The German Health Interview and Examination Survey for Adults (DEGS) survey is part of the continuous national health monitoring system carried out by the Robert Koch Institute and provides representative data on the health of adults in Germany at regular intervals [6]. The first wave (DEGS1) was conducted from November 2008 to December 2011 and the design, objectives and methods have been described in detail elsewhere [6]. Briefly, DEGS1 has a mixed design permitting both cross-sectional and longitudinal analyses. Persons aged 18–79 years randomly selected from local population registries and former German National Health Interview and Examination Survey participants in 1998 (GNHIES98) [6] were invited to participate. In DEGS1, 8152 non-institutionalised adults participated including 4193 first-time participants aged 18–79 years (response rate 42%) and 3959 previous participants of GNHIES98 aged 28–91 years (response rate 62%) [6].
DEGS1 included: standardised physician-administered computer-assisted personal interview (CAPI) with details on pre-existing physician-diagnosed health conditions; self-administered questionnaires; a range of physical, laboratory and other measurements; and robust data collection of medicines use. Complete interview and examination data for cross-sectional analyses were initially available for 7116 participants aged 18–79 years [6]. One participant was later excluded from all further analyses following withdrawal of informed consent. For this study we focused on older adults, those aged 65–79 years with interview and examination data (n = 1853). Participants whose frailty status could not be determined due to missing data on three or more of the five items used to measure frailty [7] were excluded (n = 10).Therefore, the final study sample was 1843 participants.
Frailty was defined and measured based on modified criteria used by Fried and colleagues in the Cardiovascular Health Study [7]; the most frequently used method for quantifying frailty in population-based studies [8]. Participants with three or more of the following characteristics: self-reported exhaustion, low weight, low physical activity, low walking speed and low grip strength were classified as frail [7]. Participants fulfilling one or two of the criteria were classified as ‘pre-frail’ and ‘non-frail’ if they fulfilled none. Similar to others [5,9], the measurement tools used to characterise the individual components of frailty were slightly adapted due to data availability and were determined as follows:
Self-reported exhaustion
Exhaustion was measured using a single item from the validated German language version of the Medical Outcome Short Form-36 (SF-36) [10]. Participants were asked: ‘How much of the time during the past four weeks did you have a lot of energy?’ Those responding ‘none’ or ‘little of the time’ were classified as having exhaustion consistent with other studies [11,12].
Low weight
No question related to unintentional weight loss was available in DEGS1. We used Body Mass Index (BMI) as an alternative consistent with others [9,13,14]. Those with a BMI of less than 23 were considered low weight akin to European studies investigating frailty [9,13] and taking into account contemporary obesity prevalence among older adults in Germany.
Low physical activity
Standardised questions related to physical activity and sports were included in DEGS1 [15]. Participants reporting performing no sports in the previous three months and no physical activity on any day of the week requiring the person to start to sweat or get out of breath were classified as having low physical activity.
Low walking speed
The timed up and go test is a reliable and valid test with a standardised protocol for quantifying functional mobility [16]. Participants taking 15 seconds or more were classified as having a low walking speed. This cut point has demonstrated 100% specificity for identifying all pre-frail or frail individuals according to Fried’s criteria in a large cohort of older people [17].
Low grip strength
Isometric grip strength was measured using a hand-held dynamometer (Smedley, Scandidact, Denmark, 100 kg). Two values were recorded for each hand. Maximum grip strength from all attempts was used for analyses. Low grip strength was determined using sex and BMI specific cut points specified by Fried and colleagues [7]. Few participants had valid grip strength measures but missing BMI values (n = 15). For these participants, low grip strength was determined using established grip strength values for sarcopenia (<20 kg for women and <30 kg for men) consistent with others [5].
Socio-demographic variables included age, sex and living alone. Socioeconomic status (SES) was classified as low, middle and high using an established index including information on education, professional status and household income [6]. Self-perceived levels of social support were assessed using the Oslo-3 Social Support Scale (OSS-3) with poor social support defined as <9 points (range 3–14) [18]. Additionally, participants were asked: ‘Do you think you need more help in your daily routine than you currently receive?’ Those responding ‘yes’ were classified as having self-reported lack of help.
Current depressive symptoms within the last 2 weeks were assessed using the 9-item depression module of the German version of the Patient Health Questionnaire (PHQ-9) with those scoring 10 or more points (range 0–27) as having current depressive symptoms [19]. Cognitive function was assessed using the paper-and-pencil Digit Symbol Substitution Test (DSST) from the German version of the Wechsler Adult Intelligence Scale, 3rd revision (WAIS-III) [20]. Participants are provided with a key showing unique graphic symbols corresponding to numerical digits and copy symbols onto a scoring sheet within 120 seconds (score range 0–133).
Self-rated health was measured using a standard item from the SF-36 [10] and dichotomised as excellent/very good/good versus fair/poor. Self-reported chronic diseases (“Has a doctor ever diagnosed you as having..?”) included: coronary heart disease (CHD) (including myocardial infarction (MI), angina or other CHD), stroke, heart failure, chronic renal disease, cancer, osteoarthritis, rheumatoid arthritis and osteoporosis. Hypertension and diabetes were determined based on self-reported history of disease and intake of antihypertensive [21] or antidiabetic medication [22]. Polypharmacy was defined as the use of 5 and more prescription medicines in the 7 days prior to examination [23]. History of falls was defined as those reporting two or more falls in the past 12 months. Self-reported poor hearing and poor vision were recorded. Health risk behaviours included current smoking status and alcohol consumption (first item from Alcohol Use Disorders Identification Test-Consumption [24]).
Statistical analyses
To describe the sample and the prevalence of frailty participants’ characteristics were stratified by sex. To describe the sample according to their frailty status, proportions, means, and respective 95% confidence intervals (CI) are presented. To determine differences between (a) non-frail versus pre-frail and (b) non-frail versus frail groups, separate unadjusted multinomial regression analyses were conducted using the non-frail group as the reference group. Then, all multinomial regression models were adjusted for age (in years), sex and SES. Both unadjusted and adjusted models, including relative risk ratios (RRR) and 95% CIs are presented. Participants with missing values were not included in analyses.
In order to report population estimates, all analyses were conducted with a weighting factor which corrects deviations in the sample from the population structure (as of 31 Dec 2010) with regard to age, sex, region and nationality, as well as community type and education. Calculation of the weighting factor also considered re-participation probability of GNHIES98 participants, based on a logistic regression model. To take into account the weighting as well as the correlation of the participants within a community, the confidence intervals were determined with complex samples procedures in SPSS-20 or survey procedures in STATA 12.1.
Ethics
DEGS1 was approved by the Charité-Universitaetsmedizin Berlin ethics committee (No. EA2/047/08) and conducted according to guidelines provided by the Federal and State Commissioners for Data Protection. Participants provided written informed consent prior to interview and examination.