Abstract
Objective:
Identify characteristics of Kentucky community pharmacists and community pharmacists’ practice environment associated with utilization of the Kentucky All Schedule Prescription Electronic Reporting Program (KASPER).
Methods:
Surveys were mailed to all 1,018 Kentucky pharmacists with a KASPER account and an additional 1,000 licensed pharmacists without an account. Bivariate analyses examined the association between KASPER utilization and practice type (independent or chain) and practice location (rural or urban). A multivariate Poisson regression model with robust error variance estimated risk ratios (RR) of KASPER utilization by characteristics of pharmacists’ practice environment.
Results:
Responses were received from 563 pharmacists (response rate 27.9%). Of these, 402 responses from community pharmacists were included in the analyses. A majority of responding pharmacists (84%) indicated they or someone in their pharmacy had requested a patient’s controlled substance history since KASPER’s inception. Bivariate results showed that pharmacists who practiced in independent pharmacies reported greater KASPER utilization (94%) than pharmacists in chain pharmacies (75%; p<0.001). Multivariate regression results found utilization of KASPER varied significantly among practice environments of community pharmacists with those who practiced in an urban location (RR: 1.11; [1.01–1.21]) or at an independent pharmacy (RR: 1.27; [1.14–1.40]) having an increased likelihood of KASPER utilization.
Conclusion:
Utilization of KASPER differs by community pharmacists’ practice environment, predominantly by practice type and location. Understanding characteristics of community pharmacists and community pharmacists’ practice environment associated with PDMP use is necessary to remove barriers to access and increase utilization thereby increasing PDMP effectiveness.
Keywords: Substance Abuse Detection, Adverse Drug Reaction Reporting Systems, Mandatory Reporting, Community Pharmacy Services, Professional Practice, Prescription Drugs, United States
INTRODUCTION
The abuse and misuse of prescription drugs is a major public health concern1 and characterized as an epidemic in the US.2 In an effort to address the problem of prescription drug abuse and diversion, states have enacted legislation for the creation of prescription drug monitoring programs (PDMPs) to track controlled substance (CS) dispensing.3 Reports detailing a patient’s CS prescription history can be accessed upon request, or proactively distributed depending upon state regulations. As of December 2013, PDMPs are operational in 48 states.4
In 1999, Kentucky implemented the Kentucky All Schedule Prescription Electronic Reporting (KASPER) program allowing prescribers, pharmacists, and law enforcement officials to request reports providing detailed information regarding an individual’s CS prescription history. In 2005, a fully electronic version of KASPER was launched allowing pharmacists and prescribers to receive KASPER reports in real-time, permitting them to utilize a patient’s CS prescription history to make treatment decisions at the point of care.
Characteristics and opinions of healthcare professionals who utilize PDMP reports have been assessed. In Kentucky, satisfaction surveys conducted in 2004, 2006, and 2010 captured prescriber, dispenser, and law enforcement opinions of KASPER.5,6,7 Results showed the majority of KASPER users were satisfied with the system and believed it to be effective, efficient, accurate, and useful to identify CS abuse and diversion. A 2010 study of Ohio community pharmacists examined factors influencing enrollment in the state’s PDMP.8 Factors influencing the decision to enroll were ability to assist in decreasing doctor shopping and drug abuse, Internet access, and receiving education about the PDMP. A survey of Florida pharmacists assessing attitudes regarding implementation of a PDMP found that responding pharmacists agreed a PDMP should be implemented and would reduce the incidence of doctor shopping.9 Most recently, Green and colleagues surveyed pharmacist users of PDMPs in Connecticut and Rhode Island and concluded pharmacists use PDMP data to screen for abuse and doctor shopping, but PDMP use had limited impact on other aspects of pharmacy practice including counseling patients on safer opioid use, and providing guidance on proper storage and disposal of unused medications. The impact of practice setting was not assessed and noted as being an important limitation to the study as “practice setting may influence the frequency with which pharmacists access PDMPs and how PDMP information obtained is used”.10
Studies evaluating the effectiveness of PDMPs suggest they successfully reduce the supply of CS.11,12 This reduction in supply may not be associated with improved health outcomes, specifically reductions in drug overdose mortality rates.13,14 However, some states with rigorous PDMPs report recent reductions in overdose mortality15,16 and increasing evidence suggests that utilization of PDMP reports reduces “doctor shopping”.17 Because policies regarding access to and utilization of PDMP reports by healthcare providers vary among states4, it is possible that PDMP effectiveness is limited due to inadequate use of PDMP reports at the point of care. A 2010 independent evaluation of the KASPER program revealed 16% of licensed pharmacists and 27.5% of licensed CS prescribers were registered with the PDMP.18 These registration numbers parallel estimates reported by other states, suggesting that overall less than 25% of healthcare professionals access PDMPs to obtain patient reports.19 Increased utilization of PDMP reports by healthcare professionals, including pharmacists, could assist PDMPs in meeting their stated objectives.
Understanding characteristics associated with PDMP use may assist in developing strategies to increase utilization, thereby increasing PDMP effectiveness. The purpose of this study was to examine the association between characteristics of pharmacists and pharmacists’ practice environment with KASPER use, and determine if specific characteristics increased the likelihood of utilizing KASPER.
METHODS
Survey methodology elicited community pharmacists’ perceptions regarding KASPER’s impact on CS dispensing and effectiveness in Kentucky in 2009. Survey items were developed by health policy researchers and academic pharmacists based on reviews of previous PDMP surveys5-7 and discussions with KASPER staff. The survey instrument was designed to take approximately ten minutes to complete and was pilot tested to verify clarity and length. All 1,018 Kentucky pharmacists with a KASPER account at the time of the survey, and 1,000 additional pharmacists without an account randomly selected from a list of 6,600 actively licensed pharmacists in Kentucky were contacted by mail to complete a 15-item survey.
The survey packet included a postcard with a link to an electronic version of the survey and an identification code giving the pharmacist the option of returning the survey via mail or completing it online. Reminder postcards and second surveys were sent two weeks after the initial mailing. Survey protocol was approved by the Institutional Review Board at the University of Kentucky and the Kentucky Cabinet for Health and Family Services.
Bivariate analyses examined associations between practice type (independent versus chain) and practice location (rural versus urban) with pharmacists and practice environment characteristics. Practice type classifications were based on how the pharmacist described their practice, with independent, chain, and supermarket pharmacies considered community pharmacies. For this analysis supermarket pharmacies were grouped with chain pharmacies. Rural/urban classifications were based on the 2003 Rural-Urban Continuum (RUC) Codes developed by the US Department of Agriculture.20 Counties with an RUC code of 1, 2, or 3 were grouped as urban while counties with an RUC code of 4 or greater were considered rural.20 Statistical significance was evaluated using the Wald chi-square for categorical demographic data. Data normality for continuous variables was tested using the Shapiro-Wilk test and differences assessed using the nonparametric Kruskal-Wallis test.
A Poisson regression model using a robust error variance adjusting for characteristics of pharmacists’ practice environment, years since licensure, daily CS dispensing volume, and perceived effectiveness of KASPER estimated the relative risk and associated confidence intervals (CI) of KASPER utilization by community pharmacists. For this study KASPER utilization was defined as having requested information regarding a patient’s CS prescription history since the inception of KASPER. Covariates were hypothesized to predict KASPER utilization (e.g., pharmacists with more years since licensure less likely to use KASPER; higher CS dispensing associated with greater likelihood to utilize KASPER; perception of KASPER being an effective tool associated with increased likelihood of utilization). Statistical analyses were conducted in Stata v12.0 (StataCorp LP, College Station, TX). The a priori level of significance was set at 0.05.
RESULTS
Descriptive and Bivariate
Surveys were mailed to 2,018 pharmacists in Kentucky of which 563 were returned (response rate 27.9%). Thirteen returned surveys were deemed incomplete and removed from further analysis. Another 26 observations were excluded because the respondent indicated they practiced outside Kentucky, and 120 were excluded because the pharmacist did not specify their practice type as a community pharmacy. Two additional observations were excluded because the question pertaining to KASPER use was not answered. Overall, 402 observations were included in the analyses.
Of the responding pharmacists, 339 (84%) indicated they or personnel in their pharmacy had requested a patient’s CS prescription history from KASPER since the inception of the program. Among the pharmacists who had not requested CS prescription histories, the most common reason identified for not using KASPER was “I do not have Internet access to request KASPER reports at my practice site” (n=30). All pharmacists who indicated lack of Internet access as the primary barrier to using KASPER identified their practice type as chain pharmacy. Baseline characteristics and KASPER utilization for responding pharmacists by practice type (independent and chain) and practice location (rural and urban) are presented in Table 1.
Table 1.
Variable | Practice Type | Practice Location | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Independent N= 200 | Chain N = 202 | p-value | Rural N = 173 | Urban N = 217 | p-value | |||||
KASPER Utilization | ||||||||||
Requested Reports | 187 | 94% | 152 | 75% | <0.001* | 145 | 84% | 183 | 84% | 0.890 |
Practice Location | ||||||||||
Rural | 115 | 58% | 58 | 29% | <0.001* | - | - | - | - | - |
Urban | 75 | 38% | 142 | 70% | <0.001* | - | - | - | - | - |
Practice Type | ||||||||||
Independent | - | - | - | - | - | 115 | 66% | 75 | 34% | <0.001* |
Chain | - | - | - | - | - | 58 | 33% | 142 | 65% | <0.001* |
Years in Practice | ||||||||||
Median Years (IQR)a | 27 | 16(35) | 15 | 5(29) | <0.001* | 24 | 10(34) | 19 | 8(31) | 0.028* |
CS Dispensing Behavior | ||||||||||
Median CS dispensed per day (IQR)a | 40 | 22(60) | 50 | 30(100) | <0.001* | 50 | 25(75) | 45 | 26(75) | 0.391 |
Perceived Effectiveness of KASPER | ||||||||||
Reducing Drug Abuse and Diversion | 176 | 88% | 184 | 91% | 0.219 | 148 | 86% | 201 | 93% | 0.042* |
Reducing Doctor Shopping | 172 | 86% | 174 | 86% | 0.675 | 149 | 86% | 186 | 86% | 0.734 |
Denotes statistical significance at the 5% level.
IQR: Interquartile range
A significant relationship was found in the bivariate comparison between use of KASPER and practice type, with a greater proportion of independent pharmacists having utilized KASPER (94%) than chain pharmacists (75%, p<0.001). Daily CS dispensing volume varied significantly with chain pharmacies dispensing more CS per day (median: 50, IQR: 30–100) than independent pharmacies (median: 40, interquartile range (IQR): 22–60, p=<0.001). Seventy percent (70%) of chain pharmacists stated they practiced in an urban location while only 38% of independent pharmacists indicated they practiced in an urban location (p<0.001). Responding pharmacists at independent pharmacies reported being licensed for more years (median: 27, IQR: 16–35) than chain pharmacists (median: 15, IQR: 5–29, p<0.001), respectively.
Bivariate comparisons revealed no significant association between practice location and KASPER use (p=0.878). Pharmacists in rural locations, compared to urban locations, were more likely to practice in an independent pharmacy (66% vs. 34%, p<0.001), and also reported being licensed for a longer period of time (median: 24, IQR: 10–34, median: 19, IQR: 8–31, p=0.037).
Responding pharmacists perceived KASPER as an effective tool to reduce drug abuse and diversion (92%; n=365) as well as doctor shopping (90%; n=350) in Kentucky. No difference in perceived effectiveness based on practice type was observed, however, a greater proportion of pharmacists in urban locations viewed KASPER as an effective tool to reduce drug abuse/diversion than pharmacists in rural locations (93% vs. 86%, p=0.041).
Multivariate Poisson Regression Model
Results from the Poisson regression model are presented in Table 2. Adjusted for the covariates listed in Table 2, community pharmacists in urban locations were 11% more likely to utilize KASPER than pharmacists in rural locations (p=0.032). After adjusting for the covariates, independent pharmacists were 27% more likely to utilize KASPER than chain pharmacists (p<0.001). A negative relationship was observed between the years since licensure and the likelihood of a pharmacist using KASPER (p=0.05). Neither CS dispensing volume nor perceived KASPER effectiveness influenced the likelihood of KASPER utilization.
Table 2.
Variables | Relative Risk | p-value | 95% CI |
---|---|---|---|
Practice Location | |||
Rural | Ref | ||
Urban | 1.11* | 0.037 | 1.01-1.21 |
Practice Site | |||
Chain | Ref | ||
Independent | 1.27* | <0.001 | 1.15 - 1.40 |
Years in Practice | |||
Yearsa | 0.96* | 0.050 | 0.93 - 1.00c |
CS Dispensing Behavior | |||
CS Dispensed per Dayb | 1.00 | 0.297 | 1.00c - 1.01 |
Perceived PDMP Effectiveness Reducing Drug Abuse and Diversion | |||
Ineffective | Ref | ||
Effective | 1.06 | 0.580 | 0.87 – 1.28 |
Perceived PDMP Effectiveness Reducing Doctor Shopping | |||
Ineffective | Ref | ||
Effective | 1.12 | 0.224 | 0.93 – 1.36 |
Denotes statistical significance at the 5% level
Years was divided by 10 to show the impact of practicing an additional 10 years
CS dispensed per day was divided by 10 to show the impact of dispensing an additional 10 CS per day
Confidence interval includes 1.00 due to rounding
95%CI = 95% Confidence Interval
DISCUSSION
Prescription drug monitoring programs have been implemented by states to address the prescription drug abuse crisis in the US. This study’s purpose was to examine the association between characteristics of community pharmacists and pharmacists’ practice environment with KASPER use, and determine if specific characteristics increased the likelihood of KASPER utilization. After controlling for confounding covariates, our multivariate results indicate that pharmacists who practiced in urban locations or at independent pharmacies were more likely to utilize KASPER than their counterparts in rural locations or chain pharmacies. The finding that urban pharmacists were more likely to utilize KASPER than rural pharmacists is somewhat surprising as prescription drug abuse is most prevalent in rural areas.21,22,23 The high prevalence of prescription drug abuse in rural areas may also explain why the bivariate analysis found a lower proportion of rural pharmacists perceived KASPER as an effective tool to reduce drug abuse and diversion. However, pharmacists’ perceptions concerning the effectiveness of KASPER did not impact the likelihood of utilization in the multivariate analysis.
Lack of Internet access was cited as the primary reason for not requesting a KASPER report and is consistent with previous surveys.8,10 Considering that all responding pharmacists who indicated lack of Internet access as the primary reason for not using KASPER were chain pharmacists, it is not surprising that chain pharmacists were less likely to use KASPER. In fact, it is possible that this factor (i.e., lack of Internet access) may outweigh the impact of location and years-of-practice. Although not specifically addressed in this survey, workflow issues and corporate policies within chain pharmacies are likely different from those in pharmacies independently owned and operated. Thus, to increase PDMP utilization, other barriers in chain pharmacies should be explored.
The response rate of 27.9% is considered low, though consistent with other pharmacist surveys.8,10,24 The study sample may not be representative of all community pharmacists in Kentucky as views of respondents may differ from non-responders and pharmacists familiar with KASPER may have been more inclined to participate. Due to the anonymous nature of the survey, comparisons of responders and non-responders were not possible. Additionally, the sample was limited to Kentucky community pharmacists and may not be generalizable to other states. This study analyzed a survey administered in 2009 and current KASPER utilization by Kentucky community pharmacists may have been impacted by improvements to the system, changes in corporate policies, and an increased awareness of the program. To protect anonymity extensive individual/pharmacist level questions (i.e. demographics) were not asked. Also, due to the cross-sectional design of this study an association between variables does not imply causality.
CONCLUSIONS
Utilization of KASPER at the point of dispensing varied significantly among community pharmacists and was influenced by characteristics of pharmacists’ practice environment including practice type and practice location. For PDMPs to meet their objectives of curbing CS abuse and diversion, increased utilization of reports by healthcare professionals, including community pharmacists, for treatment decisions is warranted. Understanding characteristics associated with PDMP use may assist in developing strategies to remove barriers and increase utilization, thereby increasing the effectiveness of PDMPs.
ACKNOWLEDGEMENTS
The authors have no conflicts of interest to declare. This project was supported by the Kentucky Cabinet for Health and Family Services, Office of the Inspector General. The content is solely the responsibility of the authors.
Preliminary findings presented at the International Society for Pharmacoeconomics and Outcomes Research 17th Annual International Meeting, Washington DC, June 4, 2012, and at the International Society for Pharmacoeconomics and Outcomes Research 19th Annual International Meeting, Montreal, Canada, June 2, 2014.
Footnotes
CONFLICT OF INTEREST
None declared.
Funding: The Kentucky Cabinet for Health and Family Services, Office of the Inspector General.
Contributor Information
Sarah E. Wixson, Institute for Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Kentucky. Lexington, KY (United States). sarah.wixson@uky.edu
Karen Blumenschein, Pharmacy Practice and Science. Institute for Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Kentucky. Lexington, KY (United States). kblum1@email.uky.edu.
Amie J. Goodin, Institute for Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Kentucky. Lexington, KY (United States). amie.goodin@uky.edu
Jeffery Talbert, Pharmacy Practice and Science. Institute for Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Kentucky. Lexington, KY (United States). jeff.talbert@uky.edu.
Patricia R. Freeman, Pharmacy Practice and Science. Institute for Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Kentucky. Lexington, KY (United States). trish.freeman@uky.edu
References
- 1.DuPont RL. Prescription drug abuse: an epidemic dilemma. J Psychoactive Drugs. 2010;42(2):127–132. doi: 10.1080/02791072.2010.10400685. [DOI] [PubMed] [Google Scholar]
- 2.Hernandez SH, Nelson LS. Prescription drug abuse: insight into the epidemic. Clin Pharmacol Ther. 2010;88(3):307–317. doi: 10.1038/clpt.2010.154. doi:10.1038/clpt.2010.154. [DOI] [PubMed] [Google Scholar]
- 3.Blumenschein K, Fink J, Freeman PR, James K, Kirsh K, Steinke DT, Talbert J. Review of prescription drug monitoring programs in the United States. [accessed September 12, 2013]; http://chfs.ky.gov/NR/rdonlyres/85989824-1030-4AA6-91E1-F9E3EF68827/0/KASPEREvaluationPDMPStatusFinalReport6242010.pdf .
- 4.National Alliance for Model State Drug Laws. Complication of state prescription monitoring program maps. [accessed February 3 2014]; http://www.namsdl.org/library/6D4C4D9F-65BE-F4BB-A428B392538E0663/
- 5.Kentucky Cabinet for Health and Family Services. 2010 KASPER satisfaction survey: executive summary. [accessed July 12, 2013]; http://chfs.ky.gov/NR/rdonlyres/BDC0DFC9-924B-4F11-A10A-EB17933FDDB/0/2010KASPERSatisfactionSurveyExecutiveSummary.pdf .
- 6.Kentucky Cabinet for Health and Family Services. 2006 KASPER satisfaction survey: executive summary. [accessed July 12, 2013]; http://chfs.ky.gov/NR/rdonlyres/7607D456-68C5-4C86-BF32-BACE738B0B4B/0/2006KASPERSatisfactionSurveySummary.pdf .
- 7.Kentucky Cabinet for Health and Family Services. 2004 KASPER satisfaction survey: executive summary. [accessed July 12 2013]; http://chfs.ky.gov/NR/rdonlyres/2DA936B2-C5E2-4A9C-B5BB-4483FF9DB9D3/0/2004KASPERSatisfactionSurveySummaryReport.pdf .
- 8.Ulbrich TR, Dula CA, Green CG, Porter K, Bennett MS. Factors influencing community pharmacists’ enrollment in a state prescription monitoring program. J Am Pharm Assoc (2003) 2010;50(5):588–594. doi: 10.1331/JAPhA.2010.09089. doi:10.1331/JAPhA.2010.09089. [DOI] [PubMed] [Google Scholar]
- 9.Fass JA, Hardigan PC. Attitudes of Florida pharmacists toward implementing a state prescription drug monitoring program for controlled substances. J Manag Care Pharm. 2011;17(6):430–438. doi: 10.18553/jmcp.2011.17.6.430. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Green TC, Mann MR, Bowman SE, Zaller N, Soto X, Gadea J, Cordy C, Kelly P, Friedmann PD. How does use of a Prescription Drug Monitoring Program Change Pharmacy Practice? J Am Pharm Assoc (2003) 2013;53(3):273–281. doi: 10.1331/JAPhA.2013.12094. doi:10.1331/JAPhA.2013.12094. [DOI] [PubMed] [Google Scholar]
- 11.Simeone R, Holland L. An evaluation of prescription drug monitoring programs. [accessed August 8 2013]; http://www.simeoneassociates.com/simeone3.pdf .
- 12.Reisman RM, Shenoy PJ, Atherly AJ, Flowers CR. Prescription opioid usage and abuse relationships: an evaluation of state prescription drug monitoring program efficacy. Subst Abuse. 2009;3:41–51. doi: 10.4137/sart.s2345. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Paulozzi LJ, Kilbourne EM, Desai HA. Prescription drug monitoring programs and death rates from drug overdose. Pain Med. 2011;12(5):747–754. doi: 10.1111/j.1526-4637.2011.01062.x. doi:10.1111/j.1526-4637.2011.01062.x. [DOI] [PubMed] [Google Scholar]
- 14.Li G, Brady JE, Lank BH, Giglio J, Wunsch H, DiMaggio C. Prescription drug monitoring and drug overdose mortality. Inj Epidemiol. 2014;1:540. doi: 10.1186/2197-1714-1-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Commonwealth of Kentucky, Governor Steve Beshear's Communications Office. “One year in, landmark prescription drug bill shows huge impact.”. [accessed March 24 2015]; http://migration.kentucky.gov/newsroom/governor/20130725hb1.htm .
- 16.“Prescription drug abuse: untangling the harm.”. [accessed March 24 2015]; http://www.altustimes.com/news/opinion/2400269/Prescription-drug-abuse:-Untangling-the-harm .
- 17.Finklea K, Sacco LN, Bagalman E. Prescription Drug Monitoring Programs. Congressional Research Service Briefing. [accessed March 24 2015]; https://fas.org/sgp/crs/misc/R42593.pdf .
- 18.Blumenschein K, Fink J, Freeman PR, Kirsh KL, Steinke DT, Talbert J. Independent evaluation of the impact and effectiveness of the Kentucky All Schedule Prescription Electronic Reporting program (KASPER) [accessed September 12 2013]; http://www.chfs.ky.gov/NR/rdonlyres/24493B2E-B1A1-4399-89AD-BAD43/0/KASPEREvaluationFinalReport10152010.pdf .
- 19.Green TC, Zaller N, Rich J, Bowman S, Friedmann P. Revisiting Paulozzi et al.'s “Prescription Drug Monitoring Programs and Death Rates from Drug Overdose”. Pain Med. 2011;12(6):982–985. doi: 10.1111/j.1526-4637.2011.01136.x. doi:10.1111/j.1526-4637.2011.01136.x. [DOI] [PubMed] [Google Scholar]
- 20.U.S. Department of Agriculture Economic Research Service. Measuring rurality: rural-urban continuum codes. [accessed July 14 2013]; http://webarchives.cdlib.org/sw1wp9v27r/http:/ers.usda.gov/Briefing/Rurality/RuralUrbCon/
- 21.Cicero TJ, Inciardi JA, Munoz A. Trends in abuse of Oxycontin and other opioid analgesics in the United States: 2002-2004. J Pain. 2005;6(10):662–672. doi: 10.1016/j.jpain.2005.05.004. [DOI] [PubMed] [Google Scholar]
- 22.Cicero TJ, Surratt H, Inciardi JA, Munoz A. Relationship between therapeutic use and abuse of opioid analgesics in rural, suburban, and urban locations in the United States. Pharmacoepidemiol Drug Saf. 2007;16(8):827–840. doi: 10.1002/pds.1452. [DOI] [PubMed] [Google Scholar]
- 23.Havens JR, Talbert JC, Walker R, Leedham C, Leukefeld CG. Trends in controlled-release oxycodone (OxyContin®) prescribing among Medicaid recipients in Kentucky, 1998-2002. J Rural Health. 2006;22(3):276–278. doi: 10.1111/j.1748-0361.2006.00046.x. [DOI] [PubMed] [Google Scholar]
- 24.Wendel E. Pharmacists survey gives insight into the impact of the economic downturn on patients, pharmacy practice, and their communities. American Pharmacists Association. [accessed January 23 2014];2009 Mar; Accessed at https://portal.pharmacist.com/AM/Template.cfm?Section=News Releases2&template=/CM/ContentDisplay.cfm&Conten tID=18987 .