Open Access

Marital status and its effect on lung cancer survival

  • Stacey L Tannenbaum1,
  • Wei Zhao1,
  • Tulay Koru-Sengul1, 2,
  • Feng Miao1,
  • David Lee1, 2 and
  • Margaret M Byrne1, 2, 3Email author
SpringerPlus20132:504

DOI: 10.1186/2193-1801-2-504

Received: 8 May 2013

Accepted: 25 September 2013

Published: 3 October 2013

Abstract

Purpose

The purpose of this study was to determine if marital status, including specific types of single status categories, is associated with length of survival in lung cancer patients.

Methods

Data from the 1996–2007 Florida Cancer Data System were linked with Agency for Health Care Administration data and U.S. Census data. Patients with both small cell and non-small cell lung cancer were identified (n = 161,228). Marital status was characterized by married, widowed, separated/divorced, and never married. We compared median survival time and 1, 3, and 5-year post diagnosis survival rates.

Results

Overall, 54.6% were married, 19.1% were widowed, 13.5% were separated/divorced, and 12.7% had never married. Median survival in months was longest for married (9.9) and widowed (7.7) patients, and shortest for never married (4.9) and separated/divorced (4.1) patients. Five-year survival rates were 14.2% for married, 10.7% for widowed, 8.9% for separated/divorced, and 8.4% for never married. In univariate Cox regression, marital status was a significant predictor of better survival for married (HR = 0.70; p < 0.001) and widowed (HR = 0.81; p < 0.001) patients compared with never married patients, but worse for separated/divorced patients (HR = 1.03; p = 0.003). Multivariate models demonstrated sustained survival benefits for married (HR = 0.86; p < 0.001) and widowed (HR = 0.88; p < 0.001) patients, and detriments for separated/divorced patients (HR = 1.05; p < 0.001) after adjusting for extensive confounders including demographics; tumor stage, grade, and morphology; comorbidities; treatment; and smoking status.

Conclusions

Our study demonstrated that married or widowed lung cancer patients have better survival compared to patients who were never married or separated/divorced. Research to understand the mechanism of this effect, and how the beneficial effect can be extended to those who have never married or have had the marital relationship severed through divorce or separation is needed.

Keywords

Lung cancer Marital status Outcomes Florida population-based cancer registry Support system

Introduction

Lung cancer is the second most common cancer in the U.S. but is responsible for the greatest number of deaths from cancer (American Cancer Society 2013). In 2013, it is estimated that there will be 246,210 new cases and 163,890 deaths ascribed to lung cancer (Siegel et al. 2013). Estimations for 2013 are that 14% of all incident cancers will be from lung cancer, with 28% of all cancer specific deaths in men and 26% of all deaths in women being attributable to lung cancer (American Cancer Society 2013). Despite advances in chemotherapy and radiotherapy, the 5 year survival rate for all stages combined is estimated to be approximately only 16% (Siegel et al. 2013).

Because of these dismal statistics, it is important to explore all factors that might positively affect survival and mortality outcomes. Recent and growing literature suggests that psychological factors and the presence or absence of social support may be an important factor influencing the course of cancer (Ikeda et al. 2013; Pinquart & Duberstein 2010; Cassileth et al. 1988; Rendall et al. 2011); this has been shown to be especially strong for breast cancer (Falagas et al. 2007; Nausheen et al. 2009). There have been mixed results in the literature regarding the specific association of lung cancer survival and marital status. One study showed that marital status is an independent factor for predicting overall survival in both men and women (Kravdal & Syse 2011). However another found that marriage was not significantly predictive of survival (Siddiqui et al. 2010), and others found some benefits to marriage for men (Saito-Nakaya et al. 2008). The purpose of this study was to assess, using a large comprehensive population-based dataset, whether marital status is an independent predictor of lung cancer survival.

Methods

Data

Data from two databases (1996–2007) were linked via patient ID number to form the base dataset for this study: The Florida Cancer Data System (FCDS) data and Florida’s Agency for Health Care Administration (AHCA) dataset. The matches were confirmed with the patient’s date of birth and gender. In addition, patients’ residency was used to approximate patient level socioeconomic status (SES). From the U.S. Census, we obtained tract-level information on the percentage of households in a tract with income below the federal poverty line. Each tract was categorized as: lowest (≥20%), middle-low (≥10 and <20%), middle high (≥5 and < 10%), and highest (<5%) SES based on percentage of households in the tract living in poverty. Individuals living in each tract were assigned that tract’s SES level.

Diagnoses and procedure codes on all patients with lung cancer treated at Florida in- and out-patient hospitals and free-standing surgical and radiological treatment centers were obtained from the AHCA database (Agency for Healthcare Administration 2012).

The FCDS is a population-based registry mandated by law to report all cases of cancer in the state of Florida, with the exception of those diagnosed and treated by the Veterans Affairs. Approximately 95% of all incident cases of cancer are captured. Our sample is representative of the population of lung cancer patients in Florida. As we were only interested in lung cancer, we included only those cases coded as lung cancer in the registry. From FCDS data, we captured incident cases of lung cancer, stage of disease at diagnosis and other disease characteristics, medical history, patient demographics, and methods of treatment (Florida Cancer Data System 2012).

Although we used only lung cancer cases in Florida, using FCDS data has several advantages over the main alternative, which is SEER data. First, we had the ability to link the registry data to an administrative database, AHCA data, which enabled us to enrich our control variables with information on all diagnoses and procedures. Being able to account for all comorbidities is a major strength of the study. Second, although SEER-Medicare linked data is available and would have allowed for analyses that include diagnoses, this would largely be restricted to patients 65 years and older. Our population, on the other hand, covers an age range from 18 to 110 years old. As the development of cancer in those living below the poverty line, among tobacco users, and among certain minorities commonly occurs at a younger age, a restriction to 65 years and older with the SEER-Medicare data would be much more limiting.

Variables

Overall survival, our primary endpoint, was defined as time from diagnosis to date of death or last follow-up date.

FCDS data was used to determine date of death. If FCDS did not have a date of death, FCDS and AHCA data were compared to obtain the latest date of contact. Patients without a date of death were considered to have censored data and could either be alive, or be dead and have been lost to follow up in the FCDS through moving out of the state or some other means. Our main predictor of interest was marital status which was categorized as married, widowed, separated/divorced, or never married. Following the methodology of other studies (e.g., 9,14-16), we combined separated and divorced patients into one category. In Florida, legal separation is not necessary prior to getting divorced but there are provisions of the law whereby separated partners receive the same alimony and child support payments as do divorced partners. In addition, getting divorced in Florida is easy and quick, and so divorce may be as attractive an option as separation in some cases. Therefore, those in the separated and divorced categories are likely to be more similar to each other than to other categories. Also, as the total number in the separated category was small (3.2% of the total sample), it was not feasible to analyze them separately.

Other factors used as covariates in the regression models were added in a sequential-block stepwise fashion. Demographic characteristics included race (White, Black, Other), ethnicity (Hispanic, non-Hispanic), socioeconomic status (SES; lowest [≥20% of the tract living below the federal poverty line], middle-low [≥10% and <20%], middle-high [≥5% and <10%] and, highest [<5%]), gender, primary payer at diagnosis (private, Medicaid, Medicare, Defense/Military/Veteran, Indian Health System, uninsured, other), smoking status (never, history, current), treatment facility characteristics (teaching, non-teaching; high volume, low volume), and geographic location (rural, urban). Clinico-pathological characteristics were tumor grade (undifferentiated, poorly-differentiated, moderately-differentiated, well-differentiated, other), tumor SEER summary stage (localized, regional direct extension with or without lymph nodes, regional lymph nodes only, distant), lymph node status (positive, negative), type of treatments (chemotherapy [yes/no], radiation [yes/no], surgery [yes/no]), and type of cancer (non-small cell, small cell). The final block of covariates added to the full model was the 31 Elixhauser comorbid conditions (yes/no) based on ICD-9 codes in the AHCA database.

Population

Our sample included all patients ≥18 years diagnosed with lung cancer (1996–2007) in the state of Florida (n = 179,630). We continued to follow this cohort for a 3-year period through 2010 to determine whether patients had died in this follow-up period. Non-Florida residents and patients with missing values for marital status, race, ethnicity, or SES were excluded (n = 18,402), resulting in a total sample size of 161,228.

Statistical analyses

Chi-square tests for contingency tables were used to examine the association of categorical variables. Overall median survival time and 1-, 3-, and 5-year survival rates were estimated by the Kaplan-Meier method. Log-rank tests were used to compare the survival rates by marital status. Univariate and multivariate Cox proportional hazards regression models were used to obtain unadjusted and adjusted hazard ratios (HR) and 95% confidence intervals (95% CI). Models were adjusted by adding blocks of variables sequentially whereby model 1 was univariate with marital status as the sole explanatory variable; model 2 was multivariate adjusted for race, ethnicity, and SES; model 3 was model 2 plus all remaining demographic characteristics; model 4 was model 3 plus all clinico-pathologic characteristics; and model 5, the full model, was model 4 plus all comorbidities. Because the effect of marital status has been shown to vary by gender, we considered stratification by gender for our analyses. However, when testing for interactions between gender and marital status in the multivariate Cox regressions, no interactions were found. Therefore, gender was included as an independent predictor of survival in the models.

Patients treated in the same hospital or facility share some unmeasured characteristics that may affect clinical outcomes and therefore cannot be considered as independent observations. Thus, robust standard errors to adjust for clustering of patients within medical facilities were calculated for all models. The type-I error rate was set at 5%. The SAS v9.3 (SAS Institute Inc., Cary, NC) was used to perform all analyses. This project was approved by the University of Miami Institutional Review Board.

Results

Patient demographics and clinical variables

Sociodemographic and clinico-pathologic characteristics of the sample are reported in Tables 1 and 2. Overall, 54.6% of the patients were married, 19.1% widowed, 13.5% separated/divorced, and 12.7% never married. The majority of the patients were male (55.7%), White (92.5%), non-Hispanic (93.9%), and in the middle-high and highest SES category (54.8%). Widowed patients were the oldest (median age 7.62 years, range 23–105) followed by married (69 years, range 20–104) and never married (65 years, range 18–102). More married and widowed patients received Medicare insurance (58.4 and 76.3%, respectively) than did never married (35.8%) or separated/divorced patients (34.6%). Overall, 84.5% of the patients had more than 4 comorbidities; a larger proportion of married (87.6%) and widowed (88.2%) had more than 4 comorbidities than did never married (76.3%) or separated/divorced (74.2%). More married and widowed patients were diagnosed at the localized stage (18.3% and 18.2%, respectively) than separated/divorced (11.8%) and never married (11.3%). The proportion of patients with the more treatable non-small cell lung cancer was higher in married (64.5%) and widowed (60.2%) compared with separated/divorced (47.1%) and never married (51.1%).
Table 1

Demographic characteristics of lung cancer by marital status

Variable

 

All patients

Marital status at DX

    

Never married

 

Separated/Divorced

 

Widowed

 

Married

   

N

%

N

%

N

%

N

%

All patients

161,228

100.0

20,528

100.0

21,789

100.0

30,866

100.0

88,045

100.0

Marital status at DX

          

Never married

20,528

12.7

20,528

100.0

-

-

-

-

-

-

Separated/Divorced

21,789

13.5

-

-

21,789

100.0

-

-

-

-

Widowed

30,866

19.1

-

-

-

-

30,866

100.0

-

-

Married

88,045

54.6

-

-

-

-

-

-

88,045

100.0

Race

          

White

149,178

92.5

17,163

83.6

19,844

91.1

28,941

93.8

83,230

94.5

Black

10,975

6.8

3,227

15.7

1,826

8.4

1,767

5.7

4,155

4.7

Other

1,075

0.7

138

0.7

119

0.5

158

0.5

660

0.7

Hispanic origin

          

Non-Hispanic

151,442

93.9

18,783

91.5

20,442

93.8

29,520

95.6

82,697

93.9

Hispanic

9,786

6.1

1,745

8.5

1,347

6.2

1,346

4.4

5,348

6.1

SES

          

Lowest

20,668

12.8

4,674

22.8

3,723

17.1

3,755

12.2

8,516

9.7

Middle-Low

52,264

32.4

6,912

33.7

7,818

35.9

9,999

32.4

27,535

31.3

Middle-High

60,415

37.5

6,453

31.4

7,334

33.7

12,053

39.0

34,575

39.3

Highest

27,881

17.3

2,489

12.1

2,914

13.4

5,059

16.4

17,419

19.8

Vital status

          

Alive

21,919

13.6

2,376

11.6

2,332

10.7

3,685

11.9

13,526

15.4

Dead

139,309

86.4

18,152

88.4

19,457

89.3

27,181

88.1

74,519

84.6

FCDS tobacco use

          

Never smoke

14,001

8.7

1,409

6.9

1,068

4.9

3,683

11.9

7,841

8.9

History smoke

64,008

39.7

5,247

25.6

5,244

24.1

13,505

43.8

40,012

45.4

Current smoke

54,425

33.8

7,989

38.9

8,031

36.9

9,711

31.5

28,694

32.6

Unknown

28,794

17.9

5,883

28.7

7,446

34.2

3,967

12.9

11,498

13.1

Age at diagnosis

     

Mean

 

69.8

 

65.2

 

67.9

 

76.2

 

69.0

Std

 

11.2

 

12.6

 

12.1

 

8.7

 

10.4

Median

 

71.0

 

66.0

 

68.0

 

77.0

 

70.0

Q1

 

63.0

 

56.0

 

59.0

 

71.0

 

63.0

Q3

 

78.0

 

75.0

 

76.0

 

82.0

 

76.0

Min

 

18.0

 

18.0

 

25.0

 

23.0

 

20.0

Max

 

110.0

 

102.0

 

110.0

 

105.0

 

104.0

Sex

     

Female

71,386

44.3

7,233

35.2

11,256

51.7

22,236

72.0

30,661

34.8

Male

89,842

55.7

13,295

64.8

10,533

48.3

8,630

28.0

57,384

65.2

Insurance status

          

Uninsured

5,486

3.4

1,672

8.1

1,222

5.6

426

1.4

2,166

2.5

Private insurance

30,342

18.8

3,539

17.2

3,419

15.7

3,973

12.9

19,411

22.0

Medicaid

5,644

3.5

1,877

9.1

1,440

6.6

529

1.7

1,798

2.0

Medicare

89,820

55.7

7,349

35.8

7,536

34.6

23,553

76.3

51,382

58.4

Defense/Military/Veteran

2,385

1.5

341

1.7

290

1.3

233

0.8

1,521

1.7

Indian/Public

220

0.1

65

0.3

54

0.2

31

0.1

70

0.1

Insurance, NOS

10,491

6.5

1,232

6.0

1,210

5.6

1,040

3.4

7,009

8.0

Unknown

16,840

10.4

4,453

21.7

6,618

30.4

1,081

3.5

4,688

5.3

Urban Rural by zip code

          

Urban

150,025

93.1

18,998

92.5

20,259

93.0

28,966

93.8

81,802

92.9

Rural

11,203

6.9

1,530

7.5

1,530

7.0

1,900

6.2

6,243

7.1

AAMC 2005 teaching hospital

          

Non-teaching hospital

149,258

92.6

18,574

90.5

20,184

92.6

29,165

94.5

81,335

92.4

Teaching hospital

11,970

7.4

1,954

9.5

1,605

7.4

1,701

5.5

6,710

7.6

Hospital volume

          

Low

103,348

64.1

11,804

57.5

11,038

50.7

21,685

70.3

58,821

66.8

High

57,880

35.9

8,724

42.5

10,751

49.3

9,181

29.7

29,224

33.2

SES = Socioeconomic Status (percent living below poverty line); Lowest (≥20%); Middle-low (≥10% and <20%); Middle-high (≥5% and <10%); Highest (<5%).

Table 2

Pathological and clinical characteristics

Variable

 

All patients

Marital status at DX

    

Never married

 

Separated/Divorced

 

Widowed

 

Married

 

N

%

N

%

N

%

N

%

N

%

All

161,228

100.0

20,528

100.0

21,789

100.0

30,866

100.0

88,045

100.0

Co-morbidity

          

None

12,754

7.9

2,978

14.5

3,509

16.1

1,516

4.9

4,751

5.4

1 ~ 2

3,793

2.4

667

3.2

761

3.5

583

1.9

1,782

2.0

3 ~ 4

8,477

5.3

1,216

5.9

1,348

6.2

1,544

5.0

4,369

5.0

>4

136,204

84.5

15,667

76.3

16,171

74.2

27,223

88.2

77,143

87.6

SEER stage

          

Localized

26,672

16.5

2,316

11.3

2,572

11.8

5,632

18.2

16,152

18.3

Regional, direct extension ± lymph nodes

19,478

12.1

2,184

10.6

2,153

9.9

3,765

12.2

11,376

12.9

Regional, lymph nodes only

13,820

8.6

1,371

6.7

1,486

6.8

2,697

8.7

8,266

9.4

Distant

64,374

39.9

8,049

39.2

7,415

34.0

12,571

40.7

36,339

41.3

Unknown/Unstaged

36,884

22.9

6,608

32.2

8,163

37.5

6,201

20.1

15,912

18.1

Types of lung cancer

          

SCLC

20,073

12.5

2,250

11.0

2,358

10.8

4,012

13.0

11,453

13.0

NSCLC

96,134

59.6

10,493

51.1

10,270

47.1

18,589

60.2

56,782

64.5

Other

45,021

27.9

7,785

37.9

9,161

42.0

8,265

26.8

19,810

22.5

Grade

          

Undifferentiated

11,780

7.3

1,264

6.2

1,399

6.4

2,292

7.4

6,825

7.8

Poorly-differentiated

37,134

23.0

4,161

20.3

4,049

18.6

6,745

21.9

22,179

25.2

Moderately-differentiated

18,492

11.5

1,808

8.8

1,897

8.7

3,492

11.3

11,295

12.8

Well-differentiated

5,654

3.5

507

2.5

535

2.5

1,188

3.8

3,424

3.9

Unknown/not stated

88,168

54.7

12,788

62.3

13,909

63.8

17,149

55.6

44,322

50.3

Regional nodes positive

          

No

19,699

12.2

1,737

8.5

2,066

9.5

3,358

10.9

12,538

14.2

Yes

11,604

7.2

1,105

5.4

1,271

5.8

1,770

5.7

7,458

8.5

Unknown

129,925

80.6

17,686

86.2

18,452

84.7

25,738

83.4

68,049

77.3

Chemotherapy

          

No

93,242

57.8

10,371

50.5

9,716

44.6

22,128

71.7

51,027

58.0

Yes

51,037

31.7

5,855

28.5

5,933

27.2

7,395

24.0

31,854

36.2

Unknown

16,949

10.5

4,302

21.0

6,140

28.2

1,343

4.4

5,164

5.9

Radiation Therapy

          

No

46,765

29.0

5,948

29.0

5,054

23.2

12,691

41.1

23,072

26.2

Yes

102,232

63.4

10,955

53.4

11,154

51.2

17,615

57.1

62,508

71.0

Unknown

12,231

7.6

3,625

17.7

5,581

25.6

560

1.8

2,465

2.8

Surgery

          

No

114,045

70.7

13,607

66.3

12,571

57.7

24,659

79.9

63,208

71.8

Yes

34,896

21.6

3,144

15.3

3,534

16.2

5,794

18.8

22,424

25.5

Unknown

12,287

7.6

3,777

18.4

5,684

26.1

413

1.3

2,413

2.7

SES = Socioeconomic Status (percent living below poverty line); Lowest (≥20%); Middle-low (≥10% and <20%); Middle-high (≥5% and <10%); Highest (<5%).

Survival

Median survival time (MST) in months and survival rates at 1-, 3-, and 5-years post-diagnosis are displayed in Table 3 and Figure 1. Married patients had the longest MST (9.9 months), followed by widowed patients (7.7 months), while never separated/divorced patients had the shortest (4.1 months). The 1-year survival rate was longest for married (44.5%) and widowed (38.8%) patients, and markedly shortest for never married (31.5% and separated/divorced patients (30.6%). This pattern held for 3- and 5-year survival rates.
Table 3

Median survival time and survival rates, n = 161,228

 

Median survival (months)

Survival rates (%) at time (yrs) after diagnosis

1 yr

3 yrs

5 yrs

Overall

8.1

39.9

18.2

12.1

Marital status

    

Never married

4.9

31.5

13.0

8.4

Separated/Divorced

4.1

30.6

13.5

8.9

Widowed

7.7

38.8

17.2

10.7

Married

9.9

44.5

20.9

14.2

Race

    

White

8.1

40.1

18.5

12.3

Black

7.0

36.2

14.5

8.9

Other

10.2

46.2

20.1

12.5

Hispanic origin

    

No

8.0

39.8

18.2

12.1

Yes

8.4

40.5

17.9

12.1

SES

    

Lowest

6.5

34.8

13.8

8.7

Middle-Low

7.6

38.3

16.8

11.0

Middle-High

8.5

41.1

19.4

12.8

Highest

9.5

44.0

21.7

15.1

SES = Socioeconomic Status (percent living below poverty line); Lowest (≥20%); Middle-low (≥10% and <20%); Middle-high (≥5% and <10%); Highest (<5%).

https://static-content.springer.com/image/art%3A10.1186%2F2193-1801-2-504/MediaObjects/40064_2013_Article_1419_Fig1_HTML.jpg
Figure 1

This figure illustrates proportion surviving by marital status.

Regression analysis

Results from the 5 Cox proportional hazards regression models are shown in Table 4. In the univariate model, compared to never married, a protective effect was found for married (HR 0.70; 95% CI = 0.69-0.71) and widowed (HR 0.81; 95% CI = 0.80-0.83) patients, while separated/divorced patients had slightly worse survival (HR 1.03; 95% CI = 1.01-1.05). When the final model was adjusted for all covariates (model 5), being married (HR 0.85; 95% CI = 0.81-0.89) and widowed (HR 0.88; 95% CI = 0.84-0.93) remained positively associated with better survival compared with never married, and the detrimental association of separated/divorced (HR 1.05; 95% CI = 1.02-1.08) with survival remained.
Table 4

Proportional cox regression models, n = 161,228

  

Model 1

Model 2

Model 3

Model 4

Model 5

Prognostic factors

Category

HR (95% CI)

P value

HR (95% CI)

P value

HR (95% CI)

P value

HR (95% CI)

P value

HR (95% CI)

P value

Marital status

Never married

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 
 

Separated/ Divorced

1.03 (1.01, 1.05)

0.003

1.03 (0.89, 1.20)

0.654

1.03 (0.96, 1.10)

0.461

1.04 (1.01, 1.07)

0.008

1.05 (1.02, 1.08)

<.001

 

Widowed

0.81 (0.80, 0.83)

<.001

0.82 (0.59, 1.14)

0.240

0.77 (0.63, 0.94)

0.010

0.87 (0.82, 0.91)

<.001

0.88 (0.84, 0.93)

<.001

 

Married

0.70 (0.69, 0.71)

<.001

0.71 (0.53, 0.95)

0.021

0.70 (0.60, 0.83)

<.001

0.82 (0.78, 0.87)

<.001

0.85 (0.81, 0.89)

<.001

Race

White

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 
 

Black

1.12 (1.10, 1.14)

<.001

0.97 (0.88, 1.05)

0.438

1.04 (1.01, 1.07)

0.021

0.99 (0.95, 1.02)

0.472

0.99 (0.95, 1.02)

0.391

 

Other

0.91 (0.85, 0.97)

0.005

0.91 (0.85, 0.97)

0.007

1.00 (0.93, 1.08)

0.944

0.96 (0.89, 1.04)

0.314

0.85 (0.78, 0.93)

<.001

Hispanic origin

Non-Hispanic

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 
 

Hispanic

0.98 (0.96, 1.01)

0.148

0.93 (0.85, 1.02)

0.130

0.97 (0.90, 1.06)

0.499

0.94 (0.89, 0.99)

0.026

0.91 (0.86, 0.96)

<.001

SES

Lowest

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 
 

Middle-Low

0.90 (0.89, 0.92)

<.001

0.93 (0.90, 0.97)

<.001

0.95 (0.92, 0.98)

<.001

0.96 (0.93, 0.98)

0.002

0.96 (0.94, 0.99)

0.005

 

Middle-High

0.84 (0.83, 0.85)

<.001

0.88 (0.84, 0.93)

<.001

0.90 (0.86, 0.93)

<.001

0.92 (0.89, 0.95)

<.001

0.92 (0.90, 0.95)

<.001

 

Highest

0.77 (0.76, 0.79)

<.001

0.82 (0.77, 0.88)

<.001

0.85 (0.80, 0.91)

<.001

0.88 (0.85, 0.92)

<.001

0.89 (0.85, 0.92)

<.001

Model 1: Univariate.

Model 2: Multivariate only with Marital status + Race/Ethnicity/SES.

Model 3: Multivariate - Marital status + Race/Ethnicity/SES + demographics.

Model 4: Multivariate - Marital status + Race/Ethnicity/SES + demographics + clinical.

Model 5: Multivariate - Marital status + Race/Ethnicity/SES + demographics + clinical + individual comorbidities.

Notes: there is no interaction between marital status and race, ethnicity and SES respectively.

SES = Socioeconomic Status (percent living below poverty line); Lowest (≥20%); Middle-low (≥10% and <20%); Middle-high (≥5% and <10%); Highest (<5%).

Discussion

Previous research has shown an association between marital status and survival in lung cancer, and that this association may be increasing over time (Kravdal & Syse 2011). For example, California Cancer Registry data has been used to test for overall associations of survival with marital status in lung cancer patients. This research found that for both extensive stage SCLC (HR 1.179; p < 0.001) and NSCLC (HR 1.175; 95% CI = 1.122-1.229), there are significant survival differences between unmarried and married patients (Ou et al. 2008; Ou et al. 2009). However, there are inconsistencies in the results of studies that have explored the relative survival disadvantage of different unmarried status categories. In addition, not all studies have been able to control well for treatment and comorbidity confounding variables. Thus, the goal of this study was to explore the association of marital status with survival following a diagnosis of lung cancer using data that is representative of the Florida state population and which allows for controlling for all demographic, clinical and comorbid variables. Our main finding was that married and widowed Floridian patients with lung cancer have a survival benefit compared with those who had never married, and that separated/divorced patients had worse survival than never married patients. These findings remained significant after inclusion of all demographic, clinico-pathologic, treatment and comorbidity variables in a fully adjusted Cox regression model.

Our findings are in concordance with some, but not all of the previous literature. Similar to our findings, Manzoli et al. (Manzoli et al. 2007) found that separated/divorced cancer patients had the worst survival of any marital status group. Conversely, a number of other study have found that never-married patients have worse survival than both widowed and separated/divorced patients (Pinquart & Duberstein 2010; Kravdal & Syse 2011; Kravdal 2013; Kravdal 2001), at least for some categories of patients. Early data from Norway (women diagnosed with cancer between 1996 and 1990 (Kvikstad et al. 1995)) showed that divorced women had an overall increased hazard ratio of 1.17 (95% CI = 1.07-1.27) for cancers including lung cancer compared to married women, whereas widows had no increased risk. However, in 2001, Kravdal (Kravdal 2001) found that for Norwegian women with lung cancer, being widowed was associated with the worst survival outcomes (HR 1.19; 95% CI = 1.09-1.30) compared with married women. The same study showed that, for male lung cancer patients, never married status was associated with the worst outcomes (HR 1.23; 95% CI = 1.16-1.30), whereas widowhood was associated with only half that detrimental effect (HR 1.12; 95% CI = 1.10-1.20). In the most recent data from Norway, a status of never married was found to be worst for both men and women with lung cancer, but the order of the relationship of widowed and divorced/separated status to survival was different for men and women (Kravdal 2013).

Other studies have divided divorced and separated individuals into discrete categories. One such study found that separated status carried the worst survival outcomes for 5-year and 10-year relative survival for cancer patients – approximately 72% and 64% the survival time of married patients (Sprehn et al. 2009). Another study (Lai et al. 1999), which explored SEER data for each cancer type separately, found the relative risk scores (compared to married) to be 1.18 for single, 1.16 for separated, 1.13 for divorced, and 1.08 for widowed male lung cancer patients (all significant differences); but no significant difference among relative risk scores for females.

Although many studies have found differences, albeit in inconsistent ways, among the different categories of unmarried individuals, this is not true for across the board. A review of the effect of marriage on survival broadly (Rendall et al. 2011) found little or no differences between never married, separated/divorced, and widowed statuses. A study of lung cancer in Japan found no significant increased risk of death in widowed female lung cancer patients compared to married patients, and no significant increased risk of death for separated/divorced male or female patients compared to married patients although widowed males patients had increased risk of death (HR 1.7; 95% CI = 1.2-2.5) (Saito-Nakaya et al. 2008).

One way that our results differ from much of the previous findings in the literature e.g., (Kravdal & Syse 2011; Saito-Nakaya et al. 2008; Kravdal 2013; Lai et al. 1999) is that we did not find differences between men and women in the relationship between marital status and survival. As gender and marital status interaction term in our Cox regression was not significant, indicating that marital status has the same modifying effect on survival in both genders, although gender does have a significant direct effect on survival, with males having worse survival then females with lung cancer (results not shown). The reason for this difference in our population from previous findings is unclear.

Our findings and these others suggest that some aspect of marriage and social networks in general seem to afford patients a comparatively longer time before succumbing to a disease. Previous studies on marriage and survival focused on the social support benefits that married couples have compared with never married or divorced/separated. For example, Pinquart (Pinquart & Duberstein 2010) posited that social networks, which would include marriage, would have effects on: biological pathways (neuroendocrine or neuro-immune pathways), health behaviors, access to health care systems and assistance with navigating its complexities, the likelihood of receiving vigorous and aggressive, active cancer treatment, and psychological consequences. All of these could have direct and/or indirect effects on survival. Empirically, Luszczynska, et al. (Luszczynska et al. 2012) found that patients with perceived/received family support had improved psychological and physical quality of life. Stress-related psychosocial factors have been shown to have a deleterious effect on survival in patients with lung cancer (Chida et al. 2008). Taniguchi et al. (Taniguchi et al. 2003) found that men who were not married had more psychological distress than married men (Umberson 1992). Lastly, married couples have been shown to engage in healthier lifestyle behaviors and less risky behaviors compared with unmarried couples (Krieger 1992).

This study had some limitations. It was a cross-sectional study so causality could not be assessed. However, as this was a linkage of databases some of the information was collected at a later time period. The databases that we have access to do not have individual-level indicators of SES; therefore, we used neighborhood-level poverty as a proxy. However, using neighborhood indicators of SES has been shown to be a valid and reliable methodology (29). Also, marital status was determined only at the time of diagnosis and patients’ status may have changed over time.

Our study showing marital status is a strong independent predictor of survival was unique in that we had a linkage of two large databases: 1) the FCDS registry containing incident cancer cases plus other demographic information and 2) AHCA database, providing codes for diagnoses and procedures received as the patient went forward with treatments for a large age range of patients (18–110 years). In addition, we had valid proxy of individual SES information utilizing information from the U.S. Census. With this information we were able to control for demographic and clinico-pathological characteristics, (i.e., tumor characteristics, hospital type, treatments) as well as comprehensive comorbidities.

Conclusions

We found strong evidence that married and widowed patients with lung cancer fare better in terms of survival than those who never married even after adjusting for some extensive factors including some associated with social support, whereas divorced/separated patients did worse. This suggests that some other factor(s) associated with marriage – even after the marriage has ended through widowhood, but not divorce or separation– are associated with survival. Further research to fully understand these factors and how the beneficial effect can be extended to those who have never been married or have had marriage terminated through separation or divorce is needed.

Declarations

Acknowledgments

Funding for this study was provided by James & Esther King Florida Biomedical Research Program (Grant 10KG-06).

Authors’ Affiliations

(1)
Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami
(2)
Department of Public Health Sciences, Miller School of Medicine, University of Miami
(3)
Department of Surgery, Miller School of Medicine, University of Miami

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© Tannenbaum et al.; licensee Springer. 2013

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.