Level (mother)

None

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Primary

0.74

(0.57 - 0.96)

0.02

1.11

(0.93 - 1.31)

0.25

1.12

(0.95 - 1.32)

0.17

0.88

(0.75 - 1.04)

0.13

0.98

(0.84 - 1.16)

0.84

1.15

(0.96 - 1.36)

0.12

0.98

(0.87 -1.11)

0.76

Secondary

0.46

(0.34 - 0.63)

0.00

0.62

(0.49 - 0.78)

0.00

0.79

(0.63 - 0.98)

0.03

0.74

(0.60 - 0.92)

0.00

0.73

(0.59 - 0.90)

0.00

0.93

(0.75 - 1.14)

0.47

0.87

(0.75 - 1.00)

0.05

Higher

0.22

(0.09 - 0.54)

0.00

0.34

(0.22 - 0.55)

0.00

0.58

(0.40 - 0.85)

0.01

0.13

(0.06 - 0.27)

0.00

0.39

(0.22 - 0.69)

0.00

0.65

(0.39- 1.06)

0.09

0.43

(0.32 - 0.59)

0.00

Child age

Months

1.06

(1.06 - 1.07)

0.00

.

.

1.01

(1.00 - 1.01)

0.00

1.03

(1.02 - 1.03)

0.00

.

.

1.02

(1.01 - 1.02)

0.00

1

(1.00 - 1.01)

0.00

Child Sex

Boy vs Girl

0.62

(0.53 - 0.72)

0.00

0.88

(0.78 - 0.99)

0.04

0.76

(0.69 - 0.87)

0.00

0.86

(0.77 - 0.96)

0.01

.

.

0.76

(0.68 - 0.85)

0.00

0.82

(0.76 - 0.88)

0.00

*Adjusted for all variables in the table, which were selected by forward stepwise regression

Table 5

Multivariate logistic regression model for risk factors in relation to wasting* in children in Kenya (1998–2009) and Zambia (1996–2014)

  

Kenya

Zambia

  

1998

2003

2008–09

1996

2001–02

2007

2013–14

Household characteristics

OR (CI)

P

OR (CI)

P

OR (CI)

P

OR (CI)

P

OR (CI)

P

OR (CI)

P

OR (CI)

P

Wealth index quintile

Poorest

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Poor

1.14

(0.79 - 1.65)

0.48

.

.

.

.

.

.

.

.

.

.

.

.

Middle

0.63

(0.41 - 0.97)

0.04

.

.

.

.

.

.

.

.

.

.

.

.

Rich

0.59

(0.38 - 0.93)

0.02

.

.

.

.

.

.

.

.

.

.

.

.

Richest

0.69

(0.44 - 1.10)

0.11

.

.

.

.

.

.

.

.

.

.

.

.

Household members

2-6

.

.

.

.

.

.

.

.

.

.

.

.

.

.

7+

.

.

.

.

.

.

.

.

0.74

(0.58 - 0.94)

0.01

.

.

.

.

Setting

Urban vs Rural

.

.

.

.

.

.

1.65

(1.24 - 2.18)

0.00

.

.

.

.

.

.

Toilet Type

Flush

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Pit

.

.

1.02

(0.59 - 1.76)

0.95

1.26

(0.79 - 2.02)

0.33

.

.

.

.

1.22

(0.76 - 1.99)

0.42

.

.

None

.

.

1.82

(1.02 - 3.26)

0.04

2.29

(1.39 - 3.79)

0.00

.

.

.

.

1.70

(1.04 - 2.79)

0.04

.

.

Family Characteristics

              

Mother Age

Years

.

.

.

.

.

.

.

.

.

.

.

.

1.01

(1.00 - 1.02)

0.05

Educational Level (mother)

None

.

.

.

.

.

.

.

.

.

.

.

 

.

.

Primary

.

.

0.37

(0.27 - 0.50)

0.00

0.46

(0.36 - 0.60)

0.00

.

.

.

.

.

.

.

.

Secondary

.

.

0.26

(0.16 - 0.42)

0.00

0.40

(0.27 - 0.60)

0.00

.

.

.

.

.

.

.

.

Higher

.

.

0.25

(0.10 - 0.61)

0.00

0.57

(0.31 - 1.07)

0.08

.

.

.

.

.

.

.

.

Currently employed (mother)

No vs Yes

.

.

.

.

0.72

(0.58 - 0.89)

0.00

.

.

.

.

.

.

.

.

Child age

Months

0.97

(0.96 - 0.99)

0.00

.

.

0.99

(0.99 - 0.99)

0.00

0.96

(0.95 - 0.97)

0.00

.

.

0.98

(0.97 - 0.99)

0.00

0.99

(0.98 - 0.99)

0.00

Child Sex

Boy vs Girl

.

.

.

.

.

.

0.75

(0.59 - 0.96)

0.02

.

.

0.76

(0.60 - 0.96)

0.02

.

.

*Adjusted for all variables in the table, which were selected by forward stepwise regression

Regarding maternal-child characteristics, more educated mothers were 50–80% less likely to have a stunted child compared to mothers with no or primary education only. However, the degree to which education reduced the odds of having a stunted child was not as great in 2009 compared to 1998. Older children were more likely to be stunted than younger children in 1998 and 2008, but not in other years. Finally, for all 3 years analyzed, girls were less likely to be stunted compared to boys.

The relationship between the socio-economic factors and stunting in Zambia were similar to what was found in Kenya. However, in Zambia, a large family decreased the odds of having a stunted child (1996) as did living in a rural, compared to an urban, area (2014). Also, having a flush versus pit or no toilet was protective against stunting in 1991, but not in other years. Households with electricity was protective through 2007. The results for the influence of maternal-child characteristics on stunting in Zambia were similar to Kenya such that mothers’ age had a borderline protective effect and maternal education was protective, but only in 1996 and 2001.

For wasting, in Kenya, there was a protective effect of wealth on the odds of having a wasted child in 1998 only. Not having a toilet in the household, either pit or flush, increased the odds of having a wasted child by more than 80% in 2003 and over 200% in 2009. As was reported for stunting, the higher education reported by a mother decreased the odds of having a wasted child in 2003 and 2009. Finally, maternal employed decreased the odds of having a wasted child in 2009 only.

In Zambia, the relationships between socio-economic factors and wasting differed from those in Kenya. Briefly, as was reported for stunting, a large family reduced the odds of having a wasted child (2001) while living in a rural area increased the odds (1996). Not having a pit or flush toilet in 2007 increased the odds of having a wasted child, as did being a boy in 1996 and 2007.

Summarizing the most salient outcomes, we found that the risk of stunting was higher for those with lower literacy, less education, no electricity, living in rural areas, no formal toilet, no car ownership, and those with an overall lower wealth index. This trend was consistent for both Kenya and Zambia and from year to year of available data (1998 to 2009 for Kenya and 1996 to 2014 for Zambia). Results for wasting, a condition that is a reflection of the daily nutrient intake and acute disease state, there were similar trends, but less pronounced differences between levels of each socio-economic factor.

Discussion

Undernutrition continues to be a major public health issue in Sub-Saharan Africa [23, 24], including both Kenya and Zambia [9, 25]. In fact, a number of studies have examined the nutritional status and dietary intake of children and adults in each country [11, 14]. While many of these studies have reported insufficient nutrient intake and low food security for those living in Kenya and Zambia [10, 11, 14, 26], the larger context of social factors that are associated with nutritional status and how such relationships change over time need to also be recognized. Briefly, using nationally representative data over a period of ten years in Kenya and Zambia, we found that the prevalence of stunting has not changed since 1998 in Kenya, but has decreased by 20% in Zambia since 1996. For both countries, the key predictors of chronic nutritional deficiencies (i.e., stunting) were maternal education, elements of higher socio-economic status (e.g., electricity, modern toilet, television, and piped water), while those for acute nutritional insults (i.e., wasting) included higher wealth index, type of toilet, level of maternal education, and the sex of child for some years in Zambia.

The results of our analyses are consistent with studies from other parts of Sub-Saharan Africa. In particular, as was reported by Danaei and colleagues, poor fetal growth and unsanitary conditions are the major factors predicting stunting in children under the age of 5 years [7]. This study is complemented by an economic analysis of stunting in which it was estimated stunting in Sub-Saharan African results in over $18 million in lost educational attainment [27]. Thus, given the complex nature of factors that influence growth as well as the profound effects on economics, it is imperative to understand how various programs may reduce the prevalence of stunting. Nabwera et al. reported that intensive health and nutrition interventions decreased the prevalence of undernutrition by 50% in Gambia, but that more comprehensive and sustainable programs are needed to have a more significant and lasting impact on childhood health [28]. In addition, apparent differences in nutritional status between boys and girls illustrates that there may be factors that favor higher dietary intake among one gender over the other, differences in work or physical activity patterns between the two genders, or cultural preferences for one gender over the other. Simply, any of these factors could promote healthy growth or poor growth depending on the soci-economic context in which the child is exposed.

Compared to other cross-sectional studies from each country, our data clearly demonstrate that factors influencing diet and nutritional status in Kenya and Zambia have not changed appreciably in the past 20 years. For example, one study of 1000 homes in Nairobi reported that 85% of those households surveyed were food insecure and 50% were severely food insecure [29]. The relationship between dietary, social, and environmental factors and obesity were examined in another study. A sample of 1008 women from across Kenya was used for the study, which involved anthropometric measurements and 24-h dietary recall interviews. The researchers found that overweight and obesity were highly prevalent among Kenyan women, with 43.3% of women overweight or obese. The highest prevalence of overweight and obesity occurred in women in urban areas within the high-income group [30]. The time of year was related to food security in Kenya based on the effects of the rainy and dry seasons. Using data from Meru County, it was determined that intake of energy, protein, iron, zinc, calcium, and folate increased in the rainy season and that household food security increased from the dry to the rainy season. Data was obtained from 525 households using interviews of mothers or caregivers [31]. A program involving educational intervention was found to increase the diversity of diets in a sample of 207 households. The intervention took place in Bondo and Teso South sub counties and consisted of training and cooking demonstrations for caregivers, which increased the children’s dietary diversity scores and the caregiver’s nutrition knowledge scores [32]. Surveys of adults and households illustrate the fact that many households are food insecure and reveal relationships between the nutritional status of individuals and various non-dietary factors.

Aside from those factors analyzed in this study, many other socio-demographic factors may account for the results presented, including culture, geography, maternal autonomy, and food aid [12, 33]. It is clear that specific cultural groups or geographical areas within each country have varying degrees of vulnerability to food insecurity that may influence childhood nutritional status. One study of dietary intake patterns of three ethnic groups in Kenya (Luo, Kamba, and Maasai) found that the Maasai and Kamba were vulnerable to food insecurity compared to the Luo [34]. Specifically, the prevalence of underweight, a key indicator of food insecurity, was 13.7% for the Luo, 20.5% for the Kamba, and 24.2% for the Maasai [34]. Regarding maternal autonomy, the more independent a mother is to makes decisions related to health care, education, food, and have an independent source of income , the more likely her children are to be properly nourished [3538]. In the Siaya, Kisumu, and Busia districts of Kenya, a high prevalence of childhood undernutrition was attributed to low maternal independence and high martial discord as childcare is not only affected by a mother’s direct actions with her child, but also through her social relationships with others because of the assistance that others provide [39]. In terms of food aid, a study in the Yatta district investigated the effectiveness of using local foods to improve the nutritional status of children and found that a small food ration provided to families resulted in a lower prevalence of wasting and underweight compared to control families [40]. Similar to our results, two studies in urban settlements of Nairobi (Korogocho and Viwandani) found that a higher maternal educational status was associated with higher nutritional status of her child [35, 41]. Therefore, based on our results and those of others, there has not been an appreciable change in the factors associated with nutritional status, suggesting potential avenues for nutrition interventions to combat undernutrition in these countries.

As with any study using nationally representative data and questionnaires, there are limitations that merit discussion. First, diet was assessed using different methodologies between waves of national data collection that could introduce bias or inconsistency between years studied. Second, it is unclear from DHS data exactly who was trained to measure nutritional status in each wave of data collection for each country. There is also limited information on quality control checks. Nonetheless, DHS data are considered reliable given that they are collected using established methods and have rigid oversight by the ministries responsible for administering the surveys. Still, the data should be treated with some degree of caution, but solid conclusions may be drawn given the established methods and near universal acceptance of these data as being nationally representative by major government and non-governmental organizations.

While the sub-Saharan countries of Kenya and Zambia have yet to begin the “nutrition transition” [42], it is clear that undernutrition is becoming less of a problem than before periods of economic growth. However, considering the analyses that demonstrate the intractable relationships between socio-economic factors and risk of poor nutritional status, future economic advances need to consider integrated approaches to improving economic standings of households without increasing the risk for overnutrition. Specifically, advancing maternal education and general household wealth without the introduction of processed foods and foods that are not part of the traditional diet are paramount. Research to improve the dietary diversity, one that includes nutrition dense and culturally acceptable foods, such as African-indigenous vegetables, is one example of improving economic development and nutrition without the risk of excess weight gain. Rigorous studies of how such work can impact diet and health are needed and should be part of interdisciplinary approaches to improving health and nutrition in developing countries.

Conclusions

In conclusion, based on nationally representative household data, the risk for stunting in both Kenya and Zambia was higher for those with lower literacy, less education, no electricity, living in rural areas, no formal toilet, no car ownership, and those with an overall lower wealth index. Therefore, improving the education of mothers was also a significant determinant in improving the nutritional status of children in Kenya and Zambia. In addition, the need for more broad-based efforts to reduce the prevalence of undernutrition that focus on reducing the prevalence of undernutrition without promoting excess weight gain is great. As such, future economic advances need to consider integrated approaches to improving economic standings of households without increasing the risk for overnutrition.

Abbreviations

DHS: 

Demographic household surveys

HAZ: 

Height for age Z-score

WHZ: 

Weight for height Z-score

Declarations

Acknowledgements

We thank John Bowman, USAID-Washington and Beth Mitcham, UC-Davis for their support.

Funding

This research project is supported by the Horticulture Innovation Lab with funding from the US Agency for International Development (USAID EPA-A-00-09-00004), as part of the U.S. Government’s global hunger and food security initiative called Feed the Future for the Rutgers led project “Improving Nutrition with African Indigenous Vegetables” in Eastern Africa. Funds were also provided by the New Jersey Agricultural Experiment Station, Hatch Project NJ12158.

Availability of data and materials

All datatsets are available to the public at http://microdata.worldbank.org/index.php/home and datasets cleaned and modified for use in this paper are available upon request to the corresponding author.

Authors’ contributions

DJH conceived and designed the study with input from JES. TC conducted the data collection and management. TC and PB conducted the data analyses. DJH supervised the data collection. TC wrote the first draft. All authors critically revised the draft for intellectual content. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Ethical approval for use of these data for this paper was provided by the Rutgers University Investigation Review Board #E16-092.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Nutritional Sciences, Rutgers University
(2)
New Jersey Institute for Food, Nutrition, and Health, Center for Childhood Nutrition Education and Research, Program in International Nutrition, Rutgers University
(3)
Department of Plant Biology, Rutgers University

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Copyright

© The Author(s). 2017

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