Delays in the presentation and diagnosis of women with breast cancer in Yogyakarta, Indonesia: A retrospective observational study.

PURPOSE: To investigate factors associated with delays in presentation and diagnosis of women with confirmed breast cancer (BC).
METHODS: A cross-sectional study nested in an ongoing prospective cohort study of breast cancer patients at Dr Sardjito Hospital, Yogyakarta, Indonesia, was employed. Participants (n = 150) from the main study were recruited, with secondary information on demographic, clinical, and tumor variables collected from the study database. A questionnaire was used to gather data on other socioeconomic variables, herbal consumption, number of healthcare visits, knowledge-attitude-practice of BC, and open-ended questions relating to initial presentation. Presentation delay (time between initial symptom and first consultation) was defined as ≥3 months. Diagnosis delay was defined as ≥1 month between presentation and diagnosis confirmation. Impact on disease stage and determinants of both delays were examined. A Kruskal-Wallis test was used to assess the length and distribution of delays by disease stage. A multivariable logistic regression analysis was conducted to explore the association between delays, cancer stage and factors.
RESULTS: Sixty-five (43.3%) patients had a ≥3-month presentation delay and 97 (64.7%) had a diagnosis confirmation by ≥1 month. Both presentation and diagnosis delays increased the risk of being diagnosed with cancer stage III-IV (odds ratio/OR 2.21, 95% CI 0.97-5.01, p = 0.059 and OR 3.03, 95% CI 1.28-7.19, p = 0.012). Visit to providers ≤3 times was significantly attributed to a reduced diagnosis delay (OR 0.15, 95% CI 0.06-0.37, p <0.001), while having a family history of cancer was significantly associated with increased diagnosis delay (OR 2.28, 95% CI 1.03-5.04, p = 0.042). The most frequent reasons for delaying presentation were lack of awareness of the cause of symptoms (41.5%), low perceived severity (27.7%) and fear of surgery intervention (26.2%).
CONCLUSIONS: Almost half of BC patients in our setting had a delay in presentation and 64.7% experienced a delay in diagnosis. These delays increased the likelihood of presentation with a more advanced stage of disease. Future research is required in Indonesia to explore the feasibility of evidence-based approaches to reducing delays at both levels, including educational interventions to increase awareness of BC symptoms and reducing existing complex and convoluted referral pathways for patients suspected of having cancer.

Introduction

Breast cancer has the highest cancer incidence in Indonesia and the second highest cause of cancer mortality in females. In 2018, the annual estimated incidence and mortality per 100,000 individuals were 44 and 15.3 per year respectively [1]. In the region of Yogyakarta, Indonesia, breast cancer patients are generally diagnosed at stages III and IV [2, 3]. The 5-year overall survival rate is generally unfavourable. It is 48–50% for the whole disease spectrum [2, 4] and 12% for those with advanced diseases [4].

Delays in breast cancer presentation and diagnosis are likely to be key factors in advanced-stage presentation [5]. There are disparities in the length of delays between countries. Reports from high-income countries report median times of 14–60 days [6-8], with a presentation delay of >3 months occurring in 17–35% of patients [6, 7, 9–11]. Reports from low- and middle-income countries report a longer length of delays. A study conducted in the neighbouring country of Malaysia found that the median time to consultation and diagnosis was 2 months and 5.5 months, respectively [12]. In Rwanda, a median time of 5 months for both presentation and diagnosis delays were observed [13]. In Indonesia, previous studies have observed >3-month presentation delay in 36.2% of cases and >1-month diagnosis delay in 25% of cases [14]. Furthermore, a 7-month median time of presentation delay and a 6-month median time of the delay to commencing treatment, relating to the time taken from diagnosis to initial treatment, have been observed [15].

Many sociodemographic factors, clinical factors, and patients’ experiences have been reported as influencing presentation delay. Age, residence, distance to a medical facility, marital status, education level, occupation, insurance, health facility visits, visiting traditional medicine practitioners, knowledge of breast cancer, breast self-examination, initial symptom, family history of breast cancer, and comorbidities are factors that have been associated with delays in both presentation and subsequent diagnosis [5, 6, 10, 13, 16–19]. Reasons for delays include lighter symptoms, fear of informing other people, negative attitudes toward medical health professionals and fear of treatment [13, 20]. In a local publication, initially consulting with non-medical practitioners for breast-related complaints and consuming non-medical treatments have also been associated with diagnosis and treatment delays [14]. Observed by qualitative studies, a lack of awareness and knowledge of cancer, cancer beliefs, treatment beliefs, financial problems, emotional burden, paternalistic style of communication, and unmet information needs are related to psychosocial and cultural reasons for patient delay [15, 21, 22].

To date, research focusing on delays in presentation and diagnosis for patients with breast cancer [15, 22, 23] in Indonesia have included qualitative studies [15, 23, 24], including a study undertaken in Yogyakarta, the region in which this study is focused [21]. There is very limited research quantifying factors affecting delays in Indonesia [14, 15], with none exploring factors in Yogyakarta. The Special Region of Yogyakarta, with a current population of 3,882,288 [24], has the highest frequency of cancer in the country [25], with breast cancer being the commonest malignancy [26]. The objective of this study is to quantitatively investigate the factors associated with presentation and diagnosis delays, the relationship of delays to stage at presentation and reasons for patient delay within local breast cancer cases.

Method

Study subject

This cross-sectional nested study recruited 150 Indonesian female breast cancer patients registered in a prospective ongoing main study. The main study aims to analyse the risk of side effects from chemotherapy and determine their effect on the survival and quality of life in 250 breast cancer patients. The registered participants are patients visiting and receiving their first chemotherapy treatment in the Hematology and Medical Oncology Division, “Tulip”/Integrated Cancer Clinic, Dr. Sardjito General Hospital, Yogyakarta, Indonesia, from 2018–2022. Women aged ≥18 years with histopathologically confirmed breast cancer without terminal condition and severe congestive heart failure have been recruited. Cases received chemotherapy as neoadjuvant treatment (before surgery), adjuvant treatment (after surgery), or palliative treatment with or without surgery. Study subjects were contacted and approached to participate in the cross-sectional observation study with consent before recruitment.

Method of data collection

From the main study database, we collected secondary information on demographic variables (age, education, and residence), comorbidity, family history of cancer, date of the first symptom, date of first medical visit, nutritional status (determined by body mass index/BMI), first symptom of breast cancer, and cancer staging upon diagnosis.

To acquire determinants that were unavailable initially in the study database, we developed a questionnaire with 27 questions in the initiation phase. Questions that were listed in the questionnaire were adapted from multiple sources, including the Indonesian Family Life Survey Wave 5 [27] for living arrangement, socioeconomic status and accessibility to the first medical facility visit and Breast Module of the Cancer Awareness Measure (Breast-CAM) for knowledge of breast cancer risk factors and habit of breast self-exam [28]. We also adapted variables from existing studies to develop questionnaire items [13, 17, 29, 30]. Questionnaire development started with the identification of variables that will be measured and the items from existing questions that related to the selected variables. Items were further selected before the translation process. Three independent translators performed forward-backward translation to produce a questionnaire with semantic equivalence to the original versions in English. Assessment of validity included face validity and questionnaire finalization (see S1 Fig). Face validation was conducted through pilot testing the questionnaire with six lay members of the public, convenience sampled through contacts known to the research team. Three women were primary care facility patients who came for a routine appointment, two women were primary care facility administrative officers, and one woman was a household assistant for a research team member. We conducted a discussion with the participants and gained helpful suggestions for determining which questions required refinement. Through this process, from an initial set of 27 questions, one question about the type of facilities visited to check their symptoms was removed due to redundancy. We also added nine questions to accommodate issues that had not been covered in the initial set of questions. These questions were about patients’ address based on their ID card, whether patients still lived at the same address, date of initial symptom, date of first medical contact, distance from their place of residence to the site of first medical contact, and four questions about herbal medicine consumption (the type, dose, and frequency of consumption). While the date of the first breast cancer symptom was already provided in the database, we asked it again in the questionnaire to confirm the information. Five questions were re-worded to improve readability and clarity, and open-ended questions were included at the end of the questionnaire to elicit participants’ reasons for presentation delay. The process of face validity resulted in a total set of 35 questions.

From September 2020 to February 2021, a face to face interview was conducted with the participants. Trained researchers performed the interview at the ambulatory clinic for 77 participants and, due to the pandemic situation, through phone calls for 74 participants. The questionnaire was only administered once patient informed consent had been obtained. The joint ethics committee from the Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr Sardjito General Hospital, Yogyakarta has approved the main ongoing prospective study (reference number: KE/FK/0417/EC/2018) and provided specific ethical approval for the study reported in this manuscript (amendment from the ongoing study with reference number KE/FK/0444/EC/2020).

Delays definition

Presentation delay was defined as the number of months between breast symptom onset and the patient’s first presentation to a medical professional (doctor, nurse, or midwife). Diagnosis delay was used to define the number of months between the first visit to a medical professional and the date of the first pathology report confirming a diagnosis of breast cancer. When respondents could not remember the date of when the symptoms first appeared or the date when they first visited a health facility, we asked for a time span in months. After providing the range of months, we sought to direct the patient to remember the distance between those dates from important dates/events such as family/respondent’s birthdays, or religious holidays, to further narrow the range and improve the recall closer to the exact date. When provided with a single month, we tried to ask about the exact day or its distance with other important events in the month, to further narrow the date into a single exact day. We also set breast surgery/procedure dates that were recorded in clinical notes as a benchmark, because it was considered by most patients as an important event. Participants were asked the date of first symptom and first medical visit, based on surgery date as a benchmark. When respondents were unable to provide a date for when their symptoms began or the first provider visit, they were asked to provide a month or month range and year. If they provided a month, the date was estimated as the 15th of that month; if they provided a month range, the estimated date was the midpoint between the 15th of those months. If patients were only able to provide a year, the estimated date was June 30th of that year [13].

An agreement was decided when there was conflicting information of the date of first symptoms in the existing database and that was collected from the interview. In such a case, we relied on the self-report data in the database. We used a delay of ≥3 months to define presentation delay based on substantial evidence that such delays are associated with lower survival. We used ≥1 month to define diagnosis delay because one month is indicated as an adequate time for the physician to take appropriate action and shorter delays are not clinically significant [20].

Key independent variables

The key independent variables included various parameters that were obtained from the existing study database and the interview. Sociodemographic information included age at diagnosis, residence (urban or rural, referred to the regional statistical bureau), distance to the first health facility visited (<3 km or ≥3 km), living arrangement (living alone, with spouse only, with other than a spouse, or with spouse and others), educational attainment (31]. The frequency of breast self-exam was categorized as rarely or never, at least once every 6 months, at least once a month, and at least once a week. Features of the patient’s experience with the breast problem were categorized as breast lump, other complaint, and breast lump and other complaint. Family history of cancer (none or presence) and the presence of comorbidities (none, 1, or ≥2) were categorized based on self-report from the existing database. Comorbidities included diabetes, hypertension, hepatitis, heart failure, or other health problems. Body mass index (BMI) was calculated from measured weight and height. We used the World Health Organization Asia-Pacific body mass index classifications, which classified BMI into underweight (<18.5), normal (18.5–22.9), overweight (23–24.9) and obese (≥25). For analysis purposes, we further stratified BMI into two groups: underweight to normal (<23) and overweight to obese (≥23). Information of performance index (determined as ECOG 0–1 or ≥2) was obtained from the database. Breast cancer was staged using the 7th edition AJCC staging system and then simplified as early (stage I-II), locally advanced (stage III) and metastatic (stage IV) disease [32]. A content analysis of the interview transcripts [33] was performed to assess the patients’ reasons for a delay in presentation. We coded the interview transcripts by open coding, organising responses into a meaningful set of categories that covered all relevant data.

Statistical analyses

Kruskal-Wallis statistical test was performed to assess the lengths of presentation and diagnosis delay among the patients and distribution of delays by stage at presentation. This analysis was chosen as the histogram of delay data were not normally distributed. We visually inspected the distribution of the data using histograms for data on both diagnosis time and presentation time which showed data for both variables were skewed to the right. A multivariable logistic regression analysis was conducted to elucidate the factors that are attributable to both presentation and diagnosis delays. Firstly, we conducted logistic regression to explore the association of each factor to presentation and diagnosis delays. Variables with a p-value <0.25 in the univariate logistic regression plausibly related to a presentation or diagnosis delay were included in the multiple logistic regression models. Variables that correlated highly with another variable in the model was removed. A similar approach was used to explore the association between presentation and diagnosis delay with cancer stage at diagnosis. The statistical significance was based on a two-sided p-value of <0.05. Statistical analysis was done using Stata version 14/15 (Stata Corp).

Results

Subject characteristics

When the present study started, 214 have been registered in the main study. Three cases dropped out and 38 cases had died. From the 173 eligible cases, 16 cases did not respond to our invitation and 6 cases refused to participate. Finally, 151 subjects were interviewed (recruitment rate 87.3%) with a questionnaire response rate of 100%. One patient was excluded due to participants’ inability to recall and communicate well in the interviews. Characteristics of all subjects are summarized in Table 1. The cohort was dominated by those living in the urban area (105, 70%), living within ≤3 km from the health facility they firstly visited (87, 57.6%), living with their spouse and other family members (93; 61.6%), having an education at least junior high school (88; 58.67%), possessing lower monthly income (<3,000,000 IDR) (91; 60.7%), experiencing ≤3 times of medical visit before diagnosis (91; 60.7%), visiting general practitioners as the first medical contact for breast symptoms (88; 58.7%), taking alternative medicine before presentation (106; 70.7%), having enough knowledge of breast cancer risk factor (106; 70.7%), and rarely or never performing breast self-examination (82; 54.7%). The majority of participants experienced breast lump as a first complaint (101; 67.3%), did not have a family history of cancer (93; 62%), and presented with no comorbidity (66; 44%). Upon diagnosis, the majority of patients had overweight to obese BMI (82; 54.7%), good physical performance (ECOG 0–1) (145; 96.7%), presented with stage III disease (67; 44.7%), and had the luminal B subtype (62; 41.3%). While being asked about the time when the symptoms first appeared, 13 (8.7%) provided the exact date, 2 (1.3%) provided the date range, 80 (53.3%) provided the month and year, 5 (3.3%) provided a month range and year, and 50 (33.3%) only provided a year. Regarding the date of the first visit to the health facility, 25 (16.7%) provided the exact date, 15 (10%) provided the date range, 85 (56.7%) provided the month and year, 8 (5.3%) provided a month range and year, and 17 (11.3%) only provided a year.

Table 1

Sociodemographic, clinical, and delay characteristics of study participants (n = 150) recruited from Dr. Sardjito Hospital, Yogyakarta in 2018–2021.

Variables	Frequency n (%)
Age (years)
Mean±SD	52.1±9.0
Residence
Urban	105 (70)
Rural	45 (30)
Distance to the first medical visit
≤3 km	87 (58)
>3 km	63 (42)
Living arrangement
With spouse only	19 (12.7)
With other than spouse	32 (21.3)
With spouse and other	93 (62)
Living alone	6 (4)
Education status
≥junior high school	88 (58.7)
<junior high school	62 (41.3)
Monthly income (IDR)
≥3,000,000	59 (39.3)
<3,000,000	91 (60.7)
Frequency of medical visit before diagnosis
>3 times	59 (39.3)
≤3 times	91 (60.7)
Types of health care facility first visited
Specialist (private or public)	45 (30)
General practitioner (private or public)	88 (58.7)
Midwife and other	17 (11.3)
Consumption of herbal medicine before presentation
No	44 (29.3)
Yes	106 (70.7)
Knowledge of risk factors of breast cancer
Diet-related	90 (60)
Air pollution	14 (9.3)
Smoking or being a second-hand smoker	28 (18.7)
Alcohol consumption	4 (2.7)
Stress	20 (13.3)
Radiation	1 (0.7)
Exhaustion or sleep deprivation	6 (4)
Genetic	41 (27.3)
Hormonal compound from contraception use	20 (13.3)
Breastfeeding	6 (4)
Less exercise	3 (2)
Other	11 (7.3)
Number of risk factor of breast cancer known
≥1	106 (70.7)
None	44 (29.3)
Habit of breast self-examination
At least once a week	42 (28)
At least once a month	18 (12)
Rarely or never	90 (60)
First presenting symptom
Breast lump only	101 (67.3)
Breast lump and other	35 (23.3)
Other than breast lump	14 (9.3)
Family history of cancer
No	93 (62)
Yes	57 (38)
Types of comorbidity
No comorbidity	66 (44)
Hypertension	38 (25.3)
Diabetes	14 (9.3)
Dyslipidemia	9 (6)
Other	44 (29.3)
Number of comorbidities
None	66 (44)
1	63 (42)
≥2	21 (14)
BMI (WHO Asia-Pacific)
<23	68 (45.3)
≥23	82 (54.7)
ECOG performance status
0–1	145 (96.7)
≥2	5 (3.3)
Stage
I–II	53 (35.3)
III	67 (44.7)
IV	30 (20)

Abbreviations: SD = Standard Deviation; km = Kilometre; IDR = Indonesian Rupiah; BMI = Body Mass Index; ECOG = Eastern Cooperative Oncology Group.

The magnitude of delays and their influence on disease stage

The median time to presentation from initial symptoms experienced by participants was 2 months (61 days) (Fig 1). Eighty-five (56.7%) respondents had a consultation with a medical professional within 3 months after detecting their symptoms, while 65 (43.3%) delayed the consultation by ≥3 months. The median time to diagnosis confirmation from first consultation experienced by participants was 1 month. As many as 53 (35.3%) respondents had their breast cancer diagnosed within 1 month while 97 (64.7) participants had confirmation by ≥1 month. Overall, the median time to diagnosis from initial symptom was 7 months.

Fig 1

Timeline sketch of breast cancer presentation and diagnosis.

Participants were observed to have a 2-month median presentation time and 1-month diagnosis time. The median time to diagnosis from initial symptom was 7 months. 43.3% of respondents delayed the consultation by ≥3 months and 64.7% had diagnosis confirmation by ≥1 month.

Median presentation time for patients with early, locally advanced and metastatic disease was 0, 2, and 9.5 months respectively (p <0.001). The median diagnosis time for the three groups was 0, 2, and 1.5 months respectively (p = 0.006). Median time from initial symptom to diagnosis (overall time) was 3, 10, and 25 months among patients with stage I/II, III, and IV disease (p <0.001) (Table 2).

Table 2

Median presentation, diagnosis, and overall delay for all participants (n = 150) by stage at diagnosis.

Duration of delay (months)+	Stage I-II (n = 53)	Stage III (n = 67)	Stage IV (n = 30)	Total (n = 150)	P unadjusted*
Presentation time	0 (0–3)	2 (0–10)	9.5 (1–24)	2 (0–9)	<0.001
Diagnosis time	0 (0–2)	2 (1–6)	1.5 (0–30)	1 (0–6)	0.006
Overall time#	3 (1–8)	10 (3–24)	25 (8–40)	7 (2–24)	<0.001

+ Reported as median (interquartile range).

* Kruskal-Wallis test.

# Overall time = Total duration of presentation and diagnosis time.

Factors that are attributable to both presentation and diagnosis delay

Tables 3 and 4 showed the multivariate analyses of sociodemographic, clinical, and tumor characteristics that may influence presentation and diagnosis delay. Monthly income of <3,000,000 IDR is a factor associated with an increased delay in presentation with marginal significance (odds ratio/OR 2.21, 95% confidence interval/CI 0.99–4.93, p = 0.052). Patients who visited healthcare facilities ≤3 times before diagnosis had a reduced risk of experiencing diagnosis delay (OR 0.15, 95% CI 0.06–0.37, p <0.001). Having a family history of cancer is a significant factor that is related to a higher risk of diagnosis delay (OR 2.28, 95% CI 1.03–5.04, p = 0.042).

Table 3

Sociodemographic and clinical factors associated with ≥3 months presentation delay in breast cancer patients.

Variable	<3 months presentation delay (%)	≥3 months presentation delay (%)	Crude OR (95% CI)	p	Adjusted OR (95% CI)	p
Age (cont.)	-	-	1.03 (0.99–1.07)	0.137	1.02 (0.98–1.07)	0.237
Distance to health facility (cont.)	-	-	1.00 (0.98–1.03)	0.798	-	-
Residence
Urban	60	40	Ref		Ref
Rural	48.9	51.1	1.57 (0.78–3.17)	0.210	1.55 (0.68–3.49)	0.295
Living Arrangement
With spouse only	57.9	42.1	Ref		Ref
With other than spouse	62.5	37.5	0.83 (0.26–2.63)	0.745	0.81 (0.23–2.82)	0.734
With spouse and other	57	43	1.04 (0.38–2.82)	0.942	1.46 (0.47–4.48)	0.512
Living alone	16.7	83.3	6.88 (0.67–70.8)	0.105	5.31 (0.45–63.0)	0.186
Education status#
≥junior high school	63.6	36.4	Ref
<junior high school	46.8	53.2	1.99 (1.03–3.86)	0.041	-	-
Monthly income (IDR)
≥3,000,000	69.5	30.5	Ref		Ref
<3,000,000	48.3	51.7	2.43 (1.22–4.85)	0.012	2.21 (0.99–4.93)	0.052
Consumption of herbal medicine before presentation
No	56.8	43.2	Ref		Ref
Yes	56.6	43.4	0.99 (0.49–2.02)	0.981	-	-
Number of risk factor of BC known
≥1	60.4	39.6	Ref		Ref
None	47.7	53.3	0.60 (0.30–1.22)	0.156	0.98 (0.42–2.27)	0.963
Habit of breast self-exam
At least once a week	54.8	45.2	Ref		Ref
At least once a month	61.1	38.9	0.77 (0.25–2.37)	0.650	-	-
Rarely or never	56.7	43.3	0.93 (0.44–1.93)	0.837	-	-
First presenting symptom
Breast lump only	57.4	42.6	Ref		Ref
Breast lump and other	42.9	57.1	1.80 (0.83–3.91)	0.139	1.78 (0.78–4.06)	0.174
Other than breast lump	85.7	14.3	0.23 (0.05–1.06)	0.059	0.26 (0.05–1.32)	0.104
Number of comorbidities
None	62.1	37.9	Ref		Ref
1	46	54	1.92 (0.95–3.88)	0.068	1.80 (0.81–3.97)	0.147
≥2	71.4	28.6	0.66 (0.23–1.91)	0.440	0.63 (0.19–2.12)	0.452
Family history of cancer
No	58.1	41.9	Ref		Ref
Yes	54.4	45.6	1.16 (0.60–2.26)	0.659	-	-

Abbreviations: OR = Odds Ratio; CI = Confidence Interval; Ref = Reference; Cont. = continuous data; IDR = Indonesian Rupiah; BC = Breast Cancer.

#: not included in the multivariate analysis because it is highly correlated with monthly income.

-: not applicable.

Table 4

Sociodemographic, clinical factors and service utilization associated with ≥1 month diagnosis delay in breast cancer patients.

Variable	<1 month diagnosis delay (%)	≥1 month diagnosis delay (%)	Crude OR (95% CI)	p	Adjusted OR (95% CI)	p
Age (cont.)	-	-	0.98 (0.94–1.01)	0.203	0.98 (0.93–1.02)	0.286
Residence
Urban	33.3	66.7	Ref
Rural	40	60	0.75 (0.36–1.54)	0.434	-	-
Living Arrangement
With spouse only	31.6	68.4	Ref
With other than spouse	31.2	68.8	1.02 (0.30–3.45)	0.980	-	-
With spouse and other	36.6	63.4	0.80 (0.28–2.30)	0.680	-	-
Living alone	50	50	0.46 (0.07–3.00)	0.418	-	-
Education status
≥junior high school	37.5	62.5	Ref
<junior high school	32.3	67.7	1.26 (0.64–2.50)	0.509	-	-
Monthly income (IDR)
≥3,000,000	35.6	64.4	Ref
<3,000,000	35.2	64.8	1.02 (0.51–2.02)	0.957	-	-
Frequency of medical visit before diagnosis
>3 times	13.6	86.4	Ref		Ref
≤3 times	49.4	50.6	0.16 (0.07–0.38)	<0.001	015 (0.06–0.37)	<0.001*
Types of health care facility first visited
Specialist (private or public)	37.8	62.2	Ref
General practitioner (private or public)	35.2	64.8	1.12 (0.53–2.35)	0.772	-	-
Midwife and other	29.4	70.6	1.46 (0.44–4.86)	0.540	-	-
Number of risk factor of BC known
≥1	32.1	67.9	Ref		Ref
None	43.2	56.8	1.61 (0.78–3.32)	0.197	0.79 (0.34–1.84)	0.586
Habit of breast self-exam
At least once a week	33.3	66.7	Ref
At least once a month	33.3	66.7	1 (0.31–3.23)	1.000	-	-
Rarely or never	36.7	63.3	0.86 (0.40–1.87)	0.710	-	-
First presenting symptom
Breast lump only	37.6	62.4	Ref		Ref
Breast lump and other	25.7	74.3	1.74 (0.74–4.11)	0.205	1.54 (0.59–4.02)	0.378
Other than breast lump	42.9	57.1	0.80 (0.26–2.50)	0.706	0.80 (0.23–2.81)	0.727
Number of comorbidities
None	36.4	63.6	Ref
1	33.3	66.7	1.14 (0.55–2.36)	0.718	-	-
≥2	38.1	61.9	0.93 (0.34–2.56)	0.886	-	-
Family history of cancer
No	39.8	60.2	Ref		Ref
Yes	28.1	71.9	1.69 (0.83–3.45)	0.147	2.28 (1.03–5.04)	0.042*

Abbreviations: OR = Odds Ratio; CI = Confidence Interval; Ref = Reference; IDR = Indonesian Rupiah; BC = Breast Cancer.

*: statistically significant.

-: not applicable.

Effect of delay on the likelihood of worse clinical presentation

The effect of delays on presenting with a more advanced stage at baseline was displayed in Table 5. We conducted two analysis models for each type of delay to investigate the correlation between delays and presenting with a more advanced stage at diagnosis. Model 1 for presentation delay is adjusted for education level, monthly income, number of risk factors of breast cancer known, habit of breast self-exam, first presenting symptom and number of comorbidities. Model 1 for diagnosis delay is adjusted for education level, monthly income, frequency of medical visit before diagnosis, number of risk factors of breast cancer known, habit of breast self-exam, first presenting symptom and number of comorbidities. Model 2 for presentation delay is an analysis of model 1 for presentation delay which also adjusted with diagnosis delay. Model 2 for diagnosis delay is an analysis of model 1 for diagnosis delay which is also adjusted with presentation delay. When focused on model 2, ≥3 months of delay in the presentation was associated with an increased risk of having a more advanced stage disease (III-IV) (OR 2.21, 95% CI 0.97–5.01, p = 0.059), although significance was not reached. Patients with a delay in diagnosis of ≥1 month were more likely to present with a more advanced stage (OR 3.03, 95% CI 1.28–7.19, p = 0.012). When extended to various presentation and diagnosis time, it is demonstrated that more participants with longer delays presented with more advanced stages (see S1 Table).

Table 5

Effect of presentation and diagnosis delay on likelihood of stage III-IV breast cancer at point of diagnosis.

Presence of Delay	Likelihood of stage III-IV
	Crude OR (95% CI)	p	Model 1 Adjusted OR (95% CI)	p	Model 2 Adjusted OR (95% CI)	p
Presentation Delay
Non-delay	Ref		Ref		Ref
Delay (≥3 months)	2.71 (1.29–5.72)	0.009	2.30 (1.04–5.12)	0.041	2.21 (0.97–5.01)	0.059
Diagnosis Delay
Non-delay	Ref		Ref		Ref
Delay (≥1 months)	3.25 (1.57–6.71)	0.001	3.31 (1.42–7.72)	0.006	3.03 (1.28–7.19)	0.012

Abbreviations: OR = Odds Ratio; CI = Confidence Interval. Model 1 for presentation delay: adjusted for education level, monthly income, number of risk factors of breast cancer known, habit of breast self-exam, first presenting symptom, number of comorbidities; Model 1 for diagnosis delay: adjusted for education level, monthly income, frequency of medical visit before diagnosis, number of risk factor of breast cancer known, habit of breast self-exam, first presenting symptom, number of comorbidities; Model 2 for presentation delay: Model 1 for presentation delay + adjusted for diagnosis delay; Model 2 for diagnosis delay: Model 1 for diagnosis delay + adjusting for presentation delay.

Delays also increased the risk of having a lower BMI. The risk of BMI ≤23 at the time of diagnosis was significantly higher in participants with ≥3 months of delay in presentation (OR 2.08, 95% CI 1.07–4.01, p = 0.030) compared to those with no delays. Nevertheless, the risk was not significantly increased in participants with ≥1 month of delay in diagnosis when compared to their counterparts (OR 0.85, 95% CI 0.43–1.69, p = 0.644) (see S2 Table).

Reasons for presentation delay

Table 6 displayed reasons for 65 patients who waited for ≥3 months to seek help from health professionals after their first breast complaint. When asked why they did not go to a health facility earlier, the most frequent reason was not that the initial symptom did not cause bother and thinking that it was not a serious problem (27, 41.5%). This was followed by assuming that the symptoms were not cancer or a serious condition that required medical attention (18, 27.7%) and fear for surgery (17, 26.2%). For the whole cohort, there were two participants, one presented within 3 months and the other waited for ≥3 months, who stated that they were afraid of going out because of the possibility of contracting COVID-19 in the health facility (see S3 Table).

Table 6

Reasons provided by breast cancer patients for delays of ≥3 months’ presentation (n = 65).

Reasons	Frequency (%)
The symptoms did not bother me/caused me pain.	27 (41.5)
I thought it was not serious/cancer/did not require medical attention.	18 (27.7)
I was afraid of undergoing surgery.	17 (26.2)
I was too busy.	9 (13.9)
I sought alternative treatment first.	6 (9.2)
I was afraid of seeing a physician or going to a healthcare facility.	5 (7.7)
I was afraid of the possible diagnosis.	4 (6.2)
I was concerned about the cost.	2 (3.1)
I was looking for a female physician.	2 (3.1)
I needed someone to accompany me to the healthcare facility.	1 (1.5)
I am embarrassed if my breast has to be examined.	1 (1.5)
I was afraid to going out due to COVID-19 pandemic.	1 (1.5)

COVID-19 = Coronavirus Disease 2019.

Discussion

Summary of key findings

This is the first quantitative study in Yogyakarta, Indonesia, of breast cancer cases determining the extent of delays in presentation and diagnosis exploring determinants with effect estimation. Previous quantitative reports from Indonesia compared several factors for delays but did not determine the magnitude of identified differences [14, 15]. Being a region with the highest cancer prevalence in Indonesia, data on factors influencing delays is of importance to inform the targeting of interventions by national authorities. Delays in presentation were found for 43.3%, with delays potentially associated with a monthly household income of <3,000,000 IDR (equivalent to ~USD 200). Delays in diagnosis were experienced by 64.7% of participants, attributable to visiting a healthcare centre >3 times and family history of cancer. Our findings highlighted those women who presented late often presented with an advanced stage of the disease. This is consistent with extended data for various presentation and diagnosis times (see S2 Table). Moreover, the risk of lower BMI was also observed (see S3 Table), implying an increased cancer burden and reduced survival potential.

Comparison of presentation and diagnosis delays with previous reports

The ≥3-month patient delay rate of 43.3% in the study population is higher than those observed in most high-income countries such as United States (17%) [7], Europe (17.3–20%) [6, 11] or Asia (29–35%) [9, 10]. It is comparable to reports from Asian countries including Malaysia and Iran (42.5–43.3%) [12, 29] and lower than other low- and middle-income countries like Pakistan (84%) [19] and Kenya (73.08%) [34]. A high proportion (64.7%) of ≥1 month diagnosis delay in our local breast cancer cohort is also higher than those previously documented (15.5–38%) [8, 11].

Factors that influence both delays in presentation and diagnosis have been extensively explored across international publications. However, their impact and mitigation strategies are yet to be described and evaluated in Indonesian settings. In our local setting, lower-income is potentially associated with a presentation delay, supporting findings in other countries [10, 19, 29]. The introduction of national universal health coverage in 2014 sought to increase access to healthcare to the population [35]. However, various out-of-pocket expenses remain, not covered by universal health coverage, such as transportation and logistics linked to presentation, diagnosis and subsequent treatments. Despite a large proportion of medical-related costs covered, there is still a financial burden placed on families with lower economic status. In addition, patients from lower socio-economic groups may have less health awareness and suboptimal family support [10].

Frequent (>3 times) medical visits after first initial consultation was the only significant predictor of delay in receiving a diagnosis in the present study. Although we explored patient-level factors, we did not specifically explore elements of the health service surrounding provider delays. The main factors for system-oriented delay include failure of medical practitioners to act on clinical findings, maltreatment of symptoms as benign breast disease, false-negative or misinterpretation of mammogram, and false-negative results of fine-needle aspiration cytology [9, 10, 12, 16, 36]. These factors may be targets for inclusion in future investigation and improvement initiatives. In the research literature, delays in diagnosis may be reduced through the provision of more efficient training programmes for members of the medical profession. Furthermore, our findings indicate a need for simpler and more efficient referral systems to access centres providing cancer care. As a response to long referral and waiting times in cancer care, the Swedish government launched a national policy called “standardized cancer patient pathways” [37]. This policy assigned all phases, from the first suspicion of cancer until the point of receiving treatment, with an appointed maximum time-scale, based on the optimal time for the patients and variation between diagnoses. Similar policies have been introduced across countries including Denmark and Norway [38, 39]. Furthermore, a family history of cancer is also associated with an increased risk of diagnosis delay in our local cohort, supporting other reports [40, 41]. Fear of treatment and its side effects that may have been witnessed in other family members could be a reason for delaying diagnosis and requires further exploration.

Most patients tend to ignore symptoms, are not alarmed when they initially appear and do not limit daily activity. This may lead individuals to believe it is not harmful and thus delay seeking medical help. As much as 41.5% of women discredited the symptom due to the absence of pain and 27.7% thought it was not serious and thus did not require any medical attention. Delays in help-seeking behaviour have a major effect on a patient’s prognosis and survival, yet, are potentially preventable. Recent studies reported a low awareness level of breast cancer risk factors, barriers, attitudes and breast cancer screening among Indonesian women. This may reflect an inadequate or a lack of breast cancer awareness in the country [42, 43]. Additional education programs aiming to increase awareness and public education has been recommended [43] to improve awareness of breast cancer signs and symptoms. There is an urgent need to develop communication and education strategies regarding breast cancer symptoms and early detection for Indonesia, such as specifically motivating early detection practices and breast self-examination [44]. Various methods could be explored, including mass media, various forms of literature, and programmes. One example is the ’Be Cancer Alert Campaign’, using mass media for raising breast and colorectal cancer awareness conducted in Malaysia [45]. In one systematic review, a breast cancer awareness campaign was found to increase the initiation of breast self-examination behaviours and increase the attendance of breast cancer screening [46]. There is scope to explore the feasibility and wider evaluation of similar campaigns in Indonesia. For example, a policy of breast cancer self-examination was introduced by the Indonesian Ministry of Health in 2015 but has yet to be comprehensively evaluated. Our findings reemphasize the need for cancer prevention programs to focus on making women aware of any symptoms in the breast, especially in those with lower economic status. Others indicated that women living in urban areas have a poorer level of knowledge of breast cancer risk factors compared to those living in more rural areas. This group may also serve as a target for future awareness programs [43].

During this study, reluctance to go to attend a health facility during the COVID-19 pandemic was a reason for delayed presentation in one case. Among 65 patients who delayed their first consultation, 10 initially presented to the health facility at the time of the COVID-19 pandemic in Indonesia. When considering the whole panel (n = 150), 2 cases out of 45 women stated the same reason. There was an indication that this became a reason for common delays during the study period. Its effect on the overall health problem in Indonesia still needs to be evaluated.

Study strength and limitations

We gathered detailed data from participants in an existing cohort study, using a validated questionnaire that was used by trained researchers in ambulatory clinics. Inclusion from the existing cohort study may introduce some limitations. The study mostly recruited women with good ECOG index, without terminal disease, and who were chemotherapy-naive. Some patients have died because of a terminal disease that developed after diagnosis and treatment administration. Patients with very poor clinical performance and heavily pretreated cases were not included due to the exclusion criteria of the main study. Furthermore, the vast majority of patients underwent staging in the hospital site in which the research team is based (type A hospital) so that there were time intervals between diagnostic confirmation in the district hospitals (type B hospitals) and stage establishment. Some cases possibly have already developed a more advanced stage during the elapsing time. However, a 1-month interval is indicated as representative staging time after being diagnosed in the previous health care [47]. Those who did not experience diagnosis delay in our cohort had a median referral time interval from type B hospitals to ours of 54 days. Many interviews carried out in this study were completed months after participant recruitment to the main study. The first case was recruited in May 2018 while interviews began in September 2020. As a consequence, there is a 1–29 month interval between the first intake in the main study and interviews for this study. Although strategies were in place to mitigate recall bias, this may still have influenced participant responses.

Conclusions

Many breast cancer patients in our local setting delayed seeking advice for symptoms later diagnosed as breast cancer symptoms. A high proportion of women experienced a delay in diagnosis. Delays significantly increased the risk of presentation with advanced disease and its association with high mortality probability. Frequent medical visits before diagnosis and family history of cancer were significant determinants of diagnosis delays. Feasibility testing of approaches to promoting community education to promote breast cancer awareness and training for health care professionals is required to explore strategies for potentially minimising delays and mortality from breast cancer in our local region and other settings across Indonesia.

Questionnaire in English version.

(DOC)

Click here for additional data file.

Questionnaire in Bahasa Indonesia version.

(DOC)

Click here for additional data file.

Minimal data set.

(XLSX)

Click here for additional data file.

Steps for developing the study questionnaire.

Questionnaire development started from identifying variables that will be measured and items that were obtained from existing questions related to the selected variables. It is followed by item selection and translation. The last steps included validity assessment or face validity and questionnaire finalization.

(TIF)

Click here for additional data file.

Proportion of presentation and diagnosis time based on stage at diagnosis.

(DOCX)

Click here for additional data file.

Effect of presentation and diagnosis delay on likelihood of lower BMI (<23) at point of diagnosis.

(DOCX)

Click here for additional data file.

Reasons provided across all study participants for presentation delay (n = 150).

(DOCX)

Click here for additional data file.

28 Jul 2021

PONE-D-21-15358

Delays in the presentation and diagnosis of women with breast cancer in Yogyakarta, Indonesia: a retrospective observational study

PLOS ONE

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Reviewer #1: Major comments

Line 167: you have stated that you have done content validity, but not described the result of the validity test. so explain the results of the validity test at the beginning of your result part

line 170-172: specify the exact number of respondents at the Ambulatory clinic and delivered through phone call.

Line 185: have you used any methods that can help respondents to recall their exact date of their first symptom? if so please describe it here.

Line 222: Describe the BMI classification in detail. what do you mean by (underweight to normal) and (normal to overweight)?

Line 235: why do you use Kruskal Wallis test? have you checked any assumptions not to do one way ANOVA? please explain it in your result part if you have checked the assumptions, if not it will not be the appropriate statistical analysis.

besides, you have already transform the data in to categorical variable which doesn't allow you to do One way ANOVA or Kruskal Wallis test (only applied for continuous data). so justify you reasons

Line 271: the table titles should be self explanatory which answers "what, who, where, and when", So apply it for all of your Table titles

On table 1: second column write "Frequency n (%)"

Age: Mean should not be explained alone, so write "mean (SD)"

Comorbidities: you should list the commonest comorbidities with their frequency not the number of comorbidities

Table 2: it is not the appropriate way of analysis of a Kruskal Wallis test, please make it self explanatory, the numbers in each cell are not understandable.

Table 3: this association doesn't tell us the exact relationship of stage at diagnosis and presentation delay because it is just crude odds ratio. to know their relatuion stage of diagnosis should be entered to the model and see the effect

Table 4: second column should not be put alone. so add another column and show the 'cross tab' of all independent variables with both "<3 months and >3 months" so that it will be explanatory.

Table 5: Apply the comments above on table 4

Line 537: References should be updated, Please use references that are published since 2010

Minor comments

Line 34: remove repeated words: "Extent of, impact of" Just say "factors associated with"

Line 36: "Cross-sectional study nested in an ongoing prospective cohort study of BC patients". it is not a full statement Add " were employed" after the last word

Line 78: correct grammar and English flow

Line 113: Remove the word Impact and use associated factors through out the paper

Generally the English is good but needs revision to make easily readable and understandable for the readers

Reviewer #2: Major issues:

1. Questionnaire development.

It is not clearly define in the method, how to avoid recall bias that might influence the results of the study (page 38, line 479-481).

2. The analysis (multivariable logistic regression) (comments to the question no.2)

a. The authors need to clarify the reason of choosing ‘living alone’ (in living arrangement variable) as reference in multivariable logistic regression analysis (Table 4). If ‘living alone’ is considered as the highest risk among other variables (with spouse only, with spouse and other, or with other than spouse), in my opinion, the reference should be a variable that has the lowest risk associated with > 3 months presentation delay.

b. The same reason of choosing the ‘luminal A’ as the reference to luminal B, Her2- enriched and TNBC variables, considering that each variable has different molecular and clinical characteristic. (Table 4)

c. The same reason of choosing the ‘general practitioner’ (in health care facility first visited) as the reference to specialist and midwife variables that associated with >1 month diagnosis delay, assuming that specialist is the ‘best’ or the lowest risk health care facility to avoid delay in diagnosis. (Table 5)

3. It is interesting that the authors analyze of the molecular type of the breast cancer (luminal A, luminal B, Her2-enriched and TNBC) with the presentation delays (Table 4). However, it is not clearly define in the literatures whether the molecular type is associated the diagnosis delays. Most of the delays in diagnosis process related to the stage of the disease, and the molecular types is usually associated with the prognosis of the disease.

Minor issue:

The authors provide a good point of discussion, however the use of word ‘cohort’ (page 34, line 374) is not suitable with the design study (cross sectional).

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11 Sep 2021

Dear editor and reviewers,

We are thankful for the positive feedback received from reviewers 1 and 2, and the editorial team, and for the opportunity to respond to the constructive points in our accompanying revised manuscript. Please find below our point-by-point response to reviewer feedback outlining, where relevant, the related changes we have made. We have uploaded revised versions of the manuscript as instructed, including both a clean copy and a track changes version.

Editor’s comment

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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Response:

We have checked these requirements ahead of submitting our revised manuscript.

2. Please include additional information regarding the survey or questionnaire used in the study and ensure that you have provided sufficient details that others could replicate the analyses. For instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information.

Response:

We have included as Supporting Information a copy of questionnaire that we used in this study both in Bahasa Indonesia and English.

3. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

Response:

Thank you for your remarks. We have included a statement outlining the funders and ensured they match in both the ‘Funding Information’ and ‘Financial Disclosure’ sections. We have not included specific grant reference numbers as these were funds awarded to the respective institutions that did not provide specific reference numbers in allocating the funds for this research.

4. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

"Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

Response:

We have provided as Supporting Information the minimum dataset that were used to generate Fig 1. Fig 1 is the figure that reflects the main finding presented in the paper. Sharing of the full de-identified dataset is not possible due to restrictions imposed by the ethics committee as most of these contain patient data, albeit de-identified, and it may be possible to determine the identity of participants given the extent of sociodemographic and clinical data available for each participant. Should there be a request for data, this can be sent to the corresponding author (email: susanna.hutajulu@ugm.ac.id). Future researchers can contact the institutional ethics committee (email: mhrec_fmugm@ugm.ac.id) at Universitas Gadjah Mada, Indonesia, with data access queries as well.

We have also included our response to this comment in the accompanying cover letter.

5. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please delete it from any other section.

Response:

Thank you for your comment. Indeed, we provided two ethics statements for both the parent and the present study. Both statements have been combined into one section of text and are now presented together under Methods (now page 9, lines 183-188).

6. Please note that in order to use the direct billing option the corresponding author must be affiliated with the chosen institute. Please either amend your manuscript to change the affiliation or corresponding author, or email us at plosone@plos.org with a request to remove this option.

Response:

I sent an email to plosone@plos.org with a request to remove this direct billing option.

Reviewer #1: Major comments

1. Line 167: you have stated that you have done content validity, but not described the result of the validity test. so explain the results of the validity test at the beginning of your result part

Response:

Thank you for highlighting this issue. A comprehensive overview of the questionnaire development was included on pages 7-9, lines 139-171. We conducted a rigorous development process which resulted in a set of 35 questions. However, we did not conduct a quantitative content validity and instead focused on the face validity and readability of questionnaire which were reviewed qualitatively. We have now removed mention of content validity from the manuscript. Our development process focused on making sure the questions were meaningful and easy to understand to gather data in the absence of validated approaches for Indonesia. We have outlined our approach to assessing the face validity in the methods section. We have also clarified the description of the final number of items contained in the questionnaire from 21 to 35. 21 is the number of items we developed at the earliest phase of our study.

2. Line 170-172: specify the exact number of respondents at the Ambulatory clinic and delivered through phone call.

Response:

In the manuscript text (previous lines 170-172, now lines 180-182) we have added the exact number of respondents both at the ambulatory clinic (n=77) and delivered through phone call (n=74).

3. Line 185: have you used any methods that can help respondents to recall their exact date of their first symptom? if so please describe it here.

Response:

Thank you for your question about methods used to support respondents to recall the date of their first symptom. When respondents could not remember the date of when the symptoms first appeared or the date when they first visited a health facility, we asked for a time span in months. After providing the range of months, we sought to direct the patient to remember the distance between those dates from important dates or events such as a family or respondent’s birthday, or religious holidays, to further narrow the range and improve the recall closer to the exact date. When provided with a single month, we tried to ask about the exact day or its distance with other important events in the month, in order to further narrow the date into a single exact day. We also drew on breast surgery and procedure dates that were recorded in clinical notes as a benchmark, because it was considered by most patients as an important event.

We have incorporated further details about this approach into the manuscript text in the Methods (Methods - Delays definition) (lines 195-206).

This response also applies to the question raised by Reviewer 2 relating to strategies for overcoming recall bias.

4. Line 222: Describe the BMI classification in detail. what do you mean by (underweight to normal) and (normal to overweight)?

Response:

We used the World Health Organization Asia-Pacific body mass index classifications, which classified BMI into underweight (<18.5), normal (18.5-22.9), overweight (23-24.9) and obese (≥25). In our analysis, we further stratified BMI into two groups: underweight to normal (<23) and overweight to obese (≥23). We adjusted the corresponding text in the manuscript to provide further clarity around these groupings of BMI scores (previous lines 222-223, now lines 244-248) and tables (Table 1 and S2 Table).

5. Line 235: why do you use Kruskal Wallis test? have you checked any assumptions not to do one way ANOVA? please explain it in your result part if you have checked the assumptions, if not it will not be the appropriate statistical analysis.

besides, you have already transform the data in to categorical variable which doesn't allow you to do One way ANOVA or Kruskal Wallis test (only applied for continuous data). so justify you reasons

Response:

Many thanks for your feedback. In line with our statement in statistical analyses (line 235, now line 260), “Kruskal-Wallis statistical test was performed to assess the length of presentation and diagnosis delay among the patients and distribution of delays by stage at presentation”.

We visually inspected the distribution of the data using the histogram. The histograms on diagnosis time and presentation time showed that the data of both variables are skewed to the right. Due to this violation of normality, we could not use ANOVA test (reference: Hu, Z. D., Zhou, Z. R., & Qian, S. (2015). How to analyze tumor stage data in clinical research. Journal of thoracic disease, 7(4), 566–575. https://doi.org/10.3978/j.issn.2072-1439.2015.04.09).

We have added the following text to outline our rationale for using the Kruskall Wallis test: “This analysis was chosen as data were not normally distributed. We visually inspected the distribution of the data using a histogram. The histogram on diagnosis time and presentation time showed that the data of both variables were skewed to the right” (lines 262-273). We have included a histogram of the data, below, for the reviewer’s information.

In terms of our transformation of data, as outlined in Table 2, we used the data in its original (continuous) form. We presented the median of the delays in each stage and conducted our analysis using the Kruskal Wallis test. The data differed from those in Table 1 that were already transformed to categorical variables.

6. Line 271: the table titles should be self explanatory which answers "what, who, where, and when", So apply it for all of your Table titles

Response:

Thank you very much for your suggestion. We modified all table titles to make them more self-explanatory. Related to tables, we have reordered their appearance in the findings section to accommodate a better flow with reporting our findings. For information, Table 3 became Table 5, Table 4 became Table 3 and Table 5 became Table 3.

The table titles have been outlined as below, showing the original and revised titles:

Table 1. Characteristics of study subjects (n =150)

New title: Sociodemographic, clinical, and delay characteristics of study participants (n =150) recruited from Dr. Sardjito Hospital, Yogyakarta, in 2018-2021.

Table 2. Median of presentation and diagnosis time by stage at baseline.

New title: Median presentation, diagnosis, and overall delay for all participants (n=150) by stage at diagnosis.

Table 3 � became Table 5. Delays and risk of more advance stage at time of diagnosis.

New title: Effect of presentation and diagnosis delay on likelihood of stage III-IV breast cancer at point of diagnosis.

Table 4 � became Table 3. Factors associated with ≥3 months presentation delay.

New title: Sociodemographic and clinical factors associated with ≥3 months presentation delay in breast cancer patients.

Table 5 � became Table 4. Factors associated with ≥1 month diagnosis delay.

New title: Sociodemographic, clinical factors and service utilization associated with ≥1 month diagnosis delay in breast cancer patients.

Table 6. Reasons for presentation delay (n=65).

New title: Reasons provided by breast cancer patients for delays of ≥3 months’ presentation (n=65).

S1 Table. Proportion of presentation and diagnosis time based on stage.

New title: Proportion of presentation and diagnosis time based on stage at diagnosis.

S2 Table. Delays and risk of lower BMI at time of diagnosis.

New title: Effect of presentation and diagnosis delay on likelihood of lower BMI (<23) at point of diagnosis.

S3 Table. Reasons for presentation delay in the whole panel (n=150).

New title: Reasons provided across all study participants for presentation delay (n=150).

7. On table 1: second column write "Frequency n (%)"

Age: Mean should not be explained alone, so write "mean (SD)"

Comorbidities: you should list the commonest comorbidities with their frequency not the number of comorbidities

Response:

Thank you. We have revised the manuscript in line with this suggestion. In Table 1, we have placed frequency and n (%) in the heading of second column. We have also reported standard deviation alongside the mean age of the study population.

In terms of comorbidities, we have now added specific information about condition and their frequency to Table 1. For information, the total frequency for comorbidities is >100% as participants may have reported more than 1 of the conditions reported.

8. Table 2: it is not the appropriate way of analysis of a Kruskal Wallis test, please make it self explanatory, the numbers in each cell are not understandable.

Response:

Thank you for your remarks. Our response about the rationale for the Kruskall Wallis statistical test is outlined above (Response to question no 5). In Table 2, to improve the readability of the table and ensure it is self-explanatory, we have modified title and headings. We have also added lines to group the analyses presented, making the comparison clearer of median delay across each category (presentation, diagnosis, overall) by stage of disease.

9. Table 3: this association doesn't tell us the exact relationship of stage at diagnosis and presentation delay because it is just crude odds ratio. to know their relatuion stage of diagnosis should be entered to the model and see the effect.

Response:

Thank you for this comment. In response to this comment, we modified Table 3 in the following ways:

• We reordered Table 3 into Table 5 to show factors associated with delays first. Now Table 3 is for factors associated with presentation delay and Table 4 is for factors associated with diagnosis delay.

• We now show in Table 5 the effect of delays to diagnosis at first breast cancer diagnosis in two models.

• Model 1 for presentation delay: adjusted for education level, monthly income, number of risk factor of breast cancer known, habit of breast self-exam, first presenting symptom, number of comorbidities, molecular subtype;

• Model 1 for diagnosis delay: adjusted for education level, monthly income, frequency of medical visit before diagnosis, number of risk factor of breast cancer known, habit of breast self-exam, first presenting symptom, number of comorbidities, molecular subtype

• Model 2 for presentation delay: Model 1 for presentation delay + adjusted for diagnosis delay.

• Model 2 for diagnosis delay: Model 1 for diagnosis delay + adjusting for presentation delay.

10. Table 4: second column should not be put alone. so add another column and show the 'cross tab' of all independent variables with both "<3 months and >3 months" so that it will be explanatory.

Response:

We have modified Table 4 (now Table 3) by adding another column to show variables with <3 months alongside those with ≥3 months presentation delay to make it more explanatory.

11. Table 5: Apply the comments above on table 4

Response:

We have modified Table 5 (now Table 4) by adding another column to show variables with <1 month along with ≥1 month diagnosis delay to make it more explanatory.

12. Line 537: References should be updated, Please use references that are published since 2010

Response:

Thank you for the suggestion. We have reviewed the references closely. Where possible, we have revised references used, but also outline where references remain due to, for example, a lack of other published data for Indonesia.

References that were published before 2010 and have been removed or replaced include:

Ref [6] (Arndt, 2006)

Ref [7] (Meechan, 2003)

Ref [8] (Velikova, 2004)

Ref [9] (Jenner, 2000)

Ref [10] (Nosarti, 2000)

Ref [11] (Montella, 2001)

Ref [17] (Montazeri, 2003)

Ref [18] (Pineros, 2009)

Ref [33] (Cancer Research UK, 2009)

Ref [36] (Facione, 1993)

Ref [37] (Richards, 1999)

Ref [58] (Neale, 1986)

References that remain include Ref [4] (Wahyuni, 2002), because is it the only data from Indonesia showing 5-year overall survival rate data of breast cancer in Indonesia. We keep Ref [25] (Bish, 2005) because it is the most recent literature we could find when determining our decision of choosing ≥1 month to define diagnosis delay in our study. We keep Ref [34] (Harirchi, 2005) because it provides fundamentals that underpin questionnaire development. Lastly, our citation for Braun and Clarke (2006) [42] is the leading citation for thematic analysis and its use in research, which remains in the manuscript.

We also added more recently published literature, including Cancer Research UK 2011, Brzozowska et al, 2014, Li et al, 2019, Scheel et al, 2020, Albeshan et al, 2020, Kim et al 2012, and Caplan 2014 to complement the introduction and discussion regarding questionnair variables, presentation and diagnosis delay, and explanation of molecular subtypes in the multivariable model.

Reviewer #1: Minor issues

1. Line 34: remove repeated words: "Extent of, impact of" Just say "factors associated with"

Response:

We have edited the manuscript in line 34 as suggested. We have also updated other sections of the manuscript to reflect these changes, including in the objective.

2. Line 36: "Cross-sectional study nested in an ongoing prospective cohort study of BC patients". it is not a full statement Add " were employed" after the last word

Response:

We also have edited the manuscript in line 36 as suggested: “A cross-sectional study nested in an ongoing prospective cohort study of breast cancer patients at Dr Sardjito Hospital, Yogyakarta, Indonesia, was employed.”

3. Line 78: correct grammar and English flow

Response:

We modified the sentence in line 78 (now line 77) to “Delay in breast cancer presentation and diagnosis are likely to be key factors in advanced-stage presentation. There are disparities in the length of delays between countries.”

4. Line 113: Remove the word Impact and use associated factors through out the paper

Response:

We modified the sentence in lines 112-115 (now lines 113-115) to “The objective of this study is to quantitatively investigate the factors associated with presentation and diagnosis delays, relationship of delays to stage at presentation and reasons for patient delay within local breast cancer cases.”

Reviewer #2: Major issues:

1. Questionnaire development.

It is not clearly define in the method, how to avoid recall bias that might influence the results of the study (page 38, line 479-481).

Response:

We have outlined our approach to avoiding recall bias as part of our response to comments from Reviewer 1 (Response to question no 3). Please see above for further details.

2. The analysis (multivariable logistic regression) (comments to the question no.2)

a. The authors need to clarify the reason of choosing ‘living alone’ (in living arrangement variable) as reference in multivariable logistic regression analysis (Table 4). If ‘living alone’ is considered as the highest risk among other variables (with spouse only, with spouse and other, or with other than spouse), in my opinion, the reference should be a variable that has the lowest risk associated with > 3 months presentation delay.

b. The same reason of choosing the ‘luminal A’ as the reference to luminal B, Her2- enriched and TNBC variables, considering that each variable has different molecular and clinical characteristic. (Table 4)

c. The same reason of choosing the ‘general practitioner’ (in health care facility first visited) as the reference to specialist and midwife variables that associated with >1 month diagnosis delay, assuming that specialist is the ‘best’ or the lowest risk health care facility to avoid delay in diagnosis. (Table 5)

Response:

Thank you for your comment.

In response to a, in Table 4 (now Table 3), we used “living alone” as a reference due to its highest risk associated with > 3 months presentation delay.

In response to b, we used luminal as a reference of the lowest risk based on previous report (ref: Galukande et al. 2014).

In response to c, general practitioner is a reference of the highest risk associated with > 3 months presentation delay based on report from previous studies. In the analyses of types of health care facility first visited we put midwife as the highest, based on our assumption since there is no literature including midwife in the model.

We have considered your comments and have now set all determinants with lowest risk stratification for outcome as reference in the logistic regression analyses in Table 4 and 5 (now Table 3 and 4). These tables now reflect the updated analyses, with the reference points modified as suggested.

3. It is interesting that the authors analyze of the molecular type of the breast cancer (luminal A, luminal B, Her2-enriched and TNBC) with the presentation delays (Table 4). However, it is not clearly define in the literatures whether the molecular type is associated the diagnosis delays. Most of the delays in diagnosis process related to the stage of the disease, and the molecular types is usually associated with the prognosis of the disease.

Response:

It is true that molecular types have been long observed as a prognosis factor for breast cancer. The role and association of molecular types with clinical symptom recognition has also been raised by others, too (Galukande 2014 [20], Kim 2012 [39]). It is, in fact, an area in which there is limited evidence, but we are keen to explore its relevance to self-detection in our breast cancer population given limited literature and the availability of data on molecular type available for the study population. We used luminal A as reference for analyses based on the result of Galukande 2014 showing cases with TNBC and HER2 tumors as late presenters. This is supported by other research (Kim 2012) that observed luminal A tumors to be the most symptomatic. In contrast, our study observed TNBC cases as earlier presenters compared with cases with luminal A tumors, although significance was not reached. This conflicting result reflects the early stage of evidence in this area and highlights the need for further work to explore how molecular types manifest as symptoms and their subsequent impact on outcomes that may influence presentation, such as help-seeking behaviours.

Reviewer #2: Minor issue:

1. The authors provide a good point of discussion, however the use of word ‘cohort’ (page 34, line 374) is not suitable with the design study (cross sectional).

Response:

We have edited the manuscript as suggested in line 374 (now line 424) replacing ‘cohort’ with ‘study population’: “The ≥3-month patient delay rate of 43.3% in the study population is higher than those observed in most high-income countries.”

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

25 Oct 2021

PONE-D-21-15358R1Delays in the presentation and diagnosis of women with breast cancer in Yogyakarta, Indonesia : a retrospective observational studyPLOS ONE

Dear Dr. Hutajulu

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Reviewer #2:

Thank you for the opportunity to review the manuscript “Delays in the presentation and diagnosis of women of breast cancer in Yogyakarta, Indonesia: a retrospective observational study”. Please find my review of the revised manuscript below.

Major issues:

1. Questionnaire development (recall bias)

The authors explained the approach to avoid recall bias as part of their response to Reviewer #1.

2. The analysis (multivariable logistic regression)

The authors made revision and updated the analyses as well as the Tables (Table 3 and 4).

3. The authors explained the reason of the analysis of molecular type of breast cancer with diagnosis delay (Tables 3 and 4). However, the literature used by the authors (Galukande 2014) is a cross-sectional study to determine the prevalence of breast cancer molecular phenotypes. In my opinion, this reference is not suitable with the purpose of the analysis made by the authors .

Molecular type is an immunohistochemical classification or characterization of breast cancer for hormone receptor status or HER-2 gene overexpression and it correlates with the clinical outcome or the choice of systemic therapy (e.g. ESMO Educational sessions in Annals of Oncology vol 23 suppl 10, Sept 1,2012; National Comprehensive Cancer Network Guidelines version 4.2020; or Nature/NPJ Breast Cancer 6 March 2020). Molecular types also correlate with prognosis of breast cancer (2019 ASCO education books - Molecular testing in breast cancer by Jennifer K. Litton et al). Unfortunately, there is no relevant literature of molecular type of breast cancer in association with diagnosis delays. I am concern that this issue is the major issue of the manuscript.

Minor issue:

The authors edited the design as suggested.

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12 Nov 2021

Dear editor and reviewers,

We are thankful for the positive feedback received from Reviewers 1 and 2, and the editorial team, and for the opportunity to respond to the constructive points in our accompanying revised manuscript. Please find below our point-by-point response to reviewer feedback outlining, where relevant, the related changes we have made. We have uploaded revised versions of the manuscript as instructed, including both a clean copy and a track changes version.

Editor comments

1. Do follow the acceptable repositories as recommended by Plos One, not just dataset to generate figure 1, but also table 1-6. Please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

Response:

We have revised the dataset to now include deidentified data used to generate Figure 1 and Tables 1-6. We have provided derived values for date information to avoid identification of participants. Sharing of the full de-identified dataset is not possible due to restrictions imposed by the ethics committee as most of these contain patient data, albeit de-identified, and it may be possible to determine the identity of participants given the extent of sociodemographic and clinical data available for each participant.

Should there be a request for data, this can be sent to the corresponding author (email: susanna.hutajulu@ugm.ac.id). Future researchers can contact the institutional ethics committee (email: mhrec_fmugm@ugm.ac.id) at Universitas Gadjah Mada, Indonesia with data access queries as well.

We have included again our response to this comment in the accompanying cover letter.

2. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

Response:

3. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response:

Thank you for your comment. We have checked all references and found no article with a retraction notice.

Reviewer 2

Major issues

1. Questionnaire development (recall bias)

The authors explained the approach to avoid recall bias as part of their response to Reviewer #1.

Response: Thank you for your approval.

2. The analysis (multivariable logistic regression)

The authors made revision and updated the analyses as well as the Tables (Table 3 and 4).

Response:

Thank you for your approval.

3. The authors explained the reason of the analysis of molecular type of breast cancer with diagnosis delay (Tables 3 and 4). However, the literature used by the authors (Galukande 2014) is a cross-sectional study to determine the prevalence of breast cancer molecular phenotypes. In my opinion, this reference is not suitable with the purpose of the analysis made by the authors . Molecular type is an immunohistochemical classification or characterization of breast cancer for hormone receptor status or HER-2 gene overexpression and it correlates with the clinical outcome or the choice of systemic therapy (e.g. ESMO Educational sessions in Annals of Oncology vol 23 suppl 10, Sept 1,2012; National Comprehensive Cancer Network Guidelines version 4.2020; or Nature/NPJ Breast Cancer 6 March 2020). Molecular types also correlate with prognosis of breast cancer (2019 ASCO education books - Molecular testing in breast cancer by Jennifer K. Litton et al). Unfortunately, there is no relevant literature of molecular type of breast cancer in association with diagnosis delays. I am concern that this issue is the major issue of the manuscript.

Response:

Thank you for your thoughts on the issue. We have decided to exclude molecular subtype variables out of the multivariate model and related references until there is further evidence to determine any potential influence it may have on diagnosis delays. As a result, we have also adjusted the results in Tables 3-5 and abstract and discussion sections for the exclusion of molecular subtypes in these analyses.

Minor issues

The authors edited the design as suggested.

Response:

Thank you for your favourable opinion.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

26 Dec 2021

Delays in the presentation and diagnosis of women with breast cancer in Yogyakarta, Indonesia : a retrospective observational study

PONE-D-21-15358R2

Dear Dr. Hutajulu,

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Kind regards,

Evy Yunihastuti, MD

Academic Editor

PLOS ONE

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Please provide acceptable data set as recommended by Plos One, especially data that derived table 1-6. The dataset provided was only for figure 1, which is not acceptable. Please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

5 Jan 2022

PONE-D-21-15358R2

Delays in the presentation and diagnosis of women with breast cancer in Yogyakarta, Indonesia: a retrospective observational study

Dear Dr. Hutajulu:

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