GP information (5)

GP information (5)

Documentation first General Practitioner data collection (1992-1993)
Documentation second General Practitioner data collection (2000-2001)
Documentation third General Practitioner data collection (2005-2006)
Documentation fourth General Practitioner data collection (2010)
Documentation fifth General Practitioner data collection (2016)

†: the dates mentioned refer to the investigation period in which the corresponding GP data were collected, not to the respondent cycles involved.
LASA file names: LASAHg01 (cohort 1 and 2), LAS3Bg01 (cohort 3), LASAHg00 (nonresponse), LAS3Bg00 (nonresponse)

Contact: Natasja van Schoor

Background

This is the fifth data collection at general practitioners (GPs), which was done in 2016 by S. de Breij. The GP data for respondents from the first and second LASA cohort can be found in LASAHg01, the GP data for respondents from the third LASA cohort can be found in LAS3Bg01.

The first data collection was done in 1992/93 by D. Kriegsman and B. Penninx (LASABg01.sav and LASABg01_docu 2008.doc). The second data collection was done in 2000/01 by B. Schalk. (LASACg01.sav, LASACg01_nonresponsbestand.sav and LASACg01_nonresponslabels.doc). The third was done in 2005 by L. Schaap (LASAEg01.sav, LASAEg01_nonresponsbestand.sav and LASAEg01_nonresponslabels.doc). The fourth data collection was done in 2010 by H. Galenkamp (LASAFg01.sav, LASAFg00.sav).

Selection of respondents and response rate

GPs of respondents were approached if the respondent participated in the main interview in 2011/12 (H-cycle, cohort 1 and 2, N=1522) and had given informed consent (N=1445), or if the respondent participated in the main interview in 2012/13 (3B-cycle, cohort 3, N=1023) and had given informed consent (N=874). There were 46 respondents of whom no or incorrect information on their GP was known. Therefore, 835 GP’s were approached to provide information about 2273 respondents. Overall, questionnaires of 1654 respondents were completed (72.8% of 2273 questionnaires that were sent out). The response rates of the data collection can be found here. In respondents who participated in the H-cycle, GP data is available for 67.5% (n=1028) of the respondents. In the respondents of whom GP data is missing (n=494), the reason for missing data is not known for the majority of the respondents (n=329). Other reasons for missing data were amongst others no informed consent (n=77) or refusal of the GP to participate (n=41).

In respondents who participated in the B3-cycle, GP data is available for 61.2% (n=626) of the respondents. In respondents of whom GP data is missing (n=397), the reason for missing data is not known for the majority of the respondents (n=192). Other reasons for missing data were amongst others no informed consent (n=149) or refusal of the GP to participate (n=37).

Data collection

The data collection started in March 2016. The procedures are shown in figure 1.

The GPs were asked to fill in a questionnaire for each respondent. All GP’s with less than 20 respondents were sent an introduction letter and the number of questionnaires corresponding with the number of respondents in their practice. GP’s with 20 or more respondents were sent an introduction letter together with an example of the questionnaire. These GP’s were called one week later. They were asked whether they wanted to fill in the questionnaires themselves or if they wanted to let a researcher fill in the questionnaires at their practice. The GP’s had the option to receive a voucher of €7,50 for each completed questionnaire or to donate € 7,50 to charity (WorldGranny) for each completed questionnaire.

If the questionnaires were not returned after twelve weeks the GP’s were reminded with a letter (when the GP’s had less than 20 respondents) or by a phone call (when the GP’s had 20 or more respondents). The second reminder was another twelve weeks later. After receiving completed questionnaires the GP’s received a thank you letter, accompanied by vouchers of €7,50 if this option was chosen.

Content of the GP questionnaire

The GP questionnaire of 2016 covers the presence, year of diagnosis and who diagnosed the disease (GP or medical specialist) of the following diseases: hypertension, peripheral artery disease, congestive heart failure, angina pectoris, myocardial infarction, cardiac arrhythmia (incl. type and pacemaker implantation), transient ischemic attack (TIA), stroke, diabetes mellitus (incl. medication use), asthma, COPD, chronic non-specific lung-disease, osteoarthritis (incl. number, presence in hand/wrist, fingers, hip, knee and diagnosis made with X-ray), rheumatoid arthritis, malignancies (incl. location and number), depressive disorder, anxiety disorder, memory problems, dementia, overweight (incl. treatment), undernourishment (incl. treatment). In addition, other chronic diseases that were present could be filled in (year of diagnosis was not asked). The questionnaire also includes a question on how these health problems affect daily living and some questions on end-of-life decisions.

Use of data

To increase reliability of the presence of chronic diseases and to reduce missing data, algorithms (decision trees) have been developed using data on self-reported chronic diseases, medication use (inspections of medicine bottles) and general practitioners diagnoses. Algorithms have been developed for diabetes, rheumatoid arthritis and cardiovascular diseases.

Questionnaires

huisarts_vrl_H_3B (pdf in Dutch)

Variable information

LASAHg01 / LAS3Bg01
(pdf)

Availability of information per wave
1

 BCDE
2B*
FGH

3B*
MB*IJK*
GP-information

XX-XXX-XX---

1 More information about the LASA data collection waves is available here.

* 2B=baseline second cohort;
3B=baseline third cohort;
MB=migrants: baseline first cohort;
K=future wave 2021-2022

Previous use in LASA

Kriegsman et al. (1996) state that patients’ self-reports on selected chronic diseases are fairly accurate, with the exceptions of atherosclerosis and arthritis. Overreporting and underreporting of specific diseases was associated with several personal characteristics. The results suggested that the tendency to overreport or underreport may be explained by denial by the patient, the tendency of patients to label symptoms or inaccuracy of GP records.

Galenkamp et al. (2014) compared self-reports with GP reported diseases in 2009 and examined how the prevalence of overreporting and underreporting had changed since 1992. Results showed that overreporting of chronic diseases became significantly more common over time, whereas under-reporting became less common. Agreement and change in agreement over time differed across the specific diseases. Underreporting was associated with male gender; over-reporting with female gender, worse self-rated health, and worse physical functioning. Older adults were less accurate in their self-reports than younger adults.

Studies that used the GP data were amongst others:

  • Yoneda, T.B., Rush, J., Graham, E.K., Berg, Al, Comijs, H.C., Katz, M.J., Lipton, R.B., Johansson, B., Mroczek, D.K., Piccinin, A.M. (2020). Increases in Neuroticism May Be an Early Indicator of Dementia: A Coordinated Analysis. The Journals of Gerontology: Series B, Volume 75, 2, 251-262.
  • Kleipool, E.E.F., Hoogendijk, E.O., Trappenburg, M.C., Handoko, M.L., Huisman, M., Peters, M.J.L., Muller, M.J. (2018). Frailty in Older Adults with Cardiovascular Disease: Cause, Effect or Both? Aging and Disease, 9, 489-497.
  • Marijnissen, R.M., Wouts, L., Schoevers, R.A., Bremmer, M.A., Beekman, A.T.F., Comijs, H.C., Oude Voshaar, R.C. (2014). Depression in context of low neuroticism is a risk factor for stroke A 9-year cohort study. Neurology, 83, 1692-1698.
  • Verweij, L.M., Van Schoor , N.M., Deeg, D.J.H., Dekker, J., Visser, M. (2009). Physical activity and incident clinical knee osteoarthritis in older adults. Arthritis & Rheumatism (Arthritis Care & Research), 61, 2, 152-157.
  • Schram, M.T., Frijters, D.H.M., Van de Lisdonk, E.H., Ploemacher, J., De Craen, A.J.M., De Waal, M.W.M., Van Rooij, F.J., Heeringa, J., Hofman, A., Deeg, D.J.H., Schellevis, F.G. (2008). Setting and registry characteristics affect the prevalence and nature of multimorbidity in the elderly. Journal of Clinical Epidemiology, 61, 1104-1112.
  • Licht-Strunk, E., Bremmer, M.A., Van Marwijk, H.W.J., Deeg, D.J.H., Hoogendijk, W.J.G., De Haan, M., Van Tilburg, W., Beekman, A.T.F. (2004). Depression in older persons with versus without vascular disease in the open population: Similar depressive symptom patterns, more disability. Journal of Affective Disorders, 83, 155-160.
  • Pouwer, F., Beekman, A.T.F., Nijpels, G., Dekker, J.M., Snoek, F.J., Kostense, P.J., Heine, R.J., Deeg, D.J.H. (2003). Rates and risks for co-morbid depression in patients with Type 2 diabetes mellitus: Results from a community-based study. Diabetologia, 46, 892-898.


References

  1. Galenkamp H, Huisman M, Braam AW, Schellevis FG, Deeg DJH. Disease prevalence based on older people’s self-reports increased, but patient-general practitioner agreement remained stable, 1992-2009.  Journal of Clinical Epidemiology. 2014, 67 (7): 773-780.
  2. Kriegsman, DM, Penninx BW, van Eijk JT, Boeke AJ, Deeg DJ. Self-reports and general practitioner information on the presence of chronic diseases in community-dwelling elderly. A study on the accuracy of patients’ self-reports and on determinants of inaccuracy. J Clin Epidemiol. 1996 Dec; 49 (12):1407-17.


Date of last update: February 25, 2021 (LvZ)