Baseline Demographic Profile and General Health Influencing the Post-Radiotherapy Health Related Quality-of-Life in Women with Gynaecological Malignancy Treated with Pelvic Irradiation

Abstract

Background:

Cancer specific survival and quality-of-life (QOL) assessment are important in evaluating cancer treatment outcomes. Baseline demographic profiles have significant effects on follow-up health related QOL (HRQOL) and affect the outcome of treatments.

Materials and Methods:

Post-operative gynaecological cancer patients required adjuvant pelvic radiation enrolled longitudinal assessment study. Patients had completed the short form-36 (SF-36) questionnaire before the adjuvant radiotherapy and functional assessments of cancer therapy-general module at 6^th month's follow-up period to assess the HRQOL. Baseline variables were race, age, body mass index (BMI), education, marital status, type of surgery, physical composite scores (PCS) and mental composite scores (MCS) summary scores of the SF-36. Univariate and multivariate regression analysis used to determine the influence of these variables on post-radiotherapy HRQOL domains.

Results:

Baseline PCS, MCS, age, education and marital status had positively correlation with post-radiotherapy HRQOL while higher BMI had a negative impact in univariate analysis. In multivariate regression analysis, education and MCS had a positive correlation while higher BMI had a negative correlation with HRQOL domains.

Conclusion:

Enhance our ability to detect demographic variables and modify those factors and develops new treatment aimed at improving all aspect of gynaecological cancer including good QOL.

INTRODUCTION

Women diagnosed with gynaecological cancer are at risk for depression, anxiety and impaired quality-of-life (QOL) even after treatment.[1],[2],[3],[4] Overall survival and HRQOL are important components of assessing the effects anti-cancer treatment.[5] Post-radiotherapy QOL is not only depends upon the clinical variable of the disease or treatment related morbidity and also are affected by baseline demographic variables such as socio-economic status, social characteristics and personal expectations.[6],[7] Significant differences in HRQOL found as a function of age, race, karnofsky performance status, income level, employment status and other co-morbidity.[8] Arredondo et al. examined QOL in men with prostate cancer and found men with more co-morbidities had significantly worse scores at baseline in the physical domains.[9] Pre-treatment characteristics may affect patients’ reaction to their illness and treatment, thus influence disease specific QOL scores measured during and follow-up after the treatment. The role these baseline demographic variables play in women's ability to maintain good QOL following the treatment, may therefore affect the assessment of treatment and become a factor in determining, which treatments are be selected. Health status questionnaires used to capture the general physical and mental health before radiotherapy. The SF-36 selected as it also provides a measure of the health burden of chronic disease and other medical conditions[10] that women may have at baseline.

Functional assessments of cancer therapy-general (FACT-G En and Cx) module measure the disease specific HRQOL scores during the follow-up period. Post-treatment FACT scores recorded and analyzed to determine the degree to which individual domains of QOL affected by baseline differences in demographic variables and physical composite (PCS) and mental composite health (MCS) scores measured with the SF-36.

MATERIALS AND METHODS

This prospective observational study conducted in the Department of Radiation Oncology in a tertiary cancer center in West Bengal. Consecutive patients requiring adjuvant radiotherapy following radical or total hysterectomy for endometrial or cervical Cancer enrolled in a longitudinal assessment of QOL study at their first visit in the department of radiation oncology. Data collected between May 2007 and June 2012, through interviews, at the time of Registration in the department of Radiation Oncology and at the 6^th months follow-up period. All participants signed informed consent form.

Baseline demographics ascertained by interview with a research assistant at their time of the initial visit. Private office records reviewed to obtain height, weight and diagnosis following surgery. Body mass index (BMI) was calculated (defined as weight (kg) divided by height (m²) for each woman) and categorized as normal weight (18.5-24.9), overweight (25-29.9) or obese (BMI ≥ 30).[10] Marital status categorized as being married or unmarried (single, divorced and widowed) and educational level categorized as illiterate, undergraduate and graduate.

For the evaluation of general health status before radiotherapy, the questionnaire short form (SF-36) medical outcome survey used. General health status at the time of the initial visit measured with SF-36, a comprehensive survey designed to measure physical and mental health.[11] There are eight subscales, summarized into PCS and MCS. The SF-36 is a widely used, reliable and validated instrument with population specific norms that also used to evaluate the burden of different co-morbidities.

The questionnaires used in this study to assess patients’ baseline level of physical and mental health. Eight multiple-item domain scales scored separately from 0 (lowest level of functioning) to 100 (highest level). The scales evaluate:

• Limitations in physical activities due to health problems (physical functioning)

• Limitations in social activities due to physical or emotional problems (social functioning)

• Limitations in usual role activities because of physical health problems (role-physical)

• Bodily pain

• General mental health

• Limitations in usual role activities due to emotional problems (role-emotional)

• Vitality

• General health perceptions.

These eight domain scales combined to form two summary scores, the mental component summary (MCS) scale and the physical component summary (PCS) scores.

Health related QOL after radiotherapy evaluated with the help of FACT-G, cervical cancer and endometrial cancer (FACT-G, Cx and En, Bengali format, Version 4.0).[6] FACT-G, a 27 item general module, grouped into the four domains: Physical well-being, functional well-being and social/family well-being, each with seven items and emotional well-being with six item.[12] The other 15 item corresponds to the domain “additional concerns” for endometrial or cervical cancer (FACT En and Cx). The response scales are Likert type with scores ranging from 0 (not at all) to 4 (extremely). Domain scores are obtained by summing the scores of their items ranging from 0 to 28 in the physical, functional and social/family well-being and from 0 to 24 in the emotional well-being. All domains added so that a higher score indicates higher QOL.[13]

Statistical analysis

Data stored and analyzed using Statistical Package for the Social Sciences version 17.0 (IBM).

The pre radiotherapy general health (SF-36 module) and the post-radiation HRQOL (FACT-G module) analyzed as a function of the site of cancer and scores from the FCAT-G subscales compared with the normative data of cancer female. Differences greater than 2 points considered as clinically important for all the FACT-G subscales.[13]

Univariate analysis

Age, BMI, education, PCS and MCS analyzed as continuous variables and race, marital status and type of hysterectomy analyzed as categorical variables. Pearson correlation coefficients calculated for normally distributed continuous variables such as age, BMI, SF-36, PCS and MCS with the post-radiation HRQOL scores of all the FACT subscales. Spearman's correlation coefficients calculated for categorical variables (race, type of hysterectomy) with all the FACT domains.

Multivariate analysis by Cox regression model included the prognostic significant variables from the univariate analysis. The variables regressed on individual FACT domains (physical, functional, emotional and social) using a stepwise linear regression model.

RESULTS

Consecutively 206 patients approached and of them, 194 patients agreed to participate in this study. Only one of the two modules for before and post-radiotherapy HRQOL completed by seven patients, 31 patients had inadequate operative data, nine patients were died due to other co-morbidities, accident or disease progression within 6 months and they were excluded from subsequent analysis as shown in Table 1.

Table 1.

Baseline patient's characteristic that included for statistical analysis

Nearly, 63% had endometrial cancer. Nearly 17% and 12% of patients were obese and college graduate, respectively. The majority of the patients treated with radical hysterectomy (59%).

Baseline and post-radiotherapy HRQOL measured using SF-36 and FACT-G modules, respectively and these data presented in the Table 2. There was no significant difference for any of the SF-36 summary scores or FACT-G domains scores as a function of diagnosis.

Table 2.

Mean pre and post-radiotherapy HRQOL (SF-36 summary scores and FACT-G domain scores) as a function of diagnosis

Univariate analysis showed that there is a strong correlation between post-radiotherapy patients HRQOL evaluated with FACT subscales and baseline demographic variables and general health (SF-36 PCS and MCS).

Obese patients had poor HRQOL after radiation all the domains of FACT-G. Older patients and higher education had in favor of post-radiotherapy HRQOL in all the domains of well-being except emotional subscale. Patients with higher physical and mental summary score at baseline had better physical, functional, social and emotional well-being after radiotherapy in univariate analysis. Radical hysterectomy had a negative impact on HRQOL during the follow-up period, but reached statistical significant (P < 0.05) as shown in Table 3.

Table 3.

Univariate analysis: Correlation between post radiotherapy FACT-G, baseline SF-36 and demographic variables

Multivariate linear regression model has shown that baseline MCS, education and obesity were independent prognostic factors for post-radiotherapy QOL in all the domains of FACT-G. MCS and education had a positive correlation, whereas obesity had a negative effect on all the domains of post-radiotherapy QOL. Furthermore, marital status (married) was also statistically significant prognostic factor for all the domains of FACT-G except social wellbeing as shown in Table 4.

Table 4.

Multivariate linear regression analysis for each post radiotherapy HRQOL FACT-G domain

MCS, education and obesity accounted for a significant amount of the variance (adjusted R²) in the regression model for each of the FACT-G subscales. The models accounted for 42-54% of the variability in the post-radiotherapy HRQOL in the domains of physical, functional, social and emotional well-being.

DISCUSSION

The women with gynaecological cancer following radiation are affected health related QOL. The clinical and demographic variables that showed the prognostic value of post-radiation QOL scores among the endometrial and cervical cancer measured and analyzed with univariate and multivariate regression model. These results give an idea about the extent of relation to the post-radiation QOL with the pre radiation general health and demographic factors and have important implications for management of patients with gynaecological cancer.

We chose SF-36 module for pre-radiotherapy HRQOL assessment as it also provide the health burden of chronic disease and other medical condition that women may have at this age. Whereas, FACT-G module for post-radiotherapy QOL assessment because it measures all the dimension of health and evaluates disease specific QOL that is relevant to patients with endometrial and cervical cancer. In addition, both modules had undergone rigorous testing for reliability and validity and it is easy to administer, understand and answer.

The mean age of the patients reflect an older patient's population and higher degree of other com-morbidities at this older age would be expected. It is entirely possible that concomitant disease would also have influenced baseline general health status of these patients. Older women had better physical (0.205, P > 0.05), functional (0.291, P > 0.01) and social (0.243, P > 0.01) well-being compared to younger patients during the follow-up period as per univariate analyses. Wan et al. examined the relationship between demographic variables (including age), clinical factors and social characteristics and measures on the four subscales of the FACT-G in cancer patients.[7] They found lower QOL scores among those with poorer performance status and younger patients, Consistent with these results. A recent study of 2208 women with breast cancer who completed the EORTC general cancer QOL scale (breast cancer module) found that younger age was a significant risk factor for poorer QOL.[14] Movsas et al. speculated that the observation of decreased QOL in younger patients may be due to the devastating impact of a cancer diagnosis at a younger age.[8]

Physical disability caused by the pelvic radiation have traditionally been regarded the most important factor of gynecological cancer patients that causes distress and it has received the most important attention in the management of these patients. In our study, we found that the increase physical disability in post-radiation and has a strong correlation with obesity, marital status (unmarried/widow/divorced) and type of surgery (radical hysterectomy). This does accord with the result of study by Hubert and Fries.[15]

Higher BMI had a negative effect on all the subscale of FACT-G (physical − 0.505 P > 0.01, functional − 0.365, P > 0.01, social − 0.413, P > 0.01 and emotional − 0.479, P > 0.01) in univariate analyses. In this study, higher baseline BMI did continue as a negative effect on all the domains of FACT-G in multivariate regression analysis. This does accord with the result of study by Doll et al.[16] Doll et al. had shown that, obesity is associated with poor level of subjective health status, particularly in physical domains of HRQOL.

Multivariate regression analysis that included baseline physical and mental composite summary scores from the SF-36, race, age, marital status, educational level and obesity accounted 54%, 42%, 43% and 52% of variability in physical, functional, social and emotional well-being after radiation, respectively. This is similar to the findings by Wan et al. that 45% of the variability in total FACT-G scores in cancer patients who had undergone treatment was accounted for by demographic variables (age, gender, living arrangement and, race/ethnicity), clinical variables (performance status, disease type and stage) and social factors (spiritual beliefs, religious affiliation and relationship with the physician).[7]

We wanted to identify the prognostic value of different demographic variables and different dimension of QOL before adjuvant radiation on the post-treatment HRQOL of gynaecological cancer patients. Therefore, we adjusted for such inter correlation by using multiple regression analysis. This showed that there was a tendency toward higher regression co efficient for MCS and education and negatively with obesity with all the dimension of FACT-G scores during follow-up HRQOL. All the other variables showed slightly lower value and not statistically significant.

There is distinctive association between the post-radiotherapy HRQOL and baseline MCS but not with the PCS and the MCS component measure mental, role-emotional (limitation in role activities due to the emotional problem) and social functioning domains of QOL. Post-radiotherapy HRQOL is psychosocial variable. The post-HRQOL and baseline MCS may be considered mainly of psychological or emotional nature. Second, studies of QOL in the long- term female cancer survivor suggest psychological, social and spiritual concern are more important than physical concern and psychological dysfunction is a major problem observed in most of the studies of a cancer survivor. Another explanation may be that HRQOL of long-term survivor may be more affected by difficulties reintegrating into valued social roles, a domain in the computation of the MCS measure.

CONCLUSION

The realization that gynaecological cancer affect patients QOL in many areas other than cancer and treatment squeal, enhance our ability to detect these demographic variables and modify those factors and develops new treatment aimed at improving all aspect of gynaecological cancer and thus giving these patients as good QOL as possible.

The main limitation of this study is the small sample size and the lack of diversity. Decreasing diversity may have allowed the authors to determine the effect of baseline independent variables on the subscales of the FACT-G, but at the same time limits the generalization of the results.