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# Undergraduate Research work

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This was a research work done at the KATH (hospital) in Ghana.

This was a research work done at the KATH (hospital) in Ghana.

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• THE estimate for the mean age WAS 28.55549 WITH STANDARD ERROR 1.281085
• the estimate for the mean age is 30.76648 WITH STANDARD ERROR 1.787193
• the estimate for the mean age is 29.01362 years WITH STANDARD ERROR 0.3770333
• DONE TO CHECK IF THE ANOVA WAS NOT DECEIVING US
• After you have plotted data for Normality Test, Check for P-value.P-value &lt; 0.05 = not normal. normal = P-value &gt;= 0.05. Comment:since the p-value was &lt;0.001 the age distribution was not normally distributed but skewed.(P-value=0.65&gt;0.05)
• 8.84% of the cases handled by the theatre were children below 16 years with typhoid fever; this was relatively high.
• If any of the predictor variables is zero then the product is zero; number of patients becomes negative. Since, there exists no negative number of patients it implies the number of patients is zero. Therefore, most patients that visit the hospital and are admitted in the pediatric ward are very likely to be females aged below six years from the zongo community. All the three predictor variables have a positive influence in the number of patients admitted with typhoid in the pediatric ward as observed from the correlation analysis.
• ### Transcript

• 1. The Beauty of Mathematics
1 x 9 + 2 = 1112 x 9 + 3 = 111123 x 9 + 4 = 11111 234 x 9 + 5 = 1111112345 x 9 + 6 = 111111123456 x 9 + 7 = 11111111234567 x 9 + 8 = 1111111112345678 x 9 + 9 = 111111111123456789 x 9 +10= 1111111111
1 x 8 + 1 = 912 x 8 + 2 = 98123 x 8 + 3 = 9871234 x 8 + 4 = 987612345 x 8 + 5 = 98765123456 x 8 + 6 = 9876541234567 x 8 + 7 = 987654312345678 x 8 + 8 = 98765432123456789 x 8 + 9 = 987654321
9 x 9 + 7 = 8898 x 9 + 6 = 888987 x 9 + 5 = 88889876 x 9 + 4 = 8888898765 x 9 + 3 = 888888987654 x 9 + 2 = 88888889876543 x 9 + 1 = 8888888898765432 x 9 + 0 = 888888888
• 2. And look at this symmetry:
1 x 1 = 111 x 11 = 121111 x 111 = 123211111 x 1111 = 123432111111 x 11111 = 123454321111111 x 111111 = 1234565432111111 11 x 1111111 = 123456765432111111111 x 11111111 = 123456787654321111111111 x 111111111=12345678987654321
• 3. PROJECT TITLE
Estimation Of The Age Distribution Of Patients Operated And Effect Of Salmonella Typhi On The Incidence Of Typhoid Complications At The Main Surgical Theatre
Supervisor
Mr. S. K. Appiah
• 4. 6/9/2008
4
INTRODUCTION
• Komfo Anokye Teaching Hospital (Kath) is not
performing
• Many lives lost both medically and surgically
• 5. The ministry of health ensure well being of the populace
• 6/9/2008
5
PROBLEM RECOGNITION
• Reconnaissance Visits
• 6. An Interview With A Medical (Surgical) Doctor
• 6/9/2008
6
THE NEED
• Conduct a survey so as to seek information to questions unanswered
• 7. Find certain conditions that exist in this hospital
• 6/9/2008
7
QUESTIONS RAISED
• What age group has more surgical complications?
• 8. What category of surgery is high?
• 9. What proportion of the patients were females?
• 10. What are the major complications?
• 11. Probable factors that influence one of the complications?
• 6/9/2008
8
• Most of the patients operated are below 18years
• 12. Most typhoid patients undergo surgery
• 13. The mean age of the patients is in the thirty’s
• 6/9/2008
9
ASSERTION BY “EMEDICINE GROUP” ON TYPHOID
• Children aged 1 - 5 years have the highest risk of infection, morbidity and mortality
• 14. Typhoid fever in patients is highest in adolescents and young adults
• 15. Disease is generally highest in children aged 3 - 9 years
• 6/9/2008
10
RESEARCH OBJECTIVES
• IDENTIFY THE CONDITIONS IN THE MAIN SURGICAL THEATRE
• 16. To estimate the average age of patients and age range who visit the department
• 17. To determine the ratio of males to females
• 18. To determine the ratioof general surgery to pediatric surgery
• 19. To know the complications frequently observed
• 6/9/2008
11
RESEARCH OBJECTIVES contd
• TO STUDY ONE OF SUCH COMPLICATIONS
• 20. To seek sex-relation
• 21. Whether the environment or location of patients influence the number of cases.
• 22. Does the age of persons have anything to do with the complication?
• 23. Has acquired immunity play a role?
• 6/9/2008
12
DATA COLLECTION AND DATA ANALYSIS
• DATA was sourced from administrative records of KATH
• 24. January 2006 to October 2007
• 6/9/2008
13
DATA ANALYSIS
• 6/9/2008
14
ORGANIZATION OF THE STUDY
• CHAPTER ONE
Overview of the study
• CHAPTER TWO
Literature review
• CHAPTER THREE
Profile of the coverage
• CHAPTER FOUR
Analysis of data
• CHAPTER FIVE
Findings and Recommendations
• 27. TYPHOID FEVER
is also known as
ENTERIC FEVER
• 28. ENDEMIC
Developing Countries
AFRICA & South America
• 29. CAUSE
bacterium
Salmonella Typhi
• 30. TRANSMISSION
WATERBORNE
OR
FOODBORNE
• 31. SYMPTOMS
• PROCESS
BACTERIA food or water stomach
bloodstream tissues
SERIOUS COMPLICATIONS
• 40. COMPLICATIONS
• COMPLEX CASES
end up in
SURGERY
• 46.
• 47. KOMFO ANOKYE TEACHING HOSPITAL (KATH)
• a 1000-bed hospital
• 48. LARGEST in the northern sector
• 49. also known as GEE
• LOCATION OF KATH
KATH
• 50. A
REFERRAL HOSPITAL
having
POLYCLINIC
as well
• 51. THEIR VISION
To become a medical centre of excellence offering Clinical and Non-Clinical services of the highest quality standards comparable to any international standards within 5 years (2003-2008)
• 52. THEIR MISSION
“to provide quality services to meet the needs and expectations of all clients. This will be achieved through well-motivated and committed staff applying best practices and innovation”.
• 53. THEATRES
• MAIN THEATRE
• LITERATURE REVIEW
6/9/2008
31
• 62. OVERVIEW OF CONCEPTS
6/9/2008
32
• Sampling theory is a study of relationships existing between a population and samples drawn from the population.
• 63. Why sampling over complete enumeration:-saves time, reduce cost ,saves labour
• 6/9/2008
33
Sampling Distribution:-
• It is when samples of size N is been drawn from a given populationWhy Use Stratification:-Different classes of surgeryDifferent age groupsDifferent sexesThe Principle Objective Of Stratification:-stratification divides the population into a relative more homogenous age distribution groups with regard to average age sent to the surgical ward for treatment.
• STATISTICAL HYPOTHESIS
6/9/2008
34
• It is a statementabout the parameters of the model
• 64. Used to test the claim about the average age obtained in stratification and the average age obtained by the random sample generated by minitab
• 65. (Null hypothesis)
• 66. (Alternate hypothesis)
• 6/9/2008
35
•
• 67.  The use of P-values in hypothesis testing :-
• 68. P-value as the smallest level at which the data is significant.
• 69. State if the null hypothesis was or was not rejected at a specified α -value or level of significance
• CONFIDENCE INTERVAL
6/9/2008
36
• Although hypothesis testing is a useful procedure, it sometimes does not tell the entire story. It is often preferable to provide an interval within which the value of the parameter would be expected to lie.
• 70. In many engineering and industrial experiments, the experimenter already knows that the means µ1differ µ2, consequently, the hypothesis testing on is of little interest.
• 71. The experimenter would usually be more interested in a confidence interval on the difference in means . The interval
is called a percent confidence interval for the parameter.
• 72. CORRELATION ANALYSIS
6/9/2008
37
CONCERNED WITH THE STRENGTH OF ASSOCIATION BETWEEN THE VARIABLE OF INTEREST AND THE OTHERS
An error term which caters for the errors due to chance and neglected factors which we assume are not important
• 73. CORRELATION COEFFICIENT
6/9/2008
38
• This is a quantitative measure of the strength of linear relationship between two variables, say x and y. There are two types of measure:
Pearson Product – Moment
• This is used for quantitative data measured on interval or ratio scale.
• 74. Spearman’s Rank Correlation Coefficient
• 75. This is used when the data is ranked
• Scatter diagram
6/9/2008
39
The scatter diagram is a useful tool in examining relationships; especially between two variables.
A plot of the sample data on a graph gives a visual indication of the degree of association between two variables say x and y.
• 76. TYPES OF REGRESSION MODEL
6/9/2008
40
Regression models are classified according to the number of predicted variables and also the form of the regression function.
• Simple Regression model
• 77. Multiple regression model
• Simple Linear Regression Model
6/9/2008
41
Definition and features of model
The simple linear regression model is given by Y = β0 + β1 x + ε
x - is the value of the response variable in the observation
is the known value of the predictor variables in the ith observation
ε - is the random error term which caters for the errors due to chance are neglected factors which we assumed not important.
are the parameters of the model
β0 - gives the intercept on y axis
β1 - measures the slope of the linear model
• 78. ESTIMATION OF LINEAR REGRESSION MODEL
6/9/2008
42
• The linear regression model is estimated by fitting a best prediction line through the scatter diagram. This can be done by estimating the parameters of the model.
• 6/9/2008
43
METHOD OF LEAST SQUARES
This method finds the estimates
respectively by minimizing the total sum of squares error( SSE ).
• 79. ANALYSIS OF VARIANCE IN REGRESSION MODEL
6/9/2008
44
The application of analysis of variance (ANOVA) in regression analysis is based on the partitioning of the total variation and its degree of freedom into components.
• 80. DEFINITION OF SOME TERMS(ANOVA):-
6/9/2008
45
• The three quantities SSyy, SSE and SSR are measures of dispersion.
• 81. The total sum of squares of deviation (SSyy, ) is a measure of dispersion of the total variation in the observed values, y.
• 82. The explained sum of squares, (SSR ), measures the amount of the total deviation in the observed values of y that is accounted for by the linear relationship between the observed values of x and y. This is also referred to as sum of squares due to the linear regression model.
• 83. The unexplained sum of squares is a measure of dispersion of the observed y values about the regression which is sometimes called the error residual sum of squares (SSE ).

• 84. COEFFICIENT OF DETERMINATION
6/9/2008
46
r2 is called the coefficient of determination which is explained variation expressed as fraction of total variation. It is also defined as a square of the correlation coefficient.
• 85. 6/9/2008
47
MULTIPLE REGRESSION ANALYSIS
• Multiple regression analysis will include fitting an appropriate model to a collected set of data, testing for the adequacy of the model
• 86. The analysis involves a large array of data system of equations which are conveniently and effectively performed in matrix
• 87. When you have q linear combinations of the k random variables X 1 , X2…., X k .
• 6/9/2008
48
That is, for n independent observations on Yi
and the associated independent variables X1, X 2, …, Xk
We have
• 88. 6/9/2008
49
• 89. 6/9/2008
50
MULTIPLE LINEAR REGRESSIONMODEL
• From the general linear regression model for a multiple regression analysis takes the form
• 6/9/2008
51
Forms of Multiple Linear Regression Models
1. Polynomials regression models:-
• They contain one or more predictor variables in various powers.
2. Transformed regression models:-
Some non-linear functions may be transformed to linear regression models.
3.Interaction effects regression model:-
It is the joint effect of two or more predictor variables(you can use Log etc)
• 90. 6/9/2008
52
• 91. THE BEAUTY OF MATHEMATICS
ANALYSIS OF DATA AND DISCUSSION
6/9/2008
53
• 92. ANALYSIS OF DATA AND DISCUSSION
6/9/2008
54
“An unexamined life is not worth living”, similarly an unexamined organization will not be able to move forward in the right direction
At the end of this analysis, we will be able to make well informed decisions as to;
• How to raise public awareness on the age group, gender (sex) that should be extremely vigilant, cared, and etc.
• 93. Which class or nature of surgical equipments or devises that should not be limited in number.
• 94. Which complications will need to be attended by the ministry of health.
• 6/9/2008
55
CLASSIFICATION OF THE VARIOUS COMPLICATIONS REPAIRED
• 95. 6/9/2008
56
• 96. 6/9/2008
57
• 97. 6/9/2008
58
• 98. 6/9/2008
59
Estimating frequency Distribution of age of Patients
• 99. 6/9/2008
60
The estimate for the mean age was 28.55549 with standard error 1.281085
• 100. 6/9/2008
61
The estimate for the mean age is 30.76648 with standard error 1.787193
• 101. 6/9/2008
62
STRATIFICATION OF PATIENTS BY COMPLICATIONS
The estimate for the mean age is 29.352 years with standard error 1.133
• 102. 6/9/2008
63
The estimate for the mean age is 29.01362 years with standard error 0.3770333
• 103. The Claim!
The Mean Age is 29 years
6/9/2008
64
• 104. STATISTICAL HYPOTHESIS TESTING
Null Hypothesis:
The Mean Age is 29 years
6/9/2008
65
• 105. 6/9/2008
66
Descriptive Statistics: factor, formulation1
Variable N Mean Median TrMean StDev SE Mean
factor 750 3.0000 3.0000 3.0000 1.4152 0.0517
formulation 750 28.973 25.000 27.872 21.557 0.787
Variable Minimum Maximum Q1 Q3
factor 1.0000 5.0000 2.0000 4.0000
formulation 1.000 96.000 10.000 43.000
• 106. 6/9/2008
67
• 107. 6/9/2008
68
• 108. `
6/9/2008
69
• 109. 6/9/2008
70
• 110. 6/9/2008
71
One-way ANOVA: formulation1 versus factor
Analysis of Variance for formulation
Source DF SS MS F P
factor 4 842 210 0.45 0.771
Error 745 347210 466
Total 749 348051
Individual 95% CIs For Mean
Based on Pooled StDev
Level N Mean StDev ----------+---------+---------+------
1 150 27.53 19.68 (-----------*----------)
2 150 28.48 21.37 (-----------*----------)
3 150 28.70 21.90 (-----------*----------)
4 150 29.47 21.47 (----------*-----------)
5 150 30.69 23.36 (----------*-----------)
----------+---------+---------+------
Pooled StDev = 21.59 27.0 30.0 33.0
• 111. 6/9/2008
72
Multiple Comparisons
Dependent Variable: age formulation of patients
Mean
95% Confidence Interval
Difference
(J) factor
(I) factor
(I-J)
Std. Error
Sig.
Lower Bound
Upper Bound
2.00
1.00
Tukey HSD
-.9533
2.49280
.995
-7.7698
5.8631
3.00
-1.1733
2.49280
.990
-7.9898
5.6431
4.00
-1.9400
2.49280
.937
-8.7565
4.8765
5.00
-3.1667
2.49280
.710
-9.9831
3.6498
1.00
2.00
.9533
2.49280
.995
-5.8631
7.7698
3.00
-.2200
2.49280
1.000
-7.0365
6.5965
4.00
-.9867
2.49280
.995
-7.8031
5.8298
5.00
-2.2133
2.49280
.901
-9.0298
4.6031
1.00
3.00
1.1733
2.49280
.990
-5.6431
7.9898
2.00
.2200
2.49280
1.000
-6.5965
7.0365
4.00
-.7667
2.49280
.998
-7.5831
6.0498
5.00
-1.9933
2.49280
.931
-8.8098
4.8231
1.00
4.00
1.9400
2.49280
.937
-4.8765
8.7565
2.00
.9867
2.49280
.995
-5.8298
7.8031
3.00
.7667
2.49280
.998
-6.0498
7.5831
5.00
-1.2267
2.49280
.988
-8.0431
5.5898
1.00
5.00
3.1667
2.49280
.710
-3.6498
9.9831
2.00
2.2133
2.49280
.901
-4.6031
9.0298
3.00
1.9933
2.49280
.931
-4.8231
8.8098
4.00
1.2267
2.49280
.988
-5.5898
8.0431
2.00
1.00
LSD
-.9533
2.49280
.702
-5.8471
3.9404
3.00
-1.1733
2.49280
.638
-6.0671
3.7204
4.00
-1.9400
2.49280
.437
-6.8337
2.9537
5.00
-3.1667
2.49280
.204
-8.0604
1.7271
1.00
2.00
.9533
2.49280
.702
-3.9404
5.8471
3.00
-.2200
2.49280
.930
-5.1137
4.6737
4.00
-.9867
2.49280
.692
-5.8804
3.9071
5.00
-2.2133
2.49280
.375
-7.1071
2.6804
1.00
3.00
1.1733
2.49280
.638
-3.7204
6.0671
2.00
.2200
2.49280
.930
-4.6737
5.1137
4.00
-.7667
2.49280
.759
-5.6604
4.1271
5.00
-1.9933
2.49280
.424
-6.8871
2.9004
1.00
4.00
1.9400
2.49280
.437
-2.9537
6.8337
2.00
.9867
2.49280
.692
-3.9071
5.8804
3.00
.7667
2.49280
.759
-4.1271
5.6604
5.00
-1.2267
2.49280
.623
-6.1204
3.6671
1.00
5.00
3.1667
2.49280
.204
-1.7271
8.0604
2.00
2.2133
2.49280
.375
-2.6804
7.1071
3.00
1.9933
2.49280
.424
-2.9004
6.8871
4.00
1.2267
2.49280
.623
-3.6671
6.1204
• 112. 6/9/2008
73
Each sample was used for the hypothesis testing of the claim that the mean age was 29 years.
One-Sample Z: sample1
Test of mu = 29 vs mu not = 29
The assumed sigma = 21.6
Variable N Mean StDev SE Mean
Sample 1 150 27.53 19.68 1.76
Variable 95.0% CI Z P
Sample 1 ( 24.07, 30.98) -0.84 0.403
One-Sample Z: sample 2
Test of mu = 29 vs mu not = 29
The assumed sigma = 21.6
Variable N Mean StDev SE Mean
Sample 2 150 28.48 21.37 1.76
Variable 95.0% CI Z P
Sample 2 ( 25.02, 31.94) -0.29 0.768
One-Sample Z: sample 3
Test of mu = 29 vs mu not = 29
The assumed sigma = 21.6
Variable N Mean StDev SE Mean
Sample 3 150 28.70 21.90 1.76
Variable 95.0% CI Z P
Sample 3 ( 25.24, 32.16) -0.17 0.865
One-Sample Z: sample 4
Test of mu = 29 vs mu not = 29
The assumed sigma = 21.6
Variable N Mean StDev SE Mean
Sample 4 150 29.47 21.47 1.76
Variable 95.0% CI Z P
Sample 4 ( 26.01, 32.92) 0.26 0.791
One-Sample Z: sample 5
Test of mu = 29 vs mu not = 29
The assumed sigma = 21.6
Variable N Mean StDev SE Mean
Sample 5 150 30.69 23.36 1.76
Variable 95.0% CI Z P
Sample 5 ( 27.24, 34.15) 0.96 0.337
• 113. 6/9/2008
74
• 114. 6/9/2008
75
AGE AND TYPHOID STATISTICS
• 115. 6/9/2008
76
DATA FROM THE PEDIATRIC UNIT
• 116. 6/9/2008
77
Regression Analysis: patients versus zongo, age, female
The regression equation is
patients = 4.07 + 0.42 zongo + 0.824 age + 0.500 female
Predictor Coef SE Coef T P
Constant 4.068 5.642 0.72 0.494
zongo 0.420 1.003 0.42 0.688
age 0.8240 0.6766 1.22 0.263
female 0.5002 0.7478 0.67 0.525
S = 5.899 R-Sq = 84.0% R-Sq(adj) = 77.2%
Analysis of Variance
Source DF SS MS F P
Regression 3 1282.58 427.53 12.29 0.004
Residual Error 7 243.60 34.80
Total 10 1526.18
• 117. 6/9/2008
78
• 118. 6/9/2008
79
Correlations: patients, zongo, age, female
patients zongo age
zongo 0.832
0.001
age 0.909 0.886
0.000 0.000
female 0.865 0.791 0.905
0.001 0.004 0.000
Cell Contents: Pearson correlation
P-Value
• 119. A NEED FOR MODEL MODIFICATION
6/9/2008
80
• 120. 6/9/2008
81
THE PRODUCT TRANSFORMATION
Regression Analysis: patients versus zonagefem
This modification considers the product of the predictor factors as a single variable.
The regression equation is
patients = 24.3 + 0.00230 zonagefem
Predictor Coef SE Coef T P
Constant 24.293 4.256 5.71 0.000
zonagefe 0.0022960 0.0008795 2.61 0.028
S = 9.824 R-Sq = 43.1% R-Sq(adj) = 36.8%
Analysis of Variance
Source DF SS MS F P
Regression 1 657.66 657.66 6.82 0.028
Residual Error 9 868.52 96.50
Total 10 1526.18
• 121. 6/9/2008
82
THE SQUARE ROOT TRANSFORMATION
Regression Analysis: patients versus sqrt (zonagefem)
This modification considers the square root of the product of the predictor factors as a single variable.
The regression equation is
patients = 14.1 + 0.353 sqrt(zonagefem)
Predictor Coef SE Coef T P
Constant 14.079 4.162 3.38 0.008
sqrt(zon 0.35299 0.07059 5.00 0.001
S = 6.700 R-Sq = 73.5% R-Sq(adj) = 70.6%
Analysis of Variance
Source DF SS MS F P
Regression 1 1122.2 1122.2 25.00 0.001
Residual Error 9 404.0 44.9
Total 10 1526.2
• 122. 6/9/2008
83
THE NATURAL LOG TRANSFORMATION
The regression equation is
patients = - 18.5 + 6.87 Ln(zonagefem)
Predictor Coef SE Coef T P
Constant -18.480 5.142 -3.59 0.006
Ln(zonag 6.8658 0.6790 10.11 0.000
S = 3.704 R-Sq = 91.9% R-Sq(adj) = 91.0%
Analysis of Variance
Source DF SS MS F P
Regression 1 1402.7 1402.7 102.24 0.000
Residual Error 9 123.5 13.7
Total 10 1526.2
• 123. 6/9/2008
84
• 124. 6/9/2008
85
• 125. 6/9/2008
86
The regression equation is
patients = - 18.5 + 6.87 Ln(zonagefem)
where patients represents the number of patient admitted with typhoid at the Pediatric Unit;
zonagefem represents the product of the environment, age below six years and number of females. The Ln is the natural logarithm function.
• 126. MAJOR FINDINGS AND IMPLICATIONS
6/9/2008
87
• 127.
• The mean age of patients operated was 29 years
• 128. The age range which had more surgical complications was 0-9 years.
• 129. The percentage of cases were relatively high for males. It was realized that about that 62.64 of the cases worked on were males. The ratio of males to femaleswas 1.7:1
• 130. The complete data indicates that out of a total of 1831patients 27.1%and 22.17% suffered from hernia and typhoid complications
6/9/2008
88
• 131.
• The investigations proved that out of the 22.17% of the typhoid related complications, 44.58% were children. That implied 9.88% of the total cases were children with typhoid complications.
• 132. It was also observed that 39.9% of the children with typhoid complication were aged below 16years.In other words, approximately 8.84% of the cases handled by the theatre were children below 16 years with typhoid fever.
• 133. The ratio of the male to female was nearly 1:1 respectively
• 134. The known dirty environs (“Zongo”) did not contribute a high percentage in the case of typhoid.
6/9/2008
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• 135.
• This could mean that even though most of the patients lived in well sanitary locations, they probably do not take absolute good care of themselves since typhoid is water and food bone. That is to say;
• 136. Nature of the water they drink or use in cooking
• 137. Poor keeping of the kitchen and toilet facilities
• 138. Poor personal hygiene
• 139. Parent Inadequate education of nursing children
6/9/2008
90
• 140. RECOMMENDATIONS
• The findings and Implications explained above gives an idea to make good recommendation based on the sample survey.
• 141. The hospital administrators should provide more equipments and surgical devices to accommodated patients especially those with age less 16 years.
• 142. The public should be informed as to the risk of complications of people aged in interval 0-10 years so as to minimize these cases.
• 143. Counseling on ways to minimize some of these related complications should be carried out.
6/9/2008
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• 144.
• The general public should be educated on the incidence and severity of typhoid fever; ways they can minimize its infection.
• 145. The Ministry of Health can help create animations (Cartoons) on our visual media stations so as to educate the children faster.
• 146. Rural Water Projects should be encouraged in way to enhance proper distribution of water to various locations.
6/9/2008
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• 147. CONCLUSION
• Our way of Life, is based on the decisions we make. As such, there is a need for us as citizens to be cautious on the food and water we take into our body.
• 148. This survey has revealed to as certain conditions at the main theatre of the KATH. The recommendations outlined, based on the survey, above should be considered so as to ensure that the health of all are stabilize
6/9/2008
93
• 149. 6/9/2008
94
Thank you