a senior research on the factor that affect child mortality in ambo ethiopia

Factors that affect child mortality in Ambo town

AMBO UNIVERSITY

COLLEGE OF NATURAL AND COMPUTATIONAL SCIENCEDEPARTMENT OF STATISTICS

NAME OF THE MEMBER………………………………………………………………………………….IDNO1. CHALA EDESA………………………………………………………………………………………..R/2443/042. DABALA JABESSA…………………………………………………………………………………R/2446/043. DALATA BULCHA………………………………………………………………………………….R/2447/044. DAWIT GIRMA………………………………………………………………………………………R/2450/045 . ENDALE ALEMAYEHU………………………………………………………………………….R/2460/04

ADVISOR: LEGESSE A. (MSC)

The out line in this power point are:

1.introduction 1.1background 1.2Objective of the study 2.methodology

2.1 study area 2.2 study variable 2.3 Method of data collection 2.4 Method of data analysis 3.Result &discussion 4.Conclusion & recommendation 5.Limitation of the study 6.Reference used

1. INTRODUCTION 1.1BACK GROUND OF THE STUDY

Health is vital part of a national dev.t policy which is aimed to improve the quality of life of all citizens.

Infant & child hood illness &death represent the main health problem in developing countries.

Child mortality is a factor that can be associated with the well being of population & taken as one of the dev.t indicators of health. problem.

1.3 Objective of the study

General objectives The main objective of this study is to determine the major factors that are associated to child mortality in Ambo town.

Specific objectivesTo identify the major factors that influence status of child mortality in the study community. To know which type of disease are contribute for status of child mortality.To describe how the parent educational level affect status of child mortality.

2 METHODOLOGY 2.1 STUDY AREAAmbo town is located in the central part of Ethiopia in Oromia regional state. It located 114km from Addis Ababa in the western shoa zone on the road to nekempte.It was founded in 1874 and got municipal status in 1916. Ambo is one of the reform towns in the region and has a city administration.

2.2 Study variable Dependent variablestatus of Child mortality.

Independent variablesSex of child Age of childTypes of diseaseAge of mother at birth of childParent education levelEconomy of parentBirth intervalSource of waterSize of family

Cont……Assumption of logistic regressionLogistic regression doesn’t make of the assumption of linear regressionIt does not need a liner relationship between the dependent and independent variables.Outcome variables are dichotomous in case of binary logistic regression.It requires large number to be accurate. Logistic regression does not make the key assumptions of linear regression and GLM that are based on ordinary least square algorithms.Logistic regression can handle ordinal and nominal data as independent variables.

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2.3 Method of Data Collection

Data collection is the act of assembling and gathering the need of information in the context of our objective of study .

The most crucial method we used in this study is secondary source of data which is collected from CSA of Ethiopia that collected in 2011.

2.4 Method of data AnalysisThe collected data is summarizing and interpreting by both descriptive and inferential statistics.

Descriptive Statistics Descriptive statistics is a type of statistics which deals with method and techniques of organization, presenting reporting &arranging the data without making generalization by using data, tables and graph .

Inferential Statistics Inferential statistics describes the data with making inferences by generalization and by summarizing source of numerical data.

Cont……. Chi-square test of independence Chi-square test is one of the most appropriate ways to use with categorical variables; it is non parameter test method (distribution free). It is uses to determine the significant association between the two variables .It used to test the hypothesis test that the row and column variable are independent or not. Chi-square is also a function of its degree of freedom. The test statistics is given by

2

Where: - EIJ-is the expected frequency corresponding to (i, j)th

OIJ- is observed frequency has (r-1) (c-1) degree Degree of freedom r- Number of rows c- Number of columns

Cont…..Chi-square use for different purpose among those the most important are the following. - To test goodness of fit - To test independent - To test homogeneity Assumption of chi-square All individual observation in the sample should independent. Sample must be drawn from the population interest. Each cell and every individual objective is independent of each other.

Each number qualifies for one and only one cell in the table.

It required sufficiently large expected frequency for each cell.

Logistic Regression Model of Logistic regressionOne of the statistical techniques for this study is binary logistic regression and the model for logistic regression. Since the model is used to test the association of the two variables, we used odd ratio:

P = is probability of a live. 1-P= is probability of a death. = constant term. = are the independent variable.

are coefficient of independent variable.

e= 2.718

3. RESULT AND DISCUSION

Age of mother at birth of child count Percent

Below 18 age 62 12.918 to 35 age 312 64.7Greater than 36 age

108 22.4

Total 482 100.0 Based on the above table we can conclude that most age of mothers at birth 0f child (64.7%) is lies between 18-35 ages.

DESCREPTIVE STATISTICS

Cont……

4.543.532.521.5

birth interval

400

300

200

100

0

Frequ

ency

Histogram

The above graph shows that least of age of mothers at birth of child (12.9%) is <18 age are exposed to factors that affect child mortality.

Cont……otherHIVpnemoniamalariadiarria

typeas diseases

The above pi- chart shows that most of the children (41.1%) of them are affected by different disease and only 6.0% are affected by HIV

Cont…..

Size of family

AGE OF MOTHER

Total

Below 18 years

18 to 35

Greater than 35

1-3 62 152 30 2444-6 0 154 0 154>=7 0 6 78 84

Total 62 312 108 482The above table shows that out of 312 children with age of mother at birth interval of child 18-35 most of the children 154 are found in the parent with 4-6 size of family.

3.2 Result and discussion of inference statistics

variables Pearson chi-squarevalue

dfSig(two tail)

Sex of child 0.057 1 0.811Type of disease 22.349 4 0.000Parent education level 12.185 3 0.007Age of mother 3.276 2 0.194Economy of parent 95.853 3 0.000Size of family 162.844 2 0.000Source of drinking water 10.852 2 0.004Birth interval 11.856 2 0.003Age of child 110.480 2 0.000

The summery table of chi-square for explanatory variables

HO: There is no association between status of mortality and the individual explanatory variablesH1: There is association between status of mortality and the individual explanatory variables

Cont……..Based on the summery table above a researcher can conclude as follow since the p-value for sex of child and age of mother at birth time (0.811and0.194) respectively is greater than the level of significance ( =0.05) we fail to reject the null hypothesis and can be conclude as at 5% level of significance there is no association between status of mortality and sex of child as well as age mother.In contrast the p-value for the left of explanatory variable (type of disease, parent education level, economy of parent, size of family, source of drinking water, birth interval and age of children) are less than the usual level of significance ( =0.05) we forced to reject the null hypothesis and can be conclude as the above mentioned explanatory variables have an association with status of mortality.

Binary Logistic regressionVariables in the Equation

25.920 4 .0001.079 .465 5.390 1 .020 2.941 1.183 7.3124.084 1.065 14.705 1 .000 59.405 7.365 479.1206.722 1.479 20.647 1 .000 830.461 45.719 15084.8086.684 1.465 20.830 1 .000 799.610 45.316 14109.362

47.467 3 .000-6.630 1.369 23.440 1 .000 .001 .000 .019

-11.858 1.736 46.641 1 .000 .000 .000 .000-11.981 1.840 42.401 1 .000 .000 .000 .000

25.384 2 .0004.679 1.116 17.597 1 .000 107.708 12.098 958.9266.002 1.212 24.526 1 .000 404.130 37.580 4345.915

-1.398 .244 32.917 1 .000 .247

diseasdiseas(1)diseas(2)diseas(3)diseas(4)parentednparentedn(1)parentedn(2)parentedn(3)sourceofwatersourceofwater(1)sourceofwater(2)Constant

B S.E. Wald df Sig. Exp(B) Lower Upper95.0% C.I.for EXP(B)

the fitted model for logistic regression and interpretation / Log it (p/1-p) =-1.398+1.079X1(1)+4.084 X1(1)+6.722X1(2) +……….

+6.002X3(2) since the coefficient of all categories of independent variable in types of disease & source of drinking water is positive it shows that they influence the status of child mortality positively. But parent education level is negatively affect it. The odds of malaria incase of status of child mortality is 2.941 times that of diaries. It also means the estimated odds were 94.1% higher for the malaria group. The odds of status of childThe odds for the secondary educated and higher education level of parents are too higher than the odd for uneducated parents. This is because of the value for the coefficient of parent education level is negative.The odds of status of child mortality for protected source of water are 107.78 times that of pipe source of water.The odds of status of child mortality for unprotected source of water are 404.130 times that of pipe source of water.

Cont….Hosmer and Lemeshow Test

17.429 6 .078Step1

Chi-square df Sig.

since the p-valued =0.078 is greater than the level of significance we fail to reject the null hypothesis& have an evidence to say that the fitted model is good at 5% level of significance

Logistic regression Model diagnosis N Minimum MaximumAnalog of Cook's influence statistics 482 .00000 .44473

Leverage value 482 .00001 .06420Standard residual 482 -1.06566 2.27283Normalized residual 482 -.85241 2.72348Deviance value 482 -1.04514 2.17282DFBETA for constant 482 -.01188 .05938DFBETA for disease(1) 482 -.07650 .09425DFBETA for diseases(2) 482 -1.01901 .07328DFBETA for disease(3) 482 -1.90683 .23376DFBETA for disease(4) 482 -1.93549 .17259DFBETA for parente dn(1) 482 -.06541 .087544DFBETA for parente dn(2) 482 -.06541 .01495DFBETA for parente dn(3) 482 -.08823 .038535DFBETA for sourceofwater(1) 482 -1.03454 .04487

DFBETA for source of water(2) 482 -1.25924 .07912

Valid N (listwise) 482

Cont……..Based on the above table for testing of diagnosis we can conclude the following. A failure to detect outliers& hence influential cases can have severe distortion on the validity of the inferences drawn from such modeling exercise. A check of the standardized and deviance residuals reveals that all have values of less than absolute value of 3 indicating the absence of outliers in the model. In addition, there are no large values of Cook’s distance (Di<1) which means that there are no influential cases having an effect on the model and there are no high values of DFBETAS (all values less than 0.3) which means that there are no influential observations for the individual regression coefficients.Therefore, we can accept that the model as adequate.

4. CONCLUSIONS AND RECOMMENDATIONS ConclusionsThis chapter summarizes the general findings of the study in short and precise form.. Based on the descriptive pi-chart table most of the children (41.1%) of them are affected by different other desease and only 6.0% are affected by HIV.Status of mortality is independent of sex of child and ages of mother at birth of child .In other case types of disease, parent edn. level & source of drinking water is significant affected status of child mortality. The logistic regression shows that the odd of status of child mortality for unprotected source of water are 404.130 times that of pipe source of water. The variables that have association with status of mortality but not significantly fit the logistic model are ;economy of parent, size of family ,birth interval & age of children. Out of the variables that are significant; types of disease & source of drinking water are +vely affect status of mortality. But parent education levels are affects status of mortality –vely.

Recommendation

Depending on the finding of output, we recommended the following since the most the children (41.1%) are affected by different disease, the administration of Ambo town as well as the health care center must have to give immediate solution to this problem. In addition, most of the Children affected by the factor that affect child mortality are with related to source of water so the concerned body has to consider all this responsibly. We have also get that more number of Childs affected for the reason of uneducated parent so the ministry of education have to do on this situation.Having the above risk all concerned body must think for solution.

5. Limitation of the study

The following shortages were faced us during we conduct this research. Due to problems of source of data the assumption of large sample size in logistic regression does no fulfill. There is no sufficient reference materials and internet access to refer related works.Shortage of finance to extend the study population to more are rather than limited to Ambo town.

6. ReferenceThe main reference used in this research are:Agresti, A. (2007). Introduction to categorical data Analysis (2nd Edition) Wiley, New JerseyChristensen, R. (1997).Log Liner Models and Logistic regression (2nd Edition).Springer, New York.Hosmer, D.W and Lemshow S. (2000).Applied Logistic Regression.Montgomery .D.C. and peck, E.A. (2001).Introduction to Liner regression Analysis

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