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. 2017 Sep 1;15:47–57. doi: 10.1016/j.dib.2017.08.038

Breast cancer patients in Nigeria: Data exploration approach

Pelumi E Oguntunde a,, Adebowale O Adejumo b, Hilary I Okagbue a
PMCID: PMC5612794  PMID: 28971122

Abstract

Breast cancer is the type of cancer that develops from breast tissue; it is mostly common in women and it is one of the most studied diseases, largely because of its high mortality (second to lung cancer). However, it occurs in males also. This article presents a statistical study of the distribution of age, gender, length of stay, mode of diagnosis, status (dead or alive) after treatment and the location of breast cancer among 300 patients admitted in the University of Ilorin teaching hospital, Ilorin, Nigeria. The study covers a period of five (5) years; from 2011 to 2016 and logistic regression was used to perform the basic analysis in this study. It was discovered that the age of patients and the location of the breast cancer (right or left) contributes significantly to the survival of the patients. However, early detection and treatment of the disease is highly encouraged. This study also recommends that awareness should be taken to the grassroots and males should not be excluded from this discussion.

Keywords: Breast cancer, Logistic regression, Mortality, Oncology


Specifications Table

Subject area Medicine
More specific subject area Biostatistics, Oncology
Type of data Table and text file
How data was acquired Unprocessed secondary data
Data format Raw, analyzed
Experimental factors Records of Breast cancer patients obtained from University of Ilorin Teaching Hospital (UITH), Nigeria.
Experimental features Computational Analysis: Histogram, Bar-chart, Contingency tables, Logistic regression analysis.
Data source location University of Ilorin Teaching Hospital (UITH), Nigeria
Data accessibility All the data are available in this data article as supplementary materials

Value of the data

  • The data on breast cancer could be useful for government and health workers to make decisions that would reduce the risk of breast cancer among the populace.

  • The data provides the analysis of the age, gender, location of the breast cancer, mode of diagnosis, length of stay (LOS), outcome of treatment of breast cancer patients for the population studied.

  • The data can further be analyzed using other statistical tools like chi square test, multiple linear regression and Poisson regression analysis.

  • The result from the analysis can be compared with other oncologic studies.

  • The interpretation of the data could be helpful in educational studies, epidemiologic oncology, molecular pathologic epidemiology, and breast cancer awareness, screening and so on.

  • The study can be replicated or extended to longitudinal studies.

  • The article provides insight on the impact and consequence of age and location of breast cancer on the survivability of breast cancer patients.

1. Data

The data set used in this article was collected as a secondary data and it contains information on 300 breast cancer patients. The data set was obtained from the Cancer Registry Department under the Department of Admission and Discharge Unit, University of Ilorin Teaching Hospital (UITH) Ilorin, Nigeria. It involves information on 275 females and 25 males and it covers a period of five (5) years; from 2011 to 2016. The patients were all treated as in-patients and were later discharged, of these, 97 patients were discharged dead while 203 patients were discharged alive. The raw data is available and can be assessed as Supplementary data.

Descriptive analyses were performed and logistic regression analysis was also used to describe and analyze the data set.

The data is summarized under different classifications: gender (sex), location of the breast cancer, mode of diagnosis, survival after treatment, age and length of stay in the hospital during treatment.

1.1. Analysis of age of the patients

The frequency table showing the analysis of the age of all the 300 patients is shown in Table 1.

Table 1.

Analysis of age.

Statistics
Age


 

 


N Valid 300
Missing 0
Mean 49.71
Median 50.00
Mode 60
Std. Deviation 13.884
Variance 192.768
Skewness .572
Std. Error of Skewness .141
Kurtosis .479
Std. Error of Kurtosis .281
Minimum 20
Maximum 96


 

 


Percentiles 25 40.00
50 50.00
75 60.00

In Table 1, it can be seen that the mean age of the patients is 49.71 years, the minimum and maximum ages are 20 years and 96 years respectively. The data set is slightly positively skewed with a coefficient of skewness of 0.572.

A diagrammatic representation of the age of the patients is as shown in Fig. 1.

Fig. 1.

Fig. 1

The distribution of age using histogram.

The age of the patients were classified into three different groups (or classes) and the respective frequencies are as shown in Table 2.

Table 2.

Classification of age of the patients.

Agecode
Frequency Percent Valid Percent Cumulative Percent
Valid <41years 88 29.3 29.3 29.3
41–55years 115 38.3 38.3 67.7
> 55years 97 32.3 32.3 100.0
Total 300 100.0 100.0

It can be seen from Table 2 that majority (115) of the patients are in the age group 41–55 years which accounts for 38.3% of the total population under study.

The diagrammatic representation of the information in Table 2 is as shown in Fig. 2.

Fig. 2.

Fig. 2

Bar chart showing the classification of age.

1.2. Analysis on length of stay of the patients at the hospital

Information on the length of stay of the patients in the hospital before discharge is as shown in Table 3 and the respective frequencies are also displayed.

Table 3.

Classification of length of stay.

Loscode
Frequency Percent Valid Percent Cumulative Percent
Valid < 11days 106 35.3 35.3 35.3
11–21days 101 33.7 33.7 69.0
> 21days 93 31.0 31.0 100.0
Total 300 100.0 100.0

From Table 3, it can be seen that most (106) of the patients were discharged early and particularly in less than 11 days.

The diagrammatic representation is as shown in Fig. 3.

Fig. 3.

Fig. 3

Bar chart showing the classification of length of stay.

1.3. Analysis on the gender of the patients

The information on the gender of the patients is as shown in Table 4.

Table 4.

Distribution of gender of the patients.

Gender/sex Frequency Percent Cumulative Percent
Female 275 91.7 91.7
Male 25 8.3 100.0
Total 300 100.0

It can be seen in Table 4 that majority (275) of the patients are females. Also, the table revealed the incidence of breast cancer among male patients.

The information in Table 4 is represented diagrammatically in Fig. 4.

Fig. 4.

Fig. 4

Bar chart showing the distribution of gender.

2. Experimental design, materials and methods

Research on breast cancer and other form of cancer are intense because of the high fatality rate of the disease if not properly managed. Several aspects of breast cancer has been studied, some of which have generated data sets. The analysis on those data sets is based on the various experimental designs, research materials and referred scientific methods. Some of such areas are: CT images, growth factor levels in incident breast cancer, hormone receptor status, cytokine circulation, secretagogue users in breast cancer treatments, chemokine levels, breast cancer and diabetes mellitus co-infection and treatment, breast cancer and HIV treatment, breast cancer and pregnancy. Others are: proteome analysis, risk factors analysis, breast examination, screening, management and breast cancer awareness, epidemiology, risk assessment tools, treatment options: radiotherapy treatment versus chemotherapy, survival analysis, breast cancer subtypes, biomarkers, socio-cultural barriers to treatment, socio-demographic factors and alternative medicine approach, genetic risk, dietary patterns, early diagnostics and treatment and others [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26].

Chi-square test of independence can be used to analyze the data collected, for instance, a cross-tabulation of gender and outcome of the patients at the point of discharge can be classified into a r x c contigency table as shown in Table 5. In this research however, logistic regression analysis was used to analyze the data set. See similar analysis in [27], [28], [29], [30]

Table 5.

Crosstabulation for gender and outcome of patients.

sex * Outcome Crosstabulation
Count
Outcome
Total
Alive Dead
Sex female 188 87 275
male 15 10 25
Total 203 97 300

Table 6 represents the coding for variables length of stay, age, location of cancer, mode of diagnosis and gender of the patients.

Table 6.

Categorical variable coding.

Frequency Parameter coding
(1) (2)
Loscode < 11days 106 1.00 0.00
11–21days 101 0.00 1.00
> 21days 93 0.00 0.00
Agecode <41years 88 1.00 0.00
41–55years 115 0.00 1.00
> 55years 97 0.00 0.00
Location of Cancer Both breasts 25 1.00 0.00
Left breast 140 0.00 1.00
Right breast 135 0.00 0.00
Mode of Diagnosis Cytological 166 1.00
Histological 134 0.00
sex Female 275 1.00
Male 25 0.00

Table 7 shows the classification table at step 0.

Table 7.

Classification Table.

Classification Tablea,b
Observed Predicted
Outcome
Percentage Correct
Alive Dead

Step 0 Outcome Alive 203 0 100.0
Dead 97 0 .0
Overall Percentage 67.7

Table 8 shows the variables in the equation at Step 0.

Table 8.

Variables in the equation.

B S.E. Wald df Sig. Exp(B)
Step 0 Constant −.738 .123 35.797 1 .000 .478

Block 1: Method = Backward Stepwise (Conditional).

Table 9 shows the omnibus tests of model coefficients.

Table 9.

Tests of model coefficients.

Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 20.742 8 .008
Block 20.742 8 .008
Model 20.742 8 .008
Step 2a Step −.892 2 .640
Block 19.850 6 .003
Model 19.850 6 .003
Step 3a Step −.235 1 .628
Block 19.616 5 .001
Model 19.616 5 .001
Step 4a Step −.461 1 .497
Block 19.155 4 .001
Model 19.155 4 .001
a

A negative Chi-squares value indicates that the Chi-squares value has decreased from the previous step.

Table 10 shows the model summary using the log-likelihood, Cox & Snell R square and Negelkerke R square.

Table 10.

Model summary.

Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 356.872a .067 .093
2 357.764a .064 .089
3 357.998a .063 .088
4 358.459a .062 .086
a

Estimation terminated at iteration number 4 because parameter estimates changed by less than .001.

Table 11 shows the variables in the equation from Step 1 to Step 4:

Table 11.

Variables in the equation.

B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Step 1a sex(1) −.232 .454 .261 1 .609 .793 .325 1.932
agecode 9.641 2 .008
agecode(1) −.827 .332 6.194 1 .013 .437 .228 .839
agecode(2) −.875 .309 7.996 1 .005 .417 .227 .765
Location of Cancer 9.209 2 .010
Location of Cancer(1) 1.092 .470 5.407 1 .020 2.981 1.187 7.485
Location of Cancer(2) .721 .276 6.847 1 .009 2.057 1.198 3.531
Mode of Diagnosis(1) −.156 .263 .353 1 .552 .855 .511 1.432
loscode .883 2 .643
loscode(1) −.238 .319 .559 1 .455 .788 .422 1.471
loscode(2) .031 .316 .010 1 .921 1.032 .555 1.918
Constant −.271 .503 .289 1 .591 .763


 

 

 

 

 

 

 

 

 


Step 2a sex(1) −.220 .453 .237 1 .626 .802 .330 1.948
agecode 9.669 2 .008
agecode(1) −.827 .331 6.253 1 .012 .437 .229 .836
agecode(2) −.871 .309 7.964 1 .005 .419 .229 .766
Location of Cancer 9.573 2 .008
Location of Cancer(1) 1.093 .468 5.460 1 .019 2.983 1.193 7.462
Location of Cancer(2) .742 .274 7.323 1 .007 2.100 1.227 3.593
Mode of Diagnosis(1) −.166 .263 .397 1 .529 .847 .506 1.418
Constant −.359 .459 .613 1 .434 .698


 

 

 

 

 

 

 

 

 


Step 3a agecode 10.684 2 .005
agecode(1) −.852 .326 6.814 1 .009 .427 .225 .809
agecode(2) −.898 .304 8.743 1 .003 .407 .225 .739
Location of Cancer 9.389 2 .009
Location of Cancer(1) 1.076 .466 5.325 1 .021 2.933 1.176 7.318
Location of Cancer(2) .728 .272 7.154 1 .007 2.072 1.215 3.533
Mode of Diagnosis(1) −.178 .261 .461 1 .497 .837 .502 1.398
Constant −.528 .303 3.033 1 .082 .590


 

 

 

 

 

 

 

 

 


Step 4a agecode 10.359 2 .006
agecode(1) −.832 .324 6.581 1 .010 .435 .230 .822
agecode(2) −.877 .302 8.446 1 .004 .416 .230 .752
Location of Cancer 9.581 2 .008
Location of Cancer(1) 1.114 .463 5.784 1 .016 3.047 1.229 7.554
Location of Cancer(2) .722 .272 7.055 1 .008 2.059 1.208 3.509
Constant −.640 .256 6.256 1 .012 .528
a

Variable(s) entered on step 1: sex, agecode, LocationofCancer, ModeofDiagnosis, loscode.

Table 12 shows the Hosmer and Lemeshow Test.

Table 12.

Hosmer and Lemeshow Test.

Hosmer and Lemeshow Test
Step Chi-square df Sig.
1 8.566 8 .380
2 1.502 8 .993
3 1.380 8 .995
4 1.193 5 .946

Table 13 shows the classification table for all the steps; steps 1–4.

Table 13.

Classification Table.

Observed Predicted
Outcome
Percentage Correct
Alive Dead
Step 1 Outcome Alive 187 16 92.1
Dead 74 23 23.7
Overall Percentage 70.0
Step 2 Outcome Alive 193 10 95.1
Dead 81 16 16.5
Overall Percentage 69.7
Step 3 Outcome Alive 180 23 88.7
Dead 68 29 29.9
Overall Percentage 69.7
Step 4 Outcome Alive 180 23 88.7
Dead 68 29 29.9
Overall Percentage 69.7

a. The cut value is .500

The predictive probability is as shown in Fig. 5.

Fig. 5.

Fig. 5

Diagram of predictive probabilities.

Breast cancer is one of the dangerous diseases. It occurs in both males and females but the incidence is more in females. Based on this present study, the age of the patient and the location of the breast cancer (right breast or left breast) both contribute significantly to whether a patient would survive the breast cancer disease or not.

Acknowledgement

The authors are grateful to Covenant University for funding this research and UITH, Ilorin for making the data available.

Footnotes

Transparency document

Supplementary data associated with this article can be found in the online version at 10.1016/j.dib.2017.08.038.

Appendix A

Supplementary data associated with this article can be found in the online version at 10.1016/j.dib.2017.08.038.

Transparency document. Supplementary material

Supplementary material

mmc1.pdf (119.3KB, pdf)

.

Appendix A. Supplementary material

Supplementary material

mmc2.xlsx (20.8KB, xlsx)

.

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Associated Data

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Supplementary Materials

Supplementary material

mmc1.pdf (119.3KB, pdf)

Supplementary material

mmc2.xlsx (20.8KB, xlsx)

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