Using Electronic Medical Record Data to Characterize the Level of Medication Use by Age-Groups in a Network of Primary Care Clinics

Abstract

Purpose: Our primary aim was to characterize the level of medication use across age-groups by examining electronic medical record data for a large number of patients receiving care in primary care clinics. A secondary aim was to identify factors associated with higher levels of medication use or polypharmacy. Methods: We conducted a retrospective query of electronic medical record data from a clinical data warehouse, evaluating 114 012 patients seen in primary care clinics at least once in the previous 6 months. Medication use was evaluated in 3 different categories: level 1 (0-4 medications), level 2 (5-9 medications), and level 3 (≥10 medications). Multivariate analysis was used to analyze different patient demographics and comorbidities for association with level of medication use. Results: At ages 18 to 24 years, 15% (male) to 23% (female) of patients were taking greater than 5 medications, a trend that continued to increase with older cohorts. Female patients were more likely to have level 2 (odds ratio [OR] = 1.76) and level 3 (OR = 2.73) use compared with men. Level 2 and level 3 use was associated with other patient characteristics, including number of patient encounters (level 2 OR = 2.99; level 3 OR = 8.08 for >7 encounters) and common chronic conditions such as chronic pain (level 2 OR = 2.56; level 3 OR = 6.40), diabetes (level 2 OR = 2.4; level 3 OR = 4.61), heart disease (level 2 OR = 1.99; level 3 OR = 3.65), hypertension (level 2 OR = 2.27; level 3 OR = 2.87), and dyslipidemia (level 2 OR = 1.82; level 3 OR = 2.12). Conclusion: Electronic medical record data may be an important tool for providing more comprehensive information regarding medication usage. Medication usage assessed by electronic medical records, even among the youngest cohort, appears to be greater than other sources of medication usage indicate. Higher levels of medication use were associated with a number of factors, including gender, body mass index, number of patient encounters, and comorbid conditions.

Keywords

polypharmacy primary care family medicine medication use sociodemographic characteristics

Introduction

Polypharmacy is frequently defined as the excessive or unnecessary use of prescription or nonprescription medications and is associated with drug therapy problems and adverse side effects.^1,2 Drug therapy problems are defined as undesirable events or risks experienced by the patient secondary to drug therapy that inhibit or delay the patient from achieving the desired goals of therapy. Classification of specific drug therapy problems includes problems with drug indication, effectiveness, safety, and patient adherence and has been described in detail previously.³ Drug therapy problems have been estimated to account for more than $76 billion in annual health care expenditures.⁴

Most published research describes polypharmacy in geriatric populations; however, information on medication use in the general population is limited.^{5

-8} Identifying risk factors for polypharmacy will help physicians and pharmacists in primary care practices identify patients who would benefit from medication therapy management, improve quality of care, decrease adverse consequences, and decrease the costs of care. To our knowledge, there have been no studies using electronic medical record data to evaluate medication use in a general population of patients receiving care in primary care clinics. Our aim in conducting this study was to characterize the level of medication use by age-groups, demographics, and diagnoses for a large group of patients receiving services in a network of primary care clinics through evaluation of electronic medical record information.

Methods

Sample and Study Population

The study sample consisted of patient demographic and medical data drawn from the University of Wisconsin Department of Family Medicine clinical data warehouse (CDW), a database of electronic medical record information from 22 primary care clinics across the state of Wisconsin. The CDW has been in use since 2005 and provides comprehensive information regarding patients seen at these clinics including: medication use, medical diagnoses, demographic information, and biometric data. We analyzed a sample of de-identified data for all adult patients listed in the database on November 15, 2011. The study received exemption from the Health Sciences Institutional Review Board. To ensure that medication records were accurate, the sample was limited to adult patients who had medication records documented as reviewed by clinic staff within the past 6 months. Patient comorbidities were chosen for analysis based on aggregate data, generated by the CDW, of the most common conditions in the patient population and conditions shown to be associated with greater use of medications in previous studies.^{2,8

-11}

Outcome Measure

Similar to previous population studies of polypharmacy, medication use was categorized into three groups: level 1 medication use was defined as 0 to 4 medications, level 2 as 5 to 9 medications, and level 3 as ≥10 medications.^10,12 We adopted a broad definition of medication, including all prescription drugs, over-the-counter drugs, vitamins and supplements, and herbal preparations, similar to prior epidemiological studies of medication use.¹² A complete listing of all medications taken by patients in our sample was reviewed to exclude nonmedication items such as testing supplies. Topical preparations, both prescription and over-the-counter, with low likelihood of systemic effects were also excluded.

Independent Variables

Several demographics and comorbidities were analyzed for association with level of medication use. Information on patient age, gender, smoking status, marital status, employment status, race, number of patient encounters within the past year, and body mass index (BMI) were obtained from the clinical database as documented in the patient’s chart on November 15, 2011. Major comorbidities data were obtained based on the International Classification of Diseases, Ninth Revision diagnostic codes shown in Table 1.

Table 1.

Major Comorbidities.

Heart disease	390.XX-398.XX, 402.XX, 404.XX, 410.XX-429.XX
Diabetes	250.XX
Hypertension	401.XX
Chronic pain	338.XX
Dyslipidemia	272.XX

Statistical Analysis

Statistical analyses were performed using SPSS Version 19. Missing data for BMI, marital status, race, and smoking status were imputed. A multinomial logistic model was fit to the imputed data using forced entry for known predictors of multiple medication use based on previous literature and backward stepwise method for the remaining independent variables. Age categories were based on standard US Census cut-points. Insurance status was broken down into subcategories with the employer-provided insurance as the reference category. Models were fit for a single Medicare variable and with separate classifications for patients older than 65 years receiving coverage as an entitlement versus those younger than 65 years because of disability. Odds ratios for age categories changed significantly between models. Because our database does not allow for identification of disabled individuals beyond insurance status, separate Medicare classifications were used in the final model.

Results

Sample demographics, major comorbidities, and health metrics of the population studied are shown in Tables 2 and 3. Patient records analyzed in this study totaled 114 012.

Table 2.

Study Population, by Age-Group and Gender.

	Male		Female
Age-Group (Years)	n	%	n	%
18-24	3957	8.3	6764	10.2
25-34	7385	15.5	12 677	19.1
35-44	8752	18.4	12 173	18.3
45-54	10 297	21.6	12 943	19.5
55-64	9210	19.3	10 885	16.4
65-74	4989	10.5	5859	8.8
75-84	2268	4.8	3277	4.9
85+	784	1.6	1792	2.7
Total	47 642	100.0	66 370	100.0

Table 3.

Study Population: Demographics and Comorbidities.

	n	%^a
Employment
Retired	17 987	16.0
Unemployed	25 726	22.9
Employed	68 421	61.0
Marital status
Married/partner	63 383	56.0
Widowed	4731	4.2
Divorced/separated	8176	7.2
Single	36 955	32.6
Insurance
Employer	76 900	67.4
Medicaid	10 181	8.9
Medicare	14 126	12.4
Medicare (disability)	4271	3.7
Uninsured	7835	6.9
Unclassified^b	699	0.6
Race
Non-Hispanic white	94 712	87.3
Other	13 767	12.7
Smoking status^c
Current	18 690	16.6
Former	30 696	27.3
Never	63 124	56.1
Body mass index category
Normal	30 364	29.5
Underweight	1318	1.3
Overweight	32 810	31.9
Obese	38 329	37.3
Number of patient encounters
1	31 131	27.3
2	20 810	18.3
3-4	25 643	22.5
5-6	13 637	12.0
≥7	22 791	20.0
Major comorbidities
Heart disease	11 843	10.4
Diabetes	11 172	9.8
Hypertension	28 931	25.4
Dyslipidemia	32 050	28.1
Chronic pain	1667	1.5

Subtotals for each category unequal because of missing values.

“Unclassified” includes state and private charity programs. A total of 611 patients were women receiving partial health benefits through a state-sponsored program.

Nonsmokers include “never smokers” and “passive” smokers.

Table 4 shows the patient characteristics at each level of medication use. Figures 1 and 2 provide the level 1, 2, and 3 medication use in different age-groups and reveal that the amount of medication used increased with age for both men and women. In the 18- to 24-year-old group, 15% (male) to 23% (female) used more than 5 medications per day; this increased to 85% to 89% in the 75- to 84-year-old cohort.

Table 4.

Number (n) and Percentage of Patients With Various Levels of Medication Use According to Demographic Variables, Body Mass Index, Number of Patient Encounters, and Major Comorbidities.

	Medication Use						P ^a
	Level 1 (0-4 Drugs)		Level 2 (5-9 Drugs)		Level 3 (≥10 Drugs)
	n	%	n	%	n	%
Gender
Male	24 946	52	14 299	30	8397	18	<.001
Female	29 325	44	21 615	33	15 430	23	<.001
Age (years)
18-24	8575	80	1855	17	291	3	<.001
25-34	14 264	71	4616	23	1182	6
35-44	12 407	59	6158	29	2360	11
45-54	10 280	44	8450	36	4510	19
55-64	5789	29	7951	40	6355	32
65-74	1884	17	4220	39	4744	44
75-84	703	13	1921	35	2921	53
85+	369	14	743	29	1464	57
Employment
Employed	37 382	55	21 950	32	9089	13	<.001
Unemployed	12 999	51	7053	27	5674	22
Retired	2483	14	6582	37	8922	50
Marital status
Single	21 545	58	9836	27	5574	15	<.001
Divorced/separated	2940	36	2823	35	2413	30
Widowed	746	16	1503	32	2482	53
Married	28 505	45	21 597	34	13 281	21
Insurance
Employer	40 267	52	24 586	32	12 047	16	<.001
Medicaid	5455	54	3004	30	1722	17
Medicare	1984	14	4983	35	7159	51
Medicare (disability)	731	17	1279	30	2261	53
Uninsured	5466	70	1831	23	538	7
Unclassified	368	53	231	33	100	14
Race/ethnicity
Non-Hispanic white	42 520	45	30 930	33	21 262	22
Other	8260	60	3600	26	1907	14	<.001
Smoking status
Current	9421	50	5884	32	3385	18	<.001
Former	10 590	35	10 783	35	9323	30
Never	32 974	52	19 129	30	11 021	18
Body mass index
Normal (18.5-24.9)	16 689	55	9060	30	4615	15	<.001
Under (<18.5)	663	50	364	28	291	22
Over (25-29.9)	15 612	48	10 835	33	6363	19
Obese (>30)	13 908	36	13 286	35	11 135	29
Number of patient encounters
1	20 527	66	7807	25	2797	9
2	11 859	57	6446	31	2505	12
3-4	11 557	45	9141	36	4945	19
5-6	5031	37	4953	36	3653	27
≥7	5297	23	7567	33	9927	44	<.001
Major comorbidities
Heart disease	1367	12	3809	32	6667	56	<.001
Diabetes	1186	11	3741	34	6245	56	<.001
Hypertension	4769	17	11 516	40	12 646	44	<.001
Dyslipidemia	6735	21	12 692	40	12 623	39	<.001
Chronic pain	196	12	450	27	1021	61	<.001

P values obtained from χ² analysis.

Figure 1.

Medication use for men, by age category.

Figure 2.

Medication use for women, by age category.

Table 5 shows the results of the regression analysis. Female patients were more likely to have level 2 (odds ratio [OR] = 1.76) and level 3 medication usage (OR = 2.73) compared with men. ORs began to decline after age 75 years for level 2 use and after age 85 years for level 3 use (Table 5). All comorbidities increased the odds of level 2 and level 3 medication use. Of those evaluated, a diagnosis of chronic pain had the highest odds of both levels 2 and 3 use compared with other comorbidities (level 2 OR = 2.56; level 3 OR = 6.40). Level 2 and level 3 medication use increased with the number of patient encounters. Lack of insurance was associated with lower medication use (level 2 OR = 0.74; level 3 OR = 0.47) compared with patients with employer-provided insurance. Patients with Medicaid were more likely to use medications compared to employer-provided insurance (level 2 OR = 1.09; level 3 OR = 1.20) as were patients with Medicare either as entitlement by age or through disability (Medicare, level 2 OR = 1.12, level 3 OR = 1.32; Medicare via disability, level 2 OR = 2.22, level 3 OR = 4.91).

Table 5.

Determinates of Medication Usage in Multinomial Logistic Regression.

	Level 2 (5-9 Medications)		Level 3 (≥10 Medications)
	Odds Ratio	(95% Confidence Interval)	Odds Ratio	(95% Confidence Interval)
Female (vs male)	1.76	(1.7, 1.81)	2.73	(2.61, 2.85)
Age (vs 18-24 years)
25-34	1.42	(1.33, 1.52)	2.42	(2.10, 2.79)
35-44	2.06	(1.92, 2.21)	5.24	(4.57, 6.01)
45-54	2.86	(2.67, 3.07)	9.18	(8.01, 10.52)
55-64	3.73	(3.46, 4.02)	15.46	(13.46, 17.76)
65-74	3.85	(3.43, 4.32)	16.03	(13.53, 18.99)
75-84	3.76	(3.24, 4.36)	17.18	(14.14, 20.87)
85+	2.80	(2.32, 3.38)	15.89	(12.68, 19.90)
Employment (vs employed)
Retired	1.47	(1.36, 1.59)	2.03	(1.86, 2.21)
Unemployed	1.16	(1.11, 1.21)	1.89	(1.78, 2.00)
Marital status (vs single)
Married/partner	0.90	(0.86, 0.93)	0.75	(0.71, 0.79)
Widowed	0.79	(0.71, 0.88)	0.75	(0.67, 0.85)
Divorced/separated	0.99	(0.93, 1.05)	0.98	(0.90, 1.06)
Race (other ves non-Hispanic white)
Other	0.66	(0.63, 0.69)	0.45	(0.41, 0.48)
Smoking status (vs nonsmokers)
Current	1.06	(1.01, 1.10)	0.95	(0.90, 1.01)
Former	1.26	(1.22, 1.31)	1.45	(1.39, 1.52)
Body mass index (vs normal, 18.5-24.9)
Underweight (<18.5)	0.80	(0.67, 0.96)	1.06	(0.89, 1.25)
Overweight (25-29.9)	1.02	(0.98, 1.07)	1.07	(1.01, 1.13)
Obese (>30)	1.12	(1.08, 1.17)	1.45	(1.37, 1.53)
Number of patient encounters (vs 1 encounter)
2	1.25	(1.20, 1.30)	1.25	(1.17, 1.34)
3-4	1.69	(1.63, 1.76)	2.17	(2.04, 2.31)
5-6	2.05	(1.95, 2.16)	3.42	(3.19, 3.66)
≥7	2.99	(2.85, 3.14)	8.08	(7.59, 8.59)
Insurance (vs employer insurance)
Medicaid	1.09	(1.03, 1.16)	1.20	(1.11, 1.30)
Medicare	1.12	(1.01, 1.24)	1.32	(1.18, 1.47)
Medicare (disability)	2.22	(2.00, 2.47)	4.91	(4.39, 5.49)
Uninsured	0.74	(0.70, 0.79)	0.47	(0.42, 0.53)
Unclassified	0.67	(0.56, 0.80)	0.37	(0.28, 0.48)
Major comorbidities
Heart disease	1.99	(1.85, 2.14)	3.65	(3.38, 3.93)
Chronic pain	2.56	(2.14, 3.07)	6.40	(5.32, 7.71)
Dyslipidemia	1.82	(1.75, 1.89)	2.12	(2.02, 2.22)
Diabetes	2.40	(2.23, 2.59)	4.61	(4.26, 5.00)
Hypertension	2.27	(2.17, 2.37)	2.87	(2.73, 3.02)

Underweight individuals (BMI < 18.5) had lower likelihood of level 2 use (OR = 0.80) and no statistically significant increase for level 3 use compared with patients with a normal weight (BMI = 18.5-24.9). Overweight patients had no statistically significant increase in level 2 use but were more likely to have level 3 (OR = 1.06) usage, whereas patients with BMI > 30 had increased for both (level 2 OR = 1.12; level 3 OR = 1.45) compared with patients with normal BMI.

Unemployed and retired patients showed higher level 2 and level 3 use compared with working individuals. There were no statistically significant findings for separated or divorced patients compared with single individuals, but married and widowed patients had lower use of medications overall compared with single patients. Non-Hispanic white patients had higher rates of level 2 and level 3 medication use than patients from other racial and ethnic groups. Former smokers were at more likely to use greater amounts of medications compared with never-smokers (level 2 OR = 1.26; level 3 OR = 1.45) and current smokers showed increased rates for level 2 (OR = 1.06) use, but no statistically significant increased risk for level 3 use.

Discussion

This study provides evidence that use of 5 or medications is prevalent across all adult age-groups, including the younger cohorts receiving care in primary care clinics. We found that in female patients aged 18 to 24 years, 25 to 34 years, and 35 to 44 years, the percentage using 5 or more medications was 23%, 34%, and 57%, respectively. In contrast, survey data collected by the Centers for Disease Control and Prevention¹³ reported 4.0% of 18- to 44-year old women used 5 or more prescription drugs in the past 30 days, similar to 3% reported (survey data) for prescription drug use in the same age-group by the Slone Epidemiology Center.¹⁴ Similarly, for male patients in our study, the percentage using 5 or medications was 15% for 18- to 24-year-olds, 20% for 25- to 34-year-olds, and 33% for 35- to 44-year-olds. Again, these numbers are much higher than prescription data from the Centers for Disease Control and Prevention (2.1%)¹³ and from the Slone Epidemiology Center (4%).¹⁴ Because we did not evaluate the medication lists or health problem lists for each of these age cohorts, it is difficult to speculate about the large difference between our results and that of the other sources. Our patients may use a large number of nonprescription or alternative/complementary products, documented in the electronic medical record, or have a larger number of comorbid conditions requiring prescription medications. Our information regarding medication use was reported directly from patients during clinic visits, including all patients seen during the study time frame, whereas surveys conducted by other means (eg, telephone) may omit patients in specific demographic groups. Another possible explanation for the difference is that our sample is biased toward heavy health care utilizers. Because we limited the population to patients who had an appointment with their primary care provider in the previous 6 months, we may have excluded some healthier patients who did not visit their primary care provider. It is also possible that our electronic medical records may include inaccurate information, such as medications no longer used, thereby inflating the number of medications used. However, a recent electronic medical record medication reconciliation study revealed that comprehensive medication histories taken by pharmacists were likely to reveal greater medication use (primarily nonprescription drugs) than the electronic medical record had listed, suggesting that electronic medical records are more likely to underestimate, rather than overestimate, actual medication use.¹⁵ The fact that our inclusion criteria require patients for whom medications were recently reviewed by clinic staff likely reduced the risk that our medical records contain medications no longer used.

Although our study demonstrates significant use of multiple medications across the age span of patients, it is not known what, if any, types of drug therapy problems are experienced by this younger population. From studies in the elderly, we would anticipate drug therapy problems such as drugs lacking indications, drugs used to treat side effects from other drugs, and drug interactions.¹⁶

This study also sought to examine patient characteristics associated with medication use. Previous population studies evaluating medication use in older populations showed results similar to ours with regard to the association of gender with medication use.^{8,11,12,17

-20} Three studies found that rates of medication use were higher in women across all age-groups, except for 65 or 70 years and older.^8,11,12 Two studies found that women older than 65 years used more medications than men in the same age-group.^19,20 However, one recent study of polypharmacy in patients >65 years of age being discharged from a hospital showed no association between gender and polypharmacy.²¹ In our study population, females were more likely to have level 2 and level 3 medication use, independent of age.

Most studies have shown that increasing age is associated with increased medication use.^11,17,18 Our study agrees with these findings, as the prevalence of level 2 and level 3 use increased with advanced age. However, our analysis indicates that the oldest (85+ years) age-group had lower odds of level 2 and level 3 use compared with the next oldest group. Although we controlled for major comorbidities in our model, there are other variables we did not include. The higher degree of medication use attributable to these excluded variables is likely captured, in part, by the age variable. This finding may suggest that the oldest patients who lack comorbidities may be healthier than slightly younger age cohorts, and hence less likely to use medications. Some studies, in fact, have found decreasing medication use at the extremes of age. This finding has been attributed to the oldest survivors being the healthiest and requiring fewer medications.^11,22

Previous studies have demonstrated increased medication use with chronic conditions, including diabetes, heart disease, chronic pain, dyslipidemia, and hypertension.^{2,8
-10,23
-25} Our study found similar results, demonstrating greater level 2 and level 3 medication use in all conditions listed above. Additionally, our study found that chronic pain was the strongest predictor of use of more than 5 medications among the chronic conditions evaluated. A previous study found that 21% of adult patients using chronic opioid therapy were taking more than 10 concurrent medications, putting them at increased risk for drug interactions.²⁶

The number of patient encounters with health care providers correlated with both level 2 and level 3 medication use, even when controlling for other confounders. Jörgensen et al¹⁹ reported similar results in their study as patients with greater than 5 office visits were 15 times more likely to have multiple medication use. However, their study population was elderly, defined as 65 years or older, whereas our study population included ages 18 to 85+ years. The wider age range of our study suggests that as contact with medical providers increases, the risk of level 3 medication use increases for all age-groups, not solely elderly patients. Several explanations exist for increased medication use associated with increased health care utilization, including an emphasis on providers to see patients more quickly resulting in prescription of a medication rather than spending time exploring nonpharmacologic treatments and patient education.²⁷ In addition, direct to consumer advertising has given patients more information to request specific medications from their doctors.²⁸

Limitations

We acknowledge that there are several limitations related to our methods and analysis. Data were taken from electronic medical records, and over-the-counter medication and complementary/alternative medication use was self-reported. Sensitivity of the regression analysis was limited by the need to impute some demographic information and likely accounts for nonsignificant findings related to BMI, smoking, and marital status. As discussed above, our sample may be biased toward heavy health care utilizers. Although major comorbidities were included in the analysis, there are other indications of health status and drivers of polypharmacy that were not included in the model.

Conclusions

Electronic medical record data has the capacity to provide comprehensive information regarding medication use (prescription and nonprescription). These data can serve as a valuable tool to characterize and evaluate medication use in various demographic groups receiving care in primary care settings. Further investigation of medication use and medication-related problems are warranted in younger patients seen in primary care settings.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Jeff Freund graduated with a PharmD from from the University of Minnesota College of Pharmacy and is currently a Senior Clinical Instructor at Skaggs School of Pharmacy and Phamaceutical Science at the University of Colorado Anschutz Medical Campus. He is a board certified ambulatory care pharmacist and has a clinical practice at Salud Family Health Centers in Commerce City, CO.

Jon Meiman graduated with an MD from the University of AlabamaSchool of Medicine and is currently a PGY3 resident at University ofWisconsin Department of Family Medicine. He will be joining the CDC’sEpidemic Intelligence Service upon completion of his residency.

Connie Kraus is Professor (CHS) and Director of the Office of Global Health at the University of Wisconsin School of Pharmacy. She is a board-certified ambulatory care pharmacist and has a clinical practice at Access Community Health Centers in Madison, WI.

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