Perceptions about Cancer and Its Treatment among Cancer Patients – The PACT Study

We conducted a cross-sectional and observational study involving face-to-face interviews with the cancer patients attending the Radiation Oncology Department of a peripherally-located tertiary cancer care hospital in Eastern India. A convenience sampling design was used to enroll the subjects.

We included patients with definite cytological/histological proof of malignancy, older than 18 years of age, who provided written informed consent. Those with significant physical or psychiatric issues rendering them unsuitable for the interview were excluded from the study (Fig. 1).

The sample size for the study was estimated to be of 282 subjects. Details on the sample size calculation are shown in Supplementary File 1.

A questionnaire was developed for the study after an interaction with 50 subjects (25 patients and 25 caregivers) to identify the main perceptions and myths prevalent in the target population. The aspects that were addressed included sociodemographic factors, disease-related parameters, and the perceptions about cancer and its treatment (Fig. 2). Perceptions related to cultural beliefs, contagiousness, diet, screening, the hazards of biopsy and surgery, alternative medication use, chemotherapy-induced pain, hair loss, and curability of the disease were assessed using 34 questions which could be answered as “yes”, “no”, or “I don't know”. The questionnaire was pretested among a group of 15 patients to gauge the clarity of understanding, ambiguity, comprehensibility, and uniformity. The validation resulted in some changes; thereafter, the final version was prepared. The study proposal was submitted to the institutional Ethics in Research Committee and, after getting written clearance, trained physicians conducted face-to-face interviews with the subjects. The interviewees were assured of data confidentiality and that this interview would have no impact on their treatment. The data thus collected was compiled and subjected to a statistical analysis.

The questions were framed in such a manner that answering “yes” would indicate agreeing to the false statement embodied by the question or believing in the myth. “Yes” answers were assigned a score of 1, and the “no” or “I don't know” answers, 0.

Myth score: A myth may be defined as beliefs held to be true despite refuting evidence. The myth score was calculated by adding all the “yes” answers of an individual subject. Thus, the score ranged from 1 to 34 points.

Myth index (MI): it was calculated for each subject using the following formula:

in which M₁, M₂, ............. M_n are the “yes” answers to the first, second, ........ and nth question, and N is the number of questions (in this case, N = 34). The MI is expressed as a percentage.

A high myth score and a high MI would indicate harboring a significantly-high number of false beliefs on cancer. We assumed that subjects who had myth scores greater than 10 (corresponding approximately to an MI ≥ 30%) had sufficient belief in wrong perceptions or myths to be considered “high”. Hence, we chose an arbitrary value of MI ≥ 30% as high, for the subsequent analyses with respect to sociodemographic predictor variables.

The predictor variables assessed were namely age, gender, domicile, schooling, income, disease stage, Performance Status (PS) score on the Eastern Cooperative Oncology Group (ECOG) Scale, intent of treatment, and treatment status.

A comparative analysis was performed with cross tables, as well as the Kruskal Wallis test, to try to find associations involving the predictor variables and the MI. The role of sociodemographic and disease-related parameters as predictors of high MI were assessed using logistic regression analysis. The Spearman rank test was used to measure the direction and strength of the correlations regarding the sociodemographic variables and the MI. Values of p < 0.05 were considered statistically significant. All p-values mentioned are two-tailed.

Data analysis was performed using the IBM SPSS Statistics fro Windows (IBM Corp.) software, version 26.0, and the Microsoft Excel (Microsoft Corp.) software.

The median age of the subjects was of 51.5 (interquartile range [IQR]: 44–60) years, and 129 (58.1%) of the participants were of the female gender. Most of them lived in rural households (76.6%) and had completed primary education (48.2%). Out of all the subjects, 88 (39.5%) were homemakers and most (62.2%) had a monthly family income below INR 10,000. Details on the demographic characteristics are presented in Table 1.

The most common primary site of cancer diagnosis was the breast (20.50%), followed by the uterine cervix (16.2%) and the head and neck (15.4%). The most common stage group (as per the 8th edition of the American Joint Committee on Cancer [AJCC] Tumor, Node, Metastasis [TNM] staging system) at presentation was III (32.6%), followed by IV (31.7%) and II (27.5%). The median follow-up was of 14.5 months.

Table 2 shows the percentage of subjects who believed in myths regarding various aspects of cancer. More than half (56%) believed cancer was a result of God's wish, and about 20% believed it to be contagious (thus, sharing same bed, food or utensils with a patient could give rise to cancer). Around 28% and 24% believed that cancer was caused by eating sweet or sour food respectively. Alarmingly, 18% and 16.7% believed that a needle biopsy or surgery for cancer, respectively, causes cancer to spread. About 15% believed that alternative medicines can cure cancer. Almost 28.8% felt chemotherapy was very painful. and a 61.3% of the caregivers felt that cancer treatment always left behind permanent physical sequelae.

The maximum myth score was 22 (median; 7; IQR: 4–10) points. In total 25.4% of the subjects had a myth score < 5 points, 53.8%, scores from 5 to 10 points, and 47 subjects (21.3%), ≥ 10 points.

The maximum MI was of 64.71%. A total of 33 (14.86%) patients had an MI ≤ 10%, 61 (27.47%), MI from 10 to 20%, 81 (36.48%), MI from 20 to 30%, and 47 (21.17%), MI > 30%.

Of all the predictors of high MI, lack of schooling (the MI was high in 25.0% of the illiterate subjects versus 22.0% among those with primary education) was found to have a statistically significant association (Table 3).

Very weak correlations were observed regarding the various sociodemographic variables and the MI (Supplementary File 2; Supplementary Table S1).

Univariate logistic regression detected no significant predictors of high MI (≥ 30%) among the subjects. (Supplementary File 3; Supplementary Table S2).

To the best of our knowledge, the current is the first study from Eastern India exploring the various myths and perceptions held by cancer patients regarding various aspects of cancer causation, diagnosis, and treatment. Our population of 222 cancer patients was sizeable, and a significant number of factors associated with higher levels of misconceptions were identified.

The present was a single-center study conducted over a brief time period implementing the technique of convenience sampling, thus limiting the generalizability of the findings to the entire population. Moreover, there was a possibility that the subjects were aware of being judged during the interview and gave ideal answers as opposed to what they actually believe.