Cancer continues to be a major public health challenge globally, ranking as one of the leading causes of morbidity and mortality (1). With increasing life expectancy and advancements in diagnostic techniques, the global cancer burden is steadily rising. Epidemiological data indicate that approximately 60% of malignancies are diagnosed in individuals aged 65 years or older, a figure projected to rise to 70% as the population ages (2). This shift poses significant challenges to healthcare systems, not only in managing the physical aspects of cancer but also in addressing the psychological burden that accompanies the disease (3).

Beyond the physical and biological implications of cancer, a substantial proportion of patients suffer from psychological disturbances. Among these, depression and anxiety are the most frequently reported psychiatric morbidities (4). Studies suggest that nearly half of all cancer patients experience some form of psychological distress during the course of their illness (5). Despite their high prevalence, these conditions often go undetected and untreated, especially in busy oncology settings where clinical focus remains primarily on the physical symptoms and treatment regimens.

Undiagnosed and untreated depression and anxiety in cancer patients have been linked to various adverse outcomes, including poor adherence to treatment, increased frequency of hospital admissions, longer recovery times, and a significant decline in overall quality of life (6). Moreover, psychological distress can exacerbate physical symptoms such as pain, fatigue, and insomnia, thereby creating a vicious cycle that further deteriorates a patient’s health and well-being.

Health-related quality of life (HRQoL) has emerged as an important outcome measure in cancer care. It encompasses the physical, psychological, social, and environmental domains of a patient’s life (7). Depression and anxiety can severely impair HRQoL, affecting the patient’s ability to cope with the disease, maintain interpersonal relationships, and engage in daily activities. Thus, there is a growing recognition of the need for holistic cancer care that integrates psychological evaluation and intervention alongside medical treatment.

Evidence from international and cross-cultural studies consistently shows a strong inverse correlation between psychological distress and quality of life in cancer patients (8). Despite this, routine psychological assessment is not commonly implemented in oncology practice, particularly in resource-constrained settings. A major barrier is the lack of standardized and easily administered tools that can help identify patients in need of psychological support.

The aim of this study is to evaluate depression, anxiety, and quality of life in patients undergoing cancer treatment. The objectives include assessing the levels of anxiety and depression among these patients, and examining how demographic factors relate to anxiety and depression in this group. Additionally, the study seeks to explore the relationship between psychiatric morbidity and the overall quality of life in patients receiving cancer treatment.

This cross-sectional study was conducted over a period of two years on 100 cancer patients undergoing treatment at MNJ Cancer Hospital and Regional Cancer Centre, Hyderabad. The objective was to assess depression, anxiety, and quality of life using standard assessment tools.

Inclusion Criteria:

• Patients aged between 18 and 60 years.
• Patients currently undergoing cancer treatment.
• Patients who provided informed consent.

Exclusion Criteria:

• Patients diagnosed with mental retardation.
• Patients with severe physical disability.
• Uncooperative patients.
• Patients with other serious medical co-morbidities.

Tools Used:

1. Intake Proforma - Used to collect socio-demographic details such as age, gender, education, occupation, marital status, domicile, family type, religion, and socioeconomic status.
2. Hospital Anxiety and Depression Scale (HADS):
   • Contains 14 items, 7 each for anxiety and depression.
   • Each item is rated from 0 to 3.
   • Total scores for each subscale range from 0 to 21.
     • 0–7: Normal
     • 8–10: Borderline
     • 11–21: Abnormal

3. WHO Quality of Life-BREF (WHO-BREF) Scale:
   • A 26-item scale evaluating quality of life in four domains: physical health, psychological health, social relationships, and environment.
   • It has good internal consistency (Cronbach’s alpha = 0.87), and strong content, construct, and predictive validity.

Statistical Analysis:

• Data was analyzed using SPSS version 22.
• Descriptive statistics were used for socio-demographic details.
• Mean, standard deviation, frequencies, and percentages were calculated.
• Chi-square test was applied for categorical variables.
• Student’s t-test was used to assess differences between two groups.
• ANOVA was applied for comparison among more than two groups.
• Pearson and Spearman correlation tests were used to study the association between psychological morbidity and quality of life.

Table 1: Demographic Profile of Study Subjects (N = 100)

The study included 100 cancer patients, with the majority aged between 51–60 years (37%), followed by 18–30 years (26%), 41–50 years (23%), and 31–40 years (14%). The gender distribution was equal, with 50 males and 50 females. Most participants were married (78%) and came from rural backgrounds (68%). In terms of education, 48% were illiterate, 21% had studied up to 10th class, and 31% had education above 10th class. Occupation-wise, 98% were semi-skilled workers, while only 2% were skilled (Table 1).

Table 2: HADS – Depression Scores Among Study Population (N = 100)

Out of 100 cancer patients assessed using the HADS depression scale, 60% had normal scores, 2% had borderline levels of depression, while 38% showed abnormal scores, indicating a high prevalence of depressive symptoms in the study population (Table 2).

Table 3: Association of Sociodemographic Profile with Depression Scores (HADS)

Depression levels were significantly higher among younger patients (especially those aged 18–30), unmarried individuals, those from nuclear families, and patients with higher educational levels. Religion also showed a significant association, with Muslim participants having higher mean scores. No significant association was observed with gender, occupation, or domicile status (Table 3).

Table 4: HADS – Anxiety Scores by Socio-Economic Status (N = 100)

The table 4 shows no significant association between socio-economic status and anxiety levels among cancer patients (p = 0.81). Most patients belonged to the upper lower SES group, with a mix of normal, borderline, and abnormal anxiety scores. Similar patterns were seen across other SES groups, indicating that anxiety levels were not strongly influenced by socio-economic status in this study.

Table 5: Distribution of Anxiety Scores Among Cancer Patients (N = 100)

The table 5 shows the distribution of anxiety levels among the study participants. Out of 100 cancer patients, 55% had normal anxiety scores, 15% were borderline, and 30% had abnormal anxiety levels, indicating that nearly one-third of the patients experienced clinically significant anxiety.

Table 6: Association of Socio demographic Profile with Anxiety Scores (HADS)

Anxiety scores were significantly associated with age and education level. Younger patients (especially 18–40 years) and those with higher education had higher anxiety levels. Other variables such as gender, marital status, family type, occupation, religion, and domicile did not show statistically significant associations with anxiety

This study involved 100 patients diagnosed with cancer and was conducted at MNJ Institute of Oncology and Regional Cancer Centre, Hyderabad. After obtaining informed consent, patients completed a structured intake proforma for socio-demographic data, followed by assessments using the Hospital Anxiety and Depression Scale (HADS) and WHOQOL-BREF scale to evaluate psychiatric morbidity and quality of life (QoL). The findings were statistically analyzed, and the results offer significant insights.

The mean age of the study population was 43.34 years. This is comparatively lower than findings from studies conducted in the UK and Italy, where the mean age of cancer patients ranged from 53 to 70 years (9). Differences in gender inclusion, cancer types, and regional demographics may account for the variation (10).

Equal gender distribution was observed in our study (50 males and 50 females), differing from findings by Matsushita et al. (2005) who noted male predominance. This may reflect population differences or sample collection strategies (11).

Marital status showed that 78% of patients were married, consistent with Indian cultural norms. Western studies (9) demonstrated more varied marital statuses, including higher rates of cohabitation and divorce, indicating socio-cultural influences.

Education levels revealed that nearly half the sample was illiterate, which aligns with findings from Indian settings but contrasts with Western literature where a higher proportion had secondary or tertiary education (12). Occupational distribution was skewed toward semi-skilled laborers, which reflects the socio-economic profile of the catchment area, differing from studies in more urban or developed regions (13).

Most participants were from rural backgrounds (68%), contrasting with studies from urban-centric populations (14). In terms of religion, the majority were Hindus (80%), reflective of regional demographics, while other international studies show broader religious diversity (15).

HADS-Depression scores revealed that 38% of patients had abnormal levels, consistent with findings by Liu et al. (2018) and higher than some Indian studies such as Pandey et al. (2006). The variation may be attributed to sample differences and assessment settings (16, 17).

Sociodemographic factors significantly associated with higher depression scores included younger age, unmarried status, nuclear family setting, and higher education levels. No significant association was observed with gender, occupation, or domicile, though religion showed significant differences (p = 0.019).

Similarly, 30% of patients had abnormal anxiety scores on the HADS scale. This is consistent with studies (16, 18), but higher than reported in others (Pandey et al., 2006; Park et al., 2018). Significant anxiety correlates included younger age and higher educational attainment. Gender, marital status, occupation, and socio-economic status did not show significant associations.

There was a moderate positive correlation between depression and anxiety scores (R = 0.47, p < 0.0001), affirming findings from earlier literature that these conditions often co-exist in cancer patients.

In terms of QoL, depression showed strong negative correlations with all WHO-BREF domains: physical health (R = -0.66), psychological (R = -0.51), social relationships (R = -0.50), and environment (R = -0.67), all with p < 0.0001. These results are in agreement with Sadoughi et al. (2015), who also reported significant associations between depression and reduced QoL scores (19).

Anxiety, though less strongly, was also negatively correlated with QoL domains: physical health (R = -0.38), psychological (R = -0.24), social relationships (R = -0.19), and environment (R = -0.27). Significant correlations were seen with all except social relationships (p = 0.058), again aligning with previous research by Sadoughi et al. (2015).

This study shows the necessity of integrating psycho-oncological assessment in routine cancer care. Depression and anxiety are prevalent among cancer patients and significantly affect their quality of life across physical, psychological, social, and environmental domains. Early identification and management of these psychiatric symptoms using validated tools like HADS and WHOQOL-BREF can substantially enhance patient well-being and treatment outcomes.

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