Literature DB >> 31372472

Data on adherence to medication in neurological patients using the German Stendal Adherence to Medication Score (SAMS).

Tino Prell1,2, Julian Grosskreutz1,2, Sarah Mendorf1, Otto W Witte1,2, Albrecht Kunze1.   

Abstract

This article presents demographic, socio-economic and detailed adherence to medication data from 429 patients with neurological disorders. Adherence to medication was assessed using the German Stendal Adherence to Medication Score (SAMS). The SAMS questionnaire includes 18 questions forming a cumulative scale (0 - 72) in which 0 indicates complete adherence and 72 complete non-adherence. The SAMS covers different aspects of adherence/non-adherence, such as intentional modification of medication, missing knowledge about reasons/dosage/timing of medication and forgetting to take medication. The dataset allows determining different reasons and clusters of adherence to medication. The dataset can be used as by clinicians, pharmacists and academia for further research and as reference. The dataset can also be used in a large range of other topics where demographic and socio-economic parameters are relevant. The data presented herein is associated with the research article "Clusters of non-adherence to medication in neurological patients" [1] and available on Mendeley https://data.mendeley.com/datasets/ny2krr3vgg/1.

Entities:  

Year:  2019        PMID: 31372472      PMCID: PMC6660612          DOI: 10.1016/j.dib.2019.103855

Source DB:  PubMed          Journal:  Data Brief        ISSN: 2352-3409


Specifications table The data presented in this article provide information about patient-related factors for non-adherence to medication. The data can be used to investigate distribution and reasons for non-adherence in a mixed cohort of neurological patients. The data can be used by clinicians and academia for further research and as reference.

Data

The data article presents demographical, clinical data and data from a self-report adherence questionnaire. In total, data from 429 subjects are provided (age 63, SD = 16, 240 male, 189 female) who took an average of 6 (SD = 3) drugs per day. The majority of patients were married (275 married, 77 widowed or divorced, 69 single, 8 missing data), pensioned (317 pensioned, 88 employed, 22 unemployed, 2 missing data) and had graduated from high schools or college (157 German Realschule, 153 German Abitur and/or university, 116 German Hauptschule or no school, 3 missing data). The mean SAMS was 6.9 (SD = 9) points. According to the SAMS total score 74 (17.2%) reported to be fully adherent (SAMS = 0), 252 (58.7%) showed moderate non-adherence (SAMS 1–10) and 103 (24%) clinical significant non-adherence (SAMS > 10).

Experimental design, materials, and methods

Data from 429 patients were collected (consecutive sampling) either during their visit in the outpatient clinic or during their stay on the neurological ward in the Department of Neurology at the Jena University Hospital between January and May 2018. All subjects gave written informed consent and the study was approved by the ethics committee of the Jena University Hospital, Jena, Germany. Neurological disorders mainly comprised movement disorders, cerebrovascular disorders, epilepsy, peripheral neurological disorders, inflammatory central nervous disorders. Adherence was assessed using the German Stendal Adherence to Medication Score (SAMS) (Table 1). It is an extension of the validated German Essen Compliance Score [2], [3], [4]. The SAMS includes 18 questions forming a cumulative scale (0 = complete adherence to 72 = complete non-adherence). The SAMS covers different aspects of adherence/non-adherence: intentional modification of medication (items 8–13, 17), missing knowledge about reasons/dosage/timing of medication (items 1–3, 5), forgetting to take medication (items 14–15). The presented data provides the 18 SAMS items and SAMS total score from each subject. Demographical data provided in the dataset include: age, gender, marital status, living situation, level of education, occupation, medical history such as diseases and details of their medication (whether medication was self-administered or who takes care of their daily medication, and also total daily number of medications administered in any pharmaceutical form).
Table 1

Stendal adherence to medication score (SAMS).

for all
for most
for half
for some
for none
01234
1Do you know the reason for taking your medication?
2Do you know the dosages of your medication?
3Are you familiar with the timing for taking the medication?

all
most
half
some
none
01234
4Do you take your medication regularly?
5Do you know the names of medications you are taking?

never
rare
sometimes
often
mostly
01234
6Do you forget to take your medication?
7Are you untroubled about taking the medication?
8Do you stop taking your medication when you feel better?
9Do you stop taking your medication if you sometimes feel worse after taking the medication?
10Do you take any wrong or other/unprescribed medications (such as those of your partner)?
If you think you have side effects due to of the medications (such as tremors, nausea etc.)
11- do you reduce the dose without consulting a doctor?
12- do you not take the medication for a while, i.e. take a break?
13If you feel you have to take too many, do you stop taking those medications you consider to be less important than the others without consulting your doctor?
If you forget or omit your medication, do you forget it …
14in the morning?
15at noon?
16in the evening?
17Do you deliberately not take medications you do not consider important, but take the rest?
18If you take medication as a syringe or a weekly tablet, have you ever forgotten it?
Stendal adherence to medication score (SAMS). There is no established threshold to determine non-adherence. It is generally considered that suboptimal adherence becomes clinically significant when < 80% of prescribed medication is taken [5], [6], [7], [8]. Across several highly prevalent chronic diseases 0.80 was found to be a reasonable and valid cut-off point that stratifies adherent and non-adherent patients based on predicting subsequent hospitalization [8]. This leads to a study- and sample-specific SAMS cut-off of 10 points for a clinical meaningful/significant non-adherence in the current dataset. The patients can then be categorized into i) fully adherent (SAMS = 0), ii) moderate non-adherent (SAMS 1–10) and non-adherent (SAMS > 10).

Specifications table

Subject areaMedicine
More specific subject areaHealth Services Research
Type of dataTable, link
How data was acquiredSurvey using the German Stendal Adherence to Medication Score (SAMS). Data from 429 patients with neurological disorders were collected (consecutive sampling) either during their visit to the outpatient clinic or during their stay on the neurological ward in the Department of Neurology at the Jena University Hospital.
Data formatRaw
Experimental factorsThe criteria used for including patients in the study and how data were collected has been described in Prell et al., in press. (1)
Experimental featuresStendal adherence to medication questionnaire (SAMS) and demographical data were collected in patients with neurological disorders. Principal component analysis with Varimax rotation was used to determine independent factors explaining non-adherence to medication in these subjects.
Data source locationDepartment of Neurology, Jena University Hospital, Jena, Germany
Data accessibilityMendeley Data – direct URL: https://data.mendeley.com/datasets/ny2krr3vgg/1https://doi.org/10.17632/ny2krr3vgg.1
Related research articleTino Prell, Julian Grosskreutz, Sarah Mendorf, Gabriele Helga Franke,Otto W. Witte, Albrecht Kunze. Clusters of non-adherence to medication in neurological patients. Research in Social and Administrative Pharmacy, 2019, https://doi.org/10.1016/j.sapharm.2019.01.001, in press [1]
Value of the data

The data presented in this article provide information about patient-related factors for non-adherence to medication.

The data can be used to investigate distribution and reasons for non-adherence in a mixed cohort of neurological patients.

The data can be used by clinicians and academia for further research and as reference.

  5 in total

1.  Difficulties in reporting purpose and dosage of prescribed medications are associated with poor cognition and depression.

Authors:  Hannah M Zipprich; Tino Prell
Journal:  PLoS One       Date:  2021-05-13       Impact factor: 3.240

2.  The impact of poor medication knowledge on health-related quality of life in people with Parkinson's disease: a mediation analysis.

Authors:  Hannah M Zipprich; Sarah Mendorf; Aline Schönenberg; Tino Prell
Journal:  Qual Life Res       Date:  2021-11-19       Impact factor: 3.440

3.  Self-Reported Nonadherence Predicts Changes of Medication after Discharge from Hospital in People with Parkinson's Disease.

Authors:  Francis Feldmann; Hannah M Zipprich; Otto W Witte; Tino Prell
Journal:  Parkinsons Dis       Date:  2020-07-04

4.  What Predicts Different Kinds of Nonadherent Behavior in Elderly People With Parkinson's Disease?

Authors:  Sarah Mendorf; Otto W Witte; Julian Grosskreutz; Hannah M Zipprich; Tino Prell
Journal:  Front Med (Lausanne)       Date:  2020-03-25

5.  Association Between Nonmotor Symptoms and Nonadherence to Medication in Parkinson's Disease.

Authors:  Sarah Mendorf; Otto W Witte; Hannah Zipprich; Tino Prell
Journal:  Front Neurol       Date:  2020-10-19       Impact factor: 4.003

  5 in total

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