Identificación de las necesidades de capacitación y entrenamiento

Summary

As the population ages and individuals are more frequently placed on long-term prescription regimens, understanding factors associated with medication refill adherence behavior is an important endeavor. Utilizing data collected from patients managing CHD, independent variables including background characteristics, cognitive status, health literacy, number of medications, presence of diseases, self-efficacy, patient perceived concerns about medication use, patient perceived necessity for medication use, enablers to medication-taking behavior and barriers to medication-taking behavior were examined through a path analysis to see how effectively they work together in a model that explains the determinants of medication refill adherence behavior. Overall, the hypothesized model (with CMG as the dependent variable) did not fit the data. However, there were some interesting findings that help elucidate the relationships between the independent variables and medication refill adherence behavior.

In the hypothesized model, background characteristics were proposed to directly affect cognitive status and health literacy. The variable age was significantly correlated with cognitive status, r = -.323 (p = .00). Age was also significantly correlated with health literacy, r = -.133 (p = .01). Education was significantly correlated with both cognitive status, r = .483 (p = .00) and health literacy, r = .617 (p = .00).

The hypothesized model proposed that the effects of a person‟s cognitive status, along with their health literacy, directly correlate with the ability to manage chronic illness, specifically, the number of medications taking and managing co-morbidities such as diabetes, hypertension, and hypercholestermia. The current study did not find any significant correlations to this effect.

The hypothesized model proposed that patient health, including number of medications taking and presence of co-morbidities such as diabetes, hypertension, and hypercholestermia, would directly influence self-efficacy. Additionally, Phatak‟s (2006) regression analysis found that the total number of medications, along with

concerns/necessity beliefs, and age, explained 26.5% of the variation in non-adherence. The same analysis with the current study found that 5.6% of the variation in adherence was explained, and this finding was statistically significant.

Self-efficacy was proposed to directly influence patient perceived concerns and perceived necessity for medication use. Results indicated that self-efficacy significantly correlated with patient perceived concerns, r = .303 (p=.00). Similar to Aljasem‟s (2001) study where regression analysis found that greater self-efficacy accounted for statistically significant 8% of the variance in less frequent skipping of medication, and Gatti‟s (2009) study findings that patients with lower self-efficacy had greater odds of low medication adherence compared with patients with higher medication self-efficacy, the current study found through regression analysis that self-efficacy accounted for statistically significant 2.9% of the variance in refill adherence.

The model proposed that patient perceived concerns and perceived necessity for medication use would influence enablers and barriers to medication-taking behavior.

There were significant correlations between perceived concerns and enablers, r = -.185 (p=.00) and barriers, r = .343 (p=.00). There was a significant correlation with perceived necessity and enablers, r = -.139 (p=.01). Previous research regression analysis found that necessity (Horne & Weinman, 1999) and concerns/necessity (Byrne et al., 2005)

explained 19% and 7% respectively, the variation in adherence. Similarly, the current study found that together patient perceived concerns and perceived necessity for

medication accounted for a statically significant 2.8% of the variance in medication refill adherence. In another study (Phatak & Thomas, 2006), concerns/necessity beliefs, age and total number of medications explained 26.5% of the variance in non-adherence to medication. The current study found that together concerns/necessity beliefs, age and total number of medications explained a statistically significant 5.6% of the variation in medication refill adherence.

Enablers and barriers to medication-taking were proposed to directly influence medication adherence. More work needs to be conducted examining enablers as an explanatory variable for medication refill adherence. Only one study (Svensson et al., 2000) examined enablers and it found that the top reasons for medication adherence were physician trust and desire to control the condition. When examined in the literature, barriers to medication-taking behavior included a need for information and support (Barber et al., 2004) and changing a daily routine (George & Shalansky, 2007). Gellad (2007) found both cost and skipping doses statistically significant in explaining refill adherence. The current study found that barriers significantly correlated with, self- efficacy, concerns and enablers, r = .47 (p=.00), r = .34 (p=.00), r = -.16(p=.00),

to accommodate taking medications a statistically significant independent predictor of refill non-adherence. The current study found that barriers to taking medications explained a statistically significant 1.9% of the variance in refill adherence.

Because the hypothesized model did not fit the data, the ARMS self-report measure of adherence was analyzed with the independent variables. A major advantage of the ARMS is its appropriateness for use among minority populations and patients with limited literacy skills (Kripalani, 2009). The current study participants included 90.1% African-Americans whose mean health literacy was equivalent to a fourth to sixth grade reading level. In this study, the ARMS variable had greater linear relationships with the independent variables than the CMG variable. The ARMS variable did not fit the hypothesized model. However, there were stronger linear relationships between ARMS and age, self-efficacy, concerns, and necessity variables when compared to CMG. These relationships are substantiated and supported by the literature. Therefore, these four variables were tested in a revised path model with the ARMS dependent variable and results found that this revised model fit the data.

Strengths and Limitations

A major strength of this study is that, for the first time, all variables found to have an association with medication adherence were tested together in a model of determinants of medication refill adherence behavior. Unfortunately, this hypothesized model did not work. However, the condensed revised model, using ARMS as the dependent variable, was successful in fitting a smaller group of variables.

A limitation to this study was that the independent variables were self-reported (except for number of medications and presence of diseases, which were collected from

medical records). A second limitation was that the participants were predominately African-American attending a pharmacy in a large urban city, resulting in limited generalization possibilities. A third limitation of the study was that the unsuccessful exploratory model was based on research that looked at two different constructs,

medication adherence and medication refill adherence. It may have been fruitful to tease out a model that was purely based on either medication refill adherence specifically or medication adherence in general.

Conclusion

Overall, the study found that the proposed interactions in the hypothesized model of refill adherence behavior did accurately reflect some of the relationships between the independent variables. However, the present findings do not support the hypothesized model of refill adherence behavior. The ARMS variable had greater linear relationships with the independent variables than the CMG variable. Although the hypothesized model did not fit the data, a condensed revised model using age, self-efficacy, concerns, and necessity variables with ARMS as the dependent variable successfully fit the data. However, the revised model did not explain a statistically significant amount of the variance in medication adherence.

Future research needs to identify additional variables that may also be associated with medication refill adherence. This is especially salient given that studies examining variables that play a role in adherence to medication regimens did not predict high levels of variance in adherence. Overall, the studies demonstrated that the proportions of variance in adherence explained was low, including 6.8% for age and other background characteristics (Schectman et al., 2002), 4% for cognition (Insel et al., 2008), 7% for

concerns/necessity, 8% for self-efficacy (Aljasem et al., 2001), 19% for necessity (Horne & Weinman, 1999), and 26.5% for number of medications, concerns/necessity, and age (Phatak & Thomas, 2006). Additional research is needed in this area to reveal all the determinants of medication refill adherence behavior and to identify the most appropriate measure of adherence behavior.

A better understanding of the motivational and behavioral factors that influence medication refill adherence will assist the development of effective strategies to improve patient adherence. Given the number of people who suffer from chronic diseases such as CHD, diabetes, hypertension, and hypercholestermia, and the often low rates of

medication adherence, research that continues to explore and improve medication refill adherence will have a significant impact on morbidity and mortality rates.

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