• Center on Health Equity and Access
  • Clinical
  • Health Care Cost
  • Health Care Delivery
  • Insurance
  • Policy
  • Technology
  • Value-Based Care

Disparities in Antidepressant Adherence in Primary Care: Report From Israel

The American Journal of Managed CareSeptember 2011
Volume 17
Issue 9

Patient characteristics such as psychiatric diagnosis were associated with variations in adherence, although physician characteristics were not.


To evaluate patient and physician characteristics associated with the purchase of antidepressant medication for at least 6 months.

Study Design and Methods:

Clalit Health Services is the largest managed care health fund in Israel, a country that uses a universal healthcare system. We randomly sampled 30,000 primary care patients over the age of 22 years. Our analytic sample consisted of those 949 patients who did not purchase antidepressant medication during the last quarter of 2005 and purchased antidepressant medication at least once in 2006. We used multilevel analysis, with whether or not medication was purchased for at least 6 months as an indicator of adherence. Patient and physician characteristics were evaluated as potential predictors.


Only 23% of the sample was classified as adherent. Physician characteristics explained only a small portion of the variance in adherence and, as a result, were not included in multivariate analysis. Patients who did not have a somatic diagnosis, had a depression diagnosis, and were of higher socioeconomic status were more likely to be classified as adherent. Patients who purchased tricyclic antidepressants were less likely to be classified as adherent.


The findings suggest that in a managed care setting, there is high uniformity among physicians. Although physician characteristics explain little of the variability associated with adherence, certain patient characteristics as determined by their physicians (eg, antidepressant drug class, psychiatric diagnosis) do play a role in adherence.

(Am J Manag Care. 2011;17(9):e340-e347)

Characteristics of patients and physicians at Israel’s largest HMO were evaluated as potential predictors of antidepressant adherence (ie, the medication was purchased for 6 months or longer).

  • Only 23% of the patient sample was classified as adherent.

  • Although physician characteristics explain little of the variability in adherence, certain patient characteristics as determined by their physicians (eg, antidepressant drug class, psychiatric diagnosis) do play a major role in adherence.

  • Even in a country that has universal healthcare, population group differences exist and are associated with socioeconomic status.

The public health costs associated with untreated or undertreated depression or anxiety are substantial.1-4 Primary care providers serve as the main gatekeepers for mental health problems in Israel as in other countries,5 with both depression and anxiety being most often managed in primary care clinics by primary care providers.6-8 Fortunately, the past few decades have seen a tremendous improvement in the management of depression and anxiety.9,10 According to current clinical guidelines, the first line of treatment for the management of either depression or anxiety should be an antidepressant such as a serotonin reuptake inhibitor (SSRI) or a serotonin-norepinephrine reuptake inhibitor. Older antidepressant medications, such as tricyclic antidepressants (TCAs) or monoamine oxidase inhibitors, may also be indicated for specific populations or as augmentation therapy.11 Clinical guidelines recommend the use of these agents for a minimum of 6 consecutive months in order to achieve adequate efficacy and stabilize the psychiatric condition.11-14

Despite the documented efficacy of these pharmacologic agents in the management of depression and anxiety, reports of nonadherence are staggering.15-17 Studies have shown that more than 30% of the patients discontinue their medications within the first 4 weeks of treatment15,18 and more than 60% discontinue within the first 6 months.16

Reasons for nonadherence vary and may relate to patient, drug, and provider characteristics. Studies that have focused primarily on patient characteristics found that individuals of ethnic minority background, younger or older adults,19 individuals of lower socioeconomic status (SES),17,18 and those with a personality, substance use,19 somatization, depression, or anxiety diagnosis20 are less likely to adhere to their regime. In contrast, others found that carrying a dual depression and anxiety diagnosis results in better adherence.21 Beliefs concerning psychotropic medications,22,23 the stigma of mental illness,24 or a mismatch between patients’ preferred and assigned treatment25 have also been shown to be barriers to adherence. In addition, the side effect profile of a particular drug26 is of importance, with certain psychotropic drugs resulting in lower adherence than others.17,18,27

It has been argued that physicians’ knowledge about appropriate treatments, and their ability to establish rapport, assess patients’ attitudes, and provide appropriate consultation and information are all essential for establishing appropriate adherence.28 Hence, as expected, physician characteristics have also shown to be associated with adherence to antidepressant medication. Whereas some found that receiving services from providers with a mental health specialty results in better adherence to psychotropic medication,21,29 others noted that simply providing patients with educational messages concerning the importance of the medication improves adherence.30 Using a guideline-concordant follow-up13 or concurrent psychotherapy31 was also associated with improved adherence. Nevertheless, several different interventions specifically geared toward increasing adherence to psychotropic medication were found to be largely ineffective,32 leading researchers to argue for a need for a substantial shift in services at the organizational level in order to improve adherence.33 Thus, questions have arisen about the provider role in improving adherence to psychotropicmedications.

Although informative, the majority of research to date focused either on patient characteristics or on provider characteristics as facilitators of adherence, but did not take both into account simultaneously. In addition, results are still equivocal with regard to the role of primary care physicians as facilitators of adherence to antidepressant medications. Moreover, the majority of research was conducted in countries that do not use a universal healthcare system. Thus, although we know that the prohibitive costs of psychotropic medications may be a potential barrier for some patients,34 we do not know whether disparities in adherence continue in the absence of financial constraints to the patient.

The present study provided a unique opportunity to evaluate patient and provider characteristics associated with adherence to antidepressant medications in primary care clinics in Israel, a country that uses a universal healthcare system, providing a comprehensive, uniform basket of services that includes psychotropic medications, but not psychotherapy.35 We used a comprehensive computerized medical registry of Clalit Health Services, the largest healthcare fund in Israel, which insures 53% of the Israeli population and operates as a managed care organization. Under the Israeli system, the primary care provider is considered responsible for all patient healthcare needs. The provider is responsible for the care of a defined number of patients and refers patients for consults as needed. Based on expert recommendations, the primary care provider determines patients’ treatment. The database includes information on patient characteristics as well as primary care provider characteristics, thus allowing a distinctive analysis of the actual purchasing behavior of patients instead of relying on self-reports, which have known limitations, in particular regarding sensitive issues.36

Based on past research concerning nonadherence to psychotropic medications, we expected both patient and physician characteristics to be associated with nonadherence. Specifically, we expected older and younger individuals as well as ethnic minorities to show higher levels of nonadherence. We also expected those who did not carry a psychiatric diagnosis to have lower levels of adherence. We expected physician specialty to be associated with adherence (ie, patients of physicians who specialize in family medicine would demonstrate higher levels of adherence). This expectation was based on past research, which has shown that family medicine physicians are more likely than internal medicine physicians to manage depression by themselves.37


We randomly sampled 30,000 primary care patients over the age of 22 years as of January 2006 from Clalit Health Services’ computerized medical registry. Of these patients, 591 died in 2006 and 4446 had incomplete physician data. Thus, the source sample consisted of 25,086 primary care patients. (These are not mutually exclusive; one can have missing values and be dead at same time.) Of these, 1219 had a documented purchase of antidepressant medication in the last quarter of 2005 and were excluded from analysis. Our study group consisted of the 949 patients who did not purchase antidepressant medication during the last quarter of 2005, but did purchase antidepressant medications at least once in 2006.


Clalit Health Services uses a computerized medical registry that contains both patient and physician data. All physician visits are recorded in this registry and are matched with pharmacy data using patient and physician unique IDs. For the purpose of the study, data were deidentified to prevent patients’ breach of confidentiality.

Adherence to Antidepressant Medications. We extracted data regarding antidepressant (eg, SSRIs, TCAs, other) purchase. Adherence to a new antidepressant regime was defined as no purchases of antidepressant medication in the last quarter of 2005, followed by at least 6 months of purchase starting in 2006. Although purchasing is not necessarily synonymous with adherence as indicated by actual usage of the medication, it does indicate an active decision on the part of the patient and involves a financial investment. In addition, once antidepressants are purchased for 3 months or longer, they are considered a chronic medication by Clalit Health Services. As such, they are listed in all patient-related correspondence. According to the clinical guidelines of Clalit Health Services, discontinuation of a chronic medication is not automatic, but instead requires a discussion between patient and provider.

Patient-Level Predictors

Demographic Information. Age, sex, birth country, and number of years in Israel are listed in the registry based on patients’ self-report during their first visit.

The Charlson Comorbidity Index. The Charlson Comorbidity Index (CCI) score uses both the number and seriousness of medical conditions to predict mortality. The CCI includes 19 medical conditions that are weighted on a scale of 1 to 6 based on their 12-month relative mortality risk, with total scores ranging from 0 to 37. Because age was found to be an independent risk factor for mortality, one score is added to the CCI total score for each decade of life over the age of 50 years.38 Past research has recommended the use of the CCI as a continuous measure rather than a categorical one, in order to refine its ability to distinguish between patients.39 This score is calculated by Clalit Health Services’ medical division for all primary care patients, adjusted for their age.

Somatic Diagnosis. A list of common nonpsychiatric somatic conditions that may indicate a psychiatric condition was constructed by the authors based on the medical literature.40-44 These conditions include the various types of arthritis pain, head and back pain, abdominal pain, and insomnia. The presence of any of these diagnoses was classified as a somatic diagnosis.

Psychiatric Diagnosis. Mood disorders, anxiety disorders, adjustment disorders, and somatoform disorders were included under this category, using the International Classification of Diseases, 10th Edition, codes. We excluded post-traumatic stress disorder, obsessive compulsive disorder, bipolar disorder, and body dysmorphic disorder, as our aim was to focus on psychiatric conditions that are within the mandate of Clalit’s primary care physicians,11,45 rather than psychiatrists.

Medication Type. Medications were classified into 4 groups: SSRIs, TCAs, other, or a combination of at least 2 of these classes.11

Socioeconomic Status. Socioeconomic status (low, medium, high) was based on the Israeli Central Bureau of Statistics classification.46 Although these data are available at the primary care clinic level and not at the patient level, they are considered a good proxy of patient SES.47,48 The vast majority of patients are registered in neighborhood clinics; thus, the SES of clinics reflects that of the patients’ place of residence.

Population Group. Individuals were classified as Arabs or Jews based on Clalit Health Services’ classification of primary care clinics into locations that serve at least 70% Israeli Jews versus Israeli Arabs. Although these data are available at the primary care clinic level, they are considered a good proxy of patients’ ethnicity, given Israel’s highly segregated nature.49

Physician-Level Predictors

Physician age, sex, place of birth, number of years in the country, number of years of experience, and specialty (none, family, other) were available from employee records.


We first conducted univariate and bivariate analyses. Next, we conducted multilevel analysis, with patient-level data representing the first level of predictors (eg, patient age, sex, SES) and physician-level data (eg, physician age, sex, number of years in the country) representing the second level of predictors. The outcome variable was whether or not antidepressant medications were purchased for at least 6 months starting in 2006 with no purchases in the last quarter of 2005.

The first step of a multilevel analysis used an empty model with physician random effect. This model estimated the outcome per physician rather than per patient. This analysis yielded an intraclass correlation (ICC) score, which ranged between 0% and 100%. The ICC reflected the degree to which patients of the same physician were more similar to one another than to patients of other physicians. Thus, it reflected the proportion of the total variance that was due to differences between physicians. If the ICC is relatively large, a multilevel analysis is justified. On the other hand, if the ICC is relatively low, a multilevel analysis is unjustified, and analysis should take into consideration only patientlevel variables (ie, level 1). As a rule of thumb, ICCs of .05, .10, and .15 represent small, medium, and large effect sizes, respectively.50

Given the binary nature of the outcome, we used the linear threshold model to obtain an ICC.51 Following the calculation of ICC (which was less than 1%), logistic analysis was conducted with the purchase of antidepressant medications for at least 6 months as an outcome. Only significant predictors identified in bivariate analyses were entered into the model.


The final sample consisted primarily of women (63%); most patients were over the age of 46 years (72%). The sample consisted primarily of Jews born in Israel (34.8%) or in East Europe (24%). Most of the sample had no psychiatric diagnosis (69.3%) and most purchased SSRIs (57%). The majority of physicians were female (54.8%) and were born in East Europe or the former Soviet Union (54.8%). Most had a specialty in family medicine (51.4%). See Table 1.

Only 23% of the sample was classified as adherent. In bivariate analysis, those purchasing for 6 months or longer were less likely to have a somatic diagnosis, more likely to have a psychiatric diagnosis, and more likely to purchase SSRIs or combined psychotropics. They also were more likely to be of higher SES. See Table 1.

Table 2

Results of logistic regression analysis revealed that those who did not have a somatic diagnosis, had a diagnosis of depression, and were of higher SES were more likely to be classified as adherent. In addition, compared with those purchasing SSRIs, those purchasing TCAs were less likely to be classified as adherent. See .


The present study evaluates patient and provider characteristics associated with adherence to antidepressant medications. This study is unique for several reasons. First, we evaluated patient and provider characteristics simultaneously in an attempt to identify their exclusive roles in adherence. Second, we used a representative sample of patients of the largest managed care organization in Israel, a country that uses a universal healthcare system, thus eliminating financial barriers to patients. As a result, we could evaluate disparities, which may stem from reasons other than patients’ financial status. Third, we evaluated purchasing data rather than self-report data concerning nonadherence, and thus were more likely to get an accurate estimation of nonadherence. Finally, the focus on all patients who purchased antidepressant medications rather than only on those diagnosed with depression or anxiety was valuable, given the tendency to underdocument mental illness in primary care.7

The most notable finding of the present study is that only 23% of patients purchasing antidepressant medications were classified as adherent. This figure likely represents an underestimate of nonadherence, given the fact that patients who were prescribed antidepressant medications, but for some reason did not purchase the medications even once, were not included in our analysis. Findings suggest that even in a country that uses a universal healthcare system, nonadherence is substantial.

Our findings show that those patients who had a documented depression diagnosis were more likely to adhere to their medication regime, whereas those with a somatic diagnosis were less likely to adhere. Possibly, when a psychiatric diagnosis such as depression is documented in a patient’s medical record, the patient tends to agree with the psychiatric diagnosis and thus is also more likely to agree with psychiatric treatment. On the other hand, a somatic diagnosis likely is assigned to patients who are less accepting or are less willing to view their problems as psychiatric in nature and thus also are less accepting of psychotropic medications.

An important finding of the present study is the fact that physician characteristics play only a minor role in patients’ adherence. Whereas this study contradicts some research that argued for the important role of the provider,29,30 it does support other studies that found the provider’s role to be of lesser importance.33 This finding may be explained in several ways. First, we focused on purchasing behaviors and not on prescription patterns. Thus, most of the variability associated with purchasing was expected to be associated with patient characteristics rather than provider characteristics. In addition, some patient characteristics that have shown to be associated with adherence are actually determined by the provider (eg, chart diagnosis, class of medication purchased). Hence, providers likely do play an indirect role in patients’ purchasing patterns. Finally, lack of variance between physicians may be accounted for by the organizational setting—all worked in a managed care organization that uses specific guidelines for the management of mental illness.

A striking finding of this study is the fact that individuals of higher SES were more likely to purchase their medications for at least 6 months. Although SES may serve as a proxy of education as well as of more accepting attitudes toward mental health treatment, it is still possible that financial constraints limit adherence to antidepressant medications in individuals of lower SES. This finding is particularly surprising given the fact that the healthcare system in Israel is designed to ensure universal access to services, including antidepressant medications.

Finally, our study showed that patients were less likely to purchase TCAs than SSRIs for a period of 6 months or longer. This finding could be explained by the side effect profile of the TCAs and is consistent with findings demonstrating a tendency to use SSRIs in preference to these drugs.52 It may also be due to the fact that TCAs are also used for the management of other conditions such as pain which may justify a shorter period of use.53

The present study has some limitations that should be noted. First, we evaluated purchasing data, but not actual use. Although our method is likely more reliable than solely relying on self-report, purchasing data may still represent an underestimation of adherence as our data did not include patients who may have been prescribed medication but never filled the prescription or patients who purchased medication but failed to take it as prescribed. It is, however, important to note that the purchase of antidepressant medications does indicate an active decision on the part of the patient and involves a financial investment. In addition, once antidepressants have been purchased for 3 months or longer, they are considered a chronic medication. As such, they are listed in all patient-related correspondence. Discontinuation of a chronic medication is not automatic, but instead requires a discussion between patient and provider. In addition, we were unable to distinguish between continuous purchase and sporadic purchase over the study period, because under the current system, patients are allowed to purchase medications for a period of several months in advance. Second, we did not specifically focus on patients diagnosed with depression and/ or anxiety, as was the case in the majority of past research. Thus, it is possible that some patients purchased these medications for reasons other than mental illness, such as pain or smoking cessation. Nevertheless, regardless of indication, antidepressant medications should be used for at least 6 months in order to ensure efficacy.11 Third, we did not evaluate reasons for discontinuation. Hence, it is likely that some patients discontinued treatment because of medication side effects, whereas others discontinued treatment because of the stigma associated with the medication. Finally, this is a cross-sectional study that did not allow assumptions about cause and effect.

Nevertheless, this study has multiple strengths that outweigh its limitations. Our findings suggest that only 23% of all new patients who purchased antidepressant medications in 2006 did so according to clinical guidelines. Our findingsfurther demonstrate that although physician characteristics explain little in terms of the variability associated with adherence, patient characteristics that were determined by theirphysicians (eg, presence or absence of a chart diagnosis) do play a major role in adherence. Finally, the findings suggest high uniformity among physicians. This may be the result of Clalit’s attempt to direct clinical decisions by disseminating clinical guidelines as well as other administrative measures to ensure adequate care of mental health conditions, which are expected to result in reduced healthcare costs.

Author Affiliations: FAuthor Affiliations: From Louis and Gabi Weisfeld School of Social Work (LA, RG), Department of Management (RG), Bar-Ilan University, Israel; Clalit Health Services and Clalit Research Institute (AY, EF, RDB, MG), Tel Aviv, Israel; Epidemiology Department (RDB), Ben-Gurion University of the Negev, Israel; Community Division, Clalit Health Services (MG), Tel Aviv, Israel; Department of Family Health Care (MG), Bruce Rappaport Faculty of Medicine, The Technion, Haifa, Israel.

Funding Source: This study was funded by Clalit Health Services.

Author Disclosures: The authors (LA, RG, AY, EF, RDB, MG) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (LA, RG, AY, RDB, MG); acquisition of data (AY, EF, RDB, MG); analysis and interpretation of data (LA, RG, EF, MG); drafting of the manuscript (LA, MG); critical revision of the manuscript for important intellectual content (LA, RG, AY, MG); statistical analysis (LA); provision of study materials or patients (MG); obtaining funding (LA, RG, AY, MG); administrative, technical, or logistic support (EF, RDB, MG); and supervision (MG).

Address correspondence to: Liat Ayalon, PhD, Louis and Gabi Weisfeld School of Social Work, Bar-Ilan University, Ramat Gan, 52900, Israel. E-mail: liatayalon0@gmail.com.

1. Simon G, Ormel J, VonKorff M, Barlow W. Health care costs associated with depressive and anxiety disorders in primary care. Am J Psychiatry. 1995;152(3):352-357.

2. Stewart WF, Ricci JA, Chee E, Hahn SR, Morganstein D. Cost of lost productive work time among US workers with depression [published correction appears in JAMA. 2003;290(16):2218]. JAMA. 2003;289(23): 3135-3144.

3. Rovner BW, German PS, Brant LJ, Clark R, Burton L, Folstein MF. Depression and mortality in nursing homes. JAMA.1991;265(8):993-996.

4. Carta MG, Hardoy MC, Kovess V, Dell’Osso L, Carpiniello B. Could health care costs for depression be decreased if the disorder were correctly diagnosed and treated? Soc Psychiatry Psychiatr Epidemiol. 2003;38(9):490-492.

5. Tabenkin H, Gross R, Yaphe Y. The general practitioner as physician of first contact with health problems [in Hebrew]. Harefuah. 1995; 129(9):297-303, 368.

6. Kroenke K, Spitzer RL, Williams JBW, Monahan PO, Löwe B. Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection. Ann Intern Med. 2007;146(5):317-325.

7. Ornstein S, Stuart G, Jenkins R. Depression diagnoses and antidepressant use in primary care practices: a study from the Practice Partner Research Network (PPRNet). J Fam Pract. 2000;49(1):68-72.

8. Wittchen HU, Kessler RC, Beesdo K, Krause P, Höfler M, Hoyer J. Generalized anxiety and depression in primary care: prevalence, recognition, and management. J Clin Psychiatry. 2002;63(suppl 8):24-34.

9. Arroll B, Macgillivray S, Ogston S, et al. Efficacy and tolerability of tricyclic antidepressants and SSRIs compared with placebo for treatment of depression in primary care: a meta-analysis. Ann Fam Med. 2005;3(5):449-456.

10. Davidson JR, Feltner DE, Dugar A. Management of generalized anxiety disorder in primary care: identifying the challenges and unmet needs. Prim Care Companion J Clin Psychiatry. 2010;12(2).

11. Tabenkin H, Monitz H, Goldfracht M, Peled O. Clinical guidelines for the diagnosis and treatment of depression in the community. Clalit Health Services (in Hebrew). 2006.

12. Bower P, Gilbody S. Managing common mental health disorders in primary care: conceptual models and evidence base. BMJ. 2005; 330(7495):839-842.

13. Chen SY, Hansen RA, Gaynes BN, Farley JF, Morrissey JP, Maciejewski ML. Guideline-concordant antidepressant use among patients with major depressive disorder. Gen Hosp Psychiatry. 2010;32(4):360-367.

14. Davidson JR, Zhang W, Connor KM, et al. A psychopharmacological treatment algorithm for generalised anxiety disorder (GAD). J Psychopharmacol. 2010;24(1):3-26.

15. Fairman KA, Drevets WC, Kreisman JJ, Teitelbaum F. Course of antidepressant treatment drug type, and prescriber’s specialty. Psychiatr Serv. 1998;49(9):1180-1186.

16. Bull SA, Hunkeler EM, Lee JY, et al. Discontinuing or switching selective serotonin-reuptake inhibitors. Ann Pharmacother. 2002;36(4): 578-584.

17. Hansen DG, Vach W, Rosholm JU, Søndergaard J, Gram LF, Kragstrup J. Early discontinuation of antidepressants in general practice: association with patient and prescriber characteristics. Fam Pract. 2004;21(6):623-629.

18. Olfson M, Marcus SC, Tedeschi M, Wan GJ. Continuity of antidepressant treatment for adults with depression in the United States. Am J Psychiatry. 2006;163(1):101-108.

19. Akerblad AC, Bengtsson F, Holgersson M, von Knorring L, Ekselius L. Identification of primary care patients at risk of nonadherence to antidepressant treatment. Patient Prefer Adherence. 2008;2:379-386.

20. Sayuk GS, Elwing JE, Lustman PJ, Clouse RE. Predictors of premature antidepressant discontinuation in functional gastrointestinal disorders. Psychosom Med. 2007;69(2):173-181.

21. Stein MB, Cantrell CR, Sokol MC, Eaddy MT, Shah MB. Antidepressant adherence and medical resource use among managed care patients with anxiety disorders. Psychiatr Serv. 2006;57(5):673-680.

22. Aikens JE, Nease DE Jr, Nau DP, Klinkman MS, Schwenk TL. Adherence to maintenance-phase antidepressant medication as a function of patient beliefs about medication [published correction appears in Ann Fam Med. 2005;3(6):558]. Ann Fam Med. 2005;3(1):23-30.

23. Brown C, Battista DR, Bruehlman R, Sereika SS, Thase ME, Dunbar-Jacob J. Beliefs about antidepressant medications in primary care patients: relationship to self-reported adherence. Med Care. 2005; 43(12):1203-1207.

24. Ayalon L, Areán PA, Alvidrez J. Adherence to antidepressant medications in black and Latino elderly patients. Am J Geriatr Psychiatry. 2005;13(7):572-580.

25. Hunot VM, Horne R, Leese MN, Churchill RC. A cohort study of adherence to antidepressants in primary care: the influence of antidepressant concerns and treatment preferences. Prim Care Companion J Clin Psychiatry. 2007;9(2):91-99.

26. McCann TV, Boardman G, Clark E, Lu S. Risk profiles for nonadherence to antipsychotic medications. J Psychiatr Ment Health Nurs. 2008;15(8):622-629.

27. Hotopf M, Hardy R, Lewis G. Discontinuation rates of SSRIs and tricyclic antidepressants: a meta-analysis and investigation of heterogeneity. Br J Psychiatry. 1997;170:120-127.

28. McCann DP, Blossom HJ. The physician as a patient educator: from theory to practice. West J Med. 1990;153(1):44-49.

29. Simon GE, VonKorff M, Wagner EH, Barlow W. Patterns of antidepressant use in community practice. Gen Hosp Psychiatry. 1993;15(6): 399-408.

30. Lin EH, Von Korff M, Katon W, et al. The role of the primary care physician in patients’ adherence to antidepressant therapy. Med Care. 1995;33(1):67-74.

31. Croghan TW, Melfi CA, Dobrez DG, Kniesner TJ. Effect of mental health specialty care on antidepressant length of therapy. Med Care. 1999;37(4)(suppl Lilly):AS20-AS23.

32. Brook OH, van Hout H, Stalman W, et al. A pharmacy-based coaching program to improve adherence to antidepressant treatment among primary care patients. Psychiatr Serv. 2005;56(4):487-489.

33. Gilbody S, Whitty P, Grimshaw J, Thomas R. Educational and organizational interventions to improve the management of depression in primary care: a systematic review. JAMA. 2003;289(23):3145-3151.

34. Wang PS, Patrick AR, Dormuth CR, et al. The impact of cost sharing on antidepressant use among older adults in British Columbia. Psychiatr Serv. 2008;59(4):377-383.

35. Shirom A, Gross R. World health systems: Israel. In: Fried BJ, Gaydos LM, eds. World Health Systems: Challenges and Perspectives. Ann Arbor, MI: Health Administration Press; 2002:207-225.

36. Sallis JF, Saelens BE. Assessment of physical activity by self-report: status, limitations, and future directions [published correction appears in Res Q Exerc Sport. 2000;71(4):409]. Res Q Exerc Sport. 2000;71(2) (suppl):S1-S14.

37. Rabinowitz J, Feldman D, Gross R, Boerma W. Which primary care physicians treat depression? Psychiatr Serv. 1998;49(1):100-102.

38. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383.

39. Hall WH, Ramachandran R, Narayan S, Jani AB, Vijayakumar S. An electronic application for rapidly calculating Charlson comorbidity score. BMC Cancer. 2004;4:94.

40. Clauw DJ. Pharmacotherapy for patients with fibromyalgia. J Clin Psychiatry. 2008;69(suppl 2):25-29.

41. Pary R, Tobias CR, Webb WK, Lippmann SB. Treatment of insomnia: getting to the root of sleeping problems. Postgrad Med. 1996;100(5):195-198, 201-210.

42. Finocchi C, Villani V, Casucci G. Therapeutic strategies in migraine patients with mood and anxiety disorders: clinical evidence. Neurol Sci. 2010;31(suppl 1):S95-S98.

43. Teasell RW, Mehta S, Aubut JA, et al; Spinal Cord Injury Rehabilitation Evidence Research Team. A systematic review of pharmacologic treatments of pain after spinal cord injury. Arch Phys Med Rehabil. 2010;91(5):816-831.

44. Lindsay TJ, Rodgers BC, Savath V, Hettinger K. Treating diabetic peripheral neuropathic pain. Am Fam Physician. 2010;82(2):151-158.

45. Goldfracht M, Liebermann N, Gross R. Treating mental distress by primary care staff. Health Policy Monitor, October 2006. Available at http://www.hpm.org/survey/is/a8/3.

46. Central Bureau of Statistics. Classification of Municipalities by the Socio-economic Characteristics of the Population. Jerusalem, Israel: Central Bureau of Statistics; 2003.

47. Gross R, Brammli-Greenberg S, Gordon B, Rabinowitz J, Afek A. Disparities in obesity temporal trends of Israeli adolescents by ethnic origin Int J Pediatr Obes. 2011;6(2-2):e154-e161.

48. Gross R, Brammli-Greenberg S, Gordon B, Rabinowitz J, Afek A. Population-based trends in male adolescent obesity in Israel 1967-2003. J Adolesc Health. 2009;44(2):195-198.

49. Falah G. Living together apart: residential segregation in mixed Arab-Jewish cities in Israel. Urban Stud. 1996;33(6):823-857.

50. Hox J. Multilevel Analysis: Techniques and Application. Mahwah, NJ: Lawrence Erlbaum; 2002.

51. Snijders T, Bosker RJ. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Thousand Oaks, CA: Sage; 1999.

52. Anderson IM, Tomenson BM. Treatment discontinuation with selective serotonin reuptake inhibitors compared with tricyclic antidepressants: a meta-analysis. BMJ. 1995;310(6992):1433-1438.

53. Dharmshaktu P, Tayal V, Kalra BS. Efficacy of antidepressants as analgesics: a review [published online ahead of print March 17, 2011. J Clin Pharmacol. 2011.

Related Videos
Related Content
© 2023 MJH Life Sciences
All rights reserved.