HESPER
HAN
Adherence to antiretroviral medication in Romanian HIV patients: is it about taking or timing? Objective
We describe the development and validation in Romanian of the first self-report tool that assesses adherence to medication by taking into consideration two complementary behaviors: taking the medication and timing of intake.
Background
Antiretroviral treatment (ART) requires high adherence to achieve therapeutic benefits; ideally, patients need to administer daily all doses recommended (taking adherence) and do so at exact time intervals (timing adherence). These two elements have been previously measured through electronic monitoring (EM), which is less easily applicable to routine care. Developing a self-report questionnaire that targets taking and timing adherence would facilitate clinical assessment and the effective planning of adherence interventions in clinical settings.
Methods
We developed the ProMAS-Rom-HIV, a 12-item questionnaire on adherence to ART, based on ProMAS, an 18-item Dutch adherence questionnaire validated for chronic conditions with item response theory methodology. The binary response scale of ProMAS (“yes/ no”) was replaced with a 7 point response scale, to investigate the feasibility of increasing the amount of information obtained. The ProMAS items were back-translated to Romanian, adapted to ART, and piloted with a sample of 10 patients via cognitive interviewing. Patients responded to a form that contained both socio-demographics and the adherence questionnaire and results of most recent viral load tests (+/- 12 months from questionnaire completion date) were collected from medical files. As suggested by the World Health Organization, a cut-off point of 1000 copies per ml was set to distinguish between HIV detectable and undetectable levels. We used Mokken Scaling Analysis, Factor Analysis, classical test theory analysis and cluster analysis to examine the structural validity of ProMAS-Rom-HIV. Reliability was tested with Cronbach’s alpha, Guttmann’ s lambda 6, beta, omega. Regarding criterion validity, we hypothesized that adherence will be negatively correlated with viral load values and tested this hypothesis via Wilcoxon rank sum tests.
Results
Sample:
Reliability:
We included 104 Romanian HIV patients with the following characteristics: 63% women, mean age 31 +-7, 20% with detectable viral load, mean age since HIV diagnosis 11.12, 27% employed, 55% not parents, 40% living in rural areas (see table 1). Characteristic Value Count )%)/ mean (SD) Education Elementary 9 (8.7) High school 42 (40.4) Middle 28 (26.9) None 1 (1) University 24 (23.1) Number of children 0 58 (55.8) 1 31 (29.8) 2 10 (9.6) 3 5 (4.8) Family status Extended family 52 (50) Independent family 44 (42.3) Other situations 8 (7.7) Work status Employed 29 (27.9) Unemployed 69 (66.3) Occasional work 6 (5.8) Relationship status Single 33 (31.7) Stable relationship 71 (68.3) Other treatment taken for other None 89 (85.9) conditions or comorbidities Psychiatric medication 3 (2.9) Tuberculosis medication 2 (1.9) Other medication 10 (6.9)
The values for reliability were satisfactory, with omega=.87 for the “taking scale” and .75 for the “timing scale”, as shown in table 4 Scale Taking adherence Timing adherence
Calpha 0.863 0.728
G6 0.857 0.667
Beta 0.766 0.609
Omega 0.871 0.753
Table 4: Reliability indices for both scales
The items were weakly associated with each other (Spearman’s rho=.23), as seen in figure 1.
Figure 1 Heatplot Spearman correlations between item scores
Table 1: Demographic and medical data of the sample (N= 104)
Structural validity: Out of the 12 items tested, eight items reflected two separate dimensions: timing and taking adherence with 3 and 5 items, respectively (see table 2). Both scales had good psychometric properties: the first scale had a homogeneity value H(se)=.74(.07) as shown in table 2 and the value for the second subscale was of .62(.08) - table 3. Item H se Item H se ProMas.1 0.755 (0.087) ProMas.3 0.615 (0.091) ProMas.6 0.711 (0.090) ProMas.9 0.567 (0.113) ProMas.7 0.776 (0.090) ProMas.10 0.659 (0.074) Subscale 2 has a homogeneity value H(se) = 0.616, (0.084) ProMas.8 0.773 (0.087) ProMas.11 0.715 (0.086) Table 3: Item homogeneity values for subscale 2 Subscale 1 has a homogeneity value H(se) = 0.741, (0.071)
Table 2: Item homogeneity values for subscale 1
Criterion ## validity: ## Wilcoxon rank sum test with continuity correction ## ## ## Wilcoxon rank sum test with continuity correction ## data: MostData$ProMAS.Taking[MostData$datenv.prms >= ‐365] by MostData$viral.load[MostData$datenv.prms >= ‐365] ## ## W = 777, p‐value = 0.04379 ## data: MostData$ProMAS.Taking[MostData$datenv.prms >= ‐365] by MostData$viral.load[MostData$datenv.prms >= ‐365] ## alternative hypothesis: true location shift is not equal to 0 ## W = 777, p‐value = 0.04379 ## alternative hypothesis: true location shift is not equal to 0
People with undetectable viral load were more likely to show high adherence for both timing (W=857, p=.01) and taking (W=777, p=.04), as illustrated in figure 2 (bubble chart cross tab).
Authors
Ana-Maria SCHWEITZER Fundatia Baylor (Baylor Foundation), Romania
[email protected]
English During the last month it so happened that I simply forgot to take (one of ) the medication During the last month it so happened that I took (one of ) my medication at a different moment than indicated by the doctor (or the moment I usually take it) During the last month it so happened that I took less medication than prescribed by my doctor During the last month I took a break from taking (one of ) my medication During the last month I decided to stop taking (one of ) my medicines During the last month I changed by myself the timing of (one of ) my medication During the last month it so happened that I took (one of ) my medicines at a later moment than usual During the last month it has happened that I did not take (one of ) my medicines for a day.
Figure 3: Heatplot Spearman correlations between item scores
Romanian In ultima luna mi s-a intamplat pur si simplu sa uit sa iau (unul din) medicamente… In ultima luna s-a intamplat sa imi iau (unul din) medicamentele la alta ora decat cand mi-a spus doctorul (sau decat le iau de obicei)… In ultima luna s-a intamplat sa iau mai putine medicamente decat mi-a spus doctorul… In ultima luna am facut pauza din a-mi lua (unul din) medicamente… In ultima luna am hotarat sa sar cel putin unul din medicamente… In ultima luna am schimbat singur ora la care imi iau medicamentele… In ultima luna s-a intamplat sa imi iau (unul din) medicamente mai tarziu decat de obicei… In ultima luna s-a intamplat sa sar toate medicamentele o zi intreaga…
Table 5: PROMAS-Rom_HIV- 8 items Figure 45: Bubblechart crosstab Taking adherence (sum binary scores) viral load Figure 45: Bubblechart crosstab Taking adherence (sum binary scores) viral load
Luiza Stefania VLAHOPOL Fundatia Baylor (Baylor Foundation), Romania
[email protected] Mieke Kleppe Research Centre for Public Affairs, HAN University of Applied Sciences, Netherlands
[email protected] Alexandra Dima Health Services and Performance Research (HESPER), Claude Bernard Lyon 1 University, Lyon, France
[email protected] Figure 46: Bubblechart crosstab Timing adherence (sum binary scores) viral load
Figure 2: Bubble chart crosstab for Taking and Timing (sum binary scores) and viral load Figure 46: Bubblechart crosstab Timing adherence (sum binary scores) viral load
Conclusions: This is the first questionnaire that clearly measures timing and taking as two separate behaviors that compose adherence. It also has the advantage of being a very short questionnaire, with a good reliability, thus adequate for large scale use in clinical settings. Using the ProMAS-Rom-HIV in clinical practice opens up new intervention possibilities. Since both behaviors can now be measured via self-report, the specific barriers for timing and taking adherence can be identified for patient groups and also for each individual patient and targeted at a later time, with more precise interventions. The tool was validated against viral load, but it would further benefit from validation against EM data and in other clinical contexts.
