Nurse Media Journal of Nursing
e-ISSN: 2406-8799, p-ISSN: 2087-7811
https://medianers.undip.ac.id
13(2):141-153, August 2023
https://doi.org/10.14710/nmjn.v13i2.53640
ORIGINAL RESEARCH

Contextual Factors, Health History, and Daily
Living Activities for Ovarian Cancer Risks:
A Case-Control Study in Indonesia
Restuning Widiasih1, Windy Natasha1,2, Tosin Popoola3, Katherine Nelson4
1Department of Maternity Nursing, Faculty of Nursing, Universitas Padjadjaran, Bandung, Indonesia
2Hasan Sadikin Hospital, Bandung, Indonesia

3School of Nursing and Midwifery, University of Newcastle, Australia

4School of Nursing, Midwifery and Health Practice, Victoria University of Wellington, New Zealand

Article Info

Abstract

Article History:
Received: 9 April 2023
Revised: 26 August 2023
Accepted: 28 August 2023
Online: 31 August 2023
Keywords:
Ovarian cancer; risk factors of
cancer; women health
Corresponding Author:
Restuning Widiasih
Department of Maternity Nursing,
Faculty of Nursing, Universitas
Padjadjaran, Bandung, Indonesia
Email:
restuning.widiasih@unpad.ac.id

Background: Women are encouraged to aware for risk factors that may aid in the
early detection of ovarian cancer before symptoms and sign appear. However,
limited studies evaluated the broad aspects of the risk factors for ovarian cancer,
particularly in Indonesia. For a more thorough analysis of ovarian cancer risk
factors, more variables and a research approach that can make it easier to uncover
risk factors of ovarian cancer are necessary. Detecting factors associated with
ovarian cancer could serve as the foundation for developing an early warning model.
Purpose: The aim of this study was to analyze contextual factors, reproductive
health history, and Activity Daily Living (ADLs) associated with ovarian cancer
risks.
Methods: This retrospective case-control study included 408 women with a 1:1
balanced composition: 204 women diagnosed with ovarian cancer and 204 without.
The study was conducted at the Cancer Referral Hospital in West Java Province,
Indonesia, from April to November 2020. The instrument was developed based on
previous studies and hospital medical/nursing records. The data were analyzed
using the Chi-square test and the logistic regression test. The sensitivity and
specificity were examined using the Receiver Operating Characteristic (ROC).
Results: The variables significantly associated with an increased risk of developing
ovarian cancer were advanced age (≥ 45 years) with an odds ratio (OR) of 19.76, low
education (OR: 225.00), obesity (OR: 6.04), prior surgery (OR: 51.06), parity (OR:
110.38), and poor sleep quality (OR: 15.75). These factors were found to have the
strongest associations with ovarian cancer development.
Conclusion: The present study has identified risk factors that have a statistically
significant association with the occurrence of ovarian cancer. Healthcare
practitioners have the potential to employ this information as foundational data for
future research in the development of a self-detection tool for assessing the risk of
ovarian cancer.

How to cite: Widiasih, R., Natasha, W., Popoola, T., & Nelson, K. (2023). Contextual factors, health history, and daily
living activities for ovarian cancer risks: A case-control study in Indonesia. Nurse Media Journal of Nursing, 13(2),
141-153. https://doi.org/10.14710/nmjn.v13i2.53640

1.

Introduction
Globally, ovarian cancer is the fifth leading cause of morbidity and mortality in women (Arora
et al., 2023). In Indonesia, ovarian cancer is the third leading cause of death in women after breast
and cervical cancer (Ministry of Health Republic of Indonesia [MOHRI], 2019). It is estimated
that, by 2040, the cancer mortality rate will have increased significantly (National Cancer
Institute, 2020). Individual women, families, communities, health workers, and the government
are all concerned about ovarian cancer’s high morbidity and mortality rates. This cancer is known
as a silent killer because it does not present a typical symptom (Kemppainen et al., 2019; Mahoney
& Pierce, 2022; Mathieu et al., 2018). Most patients present at health services with an advanced
stadium, which increases the risk of death (Mathieu et al., 2018; Momenimovahed & Salehiniya,
2019). The annual incidence of ovarian cancer ranges from 9 to 15 per 100.000 women, with an
average mortality rate of 5.4 to 11.6 deaths per 100.000 women (Mathieu et al., 2018). Almost
300.000 new cases were reported in 2018, placing Indonesia at number 22 among the top 25
countries with the highest ovarian cancer rates, with an incidence rate of 9.7 (Arora et al., 2023).
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Nurse Media Journal of Nursing, 13(2), 2023, 142
A comprehensive approach that includes early self-detection by identifying risk factors, screening
in health services, early diagnosis, and treatment programs could help prevent diagnosis delays.
Ovarian cancer is divided into five histological subtypes, each with its own set of risk factors,
cell origin, molecular composition, clinical features, and treatment options (Matulonis et al.,
2016). Since early detection of ovarian cancer is difficult, one strategy to pursue is prevention by
understanding the risk factors (Jauhari et al., 2016). According to research conducted in several
countries, hormonal, lifestyle, women in postmenopausal ages were contributed factors to the
formation of ovarian cancer (Koskela-Niska et al., 2013; Schüler et al., 2013; Umakanthan et al.,
2019). The research in Indonesia found that menarche, parity, contraceptive use, family history,
infertility treatment, age, occupation, menopause, body mass index (BMI) are risk factors for
ovarian cancer (Agusweni et al., 2020; Arania & Windarti, 2015; Jauhari et al., 2016; Kamajaya
et al., 2021; Latief et al., 2023; Wulandari et al., 2019). Unfortunately, these studies only calculate
the frequency of occurrence and demographic characteristics without analyzing which factors
increase or decrease ovarian cancer risks. In addition, several studies on ovarian cancer risk
factors have only been conducted in cross-sectional form (Nababan et al., 2021). Previous
research found risk variables that can be divided into three categories: contextual factors, health
history, and health behavior. However, more variables and a research methodology that can aid
the finding of risk factors that influence ovarian cancer occurrence are required for a more
thorough and detailed investigation of ovarian cancer risk factors. This study aimed to investigate
the association between contextual factors, reproductive health history, ADLs, and ovarian cancer
risks. Identifying risk variables for ovarian cancer can be the foundation for developing an earlywarning ovarian cancer model.
2. Methods
2.1 Research design
The quantitative analytic observation with a retrospective case-control study approach was
used in this study. This study compares two groups of respondents, that focuses on comparing
two groups of women: those with cancer (cases group) and non-cancer (controls group) to look at
factors associated with ovarian cancer.
2.2 Setting and samples
The samples were selected using a consecutive sample technique. The unpaired categorical
comparative analytic formula was used to calculate the sample size. The case inclusion criteria
were patients diagnosed with ovarian cancer, a treatment history, or being hospitalized at a
referral hospital in West Java, Indonesia. The population in the control group had to be free of
ovarian cancer, and not hospitalized in the cancer ward. Both groups were adult women, and their
health data information was captured in the medical record. G-Power analysis was used to
calculate the sample determination, with alpha error probability 0.05, effect size 0.3, and power
0.95. The Cohen’s rule (1988) and studies by Ribeiro et al. (2010) and Oksuz et al. (2021) were
used to calculate the effect size. According to the computation, the minimum sample size for each
group was 191, with an additional 5% sample added to prevent respondents from dropping out.
As a result, 408 samples were taken from 204 individuals in the case group and 204 participants
in the control group. The study was conducted from April to November 2020. The samples’
recruitment explained in the Figure 1.
2.3 Measurement and data collection
The dependent variable in this study was the incidence of ovarian cancer. The selection of
independent variables was informed by prior research on the examination of risk factors
associated with ovarian cancer globally (Huusom et al., 2006; Kemppainen et al., 2019; Matulonis
et al., 2016; Momenimovahed & Salehiniya, 2019); these studies contained valid variables. The
contextual factors (demographic data, medical and health history, and activity daily living),
medical history, and daily activities were investigated as independent variables. The contextual
factors were categorized and adjusted with the Indonesian formal category, especially in
demographic data: age, education level, occupation, and economic level (MOHRI, 2019; Public
Relations West Java Regional Government Agency, 2020; The Central Statistics Bureau of
Indonesia, 2020). Variables of medical and health history included menstrual history, marriage
status, reproductive health, illness, family health, family planning, and gynecological health (Bell,
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Nurse Media Journal of Nursing, 13(2), 2023, 143
2013; Lowdermilk et al., 2013; Matahari et al., 2018; National Population and Family Planning
Agency, 2018; Saryono, 2009). Daily activity factors included nutrition status, elimination
pattern, sleep patterns, and exercise. Nutritional status factors include BMI, dietary patterns, food
portions, amount of drink, caffeine consumption, and smoking (MOHRI, 2018a, 2018c; 2018d).
Basic Health Research and the Cohort of the Ministry of Health of the Republic of Indonesia were
used to create the questionnaire used in this study (MOHRI, 2018b). The detail of variables
described in the results section (Table 1). The instrument’s validity was assessed through a twostage process. Initially, the assessment of content validity was conducted through a comparative
analysis of the instrument's contents with prior research. Subsequently, the establishment of
construct validity was achieved by consulting with two distinct groups of nursing professionals:
basic nursing experts and women’s health nurse specialists (Bolarinwa, 2015; Cresswell, 2013).
The administrator at the national referral hospital in West Java provinces collected data from
the medical record in the case group, which was subsequently forwarded to the research team.
The data for the control group were obtained online by sharing survey formulations on WhatsApp,
which were linked to the Google Form from April to November 2020. It took them roughly 15-20
minutes to complete the survey formula. Following the collection of data, the nurse proceeded to
undertake the editing phase, wherein the information obtained from the medical records of the
hospital was cross-referenced to ensure the accuracy of the data provided by the participants. All
members of the study team were taught in the COVID-19 transmission prevention procedure,
research objectives, data collection method, how to fill out the survey format through Google
Forms, and paying attention to the general health state of participants while collecting data.

Medical
Record

Case

Control

Ovarian
Cancer
Patients

Non-Cancer
Patients

Cohen’s Rule (1998), Oksuz et al.
(2021), and Ribeiro et al. (2010)
Sampling
method

Effect size = 0.3
Alpha error probability = 0.05
Power = 0.95

204 case

204 control

Figure 1. The flowchart of samples’ recruitment
2.4 Data analysis
The SPSS version 25 was utilized by the researchers to examine the research data. The
information was entered into a spreadsheet and analyzed using the Chi-square test and logistic
regression. Chi-square analysis was performed to analyze the link between each variable and the
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Nurse Media Journal of Nursing, 13(2), 2023, 144
risk of ovarian cancer, while logistic regression was utilized to determine which variable was the
most influential (Umami et al., 2021). In this investigation, the p-value for all statistical tests was
less than 0.05, with a 95% confidence level for the adjusted odds ratio (AOR). The multivariate
logistic regression findings found the estimated coefficient and divided it by the standard error to
find the least number, which was then rounded off. The scoring accuracy was validated using ROC,
which was utilized to examine sensitivity and specificity (Nahm, 2022).
2.5 Ethical considerations
This study received ethical approval from the Health Research Ethics Committee of
Universitas Padjadjaran, Indonesia, number: LB.02.01/X.B.5/180/202. The research ethics
principles applied in this study were respect for human dignity, privacy and confidentiality, and
justice. In applying these ethic principles, this study had no risk of causing physic or mental harm
to the subjects. Respondents might leave the research at any time and with no consequences to
their health treatment. The data of the respondents were kept private by the researchers.
3. Results
The results are presented in four parts: first, a table of the relationship between respondent
characteristics, medical history, and ADLs on ovarian cancer incidence, and second, the results of
logistic regression analysis, the estimated β coefficient, and third, the ROC findings.
3.1 Relationship of respondent characteristics, health history, and ADLs with ovarian cancer
Table 1 shows the findings of risk factors associated with ovarian cancer (p<0.05). The risk
factors were age >45 years, low level of education, and unemployed. Several additional risk factors
related to reproductive history were menarche age (15 years), menstrual cycle (less than or more
than the standard time,) dysmenorrhea, marriage age 20 years, number of pregnancies, history
of pregnancy problems, breastfeeding history, fertility treatment, non-hormonal contraception
and contraception problems. Then, risk factors related to health history included family history
of cancer, surgeries, and history of disease complication. Lastly ADLs risk factors were coffee
drinking, exercise, food, drinking practices, smoking, sleep, defecation and urination patterns.
Table 1. The association of respondent characteristics, health history, and ADLs with
ovarian cancer (n=408)
Ovarian
Cancer
(n=204)
f (%)

Variable
Demography
Age (years)
>45
12-45 (ref)
Education
Junior High School or less
Senior High School or more (ref)
Occupation
Jobless
Workers (ref)
Menstrual History
Menarche
≥15 years
<15 years (ref)
Length of Menstruation
≤14 days
>14 days (ref)
Menstrual Cycle
<21 days
21-35 days (ref)
>35 days

Normal
(n=204)

p-value

Crude OR (95% CI)

f (%)

103 (50.5)
101 (49.5)

3 (1.5)
201 (98.5)

<0.001*

68.33 (21.15 – 220.72)

166 (81.4)
38 (18.6)

7 (3.4)
197 (96.6)

<0.001*

122.94 (5.49 – 282.55)

149 (73.0)
55 (27.0)

97 (47.5)
107 (52.5)

<0.001*

2.99 (1.98 – 4.52)

58 (28.4)
146 (71.6)

35 (17.2)
169 (82.8)

0.007*

1.92 (1.19 – 3.08)

204 (100.0)
0 (0.0)

204 (100.0)
0 (0.0)

-

0 (0.0)
204 (100.0)
0 (0.0)

54 (26.5)
133 (65.2)
17 (8.3)

<0.001*

167.97 (10.22 – 2726.81)

0.006*

53.61 (3.20 – 899.14)

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Nurse Media Journal of Nursing, 13(2), 2023, 145
Table 1. Continued
Ovarian
Cancer
(n=204)
f (%)

Variable
Menstrual symptoms
Yes
No (ref)
Type of menstrual symptoms
Dysmenorrhea
Others (ref)
Marriage History
Marriage status
Married
Single (ref)
Age of Marriage
<20 years
≥20 years (ref)
Reproductive Health History
Pregnant History
None (ref)
Once
More than two
Pregnancy and Delivery health
problems
Yes
No (ref)
Type of Pregnancy and Delivery
health problems
Bleeding/PROM/Eclampsia
Other problems
None/normal (ref)
Breastfeeding experience
Yes
No (ref)
Infertile History
Yes
No (ref)
Infertile Therapy
Yes
No (ref)
Illness History
History of disease complication
Yes
No (ref)
Type of health complications
Surgery
Other complications
No complication (ref)
Family health history
Family history with cancer
Yes
No (ref)
History of Family Planning
Type of Contraception
None (ref)
Hormonal Contraception
Non-Hormonal Contraception
Contraception problems
Yes
No (ref)

Normal
(n=204)

p-value

Crude OR (95% CI)

f (%)

53 (26.0)
151 (74.0)

122 (59.8)
82 (40.2)

<0.001*

0.24 (0.16 – 0.36)

52 (98.1)
1 (1.9)

84 (68.9)
38 (31.1)

<0.001*

23.52 (3.14 – 176.53)

180 (88.2)
24 (11.8)

128 (62.7)
76 (37.3)

<0.001*

4.45 (2.67 – 7.43)

67 (37.2)
113 (62.8)

12 (9.4)
116 (90.6)

<0.001*

5.73 (2.94 – 11.17)

19 (9.3)
41 (20.1)
144 (70.6)

88 (43.2)
36 (17.6)
80 (39.2)

<0.001*
<0.001*

5,28 (2.71 – 10.29)
8,34 (4.73 – 14.69)

51 (27.6)
134 (72.4)

33 (28.4)
83 (71.6)

0.868

0,96 (0.57 – 1,60

47 (25.4)
5 (2.2)
134 (72.4)

2 (1,7)
31 (26.7)
83 (71.6)

<0.001*
<0.001*

14,56 (3.44 – 61.52)
0.08 (0.03 – 0.24)

185 (90.7)
19 (9.3)

103 (50.5)
101 (49.5)

<0.001*

9,55 (5.53 – 16.49)

6 (2.9)
198 (97.1)

12 (5.9)
192 (94.1)

0,148

0,49 (0.18 – 1.32)

0 (0.0)
204 (100.0)

10 (4,9)
194 (95.1)

0.033*

0.05 (0.003 – 0.78)

136 (66.7)
68 (33.3)

27 (13.2)
177 (86.8)

<0.001*

13.11 (7.96 – 21.59)

126 (92.6)
10 (4.9)
68 (33.3)

7 (25.9)
20 (9.8)
177 (86.8)

<0.001*
0.523

46.85 (20.83 – 105.41)
1.30 (0.58 – 2.92)

3 (1.5)
201 (98.5)

18 (8.8)
186 (91.2)

0.001*

0.15 (0.05 – 0.53)

154 (75.5)
30 (14.7)
20 (9.8)

128 (62.7)
32 (15.7)
44 (21.6)

0.379
0.001*

0,78 (0.45 – 1.35)
0,38 (0.21 – 0.68)

36 (72.0)
14 (28.0)

26 (34.2)
50 (65.8)

<0.001*

4.95 (2.27 – 10.77)

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Nurse Media Journal of Nursing, 13(2), 2023, 146
Table 1. Continued
Ovarian
Cancer
(n=204)
f (%)

Variable
Type of problems
Irregular periods
Other problems
None (ref)
Gynecological Health History
History of Gynecological Problems
Yes
No (ref)
Type of Gynecology problems
Cancer in reproductive system
Others (ref)
Nutrition status
BMI
Overweight/Obese (>25,0)
Normal (18,5-25,0) (ref)
Dietary pattern
≤3 times/day
>3 times/day (ref)
Food Portion
≤1 Portion
>1 Portion (ref)
Amount of drink
<8 glasses
≥8 glasses (ref)
Caffein consumption
Yes
No (ref)
Smoking
Yes
No (ref)
Elimination pattern
Defecation pattern
1-4 times/day
>4 times/day (ref)
Characteristic of feses
Solid
Liquid/blood (ref)
Urination Pattern
Urinate pattern
<3 times/day
≥3 times/day (ref)
Colour
Clear
Yellow pale (ref)
Sleep pattern and Exercise
Length of take a nap
≤3 hours
>3 hours
Length of night sleep
≤7 hours
>7 hours (ref)
Exercise
Yes
No (ref)
Income status
< country’s standard
≥ country’s standard (ref)

Normal
(n=204)

p-value

Crude OR (95% CI)

f (%)

24 (48.0)
12 (24.0)
14 (28.0)

13 (17.1)
13 (17.1)
50 (65.8)

<0.001*
0.017*

6.59 (2.69 – 16.19)
3.30 (1.23 – 8.81)

204 (100.0)
0 (0.0)

20 (9.8)
184 (90.2)

<0.001*

3681 (221 – 61295)

204 (100.0)
0 (0.0)

0 (0.0)
22 (100.0)

<0.001*

18405 (356 – 950247)

143 (70.1)
61 (29.9)

66 (32.4)
138 (67.6)

<0.001*

4.90 (3.22 – 7.46)

204 (100.0)
0 (0.0)

199 (97.5)
5 (2.5)

0.061

11.28 (0,62 – 205.27)

204 (100.0)
0 (0.0)

185 (90.7)
19 (9.3)

0.009*

42.99 (2.58 – 717.14)

108 (52.9)
96 (47.1)

124 (60.8)
80 (39.2)

0.110

0.73 (0.49 – 1.08)

5 (2.5)
199 (97.5)

88 (43.1)
116 (56.9)

<0.001*

0.03 (0.01 – 0.08)

1 (0.5)
203 (99.5)

3 (1.5)
201 (98.5)

0.623

0.33 (0.03 – 3.20)

204 (100.0)
0 (0.0)

179 (87.7)
25 (12.3)

0.005*

58.10 (3.51 – 961.30)

202 (99.0)
2 (1.0)

200 (98.1)
4 (1.9)

0.685

2.02 (0.37 – 11.15)

0 (0.0)
204 (100.0)

8 (3.9)
196 (96.1)

0.049*

0.06 (0.003 – 0.99)
1

200 (98.0)
4 (2.0)

196 (96.1)
8 (3.9)

0.241

2.04 (0.61 – 6.89)

204 (100.0)
0 (0.0)

204 (100.0)
0 (0.0)

-

162 (79.4)
42 (20.6)

79 (38.7)
125 (61.3)

<0.001*

6.10 (3.93 – 9.49)

26 (12.7)
178 (87.3)

100 (49.0)
104 (51.0)

<0.001*

0.15 (0.09 – 0.25)

129 (63.2)
75 (36.8)

131 (64.2)
73 (35.8)

0.837

0.96 (0.64 – 1.44)

Note. Chi-square test, *Statistically significant (p<0.05)

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Nurse Media Journal of Nursing, 13(2), 2023, 147
3.2 Multivariate analysis of the most relating risk factors of ovarian cancer
The logistic regression test was used to perform a multivariate analysis. Table 2 shows the
final model, which identifies the factors most associated with ovarian cancer and increasing the
risk of ovarian cancer, such as age >45 years, junior high school education or below, the number
of pregnancies, history of surgery, overweight/obesity, and ≤ 7 hours of sleep duration. According
to the AOR results, education has the highest level of risk, with an AOR of 225 (95% CI: 34.22–
1479.52), meaning that a low education (junior high school and below) increases the risk of
ovarian cancer by 225 times, followed by the number of pregnancies (AOR: 110.38), history of
surgery (OR: 51.06), age> 45 years (AOR: 19.76), duration of sleep ≤7 hours (AOR: 15.75), and
overweight/obese (AOR: 6.04). The results also reveal factors associated with lowering cancer
risks, such as menstrual complaints, marriage history, family history of cancer, and coffee
consumption. A family history of cancer was the factor with the highest risk reduction, with an
AOR of 0.01 (95% CI: 0.00–0.21), meaning that a family history of cancer could reduce the risk
of ovarian cancer by 99%, followed by a history of marriage, coffee consumption, and menstrual
complaints.
Table 2. Results of multivariate analysis (n=408)

0.010*
<0.001*
0.006*
0.045*

Adjusted
OR
19.76
225.00
0.19
0.02

Lower
2.03
34.22
0.06
0.00

95% CI
Upper
192.55
1,479.52
0.63
0.92

0.022*
0.110

1
110.38
27.29

1.98
0.47

6,167.87
1,575.24

<0.001*
0.472
0.003*
0.003*
<0.001*
<0.001*

1
51.06
0.49
0.01
6.04
0.04
15.75

8.58
0.07
0.00
1.87
0.01
4.30

304.01
3.45
0.21
19.51
0.19
57.64

Variable

P-value

Age >45 years
Education (Junior High School and less)
Menstrual problem Symptoms
Marriage History
Pregnancy number
No history
One time
More than ≥2
History of disease complications
No history
Surgery
Other complication
Family history with cancer
BMI Overweight/Obese
Caffein consumption
Length of night sleep ≤7 Jam
Note. Logistic regression
* Statistically significant (p<0.05)

3.3 Results of the Receiver Operating Characteristic (ROC) test
The ROC test was used to verify the scoring accuracy. In Table 3, the area under the ROC
curve (AUC) value was 0.988 (95% CI 0.973–0.996), meaning that the risk factor scoring model
was very accurate in predicting the incidence of ovarian cancer.
Table 3. Scoring validation using the ROC test (n=408)
Variables
Scoring Model
Cut-off poin
Sensitivity
Specificity

AUC (95%CI)
0.988 (0.973 – 0.996)
>23
93.1%
97.1%

p-value
<0.001

Note. ROC test, AUC = Area Under the Curve
* Statistically significant (p<0.05)

The ROC analysis revealed that the cutoff score was >23, with a sensitivity of 93.1%, a
specificity of 97.1%, and a sensitivity and specificity of >80%, meaning that a cutoff score of >23
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Nurse Media Journal of Nursing, 13(2), 2023, 148
had good accuracy in predicting ovarian cancer incidence. If a woman’s total score is >23, she has
a high risk of ovarian cancer (Figure 2).
Total
Score
SkorTotal

100
Sensitivity: 93.1
Specificity: 97.1
Criterion: >23

Sensitivity

80
60
40
20
0
0

20

40
60
80
100-Specificity

100

Figure 2. ROC curve predicted score on the incidence of ovarian cancer
4. Discussion
This study analyzed contextual factors, reproductive health history, and ADLs associated with
ovarian cancer risks. The results showed that various contextual factors, medical history, and
ADLs were significantly associated with the risk of ovarian cancer. These included being over 45,
having a low education level, and being unemployed. Other factors were related to menstrual and
reproductive history including menarche more than 15 years, menstrual cycles less than 21 days,
menstruation problems, marital status, age of marriage, parity, bleeding and other issues during
childbirth, breastfeeding, and fertility treatment. Next medical history factors included history of
complications of certain diseases, history of surgery, family history of cancer, and history of nonhormonal contraception. Finally ADLs risk factors were eating less than three times, consuming
coffee, defecating one to four times a day, urinating less than three times, and sleep duration less
than seven hours at night. It can be seen that the risk factor variables in this study are more
numerous dan more complex than previous studies. Previous research in Indonesia only analyzed
between one and three factor variables; none examined as many as this study that analyzed ten
factor variables (Purwoko, 2018; Widodo et al., 2019).
This study also identifies the same associated factors and several different factors from
previous study. Previous study has shown that there is a significant relationship between age,
occupation (Crane et al., 2014; Dixon-Suen et al., 2019), number of pregnancies, oral
contraceptives, menarche, obesity, nutritional patterns, exercise, smoking, family history,
surgical history, and ovarian cancer (Aarestrup et al., 2019; Bhatti et al., 2013; Bodelon et al.,
2013; Jiang et al., 2014; La Vecchia, 2017; Tworoger & Huang, 2016). There is no significant
relationship between alcohol consumption and the incidence of ovarian cancer (La Vecchia, 2017).
Obesity is associated with ovarian cancer, but not the specific types of food consumed (Crane et
al., 2014). In line with previous studies, this study’s results and the factors that were found to be
significant give new insight into studies that examine a more diverse range of factors associated
with ovarian cancer especially from Indonesian participant’s culture, such as less alcohol
consumption in Indonesian women (13.2%) (Our World in Data, 2016), adolescent’s marriage
before 15 years old (0.5%) (UNICEF, 2018), and high rate in smoking (World Health
Organization, 2020). Multi-center research and various cultures needs to be carried out that
would describe and identify more specific ovarian cancer risk factors, then the cancer prevention
and control program approaches, especially for ovarian cancer, will develop with more specific,
effective and efficient.
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Nurse Media Journal of Nursing, 13(2), 2023, 149
The multivariate test determined the factors most associated with ovarian cancer, as well as
the characteristics of the relationship that increases or reduces the risk of ovarian cancer based
on the bivariate analysis. The multivariate test was conducted in two stages of modeling, with the
final model revealing that age >45 years, junior high school education and below, number of
pregnancies, history of surgery, overweight/obese, and ≤7 hours of sleep duration all increased
the risk of ovarian cancer. Meanwhile, menstrual problems, a history of marriage, a family history
of cancer, and coffee consumption all reduce the risk of ovarian cancer. The contextual factors
that increased risk of ovarian cancer in this study were age and low education. Previous research
has found that older age is a risk factor for ovarian cancer in women over the age of 65
(Mohammadian et al., 2017). Moreover, low education can increase risk factor of ovarian cancer
(Alberg et al., 2016). Age and education were common risks for various health problems, including
ovarian cancer. However, if a screening tool is developed for self-early warning of ovarian cancer,
this cancer can be managed appropriately and optimally. This risk factor analysis study can be
fundamental data for developing tools for self-early signs of ovarian cancer.
The health history variables identified that obesity and surgery history as factors that
increased risk ovarian cancer. Obesity has been found as predominantly impacting the hormonal,
inflammatory, and metabolic channels in the context of health history, notably gynecological
cancers (Staley et al., 2020). Next, surgery is still the most successful method of treating cancer;
nonetheless, roughly one-third of patients will experience a systemic or local recurrence of the
disease, surgery treating cancer is a double-edged sword because, as has been well discussed for
more than a century, it can sometimes encourage the growth of certain cancers. Based on
anticipated population cancer rates, a retrospective cohort analysis of 13,488 women who
underwent augmentation mammoplasty and were monitored for 12 years found a 21% increase in
total cancer risk. There was a considerable and more than doubled incidence of leukemia, brain
cancer, and stomach cancer among implant patients (Goldstein & Mascitelli, 2011). This study
focuses on ovarian cancer, which is a different type of cancer from previous studies, however
illustrates that the same thing might happen to ovarian cancer.
Daily activity, particularly sleep duration, and its association to cancer risk are still hotly
debated topics. Sleep is crucial for both physical and mental health. The findings of Chen et al’s
study found that neither short nor long sleep duration were significantly associated with cancer
risk. Surprisingly, the subgroup analysis revealed that in Asian people, inadequate sleep duration
was associated with an increased risk of cancer (Chen et al., 2018). Sleep duration is important
for women's health, espesially in Indonesia, women have big roles in taking care of their family,
so they are at risk of experiencing sleep deprivation for a long time. More study needed to assess
in detail the sleep pattern of Indonesian women.
This study found that family history, menstrual history, and coffee consumption as factor that
reduce the risk of ovarian cancer. Previous study found that family history is a significant risk
factor for several common diseases, including diabetes, coronary heart disease, stroke, and
malignancies of the breast, ovary, and colon (Acheson et al., 2010). The findings of this study
differ from Acheson et al.’s study; this could be due to the fact that almost all of the respondents
in this study did not have a family history of cancer (c: 98.5%, nc: 91.2%), which could have
influenced the results of statistical tests. Unlike consuming tea, which has been proven to
significantly prevent women from ovarian cancer (Zhan et al., 2017), there is minimal literature
analyzing this risk factor in relation to coffee that reduces the risk of cancer. More research on the
effect of coffee consumption on ovarian cancer is needed because drinking coffee habit is currently
popular among men and women both developed and developing countries. We performed the
ROC test to determine whether the results were genuine or not after identifying the most relevant
factors. The AUC value of 0.988 indicates that this result had a good accuracy to predict the risk
factor of ovarian cancer (Hajian-Tilaki, 2013). The ROC test enhanced the risk factor associated
with ovarian cancer that was noted from the AUC value (95 CI, 0.973-0.996). Although the
findings of this study indicate that the risk factors for ovarian cancer are valid, further
investigation using a qualitative approach is required to supplement and reinforce the
quantitative findings of this study.
5.

Implications and limitations
As health service providers, nurses play a role in preventing disease, improving health, and
making innovations in health services, including women’s health. The results of this study can be
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Nurse Media Journal of Nursing, 13(2), 2023, 150
an initial step toward developing an independent early detection instrument for women at high
risk of ovarian cancer, which could become an independent early warning system for women,
allowing them to take preventive actions as soon as risk factors are found, protecting them from
the advanced stages of cancer.
This study was only conducted at a cancer referral hospital in West Java, where particular
ethnicities may predominate, it has an impact on the identified risk factors for ovarian cancer.
Another limitation is that medical records, which are the main source of data in this study, are
still in paper-based form, thus patient data may not be adequately identifiable.
6. Conclusion
The ovarian cancer is influenced by various risk factors, including contextual factors, medical
history, and activities of daily living (ADLs). These factors can either reinforce or inhibit the
chance of developing ovarian cancer. The findings of this study have contributed to the existing
knowledge on the factors associated with ovarian cancer. However, it is evident that further
investigation is necessary to conduct a more comprehensive analysis of the data and gather
additional qualitative information. This can be achieved by employing alternative research
methodologies or by including a larger and more diverse sample of participants. In addition,
healthcare practitioners have the potential to employ this information as foundational data for
future research in the development of a self-detection tool for assessing the risk of ovarian cancer.
Acknowledgment
The authors would like to thank to Universitas Padjadjaran that funded this study.
Author contribution
RW: conceptualization (lead), methodology (lead), writing-original draft (lead), data
collection (lead) review (lead) and editing (lead); WN: conceptualization (supporting), data
collection (lead), data analysis (lead), and writing-developed draft (supporting); KN: review
(lead), discussion (equal) and writing-developed draft (supporting); TP: review (equal),
discussion (equal) and writing-developed draft (supporting) editing (lead)
Conflict of interest
There were no conflicts of interest in this publication.
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