Assessment of pediatric malnutrition and household food insecurity in Kisii and Nyamira, Kenya

Kevin Forey, MD-MBA Health Administration, Class of 2018

Blair Gifford, PhD Professor of Management and Health Administration

University of Colorado Denver

Objectives

1. To determine the prevalence of moderate and severe acute malnutrition among schoolchildren

2. Characterize household food insecurity and dietary diversity

3. Determine the proportion of schools that provide lunch to students and have access to a kitchen

Setting

• Kisii and Nyamira are among the most fertile land in Kenya

• Predominantly rural communities

• High access to fruits and vegetables

• However, Vitamin A deficiencies remain prevalent among children

• Minimal clinical information about this population otherwise

Subjects

• Male and female children

• Ages < 5 • Height < 120 cm

Prevalence of Acute MalnutritionNo School

Lunch Provided

84.9%(n=674)

15.1%(n=105)

n = 794

MAM or SAMNormalMAM: moderate acute malnutritionSAM: severe acute malnutrition

Prevalence of Acute MalnutritionNo School

Lunch Provided

84.9%(n=674)

15.1%(n=105)

School Lunch Provided

98.8%(n=15)

1.2%(n=1)

n = 85n = 794

MAM or SAMNormalMAM: moderate acute malnutritionSAM: severe acute malnutrition

Prevalence of Acute MalnutritionNo School

Lunch Provided

84.9%(n=674)

15.1%(n=105)

n = 794

MAM or SAMNormal

WHO Crisis Classification of Acute Malnutrition

Acceptable < 5%

Poor 5 - 9%

Serious 10 - 14%

Critical > 15%

Prevalence of Acute MalnutritionNo School

Lunch Provided

84.9%(n=674)

15.1%(n=105)

n = 794

MAM or SAMNormal

School Lunches and Kitchens

Do children ages 2-6 receive a school lunch?

70%No

Yes30%

n = 43 schools

School Lunches and Kitchens

Do children ages 2-6 receive a school lunch?

Is a kitchen available at your school?

70%No

Yes30%

Yes91%

9%No

n = 43 schools

School Lunches and Kitchens

Do children ages 2-6 receive a school lunch?

Is a kitchen available at your school?

Do older students receive food?

70%No

Yes30%

Yes91%

9%No

70%Yes

No30%

n = 43 schools

Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours

Staple Foods 100%

Root Crops 59%

Vegetables 97%

Fruits 89%

Beef, Goat, Lamb, Pork, Poultry 24%

Eggs 37%

Fish 11%

Beans, lentils 62%

Milk, Cheese, Other dairy 68%

Butter, Fats, Oil 90%

Sugar, Honey, Sweetener 84%

Tea, Coffee 95%

Household Dietary

Diversity

N = 96

Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours

Staple Foods 100%

Root Crops 59%

Vegetables 97%

Fruits 89%

Beef, Goat, Lamb, Pork, Poultry 24%

Eggs 37%

Fish 11%

Beans, lentils 62%

Milk, Cheese, Other dairy 68%

Butter, Fats, Oil 90%

Sugar, Honey, Sweetener 84%

Tea, Coffee 95%

Household Dietary

Diversity

N = 96

Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours

Staple Foods 100%

Root Crops 59%

Vegetables 97%

Fruits 89%

Beef, Goat, Lamb, Pork, Poultry 24%

Eggs 37%

Fish 11%

Beans, lentils 62%

Milk, Cheese, Other dairy 68%

Butter, Fats, Oil 90%

Sugar, Honey, Sweetener 84%

Tea, Coffee 95%

Household Dietary

Diversity

N = 96

Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours

Staple Foods 100%

Root Crops 59%

Vegetables 97%

Fruits 89%

Beef, Goat, Lamb, Pork, Poultry 24%

Eggs 37%

Fish 11%

Beans, lentils 62%

Milk, Cheese, Other dairy 68%

Butter, Fats, Oil 90%

Sugar, Honey, Sweetener 84%

Tea, Coffee 95%

Household Dietary

Diversity

N = 96

Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours

Staple Foods 100%

Root Crops 59%

Vegetables 97%

Fruits 89%

Beef, Goat, Lamb, Pork, Poultry 24%

Eggs 37%

Fish 11%

Beans, lentils 62%

Milk, Cheese, Other dairy 68%

Butter, Fats, Oil 90%

Sugar, Honey, Sweetener 84%

Tea, Coffee 95%

Household Dietary

Diversity

N = 96

Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours

Staple Foods 100%

Root Crops 59%

Vegetables 97%

Fruits 89%

Beef, Goat, Lamb, Pork, Poultry 24%

Eggs 37%

Fish 11%

Beans, lentils 62%

Milk, Cheese, Other dairy 68%

Butter, Fats, Oil 90%

Sugar, Honey, Sweetener 84%

Tea, Coffee 95%

Household Dietary

Diversity

N = 96

Household Dietary DiversityResponses Separated Into

Bottom, Upper, and Middle Thirds Range Mean Response

Mean HDDS P-value

Weekly Income (N=45)

Bottom 1/3 0 – 1000 KSH 627 KSH 8.2

Middle 1/3 1050 – 2100 KSH 1438 KSH 8.2 0.882

Upper 1/3 2160 – 15000 KSH 4258 KSH 7.8

Percent of Weekly Income Spent on Food

(N=45)

Bottom 1/3 7 – 47% 30% 7.9

Middle 1/3 50 – 95% 67% 8.3 0.547

Upper 1/3 100 – 362% 159% 8.6

Number of People Living In Household

(N=45)

Bottom 1/3 3 – 4 people 3.8 7.8

Middle 1/3 5 – 6 people 5.6 7.8 0.549

Upper 1/3 7 – 10 people 8.1 7.7

Household Dietary DiversityResponses Separated Into

Bottom, Upper, and Middle Thirds Range Mean Response

Mean HDDS P-value

Weekly Income (N=45)

Bottom 1/3 0 – 1000 KSH 627 KSH 8.2

Middle 1/3 1050 – 2100 KSH 1438 KSH 8.2 0.882

Upper 1/3 2160 – 15000 KSH 4258 KSH 7.8

Percent of Weekly Income Spent on Food

(N=45)

Bottom 1/3 7 – 47% 30% 7.9

Middle 1/3 50 – 95% 67% 8.3 0.547

Upper 1/3 100 – 362% 159% 8.6

Number of People Living In Household

(N=45)

Bottom 1/3 3 – 4 people 3.8 7.8

Middle 1/3 5 – 6 people 5.6 7.8 0.549

Upper 1/3 7 – 10 people 8.1 7.7No Statistical

Significance

No Statistical

Significance

No Statistical

Significance

In the past one month …Yes

NoRarely (1-2)

Sometimes (3-10 times)

Often (10+)

… did you worry that your household would not have enough food? 14.6% 71.9% 5.2% 8.3%

… did you or a household member have to eat a limited variety of foods? 16.8% 54.7% 15.8% 12.6%

… eat fewer meals because there was not enough food? 26.0% 47.9% 8.3% 17.7%

… was there ever no food to eat? 9.4% 30.2% 1.0% 59.4%

… did you or any household member go to sleep at night hungry? 15.6% 29.2% 1.0% 54.2%

Household Food InsecurityFood Secure Mildly

Food InsecureModerately

Food InsecureSeverely

Food InsecureProportion of

Caregivers Surveyed7.3% (7/96)

11.5% (11/96)

32.3% (31/96)

49.0% (47/96)

Conclusions

- Acute malnutrition is present among schools without lunch

- High diversity and availability of foods

- Animal proteins less common

- Malnutrition not simply a matter of poverty

- Evidence of cultural, social, and behavioral components

- Parents more willing to provide food for older children

Limitations

- Study limited to children attending school

- Selection bias

- Most important age group not included (< 2 yrs)

- Community health likely worse than our results

- Unable to accurately determine chronic malnutrition

- No access to dates of birth

Solutions

- Short-term:Community based management of acute malnutrition (CMAM) with RUTF (eg. Plumpy’Nut)

- Long-term: (1) Better understand feeding practices (eg. research) (2) Public health campaign (eg. parents -> contribute to school lunches) (3) Education among parents of affected children

What To Do

- Short-term:Community based management of acute malnutrition (CMAM) with RUTF (eg. Plumpy’Nut)

- Long-term: (1) Better understand feeding practices (eg. research) (2) Public health campaign (eg. parents -> contribute to school lunches) (3) Education among parents of affected children

Spring 2018

Thank You! Thank You!