Abstract
Objectives
Healthcare expenditures of older United States (US) adults with different pain severity levels are important to investigate given the increasing prevalence of pain in this population. This study assessed the correlation of healthcare expenditures among older US adults with different pain severities, hypothesizing that expenditures would increase as pain severity increased.
Methods
This retrospective cross-sectional database study used 2018 Medical Expenditure Panel Survey (MEPS) data and included US adults aged ≥50 with self-reported pain in the past four weeks and positive healthcare costs. Adjusted linear regression models with logarithmically transformed expenditure data compared differences in: total; office-based; outpatient; emergency room; inpatient; other; and prescription medication expenditures, between those with little, moderate, quite a bit, and extreme pain. Analyses were weighted to account for the complex MEPS design and to obtain nationally representative estimates. The a priori alpha level was 0.05.
Results
The eligible sample of 5,123 individuals produced a weighted study population of 57,134,711 US adults aged ≥50 with pain (little = 53.1%, moderate = 21.6%, quite-a-bit = 18.5%, extreme = 6.8%). In adjusted linear regression models, compared to little pain, extreme pain had the greatest level of costs, followed by quite a bit pain and moderate pain, for total, office-based, and prescription medication costs. For instance, compared to little pain, total healthcare costs were 78% greater for those with extreme pain, 51% greater for quite a bit pain, and 37% greater for moderate pain. However, this pattern was not observed for outpatient, emergency room, inpatient, and other costs, where ≥1 comparison for each cost category was not statistically significant.
Conclusions
This study found total healthcare costs increased as pain severity increased, yet this was not always the case when analyzing subcategories of healthcare costs. Further research is needed to investigate why some types of healthcare costs are greater with increasing pain severity, yet others are not.
Introduction
There are many definitions of pain, including the recent update from the International Association for the Study of Pain who define pain as “an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage” [1]. Determining a reliable estimate of pain prevalence in the United States (US) is challenging due to the variability of relevant data and definitions of pain [2]. For example, an Institute of Medicine report estimated that approximately 116 million US adults had chronic pain in 2011[2]. However, another study using the 2011 National Health Interview Survey (NHIS) estimated approximately 41 million (18.4%) of the adult population had chronic pain [3]. Data from the health and retirement study have indicated that the prevalence of pain in the US population has been increasing by 2–3% per year since 1992 and is likely to continue [4]. These increases in pain prevalence are even higher among older adults as increased age is a major risk factor for pain [5]. There are several types of pain, as recently indicated by International Classification of Disease (ICD)-11 codes [6], thus the nature of pain is important to consider when investigating pain. Studies have also shown that pain interferes with various activities, such as activities of daily living, ability to work, ability to concentrate, participate in leisure activities or social activities, and maintain relationships with others [7], [8], [9].
Pain severity is another factor that must be considered when investigating pain. A recent systematic review found that as a patient’s intensity of pain increased their Health-Related Quality of Life decreased [10]. Another study that used 2012 NHIS data concluded that individuals in the more severe pain categories had a higher probability of being in a poorer health state, to report being exhausted every day, taking medications to treat depression, and to be consistently worried, nervous, or anxious every day [11]. Meanwhile, a further study used Medical Expenditure Panel Survey (MEPS) data to identify factors that were associated with pain severity among older adults [12].
Estimates of the cost of pain also vary in the literature. For example, lost productivity from missed work due to pain amounted to $11.6 to $12.7 billion per year, and pain as a whole cost $560 to $635 billion per year in 2010 US dollars [2]. Another study using 2008 MEPS data found that individuals with severe pain had higher total and certain types of other healthcare service expenditures compared to those with low and moderate pain [13].
However, this study included adults aged 18 years and older [13]. There is a lack of data on how healthcare expenditures vary by pain severity among older adults with pain, which this current study sought to address. The objective of this study was to assess the correlation of self-reported pain severity and healthcare expenditures among older US adults with pain, using adjusted models to account for additional factors that may influence pain severity in this population.
Methods
Study design & eligibility
This study used a retrospective cross-sectional database design. Eligible participants were civilian, noninstitutionalized, US adults aged 50 years and older, alive for the full 2018 calendar year, with self-reported pain in the past four weeks that interfered with normal work inside and outside the home [14].
Data source
The 2018 MEPS data were collected through rounds 3, 4, and 5 of Panel 22 and rounds 1, 2, and 3 of Panel 23 (the most current data available at the time of the study) and compiled into the publicly available MEPS 2018 Full Year Consolidated Data File (HC-209). MEPS participants are selected based on the sampling framework of the National Health Interview Survey, where they identify civilian, noninstitutionalized individuals with specific characteristics living in the US to participate in the survey. One of the main MEPS components, the Household Component (MEPS-HC), collects self-reported data for all household members including demographic characteristics, health conditions and health status, prescription medication use, health insurance, income, and healthcare expenditures. MEPS staff use data from another MEPS component, the Medical Provider Component (MEPS-MPC), to supplement data collected in MEPS-HC to improve the reliability and validity of the data. Expenditure data included direct medical expenditures and the sum of direct payments for various healthcare services used during the year 2018, which included payments from private insurance, Medicaid, Medicare, out of pocket costs, and other sources [15, 16].
Dependent (expenditure) variables
The primary dependent variable was total healthcare expenditures for subjects for the 2018 year. The secondary dependent variables were office-based, outpatient, emergency room, inpatient, other, and prescription medication expenditures [15, 16].
Independent (pain severity) variable
The independent variable was pain severity, classified as little, moderate, quite a bit, or extreme pain, based on responses to an item that asked: “During the past 4 weeks, how much did pain interfere with your normal work (including both work outside the home and housework)?” [15, 16].
Control variables
Control variables included census region, age; gender; race; ethnicity; marital status; education completed; perceived health status; perceived mental health status; presence of a limitation, moderate/vigorous exercise 5×/week; smoking status; employment status; poverty (income) level; health insurance coverage; chronic conditions [15, 16].
Data analysis
Analyses were conducted using PROC SURVEY commands in SAS (SAS Institute Inc., Cary, NC, USA). Descriptive/demographic variables were compared between levels of self-reported pain (little, moderate, quite a bit, extreme) using chi-square tests. Unadjusted and adjusted linear regression models were used for each healthcare expenditure category (total, office-based, outpatient, emergency room, inpatient, other, and prescription medication expenditures). The unadjusted model contained only the independent variable pain severity (little, moderate, quite a bit, extreme) whereas the adjusted model included the independent variable and all control variables mentioned above. The referent group for all analyses was little pain. Analyses were weighted appropriately to account for the MEPS complex survey design and to obtain nationally representative estimates. Percent difference between pain levels was calculated using semi-logarithmic equations. The a priori alpha level for statistical significance was 0.05.
Results
Figure 1 shows the study subject eligibility flowchart. There were 30,461 individuals included in the MEPS 2018 full year consolidated data file. Of these individuals, 5,123 met the inclusion criteria of the study (civilian, noninstitutionalized, US adults aged 50 years and older, alive for the full 2018 calendar year, with self-reported pain in the past four weeks that interfered with normal work inside and outside the home). Of the subjects included, 382 (6.8%) reported extreme pain, 1,035 (18.5%) reported quite a bit of pain, 1,121 (21.6%) reported moderate pain, and 2,585 (53.1%) reported a little bit of pain.
Table 1 reports the characteristics of the study population stratified by the level of self-reported pain (little, moderate, quite a bit, extreme). The majority of participants were over the age of 65, female, white, non-Hispanic, were married, completed more than high school education, reported excellent/very good/good perceived health and mental health status, indicated the presence of limitation, reported not doing regular moderate/vigorous exercise, were non-smokers, unemployed, middle/high income, private health insurance coverage, and had less than five chronic conditions. Subjects most commonly resided in the Southern census region. There were significant differences (p<0.05) between all variables except census region, gender, and ethnicity.
Characteristics | Little pain (N=382) |
Moderate pain (N=1,035) |
Quite a bit of pain (N=1,121) |
Extreme pain (N=2,585) |
p-Value | |
---|---|---|---|---|---|---|
% (95% confidence limit) | % (95% confidence limit) | % (95% confidence limit) | % (95% confidence limit) | |||
Census region | ||||||
1 | Northeast | 17.3 (14.7, 19.9) |
18.5 (15.9, 21.2) |
15.1 (12.6, 17.7) |
18.3 (13.3, 23.4) |
0.06 |
2 | Midwest | 23.1 (20.1, 26.1) |
22.1 (18.9, 25.4) |
20.5 (16.9, 24.1) |
16.9 (11.3, 22.5) |
|
3 | South | 36.5 (33.6, 39.3) |
37.5 (33.9, 41.1) |
44.2 (40.1, 48.4) |
41.0 (34.7, 47.3) |
|
4 | West | 23.1 (20.4, 25.9) |
21.9 (19.0, 24.8) |
20.1 (16.6, 23.7) |
23.8 (18.7, 28.9) |
|
Age, years | ||||||
1 | 50–64 | 51.9 (49.2, 54.6) |
40.8 (37.1, 44.6) |
45.1 (41.5, 48.7) |
54.6 (49.0, 60.1) |
<0.0001 |
2 | ≥65 | 48.1 (45.4, 50.8) |
59.2 (55.4, 62.9) |
54.9 (51.3, 58.5) |
45.4 (39.9, 51.0) |
|
Gender | ||||||
1 | Male | 45.8 (43.8, 47.8) |
44.0 (40.7, 47.3) |
42.8 (39.0, 46.6) |
38.1 (32.3, 43.9) |
0.09 |
2 | Female | 54.2 (52.2, 56.2) |
56.0 (52.7, 59.3) |
57.2 (53.4, 61.0) |
61.9 (56.1, 67.7) |
|
Race | ||||||
1 | White | 81.3 (79.2, 83.5) |
83.7 (81.1, 86.2) |
77.8 (74.3, 81.3) |
77.4 (72.0, 82.9) |
0.02 |
2 | Other | 18.7 (16.5, 20.8) |
16.3 (13.8, 18.9) |
22.2 (18.7, 25.7) |
22.6 (17.1, 28.0) |
|
Ethnicity | ||||||
1 | Hispanic | 8.9 (7.2, 10.6) |
9.3 (6.9, 11.7) |
9.9 (7.4, 12.4) |
12.9 (8.4, 17.4) |
0.11 |
2 | Non-hispanic | 91.1 (89.4, 92.8) |
90.7 (88.3, 93.1) |
90.1 (87.6, 92.6) |
87.1 (82.6, 91.6) |
|
Marital status | ||||||
1 | Married | 60.8 (58.6, 63.0) |
55.1 (51.6, 58.5) |
51.3 (47.0, 55.6) |
44.7 (38.6, 50.8) |
<0.0001 |
2 | Other | 39.2 (37.0, 41.4) |
44.9 (41.5, 48.4) |
48.7 (44.4, 53.0) |
55.3 (49.2, 61.4) |
|
Education completed | ||||||
1 | High school or less | 39.4 (36.7, 42.1) |
44.2 (40.4, 48.0) |
55.9 (52.0, 59.7) |
61.4 (55.0, 67.7) |
<0.0001 |
2 | More than high school | 60.6 (57.9, 63.3) |
55.8 (52.0, 59.6) |
44.1 (40.3, 48.0) |
38.6 (32.3, 45.0) |
|
Perceived health status | ||||||
1 | Excellent/very good/good | 86.9 (85.3, 88.5) |
72.3 (69.4, 75.2) |
50.8 (46.6, 55.0) |
32.9 (27.0, 38.9) |
<0.0001 |
2 | Fair/poor | 13.1 (11.5, 14.7) |
27.7 (24.8, 30.6) |
49.2 (45.0, 53.4) |
67.1 (61.1, 73.0) |
|
Perceived mental health status | ||||||
1 | Excellent/very good/good | 92.6 (91.4, 93.9) |
84.0 (81.4, 86.5) |
74.1 (71.0, 77.3) |
67.6 (61.7, 73.5) |
<0.0001 |
2 | Fair/poor | 7.4 (6.1, 8.6) |
16.0 (13.5, 18.6) |
25.9 (22.7, 29.0) |
32.4 (26.5, 38.3) |
|
Presence of a limitation | ||||||
1 | Yes | 32.8 (30.4, 35.1) |
61.1 (57.2, 65.0) |
78.0 (74.6, 81.4) |
84.7 (79.7, 89.7) |
<0.0001 |
2 | No | 67.2 (64.9, 69.6) |
38.9 (35.0, 42.8) |
22.0 (18.6, 25.4) |
15.3 (10.3, 20.3) |
|
Moderate/vigorous exercise 5×/week | ||||||
1 | Yes | 49.3 (46.8, 51.8) |
38.4 (34.5, 42.3) |
31.0 (27.5, 34.6) |
26.4 (21.4, 31.4) |
<0.0001 |
2 | No | 50.7 (48.2, 53.2) |
61.6 (57.7, 65.5) |
69.0 (65.4, 72.5) |
73.6 (68.6, 78.6) |
|
Smoking status | ||||||
1 | Smoker | 13.3 (11.8, 14.7) |
16.8 (14.0, 19.6) |
21.0 (18.0, 24.0) |
21.7 (17.3, 26.0) |
<0.0001 |
2 | Non-smoker | 86.7 (85.3, 88.2) |
83.2 (80.4, 86.0) |
79.0 (76.0, 82.0) |
78.3 (74.0, 82.7) |
|
Employment status | ||||||
1 | Employed | 52.5 (49.6, 55.3) |
31.5 (27.8, 35.2) |
22.5 (18.6, 26.4) |
14.9 (9.7, 20.2) |
<0.0001 |
2 | Unemployed | 47.5 (44.7, 50.4) |
68.5 (64.8, 72.2) |
77.5 (73.6, 81.4) |
85.1 (79.8, 90.3) |
|
Poverty (Income) level | ||||||
1 | Poor/near poor/low | 23.2 (21.4, 25.1) |
32.5 (29.3, 35.8) |
41.9 (37.5, 46.2) |
56.7 (50.3, 63.1) |
<0.0001 |
2 | Middle/high | 76.8 (74.9, 78.6) |
67.5 (64.2, 70.7) |
58.1 (53.8, 62.5) |
43.3 (36.9, 49.7) |
|
Health insurance coverage | ||||||
1 | Private | 67.0 (63.7, 70.2) |
52.9 (49.1, 56.7) |
44.4 (40.4, 48.3) |
35.4 (29.3, 41.6) |
<0.0001 |
2 | Public | 30.0 (26.9, 33.1) |
43.6 (39.6, 47.5) |
53.1 (49.2, 57.0) |
62.3 (56.3, 68.3) |
|
3 | Uninsured | 3.1 (2.4, 3.7) |
3.6 (1.6, 5.5) |
2.5 (1.4, 3.7) |
2.3 (0.7, 3.8) |
|
Chronic conditions | ||||||
1 | ≥5 | 15.6 (13.9, 17.2) |
30.0 (26.2, 33.9) |
35.5 (31.4, 39.6) |
42.2 (37.0, 47.4) |
<0.0001 |
2 | 0–4 | 84.4 (82.8, 86.1) |
70.0 (66.1, 73.8) |
64.5 (60.4, 68.6) |
57.8 (52.6, 63.0) |
-
Analyses based on 5,123 civilian, noninstitutionalized, US adults aged 50 years and older, alive for the full 2018 calendar year, with self-reported pain in the past four weeks that interfered with normal work inside and outside the home. Differences between groups based on chi-square tests.
Table 2 presents unadjusted and adjusted differences in healthcare expenditures between each level of self-reported pain for each category of healthcare expenditure. In the adjusted analyses, extreme pain had the greatest difference in costs compared to little pain, followed by quite a bit of pain and then moderate pain for total, office-based, and prescription medication expenditures. For outpatient expenditures, quite a bit of pain was greater than little pain, yet extreme pain or moderate pain was not significantly greater than little pain. For inpatient expenditures, extreme pain was greater than little pain and moderate pain was greater than little pain, yet quite a bit of pain was not significantly greater than little pain. For other expenditures, extreme pain and quite a bit of pain were greater than little pain, yet moderate pain was not significantly greater than little pain.
Healthcare expenditure | Unadjusted | Adjusted | ||||
---|---|---|---|---|---|---|
Beta (standard error) | p-Value | Percent change | Beta (standard error) | p-Value | Percent change | |
Total | ||||||
Intercept | 8.3 (0.03) |
<0.0001 | 7.3 (0.20) |
<0.0001 | ||
Extreme pain | 1.0 (0.07) |
<0.0001 | 182.6 | 0.6 (0.08) |
<0.0001 | 78.1 |
Quite a bit pain | 0.8 (0.06) |
<0.0001 | 112.8 | 0.4 (0.06) |
<0.0001 | 50.6 |
Moderate pain | 0.6 (0.06) |
<0.0001 | 76.9 | 0.3 (0.05) |
<0.0001 | 37.3 |
Little pain | Reference | Reference | ||||
Office-based | ||||||
Intercept | 7.1 (0.03) |
<0.0001 | 6.3 (0.21) |
<0.0001 | ||
Extreme pain | 0.4 (0.08) |
<0.0001 | 52.7 | 0.3 (0.09) |
0.01 | 39.6 |
Quite a bit pain | 0.3 (0.07) |
<0.0001 | 38.2 | 0.2 (0.06) |
0.01 | 26.4 |
Moderate pain | 0.2 (0.06) |
<0.0001 | 24.9 | 0.1 (0.05) |
0.04 | 11.1 |
Little pain | Reference | Reference | ||||
Outpatient | ||||||
Intercept | 6.8 (0.06) |
<0.0001 | 6.4 (0.46) |
<0.0001 | ||
Extreme pain | 0.3 (0.15) |
0.09 | 0.3 (0.17) |
0.05 | ||
Quite a bit pain | 0.2 (0.10) |
0.03 | 24.4 | 0.3 (0.11) |
0.01 | 35.7 |
Moderate pain | 0.2 (0.10) |
0.12 | 0.2 (0.12) |
0.08 | ||
Little pain | Reference | Reference | ||||
Emergency room | ||||||
Intercept | 6.7 (0.08) |
<0.0001 | 6.0 (0.41) |
<0.0001 | ||
Extreme pain | −0.2 (0.10) |
0.10 | −0.2 (0.17) |
0.37 | ||
Quite a bit pain | −0.1 (0.10) |
0.23 | −0.01 (0.11) |
0.91 | ||
Moderate pain | −0.2 (0.10) |
0.18 | −0.1 (0.13) |
0.61 | ||
Little pain | Reference | Reference | ||||
Inpatient | ||||||
Intercept | 9.1 (0.12) |
<0.0001 | 7.1 (0.61) |
<0.0001 | ||
Extreme pain | 0.4 (0.22) |
0.08 | 0.5 (0.22) |
0.03 | 60.0 | |
Quite a bit pain | −0.05 (0.16) |
0.77 | 0.04 (0.16) |
0.76 | ||
Moderate pain | 0.3 (0.17) |
0.12 | 0.3 (0.16) |
0.04 | 39.5 | |
Little pain | Reference | Reference | ||||
Other | ||||||
Intercept | 6.6 (0.04) |
<0.0001 | 6.5 (0.22) |
<0.0001 | ||
Extreme pain | 0.7 (0.11) |
<0.0001 | 101.8 | 0.4 (0.12) |
0.01 | 53.2 |
Quite a bit pain | 0.4 (0.08) |
<0.0001 | 52.8 | 0.2 (0.08) |
0.02 | 20.7 |
Moderate pain | 0.3 (0.07) |
0.01 | 28.8 | 0.1 (0.08) |
0.32 | |
Little pain | Reference | Reference | ||||
Prescription medications | ||||||
Intercept | 6.4 (0.05) |
<0.0001 | 5.3 (0.28) |
<0.0001 | ||
Extreme pain | 1.1 (0.12) |
<0.0001 | 202.8 | 0.3 (0.11) |
0.02 | 31.5 |
Quite a bit pain | 0.9 (0.09) |
<0.0001 | 148.9 | 0.3 (0.09) |
0.01 | 34.7 |
Moderate pain | 0.7 (0.10) |
<0.0001 | 92.0 | 0.3 (0.09) |
0.01 | 29.1 |
Little pain | Reference | Reference |
-
Analyses based on 5,123 (un-weighted) civilian, noninstitutionalized, US adults aged 50 years and older, alive for the full 2018 calendar year, with self-reported pain in the past four weeks that interfered with normal work inside and outside the home, but only includes those who had positive healthcare expenditures for each healthcare expenditure category. Unadjusted: Total N=4,965; Office-based N=4,718; Outpatient N=1,912; Emergency room N=1,233; Inpatient N=826; Other N=36,746; Prescription medications N=4,669. Adjusted: Total N=4,904; Office-based N=4,667; Outpatient N=1,895; Emergency room N=1,220; Inpatient N=817; Other N=3,636; Prescription medications N=4,613. Models adjusted for the following variables: census region, age, gender, race, ethnicity, marital status, education completed, perceived health status, perceived mental health status, presence of a limitation, Moderate/vigorous exercise 5x/week, smoking status, employment status, poverty (income) level, health insurance coverage, and chronic conditions. Percent change was not calculated for variables that were not significant.
Discussion
This study aimed to assess the correlation of self-reported pain severity and adjusted healthcare expenditures among older US adults with pain. The key finding from this study was that a pattern emerged for the correlation of pain severity and adjusted healthcare expenditures for total, office-based, and prescription medication expenditures. For these expenditures, relative to little pain, extreme pain had the greatest difference in expenditures, followed by quite a bit of pain, followed by moderate pain. However, this pattern was not consistent for outpatient, inpatient, and other healthcare expenditures (where there were instances of no statistical correlation between pain severity and expenditures for some pain severity levels), and emergency room expenditures (where there was no statistical correlation between pain severity and expenditures). These findings provide new information to the literature, which is useful given the lack of contemporary existing studies that have investigated pain severity and healthcare expenditures. These findings will be further discussed below.
The finding that adjusted total, office-based, and prescription medication expenditures were greater with greater levels of pain severity is an expected finding that is unsurprising from a clinical perspective (i.e., those with more severe pain are likely to have overall greater use of the healthcare system, including more office-based visits and greater use of prescription medications, as they seek to manage their pain). A previous study using data from the Mint (minimal invasive treatment) study [17] found that pain severity was significantly and positively correlated with total healthcare expenditures in both unadjusted and adjusted analyses [18], which parallel the findings from the current study.
The finding that adjusted outpatient, inpatient, and other healthcare expenditures were not always associated with greater levels of pain severity is perhaps more surprising. In the case of adjusted outpatient expenditures, quite a bit of pain was correlated with greater expenditures than little pain, yet extreme pain and moderate pain were not. Whereas, in the case of adjusted inpatient expenditures, extreme pain and moderate pain were correlated with greater expenditures than little pain, yet quite a bit of pain was not. For adjusted other healthcare expenditures, extreme pain and quite a bit of pain were correlated with greater expenditures than little pain, yet moderate pain was not. In addition, there was no correlation between pain severity and emergency room expenditures. A previous study using 2008–2011 MEPS data found a similar pattern to the findings in the current study for outpatient expenditures and other healthcare expenditures [13]. Interestingly, the findings for inpatient expenditure in the 2008–2011 MEPS increased with increasing pain severity [13], more akin to the findings for the total, office-based, and prescription medications in the current study. However, there were differences in the definition of pain severity and differences in the variables adjusted for in analyses between the two studies, which likely contributed to the different results observed. With regards to the emergency room expenditures, emergency room utilization is typically low (a previous study found 18% of US adults aged 18–64 used the emergency room at least one or more times in 2014) [19], which may not be influenced by pain severity. Future research is needed to understand why some types of healthcare expenditures are positively correlated with greater pain severity levels, yet others are not.
The current study found most of the personal characteristics (outlined in Table 1) were associated with pain severity. This is perhaps an expected finding, given that many of these characteristics have been shown to be associated with pain in other studies. For instance, in keeping with the findings of this study, previous research has demonstrated that individuals had greater odds of reporting quite a bit or extreme pain if they were aged 50–64 vs. ≥65 years, other vs. white race, married vs. other marital status, had fair/poor vs. excellent/very good/good perceived health, had a limitation vs. no limitation, did not do regular exercise vs. did regular exercise, were smokers vs. non-smokers, unemployed vs. employed, and had a lower vs. higher income [12]. These characteristics should therefore be considered by researchers investigating pain and clinicians helping individuals manage their pain.
The findings of the current study suggest the need for interventions to help reduce pain severity, or prevent pain from becoming severe in the first place, which in turn may help to reduce healthcare expenditures (although no causal inference can be assumed based on the study design). This may be done through improving or optimizing pain management strategies. For instance, studies have identified that individuals with pain often use a variety of pain management strategies beyond prescription medications [8, 20]. Some organizations use enhanced recovery after surgery (ERAS) techniques to help reduce the time to discharge. ERAS uses multimodal techniques to help enhance pain control, including adjunctive non-pharmacological methods such as acupuncture, music therapy, transcutaneous electrical nerve stimulation, and hypnosis [21]. Improving pain management and reducing pain severity may have implications beyond healthcare expenditures, for instance, one study found that those with lower pain severity reported improved quality of life [22].
The findings of this study represent civilian, noninstitutionalized, US adults aged 50 years and older with self-reported pain in the past four weeks that interfered with normal work inside and outside the home. However, limitations of this study included the retrospective nature of the study design, which prohibited the determination of a cause-and-effect relationship between pain severity and healthcare expenditures. Another limitation of this study is the use of self-reported pain in the past four weeks, which varies from other definitions of pain in the literature and may be subject to recall bias. Pain is also a subjective condition and therefore one of the more difficult healthcare conditions to assess objectively.
Conclusions
This study found that total, office-based, and prescription medication healthcare expenditures increased as self-reported pain severity increased, yet this pattern was not consistent when analyzing other subcategories of healthcare expenditures (i.e., outpatient, inpatient, emergency room, and other healthcare expenditures). These findings suggest that reducing pain severity, or preventing pain from becoming severe, could improve healthcare expenditures. Future research is needed to understand why some types of healthcare expenditures are positively correlated with greater pain severity levels, yet others are not, and should evaluate clinical or public health interventions to reduce and prevent pain.
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Research funding: Authors state no funding involved.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: Authors state not conflict of interest.
-
Informed consent: Informed consent has been obtained from all individuals included in this study.
-
Ethical approval: Research involving human subjects complied with all relevant national regulations, institutional policies and is in accordance with the tenets of the Helsinki Declaration (as amended in 2013), and has been approved by The University of Arizona Institutional Review Board (2006721124, June 12, 2020).
References
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