Population description

Table 1 shows the descriptive statistics of patients’ characteristics. Among the 3739 patients included, 1089 (29.1%) were screeners, 2260 (60.4%) were non-screeners, and 390 (10.4%) were unaware of mammography prior to diagnosis. The majority of the patients had secondary school qualification (44.2%), resided in 4-room/5-room/executive type HDB (HDB >3 rooms) (61.7%), and were married (70.9%). Other treatment characteristics that were explored can be found in Additional file 1: Table S1.

Table 1 Characteristics of the study population

Additionally, we looked into the study population’s trends on mammography behaviour over the years. From 2002 to 2018, mammography awareness has increased from 70.8 to 91.1%, and the proportion of women who reported ever attending mammography increased from 37.5 to 63.7% (Fig. 1). However, the attendance rate within the recommended screening interval of 2 years is lower (20.8% in 2002 and only increasing to 26% in 2018). Despite the increase in both awareness and attendance over the years, there remains a substantial gap between knowing that screening is available and the actual utilization of the screening services.

Fig. 1
figure 1

Mammography awareness and attendance among eligible participants diagnosed from 2002 to 2018. Despite the increase in both awareness and attendance over the years, there remains a substantial gap between knowing that screening is available and the actual utilization of the screening services

Mammography screening attendance is associated with more favourable breast cancer tumour characteristics at diagnosis

Table 2 shows the associations between mammography behaviour and disease characteristics, adjusted for age at diagnosis, site, ethnicity, and case type (incident/prevalent). Compared to screeners (reference category for all comparisons), non-screeners were significantly more likely to be diagnosed with late-stage cancers (ORstage II vs stage I (reference): 1.72 [1.46–2.02], p < 0.001; ORstage III vs stage I (reference): 3.17 [2.52–3.98], p < 0.001). This means that the odds of a non-screener developing stage III breast cancer is 3.17 times that of a screener. Non-screeners also showed higher odds of developing high-grade tumours (ORpoorly vs well-differentiated (reference): 1.58 [1.26–1.97], p < 0.001), positive nodal status (ORpositive vs negative nodal status (reference): 1.61 [1.38–1.88], p < 0.001), and larger tumour size (OR>5cm vs ≤2cm (reference): 3.22 [2.25–4.61], p < 0.001).

Table 2 Associations between mammography behaviour and disease characteristics

Likewise, similar trends were observed among patients who were unaware of mammography. They were associated with increased odds of being diagnosed with later stage cancers (ORstage II vs stage I (reference): 2.72 [2.02–3.65], p < 0.001; ORstage III vs stage I (reference): 4.95 [3.45–7.07], p < 0.001), high-grade tumours (ORpoorly vs well-differentiated (reference): 1.53 [1.06–2.20], p = 0.022), positive nodal status (ORpositive vs negative nodal status (reference): 1.96 [1.52–2.52], p < 0.001), and larger tumour size (OR>5cm vs ≤2cm (reference): 5.06 [3.10–8.25], p < 0.001).

In terms of HER2 status, both non-screeners and those who are unaware were less likely to be diagnosed with HER2-negative cancers (non-screeners ORHER2-negative vs HER2-positive (reference): 0.80 [0.67–0.96], p = 0.016; unaware ORHER2-negative vs HER2-positive (reference): 0.72 [0.53–0.97], p = 0.028). However, there were no significant associations between mammography behaviour and hormone receptor status. Furthermore, when looking at proxy subtypes, non-screeners are at higher odds of developing HER2-overexpressed cancers (ORHER2-overexpressed vs luminal A (reference): 1.39 [1.05–1.83], p = 0.022) and patients who are unaware have higher odds of developing luminal B (HER2-negative) cancers (ORluminal B [HER2-negative] vs luminal A (reference): 1.44 [1.01–2.05], p = 0.041).

The results did not change appreciably in sensitivity analyses including patients diagnosed with stage 0 or stage IV breast cancer (Additional file 1: Table S2). However, contrary to what we found in the main study population, a subset analysis including only incident breast cancer cases found no significant association between mammography behaviour and HER2 status (non-screeners ORHER2-negative vs HER2-positive (reference): 0.87 [0.68–1.12], p = 0.286; unaware ORHER2-negative vs HER2-positive (reference): 0.95 [0.61–1.47], p = 0.811) (Additional file 1: Table S3). Furthermore, both non-screeners and those who are unaware were significantly less likely to be diagnosed with PR-negative cancers (non-screeners ORPR-negative vs PR-positive (reference): 0.80 [0.60–0.94], p = 0.013; unaware ORPR-negative vs PR-positive (reference): 0.62 [0.41–0.92], p = 0.018). Non-screeners among incident cases were also at lower odds of developing triple-negative cancers (ORtriple negative vs luminal A (reference): 0.77 [0.49–0.99], p = 0.046).

Mammography screening attendance is associated with more favourable overall cancer survival

Figure 2 presents the Kaplan-Meier curve for overall survival in 3739 breast cancer patients. A total of 149 deaths occurred within 10 years after diagnosis. In univariate Cox regression, both non-screeners and patients who were unaware were at significantly higher risk of death (HRnon-screeners [95% CI]: 1.89 [1.22–2.94], p = 0.005; HRunaware: 2.90 [1.69–4.98], p < 0.001) (Table 3). Adjusted model 1 presents the HR after adjusting for patient characteristics that were significant in the univariate Cox regression models (Additional file 1: Table S4). Even after adjustments, non-screeners were at a significantly higher risk of death compared to screeners (HRnon-screeners: 1.77 [1.12–2.77], p = 0.014). The effect of mammography behaviour on survival was no longer significant after further adjustments with disease and tumour characteristics (adjusted models 2 and 3). In the 5-year survival analyses conducted, similar results were observed (Additional file 1: Table S5 and Fig. S6).

Fig. 2
figure 2

Kaplan-Meier curves for breast cancer patients. Ten-year overall survival is illustrated according to mammography behaviour (screeners, non-screeners, unaware). The p-value is a log-rank test

Table 3 Association of mammography behaviour with 10-year overall survival

Further sensitivity analyses were performed on a subset of the data including incident cases only. Screeners continued to show better 10-year overall survival (original analysis: HRnon-screeners: 1.89 [1.22–2.94], p = 0.005; HRunaware: 2.90 [1.69–4.98], p < 0.001; incident cases only: HRnon-screeners: 1.25 [0.68–2.29], p = 0.467; HRunaware: 2.02 [0.60–4.55], p = 0.09). However, the association was no longer significant due to the smaller number of events (Additional file 1: Table S6 and Fig. S7). In contrast, trends observed between mammography behaviour and overall survival among population including those diagnosed with stage 0 or stage IV cancer were more pronounced, where even after adjustments for patient, disease, and treatment characteristics, both non-screeners and those unaware remained at significantly higher risk of death compared to screeners (HRnon-screeners: 1.57 [1.06–2.33], p = 0.026; HRunaware: 1.64 [1.00–2.67], p = 0.048) (Additional file 1: Table S7 and Fig. S8).

Additional analyses were conducted to assess the differences between non-regular screeners (n = 1210, attended mammography but could not recall the year of the last visit/attended mammography but the last visit was more than 2 years prior to diagnosis) and true non-screeners (n = 1050, have not attended mammography). Compared to non-regular screeners, true non-screeners were at higher risk of developing late stage (ORstage III vs stage I (reference): 2.11 [1.66–2.67], p < 0.001), high-grade tumours (ORpoorly vs well-differentiated (reference): 1.52 [1.15–2.01], p < 0.001), positive nodal status (ORpositive vs negative nodal status (reference): 1.38 [1.16–1.64], p < 0.001), and larger tumour size (OR>5cm vs ≤2cm (reference): 2.75 [1.99–3.81], p < 0.001), after adjusting for age at diagnosis, site, ethnicity, and case type (incident/ prevalent) (Additional file 1: Table S8). True non-screeners were less likely to be diagnosed with HER2-negative cancers and at higher risk of developing luminal B type cancers (Additional file 1: Table S8). However, there was no difference in overall survival between the two groups (Additional file 1: Fig. S9).

Screening attendees tend to be younger, received higher education, and have had a family history of breast cancer

Table 4 shows the associations between sociodemographic factors and mammography behaviour, adjusted for age at diagnosis, site, and case type (incident/prevalent). Non-screeners were more likely to be of older age group (OR≥60 vs 50–59 (reference): 1.36 [1.18–1.58], p < 0.001), more likely to be Malay (ORMalay vs Chinese (reference): 1.42 [1.11–1.82], p = 0.005), have no formal or only primary education (ORno formal/primary vs secondary (reference): 1.76 [1.46–2.11], p < 0.001), and residing in 1 to 3 rooms HDB (OR1–3 rooms HDB vs >3 rooms HDB (reference): 1.43 [1.18–1.73], p < 0.001). Additionally, they were more likely to be past smokers (ORsmokers vs non-smokers (reference): 1.59 [1.01–2.50], p = 0.045) and less likely to be physically active (OR5 vs 2 (reference): 0.52 [0.40–0.68], p < 0.001). In terms of medical risk factors, they were less likely to have had previous surgery for benign lump (ORno vs yes (reference): 0.50 [0.41–0.62], p < 0.001) or gynaecological condition (ORno vs yes (reference): 0.79 [0.68–0.92], p = 0.002) and less likely to have family history of breast cancer (ORno vs yes (reference): 0.74 [0.62–0.87], p < 0.001). Looking into reproductive risk factors, non-screeners were more likely to be nulliparous (ORnulliparous vs 25–29 (reference): 1.27 [1.03–1.57], p = 0.028). Furthermore, they were less likely to have undergone hormone replacement treatment (ORyes vs no (reference): 0.59 [0.47–0.74], p < 0.001) compared to screeners.

Table 4 Associations between sociodemographic factors and mammography behaviour

Similarly, those unaware of mammography were more likely to be older (OR ≥60 vs 50–59 (reference): 3.60 [2.79–4.66], p < 0.001), Malay (ORMalay vs Chinese (reference): 1.89 [1.29–2.77], p = 0.001), received no formal or only primary education (ORno formal/primary vs secondary (reference): 5.04 [3.77–6.75], p < 0.001), reside in 1 to 3 rooms HDB (OR1–3 rooms HDB vs > 3 rooms HDB (reference): 2.33 [1.77–3.07], p < 0.001), and widowed (ORwidowed vs married (reference): 1.85 [1.28–2.69], p = 0.001). They were associated to be past smokers (ORsmokers vs non-smokers (reference): 2.85 [1.53–5.32], p < 0.001) and less physically active (OR5 vs 2 (reference): 0.30 [0.17–0.53], p < 0.001). In addition, they were more likely to suffer from other comorbidities (ORCCI>1 vs CCI=0 (reference): 1.57 [1.07–2.31], p = 0.021), but less likely to have had previous surgery for benign lump (ORno vs yes (reference): 0.28 [0.17–0.45], p < 0.001) or gynaecological surgery (ORno vs yes (reference): 0.64 [0.50–0.83], p < 0.001) or have had family history of breast cancer (ORno vs yes (reference): 0.54 [0.39–0.74], p < 0.001). They were also younger at their first full-term pregnancy (OR<20 vs 25–19 (reference): 3.19 [1.98–5.13], p < 0.001) and less likely to have undergone hormone replacement treatment (ORyes vs no (reference): 0.14 [0.08–0.26], p < 0.001). The results remained largely unchanged in the sensitivity analysis including incident-only cases (Additional file 1: Table S9).

Deterrents and motivators of mammography attendance

We further looked into the patterns surrounding deterrents and motivators for attending mammography among non-attendees and attendees respectively (Fig. 3). Some major deterrents flagged out were lack of perceived risk by patients, as well as fear (Fig. 3a), which can include fear of screening side effects and fear of diagnosis. However, there were no major patterns identified across the different deterrents.

Fig. 3
figure 3

Heatmap showing reasons for mammography a non-attendance among non-attendees (n = 1050) and b attendance among true screeners (n = 1089), respectively. The main deterrents were lack of perceived risk and fear, while motivators can be categorized as health consciousness or cues to action. Non-attendees exclude non-regular screeners who indicated attendance but could not recall/last visit was more than 2 years prior to diagnosis

On the other hand, in terms of motivators, there were distinct groups that can be identified from the heat map (Fig. 3b). The groups were categorized as follows: (1) those who are motivated by both cues and innate health consciousness, (2) those who are motivated solely by appropriate cues to action or (3) solely by innate health consciousness, and (4) others. To better understand ways to improve targeting of appropriate cues to increase screening attendance, we further looked into characteristics of patients who were motivated by cues to action (Table 5). In the univariate model, those who were motivated by cues to action were less likely to be health conscious (ORhealth conscious vs not health conscious (reference): 0.20 [0.15–0.26], p < 0.001), were more likely to be of older age group (OR≥ 60 vs 50–59 (reference): 1.43 [1.12–1.82], p = 0.004), have received no formal/only primary education (ORno formal/primary vs secondary (reference): 1.67 [1.21–2.29], p = 0.002), reside in 1 to 3 rooms HDB (OR1–3 rooms HDB vs >3 rooms HDB (reference): 1.48 [1.06–2.06], p < 0.021), widowed (ORwidowed vs married (reference): 1.78 [1.11–2.87], p = 0.018) or separated (ORseparated/divorced vs married (reference): 2.03 [1.09–3.76], p = 0.025), and indicated lower levels of physical activity (OR5 vs 2 (reference): 0.48 [0.32–0.71], p < 0.001). In terms of reproductive risk factors, those who were motivated by cues to action were significantly associated with younger age at first full-term pregnancy (OR<20 vs 25–19 (reference): 2.71 [1.30–5.63], p = 0.008), have more children (OR≥3 vs 2 (reference): 1.52 [1.13–2.03], p = 0.005), and have been on oral contraception (ORyes vs no (reference): 1.54 [1.16–2.04], p = 0.003). However, age at diagnosis, highest qualification attained, housing, marital status, physical activity level, parity, use of oral contraception, and menopausal status at diagnosis no longer had a significant effect on whether or not participants were motivated by cues to action after adjustments.

Table 5 Characteristics of patients motivated by cues to action

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