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

Transcription

1 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

2 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

3 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

4 Subjects Male and female children Ages < 5 Height < 120 cm

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7 Prevalence of Acute Malnutrition No School Lunch Provided 15.1% (n=105) 84.9% (n=674) n = 794 Normal MAM or SAM MAM: moderate acute malnutrition SAM: severe acute malnutrition

8 Prevalence of Acute Malnutrition No School Lunch Provided School Lunch Provided 15.1% (n=105) 1.2% (n=1) 84.9% (n=674) n = % (n=15) n = 85 Normal MAM or SAM MAM: moderate acute malnutrition SAM: severe acute malnutrition

9 Prevalence of Acute Malnutrition No School Lunch Provided 15.1% (n=105) WHO Crisis Classification of Acute Malnutrition Acceptable < 5% 84.9% (n=674) n = 794 Poor 5-9% Serious 10-14% Critical > 15% Normal MAM or SAM

10 Prevalence of Acute Malnutrition No School Lunch Provided 15.1% (n=105) 84.9% (n=674) n = 794 Normal MAM or SAM

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13 School Lunches and Kitchens Do children ages 2-6 receive a school lunch? No 70% Yes 30% n = 43 schools

14 School Lunches and Kitchens Do children ages 2-6 receive a school lunch? No 70% Yes 30% Is a kitchen available at your school? No 9% Yes 91% n = 43 schools

15 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? No 70% Yes 30% No 9% Yes 91% No 30% Yes 70% n = 43 schools

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18 Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours Staple Foods 100% Root Crops 59% Vegetables 97% Household Dietary Diversity 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% N = 96

19 Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours Staple Foods 100% Root Crops 59% Vegetables 97% Household Dietary Diversity 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% N = 96

20 Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours Staple Foods 100% Root Crops 59% Vegetables 97% Household Dietary Diversity 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% N = 96

21 Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours Staple Foods 100% Root Crops 59% Vegetables 97% Household Dietary Diversity 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% N = 96

22 Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours Staple Foods 100% Root Crops 59% Vegetables 97% Household Dietary Diversity 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% N = 96

23 Proportion of Families That Have Eaten Each Food Group In the Past 24 Hours Staple Foods 100% Root Crops 59% Vegetables 97% Household Dietary Diversity 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% N = 96

24 Household Dietary Diversity Responses Separated Into Bottom, Upper, and Middle Thirds Range Mean Response Mean HDDS P-value Bottom 1/ KSH 627 KSH 8.2 Weekly Income (N=45) Middle 1/ KSH 1438 KSH Upper 1/ KSH 4258 KSH 7.8 Percent of Weekly Income Spent on Food (N=45) Number of People Living In Household (N=45) Bottom 1/3 7 47% 30% 7.9 Middle 1/ % 67% Upper 1/ % 159% 8.6 Bottom 1/3 3 4 people Middle 1/3 5 6 people Upper 1/ people

25 Household Dietary Diversity Responses Separated Into Bottom, Upper, and Middle Thirds Range Mean Response Mean HDDS P-value Weekly Income (N=45) Percent of Weekly Income Spent on Food (N=45) Number of People Living In Household (N=45) Bottom 1/ KSH 627 KSH 8.2 Middle 1/ KSH 1438 KSH No Statistical Significance Upper 1/ KSH 4258 KSH 7.8 Bottom 1/3 7 47% 30% 7.9 Middle 1/ % 67% No Statistical Significance Upper 1/ % 159% 8.6 Bottom 1/3 3 4 people Middle 1/3 5 6 people No Statistical Significance Upper 1/ people

26 Household Food Insecurity Food Secure Mildly Food Insecure Moderately Food Insecure Severely Food Insecure Proportion of Caregivers Surveyed 7.3% (7/96) 11.5% (11/96) 32.3% (31/96) 49.0% (47/96) In the past one month Rarely (1-2) Yes Sometimes (3-10 times) Often (10+) No did you worry that your household would not have enough food? did you or a household member have to eat a limited variety of foods? eat fewer meals because there was not enough food? 14.6% 71.9% 5.2% 8.3% 16.8% 54.7% 15.8% 12.6% 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%

27 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

28 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

29 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

30 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

31 Thank You! Thank You!

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