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Child undernutrition in Kenya: trend analyses from 1993 to 2008–09

  • Dennis J Matanda1Email author,
  • Maurice B Mittelmark1 and
  • Dorcus Mbithe D Kigaru2
BMC Pediatrics201414:5

DOI: 10.1186/1471-2431-14-5

Received: 7 February 2013

Accepted: 7 January 2014

Published: 13 January 2014

Abstract

Background

Research on trends in child undernutrition in Kenya has been hindered by the challenges of changing criteria for classifying undernutrition, and an emphasis in the literature on international comparisons of countries’ situations. There has been little attention to within-country trend analyses. This paper presents child undernutrition trend analyses from 1993 to 2008–09, using the 2006 WHO criteria for undernutrition. The analyses are decomposed by child’s sex and age, and by maternal education level, household Wealth Index, and province, to reveal any departures from the overall national trends.

Methods

The study uses the Kenya Demographic and Health Survey data collected from women aged 15–49 years and children aged 0–35 months in 1993, 1998, 2003 and 2008–09. Logistic regression was used to test trends.

Results

The prevalence of wasting for boys and girls combined remained stable at the national level but declined significantly among girls aged 0–35 months (p < 0.05). While stunting prevalence remained stagnant generally, the trend for boys aged 0–35 months significantly decreased and that for girls aged 12–23 months significantly increased (p < 0.05). The pattern for underweight in most socio-demographic groups showed a decline.

Conclusion

The national trends in childhood undernutrition in Kenya showed significant declines in underweight while trends in wasting and stunting were stagnant. Analyses disaggregated by demographic and socio-economic segments revealed some significant departures from these overall trends, some improving and some worsening. These findings support the importance of conducting trend analyses at detailed levels within countries, to inform the development of better-targeted childcare and feeding interventions.

Keywords

Undernutrition Wasting Stunting Underweight Trends Demographic and Health Survey Kenya

Background

Worldwide, about 2.2 million children die annually, with poor nutritional status as an underlying cause [1]. Global statistics for surviving undernourished children indicate that approximately 171 million children are chronically undernourished (stunted), 60 million are acutely undernourished (wasted), and 100 million are underweight [2]. Undernutrition is not only linked to child mortality but also to poor functional development of the child. Undernourished children are highly susceptible to common childhood ailments like diarrhea, respiratory infections and worm infestations. Recurrence of such ailments falters a child’s physical, behavioral, motor and cognitive development, and also compromises her/his health and functioning in adulthood [3]. Combatting child undernutrition is obviously crucial, and its complexity makes it hard to tackle. It results not only from macronutrient deficiencies (protein, fat and carbohydrate) but also from micronutrient deficiencies (trace minerals and vitamins), among which zinc deficiency is particularly deleterious to children’s normal growth [4]. Therefore, different aspects of food deprivation (quantity, quality and food group diversity) lead to different manifestations of undernutrition (wasting, stunting and underweight). Consequently, child undernutrition is a multidimensional problem that defies simple solutions. There is a fundamental need to better understand the public health dimensions of the problem, to provide a foundation for precisely targeted interventions in local contexts.

The burden of child undernutrition is unsurprisingly greatest in the world’s poorest countries, especially in sub-Saharan Africa and Asia [5]. This is a highly salient issue in Kenya, which is among the 20 countries that account for 80% of the world’s chronically undernourished children [6]. The most recent Kenyan national prevalence estimates are 35% for stunting, 7% for wasting and 16% for underweight [7, 8].

A child who experiences a chronic shortage of appropriate types and quantities of food is likely to grow in height/length more slowly than expected for children of the same sex and age. Such a shortfall in growth, termed ‘stunting’, is a classical indicator of underlying child undernutrition. A child who experiences acute food shortage and/or infection is likely to gain weight more slowly than expected for children of the same sex and height/length. Such a shortfall in growth is termed ‘wasting’ , which is also a classical indicator of underlying undernutrition. Underweight is a composite indicator of stunting and wasting and thus an overall indicator of the extent of child undernutrition [9, 10]. Underweight however is not a very useful indicator for interventions as it does not differentiate the extent of stunting and wasting.

According to the World Health Organisation (WHO) 2006 classification of child undernutrition, children with a Z-score below −2 Standard Deviations (SD) of the median for weight-for-height/length (WHZ) or (WLZ), height/length-for-age (HAZ) or (LAZ) and weight-for-age (WAZ) are classified as wasted, stunted and underweight respectively. Children with a Z-score below −3 SD of the median are classified as severely undernourished, while those with a Z-score between −2 SD and −3 SD are classified as being moderately undernourished. Those with a Z-score between −1 SD and −2 SD are classified as mildly undernourished [9].

There is a tendency in the literature to define and describe child undernutrition at an aggregate level, for example by reporting national prevalence for all children ages 0–59 months, without differentiation by age, sex and other factors. Yet, important differentiations do exist for specific demographic and socio-economic segments in the under-five population and nutrition interventions are correspondingly specific. In sub-Saharan Africa, boys have consistently posted higher rates of stunting compared to girls [11]. Many (sometimes contradicting) reasons have been hypothesized to explain the sex difference, such as gender-differentiated feeding practices [12, 13]. It is also postulated that girls are physically less active and therefore spend less energy compared to boys, and that boys are more vulnerable to acute respiratory infection and diarrhoea [14].

Undernutrition is most critical during the first two years of life, especially stunting, after which it is difficult to restore normal growth [15]. During this early period, poor infant and young nutrition and care practices coupled with infectious diseases increase the probability of child undernutrition [16]. Studies conducted in developing countries indicate that exclusive breastfeeding is not common, with complementary foods introduced very early [17, 18]. This leads to faltering child growth [19].

Level of maternal education has been documented as a determining factor in child undernutrition. In an environment with sufficient resources, mothers with education are more likely to utilize modern health care and have good health care knowledge and reproductive behaviours [20, 21]. Maternal education does not, however, automatically impart nutrition knowledge, and thus mothers with education may still have undernourished children.

Given this background, it cannot be assumed that international or national trends reflect sub-group trends with validity; it is an empirical question requiring appropriate sub-group analyses. This study therefore aimed to describe time trends in child undernutrition prevalence in Kenya, with overall trends decomposed by age, provincea, urban/rural residence, maternal education level and Wealth Index (WI), for boys and girls separately. Previous studies which have examined sub-groups in Kenya are inadequate for today’s needs for one or more of these reasons: the design was a single cross-sectional survey and therefore not useful to define trends over time; the study sample was not nationally representative data; the study was conducted before 2006 and hence used out-dated reference standards for child growth [17, 2226].

The present study addresses these limitations, by undertaking trend analyses of stunting, wasting and underweight, in defined sub-groups in Kenya, and using the 2006 WHO child growth standards in the analysis of data collected from four cross-sectional surveys conducted in 1993, 1998, 2003 and 2008–09. The surveys used identical methods, making their results comparable.

Methods

Data

This study used data from the Kenya Demographic and Health Survey (KDHS), a series of national cross-sectional surveys conducted in 1993, 1998, 2003 and 2008–09 (data from KDHS earlier than 1993 are not used). These datasets are publicly accessible through application to MEASURE DHSb[26]. In all survey years, data were collected using identical questionnaire items for women of reproductive age 15–49 years old. In all four surveys, a standard child anthropometry protocol was used. Children 0–59 months were weighed using scales fitted with a digital screen and measured for height using a measuring board. Weight was recorded in kilograms and height/length in centimeters. Children younger than 24 months were measured lying down on the board (recumbent length), while standing height was recorded for older children. Extensive information on data collection and management has been published elsewhere [7, 2730].

Table 1 shows the two-stage sampling design used by the Kenya Demographic and Health Survey. The first stage involved selecting data collection points (clusters) from the national master sample frame and then households were systematically sampled from the selected clusters with women of ages 15–49 years eligible for interview [7, 2729].
Table 1

Sampling design, KDHS

 

1993

1998

2003

2008-09

Clusters Selected

536

536

400

400

Households Selected

8805

9465

9865

9936

Women Interviewed

7540

7881

8195

8444

Response Rate

95%

96%

94%

96%

To enable a trend analysis, variables of interest were identified in the base year data file (1993). Thereafter, data files were sorted by their identification variables and the four cross-sectional datasets of 1993, 1998, 2003 and 2008–09 were merged into a single data file. Besides examining trends for the samples as wholes, sub-group analyses were undertaken, separately for boys and girls, by age, province, residence, maternal education and WI. In each trend analysis, logistic regression was used to test the null hypothesis that the regression coefficient β for survey year was not significantly different from zero, using the equation:
log p / 1 p = β 0 + β survey year · survey year

Due to lack of anthropometry data for children older than 36 months in the 1998 survey, the analysis reported in this paper was restricted to children aged 0–35 months. This allowed comparability of trends across the four surveys from 1993 to 2009. The age categories analyzed were 0–5 months, 6–11 months, 12–23 months and 24–35 months. During the 1993 and 1998 survey years, KDHS did not collect data in North-Eastern province. Consequently, North-Eastern province was excluded in the analysis in order to allow comparison of prevalence across all the four survey years. Provinces included in the analysis include Nairobi, Central, Coast, Eastern, Nyanza, Rift-Valley and Western.

Self-reported maternal education level was categorized as no education, incomplete primary, complete primary and incomplete secondary education. Sample size limitations in the 1993 survey for the higher education category were overcome by combining the complete secondary education and higher education categories in the analyses presented in this paper.

Standard of living measurement involved classification of children into quintiles based on the household Wealth Index. This is a proxy for standard of living based on household ownership of assets and housing quality. Each asset is assigned a factor score generated through principal component analysis, with the scores summed and standardized. All individuals are assigned the score and the quintile (poorest, poorer, middle, richer and richest) of their household [31].

Child anthropometry

In assessing children’s nutritional status, wasting (low weight-for-length/height), stunting (low length/height-for-age) and underweight (low weight-for-age) were used as the three indicators of child undernutrition. In conformity with the recommended World Health Organization (WHO) child growth standards of 2006, the SPSS syntax file ‘igrowup_DHSind.sps’ was used to calculate Z-scores for the three anthropometric indicators. Children were considered wasted, stunted or underweight if their WHZ/WLZ, HAZ/LAZ and WAZ score was less than −2 SD respectively. Extreme Z-scores considered to be biologically implausible were flagged and not used in the analysis if WHZ/WLZ score was less than −5 SD or greater than 5 SD, HAZ/LAZ score was less than −6 SD or greater than 6 SD and WAZ score was less than −6 SD or greater than 5 SD [32, 33].

Analysis

SPSS for windows version 19 was used to conduct the analyses. The design effect parameters ‘sampling weight’, ‘sample domain’ and ‘sample cluster’ [32] were incorporated using SPSS’ Complex Samples Module. In line with recommendations that emphasize provision of levels of uncertainty in the estimates of undernutrition [33], 95% confidence intervals (C.I.) for the prevalence estimates were computed and are presented in Tables 2, 3, 4. Logistic regression was used to test trends. This involved modeling change in undernutrition prevalence regressed on time (the four survey years) with probability values for Wald F tests less than 0.05 considered significant (Tables 2, 3, 4). It is important to note that in the Tables, the 95% C.I. are calculated separately for each prevalence estimate and are not associated with the Wald F statistics that were generated by the logistic regression tests for trends.
Table 2

Wasting trends by age, province, residence, maternal education and wealth index, KDHS

  

1993

1998

2003

2008-09

   
 

Sex

n

%

C.I.

n

%

C.I.

n

%

C.I.

n

%

C.I.

Wald F

P-value

Trend

Total

M/F

2,969

8.4

7.2-9.8

2,921

8.7

7.6-10.0

3,020

7.2

6.0-8.5

3,028

7.4

6.1-8.9

2.206

0.138

 
 

M

1,789

9.1

7.5-10.9

1,501

9.2

7.7-10.9

1,818

8.6

7.0-10.5

1,807

8.6

7.0-10.5

0.259

0.611

 
 

F

1,180

7.3

5.8-9.3

1,420

8.2

6.7-10.1

1,202

5.0

3.8-6.5

1,221

5.6

4.3-7.4

5.338

0.021

Age

                

0-5 months

M

275

9.7

6.3-14.7

273

9.9

6.5-14.6

342

10.0

6.9-14.4

308

8.0

5.2-12.2

0.293

0.589

 
 

F

160

8.3

4.8-14.1

179

8.6

5.0-14.4

170

5.6

2.9-10.6

142

12.8

7.8-20.2

0.391

0.532

 

6-11 months

M

367

10.4

7.3-14.6

293

14.9

10.4-20.9

366

10.0

7.1-13.9

373

13.2

8.1-20.8

0.104

0.747

 
 

F

179

7.0

4.0-12.0

232

9.9

6.4-15.2

221

4.7

2.6-8.3

185

6.5

3.2-13.0

0.056

0.812

 

12-23 months

M

648

10.2

7.4-14.0

528

9.1

6.8-12.1

638

8.9

6.6-12.0

583

5.3

3.6-7.8

5.714

0.017

 

F

360

8.9

5.9-13.3

489

9.8

7.1-13.3

385

6.4

4.3-9.5

430

4.2

2.7-6.6

8.977

0.003

24-35 months

M

500

6.2

4.3-9.0

407

4.6

2.8-7.4

472

6.1

4.0-9.4

543

9.2

5.8-14.3

3.139

0.077

 
 

F

482

6.0

4.0-8.8

520

5.9

4.0-8.6

426

3.5

2.0-6.1

464

4.4

2.4-8.0

1.431

0.232

 

Province

                

Nairobi

M

58

4.8

1.3-16.3

120

13.8

8.3-22.2

111

4.6

2.1-10.1

84

9.7

4.0-21.7

0.010

0.922

 
 

F

43

0.0

 

68

10.8

3.8-26.9

88

4.9

1.2-18.2

73

2.9

0.6-12.5

0.089

0.766

 

Central

M

199

3.5

1.4-8.3

127

9.2

5.2-15.9

182

5.2

2.8-9.3

122

5.2

2.5-10.5

0.213

0.645

 
 

F

159

4.6

2.0-10.2

142

8.2

3.7-17.1

145

6.6

3.6-11.6

110

7.2

3.6-14.1

0.409

0.524

 

Coast

M

146

13.7

9.5-19.4

126

7.4

4.4-12.2

167

8.2

5.0-13.2

173

13.7

8.9-20.5

0.012

0.913

 
 

F

93

13.9

8.3-22.3

112

6.4

3.7-10.8

97

4.7

2.1-10.0

108

11.8

7.2-18.7

0.172

0.679

 

Eastern

M

344

10.5

7.0-15.6

230

6.8

4.0-11.3

321

7.9

4.7-12.8

268

4.8

2.7-8.2

3.981

0.048

 

F

248

11.7

7.3-18.0

259

6.4

3.3-12.1

184

2.7

1.0-7.5

233

5.7

2.8-11.1

3.583

0.060

 

Nyanza

M

292

9.6

6.7-13.6

312

12.9

9.0-18.2

278

6.3

3.7-10.6

385

6.1

4.1-9.0

6.403

0.012

 

F

189

5.2

2.6-10.0

297

11.2

8.1-15.4

201

1.5

0.5-5.0

222

4.5

2.3-8.7

3.496

0.063

 

Rift-Valley

M

418

10.9

7.2-16.3

396

7.8

5.4-11.2

520

12.6

9.0-17.5

533

13.4

10.0-17.8

1.794

0.182

 
 

F

268

7.5

4.8-11.6

340

7.7

5.3-11.2

323

6.3

3.9-10.2

318

6.4

3.6-11.2

0.375

0.541

 

Western

M

333

6.8

4.4-10.4

193

6.9

4.0-11.8

239

8.3

4.5-14.9

242

3.8

1.6-8.8

0.777

0.379

 
 

F

181

4.2

2.0-8.6

203

7.2

4.0-12.6

163

7.8

4.7-12.6

158

1.5

0.5-4.8

1.199

0.275

 

Residence

                

Urban

M

192

6.4

3.8-10.7

283

7.9

4.8-12.7

324

5.6

3.6-8.6

305

7.4

4.7-11.5

0.001

0.973

 
 

F

135

5.9

3.0-11.5

244

7.9

4.8-12.9

222

3.8

1.7-8.6

244

3.5

1.8-6.6

3.611

0.058

 

Rural

M

1,598

9.4

7.7-11.4

1,218

9.5

7.8-11.4

1,494

9.3

7.4-11.5

1,502

8.8

7.0-11.1

0.182

0.670

 
 

F

1,046

7.5

5.8-9.6

1,177

8.3

6.6-10.3

980

5.2

3.9-7.0

977

6.2

4.6-8.3

2.982

0.085

 

Maternal Education

                

No education

M

309

16.2

11.8-21.9

164

7.6

4.2-13.5

236

18.4

13.0-25.5

182

18.6

13.4-25.3

1.126

0.289

 
 

F

206

12.4

8.4-17.8

144

11.3

6.8-18.4

155

6.0

2.7-12.6

114

8.7

4.7-15.4

2.498

0.115

 

Incomplete primary

M

710

9.4

7.1-12.2

557

12.2

9.5-15.4

678

11.1

8.5-14.4

666

8.5

6.1-11.6

0.296

0.587

 
 

F

476

6.6

4.5-9.7

548

8.6

6.3-11.7

456

5.7

3.8-8.4

411

4.8

2.6-8.4

2.006

0.157

 

Complete primary

M

366

8.0

5.6-11.4

377

9.1

6.3-13.1

519

3.4

2.0-5.5

562

6.6

4.5-9.7

1.994

0.158

 
 

F

226

6.4

3.6-11.0

370

8.6

5.7-12.8

326

5.1

3.1-8.3

363

8.9

5.6-13.8

0.186

0.666

 

Incomplete secondary

M

363

4.4

2.4-7.8

148

7.3

3.7-14.2

165

5.3

2.5-11.0

157

7.4

3.7-14.1

1.048

0.306

 
 

F

227

4.6

2.5-8.0

132

5.1

2.2-11.6

97

4.7

1.8-11.6

111

4.1

1.5-10.7

0.024

0.878

 

Secondary +

M

42

1.1

0.2-7.9

256

4.8

2.4-9.1

220

5.3

2.8-9.7

240

6.7

3.9-11.5

1.718

0.190

 
 

F

45

10.5

3.7-26.5

225

6.6

3.8-11.3

168

1.9

0.7-4.7

223

1.2

0.4-3.4

14.170

0.000

Wealth Index

                

Poorest

M

403

15.2

11.3-20.2

378

11.3

8.1-15.6

442

10.7

7.2-15.7

433

13.0

9.7-17.2

0.601

0.439

 
 

F

279

7.6

4.5-12.4

345

9.3

6.5-13.1

263

6.9

4.2-11.2

264

7.6

4.5-12.6

0.086

0.769

 

Poorer

M

395

10.1

7.3-13.7

302

12.2

8.8-16.5

391

10.8

7.4-15.4

415

9.6

5.1-17.4

0.053

0.818

 
 

F

240

10.9

7.4-15.7

303

8.0

5.2-12.2

269

5.2

3.0-9.1

243

7.0

3.4-13.7

1.858

0.173

 

Middle

M

385

6.6

4.4-9.6

288

5.9

3.2-10.4

347

7.5

4.8-11.3

350

5.8

3.1-10.6

0.010

0.922

 
 

F

227

8.0

4.7-13.3

258

9.8

6.3-14.8

236

3.5

1.8-6.6

240

4.9

2.7-8.9

3.945

0.047

Richer

M

343

7.8

4.9-12.2

271

6.0

3.6-9.9

316

7.4

4.5-11.7

305

7.1

4.5-10.9

0.028

0.868

 
 

F

231

5.1

2.6-9.8

258

8.1

4.7-13.5

213

4.8

2.4-9.1

246

5.7

3.1-10.3

0.036

0.849

 

Richest

M

264

3.4

1.8-6.4

262

9.5

6.1-14.5

323

5.6

3.5-8.8

305

5.6

3.1-9.8

0.100

0.752

 
 

F

204

4.7

2.5-8.8

256

5.6

3.2-9.9

221

4.1

1.8-9.1

229

2.5

1.1-5.5

1.983

0.160

 

C.I, 95% confidence intervals; Secondary +, complete secondary and/or higher education; , significant decreasing trend.

Table 3

Stunting trends by age, province, residence, maternal education and wealth index, KDHS

  

1993

1998

2003

2008-09

   
 

Sex

n

%

C.I.

n

%

C.I.

n

%

C.I.

n

%

C.I.

Wald F

P-value

Trend

Total

M/F

2,996

39.5

37.3-41.7

2,951

37.1

34.9-39.2

3,033

36.1

33.9-38.4

3,051

36.5

33.6-39.5

2.681

0.102

 
 

M

1,805

41.7

38.9-44.7

1,511

39.5

36.8-42.2

1,827

38.8

36.0-41.6

1,822

36.9

33.7-40.2

4.634

0.032

 

F

1,191

36.0

32.8-39.3

1,440

34.5

31.7-37.5

1,206

32.1

29.0-35.4

1,229

35.9

31.7-40.3

0.089

0.766

 

Age

                

0-5 months

M

276

20.5

15.4-26.7

286

17.9

13.4-23.6

355

17.8

13.4-23.2

320

14.3

9.0-22.1

2.288

0.131

 
 

F

167

10.6

6.4-16.9

190

15.7

10.7-22.5

169

14.5

9.5-21.6

149

15.5

8.9-25.8

0.135

0.713

 

6-11 months

M

372

24.9

20.5-29.9

290

24.5

19.1-30.7

363

21.5

16.9-26.9

372

27.3

21.8-33.6

0.011

0.917

 
 

F

178

22.9

16.4-31.0

239

18.6

13.7-24.7

223

16.0

11.1-22.4

184

24.3

16.6-34.0

0.108

0.742

 

12-23 months

M

655

52.9

48.0-57.8

528

50.5

45.9-55.1

642

50.4

45.5-55.3

580

49.7

43.8-55.5

0.488

0.485

 
 

F

360

31.3

26.1-37.0

489

36.9

32.5-41.7

386

38.7

33.3-44.3

432

40.1

31.5-49.4

4.179

0.041

24-35 months

M

502

51.3

46.1-56.5

407

51.0

45.5-56.5

467

52.2

46.8-57.6

550

43.1

37.1-49.4

3.242

0.072

 
 

F

485

53.1

47.6-58.5

522

46.4

41.6-51.3

428

41.5

35.9-47.4

464

43.1

37.6-48.7

9.880

0.002

Province

                

Nairobi

M

65

61.7

46.3-75.1

120

32.3

24.0-41.8

111

29.2

22.0-37.6

85

30.4

20.9-41.9

8.505

0.005

 

F

46

18.2

10.7-29.1

66

27.8

15.4-44.9

89

18.4

11.8-27.6

73

30.6

20.8-42.6

0.880

0.351

 

Central

M

202

45.2

36.0-54.7

127

41.9

33.5-50.8

188

41.2

33.7-49.2

126

37.2

29.0-46.1

1.317

0.253

 
 

F

160

34.4

27.1-42.4

145

32.4

21.8-45.2

145

31.0

23.9-39.0

110

24.1

15.3-35.8

2.149

0.145

 

Coast

M

145

42.0

34.4-50.0

128

48.5

36.7-60.5

168

41.0

32.6-50.0

174

39.9

34.3-45.8

0.505

0.478

 
 

F

96

47.3

37.8-57.0

113

41.0

32.4-50.2

98

41.3

31.4-51.9

108

42.2

28.1-57.7

0.224

0.636

 

Eastern

M

345

46.3

39.5-53.2

238

42.3

36.5-48.4

319

42.6

35.0-50.5

269

43.1

37.1-49.4

0.457

0.500

 
 

F

246

45.5

37.5-53.8

261

38.9

32.5-45.7

189

33.7

24.4-44.5

235

39.7

31.9-48.1

1.249

0.265

 

Nyanza

M

297

40.6

34.7-46.8

310

33.8

28.0-40.1

279

33.2

27.3-39.7

381

30.8

26.1-35.9

5.347

0.022

 

F

191

37.9

31.2-45.1

307

33.5

27.4-40.1

204

32.2

25.1-40.2

220

35.0

28.1-42.6

0.262

0.609

 

Rift-Valley

M

420

39.1

32.9-45.6

393

39.8

36.1-43.7

516

40.3

35.0-45.8

545

41.2

33.5-49.4

0.178

0.673

 
 

F

269

27.6

22.0-34.0

346

32.9

27.4-38.9

318

32.7

27.0-38.9

326

38.3

27.3-50.6

2.240

0.136

 

Western

M

331

35.3

29.6-41.5

196

41.3

33.1-50.0

245

38.0

31.2-45.3

242

30.1

25.5-35.2

1.235

0.268

 
 

F

184

33.6

25.9-42.2

201

33.4

26.9-40.6

165

32.0

24.1-41.1

158

32.9

25.1-41.8

0.038

0.846

 

Residence

                

Urban

M

199

41.9

32.7-51.6

281

30.4

24.9-36.5

327

35.3

29.4-41.7

307

29.5

23.7-36.0

2.647

0.105

 
 

F

138

17.7

12.0-25.5

247

27.7

20.9-35.6

223

24.4

19.5-30.0

244

25.7

16.4-37.7

0.452

0.502

 

Rural

M

1,606

41.7

38.7-44.8

1,230

41.5

38.6-44.5

1,500

39.5

36.4-42.7

1,515

38.4

34.9-42.1

2.300

0.130

 
 

F

1,052

38.4

35.0-41.9

1,193

36.0

32.9-39.2

983

33.9

30.3-37.6

985

38.4

34.1-43.0

0.057

0.812

 

Maternal Education

                

No education

M

313

44.0

37.5-50.8

166

49.3

39.4-59.2

233

41.7

32.8-51.2

190

36.8

29.1-45.3

1.975

0.161

 
 

F

209

42.9

35.3-50.9

144

43.8

34.3-53.8

152

41.3

31.3-52.0

114

44.0

29.9-59.2

0.000

0.998

 

Incomplete primary

M

716

48.8

44.6-53.0

573

44.7

40.4-49.1

683

42.1

37.9-46.4

664

41.5

36.2-47.1

5.069

0.025

 

F

475

41.1

36.0-46.5

561

39.1

34.9-43.5

457

35.9

30.6-41.5

410

40.6

34.6-47.0

0.180

0.671

 

Complete primary

M

367

40.4

34.9-46.2

380

37.8

32.7-43.2

526

38.6

33.9-43.5

565

40.1

33.9-46.8

0.004

0.951

 
 

F

228

31.8

25.5-38.8

371

38.0

32.4-43.8

330

35.8

30.5-41.5

369

34.7

28.7-41.2

0.041

0.839

 

Incomplete secondary

M

367

29.3

23.5-35.9

139

31.9

23.8-41.2

165

39.4

30.2-49.4

156

27.8

20.0-37.3

0.229

0.633

 
 

F

232

24.3

18.7-31.0

135

27.5

20.5-35.8

96

21.1

13.0-32.3

112

27.4

18.3-38.7

0.043

0.836

 

Secondary +

M

42

24.0

12.6-40.9

254

28.0

22.7-34.0

219

25.2

19.7-31.5

247

23.1

16.7-31.1

0.700

0.403

 
 

F

46

31.2

18.1-48.2

228

16.0

11.6-21.7

171

12.8

8.8-18.2

224

29.3

20.9-39.5

1.749

0.187

 

Wealth Index

                

Poorest

M

405

48.6

42.8-54.5

384

50.6

44.5-56.6

448

45.8

40.9-50.8

450

42.9

36.8-49.3

2.568

0.110

 
 

F

281

44.0

37.8-50.5

353

44.0

38.1-50.1

261

40.4

33.4-47.9

268

46.6

39.8-53.4

0.066

0.797

 

Poorer

M

399

43.9

38.3-49.7

303

41.7

36.0-47.5

389

38.5

33.0-44.3

411

44.7

37.6-51.9

0.003

0.960

 
 

F

243

37.7

31.6-44.2

307

40.9

34.8-47.3

272

35.1

29.2-41.6

247

37.4

28.8-46.9

0.174

0.677

 

Middle

M

385

40.9

34.9-47.2

288

35.8

29.5-42.6

350

34.5

28.5-40.9

345

32.7

26.9-39.1

3.596

0.058

 
 

F

227

34.4

28.0-41.5

261

35.6

30.1-41.5

236

34.0

27.5-41.1

241

39.6

31.2-48.7

0.633

0.426

 

Richer

M

346

41.7

35.9-47.9

279

38.2

32.6-44.1

312

40.1

33.8-46.7

304

28.9

22.5-36.3

5.982

0.015

 

F

232

41.8

34.8-49.1

262

31.1

24.7-38.4

211

29.2

22.9-36.5

241

32.5

23.4-43.1

1.999

0.158

 

Richest

M

270

29.4

23.4-36.2

258

26.0

20.2-32.7

328

32.9

26.5-39.9

312

30.7

23.5-38.9

0.439

0.508

 
 

F

207

18.4

13.3-25.0

259

16.5

11.7-22.7

227

19.5

15.1-24.8

231

21.6

14.6-30.7

0.713

0.399

 

C.I, 95% confidence intervals; Secondary +, complete secondary and/or higher education; , significant decreasing trend; , significant increasing trend.

Table 4

Underweight trends by age, province, residence, maternal education and wealth index, KDHS

  

1993

1998

2003

2008-09

   
 

Sex

n

%

C.I.

n

%

C.I.

n

%

C.I.

n

%

C.I.

Wald F

P-value

Trend

Total

M/F

3,115

19.7

17.9-21.6

3,051

17.6

16.0-19.4

3,148

16.0

14.4-17.8

3,147

15.0

13.0-17.2

11.804

0.001

 

M

1,881

21.4

19.1-23.8

1,580

19.4

17.2-21.9

1,880

18.7

16.4-21.2

1,890

16.4

14.1-19.1

7.964

0.005

 

F

1,234

17.2

14.6-20.2

1,471

15.6

13.6-18.0

1,269

12.1

10.0-14.5

1,257

12.8

10.3-15.7

7.237

0.007

Age

                

0-5 months

M

307

13.4

9.6-18.4

309

9.3

6.1-14.0

381

9.8

6.7-13.8

352

5.1

3.0-8.4

9.369

0.002

 

F

178

7.6

4.5-12.5

195

8.9

5.4-14.4

2,001

4.4

2.2-8.6

160

5.8

2.6-12.5

1.781

0.182

 

6-11 months

M

379

18.0

14.0-22.8

305

16.4

12.3-21.4

370

17.0

13.3-21.6

386

18.2

12.3-26.0

0.016

0.898

 
 

F

189

14.1

9.4-20.6

242

10.6

6.9-16.2

229

8.4

5.3-13.2

187

6.2

3.2-11.9

4.093

0.043

12-23 months

M

674

26.4

22.4-30.9

546

22.1

18.4-26.1

649

22.1

18.0-26.9

597

17.9

14.5-22.0

8.317

0.004

 

F

371

17.6

13.5-22.5

504

16.8

13.4-20.8

396

15.0

11.4-19.5

439

11.4

7.5-16.8

1.720

0.190

 

24-35 months

M

520

22.1

18.4-26.2

420

25.8

21.2-30.9

480

22.2

18.0-27.2

555

20.9

16.1-26.7

0.146

0.702

 
 

F

496

21.6

17.3-26.6

530

19.3

16.0-23.2

443

14.9

11.3-19.5

470

19.0

15.1-23.8

3.309

0.069

 

Province

                

Nairobi

M

72

11.5

4.9-24.9

121

13.6

8.2-21.8

114

6.2

3.1-12.2

93

9.2

5.2-15.7

1.198

0.277

 
 

F

51

2.7

0.4-15.4

72

2.6

0.4-14.7

91

4.5

2.1-9.5

80

7.3

2.7-18.4

1.330

0.252

 

Central

M

201

16.0

12.1-21.0

138

8.4

4.6-14.7

188

12.3

8.4-17.8

131

12.7

5.7-25.7

0.313

0.576

 
 

F

165

10.8

6.5-17.2

149

10.8

5.8-19.1

154

9.0

5.3-15.0

115

10.3

5.6-18.1

0.077

0.782

 

Coast

M

173

29.5

23.0-36.9

134

23.4

15.1-34.5

178

19.9

14.7-26.3

178

23.2

17.3-30.4

1.807

0.181

 
 

F

110

25.7

17.5-36.2

115

19.4

14.4-25.4

102

13.8

8.2-22.3

109

20.9

14.4-29.5

1.015

0.315

 

Eastern

M

357

23.7

18.2-30.4

256

22.2

16.9-28.7

326

20.7

15.8-26.8

275

16.9

12.0-23.2

2.548

0.112

 
 

F

262

24.3

16.9-33.7

270

18.4

14.0-23.8

195

11.4

6.6-19.1

235

14.3

8.6-23.1

3.711

0.056

 

Nyanza

M

308

21.6

17.4-26.6

315

22.7

17.6-28.8

291

14.6

10.1-20.8

398

14.0

9.9-19.4

6.951

0.009

 

F

193

20.9

15.1-28.1

302

20.2

14.8-27.0

214

9.1

5.2-15.5

226

10.8

7.0-16.3

10.394

0.002

Rift-Valley

M

432

24.0

18.9-30.1

417

19.0

15.3-23.5

537

23.0

18.0-28.9

570

20.2

15.7-25.5

0.403

0.526

 
 

F

271

12.8

9.2-17.6

357

13.8

10.1-18.4

347

15.1

10.7-21.0

331

13.6

8.2-21.6

0.075

0.784

 

Western

M

337

16.4

12.1-22.0

197

20.0

13.2-29.1

245

21.0

14.8-28.9

245

11.1

6.4-18.5

0.768

0.382

 
 

F

181

14.4

9.8-20.6

207

14.6

9.7-21.5

165

16.4

11.3-23.2

161

10.4

6.7-15.8

0.751

0.388

 

Residence

                

Urban

M

215

12.2

8.2-17.8

294

12.0

8.0-17.6

335

13.7

9.2-19.9

319

13.0

8.9-18.7

0.144

0.704

 
 

F

145

8.3

4.1-15.9

256

9.5

6.5-13.5

241

7.4

4.4-12.1

254

8.5

4.6-15.3

0.025

0.874

 

Rural

M

1,665

22.6

20.1-25.2

1,286

21.2

18.7-23.9

1,544

19.7

17.2-22.6

1,570

17.1

14.5-20.1

8.309

0.004

 

F

1,089

18.4

15.5-21.6

1,215

16.9

14.5-19.6

1,028

13.2

10.8-16.0

1,003

13.8

11.1-17.1

6.301

0.012

Maternal Education

                

No education

M

331

30.7

25.0-37.2

174

31.1

22.3-41.6

244

32.1

25.1-40.0

197

27.4

21.1-34.8

0.233

0.629

 
 

F

220

27.0

20.3-35.0

148

27.0

19.5-36.0

168

19.2

13.5-26.7

115

22.2

13.9-33.4

1.540

0.215

 

Incomplete primary

M

743

26.1

22.7-29.8

596

25.8

21.9-30.2

704

21.5

18.0-25.4

681

19.4

15.5-23.9

7.439

0.006

 

F

494

18.6

15.1-22.8

569

17.9

14.2-22.4

476

13.4

10.1-17.5

419

15.3

11.5-20.2

2.466

0.117

 

Complete primary

M

383

16.8

13.1-21.3

395

13.8

10.7-17.7

538

15.1

11.8-19.2

590

15.5

11.4-20.6

0.034

0.854

 
 

F

231

11.8

7.9-17.4

384

16.1

12.6-20.4

348

12.8

8.8-18.3

376

14.2

9.8-20.1

0.023

0.880

 

Incomplete secondary

M

383

10.3

7.3-14.3

152

11.0

6.5-17.9

167

13.0

7.6-21.4

164

11.0

6.4-18.3

0.239

0.625

 
 

F

243

11.4

7.5-16.9

139

9.4

5.3-15.9

102

4.1

1.3-12.4

115

7.1

3.7-13.3

2.837

0.093

 

Secondary +

M

42

8.6

2.4-26.4

263

10.5

6.4-16.7

227

8.1

4.9-13.1

258

6.0

3.6-9.8

2.163

0.142

 
 

F

46

12.8

5.2-28.2

232

5.6

3.3-9.5

175

5.0

2.5-9.6

232

3.9

1.8-8.1

2.529

0.112

 

Wealth Index

                

Poorest

M

419

31.7

26.3-37.5

398

30.9

25.6-36.7

457

24.1

19.3-29.6

463

24.2

19.3-29.8

5.402

0.020

 

F

290

22.6

17.4-28.7

353

22.0

17.6-27.1

277

18.4

13.2-25.1

271

16.9

12.4-22.6

2.718

0.100

 

Poorer

M

415

25.4

20.9-30.6

317

21.6

17.5-26.3

398

20.7

15.8-26.6

424

19.7

14.6-26.0

2.204

0.138

 
 

F

248

20.3

15.2-26.5

317

17.6

12.8-23.7

282

12.4

8.5-17.9

251

15.9

10.5-23.3

1.788

0.182

 

Middle

M

396

17.5

13.9-21.7

308

14.3

10.3-19.5

359

16.9

12.8-22.0

358

10.9

7.3-15.9

3.208

0.074

 
 

F

237

17.6

12.7-23.9

271

16.0

11.8-21.5

243

14.5

10.4-19.8

246

17.9

11.9-26.0

0.001

0.980

 

Richer

M

367

19.3

15.2-24.2

289

17.4

12.8-23.2

329

18.1

13.9-23.2

318

13.4

8.2-21.0

1.808

0.179

 
 

F

241

15.2

10.5-21.5

263

11.9

7.9-17.6

223

7.8

4.3-13.6

248

7.6

3.8-14.7

4.264

0.039

Richest

M

285

8.6

5.6-12.9

268

8.1

5.2-12.3

337

11.3

7.2-17.3

326

10.4

6.0-17.4

0.691

0.406

 
 

F

219

8.4

5.0-13.8

269

8.2

5.4-12.3

244

6.1

3.4-10.8

241

4.9

2.6-9.2

2.238

0.135

 

C.I, 95% confidence intervals; Secondary +, complete secondary and higher education; , significant decreasing trend.

Results

Description of the study samples

Table 5 shows the sample distributions for each year by child’s growth, sex and age, and by province, urban/rural residence, maternal education and Wealth Index. Sample sizes in the various socio-demographic groups varied considerably, affecting the comparability of the Wald F Statistics generated by logistic regression in the tests of trends (shown in Tables 2, 3, 4). This variability should be kept in mind in the examination of the data in Tables 2, 3, 4.
Table 5

Growth and socio-demographic characteristics of the samples, KDHS

 

1993

 

1998

 

2003

 

2008-09

 
 

n

%

n

%

n

%

n

%

Growth

        

Wasted

249

8.4

255

8.7

216

7.2

224

7.4

Stunted

1,182

39.5

1,094

37.1

1,095

36.1

1,114

36.5

Underweight

615

19.7

537

17.6

504

16.0

471

15.0

Sex

        

Male

2,020

60.1

1,647

51.4

1,930

59.4

1,917

60.0

Female

1,343

39.9

1,559

48.6

1,320

40.6

1,281

40.0

Age

        

0-5 months

521

15.5

523

16.3

603

18.6

516

16.1

6-11 months

593

17.6

564

17.6

615

18.9

585

18.3

12-23 months

1,124

33.4

1,097

34.2

1,080

33.2

1,048

32.8

24-35 months

1,126

33.5

1,021

31.9

952

29.3

1,049

32.8

Province

        

Nairobi

157

4.7

213

6.7

216

6.7

181

5.6

Central

390

11.6

296

9.2

357

11.0

249

7.8

Coast

310

9.2

263

8.2

290

8.9

289

9.0

Eastern

668

19.9

546

17.0

537

16.5

514

16.1

Nyanza

526

15.6

641

20.0

510

15.7

630

19.7

Rift-Valley

769

22.9

824

25.7

924

28.4

927

29.0

Western

543

16.2

423

13.2

417

12.8

408

12.8

Residence

        

Urban

430

12.8

600

18.7

607

18.7

591

18.5

Rural

2,934

87.2

2,606

81.3

2,644

81.3

2,608

81.5

Maternal Education

        

No education

614

18.3

339

10.6

432

13.3

318

9.9

Incomplete primary

1,320

39.2

1,221

38.1

1,209

37.2

1,115

34.9

Complete primary

665

19.8

811

25.3

914

28.1

982

30.7

Incomplete secondary

670

19.9

306

9.6

281

8.6

283

8.8

Secondary +

94

2.8

528

16.5

416

12.8

501

15.7

Wealth Index

        

Poorest

762

22.7

782

24.4

752

23.1

745

23.3

Poorer

702

20.9

663

20.7

695

21.4

684

21.4

Middle

672

20.0

606

18.9

620

19.1

610

19.1

Richer

657

19.5

581

18.1

571

17.6

576

18.0

Richest

570

16.9

573

17.9

614

18.9

584

18.3

Secondary +, complete secondary and/or higher education.

Trends in wasting

National trends for boys and girls combined and for boys aged 0–35 months showed no decline in wasting across the study period (Table 2), while wasting did decrease significantly for girls from 7.3% in 1993 to 5.6% in 2008–09 (F(1, 1136) = 5.34, p < 0.021). The decline in girls was concentrated in the age group 12–23 months (F(1, 1046) = 8.98, p < 0.003), and the decline in boys was concentrated in the same age group (F(1, 1046) = 5.71, p < 0.017).

By province, a departure from the overall trends was observed in Eastern and Nyanza provinces. In Eastern province, wasting among boys decreased significantly from 10.5% in 1993 to 4.8% in 2008–9 (F(1, 172) = 3.98, p < 0.048). Boys in Nyanza province posted a significant decline in wasting from 9.6% in 1993 to 6.1% in 2008–9 (F(1, 161) = 6.40, p < 0.012). Analyses by maternal education showed that the prevalence of wasting among girls with mothers having complete secondary and/or higher education declined significantly from 10.5% to 1.2% from 1993 to 2008–9 (F(1, 611) = 14.17, p < 0.000). Trends by urban/rural residence were not statistically significant while those by WI showed girls in the middle quintile decrease from 8.0% in 1993 to 4.9% in 2008–09 (F(1, 735) = 3.95, p < 0.047).

Comparing wasting prevalence between two survey years (1993 versus 2008–09), boys recorded poor growth patterns as compared to girls. Prevalence for boys increased among 6–11 months olds (10.4% to 13.2%), boys in Rift-Valley increased (10.9% to 13.4%), and boys born to mothers with no education (16.2% to 18.6%).

Trends in stunting

Nationally, prevalence in stunting for boys and girls combined remained stagnant across the survey years. The gender-specific trends showed boys’ trend declining from 41.7% in 1993 to 36.9% in 2008–9 (F(1, 1137) = 4.63, p < 0.032) while the trend for girls was stable (Table 3). There was a worsening trend in stunting for girls aged 12–23 months, with stunting increasing from 31.3% in 1993 to 40.1% in 2008–09 (F(1, 1044) = 4.18, p < 0.041). However among girls aged 24–35 months, stunting declined significantly from 53.1% in 1993 to 43.1% in 2008–09 (F(1, 1017) = 9.88, p < 0.002). Analyses by province showed significant decreases in stunting prevalence for boys in Nyanza from 40.6% in 1993 to 30.8% in 2008–09 (F(1, 162) = 5.35, p < 0.022).

The trends by maternal education were not significant for most sub-groups except a decline in stunting among boys born to mothers with incomplete primary education, from 48.8% in 1993 to 41.5% in 2008–09 (F(1, 956) = 5.05, p < 0.025). By WI, most trends were not statistically significant, with the exception of a decline among boys living in households in the richer WI quintile (F(1, 717) = 5.98, p < 0.015).

While the overall national trend in stunting for boys and girls combined stagnated during the study period, girls’ prevalence seemed to have gotten worse in certain socio-demographic segments comparing 1993 versus 2008–09. Stunting prevalence was severe in 1993 and still increased by 2008–09 among girls aged 12–23 months (31.3% to 40.1%), girls born to mothers with no education (42.9% to 44.0%), girls born to mothers with complete primary education (31.8% to 34.7%), and girls belonging to the poorest (44.0% to 46.6%) and middle (34.4% to 39.6%), wealth quintiles.

Trends in underweight

Table 4 provides the detailed trend analysis for underweight. The national trend for all children and separate trends for boys and girls showed significant declines in underweight. Underweight declined among boys and girls combined, from 19.7% in 1993 to 15.0% in 2008–9 (F(1, 1136) = 11.80, p < 0.001), among boys from 21.4% in 1993 to 16.4% in 2008–09 (F(1, 1136) = 7.96, p < 0.005), and among girls from 17.2% in 1993 to 12.8% in 2008–09 (F(1, 1136) = 7.24, p < 0.007). Age specific analysis showed significant declines among boys aged 0–5 months (F(1, 932) = 9.37, p < 0.002), girls aged 6–11 months (F(1, 925) = 4.09, p < 0.043), and boys aged 12–23 months (F(1, 1048) = 8.32, p < 0.004).

Provincial analyses showed significant declines in underweight among boys and girls in Nyanza. Boys’ prevalence reduced from 21.6% in 1993 to 14.0% in 2008–09 (F(1, 161) = 6.95, p < 0.009) and that for girls reduced from 20.9% in 1993 to 10.8% in 2008–09 (F(1, 161) = 10.39, p < 0.002). Boys and girls residing in rural areas recorded significant declines in underweight with boys’ levels reducing from 22.6% in 1993 to 17.1% in 2008–09 (F(1, 871) = 8.31, p < 0.004), and girls’ levels declining from 18.4% in 1993 to 13.8% in 2008–09 (F(1, 871) = 6.30, p < 0.012).

Most of the trend analyses of maternal education were not statistically significant. Only boys born to mothers with incomplete primary education showed a significant decline from 26.1% in 1993 to 19.4% in 2008–09 (F(1, 967) = 7.44, p < 0.006). There was a significant declining trend in underweight among boys in the poorest wealth quintile, from 31.7% in 1993 to 24.2% in 2008–09 (F(1, 551) = 5.40, p < 0.020) and among girls in the richer wealth quintile, from 15.2% in 1993 to 7.6% in 2008–09 (F(1, 716) = 4.26, p < 0.039). Comparison between the 1993 and 2008–09 surveys showed that prevalence of underweight dropped in 2008–09 in almost all sub-groups.

Discussion

For each survey year, the wasting prevalence estimate was slightly lower for girls than for boys, which is consistent with previous studies from sub-Saharan Africa [34, 35]. The overall national trend for wasting showed no significant change in the study period but there were important differences in the trends by age and sex. Older children aged 12–23 months showed a declining trend. Evidence on child growth patterns from many countries in the developing world shows that the prevalence of wasting is stable at all measurement points from about 12 months of age and on, after a six month period of sharply increasing wasting prevalence following weaning [36]. Therefore, the lessened risk of wasting over time observed in this study among Kenyan 12–23 month olds may be a result of improved post-weaning child care and feeding from the mid-1990’s on. This calls for closer investigation of archival data from KDHS and other sources on care and feeding patterns during the past two decades, to observe which care and feeding factors and trends may account for the reduction in wasting. The emphasis on overall care, and not just feeding, is in concert with recent conclusions that proper hygiene practices and access to adequate water, proper sanitation and reliable health services may be as important or even more important determinants of child growth than feeding practices [37].

As to sex differences, wasting among girls overall declined significantly, while remaining stable among boys. Yet some groups of boys did improve. Using a liberal criterion for significance of p < 0.10, the pattern of significant trends in wasting (12 trends as shown in Table 2) were all in the direction of improvement, observed predominantly in females. But trends in wasting also showed significant improvement among older boys and those living in Eastern and Nyanza provinces. The favourable trends in these provinces for both girls and boys are noteworthy, since Eastern province experiences marked perennial food shortages, while Nyanza is among the provinces with the highest poverty levels in Kenya [38, 39]. Climate research in the Eastern province has observed no discernible increasing or decreasing trend either in the annual or seasonal rainfall from 1960’s to the present [40]. It seems unlikely that changing weather conditions might have resulted in improved local food production. In light of this, one possible explanation for the improved wasting trends is the impact of food security initiatives, such as the Kenya Special Programme [39]. However, returning to the theme that overall care may be as important as feeding care, evidence from many countries suggests the importance to child growth of policies in diverse arenas. These include immunization, safe water provision, female literacy, income distribution and support for agriculture [41]. Since it is unlikely that there is any single source within countries with expertise and information on all these features of social and political life, transdisciplinary research [42] seems essential to develop better appreciation of the factors that underpin the trends in child growth reported here.

Similar to wasting, trends in stunting at a national level remained stagnant. However, stratification by sex showed a decline among boys. The high prevalence in stunting among boys as compared to girls is in agreement with the literature on stunting in sub-Saharan Africa [11], but the improvement over time in boys, more so than in girls, is difficult to explain. Looking to family dynamics, the literature on parental sex bias in relation to child care and feeding practices is contradictory and the evidence for bias is scarce [12, 13, 35]. DHS data have been brought to bear on this subject, but only via indirect inferences based on parental education differences [35]. Due to data limitations, the DHS, and most other survey data for that matter, may be inadequate for direct investigations of social and psychological factors underlying sex differences in child growth. Supporting this view is Marcoux’s meta study of 306 child nutrition surveys from across the developing world, of which 74 percent showed no sex differences in wasting, stunting and underweight [43]. That sex differences are difficult to detect reliably in survey research recommends against the use of the survey study design in the search for factors underlying sex differences in child growth. Mixed methods studies of cohorts, and of cases and controls, may be more illuminating.

Analyses by age showed stunting to be relatively lower in younger children and increased with age, in line with other research evidence that the prevalence of stunting increases with age [44]. The comparatively low and stable prevalence posted by children in the youngest age category (0–5 months) is likely due to stable childcare and feeding practices during the pre-weaning stage of development. Actually, in Kenya exclusive breastfeeding increased from 12.7% in 2003 to 31.9% in 2009, while early complimentary feeding at the age of 2–3 months decreased from 81% in 1993 to 32% by 2008 [7]. That stunting in this age group did not show a decline is likely due to a ‘floor effect’, with near lowest feasible levels of stunting already achieved by the mid-1990’s.

The high levels of stunting among children above 12 months and the increasing trend in stunting among girls aged 12–23 months indicates the seriousness of stunting, which seems to manifest itself at the onset of complimentary feeding. Studies have shown that foods used to compliment breastfeeding in Kenya are of low nutritive value [45]. The most preferred porridge is made of composite flours causing negative nutrient-nutrient interactions and also causing mal-absorption due to the child’s immature gut. Such foods are also high in anti-nutrients such as phytates and tannins that bind available nutrients and thus reduce bioavailability [45]. Further research is needed to explore the possibility that the nutritive value of the food served to girls in this age segment has worsened over the study period. The significant improvement among older children, especially among girls aged 24–35 months, could be an indication of older girls responding better to nutritional interventions leading to catch up growth [36], but more research is needed to investigate this issue.

The significant improvements in stunting levels in Nairobi could be attributed to the accrued social-economic and infrastructural advantage enjoyed in the capital region in terms of the number of health facilities and personnel, higher literacy levels and better economic performance [46]. As in the case of wasting, the improvement in stunting among boys in Nyanza province was unexpected due to its high incidence of poverty. Nevertheless, Nyanza province has witnessed an increase in literacy levels [38] and this could be one of the contributing factors to better growth, as maternal literacy is associated with reduced risk of stunting [47, 48].

Higher socio-economic status is associated with better utilization of health care services, better access to food of high quality and quantity, better nutrition, improved sanitation and household possessions [49, 50]. This advantage was observed in the present study, with a significant reduction in stunting observed among children in rich households but not in poor ones. The public health significance of this pattern is alarming, even though Kenya experienced a decrease in the percentage of people living in poverty. It is estimated that the number of people living below the poverty line increased from 13.4 million in 1997 to about 16.6 million in 2006 [51], increasing the number of children in poor households and at risk of stunting.

Overall, the results show that faltering child growth remains a significant public health challenge in Kenya. It is beyond the present scope to undertake an analysis of causes of faltering growth in the Kenyan context. Here, we must be content merely to point to the complexity of the causal landscape, and the need for research that goes beyond the simple descriptive analyses presented in this paper. Among the critical causes of faltering child growth are poor agricultural performance and food distribution at a macro level, and micronutrient deficiency at the level of individuals. At the macro level, there was a worrying decline in productivity in the Kenyan agricultural sector from a real growth rate of 4.4% in 1996 to −5.4% in 2008. This poor performance translates to less food for the fast-growing Kenyan population, poor economic returns as a result of a decline in agricultural export earnings, and increased unemployment due to the decrease in household farm incomes [46, 50, 52].

At the individual level, dietary zinc in particular is essential in bolstering immunity, protein metabolism and linear growth, and its deficiency precipitates retarded growth [49]. Bwibo and Nuemann observed that food served to Kenyan children has multiple micronutrient deficiencies, placing child at risk of poor growth regardless of the quantity of food provided by the agricultural sector [45].

This span from macro to micro level causal factors illustrates the complexity of the causal web that underlies faltering child growth, to which an array of sub-optimal childcare practices and inadequate access to health care also contribute. This complexity is signaled strongly in the present findings that compare child growth trends in urban and in rural areas. While the terms ‘urban’ and ‘rural’ are demographic concepts referring to population number and density, urban versus rural living conditions include important variation in social factors, such as rates of unemployment and illiteracy, access to health facilities, and household and community poverty level [53]. The declining trends in underweight in rural areas as compared to stagnant trends in urban areas underscore the possibility that urban areas are experiencing a decline in their perceived advantage over rural areas [54]. The high urbanization rate brought about by rural–urban migration has significantly reduced the infrastructural advantage urban areas used to enjoy, and has resulted in increased urban poverty. While hardcore poverty declined in rural areas from 34.8% in 1997 to 21.9% in 2005/06, it increased marginally in urban areas from 7.6% in 1997 to 8.3% in 2005/06 [46]. Further research on urban/rural child growth patterns should therefore be complemented by studies of changing urban and rural living conditions, so that the context of child growth is better appreciated. This should include differentiation between urban areas generally and those in capital regions such as Nairobi, which may enjoy special advantages due to proximity to central government.

Study limitations

The more data points over time the more robust the trend analysis. With just the four data points (1993, 1998, 2003 and 2008–09) available for the present analyses, we treat our findings and interpretations with due caution. However, we are not aware of any other data on child undernutrition in Kenya with more than four data points over time, and therefore consider the present effort defensible in the interest of providing the best trend estimates possible with the limited data now available.

While decomposed analysis enables detailed understanding of trends within socio-demographic groups, it results in reduction in sample size. Trends in certain groups may fail to reach statistical significance, not necessarily due to lack of changes in prevalence, but rather to limited sample size giving rise to wider confidence intervals around prevalence estimates. It is not only relatively small sample size, but also sample size variation, that may hinder comparison of trends across socio-demographic groups. For example, two sub-group trend analyses with identical prevalence estimates at four points in time may be judged statistically significant in the sub-group with a relatively large n and insignificant in the sub-group with a relatively smaller n. In surveys wherein the sampling design has not included sampling strata at the level of socio-demographic sub-groups, such as the DHS, the limitations associated with variable n that were encountered in this study cannot be overcome.

An alternative to the decomposition approach (stratification) we have taken is to use multivariate analysis to control statistically for population composition changes over time, and to control for confounding and for effect modification (interactions amongst risk and protective factors). While statistically elegant, the main value of such multivariate analyses is to produce equations that predict future changes in outcome variables as a function of hypothesized changes in risk and protective factors. This approach has less utility for policy work than the decomposition approach, which produces more easily digestible information about sub-group trends. Nevertheless, it is a limitation of the present study that the multivariate relationships among the factors defining socio-demographic sub-groups have not been taken into account in the analyses of undernutrition prevalence.

Conclusions

The national trends in childhood undernutrition in Kenya showed significant declines in underweight, but trends in wasting and stunting were stagnant. Analyses disaggregated by demographic and socio-economic segments revealed some departures from the overall trends. There were more declines in wasting among girls than boys in the various socio-demographic stratifications studied, and the opposite was true for stunting, with boys posting more declining trends compared to girls. These findings support the importance of conducting trend analyses at disaggregated levels within countries, if findings are to be useful in informing public health policy and the development of better-targeted childcare interventions. Concerted efforts should be made by relevant stakeholders to reduce the stagnating trends of undernutrition, especially for stunting, which has consistently remained high in most socio-demographic segments in Kenya.

Endnotes

aSince the promulgation of the new constitution in 2010, provinces were renamed as regions.

bThe KDHS is one of the MEASURE DHS projects in developing countries that collect data on important health indicators. It is a collaboration involving the Kenya National Bureau of Statistics, National AIDS Control Council, Ministry of Public Health and Sanitation, Kenya Medical Research Institute, National Coordinating Agency for Population and Development, ICF Macro, The United States Agency for International Development (USAID) and other non-governmental organizations.

Abbreviations

WI: 

Wealth index

KDHS: 

Kenya demographic and health survey

SPSS: 

Statistical package for the social sciences

SD: 

Standard deviations

WHZ: 

Weight-for-height Z-score

WLZ: 

Weight-for-length Z-score

HAZ: 

Height-for-age Z-score

LAZ: 

Length-for-age Z-score

WAZ: 

Weight-for-age Z-score

DHS: 

Demographic and health survey.

Declarations

Acknowledgement

Discussions with Dickson A. Amugsi and Helga B. Urke were of great value throughout the process of planning and completing this paper. We are grateful for helpful consultations early in the writing process with Professor Anna Lartey and during the analysis phase with Professor Stein Atle Lie.

Authors’ Affiliations

(1)
Department of Health Promotion and Development, University of Bergen
(2)
Department of Foods, Nutrition and Dietetics, Kenyatta University

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