Pharm World Sci (2010) 32:744–751 DOI 10.1007/s11096-010-9431-8
RESEARCH ARTICLE
Association between drug prescribing and quality of life in primary care Samanta E. Fro¨hlich • Anamaria V. Zaccolo Sabrina L. C. da Silva • Sotero S. Mengue
•
Received: 11 October 2009 / Accepted: 9 August 2010 / Published online: 29 August 2010 Springer Science+Business Media B.V. 2010
Abstract Objective To evaluate quality of life among patients of Family Health Strategy Units and how it relates to the prescribing complexity and to the number of psychotropic medications prescribed, including adjustments for sociodemographic characteristics. Setting Family Health Strategy Units in a municipality in the Brazilian state of Rio Grande do Sul. Method Cross-sectional study using face-toface interviews and prescribing analysis among users of Family Health Strategy Units. Patients were recruited by consecutive sampling. Multiple linear regression models were fitted to the different domains of quality of life in the WHOQOL-Bref questionnaire. The response rate for the patients who completed the interview was 97%. The prescribed medication data and sociodemographic characteristics of the sample were included as covariates. Prescribing complexity was analyzed by means of the Medication Regimen Complexity Index. The assumptions in the
S. E. Fro¨hlich A. V. Zaccolo S. L. C. da Silva S. S. Mengue Department of Epidemiology Program, Federal University of Rio Grande do Sul, Ipiranga Avenue, 2752, Porto Alegre, RS 90610-000, Brazil S. E. Fro¨hlich (&) Visconde do Herval Street, 556/304, Porto Alegre, RS 90130-150, Brazil e-mail:
[email protected] S. S. Mengue Department of Pharmaceutical Sciences, Federal University of Rio Grande do Sul, Ipiranga Avenue, 2752, Porto Alegre, RS 90610-000, Brazil
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estimated models were tested and the models were validated. Main outcome measure Quality of life among patients of Family Health Strategy Units. Results At total, 336 patients answered the questionnaire. Through multiple linear regression, it was observed that higher prescribing complexity was associated with significantly low scores in the physical (-2.01, 95% CI = -2.89 to -1.35) and overall (-1.93, 95% CI = -2.81 to -0.99) quality of life domains. Greater amounts of psychotropic medications prescribed were associated with significantly low scores in the physical (-1.02, 95% CI = -1.29 to -0.56), psychological (-2.52, 95% CI = -3.15 to -1.65) and overall (-0.97, 95% CI = -2.06 to -0.33) domains of the interviewees’ quality of life. The estimated models were adjusted for the sociodemographic characteristics of the sample and presented good predictive capacity. Conclusions The evaluated aspects of the prescribed medication (complexity and presence of psychotropic medications) were associated with low scores in the physical, psychological and overall quality of life domains. This may be an intrinsic characteristic of the interviewed patients, like having the quality of life at such a low level before starting the treatment, that the medication could not improve it to normal levels. Also, it can be a demonstration of the ineffectiveness of these treatments within primary health care. Keywords Brazil Prescribing complexity Multiple linear regression Primary care Psychotropic medications Quality of life
Impact of the findings on practice •
Patients using psychotropic medication do not necessarily have an improved quality of life.
Pharm World Sci (2010) 32:744–751
•
Physicians should not prescribe psychotropic medication only to improve quality of life of their patients.
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responsible for registering and attending the local population [18]. That allows better comprehension of patients and cases. This Brazilian strategy is a reference to other countries and is consolidated on most Brazilian cities [18].
Introduction Quality of life has been defined by World Health Organization’s Quality of Life Group as the perceptions of the individuals about their positions in life (within the cultural context and value system they live at), aims, expectations and standards [1, 2]. It relates to a movement that assures that its parameters are a lot more comprehensive than symptom control, decreased mortality or increased life expectancy [3]. Health-related quality of life reports have the potential to identify general health needs [4]. Through studying them, the overall impact of diseases and medical treatments can be determined from patients’ perspectives. From a social point of view, it is impossible to separate diseases from a person’s individual characteristics (measured by investigating patients’ quality of life) [5]. Moreover, quality of life is also an indicator of the results from prescribing, revealing the treatment’s benefit and/or harm caused to patients [6, 7]. When appropriately measured, it is possible to distinguish different types of patients, and the efficacy of their treatment [5, 8]. It also can help to prioritize problems, identify preferences and improve communication with patients [5, 8]. The use of multiple medications can result in drug interactions, adverse drug reactions, inappropriate dosing, potential therapeutic failure and patient non-adherence [9, 10]. These are circumstances that increase patients’ risk for drug-related morbidity and misadventures, facts that harm quality of life [11]. Besides, difficulties with medication therapy have a negative impact on the patients’ perception of their health status and quality of life [12]. The largest burden of morbidity is created by mental diseases (such as anxiety, depression and panic disorder) [13, 14]. Their multiple somatic symptoms represent a stigma and cause insufficient patient adherence to care recommendations [13, 14]. Therefore, the patients with one or more of these conditions experience poorer than expected clinical outcomes, despite the availability of efficacious treatments, which primary care physicians could provide [15–17]. The Family Health Strategy Units (FHSUs) were chosen for data collection. The units are part of a governmental initiative towards health care that focuses its actions on the global attendance of the community, the family hold and the individuals. It prioritizes prevention, education and medical follow-up to each patient. Each FHSU has a multidisciplinary team and a defined working territory, being
Aim of the study The aims of the present study were to evaluate quality of life among patients of FHSUs and how it relates to the prescribing complexity and to the number of psychotropic medications prescribed, including adjustments for sociodemographic characteristics. In general ways, this study can bring to perspective a broader approach towards the population attended at primary healthcare services. Method A cross-sectional study was conducted, with individuals registered at Family Health Strategy Units in the municipality of Santa Cruz do Sul, Brazil. The city has 119,000 inhabitants, basically concentrated in the urban area [19]. Its Human Development Index (HDI) (a comparative measure of life expectancy, literacy, education and standards of living for countries worldwide, ranging from zero to one, being the highest the value, the more developed the analyzed area is), equals 0.817 [20]. When analyzed among a wider group of indicators, it represents the economic, social and environmental real conditions [20]. In Santa Cruz do Sul, the average per capita income is US$ 211.12, child mortality rate within the first year of life is 22.11 per 1,000 live births and life expectancy is 69.7 years [20]. The eight FHSUs of Santa Cruz do Sul were established in different neighborhoods and provided health care to 24.1% of the population until September 2006 [19]. Mental health services in primary care include treatment services and preventive activities delivered by primary care professionals. Primary care services are easily accessible and are generally well accepted by people with mental health disorders. For most common and acute mental disorders, these services may have clinical outcomes as good as more specialized mental health services [13, 14]. Santa Cruz do Sul also has Psychosocial Assistance Centers that give clinical attention to people with grave and persistent mental disorders, supporting primary care on the matter [19]. During the period of data collection (March–May 2006), 336 users were selected consecutively. The inclusion criteria was to be registered at any of the FHSUs in the city, to be older than 18 years old, to accept participating in the
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study, to be able to communicate properly, to be an user of the medical services of the Family Health Strategy (FHS) during the period of data collection and to have received a medical prescription. The patients were interviewed after the medical visit. The interviewers were eight Pharmaceutical Sciences students. To collect data in the most standardized way, they followed a previously prepared interview-guideline. The patients were interviewed to gather data on sociodemographic characteristics and quality of life. The data on their prescribing was transcribed into a specific form and then analyzed. To evaluate the patients’ quality of life, the WHOQOLBref questionnaire was used [3]. This instrument has shown satisfactory characteristics regarding internal consistency, validity and test–retest reliability [21]. It has 26 questions, divided into five domains: physical, psychological, social relationships, environmental, and overall. The questions have five alternatives, each scored from 1 to 5, being 1 ‘‘very bad’’ and 5 ‘‘very good’’. Prescribing complexity has been measured based on information from medical prescribing [22], which was also validated for the Portuguese language [23]. The Medication Regimen Complexity Index includes pharmaceutical form, dosing frequency and additional instructions [22]. The pharmaceutical form was calculated as follow: (1) point for tablets, capsules and topical sprays; (2) points for gargles, mouthwashes, gums, lozenges, liquids, powders, granules, sublingual sprays and tabs for oral administration, topical pharmaceutical forms, nasal sprays, enemas, suppositories and vaginal creams; (3) points for dressings and pastes for topical use, ear, eye and nasal drops/creams/ointments, accuhalers, aerolizers and pessaries; (4) points for metered dose inhalers, ampoules and vials injections; 5 points for nebulizer and dialysis [22]. Regarding the calculation of the dosing frequency, the higher the frequency, higher the score [22]. On additional directions, the higher the complexity of the medication use, higher the score [22]. Then, after finding the related numbers, adding the points of each category leads to the index [22]. Psychotropic medications are any medication capable of affecting the mind, emotions, and behavior [24]. The prescribing data was transcribed into a specific form and classified according to the third level of the Anatomical Therapeutic Chemical Code (ATC) [25]. The classified drugs starting with the code ‘‘N’’ of the ATC [25], except the analgesics, were considered in the study as psychotropics. The statistical analysis was performed using SAS version 9.1.3. The multiple linear regression technique was used to estimate a predictive model for each domain of the WHOQOL-Bref quality of life instrument. To construct the models, 202 of the interviews were randomized. The other interviews were used to validate them.
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The variables which could be entered into the models were: prescribing complexity, number of psychotropic medications prescribed, age, sex, race, conjugal situation, schooling level, and individual income. The variables were selected by forward selection method. The criteria used to enter variables into the model was a = 0.20. Schooling level, conjugal situation, and race variables were recoded as ‘‘dummy variables’’. The interaction between the prescribing complexity and the number of the psychotropic medications prescribed was tested, as well as the number of prescribing medications multiplied by the dosage frequency, which is an indicator of the regimen complexity [22]. The appropriateness of each model was tested using the significance level of 5%, by means of residual analysis, lack-of-fit tests, verification of constant variance assumptions, normality tests, independence tests, multi co-linearity analysis and diagnostic assessment. In the validation process, the mean-squared prediction error (MSPE) of the data used for the validation was calculated for comparison with the residual mean square (RMS) of the data belonging to the model construction. The project for implementing this study was approved by the Health Department of the municipality of Santa Cruz do Sul through the official letter 530/SMS/2005/PF and by the Ethics Committee of the Federal University of Rio Grande do Sul, under number 2005450.
Results Out of the 336 patients who answered the questionnaire, 320 agreed to provide prescribing information. Table 1 shows the characteristics of the studied population. According to the interviewees, the main reasons why they sought medical help were: pain in a region of the body (30.0%); Hypertension (23.5%); alleged depression (5.3%); Diabetes Mellitus (3.8%). The most prevalent classes, among the prescribed psychotropics, were selective serotonin reuptake inhibitors (40.0%), benzodiazepine derivatives (22.0%) and barbiturates and derivatives (4.0%). In the self-assessment of quality of life, 6.0% of the individuals considered their quality of life to be ‘‘very good’’, 70.8% ‘‘good’’, 21.1% ‘‘neither good nor poor’’, 1.8% ‘‘poor’’ and 0.3% ‘‘very poor’’. Regarding satisfaction with their health, 5.7% said they were ‘‘very satisfied’’, 56.4% ‘‘satisfied’’, 28.4% ‘‘neither satisfied nor dissatisfied’’, 7.8% ‘‘dissatisfied’’, and 1.7% ‘‘very dissatisfied’’. In the physical domain, the mean score was 16.6 points (SD 2.1). Locomotion had the highest mean (4.0), followed by capacity to perform daily activities (3.7) and sleep (3.7). The lowest mean was the individuals’ need for medical
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Table 1 Characteristics of the studied population Variables
N
%
Age group From 18 to 20 years
26
7.7
From 21 to 40 years
115
34.2
From 41 to 60 years
136
40.5
59
17.6
99
29.5
237
70.5
Over 60 years Sex Male Female Race/Ethnicity White
200
59.5
Black Asian
59 8
17.6 2.4
Mixed
68
20.2
1
0.3
254
75.6
82
24.4
25
7.4
Indigenous Conjugal situation Living with spouse or companion Not living with spouse or companion Schooling Level Never went to school Elementary school
248
73.8
High school
53
15.8
University level
10
3.0
Up to US$ 143.00
141
42.0
From US$ 143.00 to US$ 286.00
148
44.0
47
14.0
Monthly income
More than US$ 286.00 Complexity of medication prescribed Up to 10 points
257
80.3
From 11 to 20 points
57
17.8
More than 20 points
6
1.9
Number of psychotropic medications in the prescribing None
274
85.6
1
38
11.9
2
8
2.5
treatment in order to lead their lives (2.5). The psychological domain mean scored 15.6 points (SD 2.1). The highest mean in this domain was the frequency which the interviewees presented negative feelings (4.0), followed by satisfaction with themselves (3.9) and satisfaction with life (3.9). Regarding social relationships, the mean score was 15.5 points (SD 2.6) and the means were close to each other for all the questions. The environment mean scored 14.0 points (SD 2.1). The highest mean in this domain was the individuals’ access to health services (3.9), followed by satisfaction with the place where they lived (3.8). Having enough money to satisfy their needs presented the lowest mean (2.8).
Table 2 presents the means of each of the five quality of life domains for the analyzed variables. Table 3 presents the estimates for the multiple linear regression models for each quality of life domain, in relation to prescribing complexity and the number of psychotropic medications prescribed, with adjusts for sociodemographic characteristics. Out of the eight variables listed on the methods section, the table only shows those considered influent, by the selection method used, for each domain. With the exception of social relationships, the coefficients found described the relative contribution of each variable to the different quality of life domains. Since the interaction between the prescribing complexity and the number of the psychotropic medications prescribed was not statistically significant (P = 0.22), it was not shown on the final models. That allowed the complexity of prescribing and number of psychotropic medications variables to stay in the final results. Through analysis of appropriateness, multi co-linearity and diagnostic evaluation, it was observed that none of the assumptions were violated. The results from the validation of the estimated models show that they have a good predictive capacity.
Discussion The results showed that prescribing complexity was associated with low scores in the physical domain and in the overall quality of life domain. In addition, higher numbers of psychotropic medications prescribed were associated with low scores in the physical, psychological, and overall quality of life domains. Being this study a cross-sectional one, the evidence for causal relationships between the factors analyzed and the outcome was weak. The directionality of this association could not be determined by this study. In other words, it can not determine if there was a decline in quality of life, whenever certain diseases (that lead to more complex prescriptions and/or the use of psychotropic medications) appeared, or if there was a specific association between these medications and the patients’ quality of life. It must also be emphasized that, because this was a sample of health service patients, the inferences for the whole population would be limited. However, associations which were likely to have been spurious were controlled by means of internal validation of the estimated models. The severity of the disease was not analyzed. However, while evaluate quality of life, the gravity of the medical symptoms (that may be included on the measure of the prescription complexity) is more informative. At times, a severe disease might not be associated with grave medical symptoms. In cases like that, the evaluation of quality of life would depend on the patient’s awareness of the
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Table 2 Means of the quality of life domains, for sociodemographic characteristics and prescribing aspects Variables
Physical domain Mean ± SD
Psychological domain Mean ± SD
Social relationships domain Mean ± SD
Environmental domain Mean ± SD
Overall quality of life domain Mean ± SD
Prescribing complexity Up to 10 points More than 10 points Number of psychotropic medications
16.6 ± 2.2
15.6 ± 2.1
15.5 ± 2.6
13.9 ± 2.1
16.2 ± 1.6
13,6 ± 2.7
14.4 ± 2.7
15.3 ± 2.9
13.4 ± 2.3
13.8 ± 2.0
None
15.9 ± 1.5
16.8 ± 2.3
15.9 ± 2.0
14.2 ± 1.9
16.2 ± 1.7
1 or more
14.1 ± 1.7
14.0 ± 2.5
15.4 ± 2.2
13.9 ± 2.3
14.1 ± 2.0
Age group From 18 to 30 years
16.2 ± 2.1
15.3 ± 2.4
15.4 ± 2.1
14.1 ± 1.6
16.0 ± 2.1
From 31 to 60 years
14.9 ± 1.9
15.1 ± 2.3
15.6 ± 1.9
13.9 ± 1.6
15.7 ± 2.2
Over 60 years
13.0 ± 1.7
15.0 ± 2.0
15.6 ± 1.7
13.8 ± 1.6
15.4 ± 2.3
Sex Male
15.9 ± 1.2
15.8 ± 1.2
15.9 ± 1.7
15.3 ± 1.3
15.1 ± 2.1
Female
14.2 ± 1.4
14.2 ± 1.5
15.5 ± 1.9
13.8 ± 1.4
14.6 ± 2.0
Race/Ethnicity White
15.0 ± 2.1
15.1 ± 1.9
15.5 ± 2.7
13.9 ± 2.2
14.9 ± 2.1
Non White
14.4 ± 2.3
14.6 ± 2.4
15.3 ± 2.0
13.4 ± 2.5
14.3 ± 2.4
14.9 ± 2.2 14.4 ± 2.6
15.3 ± 1.9 14.6 ± 2.0
15.9 ± 1.3 14.0 ± 1.7
14.1 ± 1.6 13.6 ± 1.6
15.0 ± 2.0 14.2 ± 2.1
Conjugal situation Living with spouse or companion Not living with spouse or companion Schooling Level Never went to school
13.3 ± 1.3
13.4 ± 1.5
15.8 ± 2.1
13.0 ± 1.2
14.2 ± 1.9
Elementary school
14.6 ± 1.3
15.0 ± 1.4
15.6 ± 1.8
14.2 ± 1.1
14.6 ± 1.6
High school
16.1 ± 1.2
16.3 ± 1.3
15.5 ± 1.7
15.9 ± 1.1
15.9 ± 1.2
Up to US$ 143.00
14.4 ± 2.4
13.9 ± 1.7
15.4 ± 2.7
14.0 ± 2.5
14.3 ± 2.2
More than US$ 143.00
15.1 ± 2.2
15.7 ± 1.4
15.9 ± 2.2
14.0 ± 2.4
15.0 ± 2.1
Monthly income
severity of the disease. Besides, since all interviewed individuals were attended on a primary care service, any diseases with a high level of severity would lead the patients to other stages of attention. The higher prevalence of women can be explained by the fact that females are more attentive to the signs and symptoms of a disease and they have more initiative to seek for medical help [26]. Also, women may be less inserted than men in the economically active population, which can decrease the use of FHS by the male population [26]. It was also found that the sample presented a low educational level and income. The complexity may be associated with the presence of co morbidities which are predictors for high mortality, worsening of the functional state [27], greater use of medical services and reduced quality of life [28–30]. In addition, chronic medical conditions have shown associations with the type and/or severity of physical deficiency [31]. The cumulative effect of co morbidities is not simply a matter of addition: certain combinations have greater
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effects than others do [32]. Other factors that may be associated with prescribing complexity, which in turn affect the morbidity among such individuals, are lower adherence to treatment [33, 34] and a greater risk of adverse effects and interactions between the prescribed medications [8]. Changes related to mental health (like feelings of sadness, anxiety, guilt, helplessness and irritability) can affect the results and the treatment of many chronic diseases [35, 36]. This can be an explanation for the low scores in the physical, psychological, and overall quality of life domains found for prescriptions with higher number of psychotropic medications. These associations probably occurs because these are patients with high levels of anxiety, depression and stress, whose thinking is affected by negativism and a tendency to feel dissatisfied with aspects of their lives. Stress during life may induce greater vulnerability of the organism, as well as producing chronic depression [36]. Moreover, use of psychotropic medications may increase the risk of sedation, interactions and adverse effects in the
Pharm World Sci (2010) 32:744–751 Table 3 Multiple linear regression model estimates for quality of life domains, adjusted for sociodemographic characteristics
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Models
P-value
R2
Domain 1—Physical domain
\0.0001
0.2094
Estimate for parameter
95% CI
Prescribing complexity
-2.01
-2.89 to -1.35
Age
-1.04
-1.18 to -0.77
Number of psychotropic medications prescribed
-1.02
-1.29 to -0.56
-0.89
-1.33 to -0.56
Sex Male Female Schooling level Never went to school Elementary school not completed
1.09
-0.65 to 1.97
Elementary school completed High school not completed
1.08 1.57
-0.83 to 1.74 -0.95 to 2.31
High school completed
2.45
1.84 to 3.01
1.22
-0.47 to 1.85
University level Domain 2—Psychological domain
\0.0012
0.1749
Individual income (US$)
0.65
Number of psychotropic medications prescribed
0.14 to 1.31
-2.52
-3.15 to -1.65
-0.88
-1.31 to -0.55
Sex Male Female Schooling level Never went to school Elementary school not completed
1.23
0.54 to 1.96
Elementary school completed
1.51
0.78 to 2.19
High school not completed High school completed University level Domain 3—Social relationships domain
0.3510
0.0105
0.0266
0.0622
2.35
1.76 to 3.10
3.16 3.72
2.59 to 4.04 2.16 to 4.67
0.65
-0.90 to 1.18
Conjugal situation Not living with spouse or companion Living with spouse or companion Domain 4—Environmental domain Sex Male Female
-0.69
-1.17 to -0.40
Schooling level Never went to school Elementary school not completed
0.61
0.08 to 1.28
Elementary school completed
0.51
0.03 to 1.14
High school not completed
1.08
0.76 to 1.92
0.95
0.22 to 1.78
High school completed Domain 5—Overall quality of life domain Prescribing complexity Individual income Number of psychotropic medications prescribed
0.0009
0.1438 -1.93 0.02 -0.97
-2.81 to -0.99 -0.67 to 1.84 -2.06 to -0.33
Schooling level Never went to school High school completed
2.01
0.75 to 2.95
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central nervous system, thereby affecting the physical and psychological aspects of patients’ quality of life. A study which compared the quality of life of patients with psychiatric disorders, against patients with other medical conditions, suggested that mental diseases contributed towards impairment of quality of life [37]. The results of this research agreed with the findings of a study that used WHOQOL-Bref on primary healthcare patients in Porto Alegre (State of Rio Grande do Sul), that shows that depressive symptoms were highly associated with a worse quality of life [38]. Similar results were also found in other studies [39, 40]. Also, there is evidence that depressive patients use medical services more frequently, present decreased work productivity and present lower quality of life, when compared to individuals with other diseases of chronic nature [41]. The low R2 of the models of Table 3 shows that quality of life depends on several other factors which were not described in this research. The prescribing complexity expresses a disease which needs a treatment with many medications and/ or several doses at diverse moments, or even the combination of multiple diseases of lower or higher gravity. In addition, the low R2 could be the result of generic instruments used to measure quality of life and prescribing complexity. The use of disease-specific questionnaires would result in the need for more than one instrument and, possibly, a summary score. However, the use of specific instruments in this study would cause amplitude loss of the tested association. Conclusion It was observed that the euphoria regarding the good performance of modern psychotropics may not be so accurate, since their usage seemed not to restore the participants’ quality of life to the same level as that of individuals who were not using these medications. This may be an intrinsic characteristic of the interviewed patients, like having the quality of life at such a low level before starting the treatment, that the medication could not improve it to normal levels. Also, it can be a demonstration of the ineffectiveness of these treatments within primary health care. Funding Samanta E. Fro¨hlich received assistance from the National Counsel of Technological and Scientific Development in the form of a bursary to help to fund the studies for a master’s degree in Pharmaceutical Sciences. Conflicts of Interest interest to declare.
The authors do not have any conflict of
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