Background
The overall burden of cancer has been increasing in developing countries [
1]. The World Health Organization (WHO) International Agency for Research on Cancer (IARC) estimated that there will be up to 21.7 million new cancer cases and 13 million cancer-related deaths in 2030, with 70% of those cases in low- to middle-income countries (LMICs) [
2‐
4]. Although cancer mortality rates have declined in high-income countries, LMICs have seen elevated cancer-related mortality rates [
5], owing to a lack of cancer prevention and screening programmes and limited resources to treat cancer [
4,
6].
In LMICs such as Indonesia, cancers are mostly diagnosed at an advanced stage, in which curative treatment is often no longer possible [
7]. For female cancers, breast and cervical cancers remain the leading causes of cancer mortality in Indonesia (21% and 10%, respectively) [
5]. Yet, affordable cervical cancer screening is only available in eight of 34 provinces in Indonesia, [
5,
8] with low awareness and uptake of breast and cervical cancer screening [
5,
8,
9]. The low uptake may be attributable to a range of barriers including a lack of knowledge about cancer prevention as well as widespread misconceptions and fears about cancer and its treatment [
9,
10] also further contribute to the late presentation of disease [
11]. In addition, there are often inequalities in the distribution of healthcare workers throughout the country, resulting in inequalities in healthcare access especially between urban and rural areas [
12]. Nonetheless, the extent of inequalities in cancer screening awareness and participation in LMICs, such as Indonesia, is often unclear. Additionally, breast self-examination (BSE) as a tool to screen for breast cancer is common in these countries, although there is evidence to suggest that this technique lacks effectiveness [
13].
We performed a cross-sectional study of 5397 cancer-free Indonesian women aged 40 and older, the target group for breast and cervical cancer screening based on American Cancer Society Guidelines [
14]. We used multilevel regression analyses to identify potential determinants of cervical and breast cancer screening awareness and participation to gain further insight into predisposing, enabling, and need factors which could potentially inform targeted prevention programmes in low-resource settings.
Results
Characteristics of the study participants (
N = 5397) are presented in Table
1. The mean age of participants was 52.9 years. The majority of women were of Javanese ethnicity, married, lived in urban areas, and had not completed high school. Nearly a quarter (23%) of women had three or more co-morbidities, and a similar proportion were overweight (BMI ≥ 25 kg/m
2). Only 1058 (20%) women were aware of Pap smears and 297 among them had undergone at least one Pap smear in their lifetime. A total of 251 (5%) participants were aware of mammography, among which five had had a mammogram in the previous year. Twelve percent of women reported they had performed BSE in the past year. We additionally present the demographic characteristics of women who did not respond to questions on cancer screening (Additional file
1: Table S1), which comprised 9.6% of women aged 40 and older. Compared to women who provided a response to cancer screening, non-responders were in average older, less educated, had lower household expenditure, and more likely to be non-Javanese or unmarried.
Table 1
Characteristics of study participants (N = 5397)
Predisposing | | |
Age | 40–60 | 4098 (75.93) |
≥60 | 1299 (24.07) |
Ethnicity | Not Javanese | 2900 (43.73) |
Javanese | 2497 (46.27) |
Residence | Rural | 2339 (43.34) |
Urban | 3058 (56.66) |
Marital status | Not married | 1423 (26.37) |
Married | 3974 (73.63) |
Education | Less than high school | 3534 (65.48) |
High school | 1529 (28.33) |
Higher education | 334 (6.19) |
Monthly household expenditure | Tertile 1–2 | 3561 (65.98) |
Tertile 3 | 1836 (34.02) |
Tobacco smoking | Never | 5157 (95.55) |
Former | 63 (1.17) |
Ever | 177 (3.28) |
Physical activity | Sedentary | 1627 (30.15) |
Lightly active | 1793 (33.22) |
Moderately active | 1460 (27.05) |
Vigorously active | 517 (9.58) |
Openness | < 4 | 4715 (87.36) |
≥4 | 682 (12.64) |
Conscientiousness | < 4 | 4690 (86.90) |
≥4 | 707 (13.10) |
Extroversion | < 4 | 3203 (59.35) |
≥4 | 2194 (40.65) |
Agreeableness | < 4 | 1771 (32.81) |
≥4 | 3626 (67.19) |
Neuroticism | < 4 | 3191 (59.13) |
≥4 | 2206 (40.87) |
Enabling | | |
Insured | No | 2754 (51.03) |
Yes | 2643 (48.97) |
Travel time | < 10 min | 4503 (83.44) |
≥10 min | 894 (16.56) |
Participating in social activities | No | 806 (14.93) |
Yes | 4591 (85.07) |
Need | | |
Menopausal status | Premenopausal | 2300 (42.61) |
Postmenopausal | 3097 (57.38) |
Age at menarche | < 14 | 2133 (39.52) |
≥ 14 | 3264 (60.48) |
Co-morbidity score | 0 | 1476 (27.35) |
1 | 1506 (27.90) |
2 | 1162 (21.53) |
3 and more | 1253 (23.22) |
Parent died from cancer | No | 5264 (97.54) |
Yes | 133 (2.46) |
BMI | < 25 kg/m2 | 2879 (53.34) |
≥ 25 kg/m2 | 2518 (46.66) |
CES-D | < 8 | 5097 (94.44) |
≥ 8 | 300 (5.66) |
Determinants of awareness of pap smears
Table
2 shows potential determinants of awareness of Pap smears identified through univariable regressions and grouped according to the Anderson model. Some categories, for instance education levels, were merged in the analysis due to the limited numbers of participants. In the analysis, age, ethnicity, urban residence, marital status, education level, household expenditure, physical activity, openness, extroversion, agreeableness, neuroticism, insurance, distance to healthcare providers, menopausal status, age at menarche, comorbidity score, parental deaths of cancer, overweight, and CESD were associated with awareness of Pap smears. In the multivariable analysis (Table
4), being Javanese (OR: 1.91, 95% CI: 1.52–2.40), living in urban area (OR: 4.28, 3.22–5.67), graduating high school (OR: 7.82, 6.30–9.70), greater household expenditure (OR: 2.31, 1.91–2.80), physical activity (OR: 1.54, 1.25–1.91), agreeable (1.63, 1.30–2.03) and neuroticism traits (OR: 1.23, 1.02–1.49), having insurance (OR: 2.05, 1.69–2.49), and participating in social activities (OR: 2.12, 1.50–2.98) corresponded to higher likelihood of being aware of Pap smears. As shown in Table
4, a decrease in the odds of Pap smear awareness was shown as the distance to a healthcare provider increased (OR: 0.73, 0.55–0.98) and CESD score (OR: 0.68, 0.55–0.85) in the multivariable model.
Table 2
Univariable associations of potential determinants with cancer screening awareness among women 40 years and older without known history of any cancer
Predisposing |
Age | 40–60 | 919 (22.42) | Ref | 220 (5.37) | Ref |
≥60 | 139 (10.70) | 0.32 (0.25–0.40) | 31 (2.39) | 0.44 (0.43–0.45) |
Ethnicity | Not Javanese | 478 (16.48) | Ref | 146 (5.03) | Ref |
Javanese | 580 (23.23) | 1.49 (1.18–1.88) | 105 (4.21) | 0.85 (0.59–1.22) |
Residence | Rural | 166 (7.09) | Ref | 54 (2.31) | Ref |
Urban | 892 (29.16) | 7.74 (5.77–10.37) | 197 (6.44) | 3.57 (2.34–5.44) |
Marital status | Not married | 193 (13.56) | Ref | 43 (3.02) | Ref |
Married | 865 (21.77) | 2.14 (1.74–2.62) | 208 (5.23) | 1.93 (1.34–2.75) |
Education | Less than high school | 223 (5.31) | Ref | 70 (1.98) | Ref |
High school or higher education | 835 (44.82) | 14.01 (11.44–17.16) | 181 (9.72) | 5.08 (3.75–6.90) |
Monthly household expenditure | Tertile 1–2 | 446 (12.52) | Ref | 95 (2.67) | Ref |
Tertile 3 | 612 (33.33) | 3.66 (3.08–4.35) | 156 (8.50) | 3.29 (2.49–4.33) |
Tobacco smoking | Never | 1019 (19.76) | Ref | 241 (4.67) | Ref |
Ever | 39 (16.25) | 0.78 (0.50–1.21) | 10 (4.17) | 0.87 (0.43–1.76) |
Physical activity | Active | 268 (16.47) | Ref | 51 (3.13) | Ref |
Sedentary | 790 (20.95) | 1.49 (1.23–1.80) | 200 (5.31) | 1.85 (1.32–2.60) |
Openness | < 4 | 957 (20.30) | Ref | 226 (4.79) | Ref |
≥4 | 101 (14.81) | 0.77 (0.59–1.00) | 25 (3.67) | 0.94 (0.62–1.44) |
Conscientiousness | < 4 | 929 (19.81) | Ref | 218 (4.64) | Ref |
≥4 | 129 (18.25) | 1.00 (0.78–1.28) | 33 (4.67) | 1.02 (0.69–1.53) |
Extroversion | < 4 | 688 (21.48) | Ref | 167 (5.21) | Ref |
≥4 | 370 (16.86) | 0.75 (0.63–0.88) | 84 (3.83) | 0.76 (0.57–1.00) |
Agreeableness | < 4 | 199 (11.24) | Ref | 50 (2.82) | Ref |
≥4 | 859 (23.69) | 2.53 (2.08–3.08) | 201 (5.54) | 1.91 (1.37–2.64) |
Neuroticism | < 4 | 528 (16.55) | Ref | 111 (3.48) | Ref |
≥4 | 530 (24.03) | 1.65 (1.40–1.95) | 140 (6.35) | 1.88 (1.87–1.89) |
Enabling |
Insured | No | 340 (12.35) | Ref | 82 (2.98) | Ref |
Yes | 718 (27.17) | 2.52 (2.11–3.01) | 169 (6.39) | 2.10 (1.57–2.82) |
Travel time | < 10 min | 952 (21.14) | Ref | 232 (5.15) | Ref |
≥10 min | 106 (11.86) | 0.48 (0.37–0.62) | 19 (2.12) | 0.42 (0.26–0.70) |
Participating in social activities | No | 65 (8.06) | Ref | 20 (2.48) | Ref |
Yes | 993 (21.63) | 3.27 (2.39–4.47) | 231 (5.03) | 2.25 (1.35–3.75) |
Need |
Menopausal status | Premenopausal | 641 (27.87) | Ref | 160 (6.96) | Ref |
Postmenopausal | 417 (13.46) | 0.36 (0.30–0.42) | 91 (2.94) | 0.41 (0.31–0.54) |
Age at menarche | < 14 | 472 (22.13) | Ref | 115 (5.39) | Ref |
≥ 14 | 586 (17.95) | 0.77 (0.65–0.91) | 136 (4.17) | 0.73 (0.56–0.95) |
Co-morbidity score | 0–1 | 692 (23.21) | Ref | 179 (6.00) | Ref |
≥ 2 | 366 (15.15) | 0.53 (0.45–0.63) | 72 (2.98) | 0.54 (0.40–0.72) |
Parent died from cancer | No | 1009 (19.17) | Ref | 238 (4.52) | Ref |
Yes | 49 (36.84) | 2.58 (1.62–4.11) | 13 (9.77) | 2.25 (1.15–4.41) |
BMI | < 25 kg/m2 | 457 (15.87) | Ref | 121 (4.20) | Ref |
≥ 25 kg/m2 | 601 (23.87) | 1.49 (1.26–1.76) | 130 (5.12) | 1.12 (0.85–1.48) |
CES-D | < 8 | 820 (21.42) | Ref | 190 (4.96) | Ref |
≥ 8 | 238 (15.18) | 0.73 (0.60–0.88) | 61 (3.89) | 0.86 (0.63–1.17) |
Determinants of awareness of mammography
Similar patterns of associations between potential predictors and awareness of Pap smears were observed for awareness of mammography in the univariable analysis (Table
2). In the multivariable model, we found higher odds of being aware of mammography in women living in urban areas (OR: 4.51, 95% CI: 3.36–6.06), women who had graduated high school (OR: 7.70, 6.19–9.58), women with higher household expenditure (OR: 2.28, 1.88–2.76), women that do physical activity (OR: 1.54, 1.24–1.90), women who have greater agreeableness (OR: 1.67, 1.33–2.09), women with neuroticism traits (OR: 1.24, 1.03–2.09), women who have insurance (OR: 2.01, 1.65–2.44), and women who participate in social activities (OR: 2.29, 1.62–3.23) (Table
4). Living further from health services (OR: 0.70, 0.52–0.94) and being postmenopausal (OR: 0.79, 0.63–0.99) were inversely associated with being aware of mammography in the multivariable model.
Determinants of pap smear participation
We assessed factors associated with participation in Pap smears (Table
3), and only found education level, household expenditure, insurance, menopausal status and comorbidity score to be associated with participation in Pap smears in the univariable analysis. In the multivariable models, women were more likely to have had a Pap smear if they had graduated high school (OR: 1.58, 95% CI: 1.04–2.41), had higher household expenditure (OR: 1.94, 1.40–2.69), had insurance (1.57 (1.12–2.22), and had two or more co-morbidities (1.45, 1.01–2.08) (Table
4).
Table 3
Univariable associations of potential determinants with cancer screening practice among women 40 years and older without known history of any cancer
Predisposing |
Age | 40–60 | 254 (27.64) | Ref | 538 (13.13) | Ref |
≥60 | 43 (30.94) | 1.06 (0.55–2.03) | 67 (5.16) | 0.33 (0.25–0.43) |
Ethnicity | Not Javanese | 133 (27.82) | Ref | 302 (10.41) | Ref |
Javanese | 164 (28.28) | 0.58 (0.72–1.33) | 303 (12.13) | 1.20 (0.96–1.51) |
Residence | Rural | 50 (30.12) | Ref | 129 (5.52) | Ref |
Urban | 247 (27.69) | 0.85 (0.56–1.28) | 476 (15.56) | 3.54 (2.74–4.58) |
Marital status | Not married | 52 (26.94) | Ref | 99 (6.96) | Ref |
Married | 245 (28.32) | 1.10 (0.76–1.60) | 506 (12.73) | 2.06 (1.62–2.61) |
Education | Less than high school | 48 (21.52) | Ref | 141 (3.99) | Ref |
High school or higher education | 249 (29.82) | 1.70 (1.16–2.50) | 464 (24.91) | 8.24 (6.67–10.18) |
Monthly household expenditure | Tertile 1–2 | 92 (20.63) | Ref | 265 (7.44) | 1.70 (1.54–1.88) |
Tertile 3 | 205 (33.50) | 2.08 (2.07–2.09) | 340 (18.52) | |
Tobacco smoking | Never | 286 (28.07) | Ref | 580 (11.25) | Ref |
Ever | 11 (28.21) | 1.06 (0.50–2.26) | 25 (10.42) | 0.95 (0.60–1.51) |
Physical activity | Active | 72 (26.87) | Ref | 157 (9.64) | Ref |
Sedentary | 225 (28.48) | 1.12 (0.80–1.56) | 448 (11.88) | 1.30 (1.06–1.60) |
Openness | < 4 | 272 (28.42) | Ref | 544 (11.54) | Ref |
≥4 | 25 (24.75) | 0.89 (0.55–1.45) | 61 (8.94) | 0.79 (0.59–1.07) |
Conscientiousness | < 4 | 264 (28.42) | Ref | 531 (11.32) | Ref |
≥4 | 33 (25.58) | 0.83 (0.53–1.29) | 74 (10.47) | 0.95 (0.72–1.25) |
Extroversion | < 4 | 199 (28.92) | Ref | 400 (12.49) | Ref |
≥4 | 98 (26.49) | 0.96 (0.72–1.30) | 205 (9.34) | 0.74 (0.61–0.90) |
Agreeableness | < 4 | 59 (29.65) | Ref | 107 (6.04) | Ref |
≥4 | 238 (27.71) | 0.97 (0.69–1.39) | 498 (13.73) | 2.48 (1.98–3.11) |
Neuroticism | < 4 | 135 (25.57) | Ref | 294 (9.21) | Ref |
≥4 | 162 (20.57) | 1.31 (0.99–1.74) | 311 (14.09 | 1.61 (1.34–1.93) |
Enabling |
Insured | No | 76 (22.35) | Ref | 217 (7.87) | Ref |
Yes | 221 (30.78) | 1.70 (1.23–2.37) | 388 (14.68) | 1.97 (1.63–2.39) |
Travel time | < 10 min | 275 (28.87) | Ref | 545 (12.10) | Ref |
≥10 min | 22 (20.75) | 0.61 (0.36–1.02) | 60 (6.71) | 0.52 (0.39–0.70) |
Participating in social activities | No | 13 (20.00) | Ref | 38 (4.71) | Ref |
Yes | 284 (28.60) | 1.56 (0.81–3.00) | 567 (12.35) | 2.85 (1.99–4.08) |
Need |
Menopausal status | Premenopausal | 163 (25.42) | Ref | 403 (17.52) | Ref |
Postmenopausal | 134 (32.13) | 1.38 (1.04–1.85) | 202 (6.52) | 0.32 (0.26–0.38) |
Age at menarche | < 14 | 138 (29.24) | Ref | 274 (12.85) | Ref |
≥ 14 | 159 (27.13) | 0.87 (0.65–1.15) | 331 (10.14) | 0.80 (0.66–0.96) |
Co-morbidity score | 0–1 | 73 (25.29) | Ref | 412 (13.82) | Ref |
≥ 2 | 58 (33.33) | 1.58 (1.18–2.12) | 193 (7.99) | 0.52 (0.43–0.63) |
Parent died from cancer | No | 281 (27.85) | Ref | 573 (10.89) | Ref |
Yes | 16 (32.65) | 1.30 (0.68–2.50) | 32 (24.06) | 2.42 (1.54–3.83) |
BMI | < 25 kg/m2 | 119 (26.04) | Ref | 264 (9.17) | Ref |
≥ 25 kg/m2 | 178 (29.62) | 1.18 (0.89–1.58) | 341 (13.54) | 1.44 (1.20–1.74) |
CES-D | < 8 | 235 (28.66) | Ref | 439 (11.47) | Ref |
≥ 8 | 62 (26.05) | 0.91 (0.65–1.28) | 166 (10.59) | 0.99 (0.81–1.22) |
Table 4
Multivariable associations of potential determinants with cancer screening awareness among women 40 years and older without known history of any cancer
Predisposing |
Age | 0.72 (0.52–0.99) | 0.74 (0.53–1.01) | | 0.82 (0.58–1.18) |
Javanese | 1.91 (1.52–2.40) | | | |
Urban residence | 4.28 (3.22–5.67) | 4.51 (3.36–6.06) | | 1.97 (1.54–2.51) |
Married | 1.12 (0.88–1.43) | 1.13 (0.88–1.44) | | 1.10 (0.85–1.44) |
High school or higher education | 7.82 (6.30–9.70) | 7.70 (6.19–9.58) | 1.58 (1.04–2.41) | 4.26 (3.39–5.36) |
Monthly household expenditure – higher tertile | 2.31 (1.91–2.80) | 2.28 (1.88–2.76) | 1.94 (1.40–2.69) | 1.68 (1.38–2.05) |
Physically active | 1.54 (1.25–1.91) | 1.54 (1.24–1.90) | | 1.24 (1.00–1.54) |
Openness ≥4 | 0.90 (0.67–1.22) | | | |
Extroversion ≥4 | 0.87 (0.71–1.05) | 0.83 (0.69–1.01) | | 0.83 (0.68–1.02) |
Agreeableness ≥4 | 1.63 (1.30–2.03) | 1.67 (1.33–2.09) | | 1.61 (1.26–2.05) |
Neuroticism ≥4 | 1.23 (1.02–1.49) | 1.24 (1.03–2.09) | | 1.19 (0.98–1.44) |
Enabling |
Have insurance | 2.05 (1.69–2.49) | 2.01 (1.65–2.44) | 1.57 (1.12–2.22) | 1.44 (1.18–1.76) |
Travel ≥10 min to health service | 0.73 (0.55–0.98) | 0.70 (0.52–0.94) | | 0.76 (0.56–1.03) |
Participating in social activities | 2.12 (1.50–2.98) | 2.29 (1.62–3.23) | | 2.00 (1.38–2.88) |
Need |
Postmenopausal | 0.76 (0.61–0.96) | 0.79 (0.63–0.99) | 1.29 (0.90–1.83) | 0.58 (0.56–1.03) |
Age at menarche ≥14 | 0.98 (0.93–1.02) | 0.98 (0.93–1.03) | | 0.95 (0.90–1.00) |
Co-morbidity score ≥ 2 | 1.18 (0.92–1.50) | 1.09 (0.86–1.40) | 1.45 (1.01–2.08) | 1.10 (0.85–1.41) |
Parent died from cancer | 1.50 (0.90–2.50) | 1.50 (0.89–2.52) | | 1.59 (0.99–2.54) |
BMI ≥ 25 kg/m2 | 1.15 (0.95–1.39) | | | 1.07 (0.89–1.30) |
CES-D ≥ 8 | 0.68 (0.55–0.85) | | | |
Determinants of BSE practice
A number of factors were associated with having performed BSE in the past year in univariable analyses (Table
3). In the multivariable analysis, those associated with higher odds of practicing BSE were living in urban areas (OR: 1.97, 95% CI: 1.54–2.51), had higher education (OR: 4.26, 3.39–5.36), had higher household expenditure (OR: 1.68, 1.38–2.05), had higher agreeable traits (OR: 1.61, 1.26–2.05), had insurance (OR: 1.44, 1.18–1.76), and engaged in social activities (OR: 2.00, 1.38–2.88) (Table
4). A borderline association was shown for physical activity (OR: 1.24, 95% CI: 1.00–1.54) and having menarche at age 14 or older (OR: 0.95, 0.90–1.00 compared to at younger ages) in the multivariable model.
Sensitivity analyses
Results were also similar when we used BSE at least twice (N = 723) instead of once in the past year (N = 796) to define women who practiced BSE as the outcome, but this did not alter our findings (data not shown).
Discussion
Our study identified predisposing, enabling, and need factors associated with awareness of cancer screening and participation in Indonesian women. Most persistent associations were observed for socio-economic determinants, particularly higher education, household expenditure, and ownership of health insurance, which were associated with higher awareness of Pap smears and mammography, and higher odds of participating in Pap smears and BSE. A similar positive association was observed for social activity participation with awareness of Pap smears and BSE practice, whereas distance to nearest health centres was inversely associated with awareness of Pap smears and mammography. Our findings also uncovered associations between personality traits, and Pap-smear awareness and participation and BSE practice which remained when taking into account other determinants.
Despite the increasing cancer burden, most LMICs are yet to publish national guidelines for screening and early detection of breast and cervical cancers [
5,
23]. In other LMICs in which national cancer screening programmes have been introduced, such as those in the Middle East and North Africa where screening ranges from 2% to 70% of the at-risk population, improving participation rates remains a challenge [
24]. In Sub-Saharan Africa, fewer than 5% of women at risk are estimated to have been screened for cervical cancer [
25,
26]. Population-based cervical cancer screening programmes have been in place for more than 10 years in India, however, participation rates are also relatively low [
27,
28]. The Indonesian Ministry of Health has recently released new recommendations for preventive measures against cervical and breast cancer (PERMENKES RI No.34/2015) [
29]. Approximately 34.5 million Indonesian women are expected to participate in this breast and cervical cancer screening program [
29]. According to government recommendations, health promotion should be conducted through public events, media, religious communities, and other civic society channels. Preventive measures include mass screening, mainly for cervical cancer using visual inspection with acetic acid, should be organised as public events. Women in the target age groups may also request examinations for early detection at healthcare facilities. However, no formal invitation for screening is sent to individuals, and there is a lack of clear guidelines regarding the use of mammography. In 2015, only 904,099 (4.94%) women had completed screening and early detection examination for breast and cervical cancer, a similar figure to that observed in this study. The target coverage, however, is 50% by 2019 [
29].
Most women in developing countries seek medical care after they develop symptoms. For instance, more than 70% of cervical cancer patients in developing countries visited a hospital once their cancer had already infiltrated the parametrium [
30,
31]. A population based-study conducted in Indonesia demonstrated that implementation of small-scale cervical cancer screening project reached only 24% of females in the target group despite the implementation of a mobile screening service to reach more inaccessible areas [
32]. We did not find any report evaluating existing programmes or intervention approaches for breast cancer screening. However, it is worth noting that mammography and breast ultrasonography are currently only covered by the national universal health insurance in particular health facilities, which may explain the low cancer screening awareness and participation more generally.
Only a few studies have addressed the role of mental health and personality traits in cancer screening awareness and participation in LMICs [
33,
34]. In our study, a higher CES-D score, which is linked to symptoms of depression, was associated with low awareness of Pap smears, but higher odds of BSE practice. This corroborates previous findings linking stress and depression, which are generally more common in individuals of low SES [
35], with health-related behaviours [
34]. Community support might be required to achieve the desirable level of awareness and participation in cancer screening, especially in women with psychiatric comorbidities. We found associations between higher agreeableness and higher awareness of Pap smears and BSE practice, whereas higher neuroticism was linked with higher awareness of cancer screening. Using a similar approach, two studies also reported associations between personality traits and cancer-related health behaviours, with higher conscientiousness associated with higher participation in bowel and prostate cancer screenings [
36,
37]. The positive correlation between conscientiousness and cancer screening awareness did not reach statistical significance in our study. However, our findings support the use of personality-tailored approaches to raise awareness of and participation in cancer screening among women.
As shown in our study and previous ones, sociodemographic determinants including household socio-economic status, ethnicity [
38], rural residence, health expenditure, and healthcare access [
38] are associated with participation in breast and cervical cancer screening [
39]. In addition to these factors, we demonstrated that existing comorbidities were associated with awareness of and participation in screening of breast and cervical cancers. These findings may indicate a complex relationship between health and sociodemographic factors in determining population awareness of, and participation in cancer screening. Therefore, multiple health policies are required to improve the public’s awareness of screening and other initatives as well as the healthcare system’s ability to deliver these initiatives. Interventions may also be needed to advance the skills of primary caregivers for detecting breast and cervical cancer, to promote prompt referrals, to strengthen the system’s capacity for diagnostic imaging, cytology, and histopathology, and to deliver multimodal breast and cervical cancer treatment. Moreover, an effective nationwide cancer registry needs to be established to map cancer incidence and to coordinate screening and evaluation efforts.
The main strength of this study lies in the large number of participants, who live in areas covering 83% of the population in Indonesia in 1993. We were able to account for community clustering and various potential determinants of cancer screening awareness and participation in women. A limitation of this study was that cancer screening awareness only relied on dichotomous responses of questionnaires, without any additional responses allowing for cross-validation and potentially more qualitative work. Additionally, most information was self-reported. However, any misclassification is likely to have been non-differential. We did not use specific cancer questionnaires to measure awareness such as the UK Cancer Awareness Measure [
40], since the survey was not originally designed for this particular purpose. Development and validation of a cancer awareness measurement tool that is socioculturally relevant to the Indonesian population is therefore necessary to refine our understanding of the variability in awareness of cancer screening in Indonesia. We were only able to capture mammography use in the past year due to data availability, and this may be a subject of further investigations. It should also be noted that less educated women may have been less familiar with certain medical terminology, although in Indonesia, the terms ‘Pap smear’ and ‘mammography’ are commonly used in the primary care settings [
29]. However, we still observed associations between other factors and awareness to either Pap smears or mammography when adjusting for educational levels. Spurious correlations may be of concern when performing multiple comparisons as shown in our study. However, we planned our analyses based on a priori models and our results are explained by potential socioeconomic and health-related mechanisms, and are confirmed by findings from other studies. Therefore, the observed association is unlikely to be spurious [
41], although a discrepancy with the strength of the true association is possible due to the small number of participants. Women who responded to screening questionnaires may have different characteristics compared to all women aged 40 and older. Furthermore, although IFLS5 covered most respondents from the original IFLS1 survey, there have been rapid demographic changes in Indonesia [
42]. These patterns may reduce the generalisability of our findings. However, demographic transition is well-reflected in the study population, such as the greater number of women living in urban areas in IFLS5 as opposed to the majority living in rural areas in 1993 [
42]. Furthermore, this cohort effect is unlikely to affect the internal validity of the results. Finally, our analyses were cross-sectional and only imply associations. Untangling causal associations is necessary to identify key modifiable factors that improve or worsen awareness of and participation in cancer screening.