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Choosing Wisely? Measuring the Burden of Medications in Older Adults near the End of Life: Nationwide, Longitudinal Cohort Study

Open AccessPublished:April 25, 2017DOI:https://doi.org/10.1016/j.amjmed.2017.02.028

      Abstract

      Background

      The burden of medications near the end of life has recently come under scrutiny, because several studies suggested that people with life-limiting illness receive potentially futile treatments.

      Methods

      We identified 511,843 older adults (>65 years) who died in Sweden between 2007 and 2013 and reconstructed their drug prescription history for each of the last 12 months of life through the Swedish Prescribed Drug Register. Decedents' characteristics at time of death were assessed through record linkage with the National Patient Register, the Social Services Register, and the Swedish Education Register.

      Results

      Over the course of the final year before death, the proportion of individuals exposed to ≥10 different drugs rose from 30.3% to 47.2% (P <.001 for trend). Although older adults who died from cancer had the largest increase in the number of drugs (mean difference, 3.37; 95% confidence interval, 3.35 to 3.40), living in an institution was independently associated with a slower escalation (β = −0.90, 95% confidence interval, −0.92 to −0.87). During the final month before death, analgesics (60.8%), anti-throm-botic agents (53.8%), diuretics (53.1%), psycholeptics (51.2%), and β-blocking agents (41.1%) were the 5 most commonly used drug classes. Angiotensin-converting enzyme inhibitors and statins were used by, respectively, 21.4% and 15.8% of all individuals during their final month of life.

      Conclusion

      Polypharmacy increases throughout the last year of life of older adults, fueled not only by symptomatic medications but also by long-term preventive treatments of questionable benefit. Clinical guidelines are needed to support physicians in their decision to continue or discontinue medications near the end of life.

      Keywords

      See related Editorial, p875.
      Clinical Significance
      • The burden of medications increases near the end of life (47.2% of older adults receive ≥10 prescription drugs during their last month of life).
      • Polypharmacy is fueled not only by symptomatic medications but also by long-term preventive treatments of questionable benefit.
      • Guidelines are needed to support physicians in their decision to continue or discontinue drug treatments near the end of life.
      Under the combined effect of increased longevity, chronic multimorbidity, and single-disease clinical guidelines, the concomitant use of multiple medications has become commonplace among older adults.
      • Barnett K.
      • Mercer S.
      • Norbury M.
      • Watt G.
      • Wyke S.
      • Guthrie B.
      Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.
      Polypharmacy increases inappropriate drug use and drug–drug interactions and exposes older adults to serious adverse effects.
      • Johnell K.
      • Klarin I.
      The relationship between number of drugs and potential drug-drug interactions in the elderly: a study of over 600,000 elderly patients from the Swedish Prescribed Drug Register.
      Yet, it is estimated that 25% to 40% of adults aged 65 years or older are prescribed at least 5 medications.
      • Kantor E.D.
      • Rehm C.D.
      • Haas J.S.
      • Chan A.T.
      • Giovannucci E.L.
      Trends in prescription drug use among adults in the United States from 1999-2012.
      When considering older people near the end of life, poly-pharmacy poses 2 problems. First, as death approaches, age-related physiologic changes are amplified by the changing metabolism, the decline of renal and hepatic functions, and the loss of body mass. As a result, pharmacokinetics and pharmacodynamics are altered, making older adults with life-limiting illness particularly vulnerable to the harmful side effects of medications.
      • Morgan N.A.
      • Rowett D.
      • Currow D.C.
      Analysis of drug interactions at the end of life.
      Second, the accumulation of prescriptions in the context of limited life expectancy raises questions about the intended or expected benefit of the treatments.
      • Holmes H.M.
      • Sachs G.A.
      • Shega J.W.
      • Hougham G.W.
      • Cox Hayley D.
      • Dale W.
      Integrating palliative medicine into the care of persons with advanced dementia: identifying appropriate medication use.
      The burden of medications near the end of life has recently come under scrutiny, because several studies have shown that people with specific life-limiting diseases are prescribed medications whose benefit is unlikely to be achieved within their remaining lifespan.
      • Todd A.
      • Husband A.
      • Andrew I.
      • Pearson S.A.
      • Lindsey L.
      • Holmes H.
      Inappropriate prescribing of preventative medication in patients with life-limiting illness: a systematic review.
      However, these studies have all been conducted in selected samples of individuals who shared a common disease
      • Raijmakers N.J.H.
      • van Zuylen L.
      • Furst C.J.
      • et al.
      Variation in medication use in cancer patients at the end of life: a cross-sectional analysis.
      • Bayliss E.A.
      • Bronsert M.R.
      • Reifler L.M.
      • et al.
      Statin prescribing patterns in a cohort of cancer patients with poor prognosis.
      • Riechelmann R.P.
      • Krzyzanowska M.K.
      • Zimmermann C.
      Futile medication use in terminally ill cancer patients.
      • Tjia J.
      • Briesacher B.A.
      • Peterson D.
      • Liu Q.
      • Andrade S.E.
      • Mitchell S.L.
      Use of medications of questionable benefit in advanced dementia.
      or care setting
      • Ma G.
      • Downar J.
      Noncomfort medication use in acute care inpatients comanaged by palliative care specialists near the end of life: a cohort study.
      • van Nordennen R.T.C.M.
      • Lavrijsen J.C.M.
      • Heesterbeek M.J.A.B.
      • Bor H.
      • Vissers K.C.P.
      • Koopmans R.T.C.M.
      Changes in prescribed drugs between admission and the end of life in patients admitted to palliative care facilities.
      • Dwyer L.L.
      • Lau D.T.
      • Shega J.W.
      Medications that older adults in hospice care in the United States take, 2007.
      • Heppenstall C.P.
      • Broad J.B.
      • Boyd M.
      • et al.
      Medication use and potentially inappropriate medications in those with limited prognosis living in residential aged care.
      or were recruited for a clinical trial.
      • McNeil M.J.
      • Kamal A.H.
      • Kutner J.S.
      • Ritchie C.S.
      • Abernethy A.P.
      The burden of polypharmacy in patients near the end of life.
      Future research and clinical guidelines need to be informed by findings that are generalizable beyond a specific illness or care setting.
      This study aimed to measure the change in the prevalence of polypharmacy and to identify the most commonly used medications over the course of the last year of life of older people, using data with national coverage in Sweden.

      Methods

      Study Design and Population

      We conducted a nationwide, follow-back cohort study of all older adults who died at age >65 years in Sweden between January 1, 2007 and December 31, 2013. Individuals were excluded from the study population if they had no reported cause of death or had no prescription data available during the final 3 months before the date of death (Supplementary Figure 1, available online). Death certificate data were obtained from the Swedish National Board of Health and Welfare and were linked at the individual level to several other registries with national coverage: the Swedish Prescribed Drug Register, Social Services Register, National Patient Register, and Swedish Education Register. Data were anonymized, and the Regional Ethical Review Board in Stockholm approved the study (no. 2013/1941-31/3 and 2015/1319-32).

      Assessment of Polypharmacy and Drug Exposure

      Polypharmacy was considered as the primary outcome. Although there is no consensual definition, studies conducted in the general geriatric population typically use a threshold of ≥4 or ≥5 medications to characterize polypharmacy and ≥9 or ≥10 to describe “excessive polypharmacy.”
      • Turner J.P.
      • Jamsen K.M.
      • Shakib S.
      • Singhal N.
      • Prowse R.
      • Bell J.S.
      Polypharmacy cut-points in older people with cancer: how many medications are too many?.
      • Gnjidic D.
      • Hilmer S.N.
      • Blyth F.M.
      • et al.
      Polypharmacy cutoff and outcomes: five or more medicines were used to identify community-dwelling older men at risk of different adverse outcomes.
      • Onder G.
      • Liperoti R.
      • Fialova D.
      • et al.
      Polypharmacy in nursing home in Europe: results from the SHELTER study.
      In light of the considerable burden of chronic diseases and symptoms near the end of life,
      • Singer A.E.
      • Meeker D.
      • Teno J.M.
      • Lynn J.
      • Lunney J.R.
      • Lorenz K.A.
      Symptom trends in the last year of life from 1998 to 2010: a cohort study.
      we opted for the latter, more conservative cut-off. Hence polypharmacy was hereafter defined as the monthly exposure to 10 or more prescription drugs, that is, distinct substances according to the fifth level of the Anatomical Therapeutic Chemical (ATC) classification system.
      Prescription drug data were derived from the Swedish Prescribed Drug Register to evaluate the total number of medications during each of the final 12 months before death. In Sweden, drug prescriptions cover a maximum period of 90 days. Drug exposure was estimated according to 1) the date of dispensing, 2) the total amount dispensed to the patient, and 3) the prescribed daily dose, as described in Supplementary Figure 2 (available online).
      • Lau H.S.
      • De Boer A.
      • Beuning K.S.
      • Porsius A.
      Validation of pharmacy records in drug exposure assessment.
      • Johnell K.
      • Fastbom J.
      Comparison of prescription drug use between community-dwelling and institutionalized elderly in Sweden.
      In addition, we calculated the prevalence of the 20 most common individual drug classes for the 12th, 6th, and final months before death. As recommended by the World Health Organization, drugs were classified by ATC code.

      Descriptive Variables

      Sex and age at time of death were both extracted from death certificates. International Classification of Diseases, 10th revision diagnosis codes for all contributing causes of death were categorized into 4 distinct “illness trajectories” indicative of the potential timeframe of care needs near the end of life: cancer, organ failure, prolonged dwindling, and sudden death.
      • Murray S.A.
      • Kendall M.
      • Boyd K.
      • Sheikh A.
      Illness trajectories and palliative care.
      • Morin L.
      • Aubry R.
      • Frova L.
      • et al.
      Estimating the need for palliative care at the population level: a cross-national study in 12 countries.
      Individuals were assigned a single illness trajectory using a modified version of the protocol developed by Lunney et al
      • Lunney J.R.
      • Lynn J.
      • Foley D.J.
      • Lipson S.
      • Guralnik J.M.
      Patterns of functional decline at the end of life.
      (Supplementary Table 1, available online). When multiple causes of death indicated more than 1 illness trajectory, we applied a predefined hierarchy (ie, from cancer to prolonged dwindling to organ failure to sudden death).
      • Chaudhry S.I.
      • Murphy T.E.
      • Gahbauer E.
      • Sussman L.S.
      • Allore H.G.
      • Gill T.M.
      Restricting symptoms in the last year of life: a prospective cohort study.
      In addition, we used a multimorbidity assessment tool recently validated in the general elderly population
      • Calderón-Larrañaga A.
      • Vetrano D.L.
      • Onder G.
      • et al.
      Assessing and measuring chronic multimorbidity in the older population: a proposal for its operationalization.
      to provide a comprehensive account of the burden of disease near the end of life (Supplementary Table 2, available online). In brief, a total of 60 chronic diseases were identified through the National Cause of Death Register (all causes of death), the National Patient Register (inpatient and specialized outpatient diagnoses reported during the last 24 months before death), and the Swedish Prescribed Drug Register (use of specific medications in the last 24 months before death). Living arrangement of the individuals during the final year of life was categorized as “community-dwelling” or “institutionalized,” using data from the Social Services Register. Level of education was used as a measure of socioeconomic position and operationalized as “primary education,” “secondary education,” and “tertiary education.”

      Statistical Analysis

      Descriptive analysis was performed by calculating the proportion and means of the decedents' characteristics according to their living arrangement. Statistical differences were tested with Pearson's χ2 for categorical variables and median test for continuous variables, with P <.001. The proportion of individuals receiving ≥10 drugs was reported together with the mean number of prescription drugs throughout the final year of life. Results comparing community-dwelling and institutionalized individuals were standardized for both sex and age by using the total population as reference.
      We investigated trends in polypharmacy and in the number of prescribed drugs between the 12th month and the final month before death. Unadjusted logistic regression models were computed to calculate the odds of being exposed to polypharmacy during the final month before death compared with 12 months before, using clustered robust standard error to account for intra-individual correlation. We also identified factors independently associated with change in the number of prescribed drugs over time by means of generalized estimating equation models with identity link functions at the individual level and unstructured correlation. The number of distinct medications was considered as a continuous dependent variable, whereas sex, age, living arrangement, number of chronic diseases, illness trajectory, and level of education were entered as independent variables. Results are presented as adjusted β coefficients with their 95% confidence intervals (CIs). In sensitivity analyses the same models were computed by removing analgesics (ATC code N02) from the total number of prescription drugs, because this class of medications is used for specific purposes near the end of life. Finally, changes in the prevalence of individual drug classes were assessed with variation rates (with 95% CI) from the 12th to the final month before death. Bivariate logistic regressions were used to assess P for trend (2-sided α = .01). All analyses were performed with SAS JMP 12.1 (SAS Institute, Cary, NC) and Stata 14.1 (StataCorp, College Station, TX) software.

      Results

      Study Population

      Of all 545,212 older adults who died in Sweden between 2007 and 2013, 511,843 (93.9%) met our eligibility criteria. Table 1 shows the decedents' main characteristics. Compared with community dwellers, institutionalized individuals were more often women, died at older age, and were more likely to follow a trajectory of prolonged dwindling. Overall, 51% of individuals had 5 or more diagnosed chronic conditions at time of death. The most common chronic illnesses were ischemic heart diseases (42%), hypertension (40%), congestive heart failure (38%), cancer (31%), atrial fibrillation (27%), and cerebrovascular diseases (26%).
      Table 1Characteristics of Older Adults Who Died Between 2007 and 2013 in Sweden, by Living Arrangement
      CharacteristicCommunity-DwellingInstitutionalizedTotalP Value
      Pearson's χ2 test, except for the mean age and mean number of chronic conditions (median test).
      Decedents in cohort (n)350,977160,866511,843
      Sex, n (%)<.001
       Men176,490 (50.3)57,533 (35.8)234,023 (45.7)
       Women174,487 (49.7)103,333 (64.2)277,820 (54.3)
      Age at time of death (y)
       Mean (SD)82.6 (8.2)87.7 (6.9)84.2 (8.2)<.001
       n (%)<.001
      66-7468,128 (19.4)7621 (4.7)75,749 (14.8)
      75-84124,701 (35.5)38,654 (24.0)163,355 (31.9)
      85-94136,847 (39.0)90,544 (56.3)227,391 (44.4)
      ≥9521,301 (6.1)24,047 (14.9)45,348 (8.9)
      Level of education,
      Missing values for level of education: 24,319 (4.7% of the total cohort).
      n (%)
      <.001
       Primary education177,861 (53.0)89,458 (59.0)267,319 (54.8)
       Secondary education124,470 (37.1)50,613 (33.4)175,083 (35.9)
       Tertiary education33,502 (10.0)11,620 (7.7)45,122 (9.3)
      Illness trajectory,
      Illness trajectories were constructed using all the diagnoses mentioned on the death certificates (ie, both underlying and contributing causes of death). When the different causes of death indicated more than 1 illness trajectory, we applied a previously described hierarchy (ie, from cancer to prolonged dwindling to organ failure to sudden death).25
      n (%)
      <.001
       Cancer121,569 (34.6)23,309 (14.5)144,878 (28.3)
       Organ failure143,840 (41.0)54,758 (34.0)198,598 (38.8)
       Prolonged dwindling56,514 (16.1)72,243 (44.9)128,757 (25.2)
       Sudden death29,054 (8.3)10,556 (6.6)39,610 (7.7)
      No. of chronic diseases
       Mean (SD)5.0 (2.7)4.7 (2.7)4.9 (2.7)<.001
       n (%)<.001
      03192 (0.9)2352 (1.5)5544 (1.1)
      121,116 (6.0)13,020 (8.1)34,136 (6.7)
      239,361 (11.2)21,137 (13.1)60,498 (11.8)
      349,957 (14.2)24,215 (15.1)74,172 (14.5)
      452,361 (14.9)23,904 (14.9)76,265 (14.9)
      ≥5184,990 (52.7)76,238 (47.4)261,228 (51.0)
      SD = standard deviation.
      Pearson's χ2 test, except for the mean age and mean number of chronic conditions (median test).
      Missing values for level of education: 24,319 (4.7% of the total cohort).
      Illness trajectories were constructed using all the diagnoses mentioned on the death certificates (ie, both underlying and contributing causes of death). When the different causes of death indicated more than 1 illness trajectory, we applied a previously described hierarchy (ie, from cancer to prolonged dwindling to organ failure to sudden death).
      • Chaudhry S.I.
      • Murphy T.E.
      • Gahbauer E.
      • Sussman L.S.
      • Allore H.G.
      • Gill T.M.
      Restricting symptoms in the last year of life: a prospective cohort study.

      Change in the Prevalence of Polypharmacy

      The prevalence of polypharmacy increased significantly throughout the last year of life (Figure). Between the 12th and the final month before death, the proportion of individuals exposed to ≥10 different drugs rose from 30.3% to 47.2% (P <.001 for trend), and the mean (standard deviation) number of prescription drugs increased from 7.6 (4.4) to 9.6 (4.7). Older adults living in institutions were found to receive a greater number of medications than those living in the community (P <.001 for each of the final 12 months of life). These trends remained after excluding analgesics from the total count of prescription drugs (Supplementary Figure 3, available online). However, we found significant differences across age groups and illness trajectories (Supplementary Figure 4, available online).
      Figure thumbnail gr1
      Figure(A) Polypharmacy and (B) number of prescription drugs over the course of the last 12 months of life of older people in Sweden, by living arrangement. Proportions and means are standardized for both sex and age, using the total population as reference. All trends statistically significant (P <.01).

      Factors Associated with Change in the Number of Prescription Drugs

      The magnitude of the increase in polypharmacy over the course of the last year of life varied significantly according to the decedents' characteristics (Table 2). Hence, the likelihood of being exposed to polypharmacy during the final month of life compared with 12 months before death was greater for individuals who died from cancer (odds ratio 3.34; 95% CI, 3.29-3.39) than for those who died from dementia or other neurodegenerative disorders (odds ratio 1.74; 95% CI, 1.71-1.77). As shown in Table 3, female sex, older age at time of death, and institutionalization were independently associated with a slower increase in the average number of medications over time. Older adults who died from cancer had the greatest increase in the number of prescription drugs (mean difference 3.37; 95% CI, 3.35-3.40). These associations were only partly attenuated when analgesics were excluded from the total count of prescribed drugs (Supplementary Tables 3 and 4, available online).
      Table 2Change in the Likelihood of Being Exposed to Polypharmacy During the Last Year of Life of Older Adults in Sweden, 2007-2013
      ParameterN TotalPolypharmacy (≥10 Drugs), %Last Month vs 12th Month Before Death
      12th Month Before DeathLast Month Before DeathVariation Rate, % (95% CI)
      Variation rates indicate the relative change in prevalence of polypharmacy between the 12th month and the final month before death.
      Odds Ratio (95% CI)
      Odds ratio calculated by means of logistic regression models including polypharmacy (≥10 prescription drugs) as a binary dependent variable and time (last month before death vs. 12th month before death) as sole independent variable. Clustered standard errors at the individual level were used to account for correlation of observations within groups.
      Total cohort511,84330.347.255.9 (55.1-56.7)2.06 (2.05-2.07)
      Sex
       Men234,02327.546.468.6 (67.3-69.9)2.28 (2.25-2.31)
       Women277,82032.647.846.9 (45.9-47.8)1.90 (1.88-1.92)
      Age at time of death (y)
       66-7475,74926.847.175.7 (73.2-78.1)2.43 (2.38-2.48)
       75-84163,35531.649.857.7 (56.3-59.0)2.15 (2.12-2.18)
       85-94227,39131.447.350.6 (49.5-51.7)1.96 (1.94-1.98)
       ≥9545,34825.737.646.2 (43.4-49.1)1.74 (1.69-1.79)
      Living arrangement
       Community-dwelling350,97727.145.969.4 (68.3-72.5)2.28 (2.26-2.31)
       Institutionalized160,86637.250.034.4 (33.4-35.5)1.69 (1.66-1.71)
      Level of education
      Missing values for level of education: 24,319 (4.7% of the total cohort).
       Primary education267,31931.248.355.0 (53.9-56.1)2.06 (2.04-2.09)
       Secondary education175,08330.047.056.8 (55.4-58.2)2.07 (2.04-2.10)
       Tertiary education45,12227.644.661.7 (58.8-64.7)2.11 (2.05-2.17)
      Illness trajectory
      Illness trajectories were constructed using all the diagnoses mentioned on the death certificates (ie, both underlying and contributing causes of death). When the different causes of death indicated more than 1 illness trajectory, we applied a previously described hierarchy (ie, from cancer to prolonged dwindling to organ failure to sudden death).25
       Cancer144,87824.351.8112.8 (110.5-115.9)3.34 (3.29-3.39)
       Organ failure198,59837.050.536.3 (35.3-37.3)1.73 (1.71-1.76)
       Prolonged dwindling128,75728.440.943.8 (42.3-45.4)1.74 (1.71-1.77)
       Sudden death39,61023.934.343.2 (40.0-46.4)1.66 (1.61-1.71)
      No. of chronic diseases
       055448.312.854.7 (38.4-72.9)1.63 (1.44-1.84)
       134,1368.918.6109.8 (101.5-118.5)2.35 (2.24-2.46)
       260,49813.326.297.1 (92.3-141.9)2.32 (2.25-2.39)
       374,17218.033.686.9 (83.5-92.4)2.31 (2.25-2.37)
       476,26522.841.381.1 (78.3-84.4)2.38 (2.33-2.44)
       ≥5261,22843.162.144.0 (43.2-44.7)2.16 (2.14-2.18)
      CI = confidence interval.
      Variation rates indicate the relative change in prevalence of polypharmacy between the 12th month and the final month before death.
      Odds ratio calculated by means of logistic regression models including polypharmacy (≥10 prescription drugs) as a binary dependent variable and time (last month before death vs. 12th month before death) as sole independent variable. Clustered standard errors at the individual level were used to account for correlation of observations within groups.
      Missing values for level of education: 24,319 (4.7% of the total cohort).
      § Illness trajectories were constructed using all the diagnoses mentioned on the death certificates (ie, both underlying and contributing causes of death). When the different causes of death indicated more than 1 illness trajectory, we applied a previously described hierarchy (ie, from cancer to prolonged dwindling to organ failure to sudden death).
      • Chaudhry S.I.
      • Murphy T.E.
      • Gahbauer E.
      • Sussman L.S.
      • Allore H.G.
      • Gill T.M.
      Restricting symptoms in the last year of life: a prospective cohort study.
      Table 3Factors Associated with Change in the Number of Prescription Drugs During the Last Year of Life of Older Adults in Sweden, 2007-2013
      FactorN TotalNo. of Prescription Drugs, Mean (SD)Mean Difference (95% CI)Factor × Time, Adjusted β (95% CI)
      β-Coefficients computed by the mean of generalized estimating equation models with identity link functions at the individual level and unstructured correlations. The total number of distinct prescription drugs (5-digit Anatomical Therapeutic Chemical codes) was entered as continuous dependent variable with Gaussian distribution, while including all presented individual characteristics as independent variables. β-Coefficients for the interaction with time can be interpreted as the rate of change between the 12th month and the final month before death, compared with the reference category.
      12th Month Before DeathLast Month Before Death
      Total cohort511 8437.6 (4.4)9.6 (4.7)2.01 (2.00 to 2.02)
      Sex
       Men234 0237.2 (4.3)9.5 (4.7)2.27 (2.25-2.29)Ref
       Women277 8207.9 (4.4)9.6 (4.7)1.80 (1.78-1.81)−0.46 (−0.48 to −0.44)
      Age at time of death (y)
       66-7475 7496.9 (5.0)9.6 (5.3)2.77 (2.74-2.80)Ref
       75-84163 3557.7 (4.6)9.9 (4.9)2.20 (2.18-2.22)−0.57 (−0.61 to −0.54)
       85-94227 3917.8 (4.1)9.5 (4.4)1.77 (1.75-1.78)−1.00 (−1.03 to −0.96)
       ≥9545 3487.2 (3.8)8.5 (4.0)1.31 (1.29-1.34)−1.44 (−1.49 to −1.39)
      Living arrangement
       Community-dwelling350 9777.1 (4.4)9.4 (4.8)2.29 (2.28-2.31)Ref
       Institutionalized160 8668.5 (4.2)9.9 (4.4)1.40 (1.38-1.42)−0.90 (−0.92 to −0.87)
      Level of education
      Missing values for level of education: 24,319 (4.7% of the total cohort).
       Primary education267 3197.7 (4.3)9.7 (4.6)2.01 (1.99-2.01)Ref
       Secondary education175 0837.5 (4.5)9.6 (4.8)2.05 (2.04-2.07)0.05 (0.02-0.07)
       Tertiary education45 1227.2 (4.4)9.3 (4.8)2.09 (2.05-2.13)0.09 (0.05-0.12)
      Illness trajectory
      Illness trajectories were constructed using all the diagnoses mentioned on the death certificates (ie, both underlying and contributing causes of death). When the different causes of death indicated more than 1 illness trajectory, we applied a previously described hierarchy (ie, from cancer to prolonged dwindling to organ failure to sudden death).25
       Cancer144 8786.7 (4.4)10.0 (4.9)3.37 (3.35-3.40)Ref
       Organ failure198 5988.4 (4.5)10.0 (4.8)1.59 (1.57-1.61)−1.79 (−1.82 to −1.77)
       Prolonged dwindling128 7577.5 (3.9)8.9 (4.1)1.36 (1.34-1.38)−2.01 (−2.04 to −1.98)
       Sudden death39 6106.8 (4.2)8.1 (4.4)1.29 (1.26-1.31)−2.09 (−2.14 to −2.05)
      No. of chronic diseases
       055444.6 (3.2)5.6 (3.3)0.91 (0.85-0.97)Ref
       134 1364.8 (3.4)6.4 (3.6)1.60 (1.57-1.64)0.74 (0.61-0.86)
       260 4985.4 (3.6)7.3 (3.8)1.86 (1.83-1.89)0.97 (0.86-1.09)
       374 1726.1 (3.7)8.1 (4.0)1.97 (1.94-1.99)1.08 (0.96-1.20)
       476 2656.8 (3.8)8.9 (4.1)2.08 (2.06-2.11)1.19 (1.07-1.30)
       ≥5261 2289.1 (4.4)11.2 (4.7)2.12 (2.10-2.14)1.22 (1.10-1.33)
      CI = confidence interval; SD = standard deviation.
      β-Coefficients computed by the mean of generalized estimating equation models with identity link functions at the individual level and unstructured correlations. The total number of distinct prescription drugs (5-digit Anatomical Therapeutic Chemical codes) was entered as continuous dependent variable with Gaussian distribution, while including all presented individual characteristics as independent variables. β-Coefficients for the interaction with time can be interpreted as the rate of change between the 12th month and the final month before death, compared with the reference category.
      Missing values for level of education: 24,319 (4.7% of the total cohort).
      Illness trajectories were constructed using all the diagnoses mentioned on the death certificates (ie, both underlying and contributing causes of death). When the different causes of death indicated more than 1 illness trajectory, we applied a previously described hierarchy (ie, from cancer to prolonged dwindling to organ failure to sudden death).
      • Chaudhry S.I.
      • Murphy T.E.
      • Gahbauer E.
      • Sussman L.S.
      • Allore H.G.
      • Gill T.M.
      Restricting symptoms in the last year of life: a prospective cohort study.

      Most Commonly Used Drug Classes

      Antithrombotic agents, diuretics, analgesics, psycholeptics, and β-blocking agents were found to be the 5 most common prescription drug classes during the last year of life (Table 4). Agents acting on the renin–angiotensin system, antianemic preparations, antidepressants, and drugs for acid-related disorders were also highly prevalent, with more than 30% of decedents exposed to these drugs during the final month before death. The pattern of prescription drug use changed over the course of the last 12 months of life, with a notable increase in the exposure to opioids (+120.7%), antimicrobials (+74.3%), anxiolytics (+59.5%), drugs for constipation (+57.8%), and antipsychotics (+47.3%). We found only a modest decrease in the use of preventive drugs. Hence, during their last month of life, a significant share of older adults used β-blockers (41.1%), angiotensin-converting enzyme inhibitors (21.4%), vasodilators (17.4%), lipid-lowering agents (16.3%), calcium channel blockers (15.4%), or potassium-sparing agents (12.1%). The prevalence of preventive medication was found to be higher among younger individuals (Supplementary Table 5, available online).
      Table 4Prevalence of the 20 Most Commonly Used Prescription Drug Classes During the Last Year of Life of Older People in Sweden, 2007-2013
      Rank, Drug Class
      Drug classes are ranked by descending order, using the second level of the ATC (therapeutic subgroups, eg, N02 Analgesics). For some classes, details are also provided for the pharmacological subgroups (eg, N02A Opioids).
      ATC CodePrevalence, % (N = 511,843)Variation Rate Between 12th Month and Last month Before Death, % (95% CI)
      Variation rates calculated as the relative increase or decrease in prevalence of use between the 12th month and the final month before death, in with their 95 CI.
      12th Month6th MonthLast Month
      1. Antithrombotic agentsB0152.553.553.82.6 (2.2-2.9)
      Vitamin K antagonistsB01AA7.97.76.7−15.4 (−16.6 to −14.3)
      Heparin groupB01AB1.62.55.2221.6 (213.8-229.6)
      Platelet aggregation inhibitorsB01AC44.344.944.91.4 (0.9-1.8)
      2. DiureticsC0347.149.253.112.7 (12.3-13.2)
      Low-ceiling diureticsC03A-C03B6.05.75.2−12.5 (−13.9 to −11.1)
      High-ceiling diureticsC03C37.240.145.622.7 (22.1-23.3)
      Potassium-sparing agentsC03D9.610.312.126.0 (24.6-27.4)
      3. AnalgesicsN0240.245.960.851.2 (50.6-51.8)
      OpioidsN02A17.521.438.5120.7 (119.1-122.2)
      Other analgesicsN02B35.039.649.240.6 (40.0-41.3)
      4. PsycholepticsN0539.542.851.229.6 (29.0-31.1)
      AntipsychoticsN05A7.88.911.547.3 (45.6-49.1)
      AnxiolyticsN05B16.718.826.659.5 (58.3-61.7)
      Hypnotics and sedativesN05C28.129.934.523.1 (22.4-23.8)
      5. β-Blocking agentsC0739.440.341.14.1 (3.6-4.6)
      6. Drugs acting on the renin–angiotensin systemC0931.831.930.6−3.9 (−4.5 to −3.4)
      ACE inhibitorsC09A-C09B21.621.921.4−1.0 (−1.7 to −0.2)
      Angiotensin II receptor antagonistsC09C-C09D10.910.79.8−9.9 (−11.0 to −8.9)
      7. Anti-anemic preparationsB0330.632.934.612.9 (12.3-13.5)
      Iron preparationsB03A7.88.910.433.6 (32.0-35.3)
      Vitamin B12 and folic acidB03B25.927.528.29.0 (8.3-9.7)
      8. PsychoanalepticsN0627.429.532.618.9 (18.2-19.6)
      AntidepressantsN06A24.526.630.122.7 (21.9-23.5)
      Anti-dementia drugsN06D5.45.45.0−8.2 (−9.7 to −6.6)
      9. Drugs for constipationA0625.330.139.957.8 (56.9-58.7)
      10. Drugs for acid related disordersA0223.827.435.147.3 (46.4-48.2)
      11. Cardiac therapyC0122.223.024.39.3 (8.4-19.4)
      Cardiac glycosidesC01A7.98.08.35.1 (3.7-6.5)
      Vasodilators used in cardiac diseasesC01D15.516.217.412.5 (11.6-13.5)
      12. Emollients and protectivesD0222.125.328.729.9 (29.0-35.8)
      13. Lipid modifying agentsC1018.718.116.3−12.9 (−13.6 to −12.1)
      StatinsC10AA18.217.615.8−13.3 (−14.1 to −12.6)
      14. Calcium channel blockersC0817.817.115.4−13.0 (−13.8 to −12.3)
      15. Mineral supplementsA1217.618.720.516.8 (15.9-17.8)
      CalciumA12A12.613.012.92.8 (1.8-3.8)
      PotassiumA12B5.66.38.145.3 (43.2-47.4)
      16. Drugs used in diabetesA1015.115.315.42.2 (1.3-3.1)
      Insulin and analoguesA10A8.89.410.316.5 (15.1-17.9)
      Oral blood glucose lowering drugsA10B8.68.27.4−14.0 (−15.1 to −12.8)
      17. OphthalmologicalsS0114.715.215.33.8 (2.9-4.8)
      18. Drugs for obstructive airway diseasesR0312.513.314.919.5 (18.3-25.7)
      19. Antibacterials for systemic useJ0111.513.120.074.3 (72.6-75.9)
      20. Thyroid therapyH0310.510.810.94.4 (3.3-5.6)
      ACE = angiotensin-converting enzyme; ATC = Anatomical Therapeutic Chemical classification system; CI = confidence interval.
      Drug classes are ranked by descending order, using the second level of the ATC (therapeutic subgroups, eg, N02 Analgesics). For some classes, details are also provided for the pharmacological subgroups (eg, N02A Opioids).
      Variation rates calculated as the relative increase or decrease in prevalence of use between the 12th month and the final month before death, in with their 95 CI.

      Discussion

      Our study has two main findings. First, the proportion of older adults exposed to ≥10 different prescription drugs increases over the course of the last year before death, and approximately half of individuals experience polypharmacy during their last month of life. Second, polypharmacy is fueled not only by an increased use of medications directed toward symptom management but also by the frequent continuation of long-term preventive treatments and disease-targeted drugs.
      These results compare to earlier studies. McNeil et al
      • McNeil M.J.
      • Kamal A.H.
      • Kutner J.S.
      • Ritchie C.S.
      • Abernethy A.P.
      The burden of polypharmacy in patients near the end of life.
      recently reported that patients with a life-limiting disease took on average 10.7 medications at time of death, with 69% of patients using 9 or more different drugs (excluding statins). In a retrospective cohort study of 100 patients who died from advanced cancer, researchers found a median of 11 prescribed medications 9 days before death.
      • Kierner K.A.
      • Weixler D.
      • Masel E.K.
      • Gartner V.
      • Watzke H.H.
      Polypharmacy in the terminal stage of cancer.
      Despite a considerable heterogeneity in study designs and populations, other studies investigating drug use near the end of life have reported comparable results.
      • Todd A.
      • Husband A.
      • Andrew I.
      • Pearson S.A.
      • Lindsey L.
      • Holmes H.
      Inappropriate prescribing of preventative medication in patients with life-limiting illness: a systematic review.
      • LeBlanc T.W.
      • McNeil M.J.
      • Kamal A.H.
      • Currow D.C.
      • Abernethy A.P.
      Polypharmacy in patients with advanced cancer and the role of medication discontinuation.
      These findings demonstrate the challenge of managing the accumulation of health problems in older adults with life-limiting illness.
      • Stevenson J.
      • Abernethy A.P.
      • Miller C.
      • Currow D.C.
      Managing comorbidities in patients at the end of life.
      Polypharmacy near the end of life should, however, not be thought of as a homogeneous phenomenon. Data presented in our article suggest that polypharmacy does not stem from a unique drug class but is in fact propelled by 5 different types of prescriptions, each addressing specific goals of care. Medications to alleviate the burden of symptoms (eg, analgesics, loop-diuretics, anxiolytics) compose the first of these 5 categories. This is expected, because the need for comfort care and symptom management increases sharply as death approaches.
      • Singer A.E.
      • Meeker D.
      • Teno J.M.
      • Lynn J.
      • Lunney J.R.
      • Lorenz K.A.
      Symptom trends in the last year of life from 1998 to 2010: a cohort study.
      The second category includes drug regimens used in the long-term prevention of chronic diseases that pose no immediate danger. Hence we found that statins were rarely discontinued and were still prescribed to 16% of older adults during their last month of life. Although this figure is consistent with previous reports,
      • Bayliss E.A.
      • Bronsert M.R.
      • Reifler L.M.
      • et al.
      Statin prescribing patterns in a cohort of cancer patients with poor prognosis.
      • Silveira M.J.
      • Kazanis A.S.
      • Shevrin M.P.
      Statins in the last six months of life: a recognizable, life-limiting condition does not decrease their use.
      it raises serious concern: the clinical benefit of treatments drugs aiming at preventing cardiovascular diseases during the final month of life is at the very least questionable.
      • Strandberg T.E.
      • Kolehmainen L.
      • Vuorio A.
      Evaluation and treatment of older patients with hypercholesterolemia: a clinical review.
      • Maddison A.R.
      • Fisher J.
      • Johnston G.
      Preventive medication use among persons with limited life expectancy.
      Medications used to control the evolution of potentially life-threatening or disabling comorbidities form a third distinct group of prescriptions (eg, oral antidiabetics, platelet aggregation inhibitors, thyroid therapy, antidementia drugs, and cardiac stimulants). The fourth group is drugs prescribed in an attempt to cure or slow the progression of the main life-limiting illness (eg, chemotherapy, immunosuppressants). Finally, drugs administered to counteract the current or anticipated adverse effects of other medications constitute a fifth group of prescriptions. The widespread use of potassium supplementation, proton-pump inhibitors, and laxatives close to death may reflect a “prescribing cascade” (ie, prescriptions prompted by the onset of symptoms induced by other medications rather than by the disease itself).
      • Rochon P.A.
      • Gurwitz J.H.
      Optimising drug treatment for elderly people: the prescribing cascade.
      To help clinicians assess the value and the appropriateness of drug treatments in a context of limited life expectancy, Holmes et al
      • Holmes H.M.
      • Hayley D.C.
      • Alexander G.C.
      • Sachs G.A.
      Reconsidering medication appropriateness for patients late in life.
      recommended that 2 key questions be taken into account. First, is the patient's life expectancy longer than the time needed for the medication to achieve its benefit? Second, are the objectives of the prescribed medication in keeping with the goals of care that the physician and the patient discussed and agreed upon? This prescribing model relies on the idea that drug treatments should be adapted to mirror the course of the disease as the remaining life expectancy diminishes. In other words, physicians should consider discontinuing drugs that may be effective and otherwise appropriate but whose potential harms outweigh the benefits that patients can reasonably expect before death occurs. The process of deprescribing (ie, withdrawing medications with the aim of improving health outcomes
      • Gnjidic D.
      • Couteur D.G.L.
      • Hilmer S.N.
      Discontinuing drug treatments.
      • Scott I.A.
      • Hilmer S.N.
      • Reeve E.
      • et al.
      Reducing inappropriate polypharmacy: the process of deprescribing.
      ) is now supported by a growing body of evidence.
      • Gnjidic D.
      • Le Couteur D.G.
      • Kouladjian L.
      • Hilmer S.N.
      Deprescribing trials: methods to reduce polypharmacy and the impact on prescribing and clinical outcomes.
      In a recent randomized, controlled trial including patients with ≤12 months of estimated life expectancy, researchers found no significant survival difference according to statin continuation/discontinuation and showed that quality of life was significantly improved in the group of patients that discontinued statins.
      • Kutner J.S.
      • Blatchford P.J.
      • Taylor D.H.J.
      • et al.
      Safety and benefit of discontinuing statin therapy in the setting of advanced, life-limiting illness: a randomized clinical trial.
      Whether this conclusion is applicable to other drug classes remains uncertain, because data regarding the safety of discontinuing preventative medications in the context of end-of-life care remain scarce.
      • Luymes C.H.
      • van der Kleij R.M.J.J.
      • Poortvliet R.K.E.
      • de Ruijter W.
      • Reis R.
      • Numans M.E.
      Deprescribing potentially inappropriate preventive cardiovascular medication: barriers and enablers for patients and general practitioners.
      One should also balance the potential lack of benefit of a certain drug with the possible collateral effects of its discontinuation. For instance, although there is considerable uncertainty about the efficacy and safety of antidementia agents in older adults reaching the end of life,
      • Parsons C.
      Withdrawal of antidementia drugs in older people: who, when and how?.
      the discontinuation of these medications may have indirect negative consequences, such as an increased risk of institutionalization.
      • Howard R.
      • McShane R.
      • Lindesay J.
      • et al.
      Nursing home placement in the Donepezil and Memantine in Moderate to Severe Alzheimer's Disease (DOMINO-AD) trial: secondary and post-hoc analyses.
      At the end of life, the challenge of deprescribing drug treatments is amplified by the uncertainty inherent to survival predictions
      • White N.
      • Reid F.
      • Harris A.
      • Harries P.
      • Stone P.
      A systematic review of predictions of survival in palliative care: how accurate are clinicians and who are the experts?.
      and by the natural tendency of healthcare professionals and patients to believe that medical interventions are more effective than they actually are (a phenomenon described as the “therapeutic illusion”
      • Casarett D.
      The science of choosing wisely–overcoming the therapeutic illusion.
      ). The frequent continuation of potentially futile treatments may also be the consequence both of a lack of communication and of insufficient shared decision making between patients and prescribers, out of fear that discussing issues surrounding the end of life would be perceived as a failure or that it would amount to “giving up.”
      • Buiting H.M.
      • Rurup M.L.
      • Wijsbek H.
      • van Zuylen L.
      • den Hartogh G.
      Understanding provision of chemotherapy to patients with end stage cancer: qualitative interview study.
      • Gawande A.
      Quantity and quality of life: duties of care in life-limiting illness.
      To overcome this predicament, the process of deprescribing requires timely patient–family–physician dialogue about the risk/benefit ratio of medications, and close monitoring of symptoms during the following weeks. As the situation worsens, the goals of care should be frequently reassessed to ensure that the treatment target remains concordant with the patient's preferences. It is also essential that patients and their relatives receive clear information about their options in terms of palliative care, to counter the feeling of abandonment that they may experience when disease-directed treatments are withdrawn.
      • Stevenson J.
      • Abernethy A.P.
      • Miller C.
      • Currow D.C.
      Managing comorbidities in patients at the end of life.
      • Van Nordennen R.T.C.M.
      • Lavrijsen J.C.M.
      • Vissers K.C.P.
      • Koopmans R.T.C.M.
      Decision making about change of medication for comorbid disease at the end of life: an integrative review.
      Our study should be interpreted with caution, considering the following limitations. First, the Swedish Prescribed Drug Register only records prescription drugs dispensed at pharmacies. Over-the-counter medications and medications administered in hospitals or from drug store rooms in nursing homes are not reported in the register, leading to a potential underestimation of the actual drug exposure. However, approximately 86% of defined daily doses dispensed to the Swedish population annually are prescription drugs delivered through pharmacies and are therefore covered by the Swedish Prescribed Drug Register. Second, we could only estimate drug exposure with the assumption that patients use the dispensed medications at the prescribed rate; we could therefore not account for nonadherence to treatment. Additionally, we did not investigate whether drugs that were continued until the final month of life were deintensified in terms of dosage, which could correspond to a shift toward a palliative approach. Notwithstanding these methodologic restrictions, it is the first study investigating the burden of medications near the end of life in an entire population (ie, irrespective of the underlying disease or care setting).

      Conclusions

      Polypharmacy increases sharply throughout the last year of life of older adults. To reduce the burden of medications of questionable benefit in older adults with life-limiting illness, robust evidence about the benefit and safety of deprescribing is needed. Following the example set by Kutner et al
      • Kutner J.S.
      • Blatchford P.J.
      • Taylor D.H.J.
      • et al.
      Safety and benefit of discontinuing statin therapy in the setting of advanced, life-limiting illness: a randomized clinical trial.
      with the statin discontinuation trial, future clinical trials should be conducted to evaluate the effects of withdrawing preventive medications in people with advanced illness. These findings should then be embedded into clinical guidelines, in the same manner that new evidence regarding the benefit and safety of initiating drug therapy is incorporated into current practice guidelines. However, because end-of-life situations are shaped by different disease trajectories, symptoms, and personal preferences, the goals of care vary considerably from one person to another. Future clinical practice guidelines should thus foster personalized decision making rather than promote the systematic discontinuation of medications according to a one-size-fits-all set of criteria.

      Acknowledgments

      We thank Régis Aubry, Jonas Wastesson, and Emerald Heiland for their insightful comments on the manuscript.

      Supplementary Data

      Figure thumbnail fx1
      Supplementary Figure 1Study population flowchart.
      Figure thumbnail fx2
      Supplementary Figure 2Design of drug exposure calculation. Tick marks represent the date of drug dispensing (“start date”). Horizontal lines represent the estimated duration of drug exposure. Arrows represent the end of drug exposure (“end date”).
      Figure thumbnail fx3
      Supplementary Figure 3Change in the number of nonanalgesic prescription drugs over the course of the last 12 months of life, by living arrangement. Standardized for both sex and age (reference: total population). ATC = Anatomical Therapeutic Chemical classification system.
      Figure thumbnail fx4
      Supplementary Figure 4Polypharmacy (≥10 Drugs) over the course of the last 12 months of life of older people in Sweden, by illness trajectory and age at time of death.
      Supplementary Table 1Classification of Causes of Death into Illness Trajectories
      TrajectoryICD-10 Codes
      1 – Cancer, leading to a short decline with evident terminal phase
      NeoplasmC00-C26, C30-C34, C37-C41, C43-C58, C60-C85, C88, C90-C97, D00-D09, D32, D33, D37-D48
      2 – Organ failure leading to long-term limitations with intermittent acute episodes
      DiabetesE10-E14, G590, G632, H360, M142, N083
      Other endocrinal diseasesE70-E72, E75-E77, E84-E85
      Certain infectious and parasitic diseasesA520-A523, A527, A810, A812, B15-B19, B20-B24
      Diseases of the bloodD60, D61, D69, D70, D752, D758, D86
      Diseases of the cardiovascular system (including cerebrovascular diseases)I231-I233, I238, I25, I50, I60-I64, I67, I688, I69, G46, I65, I66, I680-I682, I27, I42, I43, I51, I520, I70, I73, I74, I792, I970, I971, I978, I980, I981, I988
      Diseases of the respiratory system (including abnormalities of breathing)J40-J44, J47, J60-J62, J66, J701, J80, J841, J951-J953, J96, J980-J984, R060, R062-R065, R068
      Diseases of the digestive system (including liver diseases)K70-K77, K44, K50, K51, K55, K56, K85, K86, K871, K90
      Diseases of the skinL305, L40-L42, L44, L93, L945
      Diseases of the musculoskeletal systemM360, M361, M05, M06, M13, M15, M21, M30-M35, M40-M43, M45-M51, M53, M54, M638, M80, M81, M820, M821, M843, M844, M86-M88, M907, M961
      Diseases of the genitourinary system (including renal failure)N02-N05, N11, N12, N136, N160, N18, N19, N25, N312, N318, N319, N82
      Other (including congenital conditions)Q01-Q06, Q078, Q079, Q20-Q28, Q31, Q33, Q40-Q45, Q60-Q68, Q714, Q75-Q79, Q850, Q86-87, Q89-Q93, Q95-Q97, Q99
      3 – Prolonged dwindling, characterized by a progressive loss of both physical and cognitive capacities
      Alzheimer's diseaseG30-G32
      Mental and behavioral disordersF00, F01, F02, F03, F05, F06, R54
      Parkinson's diseaseG20-G23
      Multiple sclerosisG35-G37
      Other diseases of the nervous systemG10, G12, G70-G73, G03-G05, G07, G478, G518, G551, G608, G80-G83, G90-G99
      4 – Sudden death
      None of the three trajectories above
      ICD-10 = International Classification of Diseases, 10th Revision.
      Supplementary Table 2Details of Diagnosis Codes and Drugs Used to Detect Chronic Conditions in the Last 2 Years of Life of Older People in Sweden
      Adapted with the authors' permission from Calderón-Larrañaga A, Vetrano DL, Onder G, et al. Assessing and measuring chronic multimorbidity in the older population: a proposal for its operationalization. J Gerontol A Biol Sci Med Sci. 2016 December 21.
      Chronic DiseaseICD-10 CodesATC Codes
      Source(s) of dataNational cause of death register (all contributing causes of death)

      National patient register (all inpatient and specialized outpatient diagnoses)
      Swedish Prescribed Drugs Register
      1. AllergyJ30.1-J30.4; J45.0; K52.2; L20; L23; L50.0; Z51.6
      2. AnemiaD50-D53; D55-D59 (excl. D56.3; D59.0; D59.2; D59.3; D59.6); D60-D64 (excl. D60.1; D61.1; D61.2; D62; D64.2)B03A, B03XA
      3. AsthmaJ45R03DC; R03BC
      4. Atrial fibrillationI48
      5. Autoimmune diseasesI73.1; L10 (excl. L10.5); L12; L40; L41; L93-L95; M30-M36 (excl. M32.0; M34.2; M35.7-M35.9; M36.0; M36.1; M36.2; M36.3)D05
      6. Blindness, visual lossH54 (excl. H54.3); Z44.2; Z97.0
      7. Blood and blood forming organ diseasesD66-D69 (excl. D68.3; D68.4; D69.5); D71; D72.0; D73.0-D73.2; D74 (excl. D74.8); D75.0; D76.1; D76.3; D77; D80 (excl. D80.7); D81-D84; D86; D89 (excl. D89.1; D89.3)
      8. Bradycardias and conduction diseasesI44.1-I44.3; I45.3; I45.5; Z95.0
      9. Cardiac valve diseasesI05-I08; I09.1; I09.8; I34-I38; I39.0-I39.4; Q22; Q23; Z95.2-Z95.4
      10. Cataract and other lens diseasesH25-H28; Q12; Z96.1
      11. Cerebrovascular diseaseG45; G46; I60-I64; I67; I69
      12. Chromosomal abnormalitiesQ90-Q99
      13. Chronic infectious diseasesA15-A19; A30; A31; A50-A53 (excl. A51); A65-A67; A69.2; A81; B20-B24; B38.1; B39.1; B40.1; B57.2-B57.5; B65; B92; B94; J65; M86.3-M86.6J04A, excl. J04AB01, J04AB02, J04AB03 and J04AC
      14. Chronic kidney diseaseI12.0; I13.0-I13.9; N01, N02, N04, N05; N07; N08; N11; N18.3-N18.9; Q60; Q61.1-Q61.9; Z90.5; Z94.0
      15. Chronic liver diseaseB18; K70 (excl. K70.0; K70.1); K71.3-K71.5; K71.7; K72.1; K73; K74; K75.3-K75.8; K76.1; K76.6; K76.7; K77.8; Q44.6; Z94.4
      16. Chronic pancreas, biliary tract and gallbladder diseasesK80.0; K80.1; K80.2; K80.8; K81.1; K86 (excl. K86.2; K86.3; K86.9); Q44.0-Q44.5; Q45.0A09AA02
      17. Chronic ulcer of the skinI83.0; I83.2; L89; L97; L98.4
      18. Colitis and related diseasesK52.0; K52.8; K55.1; K55.2; K57.2-K57.5; K57.8; K57.9; K58; K59.0; K59.2; K62 (excl. K62.0; K62.1; K62.5; K62.6); K63.4; K64 (excl. K64.5);
      19. COPD, emphysema, chronic bronchitisJ41-J44; J47R03BB
      20. Deafness, hearing lossH80; H90; H91.1; H91.3; H91.9; Q16; Z45.3; Z46.1; Z96.2; Z97.4
      21. DementiaF00-F03; F05.1; G30; G31N06DA, N06DX01
      22. Depression and mood diseasesF30-F34; F38; F39; F41.2
      23. DiabetesE10; E11; E13; E14; E89.1A10
      24. DorsopathiesM40-M43; M47-M53; Q67.5; Q76.4; Q76.1;
      25. DyslipidemiaE78
      26. Ear, nose, throat diseasesH60.4; H66.1-H66.3; H70.1; H71; H73.1; H74.1; H81.0; H83.1; H83.2; H95; J30.0; J31-J33; J34.1-J34.3; J35; J37; J38.0; J38.6; K05.1; K05.3; K07; K11.0; K11.7; Q30-Q32; Q35-Q38
      27. EpilepsyG40 (excl. G40.5)
      28. Esophagus, stomach and duodenum diseasesI85; I86.4; I98.2; I98.3; K21; K22.0; K22.2; K22.4; K22.5; K22.7; K23.0; K23.1; K25.4-K25.7; K26.4-K26.7; K27.4-K27.7; K28.4-K28.7; K29.3-K29.9; K31.1-K31.5; Q39; Q40; Z90.3A02BX
      29. GlaucomaH40.1-H40.9S01ED
      30. Heart failureI11.0; I13.0; I13.2; I27; I28.0; I42; I43; I50; I51.5; I51.7; I52.8; Z94.1; Z94.3
      31. Hematological neoplasmsC81-C96
      32. HypertensionI10-I15
      33. Inflammatory arthropathiesM02.3; M05-M14; M45; M46.0; M46.1; M46.8; M46.9M01CB
      34. Inflammatory bowel diseaseK50; K51A07E
      35. Ischemic heart diseaseI20-I22; I24; I25; Z95.1; Z95.5C01DA, C01EB18
      36. Migraine and facial pain syndromesG43; G44.0-G44.3; G44.8; G50N02C
      37. Multiple sclerosisG35
      38. Neurotic, stress-related and somatoform diseasesF40-F48 (excl. F43.0; F43.2)
      39. ObesityE66
      40. Osteoarthritis and other degenerative joint diseasesM15-M19; M36.2; M36.3
      41. OsteoporosisM80-M82M05BA; M05BB; M05BX03; M05BX53
      42. Other cardiovascular diseasesI09 (excl. I09.1; I09.8); I28.1; I31.0; I31.1; I45.6; I49.5; I49.8; I70-I72 (excl. I70.2); I79.0; I79.1; I95.0; I95.1; I95.8; Q20; Q21; Q24-Q28; Z95.8; Z95.9
      43. Other digestive diseasesK66.0; K90.0-K90.2; K91.1; K93; Q41-Q43; R15; Z90.4; Z98.0
      44. Other eye diseasesH02.2-H02.5; H04 (excl. H04.3); H05 (excl. H05.0); H10.4; H17; H18.4-H18.9; H19.3; H19.8; H20.1; H21; H31.0-H31.2; H31.8; H31.9; H33; H35.2-H35.5; H35.7-H35.9; H36; H47-H49 (excl. H47.0; H47.1; H48.1); H51; Q10-Q15 (excl. Q12); Z94.7
      45. Other genitourinary diseasesB90.1; N20.0; N20.2; N20.9; N21.0; N21.8; N21.9; N22; N30.1-N30.4; N31; N32.0; N32.3; N32.8; N32.9; N33; N35; N39.3; N39.4; N48.0; N48.4; N48.9; N70.1; N71.1; N73.1; N73.4; N73.6; N76.1; N76.3; N81; N88; N89.5; N90.5; N95.2; Q54; Q62.0-Q62.4; Q62.7; Q62.8; Q63.8; Q63.9; Q64.0; Q64.1; Q64.3-Q64.9; Z90.6; Z90.7; Z96.0
      46. Other metabolic diseasesE20-E31 (excl. E23.1; E24.2; E24.4; E27.3; E30); E34 (excl. E34.3; E34.4); E35 (excl. E35.0); E40-E46 (excl. E44.1); E64; E70-E72; E74-E77; E79 (excl. E79.0); E80 (excl. E80.4); E83-E89 (excl. E86; E87; E88.3; E89.0; E89.1); K90.3; K90.4; K90.8; K90.9; K91.2; M83; M88; N25
      47. Other musculoskeletal and joint diseasesB90.2; M21.2-M21.9; M22-M24; M25.2; M25.3; M35.7; M61; M65.2-M65.4; M70.0; M72.0; M72.2; M72.4; M75.0; M75.1; M75.3; M75.4; M79.7; M84.1; M89; M91; M93; M94; M96; M99; S38.2; S48; S58; S68; S78; S88; S98; T05; T09.6; T11.6; T13.6; T14.7; T90-T98; Q65; Q66; Q68; Q71-Q74; Q77; Q78; Q79.6; Q79.8; Q87; Z44.0; Z44.1; Z89.1-Z89.9; Z94.6; Z96.6; Z97.1
      48. Other neurological diseasesB90.0; D48.2; G04.1; G09-G14 (excl. G13.0; G13.1); G24-G26 (excl. G25.1; G25.4; G25.6); G32; G37; G51-G53 (excl. G51.0); G70; G71; G72.3-72.9; G73 (excl. G73.2-G73.4); G80-G83 (excl. G83.8); G90; G91; G93.8; G93.9; G95; G99; M47.1; Q00-Q07; Q76.0
      49. Other psychiatric and behavioral diseasesF04; F06; F07; F09; F10.2; F10.6; F10.7; F11.2; F11.6; F11.7; F12.2; F12.6; F12.7; F13.2; F13.6; F13.7; F14.2; F14.6; F14.7; F15.2; F15.6; F15.7; F16.2; F16.6; F16.7; F17.2; F17.6; F17.7; F18.2; F18.6; F18.7; F19.2; F19.6; F19.7; F50; F52; F60-F63; F68; F70-F89; F95; F99N07BB
      50. Other respiratory diseasesB90.9; E66.2; J60-J67; J68.4; J70.1; J70.3; J70.4; J84; J92; J94.1; J95.3; J95.5; J96.1; J98 (excl. J98.1); Q33; Q34; Z90.2; Z94.2; Z94.3; Z96.3
      51. Other skin diseasesL13; L28; L30.1; L43 (excl. L43.2); L50.8; L58.1; L85; Q80; Q81; Q82.1; Q82.2; Q82.9
      52. Parkinson and parkinsonismG20-G23 (excl. G21.0)N04BA; N04BX
      53. Peripheral neuropathyB91; G54-G60; G62.8; G62.9; G63 (excl. G63.1); M47.2; M53.1; M54.1
      54. Peripheral vascular diseaseI70.2; I73 (excl. I73.1; I73.8); I79.2; I79.8B01AC23
      55. Prostate diseasesN40; N41.1; N41.8G04C (excl. G04CB)
      56. Schizophrenia and delusional diseasesF20; F22; F24; F25; F28
      57. Sleep disordersG47; F51.0-F51.3
      58. Solid neoplasmsAll C (excl. C81-C96); D00-D09; D32.0; D32.1; D32.9; D33.0-D33.4; Q85
      59. Thyroid diseaseE00-E03 (excl. E03.5); E05; E06.2; E06.3; E06.5; E07; E35.0; E89.0H03AA; H03B
      60. Venous and lymphatic diseasesI78.0; I83; I87; I89; I97.2; Q82.0
      ATC = Anatomical Therapeutic Chemical classification system; excl. = excluding; ICD-10 = International Classification of Diseases, 10th Revision.
      Supplementary Table 3Change in the Likelihood of Being Exposed to ≥10 Prescription Drugs (Excluding Analgesics) During the Last Year of Life of Older Adults in Sweden, 2007-2013
      ParameterN TotalPolypharmacy (Excluding Analgesics), %Last Month vs 12th Month Before Death
      12th Month Before DeathLast Month Before DeathVariation Rate, % (95% CI)
      Variation rates indicate the relative change in prevalence of polypharmacy (≥10 prescription drugs excluding analgesics) between the 12th month and the final month before death.
      Odds Ratio (95% CI)
      Odds ratio computed by the mean of logistic regression models including polypharmacy (≥10 prescription drugs excluding analgesics) as a binary dependent variable and time (last month before death vs 12th month before death) as sole independent variable. Clustered standard errors at the individual level were used to account for correlation of observations within groups.
      Total cohort511,84324.436.047.3 (46.5-48.2)1.74 (1.72-1.75)
      Sex
       Men234,02322.835.957.4 (56.0-58.9)1.90 (1.87-1.92)
       Women277,82025.836.039.9 (38.7-41.3)1.62 (1.60-1.64)
      Age at time of death (y)
       66-7475,74922.436.863.9 (61.3-66.6)2.01 (1.97-2.06)
       75-84163,35526.339.148.8 (47.3-52.3)1.80 (1.78-1.83)
       85-94227,39124.935.643.0 (41.7-44.3)1.67 (1.65-1.69)
       ≥9545,34818.725.435.9 (32.5-39.3)1.48 (1.43-1.53)
      Living arrangement
       Community-dwelling350,97722.235.559.5 (58.3-63.8)1.92 (1.90-1.94)
       Institutionalized160,86629.237.127.1 (25.8-28.3)1.43 (1.41-1.45)
      Level of education
      Missing values for level of education: 24,319 (4.7% of the total cohort).
       Primary education267,31924.936.747.0 (45.8-48.2)1.74 (1.72-1.76)
       Secondary education175,08324.536.247.5 (45.9-49.8)1.74 (1.72-1.77)
       Tertiary education45,12222.734.552.0 (48.8-55.3)1.79 (1.74-1.85)
      Illness trajectory
      Illness trajectories were constructed using all the diagnoses mentioned on the death certificates (ie, both underlying and contributing causes of death).
       Cancer144,87819.136.591.7 (89.3-94.2)2.45 (2.40-2.49)
       Organ failure198,59831.342.134.8 (33.6-35.9)1.60 (1.58-1.62)
       Prolonged dwindling128,75721.628.933.8 (32.0-35.6)1.48 (1.45-1.50)
       Sudden death39,61018.826.038.3 (34.7-42.3)1.52 (1.47-1.57)
      No. of chronic diseases
       055444.56.644.8 (23.9-69.3)1.48 (1.25-1.75)
       134,1365.49.577.2 (67.7-87.3)1.85 (1.75-1.97)
       260,4988.915.271.7 (66.3-77.2)1.85 (1.78-1.91)
       374,17212.621.671.2 (67.2-75.2)1.91 (1.86-1.96)
       476,26517.128.969.3 (66.1-72.6)1.98 (1.93-2.02)
       ≥5261,22836.451.040.0 (39.1-46.9)1.82 (1.80-1.84)
      Variation rates indicate the relative change in prevalence of polypharmacy (≥10 prescription drugs excluding analgesics) between the 12th month and the final month before death.
      Odds ratio computed by the mean of logistic regression models including polypharmacy (≥10 prescription drugs excluding analgesics) as a binary dependent variable and time (last month before death vs 12th month before death) as sole independent variable. Clustered standard errors at the individual level were used to account for correlation of observations within groups.
      Missing values for level of education: 24,319 (4.7% of the total cohort).
      § Illness trajectories were constructed using all the diagnoses mentioned on the death certificates (ie, both underlying and contributing causes of death).
      Supplementary Table 4Factors Associated with Change in the Number of Prescription Drugs (Excluding Analgesics) During the Last Year of Life of Older Adults in Sweden, 2007-2013
      FactorN TotalNo. of Prescription Drugs, Mean (SD)Mean Difference (95% CI)Factor × Time, Adjusted β (95% CI)
      β-Coefficients computed by the mean of generalized estimating equation models with identity link functions at the individual level and unstructured correlations. The total number of distinct prescription drugs excluding analgesics was entered as continuous dependent variable with Gaussian distribution, while including all presented individual characteristics as independent variables. β-Coefficients for the interaction with time can be interpreted as the rate of change between the 12th month and the final month before death, compared with the reference category.
      12th Month Before DeathLast Month Before Death
      Total cohort511,8436.9 (4.1)8.3 (4.2)1.40 (1.40-1.41)
      Sex
       Men234,0236.7 (4.0)8.3 (4.2)1.62 (1.61-1.63)Ref
       Women277,8207.1 (4.0)8.4 (4.2)1.22 (1.21-1.24)−0.39 (−0.40 to −0.37)
      Age at time of death (y)
       66-7475,7496.3 (4.6)8.3 (4.7)2.01 (1.98-2.04)Ref
       75-84163,3557.1 (4.2)8.6 (4.4)1.56 (1.54-1.58)−0.45 (−0.48 to −0.42)
       85-94227,3917.1 (3.8)8.3 (4.0)1.21 (1.20-1.22)−0.80 (−0.83 to −0.77)
       ≥9545,3486.5 (3.5)7.3 (3.6)0.82 (0.79-0.84)−1.19 (−1.23 to −1.15)
      Living arrangement
       Community-dwelling350,9776.6 (4.1)8.3 (4.3)1.65 (1.64-1.66)Ref
       Institutionalized160,8667.6 (3.9)8.5 (4.0)0.86 (0.85-0.88)−0.79 (−0.81 to −0.77)
      Level of education
      Missing values for level of education: 24,319 (4.7% of the total cohort).
       Primary education267,3197 (4.0)8.4 (4.2)1.40 (1.39-1.41)Ref
       Secondary education175,0836.9 (4.1)8.3 (4.3)1.43 (1.42-1.45)0.03 (0.01 to 0.06)
       Tertiary education45,1226.7 (4.1)8.1 (4.3)1.47 (1.43-1.50)0.07 (0.03-0.10)
      Illness trajectory
      Illness trajectories were constructed using all the diagnoses mentioned on the death certificates (ie, both underlying and contributing causes of death).
       Cancer144,8786.1 (4.0)8.3 (4.3)2.24 (2.22-2.26)Ref
       Organ failure198,5987.8 (4.2)9.0 (4.4)1.24 (1.22-1.25)−1.01 (−1.03 to −0.99)
       Prolonged dwindling128,7576.8 (3.6)7.7 (3.8)0.84 (0.83-0.86)−1.39 (−1.42 to −1.37)
       Sudden death39,6106.2 (3.9)7.2 (4.0)1.00 (0.97-1.02)−1.25 (−1.29 to −1.21)
      No. of chronic diseases
       055444.1 (2.9)4.6 (2.9)0.57 (0.52-0.63)Ref
       134,1364.2 (3.0)5.2 (3.1)1.03 (1.00-1.05)0.48 (0.37-0.59)
       260,4984.9 (3.2)6.1 (3.3)1.23 (1.21-1.26)0.67 (0.57-0.78)
       374,1725.6 (3.4)6.9 (3.5)1.35 (1.32-1.37)0.78 (0.68-0.89)
       476,2656.3 (3.5)7.7 (3.6)1.46 (1.44-1.48)0.98 (0.78-0.99)
       ≥5261,2288.4 (4.1)9.9 (4.2)1.51 (1.50-1.53)0.93 (0.83-1.04)
      CI = confidence interval; SD = standard deviation.
      β-Coefficients computed by the mean of generalized estimating equation models with identity link functions at the individual level and unstructured correlations. The total number of distinct prescription drugs excluding analgesics was entered as continuous dependent variable with Gaussian distribution, while including all presented individual characteristics as independent variables. β-Coefficients for the interaction with time can be interpreted as the rate of change between the 12th month and the final month before death, compared with the reference category.
      Missing values for level of education: 24,319 (4.7% of the total cohort).
      Illness trajectories were constructed using all the diagnoses mentioned on the death certificates (ie, both underlying and contributing causes of death).
      Supplementary Table 5Prevalence of the 20 Most Commonly Used Prescription Drug Classes During the Last Month of Life of Older People in Sweden, by Age (2007-2013)
      Rank, Drug Class (ATC Code)
      Drug classes are ranked by descending order, using the second level of the ATC (therapeutic subgroups, eg, N02 Analgesics). For some classes, details are also provided for the pharmacologic subgroups (eg, N02A Opioids).
      Prevalence, %
      66-74 y (N = 75,749)75-84 y (N = 163,355)85-94 y (N = 227,391)≥95 y (N = 45,348)
      1. Antithrombotic agents (B01)44.254.957.548.0
      Vitamin K antagonists (B01AA)7.09.15.91.5
      Heparin group (B01AB)9.95.93.62.5
      Platelet aggregation inhibitors (B01AC)30.943.550.545.3
      2. Diuretics (C03)39.549.758.660.3
      Low-ceiling diuretics (C03A-C03B)4.95.85.23.9
      High-ceiling diuretics (C03C)31.541.851.454.1
      Potassium-sparing agents (C03D)11.912.212.311.5
      3. Analgesics (N02)55.758.463.066.9
      Opioids (N02A)42.738.837.237.0
      Other analgesics (N02B)38.946.053.257.7
      4. Psycholeptics (N05)48.449.752.654.7
      Antipsychotics (N05A)9.411.212.212.0
      Anxiolytics (N05B)26.525.626.829.2
      Hypnotics and sedatives (N05C)34.033.835.135.5
      5. β-Blocking agents (C07)37.743.542.530.4
      6. Drugs acting on the renin-angiotensin system (C09)31.934.529.817.8
      ACE inhibitors (C09A-C09B)20.823.521.613.6
      Angiotensin II receptor antagonists (C09C-C09D)12.211.88.64.3
      7. Anti-anemic preparations (B03)21.931.839.739.8
      Iron preparations (B03A)6.39.412.211.4
      Vitamin B12 and folic acid (B03B)16.925.532.933.8
      8. Psychoanaleptics (N06)25.432.935.728.5
      Antidepressants (N06A)24.329.832.727.2
      Anti-dementia drugs (N06D)2.16.05.82.3
      9. Drugs for constipation (A06)35.838.141.744.3
      10. Drugs for acid related disorders (A02)42.836.932.727.5
      11. Cardiac therapy (C01)15.223.027.926.2
      Cardiac glycosides (C01A)4.97.99.68.8
      Vasodilators used in cardiac diseases (C01D)10.416.220.219.3
      12. Emollients and protectives (D02)19.525.132.438.7
      13. Lipid modifying agents (C10)25.023.511.01.9
      Statins (C10AA)24.222.910.71.7
      14. Calcium channel blockers (C08)16.517.114.810.5
      15. Mineral supplements (A12)18.020.521.818.3
      Calcium (A12A)10.713.214.010.6
      Potassium (A12B)7.07.88.78.2
      16. Drugs used in diabetes (A10)20.819.012.76.8
      Insulin and analogues (A10A)14.612.88.24.2
      Oral blood glucose lowering drugs (A10B)10.29.16.03.2
      17. Ophthalmologicals (S01)7.212.318.623.5
      18. Drugs for obstructive airway diseases (R03)19.018.012.58.9
      19. Antibacterials for systemic use (J01)20.019.720.120.1
      20. Thyroid therapy (H03)7.910.412.111.8
      ACE = angiotensin-converting enzyme; ATC = Anatomical Therapeutic Chemical classification system.
      Drug classes are ranked by descending order, using the second level of the ATC (therapeutic subgroups, eg, N02 Analgesics). For some classes, details are also provided for the pharmacologic subgroups (eg, N02A Opioids).

      Supplementary Data

      References

        • Barnett K.
        • Mercer S.
        • Norbury M.
        • Watt G.
        • Wyke S.
        • Guthrie B.
        Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.
        Lancet. 2012; 380: 37-43
        • Johnell K.
        • Klarin I.
        The relationship between number of drugs and potential drug-drug interactions in the elderly: a study of over 600,000 elderly patients from the Swedish Prescribed Drug Register.
        Drug Saf. 2007; 30: 911-918
        • Kantor E.D.
        • Rehm C.D.
        • Haas J.S.
        • Chan A.T.
        • Giovannucci E.L.
        Trends in prescription drug use among adults in the United States from 1999-2012.
        JAMA. 2015; 314: 1818
        • Morgan N.A.
        • Rowett D.
        • Currow D.C.
        Analysis of drug interactions at the end of life.
        BMJ Support Palliat Care. 2015; 5: 281-286
        • Holmes H.M.
        • Sachs G.A.
        • Shega J.W.
        • Hougham G.W.
        • Cox Hayley D.
        • Dale W.
        Integrating palliative medicine into the care of persons with advanced dementia: identifying appropriate medication use.
        J Am Geriatr Soc. 2008; 56: 1306-1311
        • Todd A.
        • Husband A.
        • Andrew I.
        • Pearson S.A.
        • Lindsey L.
        • Holmes H.
        Inappropriate prescribing of preventative medication in patients with life-limiting illness: a systematic review.
        BMJ Support Palliat Care. 2016; ([Epub ahead of print])https://doi.org/10.1136/bmjspcare-2015-000941
        • Raijmakers N.J.H.
        • van Zuylen L.
        • Furst C.J.
        • et al.
        Variation in medication use in cancer patients at the end of life: a cross-sectional analysis.
        Support Care Cancer. 2013; 21: 1003-1011
        • Bayliss E.A.
        • Bronsert M.R.
        • Reifler L.M.
        • et al.
        Statin prescribing patterns in a cohort of cancer patients with poor prognosis.
        J Palliat Med. 2013; 16: 412-419
        • Riechelmann R.P.
        • Krzyzanowska M.K.
        • Zimmermann C.
        Futile medication use in terminally ill cancer patients.
        Support Care Cancer. 2009; 17: 745-748
        • Tjia J.
        • Briesacher B.A.
        • Peterson D.
        • Liu Q.
        • Andrade S.E.
        • Mitchell S.L.
        Use of medications of questionable benefit in advanced dementia.
        JAMA Intern Med. 2014; 174: 1763-1771
        • Ma G.
        • Downar J.
        Noncomfort medication use in acute care inpatients comanaged by palliative care specialists near the end of life: a cohort study.
        Am J Hosp Palliat Care. 2014; 31: 812-819
        • van Nordennen R.T.C.M.
        • Lavrijsen J.C.M.
        • Heesterbeek M.J.A.B.
        • Bor H.
        • Vissers K.C.P.
        • Koopmans R.T.C.M.
        Changes in prescribed drugs between admission and the end of life in patients admitted to palliative care facilities.
        J Am Med Dir Assoc. 2016; 17: 514-518
        • Dwyer L.L.
        • Lau D.T.
        • Shega J.W.
        Medications that older adults in hospice care in the United States take, 2007.
        J Am Geriatr Soc. 2015; 63: 2282-2289
        • Heppenstall C.P.
        • Broad J.B.
        • Boyd M.
        • et al.
        Medication use and potentially inappropriate medications in those with limited prognosis living in residential aged care.
        Australas J Ageing. 2016; 35: E18-E24
        • McNeil M.J.
        • Kamal A.H.
        • Kutner J.S.
        • Ritchie C.S.
        • Abernethy A.P.
        The burden of polypharmacy in patients near the end of life.
        J Pain Symptom Manage. 2016; 51: 178-183e2
        • Turner J.P.
        • Jamsen K.M.
        • Shakib S.
        • Singhal N.
        • Prowse R.
        • Bell J.S.
        Polypharmacy cut-points in older people with cancer: how many medications are too many?.
        Support Care Cancer. 2016; 24: 1831-1840
        • Gnjidic D.
        • Hilmer S.N.
        • Blyth F.M.
        • et al.
        Polypharmacy cutoff and outcomes: five or more medicines were used to identify community-dwelling older men at risk of different adverse outcomes.
        J Clin Epidemiol. 2012; 65: 989-995
        • Onder G.
        • Liperoti R.
        • Fialova D.
        • et al.
        Polypharmacy in nursing home in Europe: results from the SHELTER study.
        J Gerontol A Biol Sci Med Sci. 2012; 67: 698-704
        • Singer A.E.
        • Meeker D.
        • Teno J.M.
        • Lynn J.
        • Lunney J.R.
        • Lorenz K.A.
        Symptom trends in the last year of life from 1998 to 2010: a cohort study.
        Ann Intern Med. 2015; 162: 175-183
        • Lau H.S.
        • De Boer A.
        • Beuning K.S.
        • Porsius A.
        Validation of pharmacy records in drug exposure assessment.
        J Clin Epidemiol. 1997; 50: 619-625
        • Johnell K.
        • Fastbom J.
        Comparison of prescription drug use between community-dwelling and institutionalized elderly in Sweden.
        Drugs Aging. 2012; 29: 751-758
        • Murray S.A.
        • Kendall M.
        • Boyd K.
        • Sheikh A.
        Illness trajectories and palliative care.
        BMJ. 2005; 330: 1007-1011
        • Morin L.
        • Aubry R.
        • Frova L.
        • et al.
        Estimating the need for palliative care at the population level: a cross-national study in 12 countries.
        Palliat Med. 2016; ([Epub ahead of print])https://doi.org/10.1177/0269216316671280
        • Lunney J.R.
        • Lynn J.
        • Foley D.J.
        • Lipson S.
        • Guralnik J.M.
        Patterns of functional decline at the end of life.
        JAMA. 2003; 289: 2387-2392
        • Chaudhry S.I.
        • Murphy T.E.
        • Gahbauer E.
        • Sussman L.S.
        • Allore H.G.
        • Gill T.M.
        Restricting symptoms in the last year of life: a prospective cohort study.
        JAMA Intern Med. 2013; 173: 1534-1540
        • Calderón-Larrañaga A.
        • Vetrano D.L.
        • Onder G.
        • et al.
        Assessing and measuring chronic multimorbidity in the older population: a proposal for its operationalization.
        J Gerontol A Biol Sci Med Sci. 2016; ([Epub ahead of print])https://doi.org/10.1093/gerona/glw233
        • Kierner K.A.
        • Weixler D.
        • Masel E.K.
        • Gartner V.
        • Watzke H.H.
        Polypharmacy in the terminal stage of cancer.
        Support Care Cancer. 2016; 24: 2067-2074
        • LeBlanc T.W.
        • McNeil M.J.
        • Kamal A.H.
        • Currow D.C.
        • Abernethy A.P.
        Polypharmacy in patients with advanced cancer and the role of medication discontinuation.
        Lancet Oncol. 2015; 16: e333-e341
        • Stevenson J.
        • Abernethy A.P.
        • Miller C.
        • Currow D.C.
        Managing comorbidities in patients at the end of life.
        Br Med J. 2004; 329: 909-912
        • Silveira M.J.
        • Kazanis A.S.
        • Shevrin M.P.
        Statins in the last six months of life: a recognizable, life-limiting condition does not decrease their use.
        J Palliat Med. 2008; 11: 685-693
        • Strandberg T.E.
        • Kolehmainen L.
        • Vuorio A.
        Evaluation and treatment of older patients with hypercholesterolemia: a clinical review.
        JAMA. 2014; 312: 1136-1144
        • Maddison A.R.
        • Fisher J.
        • Johnston G.
        Preventive medication use among persons with limited life expectancy.
        Prog Palliat Care. 2011; 19: 15-21
        • Rochon P.A.
        • Gurwitz J.H.
        Optimising drug treatment for elderly people: the prescribing cascade.
        BMJ. 1997; 315: 1096-1099
        • Holmes H.M.
        • Hayley D.C.
        • Alexander G.C.
        • Sachs G.A.
        Reconsidering medication appropriateness for patients late in life.
        Arch Intern Med. 2006; 166: 605-609
        • Gnjidic D.
        • Couteur D.G.L.
        • Hilmer S.N.
        Discontinuing drug treatments.
        BMJ. 2014; 349: g7013
        • Scott I.A.
        • Hilmer S.N.
        • Reeve E.
        • et al.
        Reducing inappropriate polypharmacy: the process of deprescribing.
        JAMA Intern Med. 2015; 175: 827-834
        • Gnjidic D.
        • Le Couteur D.G.
        • Kouladjian L.
        • Hilmer S.N.
        Deprescribing trials: methods to reduce polypharmacy and the impact on prescribing and clinical outcomes.
        Clin Geriatr Med. 2012; 28: 237-253
        • Kutner J.S.
        • Blatchford P.J.
        • Taylor D.H.J.
        • et al.
        Safety and benefit of discontinuing statin therapy in the setting of advanced, life-limiting illness: a randomized clinical trial.
        JAMA Intern Med. 2015; 175: 691-700
        • Luymes C.H.
        • van der Kleij R.M.J.J.
        • Poortvliet R.K.E.
        • de Ruijter W.
        • Reis R.
        • Numans M.E.
        Deprescribing potentially inappropriate preventive cardiovascular medication: barriers and enablers for patients and general practitioners.
        Ann Pharmacother. 2016; 50: 446-454
        • Parsons C.
        Withdrawal of antidementia drugs in older people: who, when and how?.
        Drugs Aging. 2016; 33: 545-556
        • Howard R.
        • McShane R.
        • Lindesay J.
        • et al.
        Nursing home placement in the Donepezil and Memantine in Moderate to Severe Alzheimer's Disease (DOMINO-AD) trial: secondary and post-hoc analyses.
        Lancet Neurol. 2015; 4422: 1-11
        • White N.
        • Reid F.
        • Harris A.
        • Harries P.
        • Stone P.
        A systematic review of predictions of survival in palliative care: how accurate are clinicians and who are the experts?.
        PLoS One. 2016; 11: e0161407
        • Casarett D.
        The science of choosing wisely–overcoming the therapeutic illusion.
        N Engl J Med. 2016; 374: 1203-1205
        • Buiting H.M.
        • Rurup M.L.
        • Wijsbek H.
        • van Zuylen L.
        • den Hartogh G.
        Understanding provision of chemotherapy to patients with end stage cancer: qualitative interview study.
        Br Med J. 2011; 342: d1933
        • Gawande A.
        Quantity and quality of life: duties of care in life-limiting illness.
        JAMA. 2016; 315: 267-269
        • Van Nordennen R.T.C.M.
        • Lavrijsen J.C.M.
        • Vissers K.C.P.
        • Koopmans R.T.C.M.
        Decision making about change of medication for comorbid disease at the end of life: an integrative review.
        Drugs Aging. 2014; 31: 501-512

      Linked Article

      • Polypharmacy in Elderly Patients: The March Goes On and On
        The American Journal of MedicineVol. 130Issue 8
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          Polypharmacy is a major public health problem in the United States and abroad. Although there is no standard definition of polypharmacy, I see and recognize it every day in the outpatient and inpatient environment. A typical example that I saw yesterday was an 84-year-old man admitted to our internal medicine service for worsening heart failure. He had a list of 14 different medicines that he claimed to be ingesting each day. This list even included 2 different β-blockers! In addition, he was taking significant quantities of an over-the-counter nonsteroidal anti-inflammatory agent, which had caused deterioration of his renal function and was a major factor in his worsening heart failure.
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