Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Self-rated health in multimorbid older general practice patients: a cross-sectional study in Germany

  • Anna Nützel1Email author,
  • Anne Dahlhaus2,
  • Angela Fuchs3,
  • Jochen Gensichen4,
  • Hans-Helmut König5,
  • Steffi Riedel-Heller6,
  • Wolfgang Maier7,
  • Ingmar Schäfer8,
  • Gerhard Schön9,
  • Siegfried Weyerer10,
  • Birgitt Wiese11,
  • Martin Scherer8,
  • Hendrik van den Bussche8 and
  • Horst Bickel1
BMC Family Practice201415:1

DOI: 10.1186/1471-2296-15-1

Received: 24 May 2013

Accepted: 17 December 2013

Published: 3 January 2014

Abstract

Background

With increasing life expectancy the number of people affected by multimorbidity rises. Knowledge of factors associated with health-related quality of life in multimorbid people is scarce. We aimed to identify the factors that are associated with self-rated health (SRH) in aged multimorbid primary care patients.

Methods

Cross-sectional study with 3,189 multimorbid primary care patients aged from 65 to 85 years recruited in 158 general practices in 8 study centers in Germany. Information about morbidity, risk factors, resources, functional status and socio-economic data were collected in face-to-face interviews. Factors associated with SRH were identified by multivariable regression analyses.

Results

Depression, somatization, pain, limitations of instrumental activities (iADL), age, distress and Body Mass Index (BMI) were inversely related with SRH. Higher levels of physical activity, income and self-efficacy expectation had a positive association with SRH. The only chronic diseases remaining in the final model were Parkinson’s disease and neuropathies. The final model accounted for 35% variance of SRH. Separate analyses for men and women detected some similarities; however, gender specific variation existed for several factors.

Conclusion

In multimorbid patients symptoms and consequences of diseases such as pain and activity limitations, as well as depression, seem to be far stronger associated with SRH than the diseases themselves. High income and self-efficacy expectation are independently associated with better SRH and high BMI and age with low SRH.

Trial registration

MultiCare Cohort study registration: ISRCTN89818205.

Keywords

Quality of life Self-assessment Chronic disease Depression Pain Functionally- impaired elderly General practice

Background

Multimorbidity is an issue of increasing importance to the health care system. The prevalence of multimorbidity rises from 10 percent in the 0 to 19 year-olds up to 78 percent in people aged 80 years and older [1]. As a consequence of an increasing life expectancy, the number of people affected by multimorbidity will probably steadily grow. However, little is known about the impact of multimorbidity on health-related quality of life of elderly primary care patients.

A persons’ own subjective rating of their health status was found to be an important predictor of morbidity and mortality, as well as a useful indicator of health-related quality of life [2, 3].

Factors associated with health-related quality of life have been studied in the general population as well as in patient-populations suffering from different chronic diseases. Several studies indicated a positive association between higher education and income with better self-rated health (SRH) [46]. The relationship between age, gender and SRH, however, is less clear [57]. Modifiable lifestyle factors, such as obesity, smoking, risky alcohol consumption and low levels of physical activity were found to have a negative correlation with health-related quality of life [810]. Furthermore, several studies concurrently report a negative association between depressive symptoms and chronic pain and SRH [11, 12], respectively. In recent years, research has particularly focused on the relationship between different chronic diseases and health-related quality of life. Neurological diseases, cancer and rheumatoid arthritis have been reported as conditions associated with low SRH among the elderly [13]. With increasing age the prevalence of chronic diseases, disability and limitations of activities of daily living rises, with limitations of daily activities being associated with lower levels of SRH [14].

Although a lot is known about factors correlated with SRH in the general population, knowledge of corresponding factors in aged multimorbid patients is scarce. Important questions remain: Is SRH in multimorbid patients more strongly affected by the presence of single disease states or by the sequelae of illnesses (i.e. pain, limitations of daily living)? What are demographic (i.e. age, education and income), lifestyle (i.e. BMI, smoking, alcohol consumption, level of physical activity) and psychological factors (i.e. depression, social support and self-efficacy expectation) associated with SRH in this patient group? Is SRH determined by the same factors in men and women or are there gender-specific differences? Therefore, the aim of this study was twofold: First, to identify the factors that are independently related to SRH in a multimorbid primary care sample of elderly people; and second, to identify possible gender-related differences in these factors.

Methods

Study design

Multimorbidity is usually defined as the presence of two or more illnesses at the same time. In the inspection of the diagnosis distribution it however became clear that two or more chronic illnesses were present in practically all our elderly patients. We thereupon defined multimorbidity as the presence of at least three chronic illnesses. In addition, in order to ensure a large number of patterns of multimorbidity, the very frequent illnesses with a prevalence of over 25% (e.g. hypertension, hyperlipidemia) were not considered for the inclusion in the sample. Nevertheless, these highly prevalent diagnoses are frequently combined with the relatively lower prevalent ones and are therefore still part of the sample. A detailed list of the 29 diseases used for inclusion of multimorbid patients can be found elsewhere [15]. This list was newly compiled at the beginning of the MultiCare-Study and represents the most frequent chronic conditions in the population based on prevalence data.

Data analyzed in this study came from the baseline investigation of the German MultiCare-Study [15], conducted from July 2008 to October 2009.

Patients were recruited in 8 study centers across Germany (Bonn, Düsseldorf, Frankfurt/Main, Hamburg, Jena, Leipzig, Mannheim and Munich). In each city about 20 general practitiosners (GPs) were recruited and asked to provide the study group with a list containing all of their patients between 65 and 85 years (date of birth 1.7.1923 to 30.6.1943), who had at least one consultation in the most recent quarter.

In each surgery, approximately 50 patients of those who suffered from at least three different chronic diseases out of a reference list of 29 chronic conditions [15] and did not meet the exclusion criteria (see below), were drawn at random. Multimorbidity was determined by chart review. These patients were contacted and asked to participate.

Exclusion criteria were:

 Residence in a nursing home

 Severe illness probably lethal within three months according to the GP

 Insufficient ability to speak and read German language

 Insufficient ability to consent (e.g. due to dementia)

 Insufficient ability to participate in interviews (e.g. due to blindness, deafness)

 Patients with no regular consultations and therefore poorly known to the GP

 Participation in other studies

Data collection

Patients who met the inclusion criteria and were willing to participate were visited at home or in the GP practice and interviewed by a trained investigator. A set of standardized questionnaires was used to collect variables belonging to the area of socio-demography, lifestyle, psychological and illness-related factors. Table 1 provides an overview of the standardized instruments. The GPs measured height and weight at the patients’ next routine consultation in the surgery.
Table 1

Description of the instruments

Abbreviation of the instrument

Function and interpretation

AUDIT-C

Alcohol Use Disorders Identification Test [20]; 3 items with 5 possible response categories (0 to 4 points); interpretation: total score (max. 12 points): ≤ 7 points no suspicion of an alcohol related disorder, ≥ 8 points: suspicion of an alcohol related disorder

Barthel-Index

Measures performance in basic activities of daily living [26]; 10 items with 3 categories each (0, 5 and 10 points); interpretation: total score (max. 100 points); 0 to 30 points: largely dependent from others, 35 to 80 points: need of care, 85 to 95: punctual need of care, 100: independent from others

BMI

Body-Mass-Index (weight (kg)/height (m2)); interpretation: BMI < 18.5: underweight, 18.5 to < 25: normal weight, 25 to < 30: overweight and > 30: obesity

VAS of EQ-5D

Visual analogous scale of the EuroQoL-5D (EQ-5D) [16] measures subjective health related quality of life on a scale from “0” representing the worst to “100” representing the best possible health status; interpretation: higher scores represent higher rates of subjective health

F-SOZU K-14

Social support questionnaire [21]; 14 items; 5 point scale; mean of the sum of all items; interpretation: high scores indicate high social support

GCPS

Graded chronic pain scale [25]; 8 items, scale 0 to 10; 2 total scores: Characteristic pain intensity and Disability score; interpretation: higher scores represent higher pain intensity and higher disability caused by pain respectively

GDS

Geriatric Depression Scale [23]; 15 items; 0 vs. 1 point per item; max. 15 points; interpretation: 0 to 5 points: unsuspicious, ≥ 6 points: depressive episode likely

IADL

Instrumental Activities of Daily Living [27]; 8 items; 0 vs. 1 point per item; total score: men: item 1–2 and 6–8 (max. 5 points), women: item 1–8 (max. 8 points); interpretation: males: score < 5: with limitations, females: score < 8: with limitations

IPAQ-7

International Physical Activity Questionnaire [19]; 6 items buildup 3 scores: time spent on vigorous activity (weighting coefficient 8.0), on moderate activity (weighting coefficient 4.0) and on walking (weighting coefficient 3.3); “Total Metabolic Equivalent Task (MET)-minutes/week” calculated as follows: minutes x weighting coefficient; interpretation: low scores indicating low and high scores indicating high physical activity

SWE

Self-Efficacy Scale [22]; 10 items on a four-point scale (1 to 4 points); total score: sum of the 10 items divided by 10; interpretation: high scores indicating high self-efficacy expectation

4 DBL

Four-dimensional symptom questionnaire [24]; 50 items with 5 point scale; 4 sub scores reflecting the factors “somatization” (16 items), “anxiety” (12 items), “depression” (6 items) and “distress” (16 items); interpretation: somatization >10: moderate, >20 high; anxiety: >8: moderate, >12: high; depression: >2: moderate, >5: high; general distress: >10: moderate, >20: high

Self-rated health

SRH was measured with the visual analogous scale (VAS) of the EuroQoL-5D (EQ-5D) [16]. With one single question (“In general, how would you rate your health status today?”) patients were asked to rate their subjective health status on a scale from “0” representing the worst to “100” representing the best possible health status.

Socio-demographic variables

Socio-demographic variables included age, gender, marital status and living conditions. Educational level was categorized as low, intermediate or high according to the CASMIN (Comparative Analysis of Social Mobility in Industrial Nations) classification [17], a certificate-orientated classification scheme developed by an international research group. Equivalent income was calculated from the total household income by using the per capita demand weighting scale [18] of the Organization for Economic Cooperation and Development (OECD). It was calculated as household total net income per month divided by the equivalised household size, which gives 1.0 to the householder, 0.5 to other household members aged 15 or over and 0.3 to each child aged less than 15 years old.

Lifestyle variables

Lifestyle variables included physical activity (IPAQ-7) [19], alcohol consumption (AUDIT-C) [20], smoking behavior and Body Mass Index (BMI). Smoking behavior was assessed by asking the patient the following question: “Are you a regular smoker, occasional smoker, former smoker or non-smoker?”

Psychological characteristics

Psychological variables ascertained in the patient-interview included perceived social support (F-SOZU K-14) [21], patients’ self-efficacy expectation (SWE) [22], depression (GDS) [23] and symptoms belonging to the four dimensions somatization, depression, anxiety and distress (4 DBL) [24].

Illness related factors

Patients were asked for the presence of chronic diseases. The interviewer read 32 chronic diseases (Table 2) to the patients who respectively responded with either “yes” or “no” to indicate whether they did or did not suffer from a particular chronic disease. Further, pain intensity and pain associated disability (GCPS) [25] were assessed. Additionally, grade of independence in performing basic (Barthel-Index) [26] and instrumental activities of daily living (iADL) [27] were illness-related factors that were rated by the interviewer.
Table 2

Prevalence of self-reported diagnoses by gender and for the whole sample

Diagnosis group

Total (n = 3189)

Men (n = 1298)

Women (n = 1891)

p value 3

Hypertension

2307 (72.3%)

939 (72.3%)

1368 (72.3%)

n.s.

Joint arthrosis

2115 (66.4%)

718 (55.4%)

1397 (73.9%)

< 0.001

Chronic low back pain

1975 (62.0%)

700 (54.1%)

1275 (67.5%)

< 0.001

Lipid metabolism disorders

1460 (45.9%)

617 (47.6%)

843 (44.7%)

n.s.

Chronic ischemic heart disease

963 (30.3%)

549 (42.3%)

414 (22.0%)

< 0.001

Severe vision reduction

1396 (43.9%)

515 (39.7%)

881 (46.8%)

< 0.001

Prostatic hyperplasia

511 (20.8%)

511 (39.6%)

--

--

Diabetes mellitus

992 (31.2%)

479 (37.0%)

513 (27.2%)

< 0.001

Cardiac arrhythmia

1044 (32.8%)

451 (34.8%)

595 (31.5%)

n.s.

Neuropathies

1114 (34.9%)

426 (32.8%)

688 (36.4%)

0.041

Dizziness

1109 (34.8%)

381 (29.4%)

728 (38.5%)

< 0.001

Lower limb varicosis

1148 (36.0%)

321 (24.8%)

827 (43.8%)

< 0.001

Asthma/COPD1

696 (21.8%)

280 (21.6%)

416 (22.0%)

n.s.

Purine/pyrimidine metabolism disorders/Gout

536 (16.9%)

269 (20.8%)

267 (14.2%)

< 0.001

Haemorrhoids

727 (22.8%)

246 (19.0%)

481 (25.5%)

< 0.001

Cerebral ischemia/Chronic stroke

444 (13.9%)

225 (17.4%)

219 (11.6%)

< 0.001

Cardiac insufficiency

548 (17.2%)

214 (16.5%)

334 (17.7%)

n.s.

Atherosclerosis/PAOD2

347 (10.9%)

193 (14.9%)

154 (8.2%)

< 0.001

Thyroid dysfunction

991 (31.1%)

192 (14.8%)

799 (42.3%)

< 0.001

Cancer

332 (10.4%)

171 (13.2%)

161 (8.8%)

< 0.001

Noninflammatory gynaecological problems

246 (9.1%)

--

246 (13.1%)

--

Renal insufficiency

307 (9.6%)

150 (11.6%)

157 (8.3%)

0.003

Cardiac valve disorders

314 (9.9%)

139 (10.7%)

175 (9.3%)

n.s.

Intestinal diverticulosis

435 (13.7%)

138 (10.6%)

297 (15.7%)

< 0.001

Psoriasis

213 (6.7%)

117 (9.0%)

96 (5.1%)

< 0.001

Rheumatoid arthritis/Chronic polyarthritis

410 (12.9%)

112 (8.7%)

298 (15.8%)

< 0.001

Osteoporosis

690 (21.7%)

97 (7.5%)

593 (31.4%)

< 0.001

Chronic cholecystitis/Gallstones

271 (8.5%)

78 (6.0%)

193 (10.2%)

< 0.001

Urinary tract stones

124 (3.9%)

63 (4.9%)

61 (3.2%)

0.025

Anemia

169 (5.3%)

54 (4.2%)

115 (6.1%)

0.019

Migraine/chronic headache

166 (5.2%)

37 (2.9%)

129 (6.8%)

< 0.001

Parkinson’s disease

67 (2.1%)

36 (2.8%)

31 (1.6%)

0.032

Note: 1: chronic obstructive pulmonary disease; 2: peripheral arterial occlusive disease;

3: χ2-test, df = 1, two-sided p, n.s. = not significant (p > 0.05).

Missing values

Missing values were substituted by the hot-deck method. This technique identifies the most similar case in the sample (nearest neighbor distance) and uses this value for imputation [28]. If more than one case were possible for the imputation one case was selected by chance. Imputation was done with the R - 2.13.0 package StatMatch [29]. A detailed description of the substitution process can be found elsewhere [30]. For all variables the missing value rate was less than 2 percent except the variable total household income which had a missing value rate of 12.2 percent.

Data analysis

The data were analyzed using the Statistical Package for the Social Sciences (SPSS, Version 19.0). Means (M) and standard deviations (SD) were calculated for continuous variables and frequencies, as well as percentages, for categorical variables. Group differences were tested for statistical significance either by χ2-test or t-test as appropriate. Linearity of the relationship between independent variables and SRH was controlled by visual inspection. Bivariate associations between risk factors and SRH were analyzed by Pearson’s correlation and differences by t-test.

Variables that showed a significance level of p ≤ 0.01 in the bivariate analyses were entered into multivariable linear regression analyses in a stepwise forward manner, with SRH as the dependent variable. Multivariable linear regression analyses were performed for the whole sample as well as for men and women separately.

Ethical considerations

The study protocol was approved by the Ethics Committee of the Medical Association of Hamburg (Approval Nr. 2881) and by the Ethics Committees of the participating study centers. Written consent was obtained from every participant after being completely informed about the study.

Results

A total of 50,786 patients in the database of the participating GPs fulfilled the age criterion and had at least one GP contact in the last quarter. Out of those 24,862 were randomly selected and checked for the presence of at least three chronic diseases and exclusion criteria. After exclusion of the patients without multimorbidity and those who met the exclusion criteria, 7,172 patients remained and were contacted. Out of those 3,317 agreed to participate (response rate 46.2%). In total, data of 3,189 patients were included in the final analyses. The difference of 128 cases between the 3,317 patients who agreed to participate and the 3,189 whose data it was possible to include in the statistical analysis is due to the fact that patients died before they could be interviewed or that exclusion criteria became obvious only after sample selection. A more detailed description of sampling and response rate, as well as a non-responder analysis can be found elsewhere [30].

Characterization of the study population

Table 3 summarizes the mean values in demographic, lifestyle and psychological variables separately for men and women. Women represented 59.3% of the study participants. Mean EQ-VAS value of the whole sample was 62.5 (SD = 18.2); men had a significant higher mean SRH of 63.6 (SD 18.4) compared to 61.6 (SD 18.0) in women (p = 0.003, see Table 4). The prevalence of self-reported diseases is presented in Table 2. Hypertension (present in 72.3%), joint arthrosis (present in 55.4% of the men and 73.9% of the women) and chronic low back pain (present in 54.1% of the men and 67.5% of the women) were the most frequently reported diseases in the study population.
Table 3

Characteristics of the study population

 

Total (n = 3189)

Men (n = 1298)

Women (n = 1891)

P value2

 

Self-rated health (SRH) (Mean (SD))

62.4 (18.2)

63.6 (18.4)

61.6 (18.0)

0.003

 

Demographic variables

     

Age, in years (Mean (SD))

74.4 (5.2)

74.0 (5.1)

74.7 (5.3)

0.001

 

Marital status (N (%))

     

  Married

1863 (58.4%)

1026 (79.0)

837 (44.3)

  

  Single

188 (5.9%)

56 (4.3)

132 (7.0)

  

  Divorced

256 (8.0%)

74 (5.7)

182 (9.6)

  

  Widowed

882 (27.7%)

142 (10.9)

740 (39.1)

< 0.001

(df = 3)

Living conditions (N (%))

     

  One person household

1128 (35.4%)

229 (17.6)

899 (47.5)

  

  Living with partner/spouse

1847 (57.9%)

1021 (78.7)

826 (43.7)

  

  Living with others 1

214 (6.7%)

48 (3.7)

166 (8.8)

< 0.001

(df = 2)

Education (N (%))

     

  Low

1986 (62.3%)

753 (58.0)

1233 (65.2)

  

  Intermediate

856 (26.8%)

306 (23.6)

550 (29.1)

  

  High

347 (10.9%)

239 (18.4)

108 (5.7)

< 0.001

(df = 2)

Monthly income (in Euro) (Mean (SD))

1412.2 (705.9)

1517.0 (833.0)

1340.3 (593.0)

< 0.001

 

Lifestyle variables

     

Smoking behavior (N (%))

   

  Current smoker

292 (9.2%)

153 (11.8)

139 (7.3)

  

  Former smoker

1361 (42.7%)

819 (63.1)

542 (28.7)

  

  Non smoker

1532 (48.0%)

324 (25.0)

1208 (63.9)

< 0.001

(df = 2)

Body mass index (Mean (SD))

28.2 (4.9)

28.1 (4.0)

28.3 (5.4)

n.s.

 

Alcohol habits score (Mean (SD))

2.2 (1.9)

3.0 (2.2)

1.6 (1.5)

< 0.001

 

Physical activity (in 1000 MET minutes/week) (Mean (SD))

2.2 (2.5)

2.6 (2.8)

1.9 (2.2)

< 0.001

 

Psychological variables

     

Self-efficacy (SWE) (Mean (SD))

3.3 (0.6)

3.4 (0.5)

3.2 (0.6)

< 0.001

 

Social support (F-SOZU) (Mean (SD))

4.1 (0.7)

4.1 (0.7)

4.1 (0.7)

n.s.

 

Depression (GDS) (Mean (SD))

2.6 (2.6)

2.3 (2.5)

2.8 (2.7)

< 0.001

 

Somatization (4DBL) (Mean (SD))

7.0 (5.1)

5.7 (4.7)

7.8 (5.2)

< 0.001

 

Anxiety (4DBL) (Mean (SD))

1.0 (2.2)

0.6 (1.7)

1.3 (2.4)

< 0.001

 

Depression (4DBL) (Mean (SD))

0.8 (2.0)

0.7 (1.8)

1.0 (2.1)

< 0.001

 

Distress (4DBL) (Mean (SD))

5.9 (5.3)

4.7 (4.7)

6.8 (5.5)

< 0.001

 

Disease-related variables

     

Characteristic pain intensity (GCPS) (Mean (SD))

34.5 (25.5)

28.1 (24.7)

39.0 (25.2)

< 0.001

 

Disability score (GCPS) (Mean (SD))

26.0 (29.2)

20.2 (27.3)

30.0 (29.2)

< 0.001

 

1: Living with others: including living together with other family members or other persons and living in assisted living or in retirement home; 2 χ2-test or t-test as appropriate, two-sided p, n.s. = not significant (p > 0.05), df: degrees of freedom.

Table 4

Self-rated health of the whole sample in relation to demographic variables and self-reported diseases

Variables

Mean (N)

SD

Mean (N)

SD

pvalue

Effect size

Self-reported diseases

    Present

     Absent

  

Hypertension

61.7 (2307)

18.0

64.3 (882)

18.5

<0.001

0.14

Joint arthrosis

60.0 (2115)

17.9

67.2 (1072)

17.8

<0.001

0.40

Chronic low back pain

59.1 (1975)

18.2

68.0 (1208)

16.9

<0.001

0.49

Chronic ischemic heart disease

59.4 (963)

18.8

63.7 (2220)

17.8

<0.001

0.24

Severe vision reduction

60.8 (1396)

18.3

63.7 (1786)

18.0

<0.001

0.12

Diabetes mellitus

61.0 (992)

18.0

63.1 (2188)

18.3

0.004

0.12

Cardiac arrhythmia

59.0 (1046)

18.2

64.1 (2139)

18.0

<0.001

0.28

Neuropathies

57.1 (1114)

18.2

65.2 (2075)

17.6

<0.001

0.45

Dizziness

57.1 (1109)

17.9

65.3 (2078)

17.7

<0.001

0.45

Lower limb varicosis

61.1 (1148)

18.4

63.2 (2037)

18.0

0.002

0.12

Asthma/COPD1

58.0 (696)

18.5

63.7 (2491)

17.9

<0.001

0.31

Purine/pyrimidine metabolism disorders/Gout

57.7 (536)

18.1

63.4 (2643)

18.1

<0.001

0.31

Hemorrhoids

59.9 (727)

18.4

63.2 (2459)

18.1

<0.001

0.18

Cerebral ischemia/Chronic stroke

59.3 (444)

18.4

62.9 (2742)

18.1

<0.001

0.20

Cardiac insufficiency

55.7 (548)

18.4

63.8 (2632)

17.8

<0.001

0.45

Atherosclerosis/PAOD2

54.6 (347)

19.9

63.4 (2835)

17.8

<0.001

0.48

Renal insufficiency

55.1 (307)

19.1

63.2 (2875)

17.9

<0.001

0.45

Intestinal diverticulosis

60.8 (435)

17.7

62.7 (2751)

18.3

0.046

0.10

Rheumatoid arthritis

55.0 (410)

18.8

63.5 (2768)

17.8

<0.001

0.47

Osteoporosis

57.4 (690)

18.7

63.8 (2496)

17.8

<0.001

0.35

Chronic cholecystitis/Gallstones

59.1 (271)

18.4

62.7 (2917)

18.2

0.002

0.20

Anemia

55.2 (169)

18.1

62.8 (3017)

18.1

<0.001

0.42

Migraine

58.8 (166)

18.6

62.6 (3022)

18.2

0.008

0.21

Parkinson’s disease

49.5 (67)

18.4

62.7 (3121)

18.1

<0.001

0.73

Lipid metabolism disorders

61.8 (1460)

17.9

62.9 (1721)

18.4

0.10

0.06

Thyroid dysfunction

61.7 (991)

18.2

62.7 (2196)

18.2

0.15

0.05

Prostate hyperplasia

63.3 (510)

17.7

63.8 (779)

18.8

0.63

0.03

Non-inflammatory gynecological problems

60.8 (245)

19.7

61.7 (1631)

17.8

0.45

0.05

Cardiac valve disorders

61.4 (314)

18.4

62.5 (2872)

18.2

0.31

0.06

Kidney stones

60.7 (124)

18.5

62.5 (3063)

18.2

0.29

0.10

Psoriasis

61.4 (213)

19.2

62.5 (2976)

18.1

0.39

0.06

Malignant tumors

60.9 (332)

18.0

62.6 (2850)

18.2

0.11

0.09

Sex

    Males

     Females

  
 

63.6 (1298)

18.4

61.6 (1891)

18.0

0.003

0.11

Marital status

    Married

     Non married

  
 

63.3 (1863)

18.2

61.2 (1326)

18.2

0.001

0.12

Household type

    With others

 

    Living alone

   
 

63.0 (2061)

18.2

61.3 (1128)

18.2

0.011

0.09

Education

    High

     Low/Intermediate

  
 

67.1 (347)

18.7

61.8 (2842)

18.1

<0.001

0.29

Smoking behavior

    Smoker

     Non-smoker

  
 

61.5 (237)

18.4

62.5 (2948)

18.2

0.42

0.05

Note: By t-test differences in SRH between different groups were tested for significance; confidence interval: 99%; only significant results are shown in the table.

Marital status: not married includes: single, divorced, widowed; household type: living with others includes: with partner, family members and in an institution; 1: chronic obstructive pulmonary disease; 2: peripheral arterial occlusive disease.

Bivariate analyses

The bivariate analyses suggested that all continuous variables showed weak but highly significant (p < 0.001) correlations with the dependent variable SRH (Table 5). Similarly, for all demographic and lifestyle variables and for most individual chronic diseases significant differences in SRH values were found. Table 4 presents differences between groups. Age and female gender was associated with lower SRH values and income, education, being married and living together with others are socio-demographic factors that were positively associated with SRH. A negative association was found between depression, anxiety, somatization and mental distress and SRH. Self-efficacy expectation and social support were psychological factors positively associated with SRH. Among the variables which showed no significant differences in mean SRH values were smoking and the presence or absence of lipid metabolism disorders, thyroid dysfunction, prostate hyperplasia, non-inflammatory gynecological problems, cardiac valve disorders, kidney stones, psoriasis and malignant tumors.
Table 5

Correlation (Pearson correlation coefficient (r)) of patient characteristics with self-rated health

Characteristics

Men (n = 1298)

Women (n = 1891)

Total (n = 3189)

Socioeconomic variables

   

Age

−0.08 **

−0.13 ***

−0.11 ***

Income

0.12 ***

0.13 ***

0.13 ***

Lifestyle variables

   

Body mass index (BMI)

−0.13 ***

−0.16 ***

−0.15 ***

Alcohol habit score (AUDIT-C)

0.10 **

0.13 ***

0.12 ***

Physical activity (IPAQ-7)

0.26 ***

0.22 ***

0.24 ***

Psychological variables

   

Self-efficacy (SWE)

0.24 ***

0.20 ***

0.22 ***

Social support (F-SOZU)

0.15 ***

0.20 ***

0.18 ***

Depression (GDS)

−0.42 ***

−0.40 ***

−0.41 ***

Somatization (4DBL)

−0.42 ***

−0.43 ***

−0.43 ***

Anxiety (4DBL)

−0.26 ***

−0.22 ***

−0.23 ***

Depression (4DBL)

−0.27 ***

−0.30 ***

−0.29 ***

Distress (4DBL)

−0.36 ***

−0.37 ***

−0.37 ***

Disease-related variables

   

Basic activities of daily living (Barthel)

0.29 ***

0.23 ***

0.25 ***

Instrumental activities (IADL)

0.29 ***

0.27 ***

-- 1

Characteristic pain intensity (GCPS)

−0.39 ***

−0.42 ***

−0.41 ***

Pain Disability score (GCPS)

−0.43 ***

−0.47 ***

−0.46 ***

Note: *** P values are <0.001; ** P values are <0.01; 1 correlation for the whole sample not shown because of different numbers of items for men and women, respectively.

Multiple regression analyses

Table 6 shows the factors remaining in the final regression model when the whole sample was analyzed. These factors explained 35% of the variance in SRH. The intensity of chronic pain, pain associated disability, depressive symptoms, somatization, high BMI levels (all p < 0.001), age (p = 0.001), psychological distress (p = 0.01), a self-reported diagnosis of Parkinson’s disease (p = 0.003) and neuropathies (p = 0.008) had a significant negative effect on SRH. Significantly positive relationships were found between SRH and physical activity, independency in instrumental activities, higher monthly income (all p < 0.001) and self-efficacy expectation (p = 0.002).
Table 6

Correlates of self-rated health in the whole sample: results of a stepwise linear multiple regression model

 

Regression coefficient B (95% CI)

Standardized coefficient (beta)

p value

Disability score (GCPS)

−0.11 (−0.15/-0.08)

−0.18

<0.001

Depression (GDS)

−0.96 (−1.31/-0.60)

−0.14

<0.001

Somatization (4DBL)

−0.61 (−0.79/-0.43)

−0.17

<0.001

Physical activity (IPAQ-7) (in 1000 MET minutes/week)

1.00 (0.38/0.97)

0.09

<0.001

Instrumental activities (IADL)

1.11 (0.70/1.51)

0.11

<0.001

Characteristic pain intensity (GCPS)

−0.07 (−0.11/-0.03)

−0.09

<0.001

Monthly income (in 1000 Euro)

2.00 (0.56/2.51)

0.06

<0.001

BMI

−0.24 (−0.38/-0.09)

−0.06

<0.001

Age

−0.18 (−0.32/-0.05)

−0.05

0.001

Self-efficacy (SWE)

1.60 (0.27/2.94)

0.05

0.002

Parkinson’s disease

−5.72 (−10.63/-0.80)

−0.04

0.003

Neuropathies

−1.58 (−3.12/-0.04)

−0.04

0.008

Distress (4DBL)

−0.19 (−0.37/0.00)

−0.05

0.010

Note: R2= .35; variables are listed in order of inclusion in the model.

Factors associated with SRH when multiple regression analyses were conducted for men and women separately are presented in Table 7. Separate analyses were able to explain equal amounts of variance in SRH in both genders. In men, seven variables explained 34% of the variance of SRH, whereas, in women eleven variables were found to explain 35% of the variance of SRH. In both genders depression, somatization and pain associated disability had a negative effect on SRH (all p < 0.001). Physical activity (p < 0.001) had a positive effect on SRH. Low or intermediate education (p < 0.001) and a self-reported diagnosis of cardiac arrhythmia (p = 0.004) were associated with reduced SRH in men. In women pain intensity, high BMI, distress (all p < 0.001), age (p = 0.001) and chronic low back pain (p = 0.004) were significantly negatively associated with SRH. SRH was better among women with higher monthly income (p = 0.009).
Table 7

Factors associated with SRH in males and females respectively

 

Men

  

Women

  
 

Regression coefficient B (95% CI)

Standar-dized coefficient (beta)

p value

Regression coefficient B (95% CI)

Standar-dized coefficient (beta)

p value

Somatization (4DBL)

−0.79 (−1.00/-0.59)

−0.20

<0.001

−0.54 (−0.72/-0.37)

−0.16

<0.001

Disability score (GCPS)

−0.14 (−0.18/-0.11)

−0.21

<0.001

−0.10 (−0.14/-0.07)

−0.17

<0.001

Depression (GDS)

−1.51 (−1.90/-1.12)

−0.20

<0.001

−0.91 (−1.24/-0.58)

−0.13

<0.001

Physical activity (in 1000 MET minutes/week) (IPAQ-7)

1.00 (0.53/1.14)

0.13

<0.001

1.00 (0.28/0.94)

0.07

<0.001

Activities of daily living (Barthel)

0.30 (0.16/0.44)

0.10

<0.001

n.s.

  

Low or intermediate education

−4.43 (−6.57/-2.29)

−0.09

<0.001

n.s.

  

Cardiac arrhythmia

−2.57 (−4.34/-0.80)

−0.07

0.004

n.s.

  

Characteristic pain intensity (GCPS)

n.s.

  

−0.07 (−0.11/-0.03)

−0.10

<0.001

Instrumental activities (IADL)

n.s.

  

1.09 (0.42/1.75)

0.07

0.001

BMI

n.s.

  

−0.24 (−0.37/-0.11)

−0.07

<0.001

Distress (4DBL)

n.s.

  

−0.30 (−0.47/-0.14)

−0.09

<0.001

Age

n.s.

  

−0.24 (−0.38/-0.11)

−0.07

0.001

Chronic low back pain

n.s.

  

−2.36(−3.97/-0.74)

−0.06

0.004

Monthly income (in 1000 Euro)

n.s.

  

2.00 (0.39/0.27)

−0.05

.009

Note: all variables that have shown significant results in the bivariate analyses were put in the model; males: R2 = 0.34;

females: R2 = 0.35.

In both genders a negative association between SRH and restrictions in activities of daily living was found. Whereas in men lower SRH scores were associated with restrictions in basic activities of daily living (p < 0.001), in women restrictions in instrumental activities (p < 0.001) resulted in significantly lower SRH values.

Discussion

The present study aimed to identify socio-demographic, lifestyle, psychological and disease-related factors associated with SRH in a large sample of elderly multimorbid general practice patients. To the best of our knowledge there are no studies that investigated SRH in multimorbid samples of comparable size and age.

In our sample the mean EQ-VAS score was slightly below the general population’s mean score [6, 3134], but above values found in samples of chronically ill patients [31, 34, 35].

Whereas in the bivariate analyses nearly all variables showed significant correlations with SRH, the final regression model for the whole sample included 13 of the initially more than 50 variables. Lifestyle variables like current smoking and alcohol consumption [36, 37] that were often found to influence SRH were not independently associated with SRH in our sample. But BMI and physical activity were lifestyle factors independently associated with SRH in our study. We found that increasing BMI reduced and physical activity increased SRH, respectively. The finding of high BMI as a correlate of low SRH was confirmed by other studies [38, 39], but not by all [40]. Several studies support the strong relationship between high levels of physical activity and better SRH [36, 4143].

In our multimorbid sample the association between socioeconomic factors and SRH seems to be weaker than in other samples [36]. Age and income were the only socioeconomic variables independently correlated with SRH in the whole sample.Among the psychological factors depression, somatization and distress significantly reduced SRH whereas self-efficacy expectation increased SRH. It might be that in a sample of multimorbid elderly patients somatization probably reflects true physical symptoms representing diseases rather than unspecific somatic complaints. The association between mental distress [4446] and depression [42, 4749] and SRH was shown in many studies. We suspect that psychological factors exist that mediate the subjective rating of health in patients suffering from multiple chronic diseases. Those with high expectations of self-efficacy and low levels of stress and depression probably cope better with multimorbidity.

Not surprisingly, among the disease-related factors single diseases were less influential for SRH in our sample than sequelae of diseases like pain intensity, disability caused by pain and restrictions in instrumental activities. It appears as if general factors like pain, disability, depression, somatic complaints, restrictions in physical activity and independent living, which accumulate in multimorbid patients due to the presence of multiple chronic diseases, affect SRH more than single self-reported diseases. Based on the fact that every patient in our sample having at least three diagnoses, it is not surprising that most diseases do not contribute independently to the explained variance of SRH. It might be, that in a general population sample with a lower prevalence of diagnoses the result would be different.

Gender-specific analyses indicated that there are no differences in the relationship between somatization, depression, pain associated disability, and low physical activity and SRH in men and women. It seems that in both genders consequences of and complaints due to multimorbidity explain most of the variance of SRH. Besides these most important factors, we found different variables to be associated with SRH specifically in men and women. As found in a previous study [50], education was associated with SRH only in men. A possible explanation might be that in most families the total household income is more dependent on men’s than on women’s educational level. BMI values revealed a negative association with SRH exclusively in women, in line with a study from the US which showed a stronger association between high BMI values and low SRH scores in aged women compared to aged men [51]. Chronic low back pain and characteristic pain intensity were negatively related to SRH exclusively in women. Therefore, it appears that in women SRH is more affected by pain than in men.

Implications

Most importantly, we found SRH to be predominantly associated with modifiable factors. This suggests that SRH could be improved through specific interventions at the level of primary care. Main focus should be on modifiable aspects and consequences of multimorbidity: appropriate interventions of pain treatment and reduction; thorough exploration of somatic symptoms, since they could reflect sequelae of multimorbidity as well as potential side and interaction effects of polypharmacy. In order to improve SRH, physical complaints should be relieved, be it by improving patients compliance to or by adjusting the medication. Besides the reduction of pain and somatic complaints, depression provides another important starting point for improvement of SRH. The high burden of physical complaints and symptoms could make it difficult to reliably detect and diagnose depression in elderly multimorbid patients. Screening for and when indicated, treatment of depression should be standard in multimorbid patients. In addition, patients’ health-related behavior should be the target of interventions in order to improve SRH. Possible interventions are: participation on special training programs for elderly that focus on weight reduction and simultaneously increase physical activity; rehabilitation interventions for those patients who have restrictions in functional abilities.

Strengths and weaknesses

A major strength of our study was the large number of multimorbid general practice patients assessed that were spread over eight study centers distributed throughout Germany. In contrast to other studies of comparable size, which are generally based on postal or telephone surveys, our study data was collected by face-to-face interviews. In our study a larger set of variables was collected and tested for associations than in comparable studies. To enhance accuracy of the diagnoses used for inclusion of multimorbid patients, GPs’ diagnoses were used for selection of participants. Nevertheless, GPs’ diagnoses are also not entirely valid [52]. Despite the fact that participants were of advanced age and suffering from multimorbidity we obtained a satisfactory response rate.

We decided to measure the health related life quality by means of a global visual analog scale. To this it could be objected that such a simple scale were not capable of representing all facets of the complex construct: life quality. According to Idler and Benyamini [2], however, it was possible to show that global self-ratings of health reflect “the respondents’ views of global health in a way that nothing else can” (p. 34). We used an analog scale in order to allow the patients to include their own dimensions into their concept of health-related quality-of-life.

However, the present study also had some limitations. Most of the information was obtained through self-report, which may reduce the validity of the information. For example, questions about alcohol consumption, smoking behavior and physical activity may have been answered in a socially acceptable manner. Conversely, it can be seen as an advantage of our study that information regarding BMI was directly measured by the physician and the ability to perform instrumental and basic activities was rated by the interviewer based on the patients’ narration. Although electronic health records are more reliable in detecting multimorbidity in younger patients, the prevalence of multimorbidity in elderly patients appears to be the same for electronic health records as for self-reports [53]. Patients living in a nursing home and those suffering from dementia or severe illness of terminal stage were excluded from the sample. Therefore, it is possible that certain aspects of multimorbidity are not represented in our sample. Due to the cross-sectional character of our data, the direction of the relationship between SRH and independent factors remains unclear.

Conclusions

SRH is a pivotal indicator of quality of life. The identification of factors influencing health-related quality of life in elderly multimorbid patients gains in importance in our aging society.

We found the strongest correlation between SRH and disease sequelae, but only few diseases and only those with a high burden of symptoms or limitations were independently associated with low SRH. In women SRH seemed to be more strongly associated with pain, whereas, in men threats of limitations of activity seemed to play a larger role.

In conclusion, perception of health and health-related quality of life at an older age might be improved by treatment of disease sequelae such as pain and discomfort. To enhance quality of life in the elderly, particular attention might be paid to the diagnosis and treatment of depression.

Abbreviations

AUDIT-C: 

Alcohol use disorders identification test

BMI: 

Body mass index

CASMIN: 

Comparative analysis of social mobility in industrial Nations

COPD: 

Chronic obstructive pulmonary disease

EQ-5D: 

EuroQoL-5D

F-SOZU K-14: 

Fragebogen zur sozialen Unterstützung

GCPS: 

Graded chronic pain scale

GDS: 

Geriatric depression scale

GPs: 

General practitioners

iADL: 

Instrumental activities of daily living

IPAQ-7: 

International physical activities questionnaire

M: 

Mean

OECD: 

Organization for economic cooperation

PAOD: 

Peripheral arterial occlusive disease

SD: 

Standard deviation

SPSS: 

Statistical package for the social sciences

SRH: 

Self-rated health

SWE: 

Self-efficacy expectation

VAS: 

Visual analogous scale

4DBL: 

Four dimensional symptom questionnaire.

Declarations

Acknowledgement

The study is funded by the German Federal Ministry of Education and Research (grant numbers 01ET0725-31 and 01ET1006A-K).

This article is on behalf of the MultiCare Cohort Study Group, which consists of Attila Altiner, Horst Bickel, Wolfgang Blank, Monika Bullinger, Hendrik van den Bussche, Anne Dahlhaus, Lena Ehreke, Michael Freitag, Angela Fuchs, Jochen Gensichen, Ferdinand Gerlach, Heike Hansen, Sven Heinrich, Susanne Höfels, Olaf von dem Knesebeck, Hans-Helmut König, Norbert Krause, Hanna Leicht, Melanie Luppa, Wolfgang Maier, Manfred Mayer, Christine Mellert, Anna Nützel, Thomas Paschke, Juliana Petersen, Jana Prokein, Steffi Riedel-Heller, Heinz-Peter Romberg, Ingmar Schäfer, Martin Scherer, Gerhard Schön, Susanne Steinmann, Sven Schulz, Karl Wegscheider, Klaus Weckbecker, Jochen Werle, Siegfried Weyerer, Birgitt Wiese, and Margrit Zieger.

We are grateful to the general practitioners in Bonn, Dusseldorf, Frankfurt/Main, Hamburg, Jena, Leipzig, Mannheim and Munich who supplied the clinical information on their patients, namely Theodor Alfen, Martina Amm, Katrin Ascher, Philipp Ascher, Heinz-Michael Assmann, Hubertus Axthelm, Leonhard Badmann, Horst Bauer, Veit-Harold Bauer, Sylvia Baumbach, Brigitte Behrend-Berdin, Rainer Bents, Werner Besier, Liv Betge, Arno Bewig, Hannes Blankenfeld, Harald Bohnau, Claudia Böhnke, Ulrike Börgerding, Gundula Bormann, Martin Braun, Inge Bürfent, Klaus Busch, Jürgen Claus, Peter Dick, Heide Dickenbrok, Wolfgang Dörr, Nadejda Dörrler-Naidenoff, Ralf Dumjahn, Norbert Eckhardt, Richard Ellersdorfer, Doris Fischer-Radizi, Martin Fleckenstein, Anna Frangoulis, Daniela Freise, Denise Fricke, Nicola Fritz, Sabine Füllgraf-Horst, Angelika Gabriel-Müller, Rainer Gareis, Benno Gelshorn, Maria Göbel-Schlatholt, Manuela Godorr, Jutta Goertz, Cornelia Gold, Stefanie Grabs, Hartmut Grella, Peter Gülle, Elisabeth Gummersbach, Heinz Gürster, Eva Hager, Wolfgang-Christoph Hager, Henning Harder, Matthias Harms, Dagmar Harnisch, Marie-Luise von der Heide, Katharina Hein, Ludger Helm, Silvia Helm, Udo Hilsmann, Claus W. Hinrichs, Bernhard Hoff, Karl-Friedrich Holtz, Wolf-Dietrich Honig, Christian Hottas, Helmut Ilstadt, Detmar Jobst, Gunter Kässner, Volker Kielstein, Gabriele Kirsch, Thomas Kochems, Martina Koch-Preißer, Andreas Koeppel, Almut Körner, Gabriele Krause, Jens Krautheim, Nicolas Kreff, Daniela Kreuzer, Franz Kreuzer, Judith Künstler, Christiane Kunz, Doris Kurzeja-Hüsch, Felizitas Leitner, Holger Liebermann, Ina Lipp, Thomas Lipp, Bernd Löbbert, Guido Marx, Stefan Maydl, Manfred Mayer, Stefan-Wolfgang Meier, Jürgen Meissner, Anne Meister, Ruth Möhrke, Christian Mörchen, Andrea Moritz, Ute Mühlmann, Gabi Müller, Sabine Müller, Karl-Christian Münter, Helga Nowak, Erwin Ottahal, Christina Panzer, Thomas Paschke, Helmut Perleberg, Eberhard Prechtel, Hubertus Protz, Sandra Quantz, Eva-Maria Rappen-Cremer, Thomas Reckers, Elke Reichert, Birgitt Richter-Polynice, Franz Roegele, Heinz-Peter Romberg, Anette Rommel, Michael Rothe, Uwe Rumbach, Michael Schilp, Franz Schlensog, Ina Schmalbruch, Angela Schmid, Holger Schmidt, Lothar Schmittdiel, Matthias Schneider, Ulrich Schott, Gerhard Schulze, Heribert Schützendorf, Harald Siegmund, Gerd Specht, Karsten Sperling, Meingard Staude, Hans-Günter Stieglitz, Martin Strickfaden, Hans-Christian Taut, Johann Thaller, Uwe Thürmer, Ljudmila Titova, Michael Traub, Martin Tschoke, Maya Tügel, Christian Uhle, Kristina Vogel, Florian Vorderwülbecke, Hella Voß, Christoph Weber, Klaus Weckbecker, Sebastian Weichert, Sabine Weidnitzer, Brigitte Weingärtner, Karl-Michael Werner, Hartmut Wetzel, Edgar Widmann, Alexander Winkler, Otto-Peter Witt, Martin Wolfrum, Rudolf Wolter, Armin Wunder, and Steffi Wünsch.

We also thank Corinna Contenius, Cornelia Eichhorn, Sarah Floehr, Vera Kleppel, Heidi Kubieziel, Rebekka Maier, Natascha Malukow, Karola Mergenthal, Christine Müller, Sandra Müller, Michaela Schwarzbach, Wibke Selbig, Astrid Steen, Miriam Steigerwald, and Meike Thiele for data collection as well as Ulrike Barth, Elena Hoffmann, Friederike Isensee, Leyla Kalaz, Heidi Kubieziel, Helga Mayer, Karine Mnatsakanyan, Michael Paulitsch, Merima Ramic, Sandra Rauck, Nico Schneider, Jakob Schroeber, Susann Schumann, and Daniel Steigerwald for data entry.

Authors’ Affiliations

(1)
Department of Psychiatry and Psychotherapy, Technical University of Munich
(2)
Institute of General Practice, University of Frankfurt/Main
(3)
Institute of General Practice, University of Düsseldorf
(4)
Institute of General Practice, University of Jena
(5)
Department of Medical Sociology and Health Economics, University Medical Medical Center Hamburg-Eppendorf
(6)
Institute for Social Medicine, Occupational Health and Public Health, University of Leipzig
(7)
Department of Psychiatry and Psychotherapy, University of Bonn
(8)
Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf
(9)
Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf
(10)
Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University
(11)
Institute for Biometry, Hannover Medical School

References

  1. van den Akker M, Buntinx F, Metsemakers JF, Roos S, Knottnerus JA: Multimorbidity in general practice: prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. J Clin Epidemiol. 1998, 51: 367-375. 10.1016/S0895-4356(97)00306-5.View ArticlePubMedGoogle Scholar
  2. Idler EL, Benyamini Y: Self-rated health and mortality: a review of twenty-seven community studies. J Health Soc Behav. 1997, 38: 21-37. 10.2307/2955359.View ArticlePubMedGoogle Scholar
  3. Idler EL, Russell LB, Davis D: Survival, functional limitations, and self-rated health in the NHANES I epidemiologic follow-up study, 1992. First National health and nutrition examination survey. Am J Epidemiol. 1992, 2000 (152): 874-883.Google Scholar
  4. Elwell-Sutton TM, Jiang CQ, Zhang WS, Cheng KK, Lam TH, Leung GM, et al: Socioeconomic influences at different life stages on health in Guangzhou, China. Soc Sci Med. 2011, 72: 1884-1892. 10.1016/j.socscimed.2011.03.041.View ArticlePubMedGoogle Scholar
  5. Barros MB, Zanchetta LM, Moura EC, Malta DC: Self-rated health and associated factors, Brazil, 2006. Rev Saude Publica. 2009, 43 (Suppl 2): 27-37.View ArticlePubMedGoogle Scholar
  6. Lubetkin EI, Jia H, Franks P, Gold MR: Relationship among sociodemographic factors, clinical conditions, and health-related quality of life: examining the EQ-5D in the U.S. general population. Qual Life Res. 2005, 14: 2187-2196. 10.1007/s11136-005-8028-5.View ArticlePubMedGoogle Scholar
  7. Wolinsky FD, Miller TR, Malmstrom TK, Miller JP, Schootman M, Andresen EM, et al: Self-rated health: changes, trajectories, and their antecedents among African Americans. J Aging Health. 2008, 20: 143-158.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Bäckmand H, Kujala U, Sarna S, Kaprio J: Former athletes’ health-related lifestyle behaviours and self-rated health in late adulthood. Int J Sports Med. 2010, 31: 751-758. 10.1055/s-0030-1255109.View ArticlePubMedGoogle Scholar
  9. Conry M, Morgan K, Curry P, McGee H, Harrington J, Ward M, et al: The clustering of health behaviours in Ireland and their relationship with mental health, self-rated health and quality of life. BMC Public Health. 2011, 11: 692-701. 10.1186/1471-2458-11-692.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Jia H, Lubetkin EI: The impact of obesity on health-related quality-of-life in the general adult US population. J Public Health (Oxf). 2005, 27: 156-164. 10.1093/pubmed/fdi025.View ArticleGoogle Scholar
  11. Motl RW, McAuley E: Symptom cluster and quality of life: preliminary evidence in multiple sclerosis. J Neurosci Nurs. 2010, 42: 212-216. 10.1097/JNN.0b013e3181e26c5f.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Rosso AL, Gallagher RM, Luborsky M, Mossey JM: Depression and self-rated health are proximal predictors of episodes of sustained change in pain in independently living, community dwelling elders. Pain Med. 2008, 9: 1035-1049. 10.1111/j.1526-4637.2008.00533.x.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Molarius A, Janson S: Self-rated health, chronic diseases, and symptoms among middle-aged and elderly men and women. J Clin Epidemiol. 2002, 55: 364-370. 10.1016/S0895-4356(01)00491-7.View ArticlePubMedGoogle Scholar
  14. Perruccio AV, Power JD, Badley EM: Arthritis onset and worsening self-rated health: a longitudinal evaluation of the role of pain and activity limitations. Arthritis Rheum. 2005, 53: 571-577. 10.1002/art.21317.View ArticlePubMedGoogle Scholar
  15. Schäfer I, Hansen H, Schön G, Maier W, Höfels S, Altiner A, et al: The German MultiCare-study: patterns of multimorbidity in primary health care - protocol of a prospective cohort study. BMC Health Serv Res. 2009, 9: 145-10.1186/1472-6963-9-145.View ArticlePubMedPubMed CentralGoogle Scholar
  16. The EuroQol Group: EuroQol--a new facility for the measurement of health-related quality of life. Health Policy. 1990, 16: 199-208.View ArticleGoogle Scholar
  17. Brauns H, Steinmann S: Educational reform in France, West-Germany and the United Kingdom: Updating the CASMIN educational classification. ZUMA-Nachrichten. 1999, 44: 7-44.Google Scholar
  18. Burniaux J-M: Income distribution and poverty in selected OECD countries. OECD Econ Dep Working Papers. 1998, 6: 1-21.Google Scholar
  19. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al: International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003, 35: 1381-1395. 10.1249/01.MSS.0000078924.61453.FB.View ArticlePubMedGoogle Scholar
  20. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA: The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory care quality improvement project (ACQUIP). Alcohol use disorders identification test. Arch Intern Med. 1998, 158: 1789-1795. 10.1001/archinte.158.16.1789.View ArticlePubMedGoogle Scholar
  21. Fydrich T, Sommer G, Brähler E, F-Soz U: Fragebogen zur Sozialen Unterstützung. 2007, Göttingen: HogrefeGoogle Scholar
  22. Hinz A, Schumacher J, Albani C, Schmid G, Brähler E: Bevölkerungsrepräsentative Normierung der Skala zur allgemeinen Selbstwirksamkeitserwartung. Diagnostica. 2006, 52: 26-32. 10.1026/0012-1924.52.1.26.View ArticleGoogle Scholar
  23. Sheikh J, Yesavage J: Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. Clin Gerontol. 1986, 5: 165-173. 10.1300/J018v05n01_09.View ArticleGoogle Scholar
  24. Terluin B, van Marwijk HW, Ader HJ, de Vet HC, Penninx BW, Hermens ML, et al: The Four-Dimensional Symptom Questionnaire (4DSQ): a validation study of a multidimensional self-report questionnaire to assess distress, depression, anxiety and somatization. BMC Psychiatry. 2006, 6: 34-10.1186/1471-244X-6-34.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Klasen BW, Hallner D, Schaub C, Willburger R, Hasenbring M: Validation and reliability of the German version of the Chronic Pain Grade questionnaire in primary care back pain patients. Psychosoc Med. 2004, 1: Doc07-PubMedPubMed CentralGoogle Scholar
  26. Collin C, Wade DT, Davies S, Horne V: The Barthel ADL Index: a reliability study. Int Disabil Stud. 1988, 10: 61-63. 10.3109/09638288809164103.View ArticlePubMedGoogle Scholar
  27. Lawton MP, Brody EM: Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969, 9: 179-186. 10.1093/geront/9.3_Part_1.179.View ArticlePubMedGoogle Scholar
  28. Gower JC: A general coefficient of similarity and some of its properties. Biometrics. 1971, 27: 857-871. 10.2307/2528823.View ArticleGoogle Scholar
  29. D’Orazio M: StatMatch: Statistical Matching. 2009, Ref Type: Computer ProgramGoogle Scholar
  30. Schäfer I, Hansen H, Schön G, Höfels S, Altiner A, Dahlhaus A, et al: The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. first results from the multicare cohort study. BMC Health Serv Res. 2012, 12: 89-10.1186/1472-6963-12-89.View ArticlePubMedPubMed CentralGoogle Scholar
  31. König HH, Bernert S, Angermeyer MC: Health Status of the German population: results of a representative survey using the EuroQol questionnaire. Gesundheitswesen. 2005, 67: 173-182. 10.1055/s-2005-857991.View ArticlePubMedGoogle Scholar
  32. König HH, Bernert S, Angermeyer MC, Matschinger H, Martinez M, Vilagut G, et al: Comparison of population health status in six european countries: results of a representative survey using the EQ-5D questionnaire. Med Care. 2009, 47: 255-261. 10.1097/MLR.0b013e318184759e.View ArticlePubMedGoogle Scholar
  33. König HH, Heider D, Lehnert T, Riedel-Heller SG, Angermeyer MC, Matschinger H, et al: Health status of the advanced elderly in six European countries: results from a representative survey using EQ-5D and SF-12. Health Qual Life Outcomes. 2010, 8: 143-View ArticlePubMedPubMed CentralGoogle Scholar
  34. Szende A, Williams A: Measuring self-reported population health: an international perspective based on EQ-5D. 2004, Budapest: SpringMed PublishingGoogle Scholar
  35. Wang HM, Beyer M, Gensichen J, Gerlach FM: Health-related quality of life among general practice patients with differing chronic diseases in Germany: cross sectional survey. BMC Public Health. 2008, 8: 246-10.1186/1471-2458-8-246.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Darviri C, Artemiadis AK, Tigani X, Alexopoulos EC: Lifestyle and self-rated health: a cross-sectional study of 3,601 citizens of Athens, Greece. BMC Public Health. 2011, 11: 619-10.1186/1471-2458-11-619.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Haseli-Mashhadi N, Pan A, Ye X, Wang J, Qi Q, Liu Y, et al: Self-rated health in middle-aged and elderly Chinese: distribution, determinants and associations with cardio-metabolic risk factors. BMC Public Health. 2009, 9: 368-10.1186/1471-2458-9-368.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Grov EK, Fossa SD, Dahl AA: Short-term and long-term elderly cancer survivors: a population-based comparative and controlled study of morbidity, psychosocial situation, and lifestyle. Eur J Oncol Nurs. 2011, 15: 213-220. 10.1016/j.ejon.2010.06.011.View ArticlePubMedGoogle Scholar
  39. Kind P, Dolan P, Gudex C, Williams A: Variations in population health status: results from a United Kingdom national questionnaire survey. BMJ. 1998, 316: 736-741. 10.1136/bmj.316.7133.736.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Sach TH, Barton GR, Doherty M, Muir KR, Jenkinson C, Avery AJ: The relationship between body mass index and health-related quality of life: comparing the EQ-5D, EuroQol VAS and SF-6D. Int J Obes (Lond). 2007, 31: 189-196. 10.1038/sj.ijo.0803365.View ArticleGoogle Scholar
  41. Buman MP, Hekler EB, Haskell WL, Pruitt L, Conway TL, Cain KL, et al: Objective light-intensity physical activity associations with rated health in older adults. Am J Epidemiol. 2010, 172: 1155-1165. 10.1093/aje/kwq249.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Fujikawa A, Suzue T, Jitsunari F, Hirao T: Evaluation of health-related quality of life using EQ-5D in Takamatsu, Japan. Environ Health Prev Med. 2011, 16: 25-35. 10.1007/s12199-010-0162-1.View ArticlePubMedGoogle Scholar
  43. Schweikert B, Hunger M, Meisinger C, König HH, Gapp O, Holle R: Quality of life several years after myocardial infarction: comparing the MONICA/KORA registry to the general population. Eur Heart J. 2009, 30: 436-443.View ArticlePubMedGoogle Scholar
  44. Arne M, Lundin F, Boman G, Janson C, Janson S, Emtner M: Factors associated with good self-rated health and quality of life in subjects with self-reported COPD. Int J Chron Obstruct Pulmon Dis. 2011, 6: 511-519.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Burström K, Johannesson M, Diderichsen F: Swedish population health-related quality of life results using the EQ-5D. Qual Life Res. 2001, 10: 621-635. 10.1023/A:1013171831202.View ArticlePubMedGoogle Scholar
  46. Herman KM, Hopman WM, Vandenkerkhof EG, Rosenberg MW: Physical activity, body mass index, and health-related quality of life in Canadian adults. Med Sci Sports Exerc. 2012, 44: 625-636. 10.1249/MSS.0b013e31823a90ae.View ArticlePubMedGoogle Scholar
  47. Cleland JA, Lee AJ, Hall S: Associations of depression and anxiety with gender, age, health-related quality of life and symptoms in primary care COPD patients. Fam Pract. 2007, 24: 217-223. 10.1093/fampra/cmm009.View ArticlePubMedGoogle Scholar
  48. Haacke C, Althaus A, Spottke A, Siebert U, Back T, Dodel R: Long-term outcome after stroke: evaluating health-related quality of life using utility measurements. Stroke. 2006, 37: 193-198. 10.1161/01.STR.0000196990.69412.fb.View ArticlePubMedGoogle Scholar
  49. Kerse N, Elley CR, Robinson E, Arroll B: Is physical activity counseling effective for older people? A cluster randomized, controlled trial in primary care. J Am Geriatr Soc. 2005, 53: 1951-1956. 10.1111/j.1532-5415.2005.00466.x.View ArticlePubMedGoogle Scholar
  50. Unden AL, Elofsson S: Do different factors explain self-rated health in men and women?. Gend Med. 2006, 3: 295-308. 10.1016/S1550-8579(06)80218-4.View ArticlePubMedGoogle Scholar
  51. Imai K, Gregg EW, Chen YJ, Zhang P, de Rekeneire N, Williamson DF: The association of BMI with functional status and self-rated health in US adults. Obesity (Silver Spring). 2008, 16: 402-408. 10.1038/oby.2007.70.View ArticleGoogle Scholar
  52. Erler A, Beyer M, Muth C, Gerlach FM, Brennecke R: Garbage in – Garbage out? Validität von Abrechnungsdiagnosen in hausärztlichen Praxen. Gesundheitswesen. 2009, 71: 823-831. 10.1055/s-0029-1214399.View ArticlePubMedGoogle Scholar
  53. Violán C, Fouguet-Boreu Q, Hermosilla-Pérez E, Valderas JM, Bolíbar B, Fábregas-Escurriola M, Brugulat-Guiteras P, Muñoz-Pérez MÁ: Comparison of the information provided by electronic health records data and a population health survey to estimate prevalence of selected health conditions and multimorbidity. BMC Public Health. 2013, 21: 251-10.1007/s10389-012-0536-5.View ArticleGoogle Scholar
  54. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2296/15/1/prepub

Copyright

© Nützel et al.; licensee BioMed Central Ltd. 2014

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.

Advertisement