Sumadi L. Anwar1,2, Gindo Tampubolon3, Mieke Van Hemelrijck4, Susanna H. Hutajulu5, Johnathan Watkins2, Wahyu Wulaningsih2,6, for the PILAR Research Network
1Division of Surgical Oncology Department of Surgery, Faculty of Medicine Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
2PILAR Research and Education, 20 Station Road, Cambridge CB1 2JD, UK
3Cathie Marsh Institute for Social Research, University of Manchester, Manchester M13 9PL, UK
4Translational Oncology and Urology Research, King’s College London UK SE1 9RT
5Division of Medical Haematology and Oncology Department of Internal Medicine, Faculty of Medicine Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
5MRC Unit for Lifelong Health and Ageing, University College London, London WC1B 5JU, UK
MRC Unit for Lifelong Health and Ageing
University College London
33 Bedford Place London WC1B 5JU, UK
Phone: +44(0)20 7670 5726
Running title: Cancer screening awareness and participation in women
Conflict of interest: All authors declare no conflict of interest exists.
Word count: 275 (Abstract), 3,680 (Text)
Background: Cancer screening awareness and participation may vary in low- and middle-income countries lacking established national screening programmes. We evaluated potential determinants of awareness about and participation in breast and cervical cancer screening and breast self-examination (BSE) in older women in a large survey in Indonesia.
Methods: From the fifth Indonesian Family Life Survey (2014-2015), a total of 5,397 women aged 40 and older without any history of cancer who responded to questionnaires concerning Pap smear, mammography, and BSE were included. Multilevel modelling was used to assess potential determinants in relation to awareness about Pap smear and mammography, and participation in Pap smear and BSE practice. Multivariable analyses were performed to identify independent predictors of cancer screening and participation.
Results: Of the 5,397 respondents, 1,058 (20%) women were aware of Pap smears, and 297 among them ever had the procedure. Only 251 (5%) participants were aware of mammography. A total of 605 (12%) of women reported they performed BSE. Higher education and household expenditure were consistently associated with higher odds of awareness about Pap smear and mammography (e.g. odds ratio [OR] of being aware of Pap smear and mammography: 7.22 (95% CI: 5.84-8.94) and 2.55 (1.81-3.59), respectively, for high school graduates compared to women with less educational attainment in the multivariable models), and participation in Pap smear and BSE. We also identified enabling factors linked with greater cancer screening awareness and participation, including health insurance, shorter distance to health services, and social participation.
Conclusion: There are socioeconomic disparities in cancer screening awareness and participation among Indonesian women. Our findings may help inform targeted health promotion and screening for cancer in the presence of limited infrastructure.
Keywords: breast cancer, screening, cervical cancer, Pap smear, breast self-examination
The overall burden of cancer has continuously increased in developing countries. The World Health Organization (WHO) International Agency for Research on Cancer (IARC) estimated that the cancer burden will grow up to 21.7 million new cancer cases and 13 million related deaths in 2030, with 70% of cases predicted to be found 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 , owing to a lack of cancer prevention and screening programmes and limited infrastructure.[4,6]
In LMICs such as Indonesia, cancers are mostly diagnosed at advanced stages, in which curable treatment is no longer an option. For female cancers, breast and cervical cancers remain the leading causes of cancer mortality in Indonesia (21% and 10%, respectively). 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 the lack of knowledge about cancer prevention, early detection, and treatment, poverty, cultural and religious beliefs. Widespread misconceptions and fears about cancer and its treatment also further contribute to the late presentation of disease. In addition, there are often inequalities of distribution of healthcare workers throughout the country, resulting in wide gaps of overall healthcare access especially between urban and rural areas. Nonetheless, the extent of inequalities in cancer screening awareness and participation in LMICs such as Indonesia is often not clear. Additionally, breast self-examination (BSE) as a tool to screen for breast cancer is common in these countries, despite evidence suggesting its lack of benefit.
We performed a cross-sectional study of 5,397 cancer-free Indonesian women aged 40 and older, which are the target group for breast and cervical cancer screening based on the American Cancer Society Guidelines. In this population, 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.
MATERIAL AND METHODS
The Indonesian Family Life Survey (IFLS) is a longitudinal household survey in Indonesia containing information from questionnaires, as well as physical and laboratory examinations. Data were collected at individual, household, and community levels. The first IFLS (IFLS1) used a stratified sampling scheme based on provinces and urban/rural location. For cost-effectiveness, 14 of the then existing 27 provinces were excluded . The resulting sample included 13 of Indonesia’s 27 provinces, containing 83% of the population: four provinces on Sumatra (North Sumatra, West Sumatra, South Sumatra, and Lampung), all five of the Javanese provinces (DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java), and four provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi). Within each province, enumeration areas (EAs) were randomly chosen from a nationally representative sample frame used in a socioeconomic survey of about 60,000 households in 1993 . Within a selected EA, households were randomly selected. Interviews were carried out with the household head and the spouse, and up to 4 randomly selected other household members as interviewing all members of the household would have been too costly. All members of the original household have been followed up through four subsequent IFLS waves. The present study was based on the fifth wave of IFLS (IFLS5), conducted in 2014-2015. Both original and split-off households were tracked in the IFLS5, resulting in a 76% re-contact rate (including death) for the original IFLS1 household members, and 82% for IFLS1 main respondents. From the IFLS5, we included a total of 5,397 women aged 40 and older who responded to the questions on Pap smear, mammography, and BSE without self-reported history of cancer (Figure S1).
Cancer screening participation and behaviour
The outcomes of the present study measured awareness of Pap smear and mammography, and participation in Pap smear and BSE practice. All responses were self-reported and dichotomous (yes, no). First, during the interview, respondents were asked if they ever heard of Pap smear. Those who responded positively to this question were further asked whether they ever had any Pap smear in their life, and when the examination took place. Participants were also asked whether they ever heard about mammography, and those who responded positively were asked whether they had any mammography in the past year. All study participants were asked whether they ever performed BSE in the past year.
Potential determinants of screening
The Anderson model of healthcare use behaviour  was used to identify potential determinants of cancer screening awareness and participation. This model included three domains: predisposing, enabling, and need factors, which interact in determining one’s health-related behaviour. From IFLS5 (Figure 1), predisposing sociodemographic and lifestyle characteristics of the patients were collected including age, ethnicity, urban or rural residence, marital status, education, monthly household expenditure, smoking status, physical activity and personality traits. Ethnicity was categorised into Javanese, which comprises the majority of Indonesians, and non-Javanese. Education was categorised based on the highest level (less than high school, high school, higher education). Household expenditure was calculated based on the total of food, non-food and education expenditure. Smoking history was used to classify individuals into current, former and never smokers. Participants were defined as vigorously active if they reported participating in more than two vigorous physical activities in the past week for at least 10 minutes each, moderately active if they participated more than 4 times in the past week in moderate to vigorous physical activities of which no more than two could be considered vigorous. Lightly active was defined as activities which were not vigorous or moderate, or walking at least 30 minutes each time, for more than 2 times in the past week. Those who reported no moderate or vigorous physical activity and walked less than 3 times a week were categorised as sedentary. Personality traits were assessed with a short (15-item) Big Five Inventory (BFI-15) questionnaire , with scores ranging from 1-5 for openness, conscientiousness, extroversion, agreeableness, and neuroticism.
Enabling factors identified in the population included insurance ownership and travel distance in minutes to nearest healthcare centres, and participation in any social activities within the past year. On 1 January 2014, the Indonesian government launched a compulsory national health insurance which covers Pap smear , although this scheme has yet to cover mammography. However, unequal access issues for healthcare was reported within the first year the scheme was implemented (2014-2015) , the period in which the present study took place. Therefore, we took into account self-reported insurance coverage in our analysis. Factors representing needs for cancer screening included information on reproductive factors: menopausal status and age at menarche, co-morbidities, parental history of cancer death, diabetes medication, hypertension or dyslipidaemia, body mass index (BMI) calculated from measured weight and height during physical examination and blood pressure. Systolic and diastolic blood pressures were measured with sphygmomanometer and the average between the first and second measurements was used. Co-morbidities were assessed as a comorbidity score similar to the Charlson co-morbidity index, where each co-morbid condition available (hypertension, diabetes, asthma, heart disease, liver disease, stroke, cancer, arthritis, kidney disease, stomach or digestive disease, and memory-related disease) contributed one point to the composite index with additional points given for older age. Finally, to assess the role of mental health, depression was measured with a short version (10-item) of Center for Epidemiologic Studies Depression Scale (CES-D), and a cut-off of 8 was used for a screening of depressive symptoms.
We analysed each determinant as exposure for the following binary outcomes: awareness of Pap smear, awareness of mammography, ever Pap smear, and ever BSE. We did not assess use of mammography as an outcome given the small number of participants with positive response. To take into account IFLS sampling design, we performed multilevel logistic regression analyses to obtain odds ratios (ORs) and their 95% CI for associations between each determinant and outcome. Community clustering was used as a random effect in a two-level multivariable model whereas household clustering was not included to maximise numbers of samples analysed in each category. However, findings between models using both community and household clusters and those with only community clusters were similar for factors which allowed use of both clustering such as continuous age and household expenditure. Univariate analyses were conducted for all potential determinants of cancer screening awareness and practice. These factors comprised different components of the Anderson model , ranging from predisposing factors such as sociodemographic characteristics (e.g. marital status, education, income), enabling factors which included healthcare access, to predisposing factors such as family history of cancer. We additionally included comorbid conditions (e.g. diabetes, obesity as measured by BMI) given the evidence linking comorbidity to cervical and breast cancer screening participation in Western populations . A multivariable model was subsequently performed which included all of these individual determinants. We performed a series of sensitivity analyses including using a different cut-off for BSE, including only systolic or diastolic blood pressure categories in the multivariable models instead of both, and assessing systolic or diastolic blood pressure while excluding participants who received any hypertension drugs. Dataset was prepared with SAS release 9.3 (SAS Institute, Cary, NC). Logistic regression with multilevel modelling was performed with lme4 package in R version 3.3.2 (R Foundation for Statistical Computing, Vienna, Austria).
Characteristics of study participants (N=5,397) 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 area, and did not complete high school. Nearly a quarter (23%) of women had 3 or more co-morbidities, and a similar proportion were overweight (BMI ≥ 25kg/m2). Only 1,058 (20%) women were aware of Pap smear 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 mammogram in the previous year. Twelve percent of women reported they ever performed BSE in the past year. We additionally present demographic characteristics of women who did not respond to questions on cancer screening (Table S1), which comprised of 9.6% women aged 40 and older. Compared to those who provided response, this group was older and had lower household expenditure, less educated, and more likely to be non-Javanese and unmarried.
Determinants of awareness to Pap smear
Table 2 shows potential determinants of awareness to Pap smear identified in univariable regressions, 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 multivariable analysis, being Javanese, living in urban area, greater education levels, household expenditure, physical activity, agreeable and neuroticism traits, having insurance and participating in social activities corresponded to higher likelihood of being aware of Pap smear. As shown in Figure 2 and detailed in Table 4, a decrease in odds of Pap smear awareness was shown for farther distance to health service, higher systolic blood pressure and CESD score. The strongest association was observed for education, with those who finished high school having over 7 times greater odds (95% CI: 5.84-8.94) of being aware of Pap smear compared to those less educated.
Determinants of awareness to mammography
Similar patterns of associations between potential predictors and awareness of Pap smear were observed for awareness of mammography (Table 2). When all predictors were included in the same model, we found higher odds of being aware of mammography in women living in urban area, having high school degree, higher household expenditure, physical activity and greater neuroticism trait, and having insurance (Figure 2, Table 4). The association was also strongest for education, with a 2.55 higher odds (95% CI: 1.81-3.59) of being aware of mammography with graduating compared to not graduating high school.
Determinants of Pap smear participation
We assessed factors associated with participation in ever having a Pap smear in lifetime (Table 3). Unlike cancer screening awareness, no association was observed with living in urban area (Table S2). Women were more likely to have had a Pap smear if they had high school degree, higher household expenditure, insurance, two or more co-morbidities, and diabetes medication (Figure 2, Table 4). Odds of having had a Pap smear doubled in women who had diabetes medication compared to no medication (OR: 2.40, 95% CI: 1.07-5.39), although this analysis was limited by the small number of participants.
Determinants of BSE practice
A number of factors were associated with ever performing BSE in the past year in univariable analyses (Table 3). Among those factors, those associated with higher odds of practicing BSE were Javanese ethnicity, urban residence, higher education and household expenditure, physical activity, higher agreeable trait, having insurance and social participation, and if any of their parents died from cancer (Figure 2, Table 4). Women were less likely to practice BSE if they were postmenopausal. A borderline association was shown for having menarche at age 14 or older (OR for BSE practice: 0.95, 95% CI: 0.90-1.00 compared to at younger ages).
For systolic and diastolic blood pressure categories, we repeated our multivariable models only including either of the two blood pressure measures to avoid collinearity, and excluded those receiving hypertension medications (N=358). These analyses did not alter our results. Results were also similar when we used BSE at least twice (N=723) instead of once a year (N=796) to define women who practiced BSE as the outcome, but this did not alter our findings (data not shown).
Our study identified predisposing, enabling, and need factors associated with awareness of cancer screening and participation in Indonesian women. Strongest 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 smear and mammography, and higher odds of participating in Pap smear and BSE. A similar positive association was observed for social activity participation and awareness of Pap smear and BSE practice, whereas distance to nearest health centres was inversely associated with awareness of Pap smear. 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.
Most developed nations have guidelines for screening and early detection of breast and cervical cancers. The guidelines describe target population, methods and interval of screening, as well as recommended interventions according to the screening results.[22–24] It is estimated that the implementation of screening programmes has successfully decreased the incidence of cervical cancer up to 80% in developed nations. Despite the increasing cancer burden, most of LMICs are yet to have national guidelines for screening and early detection of breast and cervical cancers.[5,26] In other LMICs in which national cancer screening programmes have been introduced, participation rates remain a challenge, for instance cancer screening in the Middle East and North Africa (2-70%). In Sub-Saharan Africa, only less than 5% of women at risk are estimated to have been screened for cervical cancer.[28,29] Population-based cervical cancer screening programmes have been introduced for more than 10 years in India, however, participation rates are also relatively low.[30,31] The Indonesian Ministry of Health has recently released new regulations for management of cervical and breast cancer (PERMENKES RI No.34/2015). Approximately 34.5 million Indonesian women of the target group are expected to participate in this breast and cervical cancer screening program. According to the government program, health promotion is conducted through public events, media, through religious communities and other societies, whereas preventive measures include mass screening, mainly for cervical cancer using visual inspection with acetic acid and are organised as public events. Women within target age groups may also request 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 use of mammography. In 2015, only 904,099 (4.94%) women had completed screening and early detection for breast and cervical cancer, a similar figure as observed in this study. It is planned that the coverage is targeted to reach 50% as of 2019.
Most women in developing countries seek medical care after they develop symptoms. For instance, more than 70% of cervical cancer patients in developing countries came to a hospital when cancer has already infiltrated the parametrium.[33,34] A population based-study conducted in Indonesia demonstrated that implementation of small-scale cervical cancer screening project can reach 24% coverage of total females in the targeted group, indicating relatively low coverage in the population base although the project had implemented mobile screening service to attain hard-to-reach urban areas. We did not find any report evaluating breast cancer screening program using either breast self-examination, clinical breast examination, mammography or sonography in Indonesia. In addition, breast self-examination (BSE) is the most common practice for screening in Indonesia albeit considered lacking in efficacy. In fact, mammography and sonography were not generally accessible and the national universal health insurance only covers such procedures in particular health facilities.
Only a few studies have addressed the role of mental health and personality in cancer screening awareness and participation,[37,38] especially in LMICs. In our study, a higher CES-D score was associated with low awareness of Pap smear, but higher BSE practicing. Stress and depression, which are generally more common in low SES, have also been associated with health related behaviour. Since knowledge can be overridden by cultural and personal belief as well as debilitating depression, community support might be required to achieve the desirable level of awareness and participation in cancer screening especially in women with psychological comorbidities. We found associations between higher agreeableness and higher awareness of Pap smear and BSE practice, whereas higher neuroticism was linked with higher cancer screening awareness. Using a similar tool, two studies also reported associations between personality traits and cancer, with higher conscientiousness associated with higher participation in bowel and prostate cancer screenings [40,41]. The positive correlation between conscientiousness and cancer screening awareness did not reach statistical significance in this study. However, our findings point towards the role of personality traits, which may indicate the usefulness of tailored approaches in encouraging cancer screening awareness and participation in women.
We demonstrated that existing comorbidities were associated with awareness of and participation in screening of breast and cervical cancers. Limited availability of mammography might reduce participation of screening in women who are aware of breast cancer risk. In several studies, some determinants including household socio-economic status, ethnicities, rural residence, country health expenditure, and healthcare access are associated with participation in breast and cervical cancer screening. Our study presents the current cancer screening practices as well as determinants associated with low participation among Indonesian women. Multiple strategies of health policy are required to improve public cancer awareness and healthcare infrastructures to enhance Pap smear participation and breast clinical consultation as well as examination of symptomatic patients. Interventions may also be needed to advance skills of primary care caregivers for detection of breast and cervical cancer, education, and prompt referral, strengthen capacity for diagnostic imaging, cytology, and histopathology, and multimodal breast and cervical cancer treatment. Moreover, a nationwide cancer registry needs to be well established to map the cancer incidence and provide a basis for screening coordination and evaluation.
The main strength of this study lies in the large number of participants, who lived in areas covering 83% of the population in Indonesia. 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 were unable to assess participation in mammography due to the small numbers of study participants. We did not use specific cancer questionnaires to measure awareness such as the UK Cancer Awareness Measure , since the survey was not originally designed for this particular purpose. Development and validation of a cancer awareness measurement tool which is socioculturally relevant to the Indonesian population is therefore necessary to refine our understanding of the variability in awareness of cancer screening in Indonesia. Additionally, we were only able to capture mammography use in the past year due to the available data, and this may be a subject of further investigations. 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 explicable by potential socioeconomic and health-related mechanisms and confirmed by findings from other studies. Therefore, the observed association is unlikely to be spurious , although a discrepancy with the strength of the true association is possible due to the lack 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 such as an increased proportion of older populations . These patterns may reduce the generalisability of the 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 . 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 which improve or worsen awareness of and participation in cancer screening.
We identified a number of factors associated with cervical and breast cancer screening awareness and practice in Indonesia. Improvement of enabling factors such as access to healthcare and social participation may help enhance cancer screening in low-resource settings, particularly among subgroups of women who are socio-economically susceptible to low awareness of cancer screening. The different associations observed with different personality traits support the potential benefit of employing a range of strategies to promote cancer awareness and participation in Indonesia and potentially other LMICs without long-established cancer screening programmes.
The authors would like to thank Mr Eric Hookom for reviewing and commenting on the manuscript draft.
Ethics approval and consent
The research has been performed in accordance with the Declaration of Helsinki. The IFLS surveys and their procedures were reviewed and approved by the following ethics committees: IRBs (Institutional Review Boards) in the United States (at RAND) and in Indonesia at the Universitas Gadjah Mada (UGM) for IFLS5. Informed consent was obtained from all participants.
Consent to publish
All authors have provided their consent for publication of the manuscript.
Availability of data and materials
The dataset is publicly available at the RAND Corporation’s website (http://www.rand.org/labor/FLS/IFLS/ifls5.html).
All authors declare no competing interests.
WW is employed by grants from the UK Medical Research Council [MC_UU_12019/2] and [MC_UU_12019/4]. GT is funded by the UK Medical Research Council [G1001375/1], UK Economic & Social Research Council [ES/L001772/1], and EU Horizon 2020 . Funding for IFLS5 was provided by the National Institute on Aging (NIA), grant 2R01 AG026676-05, the National Institute for Child Health and Human Development (NICHD), grant 2R01 HD050764-05A1 and grants from the World Bank, Indonesia and GRM International, Australia from DFAT, the Department of Foreign Affairs and Trade, Government of Australia.
SLA and WW conceptualised the study. SLA, GT, JW and WW prepared the data used for the analysis. SLA and WW performed the analysis with critical feedback from GT and MVH. All authors interpreted the results of the analysis. SLA wrote the first draft. All authors reviewed and edited the draft and agreed on the final version of the manuscript.
- Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer. 2015;136:E359–86.
- Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-tieulent J, Jemal A. Global Cancer Statistics, 2012. CA a cancer J. Clin. 2015;65:87–108.
- Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Dicker D, Pain A, Hamavid H, Moradi-Lakeh M, et al. The Global Burden of Cancer 2013. JAMA Oncol. 2015;1:505–27.
- De Souza JA, Hunt B, Asirwa FC, Adebamowo C, Lopes G. Global health equity: Cancer care outcome disparities in high-, middle-, and low-income countries. J. Clin. Oncol. 2016. p. 6–13.
- World Health Organization. Cancer Country Profile. Cancer Ctry. Profile. 2014;50:1–4.
- DeSantis CE, Bray F, Ferlay J, Lortet-Tieulent J, Anderson BO, Jemal A. International Variation in Female Breast Cancer Incidence and Mortality Rates. Cancer Epidemiol. Biomarkers Prev. 2015;24:1495–506.
- Camacho R, Sepúlveda C, Neves D, Piñeros M, Villanueva M, Dangou J-M, et al. Cancer control capacity in 50 low- and middle-income countries. Glob. Public Heal. An Int. J. Res. Policy Pract. 2015;10:1017–31.
- Vet JNI, Kooijman JL, Henderson FC, Aziz FM, Purwoto G, Susanto H, et al. Single-visit approach of cervical cancer screening: see and treat in Indonesia. Br. J. Cancer. 2012;107:772–7.
- Iskandarsyah A, de Klerk C, Suardi DR, Soemitro MP, Sadarjoen SS, Passchier J. Psychosocial and cultural reasons for delay in seeking help and nonadherence to treatment in Indonesian women with breast cancer: a qualitative study. Health Psychol. 2014;33:214–21.
- Price AJ, Ndom P, Atenguena E, Mambou Nouemssi JP, Ryder RW. Cancer care challenges in developing countries. Cancer. 2012;118:3627–35.
- Anderson BO, Yip CH, Smith RA, Shyyan R, Sener SF, Eniu A, et al. Guideline implementation for breast healthcare in low-income and middle-income countries: overview of the breast health global initiative Global Summit 2007. Cancer. 2008. p. 2221–43.
- Kanchanachitra C, Lindelow M, Johnston T, Hanvoravongchai P, Lorenzo FM, Huong NL, et al. Human resources for health in southeast Asia: shortages, distributional challenges, and international trade in health services. Lancet. 2011;377:769–81.
- Smith R a, Cokkinides V, Brawley OW. Cancer screening in the United States, 2009: a review of current American Cancer Society guidelines and issues in cancer screening. CA Cancer J.Clin. 2009;59:27–41.
- Strauss J, Witoelar F, Bondan S. The Fifth Wave of the Indonesia Family Life Survey: Overview and Field Report. RAND; 2016.
- Venkataraman K, Wee HL, Ng SHX, Rebello S, Tai ES, Lee J, et al. Determinants of individuals’ participation in integrated chronic disease screening in Singapore. J. Epidemiol. Community Health. 2016;70:1242–50.
- Sujarwoto S, Tampubolon G. Inflammatory markers and physical performance in middle-aged and older people in Indonesia. Age Ageing. 2015;26:1–6.
- Ford ES. Does exercise reduce inflammation? Physical activity and C-reactive protein among U.S. adults. Epidemiology. 2002;13:561–8.
- Andresen EM, Byers K, Friary J, Kosloski K, Montgomery R. Performance of the 10-item Center for Epidemiologic Studies Depression scale for caregiving research. SAGE Open Med. 2013;1:205031211351457.
- Mahmudah M. Biaya Pap Smear Prodia di Rumah Sakit yang ada di Jakarta [Internet]. Pandu. BPJS. 2017 [cited 2017 Jul 18]. Available from: https://www.panduanbpjs.com/biaya-pap-smear-prodia-di-rumah-sakit-yang-ada-di-jakarta/
- Razavi L. Indonesia’s universal health scheme: one year on, what’s the verdict? [Internet]. Guard. 2015 [cited 2017 Jul 18]. Available from: https://www.theguardian.com/global-development-professionals-network/2015/may/15/indonesias-universal-healthcare-insurance-verdict
- Diaz A, Kang J, Moore SP, Baade P, Langbecker D, Condon JR, et al. Association between comorbidity and participation in breast and cervical cancer screening: A systematic review and meta-analysis. Cancer Epidemiol. 2017;47:7–19.
- Smith RA, Andrews K, Brooks D, DeSantis CE, Fedewa SA, Lortet‐Tieulent J, et al. Cancer screening in the United States, 2016: A review of current American Cancer Society guidelines and current issues in cancer screening. CA. Cancer J. Clin. 2016;66:95–114.
- Moyer VA. Screening for Cervical Cancer: U.S. preventive services task force recommendation statement. Ann. Intern. Med. 2012. p. 880–91.
- Autier P, Koechlin A, Smans M, Vatten L, Boniol M. Mammography screening and breast cancer mortality in Sweden. J. Natl. Cancer Inst. 2012;104:1080–93.
- Chakkalakal RJ, Cherlin E, Thompson J, Lindfield T, Lawson R, Bradley EH. Implementing clinical guidelines in low-income settings: a review of literature. Glob. Public Health. 2013;8:784–95.
- Olson B, Gribble B, Dias J, Curryer C, Vo K, Kowal P, et al. Cervical cancer screening programs and guidelines in low- and middle-income countries. Int. J. Gynecol. Obstet. 2016;134:239–46.
- Sancho-Garnier H, Khazraji YC, Cherif MH, Mahnane A, Hsairi M, Shalakamy A El, et al. Overview of cervical cancer screening practices in the extended Middle East and North Africa countries. Vaccine. 2013;31 Suppl 6:G51-7.
- Sankaranarayanan R, Budukh AM, Rajkumar R. Effective screening programmes for cervical cancer in low- and middle-income developing countries. Bull. World Health Organ. 2001. p. 954–62.
- Denny L, Anorlu R. Cervical cancer in Africa. Cancer Epidemiol. Biomarkers Prev. 2012;21:1434–8.
- Pathology G. for Cervical Cancer Screening Programme Guidelines for Cervical Cancer Screening Programme Recommendations of the expert group mee. Cancer. 2006;
- Aswathy S, Quereshi MA, Kurian B, Leelamoni K. Cervical cancer screening: Current knowledge & practice among women in a rural population of Kerala, India. Indian J. Med. Res. 2012;136:205–10.
- Kementerian Kesehatan RI. Center for Epidemiologic Studies Depression Scale. 2011.
- Denny L, de Sanjose S, Mutebi M, Anderson BO, Kim J, Jeronimo J, et al. Interventions to close the divide for women with breast and cervical cancer between low-income and middle-income countries and high-income countries. Lancet. Elsevier Ltd; 2016;6736:21–30.
- Sreedevi A, Javed R, Dinesh A. Epidemiology of cervical cancer with special focus on India. Int. J. Womens. Health. 2015;7:405–14.
- Kim YM, Lambe FM, Soetikno D, Ati A, Lu R. Preventing cervical cancer in Karawang District, Indonesia: evaluation of 5-year project. Int. J. Gynecol. Obstet. 2012;119:S391.
- Lauby-Secretan B, Scoccianti C, Loomis D, Benbrahim-Tallaa L, Bouvard V, Bianchini F, et al. Breast-cancer screening--viewpoint of the IARC Working Group. N. Engl. J. Med. 2015;372:2353–8.
- Aggarwal A, Freund K, Sato A, Adams-Campbell LL, Lopez AM, Lessin LS, et al. Are depressive symptoms associated with cancer screening and cancer stage at diagnosis among postmenopausal women? The Women’s Health Initiative observational cohort. J. Womens. Health (Larchmt). 2008;17:1353–61.
- O’Donnell S, Goldstein B, MR D, SA F, CR J, JE O. Adherence to Mammography and Colorectal Cancer Screening in Women 50–80 Years of Age: The Role of Psychological Distress. Women’s Heal. Issues. 2010;20:343–9.
- Lorant V. Socioeconomic Inequalities in Depression: A Meta-Analysis. Am. J. Epidemiol. 2003;157:98–112.
- Neeme M, Aavik A, Aavik T, Punab M. Personality and Utilization of Prostate Cancer Testing. SAGE Open. 2015;5:215824401559332.
- Gale CR, Deary IJ, Wardle J, Zaninotto P, Batty GD. Cognitive ability and personality as predictors of participation in a national colorectal cancer screening programme: the English Longitudinal Study of Ageing. J. Epidemiol. Community Health. 2015;69:530–5.
- Mortel M, Rauscher GH, Murphy AM, Hoskins K, Warnecke RB. Racial and ethnic disparity in symptomatic breast cancer awareness despite a recent screen: The role of tumor biology and mammography facility characteristics. Cancer Epidemiol. Biomarkers Prev. 2015;24:1599–606.
- Akinyemiju TF. Socio-economic and health access determinants of breast and cervical cancer screening in low-income countries: analysis of the World Health Survey. PLoS ONE [Electronic Resour. 2012;7:e48834.
- Rothman K. No adjustments needed for multiple comparisons. Epidemiology. 1990;1:43–6.
Table 1. Characteristics of study participants (N=5,397)Potential determinants
|Ethnicity||Not Javanese||2900 (43.73)|
|Marital status||Not married||1423 (26.37)|
|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)|
|Physical activity||Sedentary||1627 (30.15)|
|Lightly active||1793 (33.22)|
|Moderately active||1460 (27.05)|
|Vigorously active||517 (9.58)|
|Travel time||<10 min||4503 (83.44)|
|≥10 min||894 (16.56)|
|Participating in social activities||No||806 (14.93)|
|Menopausal status||Premenopausal||2300 (42.61)|
|Age at menarche||<14||2133(39.52)|
|≥ 14||3264 (60.48)|
|Co-morbidity score||0||1476 (27.35)|
|3 and more||1253 (23.22)|
|Parent died from cancer||No||5264 (97.54)|
|Diabetes drug||No||5269 (97.63)|
|Hypertension drug||No||5039 (93.37)|
|Lipid-lowering drug||No||5288 (97.98)|
|BMI||< 25kg/m2||2879 (53.34)|
|≥ 25kg/m2||2518 (46.66)|
|Systolic blood pressure||<140 mmHg||3087 (57.20)|
|≥ 140 mmHg||2310 (42.80)|
|Diastolic blood pressure||<90 mmHg||3903 (72.32)|
|≥ 90 mmHg||1494 (27.68)|
|≥ 8||300 (5.66)|
Table 2. Univariable associations of potential determinants with cancer screening awareness among women 40 years and older without known history of any cancer
|Potential determinants||Aware of Pap smear (N=5397)||Aware of mammography (N=5397)|
|N (%)||OR (95% CI)||N (%)||OR (95% CI)|
|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)|
|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)|
|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)|
|Diabetes drug||No||1021 (19.38)||Ref||244 (4.63)||Ref|
|Yes||37 (28.91)||1.31 (0.80-2.13)||7 (5.47)||1.10 (0.47-2.55)|
|Hypertension drug||No||971 (19.27)||Ref||233 (4.62)||Ref|
|Yes||87 (24.30)||1.07 (0.78-1.47)||18 (5.03)||0.99 (0.57-1.70)|
|Lipid-lowering drug||No||1015 (19.19)||Ref||247 (4.67)||Ref|
|Yes||43 (39.45)||2.01 (1.21-3.31)||4 (3.67)||0.70 (0.24-2.02)|
|BMI||< 25kg/m2||457 (15.87)||Ref||121 (4.20)||Ref|
|≥ 25kg/m2||601 (23.87)||1.49 (1.26-1.76)||130 (5.12)||1.12 (0.85-1.48)|
|Systolic blood pressure||<140 mmHg||725 (23.49)||Ref||165 (5.34)||Ref|
|≥ 140 mmHg||333 (14.42)||0.48 (0.41-0.58)||86 (3.72)||0.67 (0.50-0.90)|
|Diastolic blood pressure||<90 mmHg||801 (20.52)||Ref||191 (4.89)||Ref|
|≥ 90 mmHg||257 (17.20)||0.77 (0.64-0.94)||60 (4.02)||0.80 (0.59-1.12)|
|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)|
Table 3. Univariable associations of potential determinants with cancer screening practice among women 40 years and older without known history of any cancer
|Potential determinants||Ever Pap smear (N=1058)||Ever BSE (N=5397)|
|N (%)||OR (95 CI)||N (%)||OR (95 CI)|
|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)|
|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)|
|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)|
|Diabetes drug||No||280 (27.42)||Ref||589 (11.18)||Ref|
|Yes||17 (45.95)||0.96 (0.54-1.70)||16 (12.50)||0.96 (0.54-1.70)|
|Hypertension drug||No||266 (27.39)||Ref||551(10.93)||Ref|
|Yes||31 (35.63)||2.50 (1.22-5.16)||54 (15.08)||1.32 (0.94-1.83)|
|Lipid-lowering drug||No||277 (27.29)||Ref||585 (11.06)||Ref|
|Yes||20 (46.51)||1.60 (0.81-2.63)||20 (18.35)||1.46 (0.85-2.52)|
|BMI||< 25kg/m2||119 (26.04)||Ref||264 (9.17)||Ref|
|≥ 25kg/m2||178 (29.62)||1.18 (0.89-1.58)||341 (13.54)||1.44 (1.20-1.74)|
|Systolic blood pressure||<140 mmHg||200 (27.59)||Ref||396 (12.83)||Ref|
|≥ 140 mmHg||97 (29.13)||1.10 (0.81-1.49)||209 (9.05)||0.67 (0.55-0.81)|
|Diastolic blood pressure||<90 mmHg||224 (27.97)||Ref||447 (11.45)||Ref|
|≥ 90 mmHg||73 (28.40)||1.02 (0.73-1.42)||158 (10.58)||0.92 (0.75-1.14)|
|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.
|Potential determinants1||OR (95% CI)|
|Aware of pap smear||Aware of mammography||Ever pap smear||Ever BSE|
|Age||0.77 (0.56-1.07)||1.13 (0.65-1.95)||0.77 (0.44-1.35)||0.83 (0.58-1.20)|
|Javanese||1.92 (1.54-2.40)*||0.83 (0.60-1.14)||1.08 (0.78-1.51)||1.27 (1.04-1.55)*|
|Urban residence||4.28 (3.17-5.50)*||2.00 (1.35-2.95)*||0.72 (0.46-1.13)||1.91 (1.50-2.43)*|
|Married||1.07 (0.84-1.37)||1.16 (0.79-1.72)||1.15 (0.75-1.75)||1.10 (0.85-1.44)|
|High school or higher education||7.22 (5.84-8.94)*||2.55 (1.81-3.59)*||1.60 (1.03-2.49)*||4.25 (3.38-5.34)*|
|Monthly household expenditure – higher tertile||2.29 (1.90-2.77)*||2.24 (1.67-3.02)*||1.94 (1.39-2.72)*||1.70 (1.39-2.06)*|
|Ever smokers||0.89 (0.54-1.46)||1.00 (0.49-2.05)||0.98 (0.43-2.23)||1.11 (0.69-1.82)|
|Physically active||1.51 (1.23-1.86)*||1.76 (1.25-2.48)*||1.23 (0.85-1.76)||1.22 (0.98-1.51)|
|Openness ≥4||0.87 (0.64-1.18)||0.87 (0.54-1.41)||0.90 (0.51-1.56)||0.93 (0.68-1.29)|
|Conscientiousness ≥4||1.07 (0.80-1.43)||1.12 (0.72-1.72)||0.83 (0.51-1.37)||0.83 (0.70-1.27)|
|Extroversion ≥4||0.86 (0.71-1.05)||0.86 (0.63-1.17)||0.98 (0.71-1.37)||0.83 (0.68-1.27)|
|Agreeableness ≥4||1.61 (1.29-2.01)*||1.26 (0.87-1.80)||0.74 (0.50-1.37)||1.59 (1.25-2.03)*|
|Neuroticism ≥4||1.22 (1.01-1.48)*||1.47 (1.10-1.96)*||1.35 (0..98-1.84)||1.19 (0.98-1.44)|
|Have insurance||2.04 (1.68-2.47)*||1.64 (1.22-2.22)*||1.59 (1.12-2.25)*||1.43 (1.18-1.75)*|
|Travel ≥10 min to health service||0.72 (0.54-0.96)*||0.54 (0.90-2.52)||0.56 (0.32-0.98)||0.77 (0.57-1.05)|
|Participating in social activities||2.05 (1.46-2.88)*||1.51 (0.90-2.52)||1.32 (0.66-2.66)||1.91 (1.32-2.75)*|
|Postmenopausal||0.75 (0.60-0.94)*||0.74 (0.52-1.06)||1.44 (0.98-2.12)||0.57 (0.45-0.73)*|
|Age at menarche ≥14||0.98 (0.93-1.02)||0.95 (0.88-1.02)||0.89 (0.81-0.98)||0.95 (0.90-1.00)*|
|Co-morbidity score ≥2||1.19 (0.92-1.53)||0.67 (0.44-1.02)||1.52 (1.00-2.32)*||1.03 (0.79-1.34)|
|Parent died from cancer||1.43 (0.86-2.38)||1.52 (0.76-3.02)||1.31 (0.64-2.68)||1.61 (1.01-2.57)*|
|Diabetes drug||1.44 (0.84-2.48)||1.55 (0.64-3.74)||2.40 (1.07-5.39)*||0.99 (0.54-1.82)|
|Hypertension drug||1.18 (0.81-1.72)||1.10 (0.61-2.00)||1.08 (0.60-1.95)||1.45 (0.99-2.11)|
|Lipid-lowering drug||1.69 (0.97-2.93)||0.55 (0.18-1.66)||1.92 (0.91-4.04)||1.21 (0.67-2.15)|
|BMI ≥ 25kg/m2||1.16 (0.96-1.39)||0.86 (0.65-1.15)||1.09 (0.79-1.49)||1.06 (0.87-1.28)|
|Systolic blood pressure ≥ 140 mmHg||0.63 (0.48-0.81)*||1.09 (0.73-1.63)||0.92 (0.59-1.42)||1.02 (0.78-1.33)|
|Diastolic blood pressure ≥ 90 mmHg||1.19 (0.91-1.56)||0.94 (0.62-1.42)||1.03 (0.65-1.63)||1.03 (0.78-1.36)|
|CES-D ≥8||0.66 (0.53-0.83)*||0.84 (0.60-1.18)||0.85 (0.57-1.25)||1.02 (0.82-1.27)|
All potential determinants were included in the multivariable model.
1For categorical factors, odds ratios (ORs) were shown for categories displayed in the left-hand column in comparison with the remaining categories as the reference (see Table 2-3)
Figure 1. Potential determinants of cancer screening awareness and participation in IFLS5 based on the Anderson model of health behaviour.
Figure 2. Independent predictors of cancer screening awareness and participation in multivariable analyses. All potential determinants were included in the multivariable model. BP=blood pressure. Full results are presented in Table S2, Supplementary Information.