Background
Chronic pain is one of the leading conditions in later life in terms of commonness and economic burden [
1]. To manage pain in older adults is known to be complex and should always be based on proper assessment [
2]. Despite the commendable development of pain management in recent years, the overall consensus highlights the under-assessment, under-diagnosing, and under-treatment/mistreatment of persistent pain in older individuals [
3,
4].
Successful pain management is based on balancing the benefits and harms of available drugs, on lifestyle interventions, and on treating the underlining cause as much as possible [
5]. Paracetamol has been recommended as the first-line treatment for both acute and chronic pain in older people, followed by non-steroidal anti-inflammatory drugs (NSAIDs), but only if contraindications are not present [
6,
7]. Pharmacological treatment modalities need to be combined with non-pharmacological ones [
6,
7]. Opioid administration may be considered with moderate to severe cancer and non-cancer pain, but never without precise individual deliberation and careful monitoring [
7,
8]. Long-term opioid administration for chronic non-cancer pain remains controversial [
9].
It has been discussed that NSAID use does not adhere to clinical guidelines (e.g. long-term or pro re nata use despite contraindications) in older adults [
10‐
13], among whom NSAIDs have been suggested to comprise a major cause of drug-related morbidity [
14]. NSAID-related peptic ulcers, renal and cardiovascular adverse effects, and increased mortality have been reported [
14,
15]. Age-related physiological changes, cognitive impairment (assessment, adverse effects), multi-morbidity, and polypharmacy pose major challenges to opioid administration [
6,
16]. Adverse effects are relatively frequent, with potentially severe consequences [
16]. An appropriate dose needs to be carefully titrated and both the desired and adverse effects monitored regularly [
7,
8,
17].
Studies regarding analgesic administration among community-dwelling older adults are scarce [
10,
13,
18,
19]. It remains unclear how large a proportion of older adults actually use analgesics and whether the medication consists of NSAIDs or paracetamol, or of neuropathic drugs or opioids [
11,
20]. Multiple studies have suggested an increasing trend in opioid prescribing over the recent years [
21,
22], but little is known about the actual opioid administration among older adults [
7,
19,
23].
The two main research questions of the current study were as follows: 1) What are the purchasing prevalence and profile of use of separate analgesics among older community-dwelling citizens? 2) What is the relationship between reported SF-36 pain and use of different analgesics among older adults?
Based on the clinical point of view and previous studies that have presented an increasing trend in opioid prescribing over the past two decades [
22], opioid administration was expected to emerge as relatively extensive. To identify factors related to opioid use could prove beneficial in terms of prevention. Therefore, the third research question was: 3) Which factors are related to opioid purchases?
Methods
The current research was a part of the Good Ageing in Lahti Region (GOAL) study executed in the Päijät-Häme Hospital District in Southern Finland [
24]. The district consists of both rural and urban areas, with approximately 220,000 inhabitants. The GOAL was a 10-year follow-up cohort study (
N = 2815) that introduced a stratified (age, sex, 14 municipalities) random sample of Finnish seniors born in 1926–30, 1936–40, and 1946–50. At baseline (2002) and the three follow-up visits (2005, 2008, and 2012), an extensive questionnaire was filled out (overall health, attitudes, quality of life, etc.), and blood samples and physical data were collected. The design of the study has been described in more detail elsewhere [
24]. The present study focused on the 2012 data, which included the complete data regarding prescribed pain medications. The total number of participants still attending at this point of the follow-up was
N = 1697. Subjects with insufficient data regarding reported pain (
N = 277) were excluded, and all statistical analyses were therefore executed with 1420 subjects. In 2012, the participants were 62–-66, 72–76, and 82–86 years of age. Hospitalized and institutionalized older adults did not participate in the study.
In Finland, patients are entitled to a reimbursement of medication costs from the Social Insurance Institution of Finland (SII). SII maintains a nationwide register of all prescriptions and medication purchases, from which the present data regarding analgesic purchases were retrieved. All pain medication purchases 6 months prior to and 6 months after the questionnaire data collection were considered. The presence vs absence of the drugs in the participants’ purchasing history were retrieved. The exact number and dose were not retrievable. The temporal association between the pain assessment and analgesic purchases was not retrievable. The analgesics considered were level 1 analgesics (NSAIDs [Anatomical Therapeutic Chemical Classification System (ATC)] M01AE01, M01AE51 ibuprofen; M01AE03 ketoprofen; M01AH01, M01AH05, M01AH06 COX-2 selective inhibitors; M01AC06 meloxicam; M01AE02, M01AE52 naproxen; M01AB05 diclofenac; M01AB01 indometin]; as well as N02BE01, N02BE51 paracetamol) and level 2–3 analgesics (N02AA01 morphine; N02AA03 hydromorphone; N02AA05 oxycodone; N02AA55 oxycodone-naloxone; N02AA59, N02AJ06 codeine combinations; N02AB03 fentanyl; N02AE01 buprenorphine; N02AX02, N02AJ14 tramadol and tramadol combinations), in addition to gabapentinoids (N03AX23 gabapentin; N03AX16 pregabalin) and tricyclic antidepressants (TCAs [N06AA09, N06CA01 amitriptyline; N06AA10 nortriptyline]). Acetylsalicylic acid was excluded from the NSAIDs due to its major use as an antithrombotic drug.
Data on the study participants’ pain, demographics, life habits, morbidity, and symptoms were based on the GOAL questionnaire. Regarding pain, the two-item Bodily Pain section of the SF-36 questionnaire was used [
25,
26]. The participants indicated how severe pain they had experienced during the previous 4 weeks (intensity; 100 = none, 80 = very mild, 60 = mild, 40 = moderate, 20 = severe, 0 = very severe) and how much this pain had disrupted their everyday work and activity (at home or outside of home) during the previous 4 weeks (interference; 100 = not at all, 75 = a little bit, 50 = moderately, 25 = quite a bit, 0 = extremely). Bodily pain is presented herein as the mean of the pain intensity and interference scores. In the first analysis, based on the bodily pain scores, the subjects were divided into four pain groups (
group I [0–45, moderate to very severe pain intensity and interference],
group II [47.5–70],
group III [77.5–90],
group IV [100, no pain intensity and interference at all]). The groups were established in order to be able to combine pain intensity and pain-related interference and to consider analgesic administration in relation to the pain. The rationale for the four pain groups was to consider separately the subjects who had reported high levels of both pain intensity and pain interference (
I) and those who had reported none (
IV). The rest were divided into two groups (
II–III) to equate the group sizes. In the second analysis, subjects were divided into two groups (opioid, non-opioid) based on whether they had purchased opioids.
Household income was determined as the taxable household income divided by the square root of the number of people living in the household indexed to the year 2017 [
27]. Weekly alcohol consumption was measured with the 3-item AUDIT-C [
28] instrument. Leisure-time physical activity (LTPA) was determined as activities lasting over 30 min that make the participant sweat and pant at least to some degree (high [6–7 times a week], moderate [3–5 times a week], low [1–2 times a week or less, or not possible due to injury or illness]) [
29]. The provided laboratory test data (fP-Glucose, fP-Triglyceride, high-density lipoprotein [fP-HDL], estimated glomerular filtration rate [eGFR, ml/min/1.73 m2], high-sensitivity C-reactive protein [S-hs-CRP], rheumatoid factor, serum uric acid) and clinical measurements (weight, height, blood pressure, waist circumference) were considered. The morbidities examined included cardiovascular, pulmonary, musculoskeletal, psychiatric, and neurological diseases, as well as diabetes mellitus type II and neoplasms. Prior to the regression analyses, the sum of each participant’s diagnosed morbidities was calculated. Metabolic syndrome (MetS) was determined as the presence of three or more of the following components: 1) waist circumference ≥ 102 cm for men and ≥ 88 cm for women; 2) fP-Triglycerides ≥1.7 mmol/L or treatment for dyslipidemia; 3) fP-HDL ≤1.03 mmol/L for men and ≤ 1.29 mmol/L for women, or treatment for dyslipidemia; 4) systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg, or antihypertensive medication; and 5) fP-Glucose ≥5.6 mmol/L or the use of medication for hyperglycemia [
30]. Also, the number of doctor’s appointments during the previous 12 months was considered. For more details about variables and coding, please contact the corresponding author for the GOAL Codebook.
The descriptive statistics include means and SDs for continuous variables and numbers and percentages for categorical variables. Statistical significances for the hypothesis of linearity across the SF-36 categories of bodily pain were evaluated by using the Cochran–Armitage test for trend and analysis of variance with an appropriate contrast. Statistical comparisons between the opioid usage groups were performed with the t-test, the Chi-squared test, or the Fisher-Freeman-Halton test when appropriate. In the case of a violation against the assumptions (non-normality), a bootstrap-type test was applied. Multivariate logistic regression was employed to investigate factors related to analgesic purchases. As predictors, the following were included: pain levels and LTPA (as ordinal variables); sex, MetS, and smoking (as dicotomous variables); and age, education years, Audit-C and number of morbidities (as continuous variable). The Hosmer-Lemeshow goodness-of-fit statistics were used for the assessment of the final models. The normality of variables was evaluated with the Shapiro-Wilk W test. The Stata 15.1 statistical package by StataCorp LP (College Station, TX, USA) was used for the analyses.
Results
The mean age of all 1420 participants was 71.2 years. The proportion of females was 55%. Out of the participants, 84% had purchased some prescribed analgesics during the considered year. All of the prescribed analgesics were most frequently obtained by
group I subjects, who reported the most pain, with the percentage decreasing linearly with the groups. In
group I, 91% of the participants had picked up the prescribed NSAIDs. NSAIDs were also largely purchased by those who, at the moment of the questionnaire data collection, reported no SF-36 bodily pain at all (70%). Forty-one percent of all participants had purchased prescribed paracetamol. Over half of the participants in
group I had purchased the prescribed opioids, as had almost one fourth of those with no reported pain at all. In
group I, 17% had purchased gabapentinoids and 7% TCAs. Age (62–66 vs 72–76 vs 82–86 years) did not make a marked difference in drug distribution or purchasing prevalence. The analgesic purchases in relation to the four SF-36 bodily pain and according to three age groups are presented in Table
1.
Table 1
Prescribed analgesics according to four SF-36 bodily pain levels (groups I–IV)
ALL PARTICIPANTS |
Number | 244 | 382 | 478 | 316 | |
Level 1 analgesics | 225 (92) | 323 (85) | 392 (82) | 234 (74) | < 0.001 |
NSAID | 200 (91) | 303 (80) | 375 (78) | 222 (70) | < 0.001 |
Paracetamol | 151 (62) | 185 (48) | 162 (34) | 85 (27) | < 0.001 |
Level 2–3 analgesics | 127 (52) | 133 (35) | 127 (27) | 73 (23) | < 0.001 |
Mild opioid | 123 (50) | 131 (34) | 124 (26) | 71 (22) | < 0.001 |
Intermediate/strong opioid | 24 (10) | 9 (2) | 5 (1) | 4 (1) | < 0.001 |
Neuropathic pain medication |
Gabapentinoid | 42 (17) | 34 (9) | 23 (5) | 13 (4) | < 0.001 |
Tricyclic antidepressant | 16 (7) | 15 (4) | 14 (3) | 5 (2) | < 0.001 |
AGE 62–66 YEARS |
Number | 71 | 163 | 224 | 145 | |
Level 1 analgesics | 66 (93) | 133 (82) | 177 (79) | 110 (76) | < 0.001 |
NSAID | 66 (93) | 129 (79) | 176 (79) | 109 (75) | < 0.001 |
Paracetamol | 35 (49) | 58 (36) | 56 (25) | 32 (22) | < 0.001 |
Level 2–3 analgesics | 41 (58) | 51 (31) | 60 (27) | 31 (21) | < 0.001 |
Mild opioid | 40 (56) | 50 (31) | 59 (26) | 29 (20) | < 0.001 |
Intermediate/strong opioid | 4 (6) | 2 (1) | 2 (1) | 2 (1) | < 0.001 |
Neuropathic pain medication |
Gabapentinoid | 12 (17) | 10 (6) | 14 (6) | 5 (3) | < 0.001 |
Tricyclic antidepressant | 7 (10) | 7 (4) | 9 (4) | 0 (0) | 0.016 |
AGE 72–76 YEARS |
Number | 112 | 150 | 197 | 129 | |
Level 1 analgesics | 102 (91) | 132 (88) | 168 (85) | 92 (71) | < 0.001 |
NSAID | 99 (88) | 123 (82) | 158 (80) | 86 (67) | < 0.001 |
Paracetamol | 75 (67) | 83 (55) | 79 (40) | 33 (26) | < 0.001 |
Level 2–3 analgesics | 56 (50) | 56 (37) | 51 (26) | 34 (26) | < 0.001 |
Mild opioid | 54 (48) | 56 (37) | 49 (25) | 34 (26) | < 0.001 |
Intermediate/strong opioid | 12 (11) | 5 (3) | 3 (2) | 1 (1) | < 0.001 |
Neuropathic pain medication |
Gabapentinoid | 20 (18) | 17 (11) | 8 (4) | 4 (3) | < 0.001 |
Tricyclic antidepressant | 6 (5) | 5 (3) | 3 (2) | 5 (4) | 0.023 |
AGE 82–86 YEARS |
Number | 61 | 69 | 57 | 42 | * |
Level 1 analgesics | 57 (93) | 58 (84) | 47 (82) | 32 (76) | < 0.001 |
NSAID | 56 (92) | 54 (78) | 41 (72) | 27 (64) | < 0.001 |
Paracetamol | 41 (67) | 44 (64) | 27 (47) | 20 (48) | < 0.001 |
Level 2–3 analgesics | 30 (49) | 26 (38) | 16 (28) | 8 (19) | < 0.001 |
Mild opioid | 29 (48) | 25 (36) | 16 (28) | 8 (19) | < 0.001 |
Intermediate/strong opioid | 8 (13) | 2 (3) | 0 (0) | 1 (2) | < 0.001 |
Neuropathic pain medication |
Gabapentinoid | 10 (16) | 7 (10) | 1 (2) | 4 (10) | < 0.001 |
Tricyclic antidepressant | 3 (5) | 3 (4) | 2 (4) | 0 (0) | 0.019 |
In total, 32% of the participants had purchased opioids. Between the opioid and non-opioid group, only a moderate difference was found in SF-36 pain intensity (61 in the opioid group vs 72 in the non-opioid group) and interference (71 vs 82, respectively) (
p < 0.001). Cardiovascular, musculoskeletal, and pulmonary diseases, as well as neoplasms, musculoskeletal pain, and depressive symptoms were more prevalent among opioid users. MetS was present in 47% of the participants in the opioid group, compared to the corresponding 40% in the non-opioid group (
p = 0.009). The proportion of those with a BMI of over 30 was higher in the opioid group (
p = 0.049). Among male participants, waist circumference was slightly wider in those who were on opioids. Serum hs-CRP and uric acid levels were higher among the participants in the opioid group. Furthermore, 48% of the participants in the opioid group had visited a doctor more than 3 times during the previous 12 months, compared to the corresponding 29% in the non-opioid group (
p < 0.001). No differences were found between the groups in terms of cohabiting, education years, household income, LTPA, waist circumference in females, alcohol consumption, smoking, blood pressure, prevalence of diabetes mellitus type II, psychiatric or neurological disease, headache, insomnia, or other laboratory measurements. Table
2 illustrates the characteristics of the groups and all related factors examined.
Table 2
Characteristics of 1420 GOAL participants who had (Yes) or had not (No) used opioids during one year
Pain |
Bodily Pain, mean (SD) | 77 (21) | 66 (25) | < 0.001* |
Intensity, mean (SD) | 72 (23) | 61 (26) | < 0.001* |
Interference, mean (SD) | 82 (22) | 71 (27) | < 0.001* |
Demographics |
Female sex, n (%) | 534 (56) | 250 (54) | 0.65 |
Age, mean (SD) | 71 (7) | 72 (7) | 0.16 |
Cohabiting, n (%) | 662 (69) | 321 (70) | 0.75 |
Education years, mean (SD) | 9.8 (3.2) | 9.6 (3.1) | 0.37 |
OECDsgrta, mean (SD) 1000€ | 1.8 (1.1) | 1.7 (0.6) | 0.40 |
Smoking, n (%) | 141 (15) | 68 (15) | 0.97 |
AUDIT-Cb, mean (SD) | 2.6 (2.2) | 2.6 (2.2) | 0.83 |
LTPAc, n(%) | | | 0.24 |
Low | 118 (12) | 71 (15) | |
Moderate | 689 (72) | 323 (71) | |
High | 148 (16) | 64 (14) | |
Clinical |
BMId, mean (SD) | 27.8 (4.8) | 28.4 (4.7) | 0.021* |
Obese (BMId ≥ 30), n (%) | 241 (26) | 139 (31) | 0.049* |
Waist cm, mean (SD) |
Female | 92 (14) | 94 (13) | 0.057 |
Male | 100 (11) | 103 (11) | 0.002* |
MetSe, n (%) | 385 (40) | 218 (47) | 0.009* |
Blood pressure mmHg, mean (SD) |
Systolic | 138 (21) | 137 (19) | 0.32 |
Diastolic | 83 (11) | 82 (10) | 0.10 |
Morbidity (diagnosed), n (%) |
Cardiovascular disease | 413 (43) | 225 (49) | 0.037* |
Diabetes mellitus type II | 101 (11) | 56 (12) | 0.35 |
Musculoskeletal disease | 330 (34) | 231 (50) | < 0.001* |
Pulmonary disease | 69 (7) | 55 (12) | 0.003* |
Psychiatric disease | 27 (3) | 22 (5) | 0.057 |
Neurological disease | 25 (3) | 16 (6) | 0.36 |
Neoplasm | 47 (5) | 44 (10) | < 0.001* |
Symptoms, n (%) |
Joint pain | 264 (28) | 182 (40) | < 0.001* |
Back pain | 235 (24) | 163 (35) | < 0.001* |
Neck pain | 253 (26) | 157 (34) | < 0.001* |
Headache | 109 (11) | 61 (13) | 0.30 |
Insomnia | 176 (18) | 91 (20) | 0.51 |
Depression | 40 (4) | 32 (7) | 0.025* |
Laboratory tests, mean (SD) |
Glucose, mMol/L | 5.66 (1.05) | 5.68 (1.01) | 0.72 |
Triglyceride, mMol/L | 1.26 (0.65) | 1.23 (0.59) | 0.47 |
HDL, mMol/L | 1.58 (0.46) | 1.55 (0.47) | 0.35 |
eGFR, mL/min/1.73 cm3 | 71 (14) | 71 (15) | 0.89 |
hsCRP, mg/L | 4.2 (4.0) | 5.4 (9.5) | 0.001* |
Uric acid, uMol/L | 334 (73) | 343 (79) | 0.042* |
Visited physician ≥3 times, n (%) | 282 (29) | 223 (48) | < 0.001* |
According to logistic regression, only pain level and a higher number of morbidities were found to independently associate with purchases of Level 1 (NSAIDs and paracetamol; 1.30 [1.04 to 1.63],
p = 0.020) and Level 2–3 analgesics (opioids; 1.53 [1.30 to 1.79],
p < 0.001). No significant association was found between analgesic purchases and the following variables: sex, age, education years, smoking, alcohol consumption, LTPA, or MetS (Table
3).
Table 3
Analgesic purchases and associated factors in 1420 GOAL participants. A multivariate logistic regression model
Pain levelsa | | < 0.001* | | < 0.001* |
Group I | 1.00 (Reference) | | 1.00 (Reference) | |
Group II | 0.54 (0.31 to 0.93) | | 0.56 (0.40 to 0.79) | |
Group III | 0.48 (0.28 to 0.82) | | 0.41 (0.29 to 0.58) | |
Group IV | 0.30 (0.17 to 0.53) | | 0.36 (0.24 to 0.53) | |
Male sex | 0.74 (0.53 to 1.01) | 0.060 | 1.13 (0.86 to 1.48) | 0.38 |
Age | 1.01 (0.99 to 1.03) | 0.45 | 0.99 (0.97 to 1.01) | 0.36 |
Education years | 1.01 (0.97 to 1.06) | 0.56 | 1.01 (0.97 to 1.04) | 0.78 |
Smoking | 1.21 (0.78 to 1.86) | 0.39 | 1.03 (0.72 to 1.46) | 0.87 |
AUDIT-Cb | 1.00 (0.93 to 1.08) | 0.92 | 1.00 (0.94 to 1.07) | 0.99 |
LTPAc | | 0.39 | | 0.34 |
Low | 1.00 (Reference) | | 1.00 (Reference) | |
Moderate | 1.33 (0.88 to 2.00) | | 0.86 (0.61 to 1.22) | |
High | 1.27 (0.76 to 2.14) | | 0.80 (0.51 to 1.25) | |
MetSd | 0.91 (0.67 to 1.22) | 0.52 | 1.09 (0.86 to 1.40) | 0.47 |
Number of morbiditiese | 1.30 (1.04 to 1.63) | 0.020* | 1.53 (1.30 to 1.79) | < 0.001* |
At the time of the questionnaire data collection, 16% of the participants in the opioid group reported no SF-36 pain of any intensity and 30% no SF-36 pain-related interference. Of all examined variables presented in Table
4, in addition to morbidities, MetS was the only factor to independently associate with opioid administration among these subjects (OR
intensity 1.99 [CI 1.10–3.60,
p = 0.022], OR
interference 1.60 [CI1.05–2.43,
p = 0.029]).
Table 4
Associated factors in opioid-purchasing participants with no SF-36 pain. A multivariate logistic regression model
Male sex | 0.88 (0.47 to 1.63) | 0.68 | 1.20 (0.78 to 1.86) | 0.40 |
Age | 0.99 (0.94 to 1.03) | 0.58 | 0.97 (0.94 to 1.01) | 0.095 |
Education years | 1.01 (0.92 to 1.10) | 0.88 | 1.03 (0.97 to 1.09) | 0.37 |
Smoking | 1.71 (0.77 to 3.80) | 0.19 | 1.08 (0.62 to 1.90) | 0.78 |
AUDIT-Ca | 0.95 (0.82 to 1.10) | 0.47 | 0.97 (0.88 to 1.08) | 0.62 |
LTPAb | | 0.99 | | 0.64 |
Low | 1 (Reference) | | 1 (Reference) | |
Moderate | 0.99 (0.43 to 2.32) | | 0.92 (0.50 to 1.69) | |
High | 1.00 (0.34 to 2.87) | | 0.83 (0.39 to 1.79) | |
MetSc | 1.99 (1.10 to 3.60) | 0.022* | 1.60 (1.05 to 2.43) | 0.029* |
Number of morbiditiesd | 1.84 (1.17 to 2.90) | 0.008* | 1.50 (1.08 to 2.07) | 0.015* |
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.