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Master thesis Department of Statistics
Masteruppsats, Statistiska institutionen
Socio-demographic, socio-economic and household environmental characteristics associated with
diarrheal disease among children under five years of age in Ethiopia
Sukaina Nasser
Masteruppsats 30 högskolepoäng, vt 2014
Supervisor: Dan Hedlin
1
Abstract
Water and sanitation-related diseases, particularly diarrhea, are among the top three causes of
death in Ethiopia after malaria and HIV/AIDS. According to the World Health Statistics 2011,
27% of deaths of children under five years of age in Ethiopia are caused by diarrheal diseases.
This might be due to poor access to safe drinking water, inadequate sanitation and poor hygiene.
Only 37 % of the general population have access to safe drinking water. More than 60 % of the
population do not have access to adequate sanitation facilities (WHO/UNICEF, JMP 2012). This
study used the 2000, 2005 and 2011 Ethiopia Demographic and Health Survey (EDHS) data. In
order to deal with missing values, we fitted two logit models to determine the effect of socio-
demographic, socio-economic and household environmental factors on childhood diarrhea. In the
first model, only records without missing values (complete case) were used; in the second model,
we used multiple imputation. The prevalence rate of childhood diarrhea in Ethiopia at the last
eleven years decreased from 19.8% in 2000 to 13.9% in 2011. The results show that the
determinant factors of childhood diarrhea were; the age of child, religion, wealth index, number
of children under five years of age in the household, source of drinking water, toilet facility and
disposal of youngest child’s stools. In order to reduce incidence of childhood diarrhea this study
suggests that improvement of the following factors are important; personal hygiene practice,
good sanitation/rubbish disposal, use of safe including water sources and also exclusive
breastfeeding, because breastfeeding provides protective factors that may help reduce infections,
such as diarrhea and other infections
Keywords: Childhood diarrhea, water, sanitation, logistic regression, complete case analysis
and multiple imputation, Ethiopia.
2
Acknowledgements
I would like to thank Professor Dan Hedlin for all the support and guidance he has given me
during this course to complete this thesis.
I would also like to thank my parents and my brother Mohammed and the rest of my family for
their encouragement.
Finally, special thanks to my lovely husband Mohammed for his support and his help to make
my study possible.
3
Contents
1. Introduction
1.1 The World’s Water Crisis and Sanitation
1.2 Country Overview: Ethiopia
1.2.1 History and Government
1.2.2 Geography
1.2.3 Economy
1.2.4 Population
1.2.5 Health Sector in Ethiopia
1.3 Objective
1.4 Outline of the study
2. Background
2.1 Water-Borne (related) Diseases such as Diarrheal Disease
2.1.1 Transmission
2.2 What is Diarrhea and why it is dangerous?
2.3 The Global Burden of Diarrheal Diseases
and Global Efforts to Control Diarrhea
2.4 The Burden of Diarrheal Diseases in Ethiopia
and Diarrhea Control in Ethiopia today
2.5 Literature Review
3. Data and Methods
3.1 Study Design or Sample Design
3.1.1 Description of the Data
3.1.2 Sample Domains
3.1.3 Sampling Frame
3.1.4 Sample Selection
3.1.5 Response Rate
3.2 Study Variables
3.2.1 Outcome (dependent) variable
3.2.2 Explanatory (Independent) variables
3.3 Common techniques for dealing with missing data
3.3.1 Delete records (Complete case) analysis
3.3.2 Multiple imputation (MI) method
3.4 Logistic regression with multiple predictors (explanatory variables) for survey data
3.4.1 Model
3.4.2 Pseudo-maximum likelihood estimation (PMLE) for logistic regression model
4. Results
4.1 Statistical Analysis
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4.2 Descriptive statistics
4.2.1 Socio- demographic and Socio- economic characteristics
4.2.2 Household environmental characteristics
4.3 Multivariate Analysis
4.3.1 Data cleaning from Missing values and “don’t know”, DK , responses
for each survey data
4.3.2 Model 1: Logistic regression analysis for complete cases in the three
surveys
4.3.3 Model 2: Logistic regression analysis for multiple imputation (MI) in
the three surveys
4.3.4 Comparisons between complete case analysis and the multiple
imputation analysis
5. Discussion
6. Conclusion and Recommendations
References
Appendix A Millennium Development Goals (MDG).
Appendix B The report (UNICEF/WHO, 2009), calls for a 7-point plan to control
diarrheal disease.
Appendix C Prevalence rates of childhood diarrhea among the three surveys,
2000, 2005 and 2011
Appendix D SAS code
5
1. Introduction
1.1. The World’s Water Crisis and Sanitation
At the beginning of the twenty-first century, the Earth, with its diverse and abundant life forms,
including over its seven billion humans, is facing a serious water crisis. All the signs suggest that
it is getting worse, unless corrective actions are taken. This crisis is one of water governance,
essentially caused by the ways in which we mismanage water. But the real tragedy is the effect it
has on the everyday lives of poor people, who are blighted by the burden of water-related
disease, living in degraded and often dangerous environments, struggling to get an education for
their children and to earn a living, and to get enough to eat. The crisis is experienced also by the
natural environment, which is being affected under the mountain of wastes dumped onto it daily,
and from overuse and misuse, with seemingly little care for the consequences and future
generations (UN/WWAP1, 2003). Where contaminated water is a major cause of illness and
death, water quality is a determining factor in human poverty, education, and economic
opportunities. Unfortunately, worldwide water quality is declining, threatening the health of
ecosystems and humans worldwide
(http://www.cdc.gov/healthywater/global/healthburden.html).
Sanitation and hygiene are critical to health, survival and development. Many countries are
challenged in providing adequate sanitation for their entire populations. An estimated 2.5 billion
people lack basic sanitation, more than 35% of the world's population (WHO/UNICEF, JMP
2012). Basic sanitation is described as having access and facilitation for the safe disposal of
human waste (feces and urine), as well as having the ability to maintain hygienic conditions,
through services such as garbage collection, industrial/hazardous waste management, and
wastewater treatment and disposal.
1.2. Country Overview: Ethiopia
1.2.1. History and Government
Ethiopia is an ancient country with a rich diversity of peoples and cultures and a unique alphabet
that has existed for more than 3,000 years. Ethiopia is one of the oldest locations of human
existence known to scientists. Fossil studies identify Ethiopia as one of the cradles of
humankind. “Dinknesh” or “Lucy,” one of the earliest and most complete hominoids discovered
in Hadar in 1974 through archaeological excavations and dates back to 3.5 million years
(Hopkin, 2005). The country has always maintained its independence, even during the colonial
era in Africa. Ethiopia is one of the founding members of the United Nations. Successive
emperors and kings with a feudal system of government ruled Ethiopia until 1974. In 1974, the
military took over the reign of rule by force and administered the country until May 1991.
Currently, a federal system of government exists, and political leaders are elected every five
years.
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1.2.2. Geography
Ethiopia is a country located in the Horn of Africa and bordered by Eritrea to the north, Djibouti
and Somalia to the east, Sudan and South Sudan to the west, and Kenya to the south. As shown
below in Figures (1.2.1 and 1.2.2). Ethiopia is divided into nine region states and two
administrative cities, Tigray, Afar, Amhara, Benishangul-Gumuz, Gambella, Harari, Oromia,
Somali, and Southern Nation Nationalities and Peoples Region (SNNPR); and the City
Administration Councils of two cities: Dire Dawa and Addis Ababa. The regional states and city
administrations are subdivided into 817 administrative Woredas (districts). A Woreda/District is
the basic decentralised administrative unit and has an administrative council composed of elected
members. The 817 Woredas are further divided into about 16,253 Kebeles, the smallest
administrative unit. It occupies a total area of 1,100,000 square kilometers’ (420,000 sq. mill),
and its capital and largest city is Addis Ababa. It has great geographical diversity; its topographic
features range from the highest peak at Ras Dashen, which is 4,550 meters above sea level, down
to the Affar Depression at 110 meters below sea level according to Central Statistical Agency
(CSA, 2010). The climatic condition of the country varies with the topography, from as high as
47 degrees Celsius in the Affar Depression to as low as 10 degrees Celsius in the highlands. The
total area of the country is about 1.1 million square kilometers. There are three principal climatic
groups in Ethiopia, namely the tropical rainy, dry, and warm temperate climates. In Ethiopia, the
mean maximum and minimum temperatures vary spatially and temporally.
Figure 1.2.1: Map of Africa showing the position of Ethiopia.
Source:https://www.google.se/search?q=ethiopia&source=lnms&sa=X&ei=enrbUpjzMqLw4QTD3oHQDg&ved=0CAYQ_AUo
AA&biw=1366&bih=641&dpr=1#q=map+of+ethiopia
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Figure 1.2.2: Map of Ethiopia showing regions and burdens.
Source:https://www.google.se/search?q=ethiopia+region&hl=sv&tbo=u&rlz=1G1SVEE_SVSE466&tbm=isch&source=univ&sa=X&ei=Ruz
qULKcGOmo4AS56YDIBA&ved=0CD0QsAQ&biw=1366&bih=632
1.2.3 Economy
Ethiopia is an agrarian country and agriculture accounts for 54 percent of the gross domestic
product (GDP), it employs about 80 percent of the population, and accounts for about 90 percent
of the exports. Coffee is the main export of the country. The Ethiopian currency is the Birr, and
1 US dollar is equivalent to about 17 Birr, between 1974 and 1991.
1.2.4 Population
Table 1.2.1 provides a summary of the basic demographic indicators for Ethiopia from data
collected in the three population and housing censuses carried out in 1984, 1994 and 2007. The
population has increased over the decade from 42.6 million in 1984 to 53.5 million in 1994 and
73.8 million in 2007. Ethiopia is one of the least urbanized countries in the world, only 16% of
the population lives in urban areas and the majority of the population lives in the two reigns,
36.7% in the Oromia and 23.3% in the Amhara, shown in the Table 1.2.2. Christianity and Islam
are the main religions, 51 percent of the populations are Orthodox Christians, 33 percent are
Muslims, and 10 percent are Protestants. The rest follow a diversity of other religions. The
country is home to about 80 ethnic groups (CSA, 2010).
Table 1.2.1 Basic demographic indicators
Indicator 1984 1994 2007
Population (millions) 42.6 53.5 73.8
Growth rate (percent) 3.1 2.9 2.6
Density (population/k ) 34.0 48.6 67.1
Percent urban 11.4 13.7 16.1
Life expectancy
Male 51.1 50.9 na
Female 53.4 53.5 na
na= not available, 1= 2= (CSA, 1998), 3=(CSA, 2010)
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Table 1.2.2 Distribution of Population by Regional States, 2007
Region Number Percentage of Total
Tigray 4,314,456 5.8
Afar 1,411,092 1.9
Amhara 17,214,056 23.3
Oromia 27,158,471 36.7
Somali 4,439,147 6.0
Beni Shangul Gumuz 670,847 0.9
SNNP (Debub) 15,042,531 20.4
Gambella 306,916 0.4
Harar 183,344 0.2
Addis Ababa 2,738,248 3.7
Dire Dawa 342,827 0.5
Special Enumeration 96,570 0.1
Source: Markakis, J. ( 2011)
1.2.5. Health Sector in Ethiopia
The health status of the people in Ethiopia is poor in relation to even low-income countries,
including those in sub-Saharan Africa. It is estimated that 75 percent of the population suffers
from some type of preventable communicable diseases such as HIV/AIDS, malaria, tuberculosis,
intestinal parasites, acute respiratory infections and diarrheal diseases as well as from
malnutrition (EDHS 2011). Health care in Ethiopia is underdeveloped because of transportation
problems that make access to any type of modern health institution difficult. Most of the rural
population has limited access to modern healthcare services. In terms of service delivery, it is
estimated that only 75% of urban households and about 42% of rural-dwellers have access to
health facilities. There is a seasonal shortage of medicines and medical supplies. Like in many
other African countries, the main causes for the shortage of medicines and medical supplies are
lengthy procurement procedures, limited access to information and an inefficient distribution
system. The issue of healthcare services delivery to the pastoral communities, who accounts for
10% of the population, calls for special attention. The problem of high maternal mortality, high
teenage pregnancy, low contraceptive prevalence rate and a relatively high incidence rate of
sexually transmitted diseases (STD) in young people also need special thought.
In 1960, a health policy initiated by the World Health Organization (WHO) was adopted. In
1997/1998, the Federal Government of Ethiopia established the Health Sector Development
Program (HSDP), which incorporates a 20-year health development strategy divided into 3-5
year rolling plans. The principle of the program is improving maternal health, reducing child
mortality and combating HIV/AIDS, malaria, diarrhea and other diseases with the ultimate aim
of improving the health status of the Ethiopian people and achieving the Millennium
Development Goals (MDGs). According to the Human Development Report of 2001, about 45%
of the people in Ethiopia live on less than one US dollar per day. Thus 51% of children below
the age of five are stunted. While 10.5% are very slender and 42.7% were underweight.
Moreover, 3.6% of women are stunted and 30.1% were undernourished (EDHS 2000). Maternal
deaths, which are amongst the highest in the world, range from 560–850 per 100,000 live births
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(WHO, 2002-2005). In addition, Ethiopia is ranked among the top five countries in the world in
children deaths under five years of age, with an estimated 77 deaths per 1000 live births.
1.2.6. Water and Sanitation in Ethiopia
Ethiopia's major health problem is the spread of disease caused by poor water and sanitation.
Water quality is very poor and often contaminated by human and animal waste. These issues are
made worse by a shortage of medical staff and health facilities and disease prevention. Water
and sanitation-related diseases, particularly diarrhea, are among the top three causes of death in
the country after malaria and HIV/AIDS. Over half a million children under the age of five die
every year from diarrhea. In other words, for every five children born, one will die from diarrhea
before they reach their fifth birthday. The government is working on achieving 100% coverage
of sanitation and water supply through the Universal Access Plan. However, the rapid
assessment report on safe drinking water published by Federal Ministry of Health (FMOH),
WHO and UNICEF in 2007 estimated coverage at 37%. The rest of the population relies on
unsafe sources such as ponds, lakes, rivers and open dug wells.
According to Figure 1.2.3, access to an improved water source and improved sanitation was
estimated as follows: from 1990 to 2008, we see almost 38% improvement in water supply (from
77% to 98% for urban areas and from 8% to 26% for rural areas) and almost 12% improvement
in sanitation (from 21% to 29% in urban areas and from 1% to 8% in rural areas). This means a
significant increase in access of water supply and sanitation, which extends to both urban and
rural areas. Ethiopia has seven years to raise the sanitation coverage from 29% (2008) to 57%
(2015) in urban areas and from 8% (2008) to 51% (2015) in rural areas. However, even if
Ethiopia meets the Millennium Development Goal (MDG) target (see Appendix A) in both rural
and urban areas, 49% of the rural population and 43% of the urban population would still be
without access to improved sanitation. Access to drinking water, on the other hand, has reached
the MDG target in urban areas, but progress is slow in rural areas. To meet the rural water MDG
target, the coverage needs to increase from 26% (2008) to 58% (2015). Therefore, continued
investments are needed in water supply to maintain coverage in urban areas and reach the
unserved rural population.
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Figure 1.2.3: Ethiopia’s progress towards the sanitation and water MDGs 1990-2008 and progress
required to achieve the MDGs
Source: JMP, WHO/UNESCO (2010).
1.3. Objectives
The following were the study objectives:
1. To examine the effects of selected socio-demographic, socio-economic and household
environmental factors on childhood diarrhea among children under five years of age
using demographic and health survey data, 2000, 2005 and 2011 from Ethiopia.
2. To compare the results among the three survey years, 2000, 2005 and 2011.
3. To compare the results between, records, delete model and multiple imputation model.
1.4. Outline of the study
The rest of the paper is organized in the following sections:
Section 2 (the background) will provide an overview of diarrheal disease and followed by a
literature review. Data and methods are discussed in section 3, section 4 presents the results is
followed by a discussion of the findings in section 5. Conclusion and recommendations are
presented in section 6.
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2. Background
2.1. Water-Borne (related) Diseases such as Diarrheal Disease
Water-borne diseases are any illness caused by drinking water contaminated by human or animal
faeces, which contain pathogenic microorganisms. Sanitation and hygiene are critical to health,
survival, and development. A significant amount of diseases could be prevented through better
access to adequate sanitation facilities and better hygiene practices.
Improved sanitation facilities (for example, toilets and latrines) allow people to dispose of their
waste appropriately, which helps break the infection cycle of many diseases. Half the hospital
beds in developing countries are occupied by people with water and sanitation related diseases
such as diarrhea (the focus in this study), pneumonia, eye and skin infections, malaria, cholera,
typhoid, Guinea Worm Disease, Buruli Ulcer, Trachoma, and Schistosomiasis. These diseases
are most often found in places with unsafe drinking water, poor sanitation, and insufficient
hygiene practices.
Hygiene refers to acts that can lead to good health and cleanliness, such as frequent hand
washing, face washing, and bathing with soap and clean water. According to the United Nations
(U.N.), more than 14,000 people die daily from water-borne illnesses. The bulk of these deaths
are related to a number of infections, including: 2 billion cases of intestinal worms, 5 million
cases of lymphatic filariasis and trachoma, 1.4 million child diarrheal deaths; and 500,000 deaths
from malaria (http://www.lenntech.com/library/diseases/diseases/waterborne-diseases.htm).
2.1.1. Transmission
Water borne diseases spread by contamination of drinking water systems with the urine and
faeces of infected people or animal. This is likely to occur where public and private drinking
water systems get their water from surface waters (rain, creeks, rivers, lakes etc.), which can be
contaminated by infected animals or people. Runoff from landfills, septic fields, and sewer pipes,
residential or industrial developments can also sometimes contaminate surface water.
This has been the cause of many dramatic outbreaks of faecal-oral diseases such as cholera and
typhoid. However, there are many other ways in which faecal material can reach the mouth, for
instance on the hands or on contaminated food. In general, contaminated food is the single most
common way in which people become infected.
The germs in the faeces can cause the diseases by even slight contact and transfer. This
contamination may occur due to floodwaters, water runoff from landfills, septic fields, and sewer
pipes. The following Figure shows the faecal-oral routes of diseases transmission.
12
Figure 2.1.1: The faecal-oral routes of diseases transmission Source: http://www.lenntech.com/library/diseases/diseases/waterborne-diseases.htm
2.2. What is diarrhea and why is it dangerous?
Diarrhea is defined as passage of three or more loose or liquid stools per day (or more frequent
passage than normal for an individual), a loose stool being one that would take the shape of the
container. This means body fluids and salts can be quickly lost from the body. Diarrhea is an
intestinal disorder characterized by abnormal fluidity and frequency of faecal evacuations,
generally the result of increased motility in the colon; may be an important symptom of such
underlying disorders as dysenteric diseases, lactose intolerance, gastrointestinal tumors (GI) and
inflammatory bowel disease.
Causes
There are many causes of diarrheal disease, according to (WHO, 2013), they are:
Infection: Diarrhea is a symptom of infections caused by a host of bacterial, viral and parasitic
organisms, most of which are spread by faeces-contaminated water. Infection is more common
when there is a shortage of adequate sanitation and hygiene and safe water for drinking, cooking
and cleaning. Rotavirus and Escherichia coli are the two most common etiological agents of
diarrhea in developing countries.
Malnutrition: Children who die from diarrhea often suffer from underlying malnutrition, which
makes them more vulnerable to diarrhea. Each diarrheal episode, in turn, makes their
malnutrition even worse. Diarrhea is a leading cause of malnutrition in children under five years
of age.
Source: Water contaminated with human faeces, for example, from sewage, septic tanks and
latrines, is of particular concern. Animal faeces also contain microorganisms that can cause
diarrhea.
Other causes: Diarrheal disease can also spread from person-to-person, aggravated by poor
personal hygiene. Food is another major cause of diarrhea when it is prepared or stored in
unhygienic conditions. Water can contaminate food during irrigation. Fish and seafood from
polluted water may also contribute to the disease. Sometimes diarrhea occurs without infection.
13
Thus, diarrhea can be caused by certain medications, food allergies, chronic illness, addictive
foods, the wrong diet and stress.
Clinical Types of Diarrheal Diseases
There are four clinical types of diarrheal disease, according to (WHO, 2013):
Acute watery diarrhea (including cholera), which lasts several hours or days: the main danger
is dehydration; weight loss also occurs if feeding is not continued.
Acute bloody diarrhea (also called dysentery): the main dangers are intestinal damage, sepsis
and malnutrition; other complications, including dehydration, may also occur.
Persistent diarrhea (which lasts 14 days or longer): the main danger is malnutrition and serious
non-intestinal infection; dehydration may also occur.
Diarrhea with severe malnutrition (marasmus or kwashiorkor): the main dangers are severe
systemic infection, dehydration, heart failure and vitamin and mineral deficiency.
Diarrhea is very dangerous, when a person gets diarrhea, the body begins to lose a lot of water
and salts or electrolytes (sodium, chloride, potassium and bicarbonate). They are normally lost
through liquid stools, vomit, sweat, urine and breathing, they are all necessary for life but if the
water and salts are not replaced fast, the body starts to "dry up" or get dehydrated. Severe
dehydration can cause death especially for children less than five years of age.
2.3. The global burden of diarrheal diseases and global efforts to control
diarrhea
Diarrheal diseases are the second leading cause of childhood morbidity and mortality in
developing countries, especially in African countries (Kosek, Bern, and Guerrant, 2003).
Mortality from diarrhea has declined over the past two decades from an estimated 5 million
deaths among children under five to 1.5 million deaths in 2004. Despite these declines, diarrhea
remains the second, after Pneumonia, most common cause of death among children under five
globally (Figure 2.3.1). Nearly one in a five child deaths, around 1.5 million a year, is due to
diarrhea which kills more children than AIDS, malaria and measles combined. Despite the
existence of inexpensive and efficient means of treatments.
14
Figure 2.3.1: Diarrhea is the second most common cause of child deaths worldwide.
Proportional distribution of cause-specific deaths among children under five years of age, 2004.
Source: World Health Organization, Global Burden of Disease estimates, 2004 update.
In developing countries, poor access to safe drinking water, inadequate sanitation and poor
hygiene are important contributors to the risk of diarrheal diseases. The World Health
Organization estimates that over 1 billion people lack access to improved water supply
worldwide. Since the global estimates of the number of deaths due to diarrhea have shown a
steady decline, from 4.6 million in 1980 (Synder and Merson, 1982) to 3.3 million in 1990 (Bern
et al., 1992) to 2.5 million in the year 2000 (Kosek, Bern and Guerrant, 2003). The main health
benefit of water supply, sanitation, and hygiene is a reduction in diarrheal disease, but the effect
on other diseases, such as dracunculiasis, schistosomiasis, and trachoma, is substantial. Water,
sanitation, and hygiene improvements could eliminate 3 to 4 percent of the global burden of
diseases (World Bank, 2006). Each year, an estimated 2.5 billion cases of diarrhea occur among
children under five years of age, and estimates suggest that overall incidence has remained
relatively stable over the past two decades. More than half of these cases are in Africa (696
million) and South Asia (783 million) as shown below (Figure 2.3.2). More than 80 percent of
child deaths due to diarrhea occur in Africa and South Asia (46% and 38%) respectively
(WHO/UNICEF, 2004).
Figure 2.3.2: Africa and South Asia account for over half the cases of childhood diarrhea Source: Based on World Health Organization, Global Burden of Disease estimates, 2004
15
Reducing child mortality does not necessarily require new techniques or interventions, but
effective scale-up and the application of existing, cost-effective interventions. The World Health
Organization (WHO) and United Nations International Children's Fund (UNICEF) achieved
some important successes in the 1970s and 1980s through the Diarrheal Diseases Control
Program. In addition, the World Health Organization and UNICEF, in the same period, led a
global effort to reduce diarrheal deaths by promoting preventive measures such as exclusive
breastfeeding and improved access to clean water and use of oral rehydration solution to counter
dehydration. Millions of lives were saved. However, there remains a lack of adequate research,
funding, and political commitment to address these diseases globally. Oral Rehydration
Solutions (ORS) was developed in the 1970s as an efficient and cost-effective way to replace the
body's vital fluids lost to illness. Although deaths from diarrheal disease dropped 75% from 1980
to 2008, diarrheal diseases remains a leading cause of death for children under the age of five.
Many children in the developing world cannot access urgent medical care for severe illnesses,
making prevention methods, including improved hygiene, sanitation, safe drinking water,
exclusive breastfeeding, and also vaccines that prevent rotavirus which is a critical component of
diarrheal disease control. When diarrhea occurs, it can be effectively treated with simple
solutions, including oral rehydration therapy/oral rehydration solution (ORS), zinc and other
micronutrients, and continued feeding (UNICEF/WHO, 2009). Hand washing with soap is one of
the most cost-efficient and effective methods for preventing diarrheal disease, reducing
incidence up to an estimated 40%. The first Global Hand Washing Day (GHD) took place on
October 15, 2008, the date appointed by the UN General Assembly in accordance with the year
2008 as the international year of sanitation, a coalition of U.S.-based public and private
organizations, including Global Water Challenge, the Institute for One World Health,
Millennium Water Alliance, PATH, the U.S. Coalition for Child Survival, U.S. Fund for
UNICEF, Water Aid America, and Water Advocates came together to raise awareness around
the benefits of hand washing in preventing diarrheal disease (UNICEF/WHO, 2009). In addition,
the same report calls for a 7-point plan to control diarrheal diseases (see Appendix B). Recently,
more than 100 organizations worldwide signed onto a Call to Action, encouraging decision-
makers to commit political will and funding to defeat deaths from diarrheal disease. At the same
time, world leaders have committed to child survival and improving conditions around the world
for future generations by 2015 through the Millennium Development Goals.
For the very first time, there is a global plan to simultaneously tackle two of the leading killers of
children: pneumonia and diarrhea. The Integrated Global Action Plan for the prevention and
control of Pneumonia and Diarrhea (GAPPD), released on 12 April 2013, by the World Health
Organization (WHO) and UNICEF, represents an integrated and intensified effort to reduce
deaths and illnesses from these diseases and lays out a new framework for scaling up
interventions to protect children, prevent disease and treat children who become sick. This has
the potential to save up to 2 million children every year from deaths caused by pneumonia and
diarrhea, some of the leading killers of children under five globally. The Action Plan’s targets,
for example, calls for 90% of all children to have access to antibiotics for pneumonia and oral
rehydration salts for diarrhea. As an interim target, all children under six months should be
16
exclusively breastfed. All children should have access to improved sanitation and safe drinking
water, and building on the good progress already made in some countries in introducing new
vaccines against pneumococcal bacteria and rotavirus (UNICEF/WHO, 2013).
2.4. The burden of diarrheal diseases in Ethiopia and diarrhea control in
Ethiopia today
In Ethiopia, morbidity reports and community-based studies indicate that diarrheal diseases are
public health problems that causes excess morbidity and mortality among children (Dessalegn,
Kumie and Tefera, 2011; Ketsela, 1991), the child mortality rate in 2007 was 199 per 1000 births
(Fontaine et al. 2009). Thus, approximately one of every five deaths every year was due to
diarrheal disease (Boschi, Velebit and Shibuya, 2008), largely caused by water related diseases.
As mentioned above, Water supply and sanitation services in the country are inadequate and only
37% of the total populations have reasonable access to adequate water supply. According to
World Health Statistics (2011), 27% causes of under-five deaths in Ethiopia from diarrheal
disease, which kills children more than Pneumonia, AIDS, malaria, measles and other diseases as
shown below in Figure 2.4.1.
Figure 2.4.1: The Burden of Diarrhea in Ethiopia
Source: World Health Statistics 2011
The Health Extension Programme (HEP) is Ethiopia’s main programme. It was introduced in
2002/03 to provide primary healthcare at the community level. The programme gives priority to
the prevention and control of communicable diseases with active community participation, with
the goal of providing fair access to health services. In addition, it is based on expanding physical
health infrastructure and publishing health extension workers (HEWs) who will provide basic
curative and preventive health services in every rural community
17
(http://cnhde.ei.columbia.edu/aboutus.html). According to reports, diarrhea dialogues from
policies to progress, 2012: Today, the Health Extension Programme (HEP) is successfully
achieving progress in providing higher numbers of trained health workers to deliver crucial
messages. Importantly, there is now a focus on reaching rural areas. These developments show
the recognized need for balanced primitive, preventive and selected curative healthcare services.
There have also been promising developments in cross-sector collaboration for improved public
health. However, diarrhea remains the biggest killer of children of less than five years of age.
Access to treatment is low and water and sanitation coverage rates in rural areas urgently need to
be addressed. Workers also face challenges implementing health strategies at a local level
(http://www.path.org/publications/files/VAC_diarrhoea_dialogues_rpt.pdf).
2.5. Literature Review
Many studies have shown how the various socio-demographic, socio-economic and household
environmental risk factors affect children with diarrhea under the age of five in developing
countries especially in Africa, represented in this section. It seems that maternal education level
is an important factor related to the diarrhea prevalence of children under the age of five, for
example, Rohmawati, (2010); Yilgwan and Okolo, (2012) and Kahabuka, Kvåle and Hinderaker,
(2010) showed that maternal education bore a significant impact on diarrheal morbidity.
Similarly, Caruso, Stephenson and Leon, (2010) showed that maternal education has a
significant effect of diarrheal morbidity, i.e. mothers who have a secondary or higher education
compared with mothers who have no education. Other researches ( Joshi et al., 2011 and
Manun'ebo, et al., 1994) had shown that the age of child is a risk factor for diarrhea since the
incidence of diarrhea was inversely proportional to age and the most vulnerable age group for
diarrhea among children was less than two years.
There had been many studies that showed that the environmental risk factors and socio-economic
conditions of the population were significant predictors of diarrheal morbidity. Woldemicael,
(2001); Kumar and Subita, (2012) and Vafaee, Moradi and Khabazkhoob, (2008) showed that
obtaining water from storage containers by dipping showed statistically significant association
with diarrheal morbidity. Another study observed that, the odds of having diarrhea in children
who lived in households, which had no latrine facility was two times higher than the odds in
children who lived in households that had latrine facilities. Dessalegn, Kumie, and Tefera,
(2011), Al-Ali, Hossain and Pugh, (1997) and Mediratta et al., (2010) showed that the sanitation
facilities, maternal history of diarrhea and breastfeeding play an important role in reducing the
incidence and severity of infantile diarrhea. Different studies done in Ethiopia have shown that
prevalence of diarrheal diseases are very high among children under five years of age, for
example, Dessalegn, Kumie, and Tefera, (2011) showed that the prevalence of under-five
childhood diarrhea was 18%. A community based study conducted in Keffa-Sheka Zone, in
southern Ethiopia, found a two-week childhood diarrheal incidence of 15% (Teklemariam et al.,
18
2000). Another study from Jimma town, southwest Ethiopia, showed a prevalence of 36.5%
(Getaneh et al., 1997).
In addition, many different studies applied a logistic regression model for binary data, for
instance, logistic regression for survey data by Scott (1986) and logistic regression analysis of
sample survey by Robert, Rao and Kumar (1987). Logistic regression analysis was used to
determine which variables predict the occurrence of diarrhea (Mock et al., 1993). Moreover,
Dikassa et al., (1993) used logistic regression analysis to investigate that maternal behavior at
risk factor for sever childhood diarrhea disease in Kinshasa, Zaire.
3. Data and Methods
3.1. Study design or sample design
3.1.1. Description of the data
This study uses data from three nationally representative surveys from Ethiopia conducted in
2000, 2005 and 2011 as part of the worldwide Demographic and Health Survey (DHS) project
(program). The main objective of each survey (EDHS 2000, EDHS 2005 and EDHS 2011) is to
provide estimates for the health and demographic variables (indicators) of interest such as
fertility, family planning behavior, child mortality, children’s nutritional status, the utilization of
maternal and child health services, knowledge of HIV/AIDS and other sexually transmitted
infections ( ). The Ethiopia DHS for each period 2000, 2005 and 2011 collected demographic
and health information from a nationally representative sample of women and men in the
reproductive age groups 15-49. Each survey 2000, 2005 and 2011 completed interviews with
10873, 9861 and 11654 women respectively in the age group 15-49 years.
3.1.2. Sample domains
There surveys, 2000 EDHS, 2005 EDHS and 2011 EDHS, are designed to produce
representative estimates for the country (Ethiopia) as a whole, for the urban and rural areas
separately and for each of the 11 geographic areas (9 regions and 2 administrations), 9 regions,
namely: Tigray, Affar, Amhara, Oromiya, Somali, Benishangul-Gumuz, Southern Nations,
Nationalities and Peoples (SNNP), Gambela, Harari, and the two Adminitrative Council Areas of
Addis Ababa and Dire Dawa.
3.1.3. Sampling frame
The sampling frame for both the 2000 EHDS and 2005 EDHS is the list of enumeration areas
( ) that were developed for the 1994 Population and Housing Census (PHC). The sampling
frame for the 2011 EDHS is the list of enumeration areas ( ) that was developed for the 2007
Population and Housing Census (PHC). In general, Ethiopia is divided into 11 geographic
regions. Each region is subdivided into zones, each zone into waredas, each wareda into towns,
and each town into kebeles.
19
3.1.4. Sample selection
The sample for each survey, 2000 EDHS, 2005 EDHS and 2011 EDHS was selected using a
stratified, two-stage cluster design. Since the samples are two-stage stratified cluster samples,
sampling weights were calculated based on sampling probabilities separately for each sampling
stage and for each cluster. In the 2000 EDHS, at the first stage of sampling, 540 , 139 in the
urban areas and 401 in the rural areas were used. In the second stage of sampling, a complete
household listing operation was carried out in all the selected . A systematic sample of 27
households per EA was selected in all the regions to provide statistically reliable estimates of key
demographic and health variables.
In the 2005 EDHS, at the first stage of sampling, 540 clusters (145 urban and 395 rural) were
selected from the list of enumeration areas ( ) from the 1994 Population and Housing Census
(PHC) sample frame. In the second stage, a complete household listing was carried out in each
selected cluster, between 24 and 32 households from each cluster.
In addition, in the 2011 EDHS, for the first stage, the sample included 624 , 187 in urban
areas and 437 in rural areas. In the second stage of sampling a complete listing of households
were carried out in each selected cluster.
3.2. Study Variables
3.2.1. Outcome (dependent) variable
Childhood diarrhea, children under five years of age, which is the dependent variable ( ), is
based on mother’s response (yes/no) to questions on whether a particular child had experienced
diarrhea during the two weeks preceding the survey. In all cases the dependent variable was
coded as ‘1’ if the mother’s response was “yes”, if child had diarrhea preceding two weeks of
survey, and coded as ‘0’ if the mother’s response was “no”.
3.2.2. Explanatory (independent) variables
The selections of the explanatory variables in this study are:
Socio-demographic characteristics including current age of child, sex of child, mother’s age,
current marital status and religion.
Socio-economic characteristics including regions, type of place of residence, wealth index
respondent currently working, mother’s education level and number of children 5 and under.
Household environmental characteristics including source of drinking water, type of toilet
facility, main floor material, disposal of youngest child's stools (when not using toilet) and
electricity. These variables are defined in Table 3.2.1.
Table 3.2.1 Description of variables
Code Variable Descriptions
Dependent Variable
20
H11 Had diarrhea (childhood diarrhea) No/ Yes, last two weeks
Independent Variables
Socio-demographic Variables
B8 Current age of child Age of child (0 – 5)
B4 Sex of child Sex of child (Male, Female)
V013 Mother’s age (Age in 5-year groups) Age of mother (15-19, 20-24, 25-29,
30-34, 35-39, 40-44 and 45-49
V501 Current marital status Never in union (Never married), Married,
Living with partner, Widowed, Divorced and
No longer living together/separated
V130 Religion Orthodox, Catholic, Protestant, Muslim,
Traditional and Other
Socio-economic variables
V024 Regions Tigray, Affar, Amhara, Oromiya, Somali,
Ben- Gumz, SNNP, Gambela, Harari, Addis
and Dire Dawa
V025 Type of place of residence Urban, Rural
V190 Wealth index Poorest, Poorer, Middle, Richer and Richest
V714 Respondent currently working No, Yes
V106 Mother’s education level (Highest No education, Primary, Secondary and Higher
educational level)
V137 Number of children 5 or under Number of children under-five in
household (0 -5)
Household environmental variables
V113 Source of drinking water Piped into dwelling, Protected well in
dwelling,
Piped into compound, River /lakes /spring,
Rainwater, Other
V116 Type of toilet facility Flush toilet, Traditional pit latrine, Ventilated
improved PIT (VIP) latrine, No facility
(bush/field), Others
V127 Main floor material Earth/Sand, Dung, Palm/Bamboo, Parquet or
polished wood, Vinyl or asphalt strips,
Cement, Carpet, Other
V465 Disposal of youngest child's stools Used toilet/latrine, Put/rinsed in toilet/latrine,
Rinse away, bury in the yard, other
V119 Has electricity Yes, No
21
3.3. Common techniques for dealing with missing data
Missing data is a common problem for almost every health survey data. Missing data presents a
problem in statistical analyses. The first issue in dealing with the problem of missing data is
determining within the missing data mechanism. Little and Rubin (1987) distinguish between
three types of missing data mechanism
1) Missing completely at random (MCAR), which means that missingness is not related to the
variables under study.
2) Missing at random (MAR), which means that missingness is related to the observed data but
not to the missing data.
3) Not missing completely at random (NMAR), which means that missingness is related to the
missing data.
3.3.1. Delete records (complete case) analysis
A common approach and easy to perform is to delete all incomplete observations from the
analysis. The results can be unbiased when data are MCAR. Even so, the disadvantage for this
method is reduction of sample size.
3.3.2. Multiple imputation (MI) method
Multiple imputation is a procedure for filling in the missing values and it has recently become
very popular for handling incomplete-data problems. Multiple imputation offers the advantage of
increased efficiency in estimation and the ability to incorporate information in an effort to reduce
nonresponse bias. Multiple imputation replaces each missing value with a set of plausible values
that represent the uncertainty about the right value to impute instead of filling in a single value
for each missing value. More details and discussion of both the theory and practice of multiple
imputation method is provided in Rubin (1987). Moreover, multiple imputation assumes that the
missing data mechanism is MAR.
The multiple imputations follow these three steps:
Create m complete data sets whereby the missing values are filled in m times.
The m complete data sets are analyzed by using standard procedures.
The results from the m complete data sets (analyses) are combined for the
inference.
Two approaches for imputing multivariate data are joint modeling (JM) and fully conditional
specification (FCS) or chained equations approach. JM is based on parametric statistical theory,
and needs a parametric model to be known. JM lacks flexibility needed to represent typical data
features, potentially leading to bias. Comparing with that, FCS is a semi-parametric and flexible
alternative that specifies the multivariate model by a series of conditional models, one for each
incomplete variable. Moreover, it is easy to apply and also to incorporate constraints on the
imputed values (van Buuren, 2007).
22
The imputation method of choice depends on the patterns of missingness in the data. The
regression method is used to impute missing values for all variables in a data set with a
monotone missing pattern. However, for data sets with arbitrary missing patterns, we can use
either Markov Chain Monte Carlo (MCMC) method which assumes multivariate normality, or a
Fully Conditional Specification (FCS) method that assumes a joint distribution for all variables.
In addition, the imputation method of choice also depends on the type of the imputed variable, a
regression method to impute missing values for a continuous variable, a logistic regression
method for a classification variable with a binary or ordinal response, and a discriminant
function method for a classification variable with a binary or nominal response.
A popular MI approach is Fully Conditional Specification (FCS), which imputes the data on a
variable-by-variable basis by specifying an imputation model for each variable with missing data
conditional on all other variables. Starting from an initial imputation and FCS, it draws
imputations by iterating over the all conditionally specified imputation models. A low number of
iterations are often sufficient from (10-20), for more details see van Buuren, (2007), van Buuren
and Grothuis-Oudshorn, (2011). The FCS approach is also available in a number of statistical
packages, including Stata, SAS and R. In SAS software, for example, after the m completes data
sets, they are analyzed using standard SAS procedures, the MIANALYZE procedure can be used
to generate valid statistical inferences about these parameters by combining results from the m
analyses.
3.4. Logistic regression with multiple predictors (explanatory variables) for
survey data
3.4.1. Model
Logistic regression analysis is often used to investigate the relationship between discrete
responses and a set of explanatory variables. Discrete responses, either is binary responses
(dichotomous), or ordinal responses and nominal responses (more than two categories). See, for
example, Agresti, (2002); Hosmer and Lemeshow, (2000); Andersen, (1990) and Harrell, (2002).
In both traditional linear regression and logistic regression, the systematic component has the
form kk xxx ,,22110 , where 0 is an intercept (constant), k ,,1 are regression
parameters, and kxx ,,1 are the predictors (explanatory variables), as is dichotomous. This
expression is often called a linear predictor. In the logistic regression model, the mean is
transformed by a logit link, which is different than a traditional linear regression model (Jaccard,
2001).
In this study, childhood diarrhea, which is the dependent variable (dichotomous), and the
underlying probability distribution is Bernoulli in form, it can be expressed in this form.
)(~ jj PBernoulliy , and explanatory variables (as defined above in a
description of variables). In this case, the statistical model that is preferred for the analysis of
binary (dichotomous) outcome variable is binary logistic regression model.
23
The form of the logistic regression equation is:
,
nj ,,1
The estimated probability jP of binary outcome could be calculated by:
)exp(1
)exp(
1
0
1
0
ji
k
i
i
ji
k
i
i
j
x
x
P
where
jP = ),,|1( 1 jkjj xxYP is the probability that a child have the diarrheal disease.
1-jP = ),,|0( 1 jkjj xxYP is the probability of a child that did not have diarrheal disease.
0 = the constant/intercept of the equation and
i = is estimated regression coefficient of the predictor variables.
For fitting logistic regression model for binary outcomes to survey data and estimate parameters
by pseudo- maximum likelihood Fisher scoring algorithm is carried out using SAS software,
version 9.3 (Surveylogistic procedure). The procedure incorporates complex survey sample
designs, like designs with stratification, clustering, and unequal weighting. Variances of the
regression parameters and odds ratios are computed by using the Taylor series (linearization)
method or replication (resampling) methods to estimate sampling errors of estimators based on
complex sample designs, including designs with stratification, clustering, and unequal weighting
(Binder, 1983), (Roberts, Rao, and Kumar, 1987).
3.4.2. Pseudo-maximum likelihood estimation (PMLE) for logistic regression model
The PML method is often used on complex survey data for logit analysis (Lehtonen and
Pahkinen, 2004). To justify the modeling of a logistic regression in a finite population, a pseudo-
MLE modeling is often taken (Skinner et al, 1989; Binder, 1983). Applications that seem
promising include pseudo maximum likelihood estimation in mixture models, multiparameter
models without a closed form MLE, for example, PMLE’s for the scale parameter of the Weibull
distribution have been recommended and regular nuisance parameter models (Gong and
Samaniego, 1981).
In order to calculate PML-estimates,
)(~,,| 1 jjkjjj PBernoullixxyY , nj ,,1
The probability mass function of the Bernoulli distribution is
ji
k
i
i
j
j
j xP
PPit
1
0)1
(log)(log
24
jj y
j
y
jjkjjjr PPxxyYp
1
1 )1(),,|( , nj ,,1
The pseudo likelihood function of the Bernoulli distribution (weighted) for a set of n
observations is
)1(
1
)1( jjjj yw
j
n
j
yw
j PPL
, where jw is the element weight or design weights.
Putting the logistic regression equation as defined above in L, the log-pseudo likelihood becomes
)1log()1(log)|,,,(log1
10 jjjj
n
j
jjjjk PywPywywL
n
j
jj
n
j j
j
ij PwP
Pyw
11
)1log(1
log
n
j
k
ijiijji
k
i
i
n
j
jj xwxyw1 1
0
1
0
1
)exp(1log)(
Pseudo likelihood equations (1) and (2) are the derivative of the log-pseudo likelihood with
respect to and set to zero as shown below. Their solution is then the pseudo-
maximum likelihood estimate PMLE respectively for i ,0 .
n
j
j
n
j
jj
ijkwyw
ywL
110
10 ,,,log
)exp(1
)exp(
1
0
1
0
ji
k
i
i
ji
k
i
i
x
x
, (1)
n
jjij
n
jjijj
k
ijkxwxyw
ywL
111
10
,,
,,,log
)exp(1
)exp(
1
0
1
0
ji
k
i
i
ji
k
i
i
x
x
(2)
4. Results
4.1. Statistical Analysis
The analysis for this study was conducted by following these steps:
First, simple descriptive statistics such as a frequency distribution and percentages were
computed to describe the socio-demographic, socio-economic, and household environmental
characteristics, for the three surveys separately and to check the variables from missing values.
These analyses were performed using SPSS Version 17.
25
Second, multivariate logistic regression analysis was run for each set of survey data to estimate
the effect of the indicator variables on the outcome variable using the SAS procedure proc
Surverlogistic to perform logistic regression for sample survey data. This model indicates that
the link function is generalized logit. Before running multivariate logistic regression analysis, the
two procedures for handling missing data, as it is used in this study, Delete records (complete
case) analysis (model 1) and Multiple imputation (model 2) for dealing with missing values for
each data set. Proc Surveylogistic procedure uses a Taylor expansion approximation and
incorporates the sample design information, including stratification, clustering and unequal
weighting and it computes variances within each stratum and then pools the variance estimates
together. Statistical significance was set at the level 0.05 to identify the relative effects of
explanatory variables on the outcome variable.
4.2. Descriptive statistics
4.2.1. Socio- demographic and Socio- economic characteristics
A total of 10873, 9861 and 11654 households for 2000, 2005 and 2011 were included in the
study with a response rate of 87.8%, 91.1% and 92.6% respectively. The majority of mothers
were in the age range of 25-29 years for each survey years 2000, 2005 and 2011 respectively
(27.9%, 28.9% and 31.2%) according to Table 4.2.1 and 4.2.2 as shown below. More than two-
thirds of the mothers lived in rural area for each three survey years respectively (84.3%, 86.2%
and 83.0%). The majority of mothers were married for each three survey years (90.6%, 92.1%
and 87.4%); no education, (80.5%, 77.2% and 69.9%); and by religion: Orthodox (42.8%, 39.5%
and 31.0%) followed by Moslem 39.5%, 39.0% and 46.8% respectively for each three survey
years. More than half of mothers worked (56.4%) in year 2000, but this proportion decreased in
survey years 2005 and 2011 by 23.0% and 29.9% respectively.
Majority of the children were males 50.9%, 51.0% and 51.4% respectively for each survey years
2000, 2005 and 2011 while the most children were aged three years old 18.5 %, 19.1% and
19.8% respectively for each survey (Table 4.2.1).
Table 4.2.1 Socio-demographic characteristics distribution of the respondents, Ethiopia
during these survey years 2000, 2005 and 2011
Characteristics Percentage of frequency %
Survey years 2000 2005 2011
Current age of child *
0 17.7 19.1 19.3
1 17.0 17.2 16.5
2 17.3 17.2 18.0
3 18.5 19.1 19.8
4 17.4 18.3 19.0
Missing value 12.1 8.7 7.3
Sex of child
Male 50.9 51.0 51.4
Female 49.1 49.0 48.6
Mother’s age
26
(Age in 5-year groups)
15-19 4.7 5.4 4.4
20-24 21.2 20.9 20.1
25-29 27.9 28.9 31.2
30-34 20.4 20.2 20.3
35-39 15.7 15.2 15.4
40-44 7.4 6.5 6.4
45-49 2.7 2.9 2.2
Current marital status
Never married 0.7 40.5 0.6
Married 90.6 92.1 87.4
Living together 1.0 1.4 5.1
Widowed 1.8 1.7 1.8
Divorced 3.7 2.7 3.4
Not living together 2.2 1.6 1.6
Religion
Orthodox 42.8 39.5 31.0
Catholic 0.6 0.9 0.9
Protestant 13.7 18.0 19.2
Moslem 39.5 39.0 46.8
Traditional 3.3 1.5 0.8
Other 0.2 1.0 1.2
Sample size (n) = 10873, 9861 and 11654 respectively for 2000, 2005 and 2011
* 0 means (0-12) months and 1, 2, 3, 4 mean years
Table 4.2.2 Socio-economic characteristics distribution of the respondents, Ethiopia during
these survey years 2000, 2005 and 2011
Characteristics Percentage of frequency %
Survey years 2000 2005 2011
Regions
Tigray 10.3 9.9 10.3
Afar 5.9 5.8 9.7
Amhara 14.9 14.8 11.1
Oromiya 20.1 19.7 15.1
Somali 6.2 6.7 8.8
Ben-Gumz 7.4 7.1 8.8
SNNP 14.6 17.5 13.8
Gambela 5.6 5.2 7.3
Harari 5.1 5.2 5.7
Addis Abeba 4.7 3.9 3.4
Dire Dawa 5.2 4.2 6.0
Type of place of residence
Urban 15.7 13.8 17.0
Rural 84.3 86.2 83.0
Wealth index
Poorest na 25.6 31.1
Poorer na 18.7 18.1
Middle na 18.6 16.1
Richer na 17.0 16.0
Richest na 20.0 18.6
Respondent currently working
No 43.6 77.0 69.9
Yes 56.4 23.0 29.9
Mother’s education level
27
(Highest educational level)
No education 80.5 77.2 69.9
Primary 12.7 15.7 25.1
Secondary 6.4 6.4 3.3
Higher 0.4 0.7 1.7
Number of children 5 or under
0 6.3 3.5 4.1
1 35.0 34.7 33.3
2 45.5 46.3 44.5
3 11.6 13.9 15.5
4 1.4 1.3 2.2
5 0.2 0.2 0.5
Sample size (n) = 10873, 9861 and 11654 respectively for 2000, 2005 and 2011
na = not available
4.2.2. Household environmental characteristics
From the total of 14 642, 14 645 and 17 817 households for each survey years 2000, 2005 and
2011 respectively, the majority of the households, 27.4%, 26.5% and 20.6% for each survey year
2000, 2005 and 2011 respectively used river water. However, these proportions declined almost
by 7% from 2000 to 2011. More than half of the households in Ethiopia (55%) used unsafe
sources as the main source of their drinking water. Ethiopian households with access to piped
water into dwellings, protected well in dwellings, and piped into compounds had increased in
2011 (0.6%, 10.4% and 6.2% respectively) comparing with 2000 and 2005. Access to toilet
facilities is another important indicator or factor of the well-being of a population. More than
three-fourth of households (77%) in 2000, in the study populations, had no latrine facility while
this proportion decreased to 65% and 50% respectively for survey years 2005 and 2011. The
majority of households (62%, 71% and 63%) had dwellings with earth/sand floor. Moreover,
one-fourth of the households (26%, 19% and 21% respectively) for each survey years had
dwellings with dung floor comparing with other materials. Another important indicator or factor
of the well-being of a population is access to electricity. The proportion of Access to electricity
in the home increased in 2011 with 18.1% from 12.3% in 2000 while more than three-quarters of
households (80%) among in the study populations had no access to electricity in the home (Table
4.2.3 below).
Table 4.2.3 Household environmental characteristics distribution of the respondents,
Ethiopia during these Survey years 2000, 2005 and 2011
Characteristics Percentage of frequency %
Survey years 2000 2005 2011
Source of drinking water
Piped into dwelling 0.1 0.3 0.6
Protected well in dwelling 5.6 5.1 10.4
Piped into compound 5.2 5.3 6.2
River/lakes /spring 20.7 27.5 22.9 Rainwater 0.2 0.3 0.9
Other 55.2 61.6 59.0
Type of toilet facility
Flush toilet 0.6 1.1 2.8
28
Traditional pit latrine 20.9 1.3 1.3
Ventilated improved Pit latrine 0.5 1.1 1.6
(VIP)
No facility (bush/field) 77.9 65.1 50.4
Other na 31.5 43.7
Main floor material
Earth/Sand 61.9 70.5 63.4
Dung 25.9 18.9 21.1
Wood planks 0.2 0.1 0.1
Palm/Bamboo 0.3 0.4 0.4
Parquet polished wood 0.3 0.3 0.1
Vinyl or asphalt strips 2.6 3.9 5.9
Cement 3.3 3.4 3.5
Carpet 1.4 1.0 2.3
Other 4.0 1.4 3.2
Disposal of youngest child's stools
Used toilet/latrine 0.4 1.8 3.4
Put/rinsed in toilet/latrine 11.6 18.8 27.3
Rinse away 12.0 12.2 16.6
Bury in the yard 8.4 2.2 3.9
Other 66.0 65.0 48.9
Missing value 4.3 4.4 4.5
Has electricity (household)
Yes 15.6 14.3 12.4
No 84.4 85.7 79.6
Sample size (n) = 10873, 9861 and 11654 respectively for 2000, 2005 and 2011 (na = not available )
4.3. Multivariate Analysis
4.3.1. Data cleaning from Missing values and “don’t know” DK, responses for each survey
data
Missing Data
A total of 10 873, 9 861 and 11 654 observations were recorded respectively for each survey data
2000, 2005 and 2011. There were missing values for dependent variable Had diarrhea 12.2%,
8.9% and 7.4% of all records for each data sets 2000, 2005 and 2011 respectively, variable
Current age of child (12.1%, 8.7 and 7.3% of dataset) for each survey and variable Disposal of
youngest child’s stools (4.3%, 4.4% and 4.5% of dataset) respectively for each survey. However,
the data was complete for the other variables included in the study. As shown below in Table
4.3.1 we recognized these missing values when descriptive statistics such as a frequency
distribution and percentages were performed to describe the socio-demographic, socio-economic,
and household environmental characteristics for each data set as mentioned above. In this study,
two methods are used to deal with missing data, records deletion (complete case analysis) and
multiple imputation methods, details of these methods as mentioned above in section 3.
Table 4.3.1 Percentage of missing data for each survey data, 2000, 2005 and 2011
29
Characteristics Missing data %
Survey years 2000 2005 2011
Had diarrhea 12.2 8.9 7.4
Current age of child 12.1 8.7 7.3
Disposal of youngest 4.3 4.4 4.5
child's stools
The response “don’t know” (DK)
The proportion of DK response for variable had diarrhea is 1.9%, 0.7% and 0.9% respectively
for each survey years 2000, 2005 and 2011, which is very low. For this reason, it is regarded as
missing data (Table 4.3.2).
Table 4.3.2 Percentage of combination missing data and DK for (Had diarrhea) for each
survey data, 2000, 2005 and 2011
Had diarrhea %
Survey years 2000 2005 2011
Don’t Know (DK) 1.9 0.7 0.9
Combine missing data & 14.1 9.6 8.3
Don’t Know (DK)
4.3.2. Model 1: Logistic regression analysis for complete cases (CC) in the three surveys
Table 4.3.3 shows parameter estimation from binary logistic regression using delete records
(complete case) for each datasets of 2000, 2005 and 2011 and this table also shows standard
errors (S.E.) and p-values. It means that incomplete observations are excluded from the analysis.
In this case, sample size reduced from 10 873 to 9 109, 9 861 to 8 782 and 11 654 to 10 303
respectively.
We consider that at the socio-demographic characteristics, the child’s age to be strongly negative
associated with childhood diarrhea. Especially, children whose age were two and less than two
years (infant children) compared with those whose age were greater than two years, for each
survey years 2000, 2005 and 2011 respectively. Moreover, if we consider survey year 2000, we
see that, religion is significant factor with negative association with childhood diarrhea
compared with both surveys 2005 and 2011. This means children whose mother was a protestant
or muslim were less likely to be affected by childhood diarrhea. While the variables, sex of child
and mother’s age have rather no effect to influence childhood diarrhea.
At the socio-economic characteristics, wealth index and number of children under-five in
household are the most important factors impact for economic status of the family. The variable
wealth index is not available in the survey year 2000, therefore, the present study focused only of
the results for both surveys, 2005 and 2011. In the survey 2011, children in the poorer or poorest
families had significantly higher risk to incidence of childhood diarrhea compared with those in
30
the richest or richer families. In both survey years 2000 and 2011 for instance, families that had
two children or above are more likely to have diarrhea than those who had one child. However,
the variable number of children under-five in household shows no significant effect on childhood
diarrhea in the survey year 2005.
Region is the significant factor on childhood diarrhea was found for each survey. In survey year
2000, there are higher incidences of childhood diarrhea in Tigray, Afar, Amhara and Somali
regions compared to Oromiyan reference group or the other regions in the country. Whereas in
survey year 2005, children who live in the regions Afar, and Somali are more likely to be
affected by childhood diarrhea than those who live in the other regions in the country. In
addition, the lower cases of childhood diarrhea in the same survey year were found in SNNP and
Addis Abeba. Incidence of childhood diarrhea in survey year 2011 shows a decrease for all
regions in the country. Mother’s education level was found to be a significant factor on
childhood diarrhea in both survey years 2000 and 2005; mothers with primary level of education
or above was found that their children are more likely of having diarrhea compared with those
who had no education level. However, no effect on childhood diarrhea was found in the survey
year 2011, this finding may be is surprising. While, type of place of residence and mothers
working status didn’t show any significant association with childhood diarrhea for each survey.
At the household environmental characteristics, it is clearly seen, the source of drinking water
and toilet facility are an influential factor associated with childhood diarrhea for each survey.
Children who live in houses use the rainwater or river/spring water had more likely to be
affected by childhood diarrhea compared with those who had safe source of drinking water like
piped into dwelling or protected well in dwelling. However, surprising result is found in survey
year 2000, there is no association between childhood diarrhea and any type of toilet facility. In
addition, disposal of youngest child’s stools is to be significant factor on childhood diarrhea for
both surveys 2000 and 2005 compared with survey year 2011 with no effect on childhood
diarrhea. Children who live in families who use toilet or clean themselves in toilet/latrine and use
garbage for child's stool had less likely to suffer from childhood diarrhea. Whereas, access to
electricity and main floor material factors have no significant impact on the childhood diarrhea
for each survey.
Table 4.3.3 Logistic regression analysis for the three surveys 2000, 2005 and 2011 using
complete case analysis.
Complete Case model Survey years 2000 2005 2011
Parameter Estimate S.E. P-value Estimate S.E. P-value Estimate S.E. P-value
Intercept 3.9536 0.9649 <.0001 13.2861 0.6326 <.0001 3.0934 0.8677 0.0004
Socio-demographic Variables Current age of child
*
0 -0.8722 0.1501 <.0001 -0.8133 0.1269 <.0001 -1.1905 0.1482 <.0001
1 -1.2655 0.1043 <.0001 -1.2029 0.1258 <.0001 -1.4037 0.1631 <.0001
2 -0.8174 0.1138 <.0001 -0.6655 0.1277 <.0001 -0.8630 0.1592 <.0001
3 -0.2653 0.1200 0.0270 -0.2004 0.1272 0.1152 -0.3566 0.1574 0.0230
31
4 (ref)
Sex of child
Male (ref)
Female 0.0504 0.0620 0.4167 -0.0041 0.0807 0.9594 0.1273 0.0824 0.1226
Mother’s age
(Age in 5-year groups)
15-19 -0.2082 0.2629 0.4285 -0.2759 0.3402 0.4174 -0.2803 0.2891 0.3323
20-24 -0.1687 0.2109 0.4237 -0.2959 0.2436 0.2244 -0.0677 0.2935 0.8175
25-29 -0.1603 0.2282 0.4824 -0.3721 0.2412 0.1229 -0.1634 0.2752 0.5527
30-34 -0.0900 0.2561 0.7253 -0.4092 0.2538 0.1070 -0.1342 0.2790 0.6305
35-39 0.0190 0.2380 0.9362 -0.3335 0.2558 0.1923 -0.1535 0.2804 0.5840
40-44 -0.0542 0.2568 0.8328 0.0115 0.2675 0.9657 -0.4133 0.3087 0.1807
45-49 (ref)
Current marital status
Never married -0.3482 0.4298 0.4178 0.4498 0.8298 0.5878 0.5956 0.6650 0.3705
Married (ref)
Living together -0.1703 0.3774 0.6517 0.3954 0.4057 0.3298 -0.2624 0.2383 0.2707
Widowed -0.4032 0.2609 0.1222 -0.2036 0.3006 0.4983 0.5428 0.3888 0.1627
Divorced -0.2373 0.1862 0.2027 0.0261 0.2514 0.9174 -0.4852 0.2100 0.0208
Not living together 0.0481 0.3512 0.8912 0.1156 0.3057 0.7052 -0.1746 0.4026 0.6646
Religion
Orthodox (ref)
Catholic -0.2612 0.4202 0.5341 -0.5814 0.4120 0.1581 -0.4359 0.3557 0.2204
Protestant -0.3908 0.1453 0.0071 -0.1492 0.1615 0.3554 -0.1212 0.1653 0.4634
Moslem -0.2767 0.1130 0.0143 -0.1996 0.1348 0.1385 -0.0633 0.1582 0.6889
Traditional 0.2111 0.2027 0.2977 0.1976 0.3426 0.5642 1.2663 0.7178 0.0777
Other -0.5541 0.6387 0.3857 0.3218 0.4461 0.4707 -0.1821 0.2831 0.5200
Socio-economic variables Regions
Tigray 0.3903 0.1610 0.0154 0.3314 0.1885 0.0787 -0.1613 0.1845 0.3819
Afar 0.6073 0.1696 0.0003 0.4474 0.2224 0.0443 -0.0435 0.2350 0.8532
Amhara 0.3668 0.1367 0.0073 0.1702 0.1224 0.1644 -0.2455 0.1786 0.1694
Oromiya (ref)
Somali 0.4812 0.1818 0.0081 0.6002 0.2216 0.0068 -0.6689 0.2156 0.0019
Ben-Gumz 0.0909 0.1645 0.5805 -0.2117 0.1690 0.2102 -0.7440 0.1620 <.0001
SNNP -0.1499 0.1297 0.2479 -0.4549 0.1466 0.0019 -0.3481 0.1802 0.0534
Gambela -0.0425 0.2126 0.8416 0.2033 0.1886 0.2811 -0.7396 0.2459 0.0026
Harari 0.0554 0.1729 0.7485 -0.3259 0.2307 0.1578 0.0848 0.2292 0.7113
Addis Abeba 0.4283 0.2644 0.1052 -0.6352 0.2967 0.0323 -0.2927 0.3310 0.3766
Dire Dawa 0.0859 0.1906 0.6521 0.3130 0.2068 0.1301 0.3558 0.2327 0.1263
Type of place of residence
Urban (ref)
Rural -0.0685 0.1820 0.7065 0.2355 0.2113 0.2650 -0.1469 0.2684 0.5841
Wealth index
Poorest na -0.2463 0.1398 0.0781 0.3594 0.1558 0.0210
Poorer na -0.2788 0.1232 0.0237 0.4296 0.1538 0.0052
Middle na -0.2291 0.1301 0.0782 0.2910 0.1516 0.0550
Richer (ref) na
Richest na -0.1032 0.1480 0.4856 0.1241 0.2131 0.5603
Respondent currently working
No (ref)
Yes 0.0161 0.0744 0.8281 -0.1334 0.0961 0.1650 -0.1392 0.1055 0.1871
Mother’s education level
(Highest educational level)
No education (ref)
Primary 0.2366 0.1015 0.0197 -0.0419 0.0965 0.6640 0.1155 0.1196 0.3341
Secondary 0.3475 0.2255 0.1233 0.2566 0.2429 0.2908 0.5160 0.4448 0.2460
Higher -1.1726 0.6081 0.0538 1.5111 0.5862 0.0099 -0.1192 0.4438 0.7882
Number of children 5 or under
1 (ref)
2 0.2347 0.0813 0.0039 0.0491 0.0837 0.5572 0.0648 0.0992 0.5131
3 0.1984 0.1140 0.0819 0.0870 0.1326 0.5118 0.1805 0.1556 0.2463
4 0.5250 0.2867 0.0671 -0.2233 0.2921 0.4446 -0.6071 0.3013 0.0439 5 0.1624 1.3182 0.9020 0.5267 0.3589 0.1422 -1.0642 0.2638 <.0001
32
Household environmental variables Source of drinking water
Piped into dwelling 0.2090 0.9073 0.8178 -0.3034 0.9044 0.7373 1.1720 0.8716 0.1788
Protected well in
Dwelling -1.2783 0.2205 <.0001 0.0599 0.2081 0.7733 -0.1448 0.2565 0.5723
Piped into compound -1.1854 0.4147 0.0043 0.8632 0.3757 0.0217 0.5073 0.3680 0.1680
River/lakes /spring (ref)
Rainwater -1.7466 0.2475 <.0001 0.3469 0.6247 0.5787 0.7405 0.3578 0.0385
Other -1.5614 1.0576 0.1399 -0.8639 0.5443 0.1125 3.0114 0.7250 <.0001
Type of toilet facility
Flush toilet (ref)
Traditional pit latrine -0.3650 0.6964 0.6002 -9.7370 0.7165 <.0001 -0.7432 0.7446 0.3182
Ventilated improved Pit -0.7984 0.8584 0.3523 -9.9688 0.4301 <.0001 -0.0774 0.6719 0.9083
Latrine (VIP)
No facility (bush/field) -0.4331 0.7262 0.5509 -9.8902 0.4316 <.0001 11.8590 1.2282 <.0001
Other na 2.5703 1.0007 0.0102 -1.6289 0.8480 0.0547
Main floor material
Earth/Sand (ref)
Dung -0.0329 0.0868 0.7047 0.0265 0.1057 0.8021 0.1569 0.1130 0.1651
Wood planks -0.7276 1.0813 0.5010 0.9933 0.6592 0.1319 0.8028 1.0860 0.4598
Palm/Bamboo -0.0199 0.3876 0.9590 0.0960 0.5998 0.8729 -0.4360 0.5752 0.4485
Parquet polished wood -0.5052 0.4370 0.2477 -0.5602 0.7128 0.4320 0.9235 1.1718 0.4306
Vinyl or asphalt strips 0.0606 0.2477 0.8066 0.0209 0.4077 0.9591 0.0841 0.3424 0.8059
Cement -0.1791 0.8413 0.8314 0.2628 0.3243 0.4178 0.1093 0.3614 0.7623
Carpet -0.0350 0.1963 0.8585 0.0642 0.5160 0.9010 -0.3025 0.3775 0.4230
Other 1.8388 0.8574 0.0320 2.4744 1.3196 0.0608 0.5610 0.5431 0.3016
Disposal of youngest child's stools
Used toilet/latrine -0.4662 0.7565 0.5377 0.0055 0.6217 0.9929 -0.1050 0.2813 0.7088
Put/rinsed in
toilet/latrine -0.2918 0.4728 0.5371 -1.0344 0.3573 0.0038 -0.2112 0.1324 0.1108
Throw into garbage -0.2951 0.4137 0.4757 -0.8885 0.3597 0.0135 -0.0595 0.1464 0.6845
Bury in the yard -0.3936 0.4543 0.3863 -0.6747 0.4395 0.1248 -0.0644 0.3143 0.8377
Left in the open/ Rinse away
not disposed of (ref)
Other -1.7774 0.7321 0.0152 -0.8181 0.3625 0.0240 0.4279 0.2322 0.0653
Has electricity
Yes
No (ref) 0.1120 0.2151 0.6026 0.2369 0.2580 0.3585 -0.0635 0.2360 0.7879 na = not available , (ref)= reference class
4.3.3. Model 2: Logistic regression analysis for multiple imputation (MI) in the three
surveys
Table 4.3.4 presents parameter estimation, their standard errors (S.E.) and associated p-values.
Analysis using multiple imputation in SAS by these three steps as stated above in the section
3.3.2. First, we generate multiple imputed data sets using the SAS procedure PROC MI with
which five full datasets with imputed values were created. However, variables including missing
values are categorical or classification variables. Logistic model for the FCS method are used in
this study to impute missing values in classification variables in each dataset with an arbitrary
missing pattern. In order to achieve a convergence, 10 burns- in iterations were used. The results
are combined using PROC MIANALYZE (see Appendix D).
After imputing the missing values for each datasets 2000, 2005 and 2011 respectively, we see
that, at the socio-demographic characteristics, age of child is a significant factor on risk of
having childhood diarrhea respectively for each survey years 2000, 2005 and 2011, which is
33
similar with records delete analysis (model 1). Infant children are less likely to have diarrhea
compared with older children. Sex of child is a significant factor associated on childhood
diarrhea was found only in survey year 2011, female children are more likely to have diarrhea
compared with male children in survey year 2011. However, it is different with the results of
model 1. Moreover, religion is significant factor associated with childhood diarrhea, which is
found in both surveys of 2000 and 2011. This means in survey year 2000, children whose
mothers are Muslim are less likely to suffer childhood diarrhea than those who are Orthodox.
Moreover, in survey year 2011, children whose mothers are Catholic are less likely to suffer
from childhood diarrhea, whereas children whose mothers are Traditional are more likely to
contract childhood diarrhea than those who are Orthodox, which is different from the analysis
from records delete analysis (model 1). While the variables, mother’s age and mother’s current
marital status have rather no significant effect on childhood diarrhea.
Variable number of children under-five in household is the most important factor that impacted
the economic status of the family. In each survey 2000, 2005 and 2011 respectively, for example,
families that had 2, 3 or 4 children, their children were more likely to have diarrhea than those
who had one child. Region is the significant factor on childhood diarrhea was found for each
surveys. In survey year 2000, there are higher incidences of childhood diarrhea that were found
in Tigray, Afar, Amhara and Somali regions compared to Oromiyan reference group or the other
regions in the country, which is similar as found in model 1. In survey year 2005, children who
live in these regions Tigray, Afar, Somali and Gambela are more likely to be affected by
childhood diarrhea than those who live in the other regions, whereas children who live in SNNP
are less likely to be affected by childhood diarrhea than those who live in the Oromiyan. The
worst case of childhood diarrhea in survey year 2011 was found in Dire Dawa region while the
lowest case of childhood diarrhea were found in Somali, Ben-Gumz and Gambela regions.
Mother’s education level was found to be a significant factor on childhood diarrhea in both
survey years 2000 and 2005 compared with 2011, where it had has no significant effect on
childhood diarrhea. Similar findings were observed in records delete analysis. While the
variables, type of place of residence, wealth index and mothers working status did not show any
association with childhood diarrhea for each survey.
At the household environmental characteristics, it is clearly, the source of drinking water and
type of toilet facility that is seen as influential factors associated with childhood diarrhea. This is
similar with records delete analysis (model 1). Children that live in houses that use rainwater or
river/spring water were more likely to contract childhood diarrhea compared with those who had
safe source of drinking water like, piped into dwelling or protected well in dwelling. There is no
association between childhood diarrhea and any type of toilet facility that was found in survey
year 2000. Similarly, disposal of youngest child’s stools is to be a significant factor on childhood
diarrhea. Whereas, main floor material and access to electricity factors have no significant
association on childhood diarrhea for each survey years 2000, 2005 and 2011.
Table 4.3.4 Logistic regression analysis for the three surveys 2000, 2005 and 2011 using
Multiple Imputation method.
34
Multiple Imputation model
Survey years 2000 2005 2011
Parameter Estimate S.E. P-value Estimate S.E. P-value Estimate S.E. P-value
Intercept 3.3827 0.8123 <.0001 2.3151 0.8529 0.0066 2.2651 0.7036 0.0013
Socio-demographic Variables Current age of child *
0 -0.8478 0.1412 <.0001 -0.8715 0.1277 <.0001 -1.1406 0.1835 <.0001
1 -1.0982 0.1041 <.0001 -1.0548 0.1279 <.0001 -1.2368 0.1669 <.0001
2 -0.6669 0.1128 <.0001 -0.5617 0.1187 <.0001 -0.7695 0.1808 0.0005
3 -0.2211 0.1175 0.0649 -0.2201 0.1178 0.0638 -0.3389 0.1495 0.0262
4 (ref)
Sex of child
Male (ref)
Female 0.1131 0.0576 0.0496 0.0454 0.0739 0.5394 0.1379 0.0641 0.0317
Mother’s age
(Age in 5-year groups)
15-19 -0.1349 0.2419 0.5771 0.0040 0.2718 0.9882 0.0149 0.2296 0.9483
20-24 -0.3250 0.1998 0.1038 -0.0918 0.1998 0.6459 0.0692 0.2256 0.7589
25-29 -0.3635 0.2025 0.0726 -0.2062 0.1888 0.2747 -0.0785 0.2188 0.7197
30-34 -0.3002 0.2241 0.1805 -0.2767 0.2013 0.1692 0.0132 0.2153 0.9511
35-39 -0.1130 0.2088 0.5885 -0.1035 0.2014 0.6072 -0.0099 0.2041 0.9612
40-44 -0.2245 0.2215 0.3106 0.1416 0.2079 0.4959 -0.1258 0.2406 0.6012
45-49 (ref)
Current marital status
Never married -0.1179 0.3787 0.7555 0.7854 0.7567 0.2993 1.3720 0.7303 0.0603
Married (ref)
Living together -0.2222 0.3486 0.5238 0.1754 0.2998 0.5585 -0.0653 0.2233 0.7701
Widowed -0.2935 0.2444 0.2298 0.0540 0.2638 0.8378 0.4472 0.3266 0.1710
Divorced -0.1844 0.1571 0.2405 -0.0437 0.1893 0.8172 -0.3291 0.1970 0.0949
Not living together 0.0637 0.2820 0.8213 -0.0009 0.2382 0.9968 0.0946 0.3849 0.8058
Religion
Orthodox (ref)
Catholic -0.2170 0.3364 0.5190 -0.4149 0.3367 0.2178 -0.6026 0.2606 0.0208
Protestant -0.2330 0.1198 0.0518 -0.1187 0.1413 0.4010 -0.1161 0.1442 0.4208
Moslem -0.2149 0.0985 0.0293 -0.1769 0.1137 0.1198 -0.1191 0.1285 0.3544
Traditional 0.2703 0.2017 0.1804 0.4850 0.3310 0.1428 1.1302 0.5229 0.0307
Other -0.4086 0.6399 0.5231 0.2742 0.3960 0.4887 -0.3380 0.2048 0.0989
Socio-economic variables Regions
Tigray 0.4358 0.1427 0.0023 0.4205 0.1540 0.0064 -0.0987 0.1464 0.5002
Afar 0.5437 0.1384 <.0001 0.4074 0.1740 0.0192 0.0984 0.2178 0.6514
Amhara 0.3816 0.1158 0.0010 0.1411 0.1073 0.1886 -0.1039 0.1460 0.4766
Oromiya (ref)
Somali 0.5335 0.1566 0.0007 0.6236 0.1747 0.0004 -0.5508 0.1850 0.0029
Ben-Gumz 0.1818 0.1480 0.2195 -0.0997 0.1555 0.5213 -0.6106 0.1406 <.0001
SNNP -0.0846 0.1081 0.4339 -0.3573 0.1332 0.0073 -0.2656 0.1436 0.0645
Gambela 0.1076 0.1862 0.5634 0.3929 0.1864 0.0350 -0.5709 0.2061 0.0056
Harari 0.0591 0.1495 0.6927 -0.0884 0.2025 0.6624 0.0734 0.1971 0.7096
Addis Abeba 0.3883 0.2608 0.1366 -0.3418 0.2693 0.2044 0.0765 0.2705 0.7773
Dire Dawa -0.0047 0.1799 0.9792 0.2295 0.1583 0.1471 0.3915 0.1907 0.0401
Type of place of residence
Urban (ref)
Rural -0.0119 0.1605 0.9407 0.2820 0.1951 0.1484 -0.1721 0.2344 0.4628
Wealth index
Poorest na -0.1510 0.1107 0.1727 0.0949 0.1281 0.4588
Poorer na -0.1566 0.0986 0.1123 0.1116 0.1363 0.4128
Middle na -0.1585 0.1121 0.1578 0.0920 0.1283 0.4731
Richer (ref) na
Richest na -0.0275 0.1358 0.8392 0.0726 0.1864 0.6968
Respondent currently working
No (ref)
35
Yes 0.0122 0.0661 0.8534 -0.0238 0.0833 0.7745 -0.1047 0.0900 0.2449
Mother’s education level
(Highest educational level)
No education (ref)
Primary 0.2249 0.0910 0.0134 0.0315 0.0853 0.7115 0.1768 0.1046 0.0913
Secondary 0.3135 0.2196 0.1535 0.4903 0.2197 0.0256 0.5150 0.3314 0.1201
Higher -0.6485 0.5664 0.2522 1.7985 0.5105 0.0004 0.4855 0.4203 0.2480
Number of children 5 or under
1 (ref)
2 0.6192 0.0692 <.0001 0.5008 0.0686 <.0001 0.3876 0.0880 <.0001
3 0.7046 0.1072 <.0001 0.6705 0.1138 <.0001 0.6259 0.1379 <.0001
4 1.0659 0.2417 <.0001 0.3143 0.2738 0.2509 -0.0588 0.2847 0.8364
5 0.8947 1.2850 0.4863 0.7064 0.5360 0.1876 1.3174 0.6965 0.0586
Household environmental variables Source of drinking water
Piped into dwelling -0.3779 0.6119 0.5368 -0.2340 0.7230 0.7462 0.6567 0.5897 0.2654
Protected well in
dwelling -1.6458 0.1736 <.0001 0.0524 0.1782 0.7686 -0.2376 0.2264 0.2938
Piped into compound -1.4163 0.3654 0.0001 1.0921 0.3535 0.0020 0.5905 0.3039 0.0520
River/lakes /spring (ref)
Rainwater -1.7439 0.1875 <.0001 -0.5145 0.5309 0.3325 0.7898 0.2659 0.0030
Other -1.6336 1.0526 0.1207 -0.4227 0.5722 0.4601 0.8957 0.5316 0.0920
Type of toilet facility
Flush toilet (ref)
Traditional pit latrine -0.3146 0.5340 0.5557 0.0066 0.8844 0.9940 -0.5978 0.6180 0.3334
Ventilated improved -0.9041 0.7304 0.2158 -0.1195 0.7421 0.8720 0.0498 0.5380 0.9261
Pit latrine (VIP)
No facility (bush/field) -0.3225 0.5790 0.5775 9.9908 1.0427 <.0001 10.8556 1.1639 <.0001
Other na 11.4824 1.1546 <.0001 -1.2374 0.7199 0.0857
Main floor material
Earth/Sand (ref)
Dung -0.0010 0.0760 0.9894 -0.0033 0.0873 0.9701 0.1068 0.0972 0.2720
Wood planks -0.5023 0.9917 0.6125 2.9459 2.1638 0.1734 0.8461 0.9512 0.3737
Palm/Bamboo 0.2444 0.3652 0.5034 0.3824 0.6739 0.5704 -0.5431 0.4718 0.2498
Parquet polished wood) -0.2923 0.4458 0.5121 -0.5025 0.5414 0.3534 0.9270 1.3166 0.4814
Vinyl or asphalt strips -0.0121 0.2217 0.9562 -0.0177 0.3597 0.9608 -0.0904 0.2958 0.7600
Cement -0.3922 0.4613 0.3952 -0.0453 0.2471 0.8544 -0.0572 0.3143 0.8555
Carpet 0.0397 0.2326 0.8644 0.1241 0.4718 0.7926 -0.2077 0.3476 0.5502
Other 0.7258 0.4869 0.1360 2.6689 1.3121 0.0420 0.0742 0.2969 0.8028
Disposal of youngest child's stools
Used toilet/latrine -0.3786 0.7346 0.6063 -0.2138 0.4814 0.6571 -0.1087 0.2164 0.6157
Put/rinsed in toilet
/latrine -0.1441 0.4477 0.7477 -0.9942 0.3184 0.0018 -0.1315 0.1103 0.2337
Throw into garbage -0.2108 0.4064 0.6041 -1.0182 0.3093 0.0010 0.0577 0.1378 0.6751
Bury in the yard -0.3114 0.4296 0.4686 -0.9128 0.3899 0.0196 -0.1784 0.3378 0.5987
Left in the open/ Rinse away
not disposed of (ref)
Other -1.2249 0.6973 0.0790 -0.7425 0.3119 0.0174 0.4076 0.1848 0.0275
Has electricity
Yes 0.2146 0.1566 0.1707 0.2531 0.2454 0.3024 -0.1773 0.1997 0.3747
No (ref)
na = not available , (ref)= reference class
4.3.4. Comparisons between complete case analysis and the multiple imputation analysis
36
In order to compare the results of a records delete (complete case) analysis and the multiple
imputation analysis we recall Table 4.3.3 and Table 4.3.4, we can see that, in survey year 2000,
the results from records delete analysis are almost similar to those from multiple imputation
analysis, although the difference is in standard errors results. Whereas, in both surveys 2005 and
2011, there is a difference with the results between records delete analysis (model 1) and the
multiple imputation analysis (model 2). In survey year 2005, for instance, the variable number of
children under-five in household, is not significant under records delete analysis but significant
under multiple imputation analysis. We focus on the estimates of logistic regression parameters
for variable, number of children under-five in household and we see that, families that had 3
children; were significantly associated with increased probability of having diarrhea than those
who had one child. In addition, the odds of having diarrheal diseases for families that had 3
children, was 1.71 (exp (0.5318) = 1.71) times higher than for families that had one child, when
controlling other factors. This means, the risk increases as the number of children increases.
Moreover, in survey year 2011 for example, the variable sex of child is not significant under
records delete analysis but significant under multiple imputation analysis. We focus on the
estimates of logistic regression parameters for sex of child factor and we see, female children are
significantly associated with increased probability of having diarrhea compared with male
children (reference category), also the odds of having diarrhea diseases for female children aged
under-five is approximately 1.15 (exp (0.1373) = 1.15) times than for male children, when
controlling other factors. This means, the risk increases for female children aged under-five.
These differentials might be due to a smaller sample size used in the records delete analysis and
the standard errors estimates for both variables, number of children under-five in household and
sex of child are larger in the records delete analysis than those of the multiple imputation model.
In addition, we can see that for all other factors either significant or not significant associated
with childhood diarrhea, have larger standard error estimates in the records delete analysis for
each survey years 2000, 2005 and 2011 compared with those in the multiple imputation model.
This is consistent with the report by Nicholas (2007). The standard error estimates for the
complete case estimator are as much as 30% larger than those of the missing data model (MI
model). Similarly, with reports by He (2010) and Nur et al. (2009) on the other hand, we
consider our missing values for each dataset, the missing values were almost greater than 5%,
and therefore the complete case analysis is less adequate. In this case, multiple imputation
method is more convenient and efficient.
5. Discussion
This study used the 2000, 2005 and 2011 EDHS data set is to determine the association between
socio-demographic, socio-economic and household environmental factors and childhood
diarrhea among children under-five in Ethiopia. In order to deal missing values problem, we fit
two logit models to estimated and determine the impact of socio-demographic, socio-economic
and household environmental factors on childhood diarrhea. Records delete (complete case)
analysis was used in the first model and multiple imputation method that was used in the second
37
model. Results show that, the proportion of the two-week period, prevalence of childhood
diarrheal disease in this study had decreased from 19.8% in 2000 to 15.7% in 2005 and 13.9% in
2011 (see Appendix C). We see that the prevalence rate of childhood diarrhea in Ethiopia at the
last eleven years decreased by almost 6%. This might be due to the difference in basic
environmental and demographic or economic characteristics of mothers or households. This
finding is almost consistent with the study done in Ghana that was 19.1% (Gyimah, 2003) and in
Indonesia 18.9% (Rahmawati, 2010). Nevertheless, the finding is in contrast with the study done
in India showed about 9% of two- week period prevalence of diarrheal disease (Joshi et al,
2011).
At the socio-demographic characteristics, the child’s age was found to be negatively associated
with childhood diarrhea. This means that infant children are less likely to experience childhood
diarrhea compared with older children. This may be attributed to breastfeeding practice. In this
case, they might be protected from childhood diarrhea. In the final report of EDHS 2011, it was
confirmed that the duration of breastfeeding in Ethiopia is long and the mean duration of
breastfeeding is 25 months or greater than two years. Moreover, older children are more likely to
contract diarrhea, this might be due to environmental exposure for example contaminated foods
and drinks or lack of hygiene practice, this is consistent with the result of other studies which
were conducted in different parts of the country, for example Shikur et al. (2012) and Mediratta
et al. (2010). Contrary to those in Shimelis et al. (2008) reported, infant children or children of
under one year of age were at increased risk of developing acute watery diarrhea than the older
children. The result also contradicts with findings from other studies conducted in other
countries, in Malawi (Chipeta, 2004) and India (Joshi et al., 2011); younger children were
associated with more diarrhea prevalence. Religion is a significant factor associated with
childhood diarrhea. However, children whose mothers were Muslim were less likely to
experience of childhood diarrhea. This is consistent with findings in Nigeria (Feyisetan, et al.,
1997).
In model 1, children who live with divorced mothers are less likely to have diarrhea than
children who live with married mothers, this results was found in survey year 2011 compared
with surveys 2000 and 2005. However, the result of the test does not correspond with reality and
not supported in the literature, type one error may have occurred. Although the errors cannot be
eliminated, we can minimize one type of error by decreasing the value of alpha from 0.05 to
0.01. However, we have chosen to retain the level 0.05.
Among socio-economic characteristics, Wealth Index and number of children under-five in
household are the most important factors that play an impact on the economic status of the
family. Children in the poorest or poorer families had significantly higher risk of experiencing
childhood diarrhea compared with those in the richest or richer families. As mentioned earlier,
almost half of the households in Ethiopia live on less than one US dollar per day. In India also,
the economic status of the families seems to be an important factor that leads to a high
prevalence of childhood diarrhea (Joshi et al., 2011). Similarly, high economic status was found
38
to be protective against diarrhea (a study in Bolivia by Caruso et al., 2010). In this survey, it was
also found that families with two children or above were more likely to have diarrhea than those
who were with only one child. Therefore, increase in number of children reflects on a child’s
health situation, e.g. less attention of mothers on each child’s health care needs. The result is
similar to a study that was conducted in the East of the country (Mengistie et al., 2001), the
presence of two or more children under the age of five in the family was a risk factor for a child
to have diarrhea. This is also similar to reports by Woldemicael, (2001) in Eritrea. The region is
the significant factor for childhood diarrhea this was found for each survey. There are higher
incidences of childhood diarrhea in different parts of the country, especially in the Somali region
compared to Oromiyan reference group or the other regions in the country. This region is one of
the most disaster–prone regions of the country and has seen increasing numbers of floods and
droughts. According to Abaya et al. (2011), a study of the impacts of climate variability on
human health in the Somali region of Ethiopia, the health care sector in the Somali region of
Ethiopia was found to be ill-prepared to effectively respond to the health impacts that may arise
from climate variability. Other factors include problems with poor living conditions, such as a
lack of sanitation or access to safe water for the majority of people, consequently leading to
increased prevalence of diseases such as malaria, malnutrition, diarrhea and tuberculosis in the
region. Mother’s education level has very less effect on childhood diarrhea. Also, it might be due
to the fact that more than two-thirds or more than 83% of the mothers lived in rural areas, as
mentioned in the results, the majority of mothers have little or no education (81%, 77% and 70%
respectively), but this proportion has declined almost 10% from 2000 to 2011. This is because a
mother's education level has a huge effect on the health of their children and plays an important
role to improve knowledge of medical and health care practices. These findings are consistent
with findings in Bolivia (Caruso et al., 2010) but contrary to those reported in Ghana (Boadi and
Kuitunen, 2005). Findings from Ghana revealed that the prevalence of diarrhea varies according
to education of mothers, which was relatively high among children of mothers with no
education. In Nigeria (Yilgwan and Okolo, 2012), mothers are more educated to the importance
of hygiene, better childcare and feeding practices and are more aware of disease causing factors
as well as preventive measures. Similarly in Malawi (Chipeta, 2004), lower education level of
mothers were found to be associated with more diarrhea cases.
At the household environmental characteristics, the source of drinking water and toilet facilities
were found to be significant factors associated with childhood diarrhea for each survey. Children
who live in houses that use the rainwater or river/spring water and no toilet facility and having
floor of earth/sand had higher risk childhood diarrhea compared with those who had safe source
of drinking water like, piped into dwelling or protected well in dwelling and those that use latrine
flush toilets. However, surprising result is found in survey year 2000, there was no association
between childhood diarrhea and any type of toilet facilities. This might be due to children under-
five not using any type of toilet. Similar findings were observed in Pakistan (Arif and Naheed,
2012). It probably may be due to the fact that a household that have a latrine does not necessarily
mean that a child uses it. In many communities, even where basic sanitation facilities exist and
adults use them, young children are often permitted to defecate indiscriminately. This finding is
39
consistent with the result of other studies, for example, in a study that was conducted in
developing countries (Kumar and Subita, 2012), it was confirmed that improvement of water
quality and sanitation prevented diarrheal diseases. This is also similar to reports Gascon et al.
(2000) in Tanzania.
6. Conclusion and Recommendations
The determinant factors of childhood diarrhea in this study were:
Younger children were less likely at increased risk of developing childhood diarrhea
than the older children.
This study found that children whose mothers were Muslim were less likely to suffer
from childhood diarrhea.
Wealth Index and number of children under-five in household were the most important
factor that impacts the economic status of the family.
There is a higher incidence of childhood diarrhea in the Somali region.
Source of drinking water, toilet facility and disposal of young child’s stools were
significant factors on childhood diarrhea.
In addition, we conclude that multiple imputation is more convenient and efficient compared to
the records deletes (complete case) method. This might be due to a smaller sample size used in
the records delete analysis.
The following health recommendations are forwarded based on the findings of the study:
A diarrheal disease is one of the main causes of death among young children in developing
countries because of associated dehydration and malnutrition. To counter the effects of
dehydration due to diarrhea, prompt increase in the child’s fluid intake through drinking and the
use of oral rehydration therapy (ORT), or a homemade solution prepared from sugar, salt, and
water is a few of many effective and efficient means of treating diarrhea. Besides, mothers
should also be encouraged to feed the child well during episodes of diarrhea.
Government local health organizations or even world health organizations should provide health
intervention programs, and maternal health awareness (health education for mothers), in order to
reduce the incidence of childhood diarrhea such as personal hygiene practices, good
sanitation/rubbish disposal, use of safe water sources and good food and promoting hand
washing with soap before eating or preparing food, as well as cleaning up their young children
from feces. According to UNICEF and WHO, it is one of the most cost-efficient and effective
methods for preventing diarrheal disease, and is estimated to reduce occurrences of diarrheal
diseases by up to 40%. (UNICEF/WHO, 2009).
Moreover, breastfeeding strengthens the children’s physiological resistance against diseases, also
breastfeeding is less expensive and costs nothing economically. The advantages of breastfeeding
40
are confirmed by several studies; for example, see these articles by Oddy et al., (1999) and Oni,
(1996). Recently, WHO and UNICEF stated that breastfeeding should last for two years. They
addressed that all mothers all over the world should follow their recommendations. The World
Health Organization and UNICEF’s recommendations on breastfeeding are as follows:
Initiation of breastfeeding within the first hour after the birth;
Exclusive breastfeeding for the first six months; and
Continued breastfeeding for two years or more, together with safe, nutritionally adequate,
age appropriate, responsive complementary feeding starting in the sixth month
(WHO/UNICEF, 2013).
These statements were miraculously stated in the Muslim’s Holy Book 14 centuries ago. Allah
(God), said in the Holy Quran: "The mothers shall give suck to their offspring for two whole
years…" (The Noble Quran, Surah Al-Baqarah: Section 233). The wisdom of breastfeeding the
children have been infused in the Muslims mothers mind through this Quranic revelation
(http://quran-m.com/firas/en1/index.php/human/189-the-quran-and breastfeeding.html). Conforming to
the results, it is seen that the children whose mothers were Muslim, had less likely to contract
childhood diarrhea compared with other groups. Furthermore, the majority of mothers were
Muslims in the study, which was around 47% in 2011.
In addition, programs that promote family planning should be regulated by government or non-
government health organizations. Therefore, more people will reflect on the child health
situation. As mentioned earlier, Ethiopia's major health problem is the spread of diseases caused
by poor water and sanitation. The government should focus on comprehensive diarrheal disease
control strategies, including improvement of water quality, hygiene, and sanitation besides
provision of oral rehydration solution and zinc supplements especially in the Somali region.
Therefore, climate variability should be seen as just one of the many factors affecting the
increasing trend of human health problems in the region. Besides improving training to increase
health officials' knowledge of climate variability and human health impacts, the government
should also address other factors that currently hinder a successful response to increasing disease
prevalence (Abaya et al, 2011).
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Appendix A
44
Millennium Development Goals (MDG)
In September 2000, the United Nations (U.N.) adopted the Millennium Declaration, which
committed member states to support needy countries and an agenda for improving the human
condition in reaching eight Millennium Development Goals (MDGs) by 2015. The goals are as
follows:
Source: Ethiopia: 2010, Millennium Development Goals Report
Appendix B
45
19,8
15,7 13,9
0
5
10
15
20
25
2000 2005 2011
Childhood diarrhea
Yes, last two weeks
The report (UNICEF/WHO, 2009) calls for a 7-point plan to control diarrheal diseases and
ultimately improve child survival:
Ensure wide availability of low-osmolarity oral rehydration solution and zinc.
Include rotavirus vaccines in national immunization programs worldwide, which was
recently recommended by the WHO.
Develop and implement behavior-change interventions and communication programs to
encourage exclusive breastfeeding.
Ensure sustained high levels of vitamin A supplementation, combining its delivery,
where possible, with other high-impact health and nutrition interventions.
Apply results of existing consumer research on how to motivate people to wash their
hands with soap to increase this highly effective and cost-effective health practice.
Adopt household water treatment and safe storage systems, such as chlorination and
filtration, in both development and emergency situations to reduce the number of
diarrheal cases.
Implement demand-led approaches to stop community-wide open defecation.
Appendix C
This figure below shows the prevalence rates of childhood diarrhea among the three
surveys, 2000, 2005 and 2011.
46
Appendix D
SAS code, we applied the same code for each survey years 2000, 2005 and 2011
Model 1: Records delete (Complete case) analysis; we take for example, survey year 2011.
Proc format;
Value H11c
0='No'
2 = 'Yes, last two weeks'
8 = 'Dont know'
9 = 'Missing value'
;
Value V113c
11 = 'Piped into dwelling'
12 = 'Piped into compound'
30 = 'COVERED WELL/BOREHOLE'
32 = 'Protected well in dwelling'
40 = 'SURFACE WATER'
41 = 'Spring'
42 = 'River, stream'
43 = 'Pond, lake'
44 = 'Dam'
51 = 'Rainwater'
96 = 'Other'
;
Value V116c
11 = 'Flush toilet'
21 = 'Traditional pit latrine'
22 = 'Ventilated improved PIT (VIP) latrine'
30 = 'NO FACILITY'
31 = 'Bush/field'
32 = 'River'
96 = 'OTHER'
97 = 'Not dejure resident'
;
Value V127c
11 = 'Earth/Sand'
12 = 'Dung'
20 = 'RUDIMENTARY'
21 = 'Wood planks'
22 = 'Palm/Bamboo'
30 = 'FINISHED'
31 = 'Parquet or polished wood'
32 = 'Vinyl or asphalt strips'
34 = 'Cement'
35 = 'Carpet'
96 = 'Other'
;
Value V465c
1 = 'Used toilet/latrine'
47
2 = 'Put/rinsed in toilet/latrine'
3 = 'Put/rinsed into drain or ditch'
4 = 'Throw into garbage'
5 = 'Buried'
6 = 'Rinse away'
9 = 'Left in the open/not disposed of'
99 = 'Use disposable diapers'
96 = 'Other'
;
Value V119c
0 = 'No'
1 = 'Yes'
9 = 'Missing value'
7 = 'Not a dejure resident'
;
Value b8c
1 = '0'
2 = '1'
3 = '2'
4 = '3'
5 = '4'
9 = 'Missing value'
;
Value b4c
1 = 'Male'
2 = 'Female'
;
Value V013c
1 = '15-19'
2 = '20-24'
3 = '25-29'
4 = '30-34'
5 = '35-39'
6 = '40-44'
7 = '45-49'
;
Value V501c
0 = 'Never in union'
1 = 'Married'
2 = 'Living with partner'
3 = 'Widowed'
4 = 'Divorced'
5 = 'No longer living together/separated'
;
Value V130c
1 = 'Orthodox'
2 = 'Catholic'
3 = 'Protestant'
4 = 'Muslim'
5 = 'Traditional'
96 = 'Other'
48
;
Value V024c
1 = 'Tigray'
2 = 'Affar'
3 = 'Amhara'
4 = 'Oromiya'
5 = 'Somali'
6 = 'Ben-Gumz'
7 = 'SNNP'
12 = 'Gambela'
13 = 'Harari'
14 = 'Addis'
15 = 'Dire Dawa'
;
Value V025c
1 = 'Urban'
2 = 'Rural'
;
Value V190c
1 = 'Poorest'
2 = 'Poorer'
3 = 'Middle'
4 = 'Richer'
5 = 'Richest'
;
Value V714c
0 = 'No'
1 = 'Yes'
9 = 'Missing value'
;
Value V106c
0 = 'No education'
1 = 'Primary'
2 = 'Secondary'
3 = 'Higher'
8 = 'Dont know'
9 = 'Missing value'
;
Value V137c
1 = '0'
2 = '1'
3 = '2'
4 = '3'
5 = '4'
6 = '5'
9 = 'Missing value'
;
run;
data WORK.E2011;
set WORK.E2011(keep=H11 V113 V116 V127 V465 V119 b8 b4 V013 V501 V130 V024
V025 V190 V714 V106 V137 V022 V021 V005);
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if v465=99 or v116=97 then delete; * v116=97 seems to be individuals who live
elsewhere;
if h11=0 then nh11=0; * with these deleted there will be no estimates=0;
else if h11=2 then nh11=2;
else nh11=2;
h11=nh11;
drop nh11;
run;
data WORK.E2011_Delete;
set WORK.E2011;
if cmiss(of _all_) then delete;
run;
ods rtf;
proc surveylogistic data=WORK.E2011_Delete;
format H11 V113 V116 V127 V465 V119 b8 b4 V013 V501 V130 V024 V025 V190 V714
V106 V137;
strata V022;
cluster V021;
weight V005;
class V113(ref='41') V116(ref='11') V127(ref='11') V465(ref='9')
V119(ref='0') b8(ref='4') b4(ref='1') V013(ref='7') V501(ref='1')
V130(ref='1') V024(ref='4') V025(ref='1') V190(ref='4') V714(ref='0')
V106(ref='0') V137(ref='1') / param=ref;
Model H11= V113 V116 V127 V465 V119 b8 b4 V013 V501 V130 V024 V025 V190 V714
V106 V137
/ link=logit;
run;
ods rtf close;
Model 2: Multiple Imputation (MI) analysis; we take for example, survey year 2011.
proc mi data=WORK.E2011 seed=1 out=outmiE2011;
class H11 b8 V465 ;
fcs logistic(H11 b8 V465) ; * I prefer logistic regression to
discriminant analysis. logistic is less restrictive;
var H11 b8 V465 ;
run;
proc print data=outmiE2011(obs=100);
*var _imputation_ v465;
where _imputation_<3;
run;
ods trace off;
proc surveylogistic data=outmiE2011;
format H11 V113 V116 V127 V465 V119 b8 b4 V013 V501 V130 V024 V025 V190 V714
V106 V137;
strata V022;
cluster V021;
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weight V005;
class V113(ref='41') V116(ref='11') V127(ref='11') V465(ref='9')
V119(ref='0') b8(ref='4') b4(ref='1') V013(ref='7') V501(ref='1')
V130(ref='1') V024(ref='4') V025(ref='1') V190(ref='4') V714(ref='0')
V106(ref='0') V137(ref='1') / param=ref;
Model H11= V113 V116 V127 V465 V119 b8 b4 V013 V501 V130 V024 V025 V190 V714
V106 V137/ link=logit;
by _imputation_;
ods output ParameterEstimates=Parms
run;
* paramter estimates;
proc sort data=parms;
by variable ClassVal0;
run;
proc mianalyze data=parms; * an ordinary SAS dataset is input, hence keyword
DATA= ;
by variable ClassVal0;
modeleffects estimate;
stderr stderr;
ods output ParameterEstimates=miparmsvariable;
run;
ods rtf;
proc print data=miparmsvariable;
run;
ods rtf close;
