Malnutrition and development in Homabay
Declarat ion
ACKNOWLEDGEMENT
Malnutrition is currently perceived not only as a health problem but also a social and development problem. The causes, effects and relationships between malnutrition and other development challenges make it important to have a multi-sectoral approach to enable planning and programming nutritional programs efficiently and sustainably. The condition is therefore deemed to be addressed in various ways as well as by different sectors thus quite distinct since previously the condition was believed to be majorly a health issue. At the national level, 35% of children under five years are stunted, 16% are underweight and 7% are wasted this is in accordance to the Kenya Demographic and Health Survey report (2014). These conditions are increasing in most countries especially in developing and less developed countries. In Kenya the highest levels of low weight for age are found among children age 24-35 months. The Percentage is slightly higher among boys at 12% and girls 10%. In the rural areas it is at 13% and urban areas 7% and the proportion of underweight decreases as mother’s education level increases. In Homabay County, the burden of malnutrition is high i.e. 21 children under the age of five were admitted in the facility between October 2016-March 2017 as a result of Severe Acute Malnutrition. Between March -August 2017, 57 children with malnutrition conditions were admitted. This data shows that the cases are fluctuating upwards. The main objective for this study thereof is to establish prevalence of malnutrition and associated factors among children under 59 months at Homabay Refferal hospital. The specific objectives for this study will include: to establish the social economic demographic characteristics among caretakers of children under five attending Homabay referral hospital, to establish feeding practices among caretakers of children under five attending Homabay referral hospital as well as to determine risk factors associated with malnutrition among children under five attending Homabay referral hospital.
A descriptive survey will be applied in this study and a total number of 233 children at Homabay referral Hospital shall be the researchers sample size. Systematic sampling procedure will be applied in this study. Language barrier could be the major challenge for the study since majority of the participants speak Luo, which is the local language. Descriptive statistic will be used to achieve this study. Data will be organized, interpreted and arranged through computer package such as Microsoft word, excel, access and SPSS version 20 then be presented through pie charts, tables and bar graphs.
The study will provide proper interventions on malnutrition cases to the government, health care providers and the caregivers thereof. It will thus enable proper finding on the best healthful practices that would be inculcated to either eradicate or reduce the condition. This will greatly aid the health practitioners involved, various Institutions and the affected people.
TABLE OF CONTENTS
1.2 Statement of the problem.. 4
2.2.1 Social economic demographic factors. 8
2.2.2 Risk factors associated with malnutrition. 10
3.4.1 Sample Size Determination. 14
3.6.2 Independent Variables. 17
3.7 Ethical Considerations. 17
Appendix A : Research Request Letter 20
Appendix B: Data Collection Request Letter 21
LIST OF TABLES
Page no
Table 1.0 16
Table 1.2 17
LIST OF FIGURES
Page no
Conceptual Framework 12
ABBREVIATIONS
DALYs Disability-Adjusted Life Years
DRC Democratic Republic of Congo
FANTA Food and Nutrition Technical Assistant
FBP Food By Prescription
HFIAS Household Food Insecurity Access Scale
KDHS Kenya Demographic Health Survey
MNP Micro Nutrient Powder
NNO Negative Nutritional Outcome
PEM Protein Energy Malnutrition
RUFT Ready To Use Food Therapy
SAM Severe Acute Malnutrition
UNICEF United Nations Children’s Fund
USA United States of America
WHO World Health Organization
DEFINITION OF TERMS
Demographic health survey– Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition.
Malnutrition– refers to lack of proper nutrition, caused by not having enough to eat, not eating enough of the right things, or being unable to use the food that one does eat.
Mortality– Mortality is another term for death. A mortality rate is the number of deaths due to a disease divided by the total population.
Prevalence– Prevalence is a measure of disease that allows us to determine a person’s likelihood of having a disease.
Protein–energy malnutrition (PEM) -refers to a form of malnutrition where there is inadequate calorie or protein intake
Risk factors-refers to the exposure of an individual that increases the likelihood of developing malnutrition.
Stunting – refers to the impaired growth and development that children experience from poor nutrition, repeated infections and inadequate psychosocial stimulation
Under nutrition– defined as the outcome of insufficient food intake (hunger) and repeated infectious diseases.
Wasting- refers to a condition that
result from inadequate nutrition over time (low weight for age)
CHAPTER ONE
INTRODUCTION
1.1 Background information
The World Food Program (2014) defines malnutrition as a state in which the physical function of a person is impaired to the point where he/she can no longer maintain proper bodily performance process such as growth and development, pregnancy, lactation, physical work and recovering from disease (Luchuo et al., 2013).
In USA, acute malnutrition defined by weight-for-height in young children, continues to be a major health problem, (Fuchs et al, 2014).
When the nutritional status of a child deteriorates in a relatively short period of time, the child can be said to have acute malnutrition. If a child’s weight for height measurement is less than 70% of the normal range for his age (weight for height Z score <-3SD), then the child would be diagnosed as having malnutrition (Dereje, 2014).
Nutrition is designed to provide access to a safe, adequate, healthful diet to a population living in a given location. These activities include nutrition education, nutrition or health promotion, food programs, supplementation programs, preventive programs, local policy analysis and development, and the organizational infrastructure that supports it (Owen, 2004).
Globally, under-nutrition is associated with more than one-third of all deaths in children of the age group of 59 months and below. Malnutrition is a burden of ill health accounting for 60% of the 10.9 million deaths that occur annually among children under five years of age (REF). Data from UNICEF show that the highest level of underweight children is found in South Asia of which 46% are the age of under-fives (Basit et al, 2012).
According to the UNICEF report 2015,nearly half of all deaths in children under five are associated with under nutrition. This clearly shows that there is a loss of 3 million young children yearly approximately. Only a fraction of these children die in other circumstances such as famine or war. Malnutrition make children’s’ growth stunted, deprives them of essential vitamins and minerals, and makes them predisposed to illness.
Malnutrition violates a child’s right to survive, grow and develop and its consequences often remain invisible for long till later on in a child’s life. The lack of breastfeeding or inadequate breastfeeding practices result in almost 12% of all deaths among children under age (UNICEF, 2015).
Despite the current efforts in prevention and management of under five malnutrition in Bangladesh, 16% of children under the age of five years are still suffering from acute malnutrition. In India under-nutrition still bears a burden to public health which has a prevalence of 43.5% among under five years population which has been observed to be one of the highest in the world. In this age group of under five population, 46% of children are reported to be stunt, 47% are under the required weight and 16% are wasted. There is also a disparity in the prevalence of under-nutrition in the middle of the states of India, which starts at 55% to 27%, (Basit et al, 2012).
The nutritional status in childhood is one of the factors indicating the well being of households as well as determining the probability of child survival. It is one of the major causes of infant and child death rates. In sub-Saharan Africa, it stands at about 2% of deaths and about 3% of disability-adjusted life years (DALYs) in children of the age of under-five. It affects the child’s intellectual development, health and productivity in the later life of these young ones. This leads to perpetuation imbalance in health and other portions of household matters. Child malnutrition is also one of the measures that show the health status of children that the WHO recommends for assessing uniformity in health (Zere et al, 2003).
In Democratic Republic of Congo, reports from three nutritional national surveys (the 1995 and 2001 Multiple Indicator Cluster Surveys and the 2007 Demographic and Health Survey) shows that nutritional condition in the DRC remains a challenge Specifically, nutritional status of children under the age of five indicated deterioration in terms of acute malnutrition that is; stunting, wasting and underweight. Stunting rate was approximately 34% in 1995, 31% in 2001, and 46% in 2007, (Kandala et al, 2011).
In Ghana, there is a trend of continued stunting growth in the North compared to the South of the country. Malnutrition in this country highly prevalent in the form of Protein Energy Malnutrition (PEM) which leads to growth retardation, underweight and deaths in at least 54% of all deaths beyond early infancy. This makes PEM one of the greatest causes of child mortality in Ghana (Poel et al, 2007).
In developing countries, 12 million children under five years die every year. Malnutrition being prevalent in developing countries, it is rarely cited as being one of the leading causes of death. This is due to the conventional way that cause of death data are reported and analyzed. In many countries, mortality statistics are compiled from records in which a single proximate cause of death has been reported (Rice et al, 2000).
Diarrhea and malnutrition are intricately interconnected. Diarrhea may lead to malnutrition due to reduced dietary intake, mal-absorption and mal-digestion, whereas malnutrition is considered a risk factor diarrhea and other infections due to weakened immune system (Njuguna & Muruka, 2011).
In Kenya, malnutrition is a serious public health problem with the stunting, wasting and underweight prevalence of 37%, 6% and 27% respectively. Stunting is highest 44.8% among the 1-2 years age group (Ngare & Mtunga, 1999). The form of malnutrition most common in Kenya is the chronic malnutrition form with 34% of children under the age of five being stunted, 8% wasted and 23% underweight. This accounts for about 1.5 million Kenyan children under five years. Due to serious consequences of malnutrition in children, nutritional status of children should be assessed periodically to monitor the situation and appropriate action taken to combat or prevent malnutrition (Kariuki et al, 2002). The Kenyan Ministry of Health in its Strategic Plan (1999–2004) did focus to reduce malnutrition among children under the age of five by 30%. Multiple programs including Ready to Use Food Therapy (RUFT) also termed Food by Prescription (FBP) funded by United States Agency for International Development (USAID) was piloted in January 2006 to curb cases of malnutrition (Ochuka, 2013). The USAID in 2006 reported that problems of malnutrition continue to worsen despite several interventions in place toward improving food and nutrition security. In 2008, the Kenya Demographic Health Survey showed that 35% of children in Kenya under-five years of age were stunted, which is an increase in the national stunting rates from 30% in 2003, with the highest prevalence ages of 18-35 months (Central Bureau of Statistics, 2009).
In the year 2000, an approximation of 182 million pre-school children less than five years old in third world countries were stunted and 27% of 182 million populations were estimated to be underweight. Generally, the trend in nutritional status in third world countries over the last 20 years shows an improvement apart from Eastern Africa region, where the trend has not improved and this includes countries like Kenya. The prevalence of stunting is 48 % while underweight among pre-school children in this region is approximately 36%. (Dennis Magu et al., 2014).
In Homabay County, fifteen percent of children under 59 months and below are moderately underweight, 2% are classified as severely underweight, 26% are moderately stunted, 11% are severely stunted, 4% are moderately wasted and 2% are classified as overweight. These percentages have been approximated going by the World Health Organization standards (Kenya National Bureau of Statistcs.2013).
1.2 Statement of the problem
In spite of the numerous interventions put in place by the Kenyan government to curb malnutrition, it still remains a challenge to reduce the incidence and prevalence of malnutrition. This is in spite of the growth in the economy, which indeed should reflect on the nutritional status of the Kenyan population. The challenge that still remains is what could have gone wrong with these positive initiatives. For example, exclusive breastfeeding is one of the interventions championed by the Kenyan government to reduce malnutrition in children under the age of five, but only 61% of mothers do it (Global nutrition report 2015).
The other exemplary situation is in Homabay County where only 41 % of babies are promptly breastfed for the first time, 35% aged less than six months are being exclusively breastfed and 50% of age less than 2years are given food appropriate for their age. Despite the risk of contamination, bottle-feeding still occurs in this county and 13% of children aged 0-23 months have been fed using bottles with nipples. As a result of these caregiver practices, the children in this county are made vulnerable to malnutrition conditions.
Early childhood nutrition promotion is believed to be an important tool when it comes to malnutrition reduction. This can enhance long-term cognitive development and school performance, more so in children with several nutritional problems (Liu et al., 2014).
The increased cases of malnutrition suggest a probability that public health nutrition promotion and education was and has not been implemented well.
This study therefore seeks to find out why there are still high cases of malnutrition in Homabay referral hospital.
1.3 Justification
Malnutrition is one of the main health problems facing children under-five in developing countries, thus imposes significant costs on the country’s economy, loss of life of young children thus leading decreased future human resources of a country. Malnutrition also predisposes children to a plethora of infectious diseases (Zere et al, 2003). Even though the prevalence of malnutrition in Kenya is relatively well documented, limited information is available on the specific regions, localities and residence. Particularly missing is information regarding malnutrition in sub county in Homaby county. This study will look at the risk factors, social demographic, social economic and health care practices among caregivers that may influence the nutrition status of children under five. The results of this study will provide information on the prevalence and knowledge gap of the Homabay community on the causes, prevention and control of malnutrition cases in under-fives. Findings from this study will be important to the Homabay referral hospital staff and county government policy makers as well as non-governmental organizations that works on malnutrition in under-fives.
1.4 Research questions
(i) Does social economic demographic characteristics among caregivers of children under five attending Homabay referral hospital cause malnutrition?
(ii) Does feeding practices among caregivers of children under five attending Homabay referral hospital lead to malnutrition?
(iii) What risk factors are associated with malnutrition among children under five?
1.5 Study Objectives
1.5.1 General objective
To establish determinants of malnutrition and associated factors among children under 59 months at Homabay referral hospital
1.5.2 Specific objectives
(i). To establish the social economic demographic characteristics among caregivers of children under five attending Homabay referral hospital.
(ii). To establish feeding practices among caregivers of children under five attending Homabay referral hospital.
(iii). To determine risk factors associated with malnutrition among children under five attending Homabay referral hospital.
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This section presents a synthesis of the reviewed literature on the factors associated with malnutrition among under-five children including feeding practices, social demographic factors, social economic factors and risk factors.
2.2 Feeding practices
Household Food Insecurity Access Scale (HFIAS) tool, which was developed by food and nutrition technical assistant (FANTA) project, was used to identify household food insecurity status of a given place. Bealu, (2017) found that 75.8% of the households underwent some challenges of food insecurity in the one month of a preceding survey. These findings were the same as to the results of other studies conducted in Ethiopia, where 74.2% and 73.1% of households had food insecurity. This could result to inadequate intake of food hence severe malnutrition. (Bealu ,et al 2017)
It is important to know that many interventions to improve a child’s growth and development and nutritional status depends on caregivers’ behavior. For complementary feeding to be good and effective not only proper foods and nutrients be available in the household but also feeding behaviors must be appropriate to ensure that the food are delivered successfully to the child.to perform these feeding behaviors, the caregiver requires adequate resources so that he or she can put relevant knowledge into practice. (Leslie,1989).
UNICEF (1997) has recognized the importance of care giving behaviors determines nutrition status of a child. Childs survival, growth and development are affected by nutrition intake and health and these are In turn influenced by underlying factors of household food security, health care services and healthiness of the environment and care of a child. These care practices include; breast milk feeding and other feeding practices, food preparation, food storage and hygiene practices amongst others. All these factors influences nutrition status of a child.
A study that was conducted with 60 caregivers in rural Shaanxi province revealed that most caregivers do not follow the international recommendations for complementary feeding. Caregiver responses clearly states that the diets of children contained primarily of formula, starches, and fruit. Few (35.7%) children were fed vegetables daily and even fewer (23.2%) had meat in their diet (Luchuo et al., 2013)
Results show that the majority (77.5%) of the caregivers were mothers. The ages of the caregivers ranged from 16 to 55 years. About 53.6% were between the ages of 25 -34 years. The study noted a significant relationship between nutritional knowledge level and the age sub-groups of caregivers (_2 = 78.66; P value = < 0.001). Most of them (90.6%) were married. A higher number of the caregivers (78.3%) had primary education. The main occupation was herding by 38.4% of caregivers. The content of nutritional knowledge among the caregivers was mainly on a balanced diet (31.2%). Others were on food hygiene (18.1%), food preparation methods (8.7%), and food diversification (6.5%). The training and counseling received by caregivers were conducted mainly through health talks given by health workers when one attended the health facility. The caregivers in the study area had minimal knowledge on nutritional issues with more than half of the caregivers having low nutritional knowledge (57.2% while about 35.5% had very low nutritional knowledge. (Peter Maina Chege et al., 2013)
2.2.1 Social economic demographic factors
In a study that was conducted in Obunga Slums, Kisumu City, Kenya on socio-demographic factors of eligible households, Some of the factors considered for the study were; income level, religious affiliation, sex of reference child, level of education of caregivers, and literacy level. Income pattern was found that majority of households earned less than Ksh 5000 (44.7%), second by those who earned between Ksh 10001–15000 (38.8%). Some reasonable proportions earned between Ksh 5000 – 10000 (15.9%). In summary, household level of earning was lower than Ksh 15000. The findings further revealed that most of households were Pentecostals origin (49.1%), followed by protestant (21.9%) and catholic (20.9%). Muslims (1.0%) and Hindu (4%) managed to get the least percentage. Within sex of reference child, 54.6% of children were males while 45.4% were females. There was no significant difference recorded between male child participant and female child participants (z=1.818, p=0.069, Confidence Interval (CI) = -0.0071: 0.1912). a number of c of the caregivers/guardians reached secondary school level of education (36.5%) and primary level (28.0%) respectfully. 18.6% is the percentage completed secondary level of education. Literacy level was high among the caregivers (95.2%). (Edward A. et al., 1997).
In the study, Malnutrition and socio-demographic factors were found associated with Pulmonary tuberculosis .In a study 121 TB patients and 371 con-trolls participated. 56.3% were male and 43.7% female. Those patients with TB, 87% had malnutrition while on the other side 33% among con-trolls. The mean body mass index (BMI) of the patients was significantly lower than that of the controls (16.1 2.3 kg/m2 vs. 19.4 3.0 kg/m2). Factors that were associated with the development of TB were BMI (OR 0.5, 95%CI 0.4–0.6), family history of TB (OR 3.2, 95%CI 1.6–6.4), living in an extended family (OR 2.7, 95%CI 1.5–4.8), being non-indigenous to Timor and Rote Islands (OR 2.9, 95%CI 1.2–6.8) and being unemployed (OR 3.8, 95%CI 1.7–8.6). Among patients with active pulmonary TB, the prevalence of malnutrition rate was extremely high. (T. A. Pakas et al., 2009)
According to the weight-for-age index, the prevalence of malnutrition was 4.5%. Based on the weight-for-height index, 3.9% had malnutrition and 6.7% were overweight. Low height-for-age was found in 8.5% of children less than five years. Children whose women were family heads, had a lower prevalence of malnutrition (PR=0.4, 95%CI 0.2-0.8). The prevalence of malnutrition in children who received the Bolsa Família benefit was similar to that of children who did not and show the results of the multivariate analysis. Even when the effect of the socioeconomic, environmental and biological variables is controlled, they did not show a statistically significant association with malnutrition or obese. Of the variables studied, only female head of the family remained independently associated with malnutrition after adjustment (Prevalence ratios = 0.4). (Zeni C. et al 2013)
Another study manifested that socioeconomic inequality led to stunting among children of the age of five years and below. In almost all countries, stunting disproportionately affected the poor individuals. The strong indices were significant in all states, apart from Madagascar and Guatemala, which ranged from 0.0005 to 0.42 approximately. In stunting it appeared the biggest in the Latin America and the Caribbean region, where the median concentration index equaled to 0.22.Wasting was generally was found common among the poor, but socioeconomic inequality was much low than for stunting. An approximation of one third of countries was found that socioeconomic inequality was not significant. The median concentration index was higher in south and south-east Asia (–0.05 based on WHO child growth standards). (Ellen Van de Poel 2007)
2.2.2 Risk factors associated with malnutrition
A study where a total of 274 children with 137 cases and 137 controls were recruited, All respondents were Malays. Among the cases, a bigger number of them was female and came from low-income families. After changing all confounders, childhood malnutrition was associated with number of children (OR: 5.86, 95% CI: 1.96, 17.55), child hunger (OR: 16.38, 95% CI: 1.34,199.72), dietary energy intake (OR: 0.99, 95% CI: 0.98, 0.99), protein intake (OR: 1.06, 95% CI: 1.01, 1.12), vitamin A intake (OR: 0.999, 95% CI: 0.997, 1.00), low birth weight (OR: 6.83, 95% CI: 1.62, 28.89), frequent sickness (OR: 2.79, 95% CI: 1.06, 7.31), and history of worm conditions (OR: 3.48, 95% CI: 1.25, 9.70). (Sulochana Nair et al., 2013)
A study on Factors associated with malnutrition among children in internally displaced people’s camps, northern Uganda stated that the prevalence of global stunting was found to be 52.4% and of global acute malnutrition 6.0%. Male child was at risk of being stunted OR 1.57 95% CI 1.15-2.13; p value=0.004. Children in the age of 3 – 24 months were at a very high risk of acute malnutrition Adjusted OR 2.78 95% CI 1.26-6.15; p value=0.012 while de-worming was protective Adjusted OR 0.44 95% CI 0.22-0.88; p value=0.018. The main sources of foodstuff for these people (Internally Displaced People ) include food rations supplied by organizations such as WFP, cultivation and purchase from available markets. (Olwedo M.A et al., 2008)
The mean age of the 267-caregiver participants was 84.4 (SD 5.4) years and 73% (n=161) were female. A total of 229 (85.8%) participants had 23.5 points or less in the MNA screening. Those who participated and were at risk of malnutrition used more drugs and had a higher depressive score and lower Barthel Index, IADL and MMSE scores than the well-nourished participants. (T. Kaipainen et al 2017)
Nutrition assessments are used to provide information on the health status of children and are an indirect measure of quality of life in a population. (Shetty, 2002). The most common way used in various health facilities is anthropometry.
Weighing a child and measuring his or her height; involves taking a measurement of their weight and height and then comparing it against the expected average height and weight for a child of that age. Some children will be below average as they are smaller, but a significant decrease below the expected level for a child could be an indication of malnutrition (Müller &Krawinkel, 2005).
Measuring the circumference of the mid-upper arm; a mid-upper arm circumference (MUAC) measurement band has different colors along the strip. If the armband lies in the orange section it is an indication that the child is suffering from moderate acute malnutrition. If it lies on the red section, it indicates that the child is suffering from severe acute malnutrition (Mother & Child nutrition, 2009)
2.3 Conceptual framework
Adopted UNICEF Conceptual Framework of Malnutrition
The figure below shows how malnutrition is being addressed through multi sectorial, sustainable approaches. The review reveals the causes and the long term and short term consequences of malnutrition which gives the nutrition status of a child.
| Intergenerational Consequences | ||
| Inadequate Access to Services | Inadequate Financial and Human Resources | Socio-cultural, economic Context |
| Short-term consequences, Mortality, Morbidity, disability | Long Term Consequences, Cognitive, development, health, economic productivity |
| Nutrition status |
| Inadequate Dietary Intake | Diseases |
(Immediate Causes)
| Household Food Security | Inadequate foods, feeding and care practices | House, Environmental health services |
(Underlying Causes)
(Basic Causes)
CHAPTER THREE
3.0 Research Methodology
3.1 Study Site
This study will be carried out in Nyanza province of Kenya, Homabay referral Hospital in Homabay County and is inhabited mainly by the Luo community. It is a district hospital located 3 kilometers approximately away from town. It is responsible as the primary hospital for people living in its periphery. The clients attending this hospital come from the surrounding of Homabay town and various corners of Homabay County because services provided are relatively cheap.
3.2 Study Design
This is a set of procedure that allows the researcher to interact with the study area or target group and get information to answer the research question or test the hypothesis. A descriptive survey will be conducted to provide information on the nutritional status of the under-fives at Homabay referral hospital.
3.3 Study population
The target population will include all guardians/caregivers with their children aged below five years visiting Homabay referral hospital.
3.3.1 inclusion criteria
- All guardians/caregivers with children under five years attending maternal and child health clinic, outpatient department for regular weight check and vaccination during the study period.
- All guardians with children of 59 months and below admitted with malnutrition conditions at Homabay referral hospital.
- Children whose guardians/caregivers will give consent to participate in the study.
3.3.2 Exclusion Criteria
- Children who will attend maternal and child health clinic for weight check and vaccination services during the study period but are not residents of Homabay County.
- Children whose guardians will fail to complete the questionnaires
- Sick children with other illnesses not related to malnutrition will be eliminated from the study
3.4 Sampling
3.4.1 Sample Size Determination
Fisher’s model will be used to calculate the sample size (Fisher et al.,1998)
N=Z α*PQ
2
e2
N-is the desired sample size
Z-Standard normal deviation
α-confidence level of 95%
P-characteristic of the proportion being measured
Q-characteristic of the proportion not measured (1-P)
e-error n
P=which is the Prevalence of an indicator of malnutrition in Homabay County 18.7% (KDHS) whereby population for children under 59 months in homabay county is greater than 10,000
Q=1-0.187=0.813
α=100-95=5% which is equivalent to 0.05
e=0.05
1.962*0.813*0.187
0.052
Sample size of 233 children under 59 months.
3.4.2 Sampling Procedure
Systematic sampling procedure will be done whereby the first segment of the population will be selected randomly then proceeds with the selection of every kth element (sample size).in this case mothers assigned number 2 will be chosen randomly then followed by the selection of every 5th mother who will be selected on an interval basis until the sample size is done. The exercise will take 3 days a week i.e. Mondays, Wednesdays and Fridays till the sample size is done. Every mother who will meet the inclusion criteria and consent will be sampled.
3.5 Data Management
3.5.1 Data Collection
Data will be collected by the use of structured questionnaires that will be self-administered and 24 hour dietary recall form. The questionnaire will consist of open and close-ended questions to allow new ideas that may be raised from the study. The questionnaires will be formulated from the objectives of the study in plain language for easier understanding.
Anthropometric measurements for the children will be taken and height and weight parameters will be used to determine their nutritional status. The weight measurement will be taken using seca scale (Hanson mode) and the height /length portable wooden constructed scale calibrated for height measurement. Height for age (stunting), weight for age (underweight) and weight for height will be calculated using = 2 D NCHS (National Centre for Health Statistics) reference data. The height for age z-score (HAZ) of <-2 will classify children under study as stunted and Z- score cut off point of <-2SD will be used to classify low weight for age, low height for age and low weight for height.
3.5.2 Data Entry
Completed forms and questionnaires will be checked for confirmation to rule out errors.
3.5.3 Data Analysis
The data collected will be analyzed by use of quantitative methods. This will involve the use of statistical methods, as percentages, fractions, averages, modes and median through the help of SPSS. Chart, tables and graphs will be used in data presentation. The study will be analyzed using descriptive statistic and multiple regression analysis to determine the relationship between independent variables and dependent variable. The regression formulae and equation will be:
Y = β0 + β1X1 + β2X2 + β3X3 + α)
Where β0 is the regression intercept;
β1- β4 are the regression coefficients
While Y will be the dependent variable (Nutritional Status)
X1 is the social economic demographic characteristics,
X2 is feeding practices among caregivers
X3 is risk factors
3.6 Study Variables
3.6.1 Dependent Variable
The independent variables for the study will include social economic, demographic, risk factors as well as household care practices of the caregivers.
3.6.2 Independent Variables
The dependent variable for the study will be the nutritional status of the children aged 59 months and below.
3.7 Ethical Considerations
Research authorization and proposal approval will be sought from Jomo Kenyatta University Graduate School and Research Committee and Medical Officer in charge of Homabay referral hospital. The Institutional Research and Ethics Committee (IREC) will review, evaluate and decide on the scientific ethical merits of this proposal. The committee will also approve this proposal for and on behalf of the Science, Technology and innovation Act, 2013. Informed consent will be sought from mothers with children under 59 months and below and qualify for the study. The parents and caregivers who will participate will be assured total confidentiality regarding the information they will give. The data will be used for academic purposes and no personal information will be exposed to public. The entire study will be carried out in strict adherence with the regulations and recommendations of IREC.
3.8 Data dissemination
The results will be shared broadly to create awareness to the health personnel, community members of the republic of Kenya as well as outside Kenya. This work will be published for learners to get informed on determinants of malnutrition and associated factors among children under 59 months in Kenya and use it as a reference to the related studies.
Chapter 4: Results and Analysis
4.1. Introduction
This chapter presents the analysis of the data obtained in the study. The chapter focuses on analyzing the information collected from the study participants and determining the relationships between the variables included in the study.
4.2. Process of data collection and the variables included in the study
Data was collected using questionnaires that were administered to the study participants. The researcher participated in filling in the questionnaires for the participants that were unable to write. On the other hand, those willing to respond to the questionnaires through writing their responses were also given an opportunity to do so. The anthropometrics data (weight and height of the children) were drawn from the data collected during the day clinics. This data was compared with the recommended Z-scores for height for weight in accordance with the world health organization (WHO) growth standards. To obtain measurement, minimally dressed if not undressed children were eight using a SECA digital weighing machines to obtain their weight. Their recumbent weight was determined using the height board. The weight for height data was adopted in categorizing the children into different groups based on their extent of malnutrition. Children with a weight for height Z score of less than 2D were categorized as malnourished while those who has a Z score of more than 2D were reported to have no malnutrition, this group were however further classified into four other group low weight for age, low height for age and low weight for height and optimal condition.
Variables
The dependent variable for the study was the nutritional status of the children ascertained by the weight for data collected. The independent variables for the study were the socio-economic status of the family using asset index such as occupation of mother and father, , their perception on the cost of food, availability of certain assets within the house such as mobile phones, cooing fuel, modern toilet, number of rooms of the house, availability of land, computer, refrigerator amongst other. The asset index was converted into asset scores that were used to categorize the household into five groups based on their socioeconomic status. The groups were divided into three layers including poor, middle, and rich. The level of education was categorized as literate or illiterate. Literacy was charged by attainment of at least a certification at the primary school level. Bottle feeding was categorized as fed and not fed. The age of the mother was classified into three layers less than 20 years, between 20 to 35 years and more than 35 years of age. Introduction of complementary foods was categorized as initiation at less than 6 months, at 6 months and at more than 6 months. In essence the independent variables included in the study were level of education, introduction of complementary foods, and age of the mother, socio-economic status of the household and bottle-feeding.
Statistical Analysis
The data collected were analyzed using SPSS software. The relationship between the dependent variable and the independent variables was determined by conducting bivariate logistic regression analysis.
The regression equation was presented as
Y = β0 + β1X1 + β2X2 + β3X3 + α)
Where β0 is the regression intercept;
β1- β4 are the regression coefficients
While Y will be the dependent variable (Nutritional Status)
X1 is the social economic demographic characteristics,
X2 is feeding practices among caregivers
X3 is risk factors (level of education, introduction of complementary foods, age)
Analysis of Data Collected
The study proposed to engage about 233 children of not more than 59 months in the study. However, only the results of 188 children were included in the analysis. While the researcher managed to distribute 250 questionnaires, only 188 were valid and duly filled, thus were included in the analysis.
General Characteristics of Participants
Descriptive statistics
| Descriptive Statistics | |||||
| N | Minimum | Maximum | Mean | Std. Deviation | |
| AGE_MOTHER | 188 | 1.00 | 3.00 | 1.9468 | .77207 |
| EDUCATION_LEVEL | 188 | .00 | 1.00 | .8298 | .37682 |
| SOCIOECONOMIC_STATUS | 188 | 1.00 | 2.00 | 1.7074 | .45615 |
| BOTTLE_FEEDING | 188 | .00 | 1.00 | .8723 | .33460 |
| COMPLEMENTARY_FOODS | 188 | 1.00 | 3.00 | 2.1223 | .49698 |
| NUTRITIONAL_STATUS | 188 | .00 | 1.00 | .6489 | .47858 |
| Valid N (listwise) | 188 |
The descriptive statistics shows that the mean value of age of most mothers involved in the study was 2. Based on the scale provide, the value 2 was assigned for the age of 20-35 years. This shows that most of the mothers included in the study were aged between 20 to 35 years. The education level mean was 0.8 a value that is close to 1, the number assigned for literacy. The value attained shows that majority of the study participants engaged in the study had at least a certificate for primary level of education.
The descriptive data also indicates that majority of the study participants were classified under the middle level of socio economic status. The ranking was based on the score attained by an individual on their occupation and the availability of various assets in the households. The study findings also show that a significant portion of the study participants were introduced complementary foods to their children after they attained 6 months of age.
The pie chart above highlights the percentage of the participants classified according to their age. As observed, most of the mothers involved in the study were aged between 20 to 35 years while the number of women aged above 35 years remained minimal.
82.98% of the study participants were literate. Literacy in the current study was determined by the ability of an individual to read and write basic language. As such, attainment of the primary education certificate at the minimum was adopted as the benchmark for determining literacy. Based on the illustration above, most of the mothers involved in the survey had attained the basic education, thus the high number of literate participants attained.
Socio-economic status classification was based on the scores attained by each participant. As mentioned above, the occupation of both father and mother, the availability of various assets in the household and the income of the household were ll included in calculating the socio-economic scores for every persons involved in the survey. Those who attained higher scores were ranked as rich, those with moderate score were ranked as medium level of riches and those who had minimal values were classified as poor. The results attained showed that no person attained the rich, status as such; all the participants did not achieve a high score to fit in that rich status. However, a significant portion of the study participants 70.74% were classified to have attained the medium level of richness. About 20% of the study participants were classified as poor.
The nutritional status of the children was determined by their weight to height Z scores. Those who did not attain 2D score were categorized as malnourished. The pie chart above shows that 64.89% of the study participants were healthy implying that they attained more than 2D. About 35.11% of the population were classified as malnourished a value that is higher than the WHO recommended percentage for any given population. This shows that a significant percentage of the children in the region do not received quality nutrition and health care. This justifies the need for identifying and evaluating the possible factors that cause malnutrition for the implementation of appropriate strategies capable of addressing the issue.
Correlation Analysis
The correlation analysis of the variables shows there is significant relationship between most of the variables. Significance is obtained at 0.01 level, thus any lower significant value is regarded a significant relationship between the variable included in the analysis.
The findings attained shows that there is a positive significant correlation between level of education and socio-economic status of the household. Indicated by r value of 0.673 and p=0.000, It is evident that mothers with a higher level of education are likely to report a higher level of socio-economic status. Education is associated with a higher income for the family, a working husband with equally a better income and availability of a significant number of the various assets needed in the houses.
The study findings also shows that there is a strong relationship between level of education and bottle feeding r=0.421, p=0.000. Even though the correlation between the variables is not string, it can be deduced that an increase in the level of education of the mother is associated with an enhanced chance of the mother to engage in bottle feeding.
The correlation results also show that there is a significant relationship between the level of education and introduction of complementary food. The sign of correlation between the two variables is however negative, r=-0.402, p=0.000. It can thus be deduced that the level of education is associated with the introduction of complementary foods. The negative sign shows that as the level of education of the mother increases, they are likely to offer complementary foods earlier in the life of the infant.
The Relationship between Nutritional status and the Independent Variables
Bivariate Analysis
| Nutritional Status | |||
| Malnourished | Nourished | Total | |
| Bottle feeding Not fed Fed | 24 42 | 0 122 | 24 164 |
| Complimentary foods Introduced before 6 months Introduced at 6 months Introduced after 6 months | 9 23 34 | 4 116 2 | 13 139 36 |
| Exclusive Breastfeeding Exclusively Breastfed Not-Exclusively Breastfed | 33 33 | 62 60 | 95 93 |
| Socio-economic status Poor Medium | 50 16 | 5 117 | 55 133 |
| Educational Status Illiterate Literate | 29 37 | 3 119 | 32 156 |
| Age Mother Below 20 years Between 20-35 years Above 35 years | 19 27 20 | 42 49 31 | 61 76 51 |
The results obtained from the bivariate analysis show that socio-economic status and bottle-feeding have significant association with the nutritional status of the child. The chi-square findings show that an increase in the socio-economic status leads to an -enhanced well-being of the child. Households with a high socio-economic status tend to have children who fall under the nourished category.
The number of nourished children was higher for households with Medium socio-economic status (117 children). Also, a higher number of children whose parents are literate were classified as nourished.
The bivariate analysis results also shows that the introduction of complementary foods at 6 months was associated with many children being nourished (116 children). It is also observed that bottle feeding has significant effect on the nutritional status of the child. As observed about 122 children were reported to be nourished for those who were fed. While exclusive breast feeding and age of the mother also have some influence on the nutritional status, their effects are not significant as the other four variables.
The ordinal by ordinal Gamma significant values show that the introduction of complementary foods, socio-economic status, bottle feeding and education level had significant influence on the nutritional status of the children. On the contrary, the relationship between the age of the mother and exclusive breastfeeding and the nutritional status of the child was not significant.
Regression Analysis
| ANOVA | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 25.162 | 6 | 4.194 | 42.961 | .000a |
| Residual | 17.668 | 181 | .098 | |||
| Total | 42.830 | 187 | ||||
| a. Predictors: (Constant), COMPLEMENTARY_FOODS, AGE_MOTHER, EXCLUSIVE_BREASTFEEDING, EDUCATION_LEVEL, BOTTLE_FEEDING, SOCIOECONOMIC_STATUS | ||||||
| b. Dependent Variable: NUTRITIONAL_STATUS |
| Co-efficient | ||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | -.723 | .210 | -3.449 | .001 | |
| AGE_MOTHER | .005 | .030 | .008 | .159 | .874 | |
| EDUCATION_LEVEL | .019 | .084 | .015 | .227 | .820 | |
| SOCIOECONOMIC_STATUS | .681 | .073 | .649 | 9.332 | .000 | |
| EXCLUSIVE_BREASTFEEDING | .012 | .046 | .013 | .270 | .788 | |
| BOTTLE_FEEDING | .238 | .092 | .167 | 2.604 | .010 | |
| COMPLEMENTARY_FOODS | -.014 | .058 | -.014 | -.239 | .811 | |
| a. Dependent Variable: NUTRITIONAL_STATUS |
To confirm the bivariate findings, regression analysis is conducted to determine the extent through which the independent variables predict the dependent variable. As indicated in the ANOVA table, the model adopted in the current study is essential in determining the relationship between the dependent and the independent variables. The significant value of p=0.000 shows that the independent variables significantly predict the dependent variable.
The regression coefficient attained in the regression analysis shows that the nature of the relationship between every predictor variable and the dependent variable. Of the six independent variables included in the study, only social economic status and bottle feeding have significantly predicts the nutritional status of the child. The p value for social economic status was p=0.000 and for bottle feeding was p=0.01. The analysis was conducted at a significant of p<0.05, thus any co-efficient with a p value of less than 0.05 is considered to significantly predict the changes in the nutritional status of the children. Age of the mother, introduction of complementary foods, exclusive breastfeeding and level of education had no significant effects on the dependent variable.
Chapter 5: Discussion of the Findings
The study findings shows that about 35% of the children involved in the study were malnourished. This value is higher than most of the data collected in different parts of the world (Rayhan & Khan, 2006). Despite the interventions put in place to address the issue of malnutrition in different parts of Kenya, it can be deduced that the strategies have not yielded any better results in Homa Bay region. This state thus states that there is need to address the issue of malnutrition in the county.
The bivariate and multivariate analysis was conducted to establish the relationship between the dependent variable and the independent variable. From the bivariate analysis it was establish the socio-economic status, bottle-feeding, introduction of complimentary foods and level of education had significant influence on the nutritional status of the child. On the other hand, the multivariate analysis through regression analysis showed that socio-economic status and bottle-feeding significantly predict the nutritional status of the child, the relationship between the nutritional factors and other independent variables were not significant.
The two analysis conducted shows that socio-economic status of the households have significant influence on the nutritional status of the child. Nutrition is associated with the ability of the caregivers and the parents to avail well-balanced diet to the children. Also, the safety and hygiene involved in the provision of the feed. Also influences the attainment of a nutritional diet. The findings attained are realistic since a high level of socio-economic status is associated with enhanced availability of adequate nutritious foods, thus an increase in the nutritional status of the child. Households with higher socio-economic status are likely to have children that are nourished.
Similar findings were obtained by Poel et al (2007) who ascertained that the prevalence of malnutrition is higher for families with lower wealth index. The limited wealth exhibited by these families as well as their inability to acquire various assets enhances their chances of failing to offer quality and adequate foods to the children. Also, such environmental conditions and living standards negatively affect the health of lactating mother preventing them from offering adequate breast feed to the child. The low socio-economic status of a household is thus associated with poor nutritional status of the child.
The educational status of the mother is also linked with the nutritional status of the child. This finding coincides with the results of Shetty (2002) who asserted that highly-educated mothers have a better knowledge of the health and nutrition. As such, they are likely to implement the approaches put in place to ensure that a high nutritional value is achieved. Policy to address nutritional problems amongst children should not just focus on enhancing the educational level of the mother, but should also aim at creating awareness on child health and nutrition to enhance the ability and capability of the mothers to buy adequate nutritious foods.
The study finding also shows that bottle-feeding significantly predicts the nutritional status of a child. According to results obtained, a significant number of the children that were reported as “fed” were nourished. On the other hand, a considerable number of children exposed to breastfeeding were also categorized as malnourished. According to Njuguna and Muruka (2011) bottle-feeding is considered an intervention adopted to improve the nutritional status of an individual. However, the hygiene issues associated with bottle feeding can deter the attainment of an improved nutritional status. This explains why about 45 children remained malnourished yet they were being bottle-fed. Policy makers should thus emphasize on provision of quality milk and assuring hygiene as significant in ensuring that bottle-feeding or provision of milk supplements improves the nutritional status of the child.
Introduction of complimentary foods is also linked with the nutritional status of the child. While a significant relationship was not obtained in the multivariate analysis, the bivariate analysis shows that a higher number of children who were exposed to complimentary foods at 6 months of age were categorized as nutritious. A significant relationship may not be evident in all cases, since the nature of the food being introduced determines its influence on the well-being of the child. Introduction of highly nutritious foods can promise the attainment of an improved or positive nutritional status. However, low quality foods will only affect the overall well-being of the child.
While it is expected that exclusive breast feeding will promote a good health and nutritional status of the child, the nutritional status of the mother determines the effectiveness of exclusive breast feeding in accomplishing that goal. As observed, the current study found to significant relationship between exclusive breast feeding and the nutritional status. This finding are similar to the results posted by Pravana et al (2017) who ascertained that exclusive breast feeding has no direct impact on the nutritional status of the child. This is because the quality of the breast milk is determined by the nutritional well-being of the child. As such, when a malnourished mother engages in exclusive breast feeding, the effects on the nutritional status of the child remain negligible. This justifies the need to consider the nutritional well-being of the mother, as opposed to the exclusive breast-feeding variable as determinants of a child’s nutritional health.
Age has also been associated with knowledge of nutrition and child well-being. However, as Talukder (2017) ascertained, age has no significant effect on knowledge without taking into consideration the education level of an individual. In essence, being old does not guarantee enhanced nutritional knowledge, rather the educational level of the person involved determines their level of experience in the provision of nutritious foods to the children. These assertions are in line with the findings of the current study that failed to establish any significant relationship between the age of the mother and the nutritional status of the child.
The prevalence of malnutrition is still high in Homa bay County. The major factors associated with malnutrition include the socio-economic status of the household, bottle feeding, introduction of complimentary foods and educational status of the mother. The results of the current paper strongly highlight the need to address the issue of poverty and avail adequate nutritious foods to the children I Homabay County who are under 5 years. The findings also strongly recommend interventions to promote nutrition education that will create awareness on the foods recommended for the children below 59 months. Advocating for hygienic practices during bottle-feeding and improving the nutritional status of the mother are approaches that are recommended to enhance the overall nutritional status of the under-five year old children in Homa Bay County.
REFERENCES
Agozie, C., Ubesie, N. & Ibeziako,S. (2012). Under-five Protein Energy Malnutrition Admitted at the University in Nigeria Teaching Hospital, Enugu Nigeria: Nutrition Journal.
Bantamen, G., Belaynew, W. & Dube, J. (2014). Assessment of Factors Associated with Malnutrition among Under Five Years Age Children at Machakel Woreda, Northwest Ethiopia: Nutritional Food Science Journal.
Basit, A., Nair, S., Chakraborthy, K., Darshan, B. and Kamath, A. (2012). Risk Factors for under-nutrition among Children Aged one to Five years in Udupi Taluk of Karnataka, India: Australas Med J.
Carolyn D., Johanna T., David H. (2009). Handbook of Nutrition and Food. New York, NY: CRC Press.
Dr Peter W., Felicity S., Rhea F. (2010). The Working companion for food microbiologists 7th Edition. Cambridge, UK: Leatherhead Publishing.
Fuchs, C., Sultana, T., Ahmed, T. and Hossain, M. (2014). Factors Associated with Acute Malnutrition among Children Admitted to a Diarrhea Treatment Facility. Bangladesh: International Journal of Pediatrics.
John G. (1997). Essentials of food microbiology. Euston Road, London: Hodder Arnold.
Kandala, N., Tumwaka, P., Emina, J., Nzita, K. and Cappuccio, F. (2011). Malnutrition among Children Under the Age of Five: Democratic Republic of Congo (DRC): BMC Public Health.
Kenya National Bureau of Statistics. (July 2013). Homa Bay County Multiple Indicator Cluster Survey 2011, Final Report. Nairobi, Kenya: Kenya National Bureau of Statistics.
Lebing W., Chualai X. (2012). Food Immunochemistry and Immunology. Beijing, China: Science Press Beijing.
Luchuo, E., Awah, P., Geraldine, N., Kindong, P., Sigal, Y., Nsah, B. and Tanjeko,T.(2013). Malnutrition in Sub Saharan Africa: Burden, Causes and Prospects: Pan African Medical Journal.
Müller, O., and Krawinkel, M. (2005). Malnutrition and Health in Developing Countries. Montreal Canada: Canadian Medical Association Journal.
Njuguna, J. and Muruka, C. (2011). Diarrhea and Malnutrition Among Children in a Kenyan District. Nairobi Kenya: Journal of Rural and Tropical Public Health.
Poel, E., Reza, A.,Hosse, I., Jehu-Appiah, C., Vega, J. And SpeybroecK, N.(2007). Malnutrition and the Disproportional Burden on the Poor. Accra,Ghana: International Journal for Equity in Health.
Rice, L.,Sacco,L,. Hyder, A. & Black, E. (2000). Malnutrition as an Underlying Cause of Childhood Deaths Associated with Infectious Diseases in Developing Countries: World Health Organisation.
Saka A., Saka, M., Ojuawo, A., Abdulkarim, A., Bilamin,S., Latubosun, L. & Adeboye, M. (2012). Haematological Profile in Children with Protein Energy Malnutrition in North Central Nigeria. Abuja Nigeria: Global Journal of Medical Research.
Shetty, P. (2002). Food and Nutrition: The Global Challenge in, the Nutrition Society Textbook Series, Introduction to Human Nutrition. United Kingdom, UK: Blackwell Publishing.
Zere, E. and McIntyre, D. (2003). Inequities in under-five child malnutrition. South Africa: International Journal for Equity in Health.
Talukder, A. (2017). Factors associated with malnutrition among under-five children: illustration using Bangladesh demographic and health survey, 2014 data. Children, 4(10), 88.
Rayhan, M. I., & Khan, M. S. H. (2006). Factors causing malnutrition among under five children in Bangladesh. Pak J Nutr, 5(6), 558-62.
Pravana, N. K., Piryani, S., Chaurasiya, S. P., Kawan, R., Thapa, R. K., & Shrestha, S. (2017). Determinants of severe acute malnutrition among children under 5 years of age in Nepal: a community-based case–control study. BMJ open, 7(8), e017084.
APPENDICES
Appendix A : Research Request Letter
Jomo Kenyatta University of Agriculture and Technology,
Kisii CBD campus,
School of Health Sciences,
Public Health Department,
P. O. Box 638,
Kisii, Kenya.
20 May 2017
Dear Sir or Madam,
RE: REQUEST FOR INFORMATION
The researcher hereby requests your consideration of the subject to enable conclusion of the study as per the questionnaires attached. The researcher is a final year students at the Jomo Kenyatta University of Agriculture and Technology pursuing a Masters in Public Health.
This study is purely for academic purposes and is being conducted towards the fulfillment for the award of the researcher’s degree.
Yours Sincerely,
Nivah Moraa Onguso
Appendix B: Data Collection Request Letter
Medical Officer,
Ministry Of Health,
Homabay referral Hospital,
P. O. Box,
Homabay, Kenya.
20 May, 2017
Dear Sir or Madam,
RE: REQUEST FOR DATA COLLECTION
The researcher hereby requests your consideration of the subject to enable conclusion of the study as per the questionnaires attached. The researcher is a final year students at the Jomo Kenyatta University of Agriculture and Technology pursuing a Masters in Public Health.
This study is purely for academic purposes and is being conducted towards the fulfillment for the award of the researcher’s degree.
You are kindly requested to allow the researcher to collect the information on the subject. All the information given will be treated with utmost confidentiality and shall be used for the purposes of this study only.
Thanks in advance for your time and willingness.
Yours Sincerely,
Nivah Moraa Onguso
Appendix C; 24 hour recall diet form
Name of the interviewer
Date of interview
| Time of day | Food or beverage consumed | Amount consumed | Preparation method | Who prepared | Where consumed |
Table 1.3
Appendix D; Questionnaire
Jomo Kenyatta University of Agriculture and Technology
School of health sciences
A Questionnaire on prevalence of malnutrition and associated factors among children under 59 months at Homabay Referral Hospital, Homabay County, Kenya.
Dear respondent,
This is an academic research intended to establish factors associated with malnutrition among children under five attending Homabay referral hospital.
The purpose of this study and its results is purely academic. I kindly request for you to spare some minutes to participate in this study.
Please tick () Yes or No or explain where you are required to. All information provided will be private and confidential.
Thank you.
| Number of questionnaire |
Date of the interview ——–
Participants’ identification:
Name ……………………………………………………….
Name of the respondent………………………….. Relationship of the respondent to the child………………………………………………. Number of children below 5 years living in the household……………………………..
Part A; Background Information
1. Birth registration number……………………………
2. Child welfare clinic card number(if available)……………………….
3. Date of birth……………………………………….
4. Age (in months)…………………………………..
5. Place of birth…………………………………….
6. Sex …………………………………………………..
• Male
• Female
7.Birth weight……………………………………………………
• Low birth weight (< 2500 grams)
• Normal birth weight (>or = 2500 grams)
8.Age of mother/caregiver (a). <20 (b). 20-29 (c). 30-39
(d). 40 and above
Part B; Social economic factors of the mother/caregiver
9. Mother’s occupation
- Civil servant
- Housewife
- Casual worker
- Business person
- Others (specify)
- Father’s occupation.
- Civil servant
- Fisherman
- Casual worker
- Business person
- Others (specify
11. What is your education level?
1. None (never been at school)
2. Primary
3. Secondary
4. Tertiary
12. Marital status of mother /care giver (a). Married (b). Divorced (c). Single (d). Separated
13. How do you perceive the cost of food in your house?
a. Affordable
b. Cheap
d. High
e. Too expensive
14. Who is the breadwinner in your house?
- Myself
- My husband
- Husband’s relatives
- My relatives
Part c; caregiver practices
15. How many children do you have?
- ) 1 (b) 2 (c) 3 (d) 4 (e) 5 and above
16. Has the child ever been breastfed during the first 6 months?
1. Yes (ever breastfed)
2. No (never breastfed)
3. I don’t know
17. Are you the one preparing food for your child?
a. Yes
b. No
18. If yes do you wash your hands before preparing ?
- Yes
- No
18. What is the source of drinking water for members of your household?
a. Piped-in dwelling
b. Public tap
c. Protected dug well or protected
c. River
d. Other (specify)
19. What kind of toilet facility does your household use?
a. Flush latrine b. Latrine c. None
Part D. Risk factors to malnutrition
20. How often does your child fall sick?
- Twice in a week
- Once in a week
- Never fallen sick
- Once in a month
21. Has your child been hospitalized three months ago?
a) Yes b) No
22. If yes from which diseases was she/he suffering from?
- Fever
- Diarrhea
- Cough
- Anemia
23. What do you do when your child falls sick?
-
Buy
drugs from drugs tore
- Consult traditionalist
- Go to the clinic
24. Child Anthropometry
- What is the weight of the child? (Kgs)
- What is the height/length of the child? (CM)
- What is the mid Upper arm circumference of the child? (CM)
THANK YOU
Appendix E; Budget
| ACTIVITY | ITEM | COST –(Ksh) |
| Literature compilation | Library and internet | 11,000 |
| Refreshment | Fruit juice and snacks | 6,000 |
| Electricity and communication | Token and airtime | 4,000 |
| Training of research assistant and data collection | Transport and lunch | 10,000 |
| Data analysis and report writing | Pens, foolscaps, SPSS software | 13,000 |
| Miscellaneous | 5,000 | |
| Ethical Review | 5000 | |
| Publishing | 15000 | |
| TOTAL | 69000 |
Appendix F; Work plan
| Activity | 25th-28th april2017 | 1st-23rd may,2017 | 25th may 2017 | 26thmay-oct 2017 | Oct-Dec 2017 | Feb 2018 | May 2018 | June –Dec 2018 |
| Title and concept development | ||||||||
| Proposal development | ||||||||
| Proposal presentation | ||||||||
| Proposal corrections | ||||||||
| Ethical review | ||||||||
| Progressive report pre | ||||||||
| Seminar development, presentation and final defense Graduation |
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