Answers to Chapter 7

Test Yourself


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Final Project: putting all the steps together

Area Description of Western and Nyanza Region of Kenya:

With the survey information (DHS 1993), general information can be found on the region of Western Kenya and Nyanza. Below is summary of operational definitions of malnutrition, socio-economic status, educational attainment, water and sanitation, and access to healthcare.

Indicators

N

Prevalence Rates (%)

Stunting (Below –2 SD)

1046

28.6

Wasting (Below –2 SD)

1021

5.1

Underweight (Below –2 SD)

1059

20.5

Bad Roofing (Grass/Thatch)

2195

48.7

Low Education (None & Incomplete primary)

2209

63.4

Illiteracy (Cannot Read)

2203

21.5

Bad Water Source (Surface)

2195

56.5

Bad Sanitation (No Toilet/Bush)

2192

15.2

No Health Card (None or No longer have one)

1213

12.8

Not Vaccinated

493

73.4

Table 1: Summary table of indicators that are a proxy for poor levels.

Targeting of sub-regions:

Prevalence rates for nutrition, SES, education and environmental indicators are summarized below and sub-regions are ranked based on nutritional status. The other indicators are used to confirm the ranking, and to see if indicator status is consistent with nutritional status, i.e. are there a few sub-districts that are consistently worse-off in most or all categories than others?

Sub-region

Stunting

Wasting

Under-weight

Bad Roofing

Low Education

Illiteracy

Bad water

Poor Sanitation

Not vaccinated

South Nyanza

38.3%

133

5.2%

129

34.1%

136

65.9%

255

75.9%

257

27.6%

257

74.5%

255

56.9%

255

33.3%

87

Kisii/

Nyamira-Rural

26.7%

202

8.8%

195

21.0%

205

33.7%

486

63.4%

488

19.9%

487

89.3%

486

.8%

486

10.1%

149

Bungoma-Rural

33.3%

216

5.5%

214

21.0%

219

51.8%

396

58.6%

396

19.7%

395

25.5%

396

9.4%

395

22.2%

153

Siaya-Rural

31.9%

185

3.7%

182

19.8%

186

65.0%

406

69.8%

408

26.5%

407

76.5%

405

24.1%

406

13.8%

123

Kakamega-Rural

24.5%

188

4.2%

182

16.5%

191

44.9%

379

57.4%

381

17.6%

381

37.5%

379

4.0%

379

19.6%

143

Other Urban

17.7%

51

5.9%

50

14.0%

51

0.9%

113

33.0%

118

5.2%

116

8.0%

113

1.8%

113

8.9%

45

Other-Rural

11.2%

71

**

69

8.6%

71

61.3%

160

76.4%

161

28.8%

160

33.5%

161

20.9%

158

25.5%

51

Total

28.6%

1046

5.1%

1021

20.5%

1059

48.7%

2195

63.4%

2209

21.5%

2203

56.5%

2195

15.2%

2192

18.6%

751

Table 2: Indicators across sub-region (**statistic unavailable due to data limitation)

As can be seen in table 2, sub-regions were ranked according to underweight prevalence. The three sub-regions with the highest prevalence of underweight are South Nyanza, Kisii/Nyamira-Rural, and Bungoma-Rural. However, the difference in underweight prevalence between Bungoma and Siaya is small and insignificant (see table below), therefore it may be advantageous to target Saiya-Rural instead of Bungoma because this area does have higher levels of stunting (31.9%), bad roofing (65.0%), low education (69.8%), bad water source (76.5%), and poor sanitation (24.1%).

Chi-square Test
Area

Prevalence of Underweight (%)

N

Standard Deviation

Saiya-Rural

19.8

182

.3994

Bungoma-Rural

21.0

214

.4085

Total

20.4

396

.4039

Difference

1.2

**

**

p-value=.760      

 

One-Way Associations:

This summary table shows that most variables are significantly associated with both WAZ and underweight prevalence.

    WAZ Prevalence <-2SDs

N

       
Corrugated Iron/ Tiled Roof

-0.7975

17.4

460

Grass/Thatched Roof

-1.079

23.0

561

Difference

0.2815

-5.6

 
 

p = 0.001

p = 0.027

 
       
Education High (Complete primary +)

-0.7898

16.4

426

Education Low (Incomplete primary/None)

-1.0684

23.4

595

Difference

0.2786

-7.0

 
 

p = 0.001

p = 0.007

 
       
Literacy High (Can Read)

-0.8831

18.6

805

Literacy Low (Cannot Read)

-1.2224

27.4

212

Difference

0.3393

-8.8

 
 

p = 0.001

p = 0.005

 
       
Latrine Access

-0.8890

19.7

847

No Latrine Access

-1.2729

24.4

168

Difference

0.3839

-4.7

 
 

p = 0.001

p = 0.169

 
       
Safe water (Piped and Well)

-0.8194

16.6

452

Unsafe Water (Surface Water)

-1.0631

23.6

564

Difference

0.2437

-7.0

 
 

p = 0.004

p = 0.006

 
       
Vaccinated for Measles

-1.2043

23.0

564

Never been Vaccinated

-1.5204

33.9

124

Difference

0.3161

-10.9

 
 

p = 0.007

p = 0.011

 
       
Delivered at Hospital or Clinic

-0.7502

15.7

370

Delivered at Home

-1.0656

23.2

652

Difference

0.3154

-7.5

 
 

p < 0.001

p = 0.004

 

 

Multi-Way Analysis:

Does sanitation have an association with weight for age z-score independent of socio-economic and environmental factors?

After considering several factors, it was found that access to sanitation does show an association with respect to weight for age. Even with multiple factors, the coefficient of no latrine stayed relatively high and significant. As expected socio-economic status, as defined by roofing type also shows an association. Unsafe drinking water, low education level, and low literacy were significant. If improving sanitation were to be done, there would an expected maximum improvement of 0.283 for weight for age z-score. Furthermore, this would yield a theoretical reduction of the prevalence by a percentage of 9.76%.

Dependent Variable: WAZ

Independent Variable

Model: coefficient (t,p)

1

2

3

4

5

6

No Latrine

-0.384

(-3.44, 0.001)

-0.322

(-2.846, 0.005)

-0.287

(-2.513, 0.012)

-0.332

(-2.946, 0.003)

-3.08

(-2.699, 0.007)

-0.283

(2.477, 0.013)

Bad Roofing

-

-0.237

(-2.794, 0.005)

-.204

(-2.379, 0.018)

-

-0.211

(-2.479, 0.013)

-0.206

(-2.424, 0.016)

Low Education

-

-

-.197

(-2.289, 0.002)

-0.231

(-2.717, 0.007)

-

-

Low Literacy

-

-

-

-

-0.269

(-2.609, .009)

-0.283

(-2.745, 0.006)

Unsafe Water

-

-

-

-

-

-0.216

(-2.593, 0.010)

N

1017

1017

1017

1017

1017

1017

Adj. R2

0.012

0.019

0.024

0.019

0.027

0.034

 

Does access to safe drinking water have an association with weight for age z-score independent of socio-economic and environmental factors?

Taking several regression models into consideration (see table below), it was found that access to safe drinking water remained significant throughout all models. After controlling for all factors, the regression model suggests that a maximum improvement of 0.212 for weight for age z-score can be achieved. This would mean a reduction in the prevalence by 6.5%, which is a decrease of 31.7% in the number of cases of underweight.

Dependent Variable: WAZ

Independent Variable

Model: coefficient (t,p)

1

2

3

4

5

6

Unsafe Water

-0.244

(-2.92, 0.004)

-0.231

(-2.778, 0.006)

-0.231

(-2.779, 0.006)

-0.216

(-2.593, 0.01)

-0.223

(-2.665, 0.008)

-0.2.12

(2.555, 0.011)

Bad Roofing

-

-0.275

(-3.309, 0.001)

-0.243

(-2.907, 0.004)

-0.206

(-2.424, 0.016)

-

-0.176

(-2.039, 0.042)

Low Literacy

-

-

-0.311

(-3.403, 0.002)

-0.283

(-2.745, 0.06)

-

-0.239 (-2.288, 0.022)

No Latrine

-

-

-

-0.283 (-2.477, 0.013)

-0.349

(-3.359, .001)

-0.268

(-2.347, 0.019)

Delivery at Home

-

-

-

-

-

-0.212

(-2.390 0.017)

N

1011

1011

1011

1011

1011

1006

Adj. R2

0.008

0.019

0.028

0.034

0.018

0.039

 

Does access to healthcare, as defined by place of delivery, have an association with weight for age z-score independent of socio-economic and environmental factors?

After controlling for socio-economic factors (see table below), it was found that the proxy for access to healthcare, place of delivery, showed a strong and significant relationship with weight for age z-score. The statistical analysis further suggests that if access to healthcare was available, the maximum change in weight for age z-score would be 0.212. In terms of prevalence, there would be a maximum decrease of 5.07%, which means that prevalence would drop by approximately 25%.

Dependent Variable: WAZ

Independent Variable Model: coefficient (t,p)
1 2 3 4 5
Deliveray at Home -0.315

(-3.671, 0.000)

-0.267

(-3.058, 0.002)

-0.228

(-2.576, 0.01)

-0.213

(-2.388, 0.01)

-0.212

(-2.390 0.017)

Bad Roofing - -0.232

(-2.751, 0.006)

-0.214

(-2.526, 0.012)

-0.181

(-2.092, 0.036)

-0.176

(-2.039, 0.042)

Low Literacy - - -0.252

(-2.426, 0.015)

-0.224

(-2.147, 0.032)

-0.239 (-2.288, 0.022)
No Latrine - - - -0.293 (-2.568, 0.010) -0.268

(-2.347, 0.019)

Unsafe Water - - - - -0.212

(-2.555 0.011)

N 1006 1006 1006 1006 1006
Adj. R2 0.013 0.020 0.026 0.032 0.039

 

Does literacy have an association with weight for age z-score independent of socio-economic and environmental factors?

The effect of literacy on nutritional status is most compelling. It has been shown that, throughout all the regression models (see above tables), literacy has remained significant. This shows that an improvement in literacy will result in a maximum change of .239 in the z-score for underweight. This means that there would be an expected decrease of 7.26% in prevalence, which is relatively the highest proportionate change (35.4%).

 

Program Coverage and Targeting Assessment:

In the previous section, it was found that the possible key vectors of change in underweight were access to sanitation, safe drinking water, measles vaccination, place of delivery, and literacy. As there some of these factors are addressed through current programs, it is of interest to assess how well targeted these programs are to the malnourished.

 

Access to Measles Vaccination:

 

Underweight

Not Underweight

Total

Vaccinated

129

433

562

Not Vaccinated

42

82

124

Total

171

515

686

The ovearall coverage was 0.8192 and the coverage for the malnourished was 0.7544. The test for targeting (F/PP=.9209) confirmed that immunizations are not well targeted to those worse-off.

Access to Delivery in Hospital/Clinic:

 

Underweight

Not Underweight

Total

Clinic/Hosp. Del.

58

313

371

Home Delivery

151

499

650

Total

209

812

1021

 

The coverage was 0.3634 and the coverage for malnourished was 0.2775. The test for targeting (F/PP=.7636) confirmed that the worse-off do not have access to delivery in clinics or hospitals, indicating low access to health services in general. If access to clinics and modern health facilities were available, there would be a significant of decreasing the prevalence of underweight.

 

Child Feeding Practices

As can be seen from the child feeding patterns table (see excel chart), the percent of those who breastfeed after 3 to 5 months is low, and the percent of exclusive breastfeeders is extremely low, with only about 27% practicing. Exclusive breastfeeding until the age of 6 months needs to be emphasized through a nutrition education program. The introduction of complementary foods between 4 and 9 months is good, but efforts need to be made to promote continued breastfeeding after introducing liquids and solids into the infant's diet.

Nutrition Program Recommendations:

Taking the DHS data and the statistical information from this study, there are interventions that can be taken to reduce the occurrence of underweight. These measures can be done at two levels, short-term and long-term.

Short-term Interventions:

The DHS data has suggested that water and sanitation have an important effect on underweight. For example improvement of 0.212 for weight for age z-score can be achieved by improving water. This would mean a reduction in the prevalence by 6.5%, which is a decrease of 31.7% in the number of cases of underweight. If improving sanitation were to be done, there would an expected maximum improvement of 0.283 for weight for age z-score. Furthermore, this would yield a theoretical reduction of the prevalence by a percentage of 9.76. Taking this into account, I would urge that the government of Kenya invest in improving access to tap water and sanitation.

Long-term Interventions:

It is also important and beneficial for the Kenyan government to take into consideration long-term interventions. Data has suggested that access to healthcare, literacy, and income generation are areas where improvements will impact nutritional status. Healthcare access can be increased and be efficiently run if there are money and resources. It is important that the Kenyan government keep in mind that a system that has a strong referral system is a good way to improve efficiency. Another way is to implement viable social safety nets. Two possibilities are rotating drug generation programs (e.g. the Bamako Initiative and the development of community based insurance schemes. There should also be promotion of privatization of healthcare to remove the burden placed on the limited resources of the Kenyan Government.

Literacy should be improved, this analysis shows that improvement in literacy will cause a maximum change of .239 in the z-score for underweight. This means that there would be an expected decrease of 7.26% in prevalence. This can be done through the aid of Non-Governmental Organizations. Literacy is a also a good way to promote healthy and preventive measures. Furthermore, insurance schemes require a literate population to work efficiently. This tool can also enhance the human capital of Kenya and improve the fall back position of women, which in turn can aid in improving the health status of women and children.

Income generation is also important. This can be done through food for work programs, income diversification programs, and animal husbandry. Data has consistently shown that having income increases ones endowment and exchange possibilities.