The burden of chronic diseases among Australian cancer patients: Evidence from a longitudinal exploration, 2007-2017

Rashidul Alam Mahumud; Khorshed Alam; Jeff Dunn; Jeff Gow

doi:10.1371/journal.pone.0228744

. 2020 Feb 12;15(2):e0228744. doi: 10.1371/journal.pone.0228744

The burden of chronic diseases among Australian cancer patients: Evidence from a longitudinal exploration, 2007-2017

Rashidul Alam Mahumud ^1,^2,^3,^4,^*, Khorshed Alam ^1,², Jeff Dunn ^1,^5,⁶, Jeff Gow ^1,^2,⁷

Editor: Miguel Angel Luque-Fernandez⁸

PMCID: PMC7015395 PMID: 32049978

Abstract

Introduction

Cancer is a major public health concern in terms of morbidity and mortality worldwide. Several types of cancer patients suffer from chronic comorbid conditions that are a major clinical challenge for treatment and cancer management. The main objective of this study was to investigate the distribution of the burden of chronic comorbid conditions and associated predictors among cancer patients in Australia over the period of 2007–2017.

Methods

The study employed a prospective longitudinal design using data from the Household, Income and Labour Dynamics in Australia survey. The number of chronic comorbid conditions was measured for each respondent. The longitudinal effect was captured using a fixed-effect negative binomial regression model, which predicted the potential factors that played a significant role in the occurrence of chronic comorbid conditions.

Results

Sixty-one percent of cancer patients experienced at least one chronic disease over the period, and 21% of patients experienced three or more chronic diseases. Age (>65 years old) (incidence rate ratio, IRR = 1.15; 95% confidence interval, CI: 1.05, 1.40), inadequate levels of physical activity (IRR = 1.25; 95% CI: 1.09, 1.59), patients who suffered from extreme health burden (IRR = 2.30; 95% CI: 1.73, 3.05) or moderate health burden (IRR = 1.90; 95% CI: 1.45, 2.48), and patients living in the poorest households (IRR = 1.21; 95% CI: 1.11, 1.29) were significant predictors associated with a higher risk of chronic comorbid conditions.

Conclusions

A large number of cancer patients experience an extreme burden of chronic comorbid conditions and the different dimensions of these in cancer survivors have the potential to affect the trajectory of their cancer burden. It is also significant for health care providers, including physical therapists and oncologists, who must manage the unique problems that challenge this population and who should advocate for prevention and evidence-based interventions.

Introduction

Cancer is one of the most pressing public health problems worldwide [1]; an estimated 9.6 million patients die from cancer each year. In Australia, it is also an alarming issue with the health system dealing with 483 new cases per 100,000 people in 2019, while on average 136 people die from cancer each day [2]. Cancer contributes 18% of the total burden of disease in terms of disability-adjusted life years, followed by 14% from cardiovascular diseases, 13% from musculoskeletal conditions, and 12% from mental and substance use disorders in Australia [3]. Further, there are approximately one million survivors in Australia who have been diagnosed with cancer in the past [4]. The five-year survival from all cancers combined improved from 48% to 69% between 1990 and 2011–2015 [2].

However, the majority of cancer patients suffer from chronic diseases or conditions, commonly referred to as comorbidity. The risk of having comorbidity increases during treatment as well as oncology follow-up periods [3,5,6], which adversely influences treatment choices and outcomes. Chronic comorbid conditions of cancer patients contribute to a major clinical challenge in terms of cancer diagnosis, ill health, the course of treatment, long-term disability and disease management [7]. In 2014–15, more than 11 million Australians (50%) reported having at least one chronic disease, wherein approximately 1 in 4 (23%) Australians had two or more chronic conditions [8]. This rate was more pronounced for people aged 65 and over (87%) compared with people aged 0–44 (35%), females (52%) compared with males (48%), people in disadvantaged socioeconomic areas (55%) compared with those in the most advantaged socioeconomic areas (47%), and people living in regional and remote areas (54%) compared with those in the major cities (48%) [8]. Ultimately, the severity of comorbidity leads to an increased risk of hospitalisation, reduced health status, increased mortality, and increased financial burden on the healthcare system [9–11]. It may also adversely impact an individual’s access to advanced cancer treatments (e.g., chemotherapy and radiotherapy) and the effectiveness of that treatment [12]. This is a substantial prognostic factor for the long-term survival of cancer patients. There is a growing body of research on the significant impact of chronic comorbid conditions among patients with cancer. However, there are limited empirical studies on comorbidities available in the Australian setting [7,13–15].

Comorbidity has a well documented detrimental effect on cancer survival [9] and it describes the existence of a long-term health condition or disorder in the presence of primary disease or illness [16]. In the case of cancer, chronic comorbidity refers to the existence of one or more comorbid conditions in a person simultaneously. While the existence of these comorbid health conditions may be extraneous, particularly chronic diseases, there is an association between them. Further, many chronic diseases share common risk factors. Cancer patients with comorbid conditions also experience a higher physiological burden of disease [7]. The presence of specific severe comorbidities or psychiatric disorders is associated with delayed cancer diagnosis [11]. Further, patients with chronic diseases with regular medical consultations and follow-up had their cancer detected at an earlier stage [12].

The chance of improving health status and completing a course of cancer treatment in the presence of comorbidities is significantly lower among cancer patients [4,13,15,17,18] and is associated with a higher rate of mortality depending on the severity of disease and associated comorbidity [11]. For instance, the mortality rate is substantially higher among cancer patients with comorbidities (47%) compared with cancer patients without comorbidities (34%) [19]. Given the clinical significance of comorbidity and its high prevalence in cancer survivors, it is essential to have a measure for quantifying likely effects on cancer outcomes [20]. Understanding more about comorbidities among cancer patients can generate possible evidence as well as provide direction for prevention, management, and treatment of chronic diseases.

A number of studies confirm that comorbid chronic conditions were more pronounced among cancer patients [4,11,13–15,21,22]. The most prevalent risk factors were age (over 65 years) [23,24], unhealthy behaviors (e.g., alcohol consumption and smoking tobacco) [25,26], obesity, limited engagement with physical activity [27] and inadequate diet [25] and they are significantly related to a higher risk of developing cancer along with multiple chronic diseases [5,7,25]. Further, comorbid conditions of cancer patients are significantly associated with worse health status during treatment and oncology follow-up periods [28,29] as well as low or intermediate socioeconomic status [30], and poor nutritional status [31]. The ongoing evidence shows that modifying or avoiding risk factors can significantly reduce the burden of chronic comorbid conditions among cancer patients [1]. For example, cancer survivors who engage in less sedentary behavior enjoy a better quality of life [32], and this can also significantly contribute to reducing the risk of experiencing chronic comorbid conditions [33].

The primary intention of these studies was to examine the distribution, trend, pattern, and disparity in comorbidity status among cancer patients when considering a limited range of variables. The majority of these studies pay little attention to examining the long-term impact of chronic comorbid conditions for cancer survivors’ over times. Therefore, routine oncology follow-ups must explore how cancer survivors’ characteristics impact on the number of chronic comorbid conditions they experience.

This study will examine the longitudinal nature of chronic comorbid conditions of cancer patients. More specifically, the study proposes to develop a better understanding of the longitudinal distribution of chronic comorbidity status among cancer patients as well as its impact over time. This study complements and contributes to this strand of ongoing cancer research to increase awareness and improve public health practice among sufferers and survivors, and to measure impact. The findings could contribute to designing appropriate interventions and/or the provision of quality healthcare services and resources for ongoing surveillance of people living with, through and beyond cancer, and help determine what kinds of support survivors need. This study, therefore, aims to investigate the distribution, potential predictors and associated burden of chronic comorbid conditions among cancer patients by using a longitudinal data set from the Household, Income and Labour Dynamics in Australia (HILDA) survey.

Materials and methods

Study design

The study design is a longitudinal exploration using a household-based panel over an extended period of 2007 to 2017. Individuals who face the burden of life-threatening cancer were interviewed with a focus on the magnitude of the cancer burden associated with their chronic comorbid conditions. The magnitude of the cancer burden includes their course of treatment over an extended oncology follow-up period which can affect their health status burden and includes chronic comorbid conditions, disability, and adverse events.

Conceptual framework

The distribution of comorbidity varies by patient-level factors (Fig 1). Like cancer itself, it increases with age. Functional status, a measure of patients’ ability to perform everyday activities, is related to both the presence and the consequences of chronic comorbid conditions. Health status burden is associated with increased vulnerability to stressors that result from decreased health scores as well as physiological strength [34]. Further, health status burden is strongly associated with increased age and the severity of the disease. In the context of comorbidity experiences, patients assess their health status depending on the severity of disease (as either better or worse) [35]. Despite strong associations between them, comorbidity, functional status, and health status burden are separate entities, and each has an independent effect on outcomes [34]. To investigate the longitudinal effects, it is assumed that several predictors (e.g., individual background characteristics, social factors, and disease-related symptomatic factors), measured at the symptom-level might predict outcome factors (e.g., appraisal of disease severity levels, utilisation of advanced treatment, life satisfaction, and uncertainty). Moreover, the combination of predictors was expected to predict patients’ health outcomes (e.g., chronic comorbid conditions, long-term health problems or disability, and adverse events).

Data source

Data came from the Household, Income and Labour Dynamics in Australia (HILDA) survey [36]. The HILDA survey commenced in 2001 and is a nationally representative household-based panel study that produces data on the lives of Australian residents aged 15 or over. As per the HILDA protocol, written or verbal consent was collected from all potential participants before conducting the survey. Data were collected through face-to-face interviews using quantitative survey instruments, followed by re-interviews with the same people in subsequent years. The details of the methods of data collection, including the sampling technique, have been explained elsewhere [36]. The present study participants were diagnosed with cancer patients, and data were restricted to four waves (e.g., wave-7, wave-9, wave-13 and wave-17) based on the availability of data related to cancer. However, wave-3 was excluded from the analysis due to the limited data related to comorbidity status. Other survey waves were excluded from the analyses due to the paucity of cancer-related information. A total of 2,066 diagnosed cancer patients were potential study participants from the four waves: wave-7 in 2007 (n₂ = 557), wave-9 in 2009 (n₃ = 416), wave-13 in 2013 (n₄ = 517) and wave-17 in 2017 (n₅ = 576).

Study variables

Outcome variable

The chronic comorbid conditions were classified into disease groupings and cover the most common types of long-term health conditions experienced by cancer patients in the Australian community. A previous review study identified that at least 21 approaches have been executed to measure comorbidity status [37]. There is no gold-standard method for measuring comorbidity among cancer populations [37]. The selection of the method depends on the study research question, data availability, and population studied. A number of methods related to measuring comorbidity status have been used in the context of cancer-related studies including exploration of the impact of single conditions (such as diabetes or congestive heart failure) [38–40], single condition counts [41–43], weighted indices [43–47], and organ-based systems [48–50]. Although all these approaches aim to evaluate the same underlying construct, they vary in terms of the study purpose for which the measures were performed. These approaches vary in the context of study perspective and design. The simplest approach to measuring comorbidity status is to investigate the distribution of individual comorbid conditions and to treat them independently and/or to combine them by summing the total number of conditions [51]. In this study, a single condition count approach was performed to measure comorbidity status. Cancer patients reporting chronic condition(s) were considered an outcome variable in the analysis. Chronic comorbid conditions included being diagnosed with serious chronic illness, including arthritis or osteoporosis, heart disease, diabetes, hypertension, mental illness, or circulatory conditions. The count of chronic health conditions was measured for each respondent based on the number of disease exposures and who had been prescribed medication for their illness. If the respondents had multiple chronic conditions, it was counted as multiple responses.

Explanatory variables

This study considered several demographic, socio-economic and health and lifestyle-related variables based on the conceptual framework, as putative predictors of chronic comorbid conditions. Socio-demographic factors, such as sex, age, educational achievement, employment status, and marital status were considered as potential factors in the analysis. Lifestyle factors such as alcohol consumption, smoking exposure, and physical activity were also included. The level of physical activity was categorized into three groups as low, moderate, or high [27,52,53]. Further, life condition-related factors such as satisfaction with employment, financial situation, and social supports were also selected as potential predictors. Ethnic status was defined as Aboriginal or non-Aboriginal. The quality of life scores was measured using the medical outcomes study short-form (SF-36) [54]. The SF-36 is one of the most common generic measures of health-related quality of life, which is widely used to assess the burden of disease in the context of different country settings [55]. It uses psychometric properties to enable profiling of physical functional health and well-being and to quantify disease burden across eight domains, including physical functioning, role-physical, body pain, general health, vitality, social functioning, role-emotional, and mental health. Considering these dimensions, the total score on each SF-36 subscale ranges between 0 and 100, labelling ‘worst imaginable health’ and ‘best imaginable health state’, respectively. It is signified that the higher scores represent better health status. A recent review study confirmed that several studies used a total score of SF-36 items to derive quality of life scores across the eight domains of SF-36 [56]. The levels of health status burden were proposed based on the magnitude of quality of life scores as follows: (1) high burden if the short form-36 (SF-36) scores < 50.00, (2) moderate burden if 50.00 ≤SF-36 scores < 90.00, and (3) no burden if SF-36 scores ≥ 90.00. The level of health status burden captured the severity of disease for cancer patients. Work disability was measured based on the severity of disability score ranged from 0 to 10, with 10 indicating ‘able to do any work’ and 0 indicating ‘not at all’. The severity of disability level was defined as follows: (i) ‘no disability’ if disability score was equal to zero, (ii) ‘moderate disability’ for disability scores of 1 to 6, and (iii) ‘severe disability’ for disability scores of 7 to 10. Geographical locations were defined according to the accessibility to services and the Remoteness Index of Australia [57], and they were categorized into five groups: major cities, inner regional, outer regional and remote or very remote. The index of relative socioeconomic disadvantage (IRSD) was used to measure socioeconomic status (SES). The index was defined into five groups with these threshold values: Q₁ (IRSD ≤ 927.0), Q₂ (927.0 > IRSD ≤ 965.8), Q₃ (965.8 > IRSD ≤ 1001.8), Q₄ (1001.8 > IRSD ≤ 1056.0), or Q₅ (IRSD > 1056.0) [58]. This is a geographical area-based estimate of socioeconomic status using income, education level and occupation where communities are categorised from economically disadvantaged to wealthy.

Statistical analysis

This study utilised descriptive analyses to compare patients with cancer and chronic medical conditions across the characteristics. The trend of chronic comorbid conditions among cancer patients was performed using the Cochran-Armitage trend test [59]. In the analytical exploration, the adjusted fixed-effect negative binomial regression model was used to identify the potential factors that had a significant role in the exposure to chronic comorbid conditions. In the regression model, the dependent variable (number of chronic comorbid conditions) was characterised as a count measure. An unadjusted analysis was performed using only separated explanatory variables for the following reasons: (1) primary screening of the selection of qualified predictors, which were added in the adjusted model, (2) although the chi-square tests (or one-way analysis where appropriate) are only used to find the association between outcome and explanatory variables. However, the majority of the predictor variables were categorical nature with two or more labels in this study. Therefore, an un-adjusted analysis was performed to find the association between outcome and the labels of explanatory variables. The predictor variables were included in the adjusted model only if any label of the predictor was significant at 5% or less risk level in the unadjusted model, which in turn was used to adjust for the effects of other potential confounders. However, insignificant predictors were not included in the adjusted model. The model was tested for sensitivity by the forward selection procedure (e.g., including and excluding specific variables) with robust standard errors. For the independent variables, the category found to be least at risk of having chronic comorbid conditions in the analysis was considered as the reference for constructing incidence risk ratios (IRR). Statistical significance was considered at the 5% risk level. All data analyses were undertaken using the statistical software Stata/SE 13 (StataCorp, College Station, TX, USA).

Ethical considerations

The Household, Income and Labour Dynamics in Australia (HILDA) data are used under strict licensing. Data can be potentially obtained and shared subject to a peer-reviewed application. Ethical approval for the HILDA study was obtained from the Faculty of Business and Economics Human Ethics Advisory Committee at the University of Melbourne (#1647030). Approval for the use of HILDA data was provided by the Department of Social Services. Ethical approval was not required from an institutional review board because the patient information was de-identified. Appropriate approval was obtained for this study from the Department of Social Services to access the de-identified longitudinal dataset.

Results

Background characteristics of the study population

A total of 2,066 cancer patients were potential participants (Tables 1 and 2). Approximately 54% of patients were male, with 58% of patients being married. A higher proportion (46%) of the patients were senior or old senior-aged (more than 65 years), followed by middle-aged (37%). Approximately 47% had completed middle or high school level education, with 316 cancer patients (15%) having tertiary education. Sixty three percent of 63% of patients were unemployed, while 45% of patients had inadequate physical activity, with only 23% of patients having high-level physical activities per week. Two-third of 75% of patients consumed alcohol frequently. The majority of participants (89%) reported a moderate or extreme health burden, whereas 42% of patients experienced moderate or severe disability levels. In addition, 72% received prescribed medication, and 61% lived in major cities.

Table 1. Summary statistics by the number of chronic condition among cancer patients for wave 7 and wave 9.

Variables	Number of observations, n (%)	Wave-7			Wave-9
		Number of chronic comorbid conditions, n (%)			Number of chronic comorbid conditions, n (%)
		0	1–2	3 or more	0	1–2	3 or more
Sex
Male	1,123 (54.36)	234 (54.80)	77 (59.23)	na	45 (51.14)	110 (48.46)	55 (54.46)
Female	943 (45.64)	193 (45.20)	53 (40.77)	na	43 (48.86)	117 (51.54)	46 (45.54)
Age
<25 years	53 (2.57)	10 (2.34)	3 (2.31)	na	1 (1.14)	4 (1.76)	1 (0.99)
25–45 years	283 (13.70)	77 (18.03)	17 (13.08)	na	23 (26.14)	32 (14.10)	8 (7.92)
46–65 years	771 (37.32)	146 (34.19)	69 (53.08)	na	39 (44.32)	86 (37.89)	30 (29.7)
>65 years	959 (46.42)	194 (45.43)	41 (31.54)	na	25 (28.41)	105 (46.26)	62 (61.39)
Educational attainment
Year 11 or below	774 (37.46)	169 (39.58)	48 (36.92)	na	26 (29.55)	97 (42.73)	46 (45.54)
Year 12	168 (8.13)	37 (8.67)	14 (10.77)	na	10 (11.36)	15 (6.61)	9 (8.91)
Trade/certificate/diploma	808 (39.11)	149 (34.89)	54 (41.54)	na	35 (39.77)	81 (35.68)	40 (39.6)
Tertiary	316 (15.30)	72 (16.86)	14 (10.77)	na	17 (19.32)	34 (14.98)	6 (5.94)
Unemployed	1,306 (63.21)	250 (58.55)	66 (50.77)	na	40 (45.45)	150 (66.08)	86 (85.15)
Marital status
Single	258 (12.49)	52 (12.18)	20 (15.38)	na	16 (18.18)	27 (11.89)	7 (6.93)
Married	1,196 (57.89)	256 (59.95)	80 (61.54)	na	46 (52.27)	130 (57.27)	52 (51.49)
Others	612 (29.62)	119 (27.87)	30 (23.08)	na	26 (29.55)	70 (30.84)	42 (41.58)
Alcohol consumption (= yes)	1,500 (72.60)	341 (79.86)	102 (78.46)	na	64 (72.73)	158 (69.60)	66 (65.35)
Smoking exposure (= yes)	276 (13.36)	64 (14.99)	22 (16.92)	na	11 (12.50)	32 (14.10)	13 (12.87)
Physical activity status
Low	876 (42.40)	153 (55.11)	88 (55.11)		36 (55.11)	98 (55.11)	52 (55.11)
Moderate	701 (33.93)	134 (29.55)	30 (29.55)		28 (29.55)	74 (29.55)	33 (29.55)
High	489 (23.67)	140 (15.34)	12 (15.34)		24 (15.34)	55 (15.34)	16 (15.34)
Health status burden
No burden	208 (10.07)	57 (13.35)	13 (10.00)	na	24 (27.27)	19 (8.37)	1 (0.99)
Moderate burden	1,205 (58.33)	268 (62.76)	82 (63.08)	na	48 (54.55)	135 (59.47)	41 (40.59)
Severe burden	653 (31.61)	102 (23.89)	35 (26.92)	na	16 (18.18)	73 (32.16)	59 (58.42)
Disability status
No disability	1,172 (56.73)	258 (60.42)	76 (58.46)	na	76 (86.36)	124 (54.63)	32 (31.68)
Moderate disability	509 (24.64)	92 (21.55)	26 (20.00)	na	7 (7.95)	63 (27.75)	39 (38.61)
Severe disability	385 (18.64)	77 (18.03)	28 (21.54)	na	5 (5.68)	40 (17.62)	30 (29.70)
Healthcare utilisation (= yes)	1,093 (72.43)	219 (65.45)	63 (46.95)	na	22 (25.00)	181 (79.74)	98 (97.03)
Life satisfaction with-
Employment, mean (sd)	3.39 (3.96)	3.51 (4.03)	3.86 (3.94)	na	5.3 (3.98)	3.55 (3.98)	2.36 (3.88)
Financial situation, mean (sd)	6.73 (2.37)	7.05 (2.27)	6.65 (2.43)	na	6.98 (2.14)	6.63 (2.45)	6.04 (2.59)
Social supports, mean (sd)	7.83 (1.82)	8.09 (1.54)	7.97 (1.54)	na	7.91 (1.73)	7.64 (2.03)	7.78 (1.98)
Remoteness
Major Cities	1,264 (61.18)	270 (63.23)	75 (57.69)	na	48 (54.55)	128 (56.39)	63 (62.38)
Inner Regional	519 (25.12)	98 (22.95)	34 (26.15)	na	24 (27.27)	59 (25.99)	24 (23.76)
Outer Regional	247 (11.96)	50 (11.71)	21 (16.15)	na	13 (14.77)	38 (16.74)	12 (11.88)
Remote or very remote	36 (1.74)	9 (2.11)	na	na	3 (3.41)	2 (0.88)	2 (1.98)
Socioeconomic status
Q₁ (lowest 20%) (ref)	407 (19.70)	81 (18.97)	23 (17.69)	na	11 (12.50)	46 (20.26)	27 (26.73)
Q₂	470 (22.75)	87 (20.37)	27 (20.77)	na	16 (18.18)	60 (26.43)	29 (28.71)
Q₃	369 (17.86)	79 (18.50)	33 (25.38)	na	25 (28.41)	39 (17.18)	14 (13.86)
Q₄	428 (20.72)	98 (22.95)	28 (21.54)	na	20 (22.73)	39 (17.18)	21 (20.79)
Q₅ (highest 20%)	392 (18.97)	82 (19.20)	19 (14.62)	na	16 (18.18)	43 (18.94)	10 (9.90)
Overall	2,066 (100)	427 (76.66)	130 (23.34)	na	88 (21.15)	227 (54.57)	101 (24.28)

Open in a new tab

Na = not available

Table 2. Summary statistics by the number of chronic condition among cancer patients for wave 13 and wave 17.

Variables	Wave-13			Wave-17			Overall
	Number of chronic comorbid conditions, n (%)			Number of chronic comorbid conditions, n (%)			Number of chronic comorbid conditions, n(%
	0	1–2	3 or more	0	1–2	3 or more	0	1–2	3 or more
Sex
Male	70 (58.82)	122 (50.41)	87 (55.77)	73 (57.94)	160 (58.39)	90 (51.14)	422 (55.53)	469 (53.72)	232 (53.58)
Female	49 (41.18)	120 (49.59)	69 (44.23)	53 (42.06)	114 (41.61)	86 (48.86)	338 (44.47)	404 (46.28)	201 (46.42)
Age
<25 years	6 (5.04)	9 (3.72)	2 (1.28)	6 (4.76)	5 (1.82)	6 (3.41)	23 (3.03)	21 (2.41)	9 (2.08)
25–45 years	25 (21.01)	30 (12.4)	9 (5.77)	26 (20.63)	28 (10.22)	8 (4.55)	151 (19.87)	107 (12.26)	25 (5.77)
46–65 years	51 (42.86)	93 (38.43)	47 (30.13)	56 (44.44)	98 (35.77)	56 (31.82)	292 (38.42)	346 (39.63)	133 (30.72)
>65 years	37 (31.09)	110 (45.45)	98 (62.82)	38 (30.16)	143 (52.19)	106 (60.23)	294 (38.68)	399 (45.7)	266 (61.43)
Educational attainment
Year 11 or below	31 (26.05)	88 (36.36)	70 (44.87)	30 (23.81)	91 (33.21)	78 (44.32)	256 (33.68)	324 (37.11)	194 (44.8)
Year 12	9 (7.56)	20 (8.26)	8 (5.13)	12 (9.52)	21 (7.66)	13 (7.39)	68 (8.95)	70 (8.02)	30 (6.93)
Trade/certificate/diploma	51 (42.86)	107 (44.21)	59 (37.82)	47 (37.3)	117 (42.7)	68 (38.64)	282 (37.11)	359 (41.12)	167 (38.57)
Tertiary	28 (23.53)	27 (11.16)	19 (12.18)	37 (29.37)	45 (16.42)	17 (9.66)	154 (20.26)	120 (13.75)	42 (9.7)
Unemployed	58 (48.74)	159 (65.70)	128 (82.05)	54 (42.86)	177 (64.60)	138 (78.41)	402 (52.89)	552 (63.23)	352 (81.29)
Marital status
Single	21 (17.65)	30 (12.4)	12 (7.69)	24 (19.05)	30 (10.95)	19 (10.8)	113 (14.87)	107 (12.26)	38 (8.78)
Married	72 (60.5)	141 (58.26)	90 (57.69)	69 (54.76)	164 (59.85)	96 (54.55)	443 (58.29)	515 (58.99)	238 (54.97)
Others	26 (21.85)	71 (29.34)	54 (34.62)	33 (26.19)	80 (29.2)	61 (34.66)	204 (26.84)	251 (28.75)	157 (36.26)
Alcohol consumption (= yes)	91 (76.47)	178 (73.55)	100 (64.10)	84 (66.67)	205 (74.82)	111 (63.07)	580 (76.32)	643 (73.65)	277 (63.97)
Smoking exposure (= yes)	11 (9.24)	32 (13.22)	23 (14.74)	14 (11.11)	32 (11.68)	22 (12.50)	100 (13.16)	118 (13.52)	58 (13.39)
Physical activity status
Low	35 (29.41)	95 (39.26)	94 (60.26)	50 (39.68)	125 (45.62)	97 (55.11)	274 (36.05)	406 (46.51)	243 (56.12)
Moderate	44 (36.97)	81 (33.47)	44 (28.21)	36 (28.57)	89 (32.48)	52 (29.55)	242 (31.84)	274 (31.39)	129 (29.79)
High	40 (33.61)	66 (27.27)	18 (11.54)	40 (31.75)	60 (21.9)	27 (15.34)	244 (32.11)	193 (22.11)	61 (14.09)
Health status burden
No burden	30 (25.21)	15 (6.2)	0 (0)	22 (17.46)	22 (8.03)	5 (2.84)	132 (17.37)	69 (7.9)	6 (1.39)
Moderate burden	64 (53.78)	172 (71.07)	75 (48.08)	76 (60.32)	175 (63.87)	69 (39.2)	422 (55.53)	513 (58.76)	156 (36.03)
Severe burden	25 (21.01)	55 (22.73)	81 (51.92)	28 (22.22)	77 (28.1)	102 (57.95)	206 (27.11)	291 (33.33)	271 (62.59)
Disability status
No disability	96 (80.67)	146 (60.33)	41 (26.28)	104 (82.54)	153 (55.84)	66 (37.50)	534 (70.26)	499 (57.16)	139 (32.10)
Moderate disability	9 (7.56)	59 (24.38)	56 (35.9)	10 (7.94)	84 (30.66)	64 (36.36)	118 (15.53)	232 (26.58)	159 (36.72)
Severe disability	14 (11.76)	37 (15.29)	59 (37.82)	12 (9.52)	37 (13.5)	46 (26.14)	108 (14.21)	142 (16.27)	135 (31.18)
Healthcare utilisation (= yes)	39 (32.77)	175 (72.31)	152 (97.44)	47 (37.30)	209 (76.28)	170 (96.59)	108 (9.88)	565 (51.69)	420 (38.43)
Life satisfaction with-
Employment, mean (sd)	4.82 (3.89)	3.47 (3.9)	1.96 (3.43)	4.48 (3.97)	3.39 (3.97)	1.64 (3.12)	4.08 (4.04)	3.52 (3.95)	1.92 (3.43)
Financial situation, mean (sd)	7.39 (1.98)	6.5 (2.53)	5.99 (2.57)	7.33 (2.01)	6.76 (2.31)	6.32 (2.65)	7.14 (2.17)	6.64 (2.43)	6.13 (2.6)
Social supports, mean (sd)	7.94 (1.68)	7.67 (2.17)	7.74 (1.95)	7.82 (1.74)	7.92 (1.71)	7.44 (2.1)	8 (1.62)	7.78 (1.91)	7.63 (2.02)
Remoteness
Major Cities	79 (66.39)	151 (62.4)	105 (67.31)	91 (72.22)	151 (55.11)	103 (58.52)	488 (64.21)	505 (57.85)	271 (62.59)
Inner Regional	27 (22.69)	55 (22.73)	35 (22.44)	26 (20.63)	87 (31.75)	50 (28.41)	175 (23.03)	235 (26.92)	109 (25.17)
Outer Regional	11 (9.24)	32 (13.22)	16 (10.26)	6 (4.76)	28 (10.22)	20 (11.36)	80 (10.53)	119 (13.63)	48 (11.09)
Remote or very remote	2 (1.68)	4 (1.65)	0 (0)	3 (2.38)	8 (2.92)	3 (1.7)	17 (2.24)	14 (1.6)	5 (1.15)
Socioeconomic status
Q₁ (lowest 20%) (ref)	17 (14.29)	50 (20.66)	48 (30.77)	18 (14.29)	41 (14.96)	45 (25.57)	127 (16.71)	160 (18.33)	120 (27.71)
Q₂	22 (18.49)	59 (24.38)	38 (24.36)	22 (17.46)	62 (22.63)	48 (27.27)	147 (19.34)	208 (23.83)	115 (26.56)
Q₃	21 (17.65)	29 (11.98)	27 (17.31)	21 (16.67)	51 (18.61)	30 (17.05)	146 (19.21)	152 (17.41)	71 (16.40)
Q₄	32 (26.89)	51 (21.07)	23 (14.74)	27 (21.43)	64 (23.36)	25 (14.20)	177 (23.29)	182 (20.85)	69 (15.94)
Q₅ (highest 20%)	27 (22.69)	53 (21.9)	20 (12.82)	38 (30.16)	56 (20.44)	28 (15.91)	163 (21.45)	171 (19.59)	58 (13.39)
Overall	119 (23.02)	242 (46.81)	156 (30.17)	126 (21.88)	274 (47.57)	176 (30.56)	760 (36.79)	873 (42.26)	433 (20.96)

Open in a new tab

Na = not available

Distribution and changes of chronic comorbid conditions with cancer patients over time

The prevalence of comorbid conditions was reported by cancer patients as follows: arthritis or osteoporosis (45%), high blood pressure or hypertension (39%), obesity (23%), depression or anxiety (22%), heart disease (14%), and asthma (13%). These were significantly increased in the prevalence of depression or anxiety (p<0.01), mental illness (p = 0.052) and obesity (p = 0.003) over the period (Fig 2). However, a downward trend in the prevalence of comorbid conditions was observed for arthritis/osteoporosis (p = 0.012) over time.

Overall, approximately 42% of patients suffered from one to two chronic comorbid conditions, while 21% of patients experienced at least three or more comorbid conditions (Table 1). The prevalence of comorbid conditions was prominently distributed by age. The majority of comorbidities were highly pronounced in patients due to a lack of physical activity. For example, 56% of patients were more likely to report three or more comorbid conditions. This prevalence was disproportionately low (14%) in those who engaged in a high level of physical activity. Further, patients who suffered from at least one comorbid condition were significantly aligned with the magnitude of high or moderate health status burden (e.g., 62% for severe burden and 36% for moderate burden). Similarly, an upward trend of the upper extremity of disability levels was observed with an increased number of comorbid exposures among the poorest cancer survivors during the period (Fig 3). Regarding socioeconomic position, the magnitude of comorbid conditions was more pronounced in the most disadvantaged socio-economic group. For example, 28% of patients who lived in the poorest households were significantly exposed to three or more comorbid conditions compared with the richest households (13%). Also, the severity of disability score was also highest among patients in the poorest households along with an increasing number of comorbid conditions (Fig 3).

Factors influencing chronic comorbid exposure of cancer patients

Table 3 exhibits the results of the fixed effect negative binomial regression analyses. In the adjusted model, older patients, the magnitude of health status burden associated with cancer, utilisation of healthcare, and patients living in the poorest households were significant predictors associated with a higher risk of comorbid conditions. An aged patient (>65 years old) has 1.15 times higher risk of having comorbid conditions (incidence rate ratio, IRR = 1.15; 95% confidence interval, CI: 1.08, 1.45) compared with a young patient (<25 years). Patients who performed lower levels of physical activity were 1.25 times more likely to have a chronic comorbid condition (IRR = 1.25; 95% CI: 1.09, 1.59) compared with patients who engaged in high-level physical activity. Further, patients who faced an extreme health burden were 2.30 times significantly higher risk of having comorbid conditions than those with no health burden. The risks of having a comorbid condition were more pronounced among patients who suffered from extreme health burden (IRR = 2.30 times) or moderate burden level (IRR = 1.90 times) compared with patients who reported excellent health status. Similarly, a higher risk of having a comorbid exposure was significantly observed in cancer patients who lived in the poorest households (IRR = 1.21; 95% CI: 1.11, 1.29) compared with their richest counterparts.

Table 3. Factors influencing chronic comorbid conditions of cancer patients using a fixed-effect negative binomial regression model.

Variables	Unadjusted model¹		Adjusted model²
Variables	IRR (SE)	95% CI	IRR (SE)	95% CI
Female (ref = male)	1.04 (0.05)	(0.94, 1.14)	-	-
Age group
< 25 years (= ref)	1.00	-	1.00	-
25–45 years	0.72 (0.13)	(0.51, 1.03)	0.85 (0.14)	(0.61, 1.18)
46–65 years	1.15 (0.19)	(0.83, 1.58)	1.07 (0.16)	(0.79, 1.45)
>65 years	1.49^*** (0.24)	(1.09, 2.04)	1.15^** (0.17)	(1.08, 1.45)
Educational attainment
Year 11 or below	1.48^*** (0.12)	(1.26, 1.74)	1.16^** (0.09)	(1.01, 1.35)
Year 12	1.11 (0.13)	(0.88, 1.40)	1.13 (0.12)	(0.91, 1.40)
Trade/certificate/diploma	1.38^*** (0.12)	(1.17, 1.63)	1.21^*** (0.09)	(1.05, 1.40)
Tertiary (= ref)	1.00	-	1.00	-
Unemployed (ref = employed)	1.80^*** (0.10)	(1.62, 2.00)	1.08 (0.07)	(0.95, 1.23)
Marital status
Single (= ref)	1.00	-	1.00	-
Married	1.21^** (0.10)	(1.02, 1.42)	1.02 (0.08)	(0.87, 1.20)
Others	1.41^*** (0.12)	(1.19, 1.68)	1.06 (0.09)	(0.90, 1.25)
Physical activity status
Low	1.60^*** (0.12)	(1.39, 1.85)	1.25^** (0.07)	(1.09, 1.59)
Moderate	1.30^*** (0.10)	(1.12, 1.52)	1.06 (0.07)	(0.92, 1.21)
High (= ref)	1.00	-	1.00	-
Alcohol consumption (ref = yes)	1.26^*** (0.06)	(1.14, 1.39)	0.91 (0.05)	(0.82, 1.00)
Smoking exposure (ref = no)	1.02 (0.07)	(0.88, 1.18)	-	-
Healthcare utilisation (ref = no)	0.27 (0.02)	(0.24, 0.31)	0.38^*** (0.03)	(0.33, 0.45)
Health status burden
No burden (= ref)	1.00	-	1.00	-
Moderate burden	2.44^*** (0.27)	(1.96, 3.03)	1.90^*** (0.26)	(1.45, 2.48)
Severe burden	4.18^*** (0.47)	(3.36, 5.21)	2.30^*** (0.33)	(1.73, 3.05)
Disability status
No disability (= ref)	1.00	-	1.00	-
Moderate disability	1.82^*** (0.10)	(1.64, 2.02)	1.22^*** (0.07)	(1.10, 1.36)
Severe disability	1.99^*** (0.12)	(1.76, 2.24)	1.25^*** (0.08)	(1.11, 1.41)
Life satisfaction with-
Employment	0.94^*** (0.01)	(0.92, 0.95)	0.98^*** (0.01)	(0.97, 0.99)
Financial situation	0.97^** (0.01)	(0.95, 0.99)	0.96^*** (0.01)	(0.94, 0.98)
Social supports	0.96^*** (0.01)	(0.93, 0.98)	1.03^** (0.01)	(1.01, 1.05)
Remoteness
Major cities (= ref)	1.00	-	-	-
Inner regional	1.02 (0.06)	(0.91, 1.14)	-	-
Outer regional	1.04 (0.08)	(0.90, 1.21)	-	-
Remote or very remote	0.77 (0.14)	(0.54, 1.11)	-	-
Socioeconomic status
Q₁ (lowest 20%)	1.51^*** (0.12)	(1.29, 1.77)	1.21^*** (0.08)	(1.11, 1.29)
Q₂	1.35^*** (0.11)	(1.15, 1.57)	1.09 (0.08)	(0.95, 1.26)
Q₃	1.19^** (0.10)	(1.01, 1.41)	1.15 (0.09)	(0.99, 1.34)
Q₄	1.08 (0.09)	(0.92, 1.27)	0.99 (0.08)	(0.85, 1.15)
Q₅ (highest 20%) (= ref)	1.00	-	1.00	-

Open in a new tab

Note:

*p<0.05,

**p<0.01,

***p<0.001,

IRR = incidence rate ratio, SE = standard error, CI = confidence interval,

¹Single explanatory variable was included in un-adjusted model,

²Explanatory variables were included in the adjusted model only if any label of the variable was significant at 5% or less risk level in the unadjusted model

Discussion

The study results show that approximately 63% of cancer patients suffered from at least one chronic disease. The most prevalent comorbid conditions were arthritis or osteoporosis, high blood pressure or hypertension, obesity, depression or anxiety, heart disease, and asthma. However, these were significantly increased in the presence of diabetes, depression or anxiety, mental illness, heart disease and obesity over time. In the adjusted model, older patients, inadequate level of physical activities, the magnitude of health burden associated with cancer, utilisation of healthcare, and patients living in the poorest households were significant predictors associated with a higher risk of comorbid conditions.

Further, patients who faced an extreme health burden had a three times higher risk of having comorbid conditions than who reported excellent health status. Some studies have confirmed that the poor health status of cancer patients resulted in a greater burden of functional disability (e.g., specific task difficulties) [60,61] along with a higher burden of chronic diseases [15,30,62]. However, the prevalence of long-term health problems, including chronic illness, short or long-term disability, was also more concentrated in combination with a cancer diagnosis [63–68]. Advanced cancer treatments can damage healthy cells or organs [69], for example, radiation and chemotherapy may impose short and long-term chronic health problems and impact on the spinal cord, nerves, and brain, which then may significantly contribute to long-term adverse health outcomes like death, physical and mental disabilities.

The results indicate that aged cancer patients (older than 65 years) were at a 1.15 times higher risk of having chronic comorbid conditions compared with younger patients. This finding is consistent with previous studies, which revealed that elderly cancer patients reported significantly more exposure to chronic comorbid conditions [23,70,71], required more assistance with daily living activities [72], and had deficits in performing work-related activities in terms of their physical ability [60,73]. Several reasons might influence this reduction in their physical strength. For example, a course of advanced cancer treatment is associated with considerable physical and psychological side effects in elderly cancer patients (e.g., weight change, muscle loss, fatigue, and physical weakness) [74], and exposure to multiple comorbidities [64,65,75] will presumably contribute to worse health status. Although, cancer patients in older age groups are less likely to be offered cancer treatments (e.g., chemotherapy, radiotherapy and axillary lymph node dissection) that may then contribute to a greater burden of health [74]. This result indicates that rehabilitation-related interventions (e.g., physical therapies) are essential to prevent or alleviate chronic comorbid conditions and an emerging cancer research area, particularly focused on the elderly [76].

The present study found that cancer patients who performed lower levels of physical activities were strongly associated with an extreme level of chronic comorbidities compared with patients engaged in high-level physical activity. This finding is in line with other studies [52,77,78], whereby it was found that limited physical activity levels were significantly associated with a higher risk of having chronic comorbid conditions in cancer patients. The magnitude of limited physical activity level may decrease the risk for several cancers by some mechanisms, including decreasing sex hormones, metabolic hormones and inflammation, and improving immune function [77]. In terms of cancer risk, high levels of physical activities (compared with low levels) played a significant role in the prevention of several cancers (e.g., 42% for gastrointestinal cancer, 23% for renal cancer, and 20% for myeloid leukemia) [79]. This includes averting genetic damage, improving the immune system, reducing chronic infections, and controlling cancer cells [79]. In addition, some past studies confirmed that physical activity plays an effective role in controlling the side effects of cancer treatment and disease progression, reducing psychological conditions [77,80] and reducing the risk of developing future cancers [81]. Several hypotheses and mechanisms have been suggested regarding the anti-cancer effects of physical activities. The American Cancer Society guidelines for cancer survivors [82] recommend daily physical activities, including a continuation of normal daily life activities immediately after diagnosis, which help to significantly reduce physical stamina and muscle strength erosion as well as anxiety levels, thereby resulting in the prevention of long-term adverse health outcomes (e.g., extreme comorbidity burden and disability) [83]. In this context, future research could examine the influence that physical activity has on the effectiveness of chronic comorbid conditions among cancer patients.

The risks of having extreme chronic comorbidity conditions amongst cancer patients who lived in the poorest households were more pronounced compared with their richer counterparts. Recent studies confirm this result with the disadvantaged socioeconomic status of cancer survivors being negatively associated with long-term adverse health outcomes (e.g., multiple chronic illnesses, physical disability) [83–93]. Some studies also provided evidence that the magnitude of the cancer burden is adversely associated with socioeconomic status [16, 32–35]. Further, adverse cancer outcomes (e.g., worse health status and long-term chronic illness) were disproportionately found in poorer people as opposed to those of higher socioeconomic status [13, 16, 32, 34]. Some reasons that have contributed to the high rates of long term health impacts among the poorest groups include higher tobacco consumption [16,28], economic burden [36,37], increased mental illness [94], lack of health education and awareness [95], and less access to competent and effective health care services [95]. Low productivity, loss/reduction of household income, and increased healthcare expenditure are more pronounced amongst the poorest cancer patients. Growing socioeconomic disparities of cancer outcomes need the attention of governments, health systems, and decision-makers. For example, Cancer Council Australia has an optimal care pathway project, which has already addressed several cancer sites in disadvantaged areas. Such initiatives might help to reduce socio-economic disparities, which are related to poverty, gender, education, and health, and they should promote universal access to health care which can further enhance both socio-economic and human development.

This study has some limitations. Study participants were accessed from the HILDA survey, which covers health, economic, employment, income and health characteristics of household members aged 15 years and older. Children who suffered from cancer were excluded from this study. The study findings established a relationship between cancer diagnosis and chronic comorbidity conditions among cancer survivors, which might vary in terms of cancer stages and types of cancer. The authors were not able to estimate the cancer type analysis due to the paucity of relevant data. Further, the study findings were based on self-reported responses that might have been impacted by respondents’ prejudice (e.g., silence and over-response), and by problems in understanding and interpreting the survey questions.

Despite these limitations, this study has some strengths including the use of a prospective longitudinal design of long term follow-ups and the application of well-validated and reliable longitudinal wave measures of the impacts of a cancer diagnosis on the burden of chronic comorbid conditions of individuals over the 2007–2017 period. The study population captured different dimensions including ethnically, geographically, and socio-economically diverse groups. Furthermore, this study included several potential confounding factors such as health status burden, the severity of the disability level as well as life satisfaction (e.g., employment, financial situation and, social supports) that were not present in previous studies. For this study, data were gathered from four-wave of the HILDA survey for cancer survivors. The length of the survey period may have introduced uncontrolled bias, as changes in health status are not instantaneous and might emerge only after time, which was not captured in this study. Due to the paucity of funding, the authors were unable to consider cancer patients who registered for cancer surveillance as well as received health care from other health facilities (e.g., private clinics, community clinics and, secondary or tertiary hospitals). Future study is required using a similar study design, perspective, and analytical methods in terms of cancer-specific exploration.

Conclusions

This study has shown an extreme burden of chronic comorbid conditions among cancer patients in Australia. Older patients, inadequate level of physical activities, the magnitude of health burden, and patients living in the poorest households were significant predictors associated with a higher risk of having chronic comorbidity conditions. The findings have further implications for improving public health policy and reducing population-level unhealthy lifestyles, which should be recommended. The study results could be used to better outline the management of a sequelae course of treatment for those who should undergo more intensive physical rehabilitation aimed at reducing the risk of adverse health outcomes. Given the clinical significance of comorbidity in cancer survivors, this study may play a significant role in providing comprehensive evidence for health care providers, including physical therapists and oncologists, who should be aware of the unique problems that challenge this population and who should advocate for prevention and evidence-based interventions. Finally, a greater awareness of the importance of managing a patients overall health status within the context of comorbidity is warranted together with emphasised research on comorbidity to generate an appropriate scientific basis on which to build evidence-based care guidelines for these chronic comorbid conditions patients.

Acknowledgments

The study is part of the first author’s PhD research at the University of Southern Queensland, Australia. We would also like to thank the Australian Government’s Department of Social Services (DSS), the HILDA study at Melbourne Institute for providing access to the data used in the research. We would like to gratefully acknowledge the study participants, reviewers, and editors of our manuscript.

Data Availability

This paper uses unit record data from the Household, Income and Labor Dynamics in Australia (HILDA) Survey under strict licensing. Although data are not available to the public, they can be potentially obtained and shared subject to a peer-reviewed application. The data are available from the Australian Government Department of Social Services and the Melbourne Institute of Applied Economic and Social Research at https://melbourneinstitute.unimelb.edu.au/hilda.

Funding Statement

The authors received no specific funding for this work.

References

1.World Health Organization (WHO). Cancer: Key facts. 2018 [cited 11 May 2019]. https://www.who.int/news-room/fact-sheets/detail/cancer
2.Australian Institute of Health and Welfare. Cancer in Australia: In brief 2019. Cancer series no.119. Cat. no. CAN 123. Canberra: AIHW; 2019. www.aihw.gov.au
3.Australian Institute of Health and Welfare. Australian Burden of Disease Study: impact and causes of illness and death in Australia 2015. Australian Burden of Disease series no. 19. Cat. no. BOD 22. Canberra: AIHW; 2019.
4.Ng HS, Koczwara B, Roder D, Vitry A. Changes in the prevalence of comorbidity in the Australian population with cancer, 2007–2014. Cancer Epidemiol. 2018;54: 56–62. 10.1016/j.canep.2018.03.010 [DOI] [PubMed] [Google Scholar]
5.Australian Institute of Health and Welfare. Australia’s health 2018. Australia’s health series no. 16. AUS 221. Canberra: AIHW; 2018.
6.World Health Organization (WHO). Noncommunicable diseases. In: Key facts [Internet]. 2018 [cited 28 Jul 2019]. https://www.who.int/en/news-room/fact-sheets/detail/noncommunicable-diseases
7.Stairmand J, Signal L, Sarfati D, Jackson C, Batten L, Holdaway M, et al. Consideration of comorbidity in treatment decision making in multidisciplinary cancer team meetings: A systematic review. Ann Oncol. 2015;26: 1325–1332. 10.1093/annonc/mdv025 [DOI] [PubMed] [Google Scholar]
8.Australian Institute of Health and Welfare (AIHW). Australia’s health 2016. Australia’s health series no. 15. Cat. no. AUS 199. Canberra: AIHW; 2016. https://www.aihw.gov.au/reports/australias-health/australias-health-2016/contents/summary
9.Sarfati D, Gurney J, Lim BT, Bagheri N, Simpson A, Koea J, et al. Identifying important comorbidity among cancer populations using administrative data: Prevalence and impact on survival. Asia Pac J Clin Oncol. 2016;12: e47–e56. 10.1111/ajco.12130 [DOI] [PubMed] [Google Scholar]
10.Carstensen J, Andersson D, Andre M, Engstrom S, Magnusson H, Borgquist LA. How does comorbidity influence healthcare costs? A population-based cross-sectional study of depression, back pain and osteoarthritis. BMJ Open. 2012;2: e000809 10.1136/bmjopen-2011-000809 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Sogaard M, Thomsen RW, Bossen KS, Sørensen HT, Nørgaard M. The impact of comorbidity on cancer survival: A review. Clin Epidemiol. 2013;5: 3 10.2147/CLEP.S47150 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Pilleron S, Sarfati D, Janssen-Heijnen M, Vignat J, Ferlay J, Bray F, et al. Global cancer incidence in older adults, 2012 and 2035: A population-based study. Int J Cancer. 2019;144: 49–58. 10.1002/ijc.31664 [DOI] [PubMed] [Google Scholar]
13.Sarfati D, Hill S, Blakely T, Robson B, Purdie G, Dennett E, et al. The effect of comorbidity on the use of adjuvant chemotherapy and survival from colon cancer: A retrospective cohort study. BMC Cancer. 2009;9: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Gross CP, McAvay GJ, Guo Z, Tinetti ME. The impact of chronic illnesses on the use and effectiveness of adjuvant chemotherapy for colon cancer. Cancer. 2007;109: 2410–2419. 10.1002/cncr.22726 [DOI] [PubMed] [Google Scholar]
15.Cuthbert CA, Hemmelgarn BR, Xu Y, Cheung WY. The effect of comorbidities on outcomes in colorectal cancer survivors: a population-based cohort study. J Cancer Surviv. 2018;12: 733–743. 10.1007/s11764-018-0710-z [DOI] [PubMed] [Google Scholar]
16.Sarfati D, Koczwara B, Jackson C. The impact of comorbidity on cancer and its treatment. CA Cancer J Clin. 2016;66: 337–350. 10.3322/caac.21342 [DOI] [PubMed] [Google Scholar]
17.Gurney J, Sarfati D, Stanley J. The impact of patient comorbidity on cancer stage at diagnosis. Br J Cancer. 2015;113: 1375–1380. 10.1038/bjc.2015.355 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Elliott BA, Renier CM, Haller IV., Elliott TE. Health-related quality of life (HRQoL) in patients with cancer and other concurrent illnesses. Qual Life Res. 2004;13: 457–462. 10.1023/B:QURE.0000018476.11278.35 [DOI] [PubMed] [Google Scholar]
19.Van Hemelrijck M, Folkvaljon Y, Adolfsson J, Akre O, Holmberg L, Garmo H, et al. Causes of death in men with localized prostate cancer: A nationwide, population-based study. BJU Int. 2016;117: 507–514. 10.1111/bju.13059 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Pule L, Buckley E, Niyonsenga T, Banham D, Roder D. Developing a comorbidity index for comparing cancer outcomes in Aboriginal and non-Aboriginal Australians. BMC Health Serv Res. 2018;18: 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Sigel K, Wisnivesky JP. Comorbidity profiles of patients with lung cancer: A new approach to risk stratification? Ann Am Thorac Soc. 2017;14: 1512–1513. 10.1513/AnnalsATS.201706-442ED [DOI] [PubMed] [Google Scholar]
22.Lindhagen L, Van Hemelrijck M, Robinson D, Stattin P, Garmo H. How to model temporal changes in comorbidity for cancer patients using prospective cohort data. BMC Med Inform Decis Mak. 2015;15: 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Yancik R, Wesley MN, Ries LAG, Havlik RJ, Edwards BK, Yates JW. Effect of age and comorbidity in postmenopausal breast cancer patients aged 55 years and older. JAMA. 2001;285: 885–892. 10.1001/jama.285.7.885 [DOI] [PubMed] [Google Scholar]
24.Yun YH, Kim SH, Lee KM, Park SM, Kim YM. Age, sex, and comorbidities were considered in comparing reference data for health-related quality of life in the general and cancer populations. J Clin Epidemiol. 2007;60: 1164–1175. 10.1016/j.jclinepi.2006.12.014 [DOI] [PubMed] [Google Scholar]
25.Ezzati M, Riboli E. Behavioral and dietary risk factors for noncommunicable diseases. N Engl J Med. 2013;369: 954–964. 10.1056/NEJMra1203528 [DOI] [PubMed] [Google Scholar]
26.Grimmett C, Wardle J, Steptoe A. Health behaviours in older cancer survivors in the English Longitudinal Study of Ageing. Eur J Cancer. 2009;45: 2180–2186. 10.1016/j.ejca.2009.02.024 [DOI] [PubMed] [Google Scholar]
27.Loprinzi PD, Cardinal BJ. Effects of physical activity on common side effects of breast cancer treatment. Breast Cancer. 2012;19: 4–10. 10.1007/s12282-011-0292-3 [DOI] [PubMed] [Google Scholar]
28.Der-Martirosian C, Cordasco KM, Washington DL. Health-related quality of life and comorbidity among older women veterans in the United States. Qual Life Res. 2013;22: 2749–2756. 10.1007/s11136-013-0424-7 [DOI] [PubMed] [Google Scholar]
29.Banham D, Roder D, Brown A. Comorbidities contribute to the risk of cancer death among Aboriginal and non-Aboriginal South Australians: Analysis of a matched cohort study. Cancer Epidemiol. 2018;52: 75–82. 10.1016/j.canep.2017.12.005 [DOI] [PubMed] [Google Scholar]
30.Schrijvers CTM, Coebergh JWW, Mackenbach JP. Socioeconomic status and comorbidity among newly diagnosed cancer patients. Cancer. 1997;80: 1482–1488. [PubMed] [Google Scholar]
31.Capuano G, Gentile PC, Bianciardi F, Tosti M, Palladino A, Di Palma M. Prevalence and influence of malnutrition on quality of life and performance status in patients with locally advanced head and neck cancer before treatment. Support Care Cancer. 2010;18: 433–437. 10.1007/s00520-009-0681-8 [DOI] [PubMed] [Google Scholar]
32.Ni J, Feng J, Denehy L, Wu Y, Xu L, Granger CL. Symptoms of posttraumatic stress disorder and associated risk factors in patients with lung cancer: A longitudinal observational study. Integr Cancer Ther. 2018;17: 1195–1203. 10.1177/1534735418807970 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Australian Institute of Health and Welfare. Impact of physical inactivity as a risk factor for chronic conditions, Australian Burden of Disease Study. Australian Burden of Disease Study series no15. AIHW; 2017.
34.Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of disability, frailty, and comorbidity: Implications for improved targeting and care. Journals Gerontol Med Sci. 2004;59: 255–263. 10.1093/gerona/59.3.m255 [DOI] [PubMed] [Google Scholar]
35.Lazarus R. Stress and Emotion: A New Synthesis. New York: Springer; 1999. [Google Scholar]
36.Summerfield M, Bevitt A, Fok YK, Hahn M, La N, Macalalad N, et al. HILDA User Manual–Release 17. Melbourne Institute: Applied Economic and Social Research, University of Melbourne; 2018.
37.Sarfati D. Review of methods used to measure comorbidity in cancer populations: No gold standard exists. J Clin Epidemiol. 2012;65: 924–933. 10.1016/j.jclinepi.2012.02.017 [DOI] [PubMed] [Google Scholar]
38.Gross CP, Guo Z, McAvay GJ, Allore HG, Young M, Tinetti ME. Multimorbidity and survival in older persons with colorectal cancer. J Am Geriatr Soc. 2006;54: 1898–1904. 10.1111/j.1532-5415.2006.00973.x [DOI] [PubMed] [Google Scholar]
39.Meyerhardt J, Catalano P, Haller D, Mayer R, Macdonald J, Benson A, et al. Impact of diabetes mellitus on outcomes in patients with colon cancer. J Clin Oncol. 2003;21: 433–40. 10.1200/JCO.2003.07.125 [DOI] [PubMed] [Google Scholar]
40.Sarfati D, Tan L, Blakely T, Pearce N. Comorbidity among patients with colon cancer in New Zealand. N Z Med J. 2011;124: 76–88. [PubMed] [Google Scholar]
41.Tammemagi CM, Neslund-Dudas C, Simoff M, Kvale P. Impact of comorbidity on lung cancer survival. Int J Cancer. 2003;103: 792–802. 10.1002/ijc.10882 [DOI] [PubMed] [Google Scholar]
42.Satariano WA, Ragland DR. The effect of comorbidity on 3-year survival of women with primary breast cancer. Ann Intern Med. 1994;120: 104–110. 10.7326/0003-4819-120-2-199401150-00002 [DOI] [PubMed] [Google Scholar]
43.Sarfati D, Gurney J, Stanley J, Salmond C, Crampton P, Dennett E, et al. Cancer-specific administrative data-based comorbidity indices provided valid alternative to Charlson and National Cancer Institute Indices. J Clin Epidemiol. 2014;67: 586–595. 10.1016/j.jclinepi.2013.11.012 [DOI] [PubMed] [Google Scholar]
44.Klabunde CN, Legler JM, Warren JL, Baldwin LM, Schrag D. A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and lung cancer patients. Ann Epidemiol. 2007;17: 584–590. 10.1016/j.annepidem.2007.03.011 [DOI] [PubMed] [Google Scholar]
45.Sarfati D, Gurney J, Stanley J, Lim B, McSherry C. Development of a pharmacy-based comorbidity index for patients with cancer. Med Care. 2014;52: 586–593. 10.1097/MLR.0000000000000149 [DOI] [PubMed] [Google Scholar]
46.Fleming ST, Pearce KA, McDavid K, Pavlov D. The development and validation of a comorbidity index for prostate cancer among Black men. J Clin Epidemiol. 2003;56: 1064–1075. 10.1016/s0895-4356(03)00213-0 [DOI] [PubMed] [Google Scholar]
47.Charlson MmE, Pompel P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Disabil. 1987;40: 373–83. [DOI] [PubMed] [Google Scholar]
48.Miller MD, Rifai AH, Parrdis CF, Wouck PR, Stack JA, Rifai AH, et al. Rating chronic medical illness burden in geropsychiatric practice and research: Application of the cumulative illness rating scale. Psychiatry Res. 1992;41: 237–248. 10.1016/0165-1781(92)90005-n [DOI] [PubMed] [Google Scholar]
49.Piccirillo J. Importance of comorbidity in head and neck squamous cell cancer. Laryngoscope. 2000;110: 593–602. [DOI] [PubMed] [Google Scholar]
50.Piccirillo J, Spitznagel E, Vermani N, Costas I, Schnitzler M. Comparison of comorbidity indices for patients with head and neck cancer. Med Care. 2004;42: 482–486. 10.1097/01.mlr.0000124254.88292.a1 [DOI] [PubMed] [Google Scholar]
51.Mandelblatt JS, Bierman AS, Gold K, Zhang Y, Ng H, Maserejan N, et al. Constructs of burden of illness in older patients with breast cancer: A comparison of measurement methods. Health Serv Res. 2001;36: 1085–1107. 10.1111/j.1475-6773.2007.00786.x [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Kuijpers W, Groen WG, Aaronson NK, Van Harten WH. A systematic review of web-based interventions for patient empowerment and physical activity in chronic diseases: Relevance for cancer survivors. J Med Internet Res. 2013;15 10.2196/jmir.2281 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Chipperfield K, Fletcher J, Millar J, Brooker J, Smith R, Frydenberg M, et al. Factors associated with adherence to physical activity guidelines in patients with prostate cancer. Psychooncology. 2013;22: 2478–2486. 10.1002/pon.3310 [DOI] [PubMed] [Google Scholar]
54.RAND Corporation. 36-item short form survey instrument (SF-36). In: RAND Health Care Communications [Internet]. Santa Monica, CA 90407–2138; [cited 11 Nov 2019]. https://www.rand.org/health-care/surveys_tools/mos/36-item-short-form.html
55.Yarlas AS, White MK, Yang M, Saris-Baglama RN, Bech PG, Christensen T. Measuring the health status burden in hemodialysis patients using the SF-36^® health survey. Qual Life Res. 2011;20: 383–389. 10.1007/s11136-010-9764-8 [DOI] [PubMed] [Google Scholar]
56.Lins L, Carvalho FM. SF-36 total score as a single measure of health-related quality of life: Scoping review. SAGE Open Med. 2016;4: 1–12. 10.1177/2050312116671725 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.The Australian Bureau of Statistics (ABS) and the Australian Population and Migration Research Centre at the University of Adelaide. Australian statistical geography standard (ASGS): Volume 5—Remoteness Structure. 2016.
58.The Australian Bureau of Statistics. The Index of relative socio-economic disadvantage (IRSD). In: Socio-Economic Indexes for Areas (SEIFA) [Internet]. Canberra, Australia; 2011 [cited 19 Jan 2019] pp. 1–48. http://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001
59.Armitage P. Tests for Linear Trends in Proportions and Frequencies. Biometrics. 1955;11: 375–386. [Google Scholar]
60.Robb C, Haley WE, Balducci L, Extermann M, Perkins EA, Small BJ, et al. Impact of breast cancer survivorship on quality of life in older women. Crit Rev Oncol Hematol. 2007;62: 84–91. 10.1016/j.critrevonc.2006.11.003 [DOI] [PubMed] [Google Scholar]
61.Thome B, AK D, IR H. Quality of life in old people with and without cancer. Qual Life Res. 2004;13: 1067 Available: http://search.ebscohost.com/login.aspx?direct=true&db=amed&AN=0064950&site=ehost-live [DOI] [PubMed] [Google Scholar]
62.Banham D, Roder D, Brown A. Comorbidities contribute to the risk of cancer death among Aboriginal and non-Aboriginal South Australians: Analysis of a matched cohort study. Cancer Epidemiol. 2018;52: 75–82. 10.1016/j.canep.2017.12.005 [DOI] [PubMed] [Google Scholar]
63.Short PF, Vasey JJ, BeLue R. Work disability associated with cancer survivorship and other chronic conditions. Psychooncology. 2008;17: 91–97. 10.1002/pon.1194 [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Vartanian JG, Carvalho AL, Toyota J, Kowalski ISG, Kowalski LP. Socioeconomic effects of and risk factors for disability in long-term survivors of head and neck cancer. Arch Otolaryngol—Head Neck Surg. 2006;132: 32–35. 10.1001/archotol.132.1.32 [DOI] [PubMed] [Google Scholar]
65.Chrischilles EA, Riley D, Letuchy E, Koehler L, Neuner J, Jernigan C, et al. Upper extremity disability and quality of life after breast cancer treatment in the Greater Plains Collaborative clinical research network. Breast Cancer Res Treat. 2019;0: 0 10.1007/s10549-019-05184-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.van Muijen P, Duijts SFA, Bonefaas-Groenewoud K, van der Beek AJ, Anema JR. Factors associated with work disability in employed cancer survivors at 24-month sick leave. BMC Cancer. 2014;14: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
67.van Muijen P, Duijts SFA, van der Beek AJ, Anema JR. Prognostic factors of work disability in sick-listed cancer survivors. J Cancer Surviv. 2013;7: 582–591. 10.1007/s11764-013-0297-3 [DOI] [PubMed] [Google Scholar]
68.Short PF, Vasey JJ, BeLue R. Work disability associated with cancer survivorship and other chronic conditions. Psychooncology. 2008;17: 91–97. 10.1002/pon.1194 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.National Cancer Institute. Side effects of cancer treatment. In: Cancer Treatment [Internet]. 2019 [cited 17 Jun 2019]. https://www.cancer.gov/about-cancer/treatment/side-effects
70.Jørgensen TL, Hallas J, Friis S, Herrstedt J. Comorbidity in elderly cancer patients in relation to overall and cancer-specific mortality. Br J Cancer. 2012;106: 1353–1360. 10.1038/bjc.2012.46 [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Garman KS, Pieper CF, Seo P, Cohen HJ. Function in elderly cancer survivors depends on comorbidities. Journals Gerontol Ser A Biol Sci Med Sci. 2011;58: M1119–M1124. 10.1093/gerona/58.12.m1119 [DOI] [PubMed] [Google Scholar]
72.Yabroff KR, Lawrence WF, Clauser S, Davis WW, Brown ML. Burden of illness in cancer survivors: Findings from a population-based national sample. J Natl Cancer Inst. 2004;96: 1322–1330. 10.1093/jnci/djh255 [DOI] [PubMed] [Google Scholar]
73.Endo M, Haruyama Y, Takahashi M, Nishiura C, Kojimahara N, Yamaguchi N. Returning to work after sick leave due to cancer: a 365-day cohort study of Japanese cancer survivors. J Cancer Surviv. 2016;10: 320–329. 10.1007/s11764-015-0478-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Brunet J, Sabiston CM. Self-presentation and physical activity in breast cancer survivors: The moderating effect of social cognitive constructs. J Sport Exerc Psychol. 2011;33: 759–778. 10.1123/jsep.33.6.759 [DOI] [PubMed] [Google Scholar]
75.Jones JM, Olson K, Catton P, Catton CN, Fleshner NE, Krzyzanowska MK, et al. Cancer-related fatigue and associated disability in post-treatment cancer survivors. J Cancer Surviv. 2016;10: 51–61. 10.1007/s11764-015-0450-2 [DOI] [PubMed] [Google Scholar]
76.Pergolotti M, Deal AM, Williams GR, Bryant AL, Reeve BB, Muss HB. A randomized controlled trial of outpatient CAncer REhabilitation for older adults: The CARE Program. Contemp Clin Trials. 2015;44: 89–94. 10.1016/j.cct.2015.07.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
77.McTiernan A. Mechanisms linking physical activity with cancer. Nat Rev Cancer. 2008;8: 205–211. 10.1038/nrc2325 [DOI] [PubMed] [Google Scholar]
78.Vardar-Yagli N, Sener G, Saglam M, Calik-Kutukcu E, Arikan H, Inal-Ince D, et al. Associations among physical activity, comorbidity, functional capacity, peripheral muscle strength and depression in breast cancer survivors. Asian Pacific J Cancer Prev. 2015;16: 585–589. 10.7314/APJCP.2015.16.2.585 [DOI] [PubMed] [Google Scholar]
79.Moore SC, Lee M, Weiderpass E, Campbell PT, Sampson JN, Kitahara CM, et al. Leisure-time physical activity and risk of 26 types of cancer in 1.44 million adults. JAMA Intern Med. 2016;176: 816–825. 10.1001/jamainternmed.2016.1548 [DOI] [PMC free article] [PubMed] [Google Scholar]
80.Stefani L, Galanti Gensini G. Exercise as a prescription therapy for breast and colon cancer survivors. Int J Gen Med. 2013;6: 245–251. 10.2147/IJGM.S42720 [DOI] [PMC free article] [PubMed] [Google Scholar]
81.Parent MÉ, Rousseau MC, El-Zein M, Latreille B, Désy M, Siemiatycki J. Occupational and recreational physical activity during adult life and the risk of cancer among men. Cancer Epidemiol. 2011;35: 151–159. 10.1016/j.canep.2010.09.004 [DOI] [PubMed] [Google Scholar]
82.Rock CL, Doyle C, Demark-Wahnefried W, Meyerhardt J, Courneya KS, Schwartz AL, et al. Nutrition and physical activity guidelines for cancer survivors. CA Cancer J Clin. 2012;62: 242–274. 10.3322/caac.21142 [DOI] [PubMed] [Google Scholar]
83.Speck RM, Courneya KS, Mâsse LC, Duval S, Schmitz KH. An update of controlled physical activity trials in cancer survivors: A systematic review and meta-analysis. J Cancer Surviv. 2010;4: 87–100. 10.1007/s11764-009-0110-5 [DOI] [PubMed] [Google Scholar]
84.Aarts MJ, Lemmens VEPP, Louwman MWJ, Kunst AE, Coebergh JWW. Socioeconomic status and changing inequalities in colorectal cancer? A review of the associations with risk, treatment and outcome. Eur J Cancer. 2010;46: 2681–2695. 10.1016/j.ejca.2010.04.026 [DOI] [PubMed] [Google Scholar]
85.Shankaran V, Jolly S, Blough D, Ramsey SD. Risk factors for financial hardship in patients receiving adjuvant chemotherapy for colon cancer: A population-based exploratory analysis. J Clin Oncol. 2012;30: 1608–1614. 10.1200/JCO.2011.37.9511 [DOI] [PubMed] [Google Scholar]
86.Hoebel J, Kroll LE, Fiebig J, Lampert T, Katalinic A, Barnes B, et al. Socioeconomic inequalities in total and Site-Specific cancer incidence in Germany: A population-based registry study. Front Oncol. 2018;8: 1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
87.Hagedoorn P, Vandenheede H, Vanthomme K, Gadeyne S. Socioeconomic position, population density and site-specific cancer mortality: A multilevel analysis of Belgian adults, 2001–2011. Int J Cancer. 2018;142: 23–35. 10.1002/ijc.31031 [DOI] [PubMed] [Google Scholar]
88.Sharpe KH. Socioeconomic inequalities in lung and upper aero-digestive tract cancer incidence in Scotland. University of Glasgow. 2018. https://theses.gla.ac.uk/8887/
89.Yu XQ, Luo Q, Kahn C, Cahill C, Weber M, Grogan P, et al. Widening socioeconomic disparity in lung cancer incidence among men in New South Wales, Australia,1987–2011. Chinese J Cancer Res. 2017;29: 395–401. 10.21147/j.issn.1000-9604.2017.05.03 [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Teng AM, Atkinson J, Disney G, Wilson N, Blakely T. Changing socioeconomic inequalities in cancer incidence and mortality: Cohort study with 54 million person-years follow-up 1981–2011. Int J Cancer. 2017;140: 1306–1316. 10.1002/ijc.30555 [DOI] [PubMed] [Google Scholar]
91.Di Cesare M, Khang Y, Asaria P, Blakely T, Cowan MJ, Farzadfar F, et al. Inequalities in non-communicable diseases and effective responses. Lancet. 2013;381: 585–597. 10.1016/S0140-6736(12)61851-0 [DOI] [PubMed] [Google Scholar]
92.Collaborators T. Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: A systematic analysis from the Global Burden of Disease Study 2015. Lancet. 2017;389: 1885–906. 10.1016/S0140-6736(17)30819-X [DOI] [PMC free article] [PubMed] [Google Scholar]
93.Carrera PM, Kantarjian HM, Blinder VS. The financial burden and distress of patients with cancer: understanding and stepping-up action on the financial toxicity of cancer treatment. CA Cancer J Clin. 2018;68: 153–165. 10.3322/caac.21443 [DOI] [PMC free article] [PubMed] [Google Scholar]
94.Niessen LW, Mohan D, Akuoku Jonathan K M A J, Ahmed S, Koehlmoos Tracey P T A, Khan J, et al. Tackling socioeconomic inequalities and non-communicable diseases in low-income and middle-income countries under the Sustainable Development agenda. Lancet. 2018;391: 2036–2046. 10.1016/S0140-6736(18)30482-3 [DOI] [PubMed] [Google Scholar]
95.Pampel F, Krueger P, Denney J. Socioeconomic disparities in health behaviors. Annu Rev Sociol. 2010;36: 349–70. 10.1146/annurev.soc.012809.102529 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0228744.r001

Decision Letter 0

Miguel Angel Luque-Fernandez

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

14 Oct 2019

PONE-D-19-24164

The emerging burden of chronic diseases among Australian cancer patients: Evidence from a longitudinal exploration, 2003-2017

PLOS ONE

Dear Mr. Mahumud,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We would appreciate receiving your revised manuscript by Nov 28 2019 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Miguel Angel Luque-Fernandez

Academic Editor

PLOS ONE

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

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

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

3. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for the opportunity to read and comment on your manuscript, this is an interesting study. I have some comments and suggestions, which I list below.

1) The title is quite broad, and you describe the burden as “emerging”. I’m not clear if by ‘burden’ you are referring to the presence of chronic disease in cancer patients (and the complexity this comorbidity presents in the care of cancer patients), and what the evidence is from your study to suggest this is an emerging burden?

2) I would have liked more detail about the cancer patient population in this study, specifically the timing of when the cancer had been diagnosed (in relation to the timing of the survey) and the type of cancer diagnosed. The latter in particular is really important in relation to this study, given that the aetiology of cancers can vary according to their type, and that some cancers share common risk factors with some of the chronic conditions you investigated (e.g. smoking is strongly associated with both lung cancer and emphysema). In addition, was the distribution of cancer types similar across each wave of the study? Are the changes in chronic comorbid conditions over time presented in Figure 1 a like-for-like comparison among the cancer patient populations?

3) Explanatory variables.

i) Is there a main exposure of interest?

ii) Why did you include the variable 'satisfaction with household members' as a predictor in your analysis, how does this relate to comorbidity in cancer patients? Was it considered to be a proxy variable for stress?! A conceptual framework diagram may help to explain your assumptions of the relationships between your explanatory variables and outcome variable.

4) Table 1: it would be interesting to include a summary of the distribution of the number of chronic conditions according to the wave of the study.

Minor comments

5) Avoid vague statements, specifically:

i) Top of page 4: "Existing research recognises the critical role played by comorbid chronic conditions among cancer patients".

ii) Page 5: "Individuals with cancer who faced the burden of chronic comorbid conditions were investigated to see the degree of the cancer burden related to its primary evaluation as well as their ability to cope". What do you mean by the degree of the cancer burden relating to its primary evaluation? How are you quantifying ability to cope?

Reviewer #2: The present manuscript titled ‘The emerging burden of chronic diseases among Australian cancer patients: Evidence from a longitudinal exploration, 2003-2017' presents a prospective longitudinal design using data from the HILDA Australian survey. Authors have applied a fixed-effect negative binomial regression model to predict the potential factors of occurrence in chronic comorbid conditions. In general, the theme of the manuscript is important and relevant. However, I found some key points unexplained or not covered in the present form of the manuscript. I would suggest a revision (given below) before the acceptance of the manuscript for publication in the PloS One.

My suggestions/comments are as follows:

(1) Introduction: The Introduction provides information concerning cancer and comorbid conditions worldwide, but does not provide enough information regarding the Australian reality. I suggest the authors explain better how this study is important to the cancer epidemiology field, specifying the challenges, particularly to Australia.

(2) Methods:

Data source: I suggest the authors provide more information concerning HILDA Australian survey. In the text, the authors have described information for five waves but this information is unclear when we analyze the figures and tables. It is unclear why the authors selected five waves with cancer-related information but have just presented data for three waves in Figure 1.

Outcome variable: The authors have cited that “there is no gold-standard method to measuring comorbidity status in the context of cancer patients”, then the authors have listed some methods for measuring comorbidity status applied to the literature. As a reader, I would like to know why the authors have chosen the count of comorbid conditions? It was a single count? How this information was organized? Please, provide more information explaining these details.

Explanatory variables: The authors applied several demographic, socio-economic and health and lifestyle-related variables in this study. For each variable, there is a specific categorization method. For some variables the rationale of applying the scale is unclear, an example is the application of the SF-36 scale. I know what the survey means but it is necessary to explain it in the text. I didn’t get the rationale of why the authors have chosen this quality of life scale to justify health burden levels and also how the score was made in this study. I also didn’t see any information as supplementary material of these data. Please, I suggest the authors clarify them.

Statistical analysis: What the authors understand as an “insignificant predictors were not included in the adjusted model”? Please, provide more information and explain it. I understand that the adjusted model was applied as a fixed-effect negative binomial regression. But what method was applied and defined as unadjusted? I suggest again the authors clarify and explain better this important information for future readers of this manuscript.

(3) Results:

- The quality of the Figures is not good. Please, provide the Figures in better quality. I simply cannot read the legend of Figure 3.

- Why the authors have only shown information for three waves in Figure 1 if in the Methods section were described five waves? The authors have applied a Cochran- Armitage trend test. It was the rationale of present the results for three waves? Please, explain it.

- As a reader, It will be more interesting to see in Table 2 the results for each comorbid condition such as the authors have shown in Table 1 (0 chronic diseases; 1-2 chronic diseases; 3 or more chronic diseases).

- I didn’t see any information as supplementary material of all the several variables analyzed in this study.

(4) Discussion:

Your discussion is interesting and you made an effort to compare your results with the results of previous studies. However, what is your specific recommendations based on the results you produced to Australian reality? Please, provide more information on what has been studied in Australia and why your study is important on this topic.

(4) Minor comments:

Some sentences do not read well. Please consider revising.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Feb 12;15(2):e0228744. doi: 10.1371/journal.pone.0228744.r002

Author response to Decision Letter 0

12 Dec 2019

Dear Reviwer(s),

Thank you for giving us an opportunity to revise our manuscript entitled “The burden of chronic diseases among Australian cancer patients: Evidence from a longitudinal exploration, 2007-2017”. We found the reviewers’ comments/feedback very helpful in improving the manuscript and we have revised the manuscript accordingly. Please find attached the revised manuscript. We have no conflict of interest to declare. The manuscript has not been published in any other journal. Our point-by-point comments on the suggested revisions are below.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(76.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0228744.r003

Decision Letter 1

Miguel Angel Luque-Fernandez

14 Jan 2020

PONE-D-19-24164R1

The burden of chronic diseases among Australian cancer patients: Evidence from a longitudinal exploration, 2007-2017

PLOS ONE

Dear Mr. Mahumud,

Thank you for submitting your manuscript to PLOS ONE. We would like to accept for publication your article but there is still a couple of minor questions raised by one reviewer that you would like to answer/clarify. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We would appreciate receiving your revised manuscript by Feb 28 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Miguel Angel Luque-Fernandez

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Thank you for the opportunity to read the revised version of your manuscript.

I have a couple of further comments:

(Using your numbering system of the original comments - comment 3: (page 18, lines 26-29) you have used the word ‘emerging’ in this sentence, please clarify what this means. For example, are you saying that the prevalence of comorbidity among cancer patients has increased over time? I think you need to be more explicit. Likewise the sentence “The authors were not able to estimate the cancer-specific analysis due to the paucity of relevant data” – what do you mean by cancer-specific? According to cancer type?

Reviewer #2: This is the second version of the manuscript. Authors have done considerable changes explaining in detail point-by-point according to peer reviewer's suggestions/comments and it is commendable. The revision certainly improves the quality and scope of the present manuscript. I would recommend it for possible publication in PlosOne.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS One. 2020 Feb 12;15(2):e0228744. doi: 10.1371/journal.pone.0228744.r004

Author response to Decision Letter 1

22 Jan 2020

Please find the attached file. We found the reviewers’ comments/feedback very helpful in improving the manuscript and we have revised the manuscript accordingly.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(32.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0228744.r005

Decision Letter 2

Miguel Angel Luque-Fernandez

23 Jan 2020

The burden of chronic diseases among Australian cancer patients: Evidence from a longitudinal exploration, 2007-2017

PONE-D-19-24164R2

Dear Dr. Mahumud,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Miguel Angel Luque-Fernandez

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0228744.r006

Acceptance letter

Miguel Angel Luque-Fernandez

31 Jan 2020

PONE-D-19-24164R2

The burden of chronic diseases among Australian cancer patients: Evidence from a longitudinal exploration, 2007-2017

Dear Dr. Mahumud:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Miguel Angel Luque-Fernandez

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(76.5KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(32.9KB, docx)}

Data Availability Statement

[pone.0228744.ref001] 1.World Health Organization (WHO). Cancer: Key facts. 2018 [cited 11 May 2019]. https://www.who.int/news-room/fact-sheets/detail/cancer

[pone.0228744.ref002] 2.Australian Institute of Health and Welfare. Cancer in Australia: In brief 2019. Cancer series no.119. Cat. no. CAN 123. Canberra: AIHW; 2019. www.aihw.gov.au

[pone.0228744.ref003] 3.Australian Institute of Health and Welfare. Australian Burden of Disease Study: impact and causes of illness and death in Australia 2015. Australian Burden of Disease series no. 19. Cat. no. BOD 22. Canberra: AIHW; 2019.

[pone.0228744.ref004] 4.Ng HS, Koczwara B, Roder D, Vitry A. Changes in the prevalence of comorbidity in the Australian population with cancer, 2007–2014. Cancer Epidemiol. 2018;54: 56–62. 10.1016/j.canep.2018.03.010 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref005] 5.Australian Institute of Health and Welfare. Australia’s health 2018. Australia’s health series no. 16. AUS 221. Canberra: AIHW; 2018.

[pone.0228744.ref006] 6.World Health Organization (WHO). Noncommunicable diseases. In: Key facts [Internet]. 2018 [cited 28 Jul 2019]. https://www.who.int/en/news-room/fact-sheets/detail/noncommunicable-diseases

[pone.0228744.ref007] 7.Stairmand J, Signal L, Sarfati D, Jackson C, Batten L, Holdaway M, et al. Consideration of comorbidity in treatment decision making in multidisciplinary cancer team meetings: A systematic review. Ann Oncol. 2015;26: 1325–1332. 10.1093/annonc/mdv025 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref008] 8.Australian Institute of Health and Welfare (AIHW). Australia’s health 2016. Australia’s health series no. 15. Cat. no. AUS 199. Canberra: AIHW; 2016. https://www.aihw.gov.au/reports/australias-health/australias-health-2016/contents/summary

[pone.0228744.ref009] 9.Sarfati D, Gurney J, Lim BT, Bagheri N, Simpson A, Koea J, et al. Identifying important comorbidity among cancer populations using administrative data: Prevalence and impact on survival. Asia Pac J Clin Oncol. 2016;12: e47–e56. 10.1111/ajco.12130 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref010] 10.Carstensen J, Andersson D, Andre M, Engstrom S, Magnusson H, Borgquist LA. How does comorbidity influence healthcare costs? A population-based cross-sectional study of depression, back pain and osteoarthritis. BMJ Open. 2012;2: e000809 10.1136/bmjopen-2011-000809 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref011] 11.Sogaard M, Thomsen RW, Bossen KS, Sørensen HT, Nørgaard M. The impact of comorbidity on cancer survival: A review. Clin Epidemiol. 2013;5: 3 10.2147/CLEP.S47150 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref012] 12.Pilleron S, Sarfati D, Janssen-Heijnen M, Vignat J, Ferlay J, Bray F, et al. Global cancer incidence in older adults, 2012 and 2035: A population-based study. Int J Cancer. 2019;144: 49–58. 10.1002/ijc.31664 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref013] 13.Sarfati D, Hill S, Blakely T, Robson B, Purdie G, Dennett E, et al. The effect of comorbidity on the use of adjuvant chemotherapy and survival from colon cancer: A retrospective cohort study. BMC Cancer. 2009;9: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref014] 14.Gross CP, McAvay GJ, Guo Z, Tinetti ME. The impact of chronic illnesses on the use and effectiveness of adjuvant chemotherapy for colon cancer. Cancer. 2007;109: 2410–2419. 10.1002/cncr.22726 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref015] 15.Cuthbert CA, Hemmelgarn BR, Xu Y, Cheung WY. The effect of comorbidities on outcomes in colorectal cancer survivors: a population-based cohort study. J Cancer Surviv. 2018;12: 733–743. 10.1007/s11764-018-0710-z [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref016] 16.Sarfati D, Koczwara B, Jackson C. The impact of comorbidity on cancer and its treatment. CA Cancer J Clin. 2016;66: 337–350. 10.3322/caac.21342 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref017] 17.Gurney J, Sarfati D, Stanley J. The impact of patient comorbidity on cancer stage at diagnosis. Br J Cancer. 2015;113: 1375–1380. 10.1038/bjc.2015.355 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref018] 18.Elliott BA, Renier CM, Haller IV., Elliott TE. Health-related quality of life (HRQoL) in patients with cancer and other concurrent illnesses. Qual Life Res. 2004;13: 457–462. 10.1023/B:QURE.0000018476.11278.35 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref019] 19.Van Hemelrijck M, Folkvaljon Y, Adolfsson J, Akre O, Holmberg L, Garmo H, et al. Causes of death in men with localized prostate cancer: A nationwide, population-based study. BJU Int. 2016;117: 507–514. 10.1111/bju.13059 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref020] 20.Pule L, Buckley E, Niyonsenga T, Banham D, Roder D. Developing a comorbidity index for comparing cancer outcomes in Aboriginal and non-Aboriginal Australians. BMC Health Serv Res. 2018;18: 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref021] 21.Sigel K, Wisnivesky JP. Comorbidity profiles of patients with lung cancer: A new approach to risk stratification? Ann Am Thorac Soc. 2017;14: 1512–1513. 10.1513/AnnalsATS.201706-442ED [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref022] 22.Lindhagen L, Van Hemelrijck M, Robinson D, Stattin P, Garmo H. How to model temporal changes in comorbidity for cancer patients using prospective cohort data. BMC Med Inform Decis Mak. 2015;15: 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref023] 23.Yancik R, Wesley MN, Ries LAG, Havlik RJ, Edwards BK, Yates JW. Effect of age and comorbidity in postmenopausal breast cancer patients aged 55 years and older. JAMA. 2001;285: 885–892. 10.1001/jama.285.7.885 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref024] 24.Yun YH, Kim SH, Lee KM, Park SM, Kim YM. Age, sex, and comorbidities were considered in comparing reference data for health-related quality of life in the general and cancer populations. J Clin Epidemiol. 2007;60: 1164–1175. 10.1016/j.jclinepi.2006.12.014 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref025] 25.Ezzati M, Riboli E. Behavioral and dietary risk factors for noncommunicable diseases. N Engl J Med. 2013;369: 954–964. 10.1056/NEJMra1203528 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref026] 26.Grimmett C, Wardle J, Steptoe A. Health behaviours in older cancer survivors in the English Longitudinal Study of Ageing. Eur J Cancer. 2009;45: 2180–2186. 10.1016/j.ejca.2009.02.024 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref027] 27.Loprinzi PD, Cardinal BJ. Effects of physical activity on common side effects of breast cancer treatment. Breast Cancer. 2012;19: 4–10. 10.1007/s12282-011-0292-3 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref028] 28.Der-Martirosian C, Cordasco KM, Washington DL. Health-related quality of life and comorbidity among older women veterans in the United States. Qual Life Res. 2013;22: 2749–2756. 10.1007/s11136-013-0424-7 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref029] 29.Banham D, Roder D, Brown A. Comorbidities contribute to the risk of cancer death among Aboriginal and non-Aboriginal South Australians: Analysis of a matched cohort study. Cancer Epidemiol. 2018;52: 75–82. 10.1016/j.canep.2017.12.005 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref030] 30.Schrijvers CTM, Coebergh JWW, Mackenbach JP. Socioeconomic status and comorbidity among newly diagnosed cancer patients. Cancer. 1997;80: 1482–1488. [PubMed] [Google Scholar]

[pone.0228744.ref031] 31.Capuano G, Gentile PC, Bianciardi F, Tosti M, Palladino A, Di Palma M. Prevalence and influence of malnutrition on quality of life and performance status in patients with locally advanced head and neck cancer before treatment. Support Care Cancer. 2010;18: 433–437. 10.1007/s00520-009-0681-8 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref032] 32.Ni J, Feng J, Denehy L, Wu Y, Xu L, Granger CL. Symptoms of posttraumatic stress disorder and associated risk factors in patients with lung cancer: A longitudinal observational study. Integr Cancer Ther. 2018;17: 1195–1203. 10.1177/1534735418807970 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref033] 33.Australian Institute of Health and Welfare. Impact of physical inactivity as a risk factor for chronic conditions, Australian Burden of Disease Study. Australian Burden of Disease Study series no15. AIHW; 2017.

[pone.0228744.ref034] 34.Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of disability, frailty, and comorbidity: Implications for improved targeting and care. Journals Gerontol Med Sci. 2004;59: 255–263. 10.1093/gerona/59.3.m255 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref035] 35.Lazarus R. Stress and Emotion: A New Synthesis. New York: Springer; 1999. [Google Scholar]

[pone.0228744.ref036] 36.Summerfield M, Bevitt A, Fok YK, Hahn M, La N, Macalalad N, et al. HILDA User Manual–Release 17. Melbourne Institute: Applied Economic and Social Research, University of Melbourne; 2018.

[pone.0228744.ref037] 37.Sarfati D. Review of methods used to measure comorbidity in cancer populations: No gold standard exists. J Clin Epidemiol. 2012;65: 924–933. 10.1016/j.jclinepi.2012.02.017 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref038] 38.Gross CP, Guo Z, McAvay GJ, Allore HG, Young M, Tinetti ME. Multimorbidity and survival in older persons with colorectal cancer. J Am Geriatr Soc. 2006;54: 1898–1904. 10.1111/j.1532-5415.2006.00973.x [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref039] 39.Meyerhardt J, Catalano P, Haller D, Mayer R, Macdonald J, Benson A, et al. Impact of diabetes mellitus on outcomes in patients with colon cancer. J Clin Oncol. 2003;21: 433–40. 10.1200/JCO.2003.07.125 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref040] 40.Sarfati D, Tan L, Blakely T, Pearce N. Comorbidity among patients with colon cancer in New Zealand. N Z Med J. 2011;124: 76–88. [PubMed] [Google Scholar]

[pone.0228744.ref041] 41.Tammemagi CM, Neslund-Dudas C, Simoff M, Kvale P. Impact of comorbidity on lung cancer survival. Int J Cancer. 2003;103: 792–802. 10.1002/ijc.10882 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref042] 42.Satariano WA, Ragland DR. The effect of comorbidity on 3-year survival of women with primary breast cancer. Ann Intern Med. 1994;120: 104–110. 10.7326/0003-4819-120-2-199401150-00002 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref043] 43.Sarfati D, Gurney J, Stanley J, Salmond C, Crampton P, Dennett E, et al. Cancer-specific administrative data-based comorbidity indices provided valid alternative to Charlson and National Cancer Institute Indices. J Clin Epidemiol. 2014;67: 586–595. 10.1016/j.jclinepi.2013.11.012 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref044] 44.Klabunde CN, Legler JM, Warren JL, Baldwin LM, Schrag D. A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and lung cancer patients. Ann Epidemiol. 2007;17: 584–590. 10.1016/j.annepidem.2007.03.011 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref045] 45.Sarfati D, Gurney J, Stanley J, Lim B, McSherry C. Development of a pharmacy-based comorbidity index for patients with cancer. Med Care. 2014;52: 586–593. 10.1097/MLR.0000000000000149 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref046] 46.Fleming ST, Pearce KA, McDavid K, Pavlov D. The development and validation of a comorbidity index for prostate cancer among Black men. J Clin Epidemiol. 2003;56: 1064–1075. 10.1016/s0895-4356(03)00213-0 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref047] 47.Charlson MmE, Pompel P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Disabil. 1987;40: 373–83. [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref048] 48.Miller MD, Rifai AH, Parrdis CF, Wouck PR, Stack JA, Rifai AH, et al. Rating chronic medical illness burden in geropsychiatric practice and research: Application of the cumulative illness rating scale. Psychiatry Res. 1992;41: 237–248. 10.1016/0165-1781(92)90005-n [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref049] 49.Piccirillo J. Importance of comorbidity in head and neck squamous cell cancer. Laryngoscope. 2000;110: 593–602. [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref050] 50.Piccirillo J, Spitznagel E, Vermani N, Costas I, Schnitzler M. Comparison of comorbidity indices for patients with head and neck cancer. Med Care. 2004;42: 482–486. 10.1097/01.mlr.0000124254.88292.a1 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref051] 51.Mandelblatt JS, Bierman AS, Gold K, Zhang Y, Ng H, Maserejan N, et al. Constructs of burden of illness in older patients with breast cancer: A comparison of measurement methods. Health Serv Res. 2001;36: 1085–1107. 10.1111/j.1475-6773.2007.00786.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref052] 52.Kuijpers W, Groen WG, Aaronson NK, Van Harten WH. A systematic review of web-based interventions for patient empowerment and physical activity in chronic diseases: Relevance for cancer survivors. J Med Internet Res. 2013;15 10.2196/jmir.2281 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref053] 53.Chipperfield K, Fletcher J, Millar J, Brooker J, Smith R, Frydenberg M, et al. Factors associated with adherence to physical activity guidelines in patients with prostate cancer. Psychooncology. 2013;22: 2478–2486. 10.1002/pon.3310 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref054] 54.RAND Corporation. 36-item short form survey instrument (SF-36). In: RAND Health Care Communications [Internet]. Santa Monica, CA 90407–2138; [cited 11 Nov 2019]. https://www.rand.org/health-care/surveys_tools/mos/36-item-short-form.html

[pone.0228744.ref055] 55.Yarlas AS, White MK, Yang M, Saris-Baglama RN, Bech PG, Christensen T. Measuring the health status burden in hemodialysis patients using the SF-36^® health survey. Qual Life Res. 2011;20: 383–389. 10.1007/s11136-010-9764-8 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref056] 56.Lins L, Carvalho FM. SF-36 total score as a single measure of health-related quality of life: Scoping review. SAGE Open Med. 2016;4: 1–12. 10.1177/2050312116671725 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref057] 57.The Australian Bureau of Statistics (ABS) and the Australian Population and Migration Research Centre at the University of Adelaide. Australian statistical geography standard (ASGS): Volume 5—Remoteness Structure. 2016.

[pone.0228744.ref058] 58.The Australian Bureau of Statistics. The Index of relative socio-economic disadvantage (IRSD). In: Socio-Economic Indexes for Areas (SEIFA) [Internet]. Canberra, Australia; 2011 [cited 19 Jan 2019] pp. 1–48. http://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001

[pone.0228744.ref059] 59.Armitage P. Tests for Linear Trends in Proportions and Frequencies. Biometrics. 1955;11: 375–386. [Google Scholar]

[pone.0228744.ref060] 60.Robb C, Haley WE, Balducci L, Extermann M, Perkins EA, Small BJ, et al. Impact of breast cancer survivorship on quality of life in older women. Crit Rev Oncol Hematol. 2007;62: 84–91. 10.1016/j.critrevonc.2006.11.003 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref061] 61.Thome B, AK D, IR H. Quality of life in old people with and without cancer. Qual Life Res. 2004;13: 1067 Available: http://search.ebscohost.com/login.aspx?direct=true&db=amed&AN=0064950&site=ehost-live [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref062] 62.Banham D, Roder D, Brown A. Comorbidities contribute to the risk of cancer death among Aboriginal and non-Aboriginal South Australians: Analysis of a matched cohort study. Cancer Epidemiol. 2018;52: 75–82. 10.1016/j.canep.2017.12.005 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref063] 63.Short PF, Vasey JJ, BeLue R. Work disability associated with cancer survivorship and other chronic conditions. Psychooncology. 2008;17: 91–97. 10.1002/pon.1194 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref064] 64.Vartanian JG, Carvalho AL, Toyota J, Kowalski ISG, Kowalski LP. Socioeconomic effects of and risk factors for disability in long-term survivors of head and neck cancer. Arch Otolaryngol—Head Neck Surg. 2006;132: 32–35. 10.1001/archotol.132.1.32 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref065] 65.Chrischilles EA, Riley D, Letuchy E, Koehler L, Neuner J, Jernigan C, et al. Upper extremity disability and quality of life after breast cancer treatment in the Greater Plains Collaborative clinical research network. Breast Cancer Res Treat. 2019;0: 0 10.1007/s10549-019-05184-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref066] 66.van Muijen P, Duijts SFA, Bonefaas-Groenewoud K, van der Beek AJ, Anema JR. Factors associated with work disability in employed cancer survivors at 24-month sick leave. BMC Cancer. 2014;14: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref067] 67.van Muijen P, Duijts SFA, van der Beek AJ, Anema JR. Prognostic factors of work disability in sick-listed cancer survivors. J Cancer Surviv. 2013;7: 582–591. 10.1007/s11764-013-0297-3 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref068] 68.Short PF, Vasey JJ, BeLue R. Work disability associated with cancer survivorship and other chronic conditions. Psychooncology. 2008;17: 91–97. 10.1002/pon.1194 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref069] 69.National Cancer Institute. Side effects of cancer treatment. In: Cancer Treatment [Internet]. 2019 [cited 17 Jun 2019]. https://www.cancer.gov/about-cancer/treatment/side-effects

[pone.0228744.ref070] 70.Jørgensen TL, Hallas J, Friis S, Herrstedt J. Comorbidity in elderly cancer patients in relation to overall and cancer-specific mortality. Br J Cancer. 2012;106: 1353–1360. 10.1038/bjc.2012.46 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref071] 71.Garman KS, Pieper CF, Seo P, Cohen HJ. Function in elderly cancer survivors depends on comorbidities. Journals Gerontol Ser A Biol Sci Med Sci. 2011;58: M1119–M1124. 10.1093/gerona/58.12.m1119 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref072] 72.Yabroff KR, Lawrence WF, Clauser S, Davis WW, Brown ML. Burden of illness in cancer survivors: Findings from a population-based national sample. J Natl Cancer Inst. 2004;96: 1322–1330. 10.1093/jnci/djh255 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref073] 73.Endo M, Haruyama Y, Takahashi M, Nishiura C, Kojimahara N, Yamaguchi N. Returning to work after sick leave due to cancer: a 365-day cohort study of Japanese cancer survivors. J Cancer Surviv. 2016;10: 320–329. 10.1007/s11764-015-0478-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref074] 74.Brunet J, Sabiston CM. Self-presentation and physical activity in breast cancer survivors: The moderating effect of social cognitive constructs. J Sport Exerc Psychol. 2011;33: 759–778. 10.1123/jsep.33.6.759 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref075] 75.Jones JM, Olson K, Catton P, Catton CN, Fleshner NE, Krzyzanowska MK, et al. Cancer-related fatigue and associated disability in post-treatment cancer survivors. J Cancer Surviv. 2016;10: 51–61. 10.1007/s11764-015-0450-2 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref076] 76.Pergolotti M, Deal AM, Williams GR, Bryant AL, Reeve BB, Muss HB. A randomized controlled trial of outpatient CAncer REhabilitation for older adults: The CARE Program. Contemp Clin Trials. 2015;44: 89–94. 10.1016/j.cct.2015.07.021 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref077] 77.McTiernan A. Mechanisms linking physical activity with cancer. Nat Rev Cancer. 2008;8: 205–211. 10.1038/nrc2325 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref078] 78.Vardar-Yagli N, Sener G, Saglam M, Calik-Kutukcu E, Arikan H, Inal-Ince D, et al. Associations among physical activity, comorbidity, functional capacity, peripheral muscle strength and depression in breast cancer survivors. Asian Pacific J Cancer Prev. 2015;16: 585–589. 10.7314/APJCP.2015.16.2.585 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref079] 79.Moore SC, Lee M, Weiderpass E, Campbell PT, Sampson JN, Kitahara CM, et al. Leisure-time physical activity and risk of 26 types of cancer in 1.44 million adults. JAMA Intern Med. 2016;176: 816–825. 10.1001/jamainternmed.2016.1548 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref080] 80.Stefani L, Galanti Gensini G. Exercise as a prescription therapy for breast and colon cancer survivors. Int J Gen Med. 2013;6: 245–251. 10.2147/IJGM.S42720 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref081] 81.Parent MÉ, Rousseau MC, El-Zein M, Latreille B, Désy M, Siemiatycki J. Occupational and recreational physical activity during adult life and the risk of cancer among men. Cancer Epidemiol. 2011;35: 151–159. 10.1016/j.canep.2010.09.004 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref082] 82.Rock CL, Doyle C, Demark-Wahnefried W, Meyerhardt J, Courneya KS, Schwartz AL, et al. Nutrition and physical activity guidelines for cancer survivors. CA Cancer J Clin. 2012;62: 242–274. 10.3322/caac.21142 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref083] 83.Speck RM, Courneya KS, Mâsse LC, Duval S, Schmitz KH. An update of controlled physical activity trials in cancer survivors: A systematic review and meta-analysis. J Cancer Surviv. 2010;4: 87–100. 10.1007/s11764-009-0110-5 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref084] 84.Aarts MJ, Lemmens VEPP, Louwman MWJ, Kunst AE, Coebergh JWW. Socioeconomic status and changing inequalities in colorectal cancer? A review of the associations with risk, treatment and outcome. Eur J Cancer. 2010;46: 2681–2695. 10.1016/j.ejca.2010.04.026 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref085] 85.Shankaran V, Jolly S, Blough D, Ramsey SD. Risk factors for financial hardship in patients receiving adjuvant chemotherapy for colon cancer: A population-based exploratory analysis. J Clin Oncol. 2012;30: 1608–1614. 10.1200/JCO.2011.37.9511 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref086] 86.Hoebel J, Kroll LE, Fiebig J, Lampert T, Katalinic A, Barnes B, et al. Socioeconomic inequalities in total and Site-Specific cancer incidence in Germany: A population-based registry study. Front Oncol. 2018;8: 1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref087] 87.Hagedoorn P, Vandenheede H, Vanthomme K, Gadeyne S. Socioeconomic position, population density and site-specific cancer mortality: A multilevel analysis of Belgian adults, 2001–2011. Int J Cancer. 2018;142: 23–35. 10.1002/ijc.31031 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref088] 88.Sharpe KH. Socioeconomic inequalities in lung and upper aero-digestive tract cancer incidence in Scotland. University of Glasgow. 2018. https://theses.gla.ac.uk/8887/

[pone.0228744.ref089] 89.Yu XQ, Luo Q, Kahn C, Cahill C, Weber M, Grogan P, et al. Widening socioeconomic disparity in lung cancer incidence among men in New South Wales, Australia,1987–2011. Chinese J Cancer Res. 2017;29: 395–401. 10.21147/j.issn.1000-9604.2017.05.03 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref090] 90.Teng AM, Atkinson J, Disney G, Wilson N, Blakely T. Changing socioeconomic inequalities in cancer incidence and mortality: Cohort study with 54 million person-years follow-up 1981–2011. Int J Cancer. 2017;140: 1306–1316. 10.1002/ijc.30555 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref091] 91.Di Cesare M, Khang Y, Asaria P, Blakely T, Cowan MJ, Farzadfar F, et al. Inequalities in non-communicable diseases and effective responses. Lancet. 2013;381: 585–597. 10.1016/S0140-6736(12)61851-0 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref092] 92.Collaborators T. Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: A systematic analysis from the Global Burden of Disease Study 2015. Lancet. 2017;389: 1885–906. 10.1016/S0140-6736(17)30819-X [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref093] 93.Carrera PM, Kantarjian HM, Blinder VS. The financial burden and distress of patients with cancer: understanding and stepping-up action on the financial toxicity of cancer treatment. CA Cancer J Clin. 2018;68: 153–165. 10.3322/caac.21443 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0228744.ref094] 94.Niessen LW, Mohan D, Akuoku Jonathan K M A J, Ahmed S, Koehlmoos Tracey P T A, Khan J, et al. Tackling socioeconomic inequalities and non-communicable diseases in low-income and middle-income countries under the Sustainable Development agenda. Lancet. 2018;391: 2036–2046. 10.1016/S0140-6736(18)30482-3 [DOI] [PubMed] [Google Scholar]

[pone.0228744.ref095] 95.Pampel F, Krueger P, Denney J. Socioeconomic disparities in health behaviors. Annu Rev Sociol. 2010;36: 349–70. 10.1146/annurev.soc.012809.102529 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

The burden of chronic diseases among Australian cancer patients: Evidence from a longitudinal exploration, 2007-2017

Rashidul Alam Mahumud

Khorshed Alam

Jeff Dunn

Jeff Gow

Roles

Abstract

Introduction

Methods

Results

Conclusions

Introduction

Materials and methods

Study design

Conceptual framework

Fig 1. Conceptual framework of the study.

Data source

Study variables

Outcome variable

Explanatory variables

Statistical analysis

Ethical considerations

Results

Background characteristics of the study population

Table 1. Summary statistics by the number of chronic condition among cancer patients for wave 7 and wave 9.

Table 2. Summary statistics by the number of chronic condition among cancer patients for wave 13 and wave 17.

Distribution and changes of chronic comorbid conditions with cancer patients over time

Fig 2. The trend of disease pattern among patients with cancer.

Fig 3. Unequal distribution of the presence of chronic comorbidities with the severity of disability among cancer patients across socioeconomic status.

Factors influencing chronic comorbid exposure of cancer patients

Table 3. Factors influencing chronic comorbid conditions of cancer patients using a fixed-effect negative binomial regression model.

Discussion

Conclusions

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Miguel Angel Luque-Fernandez

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Miguel Angel Luque-Fernandez

Roles

Author response to Decision Letter 1

Decision Letter 2

Miguel Angel Luque-Fernandez

Roles

Acceptance letter

Miguel Angel Luque-Fernandez

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases