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Age and Sex Patterns of Drug Prescribing in a Defined American Population

      Abstract

      Objective

      To describe the age and sex patterns of drug prescribing in Olmsted County, Minnesota.

      Patients and Methods

      Population-based drug prescription records for the Olmsted County population in 2009 were obtained using the Rochester Epidemiology Project medical records linkage system (n=142,377). Drug prescriptions were classified using RxNorm codes and were grouped using the National Drug File–Reference Terminology.

      Results

      Overall, 68.1% of the population (n=96,953) received a prescription from at least 1 drug group, 51.6% (n=73,501) received prescriptions from 2 or more groups, and 21.2% (n=30,218) received prescriptions from 5 or more groups. The most commonly prescribed drug groups in the entire population were penicillins and β-lactam antimicrobials (17%; n=23,734), antidepressants (13%; n=18,028), opioid analgesics (12%; n=16,954), antilipemic agents (11%; n=16,082), and vaccines/toxoids (11%; n=15,918). However, prescribing patterns differed by age and sex. Vaccines/toxoids, penicillins and β-lactam antimicrobials, and antiasthmatic drugs were most commonly prescribed in persons younger than 19 years. Antidepressants and opioid analgesics were most commonly prescribed in young and middle-aged adults. Cardiovascular drugs were most commonly prescribed in older adults. Women received more prescriptions than men for several drug groups, in particular for antidepressants. For several drug groups, use increased with advancing age.

      Conclusion

      This study provides valuable baseline information for future studies of drug utilization and drug-related outcomes in this population.

      Abbreviations and Acronyms:

      LDL (low-density lipoprotein), NDF-RT (National Drug File–Reference Terminology), NHANES (National Health and Nutrition Examination Survey), REP (Rochester Epidemiology Project)
      Prescription drug use has increased steadily in the United States for the past decade. The percentage of people who took at least 1 prescription drug in the past month increased from 44% in 1999-2000 to 48% in 2007-2008.
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      Prescription drug use continues to increase: U.S. prescription drug data for 2007-2008.
      This increased use resulted in increased spending on prescription drugs, which reached $250 billion in 2009, and accounted for 12% of total personal health care expenditures.

      National Center for Health Statistics. Health, United States, 2011: with special feature on socioeconomic status and health. http://www.cdc.gov/nchs/data/hus/hus11.pdf. Accessed April 12, 2013.

      Drug-related spending is expected to continue to grow in the coming years.

      National Center for Health Statistics. Health, United States, 2011: with special feature on socioeconomic status and health. http://www.cdc.gov/nchs/data/hus/hus11.pdf. Accessed April 12, 2013.

      Quantification of drug-prescribing patterns in the general population is important for a variety of reasons. Prescription drug abuse has become the fastest-growing drug problem in the United States.
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      Medication-related adverse outcomes in US hospitals and emergency departments increased 52% between 2004 and 2008.

      Lucado J, Paez K, Elixhauser A. Medication-related adverse outcomes in U.S. hospitals and emergency departments, 2008. HCUP Statistical Brief #109. http://www.hcup-us.ahrq.gov/reports/statbriefs/sb109.pdf. Published April 2011. Accessed May 23, 2013.

      In addition, drug-prescribing patterns may serve as indirect measures of the burden of diseases in a population.
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      Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health.
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      Evaluating heart disease prescriptions-filled as a proxy for heart disease prevalence rates.
      Prescribing patterns also vary considerably across geographic regions
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      Geographic variation in Medicare drug spending.

      Wennberg J, Cooper M. The Dartmouth atlas of health care in the US; 1999. http://www.dartmouthatlas.org/downloads/atlases/99Atlas.pdf. Accessed May 23, 2013.

      Wennberg J, Wennberg D. Practice variations and the use of prescription drugs: Dartmouth atlas of health care in Michigan 2000. http://www.bcbsm.com/content/dam/public/Consumer/Documents/about-us/dartmouth-atlas.pdf. Accessed May 23, 2013.

      and may serve as a proxy for health system performance.
      A variety of studies have described patterns of drug prescription in some countries, including Sweden, Spain, and Canada.
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      Identifying how age and gender influence prescription drug use in a primary health care environment in Catalonia, Spain.
      However, there are few population-based studies of prescription drugs in the United States because of the lack of a centralized health care data system.
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      Some of the published US studies were conducted decades ago and may not reflect current prescription patterns.
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      Drug use in the United States in 1981.
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      Drug use and expenditures in 1982.
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      • Faich G.A.
      Prescription drug use in 1984 and changes over time.
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      Recent patterns of medication use in the ambulatory adult population of the United States: the Slone survey.
      • Kotzan L.
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      • Kotzan J.A.
      Influence of age, sex, and race on prescription drug use among Georgia Medicaid recipients.
      • LaVange L.M.
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      Outpatient prescription drug utilization and expenditure patterns of noninstitutionalized aged Medicare beneficiaries.
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      • Motheral B.R.
      Gender- and age-related prescription drug use patterns.
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      Use of chronic medications among a large, commercially-insured US population.
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      Patterns of outpatient prescription drug use among Pennsylvania elderly.
      • Khandker R.K.
      • Simoni-Wastila L.J.
      Differences in prescription drug utilization and expenditures between Blacks and Whites in the Georgia Medicaid population.
      More recent studies included only the Medicare-eligible elderly population or individuals with health insurance coverage.
      • Zhang Y.
      • Baicker K.
      • Newhouse J.P.
      Geographic variation in Medicare drug spending.
      • Kennedy J.
      • Tuleu I.
      • Mackay K.
      Unfilled prescriptions of Medicare beneficiaries: prevalence, reasons, and types of medicines prescribed.
      In this study, we examined the outpatient drug-prescribing patterns for the entire Olmsted County, Minnesota, population in 2009 using the Rochester Epidemiology Project (REP), a medical records linkage system that captures virtually all the health care visit information for the entire population.
      • St Sauver J.L.
      • Grossardt B.R.
      • Yawn B.P.
      • Melton III, L.J.
      • Rocca W.A.
      Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester Epidemiology Project.
      • St Sauver J.L.
      • Grossardt B.R.
      • Leibson C.L.
      • Yawn B.P.
      • Melton III, L.J.
      • Rocca W.A.
      Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project.
      • Rocca W.A.
      • Yawn B.P.
      • St Sauver J.L.
      • Grossardt B.R.
      • Melton III, L.J.
      History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population.
      • St Sauver J.L.
      • Grossardt B.R.
      • Yawn B.P.
      • et al.
      Data resource profile: the Rochester Epidemiology Project (REP) medical records-linkage system.

      Patients and Methods

      Study Population

      All individuals residing in Olmsted County between January 1 and December 31, 2009, were identified using the REP census (n=146,687),
      • St Sauver J.L.
      • Grossardt B.R.
      • Yawn B.P.
      • Melton III, L.J.
      • Rocca W.A.
      Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester Epidemiology Project.
      and those who had given permission for their medical records to be used for research were included in this study (n=142,377; 97.1%). The number of people included in this study represented 98.7% of the population predicted to reside in Olmsted County by the US Census, and the age and sex distributions were virtually identical to those of the US Census estimates.
      • St Sauver J.L.
      • Grossardt B.R.
      • Leibson C.L.
      • Yawn B.P.
      • Melton III, L.J.
      • Rocca W.A.
      Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project.
      Additional details about the population of Olmsted County and about the REP have been published elsewhere.
      • St Sauver J.L.
      • Grossardt B.R.
      • Yawn B.P.
      • Melton III, L.J.
      • Rocca W.A.
      Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester Epidemiology Project.
      • St Sauver J.L.
      • Grossardt B.R.
      • Leibson C.L.
      • Yawn B.P.
      • Melton III, L.J.
      • Rocca W.A.
      Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project.
      • Rocca W.A.
      • Yawn B.P.
      • St Sauver J.L.
      • Grossardt B.R.
      • Melton III, L.J.
      History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population.
      • St Sauver J.L.
      • Grossardt B.R.
      • Yawn B.P.
      • et al.
      Data resource profile: the Rochester Epidemiology Project (REP) medical records-linkage system.

      Drug Prescription Records

      Outpatient drug prescriptions written for the study population between January 1 and December 31, 2009, were obtained from Mayo Clinic and the Olmsted Medical Center (both in Rochester, Minnesota) and were linked to specific individuals in the records linkage system (n=663,736 prescription records). As described elsewhere, these 2 institutions provide most of the medical care for Olmsted County residents.
      • St Sauver J.L.
      • Grossardt B.R.
      • Yawn B.P.
      • Melton III, L.J.
      • Rocca W.A.
      Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester Epidemiology Project.
      • St Sauver J.L.
      • Grossardt B.R.
      • Leibson C.L.
      • Yawn B.P.
      • Melton III, L.J.
      • Rocca W.A.
      Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project.
      • Rocca W.A.
      • Yawn B.P.
      • St Sauver J.L.
      • Grossardt B.R.
      • Melton III, L.J.
      History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population.
      • St Sauver J.L.
      • Grossardt B.R.
      • Yawn B.P.
      • et al.
      Data resource profile: the Rochester Epidemiology Project (REP) medical records-linkage system.
      Since 2002, both institutions have used proprietary electronic prescription systems in their outpatient settings (ie, office and hospital outpatient settings). Electronic prescriptions in 2009 were retrieved from the proprietary systems and were converted into RxNorm codes retrospectively. The prescriptions were then grouped using the National Drug File–Reference Terminology (NDF-RT) classification system.
      • Pathak J.
      • Chute C.G.
      Analyzing categorical information in two publicly available drug terminologies: RxNorm and NDF-RT.
      • Pathak J.
      • Murphy S.P.
      • Willaert B.N.
      • et al.
      Using RxNorm and NDF-RT to classify medication data extracted from electronic health records: experiences from the Rochester Epidemiology Project.
      Combination drugs with multiple ingredients were counted once under the NDF-RT category of the main ingredient or, if applicable, under the combination drug category.
      Approximately 2% of the prescription records (n=12,576) were excluded because they lacked specific descriptions and could not be assigned to an NDF-RT class. All the remaining prescriptions were grouped into 28 NDF-RT classes. In this study, we focused on drug classes that were prescribed to at least 1% of the Olmsted County population; therefore, 8 NDF-RT prescription classes were excluded (prescriptions were written to <1% of the population), leaving 20 classes for the analysis. The NDF-RT classification system also includes detailed subgroups for each class. Using the NDF-RT subgroups with some modifications (combining related or rarely prescribed subgroups), we classified all the prescriptions into 70 drug groups (Supplemental Table 1, available online at http://www.mayoclinicproceedings.org). All the drug groups were mutually exclusive. A person who received multiple prescriptions in the same drug group during the 12 months of the study was counted only once, and we did not consider refills or duration of drug use. Overall, 380,441 unique prescription records were included in the analyses.

      Statistical Analyses

      Prevalence was estimated by dividing the number of individuals who received drugs from each group during a 12-month period by the 2009 Olmsted County population (1-year prevalence). Age- and sex-specific prevalence patterns were explored graphically. Age-standardized or age- and sex-standardized prevalence figures were obtained by direct standardization to the entire US population (2000 US Census) when appropriate. Because the study covered the complete population of Olmsted County and no sampling was involved, statistical tests and CIs are not included in the tables.
      • Anderson D.W.
      • Mantel N.
      On epidemiologic surveys.
      • Deming W.E.
      Boundaries of statistical inference.
      • Rocca W.A.
      • Cha R.H.
      • Waring S.C.
      • Kokmen E.
      Incidence of dementia and Alzheimer's disease: a reanalysis of data from Rochester, Minnesota, 1975-1984.

      Results

      Overall Prevalence

      The 2009 REP census population included 142,377 individuals. Approximately half of the population were men or boys (47%; n=66,865), 27% (n=38,558) were younger than 19 years, and 12% (n=17,336) were 65 years or older. Most of the population was white (92%; n=131,069). Overall, 68.1% of the population (n=96,953) received a prescription from at least 1 drug group, 51.6% (n=73,501) received prescriptions from 2 or more drug groups, and 21.2% (n=30,218) received prescriptions from 5 or more drug groups. A higher percentage of women or girls received at least 1 drug prescription compared with men or boys (72.5% [n=54,721/75,512] vs 63.2% [n=42,232/66,865]).
      Overall, 17% (n=23,734) of the population received at least 1 prescription for penicillins and β-lactam antimicrobials, which was the most commonly prescribed drug group in the entire population. Antidepressants (13%; n=18,028), opioid analgesics (12%; n=16,954), antilipemic agents (11%; n=16,082), and vaccines/toxoids (11%; n=15,918) were next in decreasing order of frequency. The Table reports the 20 most commonly prescribed drug groups and the prescription prevalence by sex and age groups. The prevalence figures for 20 additional groups of prescription drugs in decreasing order of frequency are reported in Supplemental Table 2 (available online at http://www.mayoclinicproceedings.org).
      TableAge- and Sex-Specific Prevalence (per 100 Population) of the 20 Most Common Drug Groups in the 2009 Olmsted County, Minnesota, Population (n=142,377)
      ACE = angiotensin-converting enzyme.
      Drug groupAge (y)
      Numbers indicate the actual number of cases observed. Prevalence can be computed by dividing the number of cases by the corresponding denominator listed next (and multiplying by 100). Denominators for men/boys and women/girls combined: 0-18 y, 38,558; 19-29 y, 23,968; 30-49 y, 37,927; 50-64 y, 24,588; and ≥65 y, 17,336. Denominators for men/boys: 0-18 y, 19,611; 19-29 y, 10,337; 30-49 y, 17,888; 50-64 y, 11,496; and ≥65 y, 7533. Denominators for women/girls: 0-18 y, 18,947; 19-29 y, 13,631; 30-49 y, 20,039; 50-64 y, 13,092; and ≥65 y, 9803.
      All ages
      0-1819-2930-4950-64≥65Crude
      A crude prevalence was computed by dividing cases observed across all ages by the total population.
      Standardized %
      Overall prevalence for men/boys and women/girls combined was standardized by age and sex; overall prevalence for men/boys and women/girls separately was standardized only by age (direct standardization using the 2000 US Census population).
      No.%No.%No.%No.%No.%No.%
      Penicillins and β-lactam antimicrobials
       Both sexes877122.75317713.26556314.67346214.08276115.9323,73416.6716.60
       Men/boys437722.32103510.01217012.13143312.47118115.6810,19615.2515.07
       Women/girls439423.19214215.71339316.93202915.50158016.1213,53817.9318.13
      Antidepressants
       Both sexes10102.62266311.11631016.64490019.93314518.1418,02812.6612.51
       Men/boys4092.097667.41195310.92149312.9997812.9855998.378.56
       Women/girls6013.17189713.92435721.74340726.02216722.1112,42916.4616.21
      Opioid analgesics
       Both sexes16064.17289812.09525813.86384415.63334819.3116,95411.9111.84
       Men/boys8474.32106410.29211711.83170614.84135417.97708810.6010.77
       Women/girls7594.01183413.45314115.67213816.33199420.34986613.0712.85
      Antilipemic agents
       Both sexes150.041270.5325396.69637725.94702440.5216,08211.3011.07
       Men/boys100.05770.7416359.14338629.45329243.70840012.5612.73
       Women/girls50.03500.379044.51299122.85373238.07768210.179.57
      Vaccines/toxoids
       Both sexes892623.1518787.8422595.9617427.0811136.4215,91811.1811.07
       Men/boys433022.085505.3210485.867986.944816.39720710.7810.40
       Women/girls459624.2613289.7412116.049447.216326.45871111.5411.77
      Antiasthmatics
       Both sexes392110.1716977.0835209.28247710.07208012.0013,6959.629.56
       Men/boys213810.905385.2012086.758277.1981910.8755308.278.22
       Women/girls17839.4111598.50231211.54165012.60126112.86816510.8110.83
      Topical anti-infective/anti-inflammatory agents
       Both sexes29527.6615296.3831228.23284011.55281916.2613,2629.319.22
       Men/boys14677.485034.8711446.4011309.83122916.3154738.198.20
       Women/girls14857.8410267.5319789.87171013.06159016.22778910.3110.23
      Erythromycins/macrolides
       Both sexes33648.7218437.69396310.4523859.7015078.6913,0629.179.13
       Men/boys16538.435134.9613607.609067.885987.9450307.527.51
       Women/girls17119.0313309.76260312.99147911.309099.27803210.6410.71
      Gastrointestinal medications, other
       Both sexes3951.029984.1630748.11332113.51325318.7611,0417.757.70
       Men/boys1840.943733.6113197.37137011.92127616.9445226.766.92
       Women/girls2111.116254.5917558.76195114.90197720.1765198.638.39
      Laxatives
       Both sexes6751.757273.0323526.20385815.69270515.6010,3177.257.05
       Men/boys3031.551991.938634.82176115.32123516.3943616.526.50
       Women/girls3721.965283.8714897.43209716.02147015.0059567.897.63
      β-Blockers and related medications
       Both sexes770.202350.9813573.58320113.02522930.1610,0997.096.97
       Men/boys340.17760.746333.54171714.94242032.1348807.307.45
       Women/girls430.231591.177243.61148411.34280928.6552196.916.59
      ACE inhibitors
       Both sexes300.081120.4714553.84341813.90474027.3497556.856.75
       Men/boys190.10750.738794.91192016.70219029.0750837.607.73
       Women/girls110.06370.275762.87149811.44255026.0146726.195.87
      Diuretics
       Both sexes460.121470.6113683.61310012.61509229.3797536.856.75
       Men/boys210.11540.525503.07131311.42196926.1439075.845.99
       Women/girls250.13930.688184.08178713.65312331.8658467.747.37
      Topical nasal and throat agents
       Both sexes14193.6810884.5427667.2922028.9616359.4391106.406.37
       Men/boys8224.193813.6910906.099097.917029.3239045.845.88
       Women/girls5973.157075.1916768.3612939.889339.5252066.896.84
      Antihistamines
       Both sexes20135.2213305.5526557.0019197.8011176.4490346.356.28
       Men/boys10925.573953.828764.906145.344045.3633815.065.04
       Women/girls9214.869356.8617798.8813059.977137.2756537.497.45
      Antirheumatics
       Both sexes9892.5613255.5327987.3821088.5711536.6583735.885.83
       Men/boys4662.384304.1611136.228987.814696.2333765.055.10
       Women/girls5232.768956.5716858.4112109.246846.9849976.626.54
      Sedatives/hypnotics
       Both sexes2050.539694.0428167.4222829.2816359.4379075.555.53
       Men/boys930.473082.9810595.928857.706118.1129564.424.54
       Women/girls1120.596614.8517578.77139710.67102410.4549516.566.45
      Adrenal corticosteroids
       Both sexes14983.897993.3319825.2315596.3415498.9473875.195.17
       Men/boys8624.403022.927264.065875.116578.7231344.694.71
       Women/girls6363.364973.6512566.279727.428929.1042535.635.61
      Quinolones
       Both sexes1910.509694.0420095.3018997.72227213.1173405.165.08
       Men/boys630.322372.296623.707366.4086311.4625613.833.94
       Women/girls1280.687325.3713476.7211638.88140914.3747796.336.15
      Systemic contraceptives
       Both sexes
      A total of 49 prescriptions for systemic contraceptives (0.7% [n=49/7044]) were assigned to men/boys by mistake.
       Men/boys
      A total of 49 prescriptions for systemic contraceptives (0.7% [n=49/7044]) were assigned to men/boys by mistake.
       Women/girls8804.64335224.59257512.851701.30180.1869959.269.10
      a ACE = angiotensin-converting enzyme.
      b Numbers indicate the actual number of cases observed. Prevalence can be computed by dividing the number of cases by the corresponding denominator listed next (and multiplying by 100). Denominators for men/boys and women/girls combined: 0-18 y, 38,558; 19-29 y, 23,968; 30-49 y, 37,927; 50-64 y, 24,588; and ≥65 y, 17,336. Denominators for men/boys: 0-18 y, 19,611; 19-29 y, 10,337; 30-49 y, 17,888; 50-64 y, 11,496; and ≥65 y, 7533. Denominators for women/girls: 0-18 y, 18,947; 19-29 y, 13,631; 30-49 y, 20,039; 50-64 y, 13,092; and ≥65 y, 9803.
      c A crude prevalence was computed by dividing cases observed across all ages by the total population.
      d Overall prevalence for men/boys and women/girls combined was standardized by age and sex; overall prevalence for men/boys and women/girls separately was standardized only by age (direct standardization using the 2000 US Census population).
      e A total of 49 prescriptions for systemic contraceptives (0.7% [n=49/7044]) were assigned to men/boys by mistake.

      Prevalence by Age and Sex

      The prevalence of the most commonly prescribed drugs varied by age and sex (Figure 1). In general, women had a higher prescription prevalence for most drug groups except for cardiovascular disease drugs (including antilipemic agents, β-blockers and related medications, and angiotensin-converting enzyme inhibitors). The prevalence of most of the drug groups increased with advancing age. However, vaccine/toxoids, and penicillin and β-lactam antimicrobial prescriptions were most prevalent in children, decreased in young adults, and then slowly increased with age. Prescriptions for antidepressants, opioid analgesics, gastrointestinal medications, laxatives, and cardiovascular disease drugs increased sharply with age. In contrast, prescriptions for antiasthmatics, topical anti-infective/anti-inflammatory agents, erythromycins/macrolides, topical nasal and throat agents, and antihistamines had a relatively stable prevalence across all age groups.
      Figure thumbnail gr1
      Figure 1Age-specific prevalence (per 100 population) of the 15 most commonly prescribed drug groups in men/boys compared with women/girls. The 15 panels are in descending order of overall age- and sex-adjusted prevalence (). ACE = angiotensin-converting enzyme.
      The most commonly prescribed drug groups varied by age (Table and Figure 2). In children (<19 years old), the top prescriptions were vaccines/toxoids, penicillins and β-lactam antimicrobials, and antiasthmatic drugs. In contrast, the most common prescriptions in persons 65 years or older were antilipemic agents and β-blockers and related medications. Finally, prescribing patterns varied by sex within age groups. For example, in children (<19 years old), drug-prescribing patterns were similar between boys and girls. However, central nervous system stimulants were more commonly prescribed to boys than to girls (data not shown). In young adults (19-29 years old), systemic contraceptives were the most common prescription, but were restricted to women. Similary, antidepressants were the most common drug group in the 30- to 49-year-old population, with an overall prevalence of 17% (n=6310/37,927). Again, the prevalence of antidepressants was driven by a higher frequency of prescriptions to women in this age group (22%; n=4357/20,039).
      Figure thumbnail gr2
      Figure 2Prevalence (per 100 population) of the 10 most commonly prescribed drug groups in each age category overall and by sex. ACE = angiotensin-converting enzyme.

      Discussion

      Overall Findings

      Outpatient prescriptions for drugs were highly prevalent in the Olmsted County population in this 2009 study. In a 12-month period, almost 70% of the population received a prescription from at least 1 drug group, more than 50% received prescriptions from 2 or more drug groups, and more than 20% received prescriptions from 5 or more drug groups. The most prevalent prescriptions were penicillins and β-lactam antimicrobials, antidepressants, opioid analgesics, and antilipemic agents. These drugs were prescribed to both sexes across all age groups (except for antilipemic agents, which were rarely used before age 30 years). However, prescribing patterns differed substantially across age and sex groups. Overall, women and older adults received more prescriptions.
      In general, drug-prescribing patterns in this population are consistent with those in previous population-based studies in the United States.
      • Gu Q.
      • Dillon C.F.
      • Burt V.L.
      Prescription drug use continues to increase: U.S. prescription drug data for 2007-2008.
      • Roe C.M.
      • McNamara A.M.
      • Motheral B.R.
      Use of chronic medications among a large, commercially-insured US population.
      The prevalence of prescription drug use is high in the United States. The National Health and Nutrition Examination Survey (NHANES) reported a 48% monthly use of 1 or more prescription drugs in 2007-2008.
      • Gu Q.
      • Dillon C.F.
      • Burt V.L.
      Prescription drug use continues to increase: U.S. prescription drug data for 2007-2008.
      Another survey reported that 50% of US adults took at least 1 medication weekly.
      • Kaufman D.W.
      • Kelly J.P.
      • Rosenberg L.
      • Anderson T.E.
      • Mitchell A.A.
      Recent patterns of medication use in the ambulatory adult population of the United States: the Slone survey.
      Our findings cannot be compared directly with findings from these previous studies because of differences in methods (weekly or monthly use vs annual use and data derived from drug prescriptions vs self-reports, pharmacy records, or insurance claims).
      • Lin S.J.
      • Lambert B.
      • Tan H.
      • Toh S.
      Frequency estimates from prescription drug datasets (revision of #04-11-066A).
      Antibiotics, vaccines, asthma medicines, and central nervous system stimulants were commonly prescribed to children, whereas oral contraceptives, antibiotics, antidepressants, and opioid analgesics were commonly prescribed to young and middle-aged adults. As expected, cardiovascular disease drugs were the most commonly prescribed drugs in older adults, with 41% of individuals 65 years or older receiving an antilipemic prescription. Men had a higher prevalence of cardiovascular disease drug prescriptions than women, which was consistent with cardiovascular disease patterns. Specifically, the incidence of cardiovascular disease in women lags 10 years behind the incidence in men,
      • Roger V.L.
      • Go A.S.
      • Lloyd-Jones D.M.
      • et al.
      Heart disease and stroke statistics—2012 update: a report from the American Heart Association [published correction appears in Circulation. 2012;125(22):e1002].
      and a similar pattern was reflected in the present drug prescription data. However, when considering all prescription drugs, women received more prescriptions than men. This may be caused by the higher frequency of diseases or conditions requiring medication in women or by differences in health care–seeking behavior between men and women.
      • Courtenay W.H.
      Constructions of masculinity and their influence on men's well-being: a theory of gender and health.
      For example, among patients with migraines, 73% of women seek care from physicians compared with 49% of men.
      • Lipton R.B.
      • Bigal M.E.
      Migraine: epidemiology, impact, and risk factors for progression.

      Specific Drug Groups

      This study provides an overview of prescription patterns in this community and highlights some of the commonly used drug groups that deserve further research, as described in the following paragraphs. Penicillins and β-lactam antimicrobials are the most commonly prescribed drugs, especially in children. The high prevalence of prescriptions for penicillins and β-lactam antimicrobials (approximately 25% of all children in 2009) reflects the high rate of bacterial infections (such as ear and throat infections). Appropriate use of antibiotics is a major public health concern,
      and we plan to further study antibiotic prescriptions through linkage with laboratory and medical record data to explore prescribing appropriateness, type and duration of use, and use of multiple antibiotics.
      Antidepressants are the second most prescribed drug group (13%), particularly in middle-aged women. This sex difference has been reported in other studies.
      • Roe C.M.
      • McNamara A.M.
      • Motheral B.R.
      Use of chronic medications among a large, commercially-insured US population.
      • Anthony M.
      • Lee K.Y.
      • Bertram C.T.
      • et al.
      Gender and age differences in medications dispensed from a national chain drugstore.
      • Centers for Disease Control and Prevention (CDC)
      Current depression among adults—United States, 2006 and 2008.
      The increased prescription of antidepressants in recent years has occurred concurrently with a decreasing use of psychotherapy.
      • Olfson M.
      • Marcus S.C.
      National patterns in antidepressant medication treatment.
      However, many antidepressants are not prescribed by psychiatrists
      • Mojtabai R.
      Increase in antidepressant medication in the US adult population between 1990 and 2003.
      and are prescribed to patients who may not have a psychiatric diagnosis.
      • Mojtabai R.
      • Olfson M.
      Proportion of antidepressants prescribed without a psychiatric diagnosis is growing.
      Further studies considering indications may be helpful to understand the use of antidepressant drugs for conditions other than depression.
      Opioid analgesics are the third most common prescription group in this population. In the United States, there has been a 10-fold increase in the medical use of opioid painkillers during the past 20 years.
      • Parsells Kelly J.
      • Cook S.F.
      • Kaufman D.W.
      • Anderson T.
      • Rosenberg L.
      • Mitchell A.A.
      Prevalence and characteristics of opioid use in the US adult population.
      Concerns regarding opioid misuse are increasing in the United States because deaths from opioid overdose currently outnumber deaths due to heroin and cocaine use combined.
      • Parsells Kelly J.
      • Cook S.F.
      • Kaufman D.W.
      • Anderson T.
      • Rosenberg L.
      • Mitchell A.A.
      Prevalence and characteristics of opioid use in the US adult population.
      The 12-month prevalence of opioid prescriptions (12%) in the present study was consistent with that in previous reports.
      • Gu Q.
      • Dillon C.F.
      • Burt V.L.
      Prescription drug use continues to increase: U.S. prescription drug data for 2007-2008.
      • Parsells Kelly J.
      • Cook S.F.
      • Kaufman D.W.
      • Anderson T.
      • Rosenberg L.
      • Mitchell A.A.
      Prevalence and characteristics of opioid use in the US adult population.
      Also, consistent with other studies, women had a higher prevalence of opioid prescriptions than men.
      • Gu Q.
      • Dillon C.F.
      • Burt V.L.
      Prescription drug use continues to increase: U.S. prescription drug data for 2007-2008.
      • Roe C.M.
      • McNamara A.M.
      • Motheral B.R.
      Gender- and age-related prescription drug use patterns.
      • Anthony M.
      • Lee K.Y.
      • Bertram C.T.
      • et al.
      Gender and age differences in medications dispensed from a national chain drugstore.
      • Parsells Kelly J.
      • Cook S.F.
      • Kaufman D.W.
      • Anderson T.
      • Rosenberg L.
      • Mitchell A.A.
      Prevalence and characteristics of opioid use in the US adult population.
      This finding is likely due to a higher prevalence of diseases associated with chronic pain in women
      • Wiesenfeld-Hallin Z.
      Sex differences in pain perception.
      but also to a lower pain tolerance and a higher subjective pain rating in women than in men.
      • Berkley K.J.
      Sex differences in pain.
      • Dixon K.E.
      • Thorn B.E.
      • Ward L.C.
      An evaluation of sex differences in psychological and physiological responses to experimentally-induced pain: a path analytic description.
      Osteoarthritis and joint disorders and back problems are the second and third most common chronic conditions in this community.
      • St Sauver J.L.
      • Warner D.O.
      • Yawn B.P.
      • et al.
      Why patients visit their doctors: assessing the most prevalent conditions in a defined American population.
      Therefore, it is not surprising that the use of opioid analgesics was common. However, it is surprising that opioid analgesics were prescribed in all age groups, including young adults, who generally do not have chronic pain conditions. This pattern can be explained by the inclusion of opioid analgesics prescribed for both acute and chronic pain. Opioid analgesics are often prescribed to manage acute pain after surgical procedures or trauma, and patients are instructed to use the analgesic only if needed. In addition, we included prescriptions given to patients at the time of dismissal from the hospital or emergency department (eg, hydrocodone/acetaminophen and oxycodone). These types of short-term prescriptions may be common in the younger population after dental procedures. Nevertheless, the high level of opioid prescriptions among all the individuals in this population suggests the importance of future studies to determine whether alternative pain management agents should be considered.
      In this study, antilipemic agents were the fourth most commonly prescribed drug group overall, and the high use was driven primarily by prescriptions to persons 50 years or older. In persons 65 years or older, 41% received at least 1 antilipemic prescription in 2009. This finding is similar to the monthly percentage estimated from the NHANES in 2007-2008 (45% of adults aged ≥60 years).
      • Gu Q.
      • Dillon C.F.
      • Burt V.L.
      Prescription drug use continues to increase: U.S. prescription drug data for 2007-2008.
      The NHANES data also estimated that 33.5% of US adults older than 20 years have increased low-density lipoprotein (LDL) cholesterol levels, and this prevalence increases to 58% in adults 65 years or older.
      • Centers for Disease Control and Prevention (CDC)
      Vital signs: prevalence, treatment, and control of high levels of low-density lipoprotein cholesterol—United States, 1999-2002 and 2005-2008.
      However, less than half of those with high LDL cholesterol levels were treated, and even fewer had the LDL cholesterol level controlled.
      • Centers for Disease Control and Prevention (CDC)
      Vital signs: prevalence, treatment, and control of high levels of low-density lipoprotein cholesterol—United States, 1999-2002 and 2005-2008.
      Applying similar estimates to the present population, we expect that antilipemic agents may be underprescribed in a large percentage of patients. We plan to address antilipemic agent use patterns in future studies. These studies will also incorporate serial lipid blood test results and other detailed information from medical records.

      Strengths and Limitations

      The strengths of this study include the availability of complete medical visit information for the entire Olmsted County population. For a combination of geographic and historical circumstances, almost all the county residents seek health care from a limited number of local providers. Furthermore, all residents, irrespective of insurance status, are included in both the denominator and the numerator of the prevalence figures, providing a more complete picture of prescribing patterns in the community.
      Some utilization studies rely on self-reported drug use, which may more accurately reflect actual drug exposure; however, recall bias is a problem for past use.

      National Center for Health Statistics. Health, United States, 2011: with special feature on socioeconomic status and health. http://www.cdc.gov/nchs/data/hus/hus11.pdf. Accessed April 12, 2013.

      • Kaufman D.W.
      • Kelly J.P.
      • Rosenberg L.
      • Anderson T.E.
      • Mitchell A.A.
      Recent patterns of medication use in the ambulatory adult population of the United States: the Slone survey.
      In particular, interviewees tend to underreport their medication use.
      • Hill S.C.
      • Zuvekas S.H.
      • Zodet M.W.
      Implications of the accuracy of MEPS prescription drug data for health services research.
      In addition, self-reported drug use does not necessarily reflect prescribing patterns by the health care providers because not all prescriptions are filled.
      • Kennedy J.
      • Tuleu I.
      • Mackay K.
      Unfilled prescriptions of Medicare beneficiaries: prevalence, reasons, and types of medicines prescribed.
      However, utilization estimates derived from pharmacy records, claims, and other administrative databases may have a higher sensitivity for actual drug exposure.
      A potential limitation of prescription-based studies, such as this one, is the inability to determine whether the patients actually purchased and used the drugs (adherence with the prescription). Therefore, the patterns of prescriptions that we observed may not reflect the patterns of actual drug use in the population. Nevertheless, the ability to link prescription data with diagnoses and with clinical details in the electronic medical records is a unique strength of the REP and will form the basis for future utilization and outcome studies focused on individual drugs or drug groups.
      A second limitation of this database is that many commonly used drugs are not prescription drugs and can be purchased over-the-counter (such as cold medicines); therefore, they are not found among the most commonly prescribed drugs. This also applies to vaccines that are more completely captured in vaccine registries. A third limitation is our inability to include drug prescriptions from a few smaller health care practices in Olmsted County that do not have an electronic drug prescription system.
      • St Sauver J.L.
      • Grossardt B.R.
      • Yawn B.P.
      • Melton III, L.J.
      • Rocca W.A.
      Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester Epidemiology Project.
      • St Sauver J.L.
      • Grossardt B.R.
      • Yawn B.P.
      • et al.
      Data resource profile: the Rochester Epidemiology Project (REP) medical records-linkage system.
      Thus, we may have underestimated the frequency of use for some drug groups.
      Fourth, drug formularies, prescribing guidelines, and decision support systems may vary substantially across health care practices throughout the country. Therefore, the prescribing patterns that we observed in Olmsted County may not be generalizable to other regions. On the other hand, drug formularies, prescribing guidelines, and decision support systems may influence more strongly the choice of a drug within a particular drug group than the choice of the drug group itself. Thus, the patterns of drug groups may be more generalizable to other populations than the patterns of specific drugs.
      Finally, we considered the use of drugs in a 12-month period to avoid seasonal variations in prescriptions for some drugs (eg, allergy drugs). However, the 12-month prevalence used in this study does not distinguish between long-term use (repeated prescriptions) and onetime use of drugs (eg, antibiotics), and does not reflect multiple prescriptions in the same drug group (switches) or the frequency of drug prescribing in an individual patient. We also have not assessed refills and instructions for use, such as directions to use the drug only if needed (eg, for opioid analgesics). Duration of drug use may be particularly important when investigating issues such as chronic disease management, drug abuse, and outcomes. We are currently performing additional analyses to address issues of indications, duration of use, and per capita prescriptions in each drug group to provide a more complete picture of drug utilization in this community.

      Conclusion

      A high percentage of the overall Olmsted County population received outpatient prescription drugs in 2009. The drug-prescribing patterns varied substantially by age and sex. In general, women and older individuals received more prescriptions. These findings are useful for understanding the prescribing patterns across all ages in a defined population and provide important baseline information for future studies of drug-related adverse events, drug-to-drug interactions, polypharmacy, health-seeking behaviors, and other prescription-related aspects of health care utilization.

      Acknowledgments

      We thank Carol Greenlee for formatting the submitted manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging.

      Supplemental Online Material

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