Association of Multimorbidity on Healthcare Expenditures Among Older United States Adults With Pain

Abstract

Objectives: This cross-sectional study compared the healthcare expenditures associated with multimorbidity (having ≥2 chronic conditions) versus no multimorbidity among older United States (US) adults (aged ≥ 50 years) with self-reported pain in the past 4 weeks. Methods: This research used data from the 2018 Medical Expenditure Panel Survey. Adjusted linear regression models evaluated group differences in various annual healthcare expenditures. Results: Descriptive statistics indicated multimorbidity was associated with all personal characteristics (p < 0.05) except gender and smoking status (p > 0.05). Multimorbidity had 75.8% greater annual total health expenditures (p = 0.0083), 40.6% greater office-based expenditures (p = 0.0224), 100.6% greater prescription medication costs, (p = 0.0268), yet 47.3% lower inpatient expenditures (p = 0.0158), and 56.6% lower home healthcare expenditures (p < 0.0001) than no multimorbidity. Discussion: This study found greater healthcare expenditures among older US adults with pain and multimorbidity, which captures the financial burden of comorbidity in this population.

Keywords

pain multimorbidity older adults health expenditures

Introduction

Multimorbidity may be defined as the presence of two or more chronic conditions in the same individual (Barnett et al., 2012; Fortin et al., 2012). Multimorbidity is highly prevalent among older adults in the United States (US) (Salisbury et al., 2011; Agency for Healthcare Research and Quality, 2014; Healthy Aging Team, 2017; Health Policy Data Requests - Percent of US Adults 55 and Over with Chronic Conditions, 2020). For example, the National Council on Aging estimated in 2017 that approximately 68% of US adults aged 65 years and older have multiple chronic conditions (Healthy Aging Team, 2017). Multimorbidity is positively associated with sociodemographic characteristics such as gender, socioeconomic status, and reduced functional status (Fortin et al., 2012; Van den Akker et al., 1998). In addition, the presence of multimorbidity in an individual has profound implications for healthcare expenditures (Lehnert et al., 2011). Chronic conditions are also among the leading causes of death and disability, resulting in $3.5 trillion of annual healthcare expenditures in the US in 2017 (Buttorff et al., 2017). Controlling the ever-rising healthcare expenditures associated with chronic conditions therefore remains a policy challenge in the United States (Fuster & Voûte, 2005; Norris et al., 2008).

One important chronic condition that warrants particular attention is pain. Population-based estimates of pain among US adults increased 25% over the past two decades with advanced age associated with a higher prevalence of pain (Nahin et al., 2019). The National Institute of Health estimated that 4–9% of Americans over the age of 65 years used a prescription pain relief opioid medication in 2018 (Lehmann & Fingerhood, 2018). Interviews conducted with 7601 US adults aged ≥ 65 years in a 2011 national survey showed that half of the older adults complained of bothersome pain in the last month (Patel et al., 2013). Other studies have demonstrated the burden of pain through the vast number of pain management strategies used (Axon et al., 2018, 2019). An estimated $560 billion per year is attributed to pain-related direct medical costs and lost productivity. Pain has been identified as a key factor related to increased physician visits, polypharmacy, and disability and thus leads to poorer quality of life and lower productivity (Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education, 2011).

Pain may arise from a multitude of different chronic diseases, with many older adults suffering from multimorbidity as well as pain (Butchart et al., 2009). Furthermore, around 22% of multimorbid patients suffer from pain-related morbidity such as chronic back problems, osteoarthritis, migraine, or chronic gastritis (Schäfer et al., 2010).

Although the negative effects of pain on health outcomes and health expenditures have been described previously (Dahlhamer et al., 2018; Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education, 2011), an increasing number of older adults with pain also have multiple chronic diseases (Baker et al., 2017). There are currently no up-to-date estimates of healthcare expenditures for older US adults with pain and multimorbidity. To address this gap in the literature, the objective of the current study was to determine differences in annual healthcare expenditures of older US adults with pain and multimorbidity versus those with no multimorbidity. We hypothesized that healthcare expenditures would be greater among older adults with pain and multimorbidity versus those with no multimorbidity.

Methods

Data Source

The Medical Expenditure Panel Survey (MEPS) is sponsored by the Agency for Healthcare Research and Quality and uses the sample of households drawn from the previous year’s National Health Interview Survey (Medical Expenditure Panel Survey Background, n.d.). MEPS involves a complex, stratified, panel design whereby clusters, strata, and population weights are provided to create nationally representative estimates for the US population and to account for the complex survey design (Medical Expenditure Panel Survey Background, n.d.). MEPS consists of two components: The Household Component (HC) and the Insurance Component (IC) (Medical Expenditure Panel Survey Background, n.d.). MEPS staff supplement the MEPS-HC data with data from the respondents’ medical providers (where available). This study used the publicly available 2018 MEPS full-year consolidated data file (the most recently released data at the time of the study). There were 30,461 subjects available in the 2018 MEPS full-year consolidated data file. This is a person-level file that collects detailed information during the household interview related to respondents’ demographic characteristics, health status, health conditions, healthcare use, expenditures, payment source, and health insurance coverage (Medical Expenditure Panel Survey Household Component Overview, 2019). No statistical imputation methodology was used to handle missing data. MEPS sampling weights reflect adjustments for survey nonresponse and adjustments to the national estimates. This study was deemed exempt by the University of Arizona Institutional Review Board.

Study Design and Population

This study used a cross-sectional design. Subjects were included if they were US adults aged 50 years or older, alive during the calendar year, with self-reported pain in the past 4 weeks, and positive total annual healthcare expenditures (i.e., those with zero or negative costs were excluded). Self-reported pain subjects were identified from their response to the item in the 2018 Self-Administered Questionnaire “During the past 4 weeks, pain interfered with normal work outside the home and housework.” Subjects were deemed to have pain if they responded “a little bit,” “moderately,” “quite a bit,” or “extremely,” while subjects were excluded if they responded “not at all” (Medical Expenditure Panel Survey Household Component Overview, 2019).

Primary dependent variable

The primary dependent variable was total annual per-person total healthcare expenditures. Total healthcare expenditures included hospital inpatient, hospital outpatient, office-based, emergency room, prescription drug, home health care, dental care, vision care, and other expenditures.

Secondary dependent variables

The secondary dependent variables were annual per-person expenditures for individual health service category: (1) hospital inpatient, (2) hospital outpatient, (3) office-based, (4) emergency room, (5) prescription medications, (6) home health care, (7) vision care, and (8) other expenditures (which included expenditures for dental care, medical supplies, and other equipment). Expenditure data in MEPS-HC are payments (amount paid for healthcare services) made by patients and payers and include direct payments for health care provided during the year by private insurance, Medicaid, Medicare, out-of-pocket, and other sources but not indirect payments or over-the-counter medication expenses (Medical Expenditure Panel Survey Household Component Overview, 2019).

Independent variable

The independent variable in this study was multimorbidity status (multimorbidity vs. no multimorbidity). Multimorbidity was defined as having ≥2 chronic conditions from the following list: hypertension, coronary heart disease, angina, myocardial infarction, other heart diseases, stroke, emphysema, chronic bronchitis, high cholesterol, cancer, diabetes, joint pain, arthritis, and asthma. These conditions are collected by MEPS because of their relatively high prevalence and because generally accepted standards for appropriate clinical care have been developed (Medical Expenditure Panel Survey Household Component Overview, 2019).

Covariates

The variables that were controlled for in adjusted analyses included age (50–64 years and ≥ 65 years), gender (male and female), race (white, Black, Asian/Pacific Islander/Native Hawaiian, and others), ethnicity (Hispanic and non-Hispanic), region (Northeast, Midwest, South, and West), marital status (single/never married, married, and widowed/divorced/separated), education (less than high school, high school diploma/General Educational Development Test, and bachelor’s or higher), employment (employed and unemployed), poverty status (poor/near poor, low income, middle income, and high income), health insurance (private, public, and uninsured), frequent exercise (yes or no), current smoker (yes or no), functional limitations (yes or no), social limitations (yes or no), activity of daily living (ADL) limitations (yes or no), instrumental activity of daily living (IADL) limitations (yes or no), perceived health status (excellent/very good, good, and fair/poor), perceived mental health status (excellent/very good, good, and fair/poor), and pain severity (little, moderate, and quite a bit/extreme).

Statistical analyses

Chi-square tests were used to compare descriptive characteristics between the multimorbidity and no multimorbidity groups. Differences in unadjusted mean healthcare expenditures were compared between the two groups using Student’s independent t-tests. Zero-value expenditures (indicating the people with no healthcare expenditure during the year) were removed to identify subjects with only positive healthcare expenditures for each of the abovementioned categories. The expenditure data were skewed and thus were logarithmically transformed to meet the normality assumption required for linear regression models. Ordinary least square (OLS) regressions were conducted on logged expenditures. Two models were developed for each category of healthcare expenditures. The unadjusted model contained only the multimorbidity and no multimorbidity variable (as the independent variable), while the adjusted model consisted of multimorbidity and no multimorbidity (as the independent variable) and all other covariates (described above). The assumptions of OLS regression such as independence of observations, homoscedasticity, linearity of parameters, and multicollinearity for the logarithmically transformed expenditures were verified. The logged expenditure variables had a variance inflation factor close to one that indicated no evidence of multicollinearity. Normality of the residuals was verified using the Q–Q plot, and homoscedasticity was confirmed by assessing a scatterplot of standardized residuals versus predicted values. Overall, the logarithmically transformed expenditure data met all assumptions of multivariable linear regression. Beta coefficients, estimated from OLS regression, were used to compute the percent difference in costs between the two groups (multimorbidity and no multimorbidity). Cluster, strata, and weight variables were used to adjust for the complex survey design of MEPS and to obtain nationally representative estimates (Medical Expenditure Panel Survey Computing Standard Errors for MEPS Estimates, n.d.). An a priori alpha of 0.05 was considered as the level of significance in all analyses. All analyses were conducted using survey procedures in SAS University Edition (SAS Institute Inc., Cary, NC, USA).

Results

Subject selection

After application of the study eligibility criteria, there were a total of 5123 subjects included in the analysis (Figure 1). This resulted in a weighted 2018 population of 57,134,711 older US adults with self-reported pain in the last 4 weeks, of which 46,996,409 or 82.3% (95% CI = 80.9%, 83.6%) were in the multimorbidity group (i.e., two or more comorbidities), and 10,138,302 or 17.7% (95% CI = 14.4%, 19.1%) were in the no multimorbidity group (i.e., less than two comorbidities).

Figure 1.

Study subject eligibility flowchart.

Subject characteristics

The distribution of characteristics in the multimorbidity and no multimorbidity groups is presented in Table 1. There were significant differences (p < 0.05) between the two groups for most characteristics except gender (p = 0.4766) and current smoker (p = 0.1191). Compared to the no multimorbidity group, the multimorbidity group had a greater proportion of individuals with the following characteristics: aged 65 years and older, white, non-Hispanic, from the south region, widowed/divorced/separated, had education less than high school, unemployed, poor/near poor poverty status, had public health insurance, no frequent exercise, had functional limitations, had social limitations, had ADL limitations, had IADL limitations, reported fair/poor perceived health status, reported fair/poor perceived mental health status, and had quite a bit/extreme pain.

Table 1.

Characteristics of United States Older Adults (Age ≥ 50 Years) With Self-Reported Pain in the Past 4 Weeks Stratified by Multimorbidity and No Multimorbidity in the 2018 Medical Expenditure Panel Survey Data.

	Multimorbidity (weighted N = 46,996,409)^a		No multimorbidity (weighted N = 10,138,302)^a
Characteristics	Weighed %	95% CI	Weighed %	95% CI	P
Age (years)					<0.0001
50–64	43.6	41.6–45.7	70.7	66.6–74.7
≥65	56.4	54.3–58.4	29.3	25.3–33.4
Gender					0.4766
Male	44.0	42.4–45.6	45.6	41.8–49.5
Female	56.0	54.4–57.6	54.4	50.5–58.2
Race					0.0012
White	81.2	79.4–83.0	79.9	76.9–82.9
Black	11.6	10.2–13.1	10.0	7.8–12.4
Asian/Pacific Islander/Native Hawaiian	4.1	2.9–5.2	7.5	5.6–9.5
Others	3.1	2.5–3.7	2.6	1.3–3.8
Ethnicity					0.0256
Hispanic	9.0	7.3–10.6	11.7	8.6–14.8
Non-Hispanic	91.0	89.4–92.7	88.3	85.2–91.4
Region					0.0258
Northeast	17.1	15.3–18.9	17.7	13.8–21.6
Midwest	21.6	19.5–23.7	23.8	19.6–27.9
South	39.6	37.4–41.7	33.2	28.9–37.5
West	21.7	19.7–23.8	25.4	21.3–29.4
Marital status					<0.0001
Single/never married	7.6	6.7–8.4	10.5	7.9–13.1
Married	55.5	53.5–57.5	62.1	57.6–66.6
Widowed/divorced/separated	36.9	35.0–38.8	27.4	23.8–31.0
Education					<0.001
Less than high school	12.9	11.5–14.3	7.9	6.0–9.8
High school diploma/GED	49.0	47.0–50.9	47.3	43.6–51.0
Bachelor’s or higher	38.1	36.2–40.1	44.8	41.0–48.6
Employment					<0.0001
Employed	34.6	32.0–37.2	64.0	59.5–68.6
Unemployed	65.4	62.8–68.0	36.0	31.4–40.5
Poverty status					<0.0001
Poor/near poor	17.6	16.1–19.2	13.2	10.5–15.8
Low income	14.7	13.3–16.1	11.5	9.0–14.0
Middle income	26.2	24.4–28.0	25.0	21.7–28.4
High income	41.4	39.1–43.8	50.3	45.8–54.8
Health insurance					<0.0001
Private	55.6	53.3–57.8	66.8	62.9–70.7
Public	42.3	40.0–44.6	25.9	22.1–29.6
Uninsured	2.1	1.5–2.7	7.3	5.1–9.5
Frequent exercise					<0.0001
Yes	39.7	37.6–41.9	52.6	48.6–56.7
No	60.3	58.1–62.4	47.4	43.3–51.4
Current smoker					0.1191
Yes	15.6	14.2–17.0	18.2	15.0–21.3
No	84.4	83.0–85.8	81.8	78.7–85.0
Functional limitations					<0.0001
Yes	44.1	42.1–46.1	17.7	14.8–20.5
No	55.9	53.9–57.9	82.3	79.5–85.2
Social limitations					<0.0001
Yes	17.2	15.5–19.0	6.7	4.6–8.8
No	82.8	81.0–84.5	93.3	91.2–95.4
ADL limitations					<0.0001
Yes	5.8	4.9–6.8	2.0	1.0–3.1
No	94.2	93.2–95.1	98.0	96.9–99.0
IADL limitations					<0.0001
Yes	10.4	9.3–11.5	3.0	1.9–4.2
No	89.6	88.5–90.7	97.0	95.8–98.1
Perceived health status					<0.0001
Excellent/very good	33.4	31.2–35.5	54.8	50.8–58.9
Good	36.6	34.7–38.5	34.2	30.2–38.1
Fair/poor	30.0	28.1–31.9	11.0	8.7–13.2
Perceived mental health status					<0.0001
Excellent/very good	50.6	48.5–52.7	62.4	58.0–66.8
Good	33.7	31.9–35.6	29.2	25.1–33.3
Fair/poor	15.7	14.5–16.9	8.4	6.3–10.4
Pain severity					<0.0001
Little	49.3	47.3–51.3	70.4	66.7–74.1
Moderate	22.6	21.1–24.1	16.9	14.0–19.9
Quite a bit/extreme	28.1	26.3–29.8	12.7	10.1–15.2

Abbreviations: % = percentage; CI = confidence interval; GED = General Educational Development Test; ADL = activity of daily living; IADL = instrumental activity of daily living.

Analyses were based on 5123 (unweighted) older United States adults (age ≥ 50 years) alive during the calendar year 2018 with self-reported pain in the past 4 weeks. p-values were obtained by performing a chi-square test between the multimorbidity group (defined as ≥ 2 chronic conditions; unweighted n=4293) and no multimorbidity group (defined as <2 chronic conditions; unweighted n=830).

^aWeighted numbers represent the projected number of individuals (i.e., national-level estimates).

Unadjusted mean annual healthcare expenditures

Differences in the unadjusted mean healthcare expenditures between the multimorbidity group and the no multimorbidity group are presented in Table 2. The annual unadjusted mean total healthcare expenditures were significantly higher among the multimorbidity group compared to the no multimorbidity group ($15,609 vs. $8,100, p < 0.0001). Compared to individuals with no multimorbidity, individuals with multimorbidity also had significantly higher office-based expenditures ($3816 vs. $2,568, p = 0.0002), prescription medication expenditures ($4233 vs. $2,369, p = 0.0038), and other expenditures ($1531 vs. $1,156, p = 0.0047). There were no statistically significant differences in hospital inpatient, hospital outpatient, emergency room, home health care, and vision care expenditures between the two groups (p > 0.05).

Table 2.

Mean Healthcare Expenditures of United States Older Adults (Age ≥ 50 Years) With Self-Reported Pain in the Past 4 Weeks Stratified by Multimorbidity and No Multimorbidity in the 2018 Medical Expenditure Panel Survey Data.

Type of healthcare service	Multimorbidity group (weighted N = 46,996,409)^a $		No multimorbidity group (weighted N = 10,138,302)^a $		p
	Mean	SE	Mean	SE
Total	15,609	504	8100	890	<0.0001
Hospital inpatient	21,519	1,431	21,848	4633	0.9446
Hospital outpatient	3643	241	3194	635	0.5108
Office-based	3816	144	2568	309	0.0002
Emergency room	1484	82	1935	294	0.1420
Prescription medications	4233	177	2369	634	0.0038
Home health care	8582	729	7519	2028	0.6186
Vision care	427	25	403	29	0.5119
Others^b	1531	66	1156	109	0.0047

Abbreviation: SE = standard error.

^aWeighted numbers represent the projected number of individuals (i.e., national-level estimates).

^bOther healthcare expenditures included dental, medical supplies, and equipment.

Adjusted annual healthcare expenditures

In adjusted analyses, total annual healthcare expenditures were 75.8% greater (β = 0.564, p = 0.0083) in the multimorbidity group than the no multimorbidity group. Compared to the no multimorbidity group, those with multimorbidity had 47.3% lower hospital inpatient expenditures (β = −0.640, p = 0.0158), 40.6% greater office-based expenditures (β = 0.340, p = 0.0224), 100.6% greater prescription medication expenditures (β = 0.696, p = 0.0268), and 56.6% lower home healthcare expenditures (β = −0.835, p < 0.0001) (Figure 2). There were no statistical differences between the multimorbidity and no multimorbidity groups for hospital outpatient (β = 0.426, p = 0.1140), emergency room (β = −0.514, p = 0.1055), vision care (β = −0.172, p = 0.4818), and other (β = 0.385, p = 0.0538) expenditures.

Figure 2.

Percent difference in annual adjusted healthcare expenditures of United States older adults (age ≥ 50 years) with pain in the past 4 weeks for multimorbidity versus no multimorbidity in the 2018 Medical Expenditure Panel Survey data. Analyses based on 5123 (unweighted) older US adults (age ≥ 50 years) alive during the calendar year 2018 with self-reported pain in the past 4 weeks but only includes those who had positive healthcare expenditures for each healthcare expenditure category. The adjusted model included the presence of multimorbidity (independent variable) and the following covariates: age, gender, race, ethnicity, region, marital status, education, employment, poverty status, health insurance, frequent exercise, current smoker, functional limitations, social limitations, activity of daily living limitations, instrumental activity of daily living limitations, perceived health status, perceived mental health status, and pain severity.

Discussion

The findings of this study, based on a nationally representative survey of the US civilian noninstitutionalized population, provide an analysis of healthcare expenditures associated with multimorbidity among older adults with self-reported pain in the last 4 weeks. In particular, older US adults with pain and multimorbidity had greater total annual healthcare expenditures, greater office-based expenditures, greater prescription medication expenditures, yet lower hospital inpatient and home healthcare expenditures than those with no multimorbidity. This is useful new information, given that there is limited literature available on healthcare expenditures among older adults with pain and multimorbidity.

Our results revealed that older US adults with pain and multimorbidity had greater total annual healthcare expenditures compared to those without multimorbidity. Higher total expenditures in multimorbid older adults have been observed in other studies although these studies were not conducted among individuals with pain specifically. For instance, in one US study, the mean expenditures in 1999 of individuals with four or more chronic conditions were $13,973 per capita (Wolff et al., 2002). In our study, the 2018 unadjusted mean total healthcare expenditures for multimorbid older adults with two or more chronic conditions were $15,609. The relatively greater mean cost in our study may be due to inflation over time (1999 vs. 2018 data) or differences in the study population (Medicare beneficiaries greater than 65 years old vs. adults greater than 50 years old with self-reported pain in our study). Another US study of Medicare beneficiary reported an adjusted 108.2% increase in 2005 average total cost associated with two chronic conditions (Schneider et al., 2009), which is higher than the 75.8% reported in our study. Our findings are also similar to those found in studies conducted outside the US: a cross-sectional survey of a nationally representative sample of people aged 60 years and older in Singapore found multimorbid respondents were associated with substantially higher healthcare expenditures (Picco et al., 2016), while a claims-based observational study among a community-dwelling population aged 65 years and older in Switzerland reported that the mean total healthcare expenditures were 5.5 times higher among those with multimorbidity compared to no multimorbidity (Bähler et al., 2015).

In our study, prescription medication expenditure was 100.6% higher in the multimorbidity group. This is supported by the findings of several studies whereby the mean annual costs for prescription medications were calculated within these studies (Fahlman et al., 2006; Moxey et al., 2003; Rogowski et al., 1997; Sambamoorthi et al., 2003), all of which found that spending significantly increased with each additional chronic condition. This is unsurprising, given that multimorbidity and polypharmacy are common among older adults (Gnjidic et al., 2012).

Our study also found that multimorbid patients had 40.6% greater office-based healthcare expenditures. This is unsurprising as multimorbid patients are more likely to consult specialists (Starfield et al., 2005). The consequences of multimorbidity include polypharmacy and repeated referrals for specialized care (Starfield et al., 2005). Outside the US, a claims-based study of adults ≥ 65 years in Switzerland found the mean number of office-based consultations per year was 15.7 in the multimorbid compared to 4.4 in the non-multimorbid group, and each additional chronic condition was associated with a 33% increase in expenditures (Bähler et al., 2015).

In contrast, for hospital inpatient care, a cross-sectional study using data from the 2010 US Veterans Affairs Health Care System showed that each additional body system affected by chronic conditions was associated with an average increase of $577 in inpatient expenditures (Zulman et al., 2015), while our study showed a 47.3% decrease in costs. Our study also found lower home healthcare expenditures among multimorbid patients versus those with no multimorbidity, which is interesting, given that multimorbid patients are more likely than others to require nursing home placement (Hartmann et al., 2011). Reasons to explain these unexpected findings on lower hospital inpatient and home health care are unknown and therefore warrant further investigation.

Aside from healthcare expenditures, this study also identified that the prevalence of multimorbidity among older adults with self-reported pain in 2018 was 82.3%. This represents a greater prevalence trend than that reported in a previous study that used US administrative claims data, which found that the prevalence of multimorbidity in 2008 was 67% (Salive, 2013) and supports the continuing increase in the multimorbid older adult population. The same study also reported that the prevalence of multimorbidity increased with age, from 62% in those aged 65–74 years to 81.5% in those aged ≥ 85 years (Salive, 2013). Another cross-sectional analysis using 2005–2012 National Health and Nutrition Examination Survey data among adults aged 65 years or older showed a multimorbidity prevalence of 67% (Jindai et al., 2016). A systematic review of 16 other community-based older adult prevalence studies reported a median prevalence of 63% (Salive, 2013). Outside the US, a study from the Irish health system found the prevalence of multimorbidity in primary care settings to be 66.2% among adults aged >50 years of age (Glynn et al., 2011). Reasons why the prevalence of multimorbidity was higher in our study versus others may be due to our more restricted study eligibility criteria (i.e., the individuals in our study all had pain in the past 4 weeks), more up-to-date data, variations in the definition of multimorbidity (type and number of chronic diseases included), and differences in study designs.

Strengths and Limitations

The strength of this study is the use of a nationally representative dataset that allows high generalizability of the findings and the inclusion of many variables in adjusted analyses, such as perceived physical and mental health status, ADL, IADL, and functional and social limitations, which could be independent predictors of healthcare expenditures among our study population. The study findings are therefore able to identify the economic burden of multimorbidity among the older US adult population with pain.

However, this cross-sectional analysis has a number of limitations. First, MEPS data on pain in the last 4 weeks are self-reported, which may lead to recall bias and nonresponse bias, and a consequent underreporting or overreporting of medical information. It was also not possible to determine the type of pain (e.g., acute vs. chronic pain), which could have influenced the results. Second, although several potential confounding variables are available in the MEPS dataset, there may be other unmeasured or unidentified covariates that could have influenced the study findings that were not accounted for. Healthcare expenditures may also have varied depending on the combinations of chronic conditions (i.e., an individual with two conditions may have different expenditures from another individual with two different conditions), as well as the severity and duration of the chronic diseases. Third, these findings are generalizable only to the civilian, noninstitutionalized population, as the dataset does not include individuals in institutions and nursing homes who may have different healthcare expenditures.

Clinical/policy implications

This study provides unique contributions to the pain literature by estimating mean healthcare expenditures associated with multimorbidity in total and for other different type of health service categories. These cost estimates may be of interest to policy makers, providers, and payers to prevent, control, and reduce comorbidities among older adults with pain.

Conclusions

The findings of this nationally representative cross-sectional study found that in 2018, older US adults with pain and multimorbidity had greater total, office-based, and prescription medication expenditures, yet lower hospital inpatient and home healthcare expenditures than those with no multimorbidity. Our results support the high total and prescription healthcare expenditures seen in multimorbid older adults with pain and highlight the need for better chronic disease management strategies and treatments.

Footnotes

Author Contributions

Conceptualization, David R Axon. and Srujitha Marupuru.; methodology, Srujitha Marupuru.; software, Srujitha Marupuru.; validation, David R Axon. and Srujitha Marupuru.; formal analysis, Srujitha Marupuru.; investigation, Srujitha Marupuru.; resources, Srujitha Marupuru.; data curation, Srujitha Marupuru.; writing—original draft preparation, David R Axon. and Srujitha Marupuru.; writing—review and editing, David R Axon.; visualization, David R Axon. and Srujitha Marupuru.; supervision, David R Axon.; project administration, David R Axon.; and funding acquisition, David R Axon. All authors have read and agreed to the published version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr. Axon has received research funding from the American Association of Colleges of Pharmacy, Arizona Department of Health Services, Pharmacy Quality Alliance, Merck & Co., and Tabula Rasa HealthCare Group outside of this study. Ms. Marupuru has received funding from Tabula Rasa HealthCare Group outside of this study.

Funding

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

ORCID iD

Srujitha Marupuru

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