Saturday, January 30, 2016

Prediction of Unwanted Pregnancies Using Logistic Regression, Probit Regression & Discriminant Analysis

BACKGROUND:
Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population.

METHODS:
In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used.

RESULTS:
The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively.

CONCLUSION:
Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.


The frequency distribution of all pregnancies and unwanted pregnancies among women referred to the health centers in Khorramabad in 2012
VariableCategoryTotal
pregnancies*
Unwanted pregnancies ***
Mother's age
(years)
< 20
20-34
≥ 35
7.1 (63)
82.4 (730)
10.5 (94)
20.6 (13)
23.0 (168)
45.7 (43)
<0.001
Husband's age
(years)
< 25
25-39
≥ 40
9.4 (84)
78.7 (698)
11.9 (106)
16.7 (14)
25.0 (174)
32.1 (34)
0.050
Age difference between husband and wife
(years)
< 0
0-4
5-9
≥10
(71)
(369)
(335)
(107)
18.3 (13)
24.4 (90)
26.6 (89)
28.0 (30)
0.439
The number of living male children0
1
≥ 2
68.9 (592)
24.0 (206)
7.1 (61)
18.8 (111)
34.0 (70)
67.2 (41)
<0.001
The number of living female children0
1
≥ 2
70.3 (604)
23.2 (199)
6.5 (56)
21.2 (128)
34.7 (69)
44.6 (25)
0.003
Parity1
2
≥3
47.1 (417)
35.7 (317)
17.2 (153)
15.8 (66)
22.4 (71)
56.9 (87>)
<0.001
Pregnancy spacing
(years)
First pregnancy
<2
2 - 4
≥ 4
48.3 (417)
7.2 (62)
9.6 (83)
34.9 (301)
15.8 (66)
51.6 (32)
42.2 (35)
29.6 (89)
<0.001
Mother’s educational attainmentIlliterate
high school diploma or lower
University degree
2.8 (25)
77.4 (686)
19.8 (176)
60.0 (15)
24.8 (170)
22.2 (39)
<0.001
Husband’s educational attainmentIlliterate
high school diploma or lower
University degree
2.7 (24)
73.6 (653)
23.7 (210)
58.3 (14)
24.3 (159)
24.3 (51)
0.005
Mother’s occupationHousewife
Employee
Self-employed
91.0 (807)
6.9 (61)
2.1 (19)
25.4 (205)
21.3 (13)
31.6 (6)
0.664
Husband’s occupationUnemployed
Worker
Employee
Self-employed
2.8 (25)
16.0 (142)
29.8 (267)
51.3 (454)
36.0 (9)
29.6 (42)
22.7 (60)
24.9 (113)
0.264
Monthly household income
(Rials)
<7.5 million
7.5 million-9.9 million
≥10 million
23.2 (206)
51.3 (455)
25.5 (226)
32.5 (67)
20.9 (95)
27.4 (62)
0.004
Contraceptive methodNone
Natural/Traditional
Condoms
Oral Contraceptive Pills
Other
15.3 (136)
37.4 (332)
13.2 (117)
26.0 (231)
8.0 (71)
16.2 (22)
21.4 (71)
29.1 (34)
34.6 (80)
23.9 (17)
<0.001
Ownership status of the residencePersonal
Parent’s
Rented
27.0 (237)
25.5 (224)
47.6 (418)
26.2 (62)
24.1 (54)
25.6 (107)
0.860
The substructure of the residence
(m2)
< 100
100-199
≥200
34.4 (302)
53.8 (473)
11.8 (104)
28.1 (85)
24.3 (115)
22.1 (23)
0.338
Ownership of a personal vehicleYes
No
42.2 (373)
57.8 (511)
23.1 (86)
26.4 (135)
0.149
*: The numbers in each cell indicates % (frequency).
**: The statistical test used is chi-square.

Full article at:   http://goo.gl/azTrPz

  • 1Biostatistics Instructor, Department of Statistics and Epidemiology, Faculty of Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, & PhD Candidate in Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. farzadebrahimzadeh2012@gmail.com , ebrahimzadeh@modares.ac.ir.
  • 2Associate Professor, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. hajizadeh@modares.ac.ir.
  • 3PhD Candidate in Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. nasim.vahabi@modares.ac.ir.
  • 4English Language Instructor, Department of the English Language, Faculty of Medicine, Lorestan University of Medical Sciences, Khorramabad, Iran. almasian2@gmail.com.
  • 5Midwifery Instructor, Department of Public Health, Faculty of Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, Iran. k_bakhteyar@yahoo.com. 





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