undergraduate research work

1 x 8 + 1 = 9

12 x 8 + 2 = 98

123 x 8 + 3 = 987

1234 x 8 + 4 = 9876

12345 x 8 + 5 = 98765

123456 x 8 + 6 = 987654

1234567 x 8 + 7 = 9876543

12345678 x 8 + 8 = 98765432

123456789 x 8 + 9 = 987654321

1 x 9 + 2 = 11

12 x 9 + 3 = 111

123 x 9 + 4 = 1111

1 234 x 9 + 5 = 11111

12345 x 9 + 6 = 111111

123456 x 9 + 7 = 1111111

1234567 x 9 + 8 = 11111111

12345678 x 9 + 9 = 111111111

123456789 x 9 +10= 1111111111

9 x 9 + 7 = 88

98 x 9 + 6 = 888

987 x 9 + 5 = 8888

9876 x 9 + 4 = 88888

98765 x 9 + 3 = 888888

987654 x 9 + 2 = 8888888

9876543 x 9 + 1 = 88888888

98765432 x 9 + 0 = 888888888

The Beauty of Mathematics

1 x 1 = 1

11 x 11 = 121

111 x 111 = 12321

1111 x 1111 = 1234321

11111 x 11111 = 123454321

111111 x 111111 = 12345654321

11111 11 x 1111111 = 1234567654321

11111111 x 11111111 = 123456787654321

111111111 x 111111111=12345678987654321

And look at this symmetry:

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

3/25/2010 4

• Komfo Anokye Teaching Hospital (Kath) is not

performing

• Many lives lost both medically and surgically

• The ministry of health ensure well being of the populace

INTRODUCTION

3/25/2010 5

PROBLEM RECOGNITION

• Reconnaissance Visits

• An Interview With A Medical (Surgical) Doctor

3/25/2010 6

THE NEED

• Conduct a survey so as to seek information to questions unanswered

• Find certain conditions that exist in this hospital

3/25/2010 7

QUESTIONS RAISED

• What age group has more surgical complications?

• What category of surgery is high?

• What proportion of the patients were females?

• What are the major complications?

• Probable factors that influence one of the complications?

3/25/2010 8

ASSUMPTIONS MADE

• Most of the patients operated are below 18years

• Most typhoid patients undergo surgery

• The mean age of the patients is in the thirty’s

3/25/2010 9

ASSERTION BY “EMEDICINE GROUP” ON TYPHOID

• Children aged 1 - 5 years have the highest risk of infection, morbidity and mortality

• Typhoid fever in patients is highest in adolescents and young adults

• Disease is generally highest in children aged 3 - 9 years

3/25/2010 10

RESEARCH OBJECTIVES

IDENTIFY THE CONDITIONS IN THE MAIN SURGICAL THEATRE

• To estimate the average age of patients and age range who visit the department

• To determine the ratio of males to females

• To determine the ratio of general surgery to pediatric surgery

• To know the complications frequently observed

3/25/2010 11

RESEARCH OBJECTIVES contd

TO STUDY ONE OF SUCH COMPLICATIONS

• To seek sex-relation

• Whether the environment or location of patients influence the number of cases.

• Does the age of persons have anything to do with the complication?

• Has acquired immunity play a role?

3/25/2010 12

DATA COLLECTION AND DATA ANALYSIS

• DATA was sourced from administrative records of KATH

• January 2006 to October 2007

3/25/2010 13

DATA ANALYSIS

• Microsoft office excel 2007

• SPSS

• Minitab

3/25/2010 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

TYPHOID FEVER

is also known as

ENTERIC FEVER

ENDEMIC

Developing Countries

AFRICA & South America

CAUSE

bacterium

Salmonella Typhi

TRANSMISSION

WATERBORNE

OR

FOODBORNE

SYMPTOMS• Fever

• Headache

• Sore throat

• Constipation

• Joint pain

• Abdominal pain

• Loss of appetite

• Fatigue

• Rose spots

PROCESS

BACTERIA food or water stomach

bloodstream tissues

SERIOUS COMPLICATIONS

COMPLICATIONS

• Intestinal perforation

• Peritonitis

• Encephalopathy

• Intestinal hemorrhage

• Hepatosplenomegaly

• Diarrhea

COMPLEX CASES

end up in

SURGERY

KOMFO ANOKYE TEACHING

HOSPITAL (KATH)

• a 1000-bed hospital

• LARGEST in the northern sector

• also known as GEE

LOCATION OF KATH

KATH

A

REFERRAL HOSPITAL

having

POLYCLINIC

as well

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)

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”.

THEATRES

• Main Theatre

• A1 Theatre

• Poly Theatre

MAIN THEATRE

• General Surgery

• Urology

• Neurosurgery

• ENT, Eye

• Paediatric Surgery

• Plastic Surgery

• Gynaecology

LITERATURE

REVIEW

3/25/2010 31

OVERVIEW OF CONCEPTS

3/25/2010 32

Sampling theory is a study of relationships

existing between a population and samples

drawn from the population.

Why sampling over complete

enumeration:-saves time, reduce cost

,saves labour

3/25/2010 33

Sampling Distribution:- It is when samples of size N is been drawn from a given population

Why Use Stratification:-Different classes of surgeryDifferent age groupsDifferent sexes

The 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

3/25/2010 34

It is a statement about the parameters of the model

Used to test the claim about the average age

obtained in stratification and the average age

obtained by the random sample generated by

minitab

(Null hypothesis)

(Alternate hypothesis)

3/25/2010 35

The use of P-values in hypothesis testing :-

P-value as the smallest level at which the data

is significant.

State if the null hypothesis was or was not

rejected at a specified α -value or level of

significance

CONFIDENCE INTERVAL

3/25/2010 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.

In many engineering and industrial experiments, the experimenter already knows that the means µ1differ µ2 , consequently, the hypothesis testing on is of little interest.

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.

CORRELATION ANALYSIS

3/25/2010 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

CORRELATION COEFFICIENT

3/25/2010 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.

Spearman’s Rank Correlation Coefficient

This is used when the data is ranked

Scatter diagram

3/25/2010 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.

TYPES OF REGRESSION MODEL

3/25/2010 40

Regression models are classified

according to the number of predicted

variables and also the form of the

regression function.

Simple Regression model

Multiple regression model

Simple Linear Regression Model

3/25/2010 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

ESTIMATION OF LINEAR

REGRESSION MODEL

3/25/2010 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.

3/25/2010 43

METHOD OF LEAST SQUARES

This method finds the estimates

respectively by minimizing the total sum of squares

error( SSE ).

ANALYSIS OF VARIANCE IN

REGRESSION MODEL

3/25/2010 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.

DEFINITION OF SOME

TERMS(ANOVA):-

3/25/2010 45

The three quantities SSyy, SSE and SSR are measures of dispersion.

The total sum of squares of deviation (SSyy, ) is a measure of dispersion of the total variation in the observed values, y.

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.

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 ).

COEFFICIENT OF

DETERMINATION

3/25/2010 46

r2 is called the coefficient of determination which is explainedvariation expressed as fraction of total variation. It is also defined asa square of the correlation coefficient.

3/25/2010 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

The analysis involves a large array of data system of

equations which are conveniently and effectively

performed in matrix

When you have q linear combinations of the k

random variables X 1 , X2…., X k .

3/25/2010 48

That is, for n independent observations on Yi

and the associated independent variables X1, X 2, …, Xk

We have

3/25/2010 49

3/25/2010 50

MULTIPLE LINEAR REGRESSION

MODEL From the general linear regression model for a

multiple regression analysis takes the form

3/25/2010 51

Forms of Multiple Linear Regression

Models1. 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)

3/25/2010 52

THE BEAUTY OF

MATHEMATICS

ANALYSIS OF DATA AND

DISCUSSION

3/25/2010 53

ANALYSIS OF DATA AND

DISCUSSION

3/25/2010 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.

Which class or nature of surgical equipments or devises

that should not be limited in number.

Which complications will need to be attended by the

ministry of health.

3/25/2010 55

CLASSIFICATION OF THE VARIOUS COMPLICATIONS REPAIRED

COMPLICATION NUMBER OF CASES PERCENTAGE

HERNIA 496 27.1

GOITER 80 4.4

TYPHOID 406 22.17

BREAST 127 6.9

APPENDICITIS 72 3.93

OTHERS 650 35.5

TOTAL 1831 100

3/25/2010 56

3/25/2010 57

age range Frequency relative frequency

0-9 32 0.21

10-19 23 0.15

20-29 34 0.23

30-39 27 0.18

40-49 13 0.09

50-59 9 0.06

60-69 5 0.03

70-79 7 0.05

80-89 0 0.00

90-99 0 0.00

3/25/2010 58

3/25/2010 59

Estimating frequency Distribution of age of Patients

9080706050403020100

0.25

0.20

0.15

0.10

0.05

0.00

age point

rela

tiv

e f

req

ue

nc

y

S catterplot of re lative frequency vs age point

3/25/2010 60

STRATIFICATION OF PATIENTS USING THE CLASS OF SURGERY

stratum Nh nh

2

Yh S2h NhYh p=Sh

2/nh V=Nh(Nh-nh)p

General surgery 1310 10

7

37.748 366.4927 49449.88 3.425165421 5397820.941

Pediatric surgery 521 43 5.4419 18.01408 2835.2299 0.418932093 104330.0106

Total 1831 15

0

43.1899 384.5068 52285.1099 3.844097514 5502150.952

The estimate for the mean age was 28.55549 with standard error

1.281085

3/25/2010 61

STRATIFICATION OF PATIENTS BY SEX

stratum Nh nh

2

Yh S2h NhYh p=Sh

2/nh V=Nh(Nh-nh)p

female 684 56 29.518 447.7456 20190.312 7.995457143 3434464.607

male 1147 94 31.511 566.1068 36143.117 6.022412766 7273815.937

Total 1831 150 61.029 1013.852 56333.429 14.01786991 10708280.54

The estimate for the mean age is 30.76648 with standard

error 1.787193

3/25/2010 62

The estimate for the mean age is 29.352 years with standard error

1.133

STRATIFICATION OF PATIENTS BY COMPLICATIONS

3/25/2010 63

STRATIFICATION OF THE PATIENTS IN TERMS OF AGE RANGE

stratum Nh nh: Yh Sh2 NhYh p=Sh

2/nh Nh(Nh-nh)p

0-10 479 10 5.6 6.267 2682.4 0.6267 140788.7817

11-20 240 6 13.5 6.7 3240 1.116666667 62712

21-30 332 8 24.25 7.929 8051 0.991125 106613.334

31-40 245 6 34.5 5.5 8452.5 0.916666667 53675.41667

41-50 202 5 44.8 5.7 9049.6 1.14 45365.16

51-60 142 4 55 9 7810 2.25 44091

61-70 91 3 66 3 6006 1 8008

71-80 71 3 74.33 9.3 5277.43 3.1 14966.8

81-90 20 2 85 2 1700 1 360

91-100 9 1 95 0 855 0 0

Total 1831 48 497.98 55.396 53123.93 12.14115833 476580.4924

The estimate for the mean age is 29.01362 years with standard

error 0.3770333

The Claim!

The Mean Age

is 29 years

3/25/2010 64

STATISTICAL HYPOTHESIS

TESTING

Null Hypothesis:

The Mean Age is 29 years

3/25/2010 65

3/25/2010 66

Descriptive Statistics: factor, formulation1Variable 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

3/25/2010 67

1 2 3 4 5

0

10

20

30

40

50

60

70

80

90

100

factor

form

ula

tio

n1

Boxplots of formulation by factor

(means are indicated by solid circles)

3/25/2010 68

-30 -20 -10 0 10 20 30 40 50 60 70

-3

-2

-1

0

1

2

3

No

rma

l S

co

re

Residual

Normal Probability Plot of the Residuals

(response is f ormulat)

`

3/25/2010 69

282318

95% Conf idence Interv als f or Sigmas

P-Value : 0.191

Test Statistic: 1.532

Levene's Test

P-Value : 0.350

Test Statistic: 4.440

Bartlett's Test

Factor Lev els

5

4

3

2

1

Test for Equal Variances for formulation1

3/25/2010 70

3/25/2010 71

One-way ANOVA: formulation1 versus factorAnalysis 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

3/25/2010 72

Multiple Comparisons

Dependent Variable: age formulation of patients

-.9533 2.49280 .995 -7.7698 5.8631-1.1733 2.49280 .990 -7.9898 5.6431-1.9400 2.49280 .937 -8.7565 4.8765-3.1667 2.49280 .710 -9.9831 3.6498

.9533 2.49280 .995 -5.8631 7.7698-.2200 2.49280 1.000 -7.0365 6.5965-.9867 2.49280 .995 -7.8031 5.8298

-2.2133 2.49280 .901 -9.0298 4.60311.1733 2.49280 .990 -5.6431 7.9898

.2200 2.49280 1.000 -6.5965 7.0365-.7667 2.49280 .998 -7.5831 6.0498

-1.9933 2.49280 .931 -8.8098 4.82311.9400 2.49280 .937 -4.8765 8.7565

.9867 2.49280 .995 -5.8298 7.8031

.7667 2.49280 .998 -6.0498 7.5831-1.2267 2.49280 .988 -8.0431 5.58983.1667 2.49280 .710 -3.6498 9.98312.2133 2.49280 .901 -4.6031 9.02981.9933 2.49280 .931 -4.8231 8.80981.2267 2.49280 .988 -5.5898 8.0431-.9533 2.49280 .702 -5.8471 3.9404

-1.1733 2.49280 .638 -6.0671 3.7204-1.9400 2.49280 .437 -6.8337 2.9537-3.1667 2.49280 .204 -8.0604 1.7271

.9533 2.49280 .702 -3.9404 5.8471-.2200 2.49280 .930 -5.1137 4.6737-.9867 2.49280 .692 -5.8804 3.9071

-2.2133 2.49280 .375 -7.1071 2.68041.1733 2.49280 .638 -3.7204 6.0671

.2200 2.49280 .930 -4.6737 5.1137-.7667 2.49280 .759 -5.6604 4.1271

-1.9933 2.49280 .424 -6.8871 2.90041.9400 2.49280 .437 -2.9537 6.8337

.9867 2.49280 .692 -3.9071 5.8804

.7667 2.49280 .759 -4.1271 5.6604-1.2267 2.49280 .623 -6.1204 3.66713.1667 2.49280 .204 -1.7271 8.06042.2133 2.49280 .375 -2.6804 7.10711.9933 2.49280 .424 -2.9004 6.88711.2267 2.49280 .623 -3.6671 6.1204

(J) factor2.003.004.005.001.003.004.005.001.002.004.005.001.002.003.005.001.002.003.004.002.003.004.005.001.003.004.005.001.002.004.005.001.002.003.005.001.002.003.004.00

(I) factor1.00

2.00

3.00

4.00

5.00

1.00

2.00

3.00

4.00

5.00

Tukey HSD

LSD

MeanDifference

(I-J) Std. Error Sig. Lower Bound Upper Bound95% Confidence Interval

3/25/2010 73

Each sample was used for the hypothesis testing of the claim that the mean age was 29 years.

One-Sample Z: sample1Test 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 2Test 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 3Test 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 4Test 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 5Test 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

3/25/2010 74

3/25/2010 75

AGE NUMBER

OF CASES

PERCENTAG

E

FEMALE

S

TYPHIO

D CASES

PERCENT

AGE

WITH

TYPHIOD

% OF

TYPHIOD

IN RANGE

NUMB

ER

0-17 623 34.00655 221 181 9.8799 44.5813 0-15 162(8.8

4%)

18-

54

944 51.53 360 191 10.4258 47.04 18-30 117

55+ 264 14.47 103 34 1.8559 8.37438

TOT

AL

1831 100 684 406 22.1615

AGE AND TYPHOID STATISTICS

3/25/2010 76

Month PATIENTS ZONGO AGE <6years FEMALE

June 10 1 8 7

July 35 5 25 22

August 41 15 35 24

September 34 7 20 17

October 27 7 16 13

November 46 10 27 20

December 39 7 26 15

January 37 9 23 22

February 31 5 16 16

March 45 11 25 18

April 10 2 6 6

Total 355 79 227 180

DATA FROM THE PEDIATRIC UNIT

3/25/2010 77

Regression Analysis: patients versus zongo, age, femaleThe 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

3/25/2010 78

1086420-2-4-6

2

1

0

Residual

Fre

qu

en

cy

Histogram of Residuals

1050

10

0

-10

Observation Number

Re

sid

ua

l

I Chart of Residuals

Mean=-1.8E-04

UCL=13.75

LCL=-13.75

5040302010

10

5

0

-5

Fit

Re

sid

ua

l

Residuals vs. Fits

210-1-2

10

5

0

-5

Normal Plot of Residuals

Normal Score

Re

sid

ual

residual plot for Normal linear

3/25/2010 79

Correlations: patients, zongo, age, femalepatients 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

A NEED FOR MODEL

MODIFICATION

3/25/2010 80

3/25/2010 81

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

THE PRODUCT TRANSFORMATION

3/25/2010 82

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

THE SQUARE ROOT TRANSFORMATION

3/25/2010 83

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

THE NATURAL LOG TRANSFORMATION

3/25/2010 84

10 9 8 7 6 5 4

50

40

30

20

10

Ln(zonagefem

patients

S = 3.70396 R-Sq = 91.9 %

R-Sq(adj) = 91.0 %

patients = -18.4801 +

6.86584 Ln(zonagef em

Regression Plot

3/25/2010 85

6420-2-4-6

5

4

3

2

1

0

Residual

Fre

quency

Histogram of Residuals

1050

10

0

-10

Observation Number

Resid

ual

I Chart of Residuals

Mean=0.01085

UCL=12.10

LCL=-12.08

5040302010

5

0

-5

Fit

Resid

ual

Residuals v s. Fits

210-1-2

5

0

-5

Normal Plot of Residuals

Normal Score

Resid

ual

residual plot for Natural log transform

3/25/2010 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.

MAJOR FINDINGS AND

IMPLICATIONS

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The mean age of patients operated was 29 years

The age range which had more surgical complicationswas 0-9 years.

The percentage of cases were relatively high for males.It was realized that about that 62.64 of the casesworked on were males. The ratio of males to femaleswas 1.7:1

The complete data indicates that out of a total of 1831patients 27.1% and 22.17% suffered from hernia andtyphoid complications

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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 typhoidcomplications.

It was also observed that 39.9% of the children with typhoidcomplication 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.

The ratio of the male to female was nearly 1:1 respectively

The known dirty environs (“Zongo”) did not contribute a high percentage in the case of typhoid.

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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;

• Nature of the water they drink or use in cooking

• Poor keeping of the kitchen and toilet facilities

• Poor personal hygiene

• Parent Inadequate education of nursing children

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RECOMMENDATIONS The findings and Implications explained above gives

an idea to make good recommendation based on the sample survey.

• The hospital administrators should provide more equipments and surgical devices to accommodated patients especially those with age less 16 years.

• 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.

• Counseling on ways to minimize some of these related complications should be carried out.

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• The general public should be educated on the incidence and severity of typhoid fever; ways they can minimize its infection.

• The Ministry of Health can help create animations (Cartoons) on our visual media stations so as to educate the children faster.

• Rural Water Projects should be encouraged in way to enhance proper distribution of water to various locations.

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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.

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

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Thank

you