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RESEARCH FINDINGS #45:
Average Annual Morphine Milligram Equivalents (MMEs) of Outpatient Prescription Opioids and Share of Total MMEs among Elderly and Non-Elderly Adults in 2015–2016

November 2019

Asako S. Moriya, PhD and G. Edward Miller, PhD

Highlights

Overall in 2015–2016, non-elderly adults in the United States obtained an annual average of 376 milligram morphine equivalents (MMEs), and elderly adults obtained an annual average of 774 MMEs of outpatient prescription opioids to treat chronic and acute pain.
In 2015–2016, individuals reported to be in fair or poor health comprised about one-tenth (11.1 percent) of the non-elderly adult population and one-fifth (21.3 percent) of the elderly population, but accounted for about three-fifths of total MMEs acquired by each group (61.4 and 58.2 percent, for non-elderly adults and the elderly, respectively).
Among non-elderly adults in 2015–2016, subgroups with shares of total MMEs that were significantly higher than their population shares included non-Hispanic whites (78.5 versus 60.3 percent), those who were not employed (76.2 versus 25.3 percent), those with incomes below the federal poverty line (28.2 versus 11.9 percent), those with public disability-related coverage (39.0 versus 3.9 percent) and those who were in fair or poor mental health (34.8 versus 7.0 percent).
Among elderly adults in 2015–2016, subgroups with shares of total MMEs that were significantly higher than their population shares included those who were low income (34.6 versus 21.9 percent), those with both Medicare and other public insurance (16.8 versus 11.1 percent), those in fair or poor mental health (32.1 versus 12.6 percent), and those living in non-MSAs (28.6 versus 17.6 percent).

Introduction

Examining patterns of use of outpatient prescribed opioids can contribute to efforts to make appropriate use of these drugs in the treatment of acute and chronic pain. The number of outpatient prescription fills acquired during the year is a common metric to compare levels of opioid use across individuals.¹ Fills, however, vary in terms of which opioid is prescribed, the quantity of pills or other units (e.g., patches) dispensed, and the strength (e.g., milligrams or micrograms) per unit. Morphine milligram equivalents (MMEs) provide a useful measure that combines all of these dimensions in a single, standardized measure of the potency of each fill. MMEs can also be summed across fills to calculate total annual MMEs for each individual.

This Research Findings report presents estimates from the 2015 and 2016 Medical Expenditure Panel Survey Household Component (MEPS-HC) of average annual MMEs of outpatient prescription opioids acquired by adults (age 18 and older) in the U.S. civilian noninstitutionalized population. We include all opioid medications that are commonly used to treat pain, but exclude respiratory agents, antitussives, and drugs commonly used in Medication Assisted Treatment. Only prescription fills purchased or obtained in an outpatient setting are included in these estimates. Prescription medicines administered in an inpatient setting or in a clinic or physician's office are excluded.

We present estimates for all non-elderly adults (ages 18–64) and all elderly adults (age 65 and older) and for subgroups of each adult population defined by demographic, socioeconomic, health, and geographic characteristics. We present three types of estimates. First, we present an estimate of average annual MMEs for each subgroup. These estimates include individuals with no opioid use during the year, so differences across subgroups reflect differences in the probability of any opioid use, as well as levels of use conditional on any use. Second, we present estimates of each subgroup's share of the non-elderly (or elderly) adult population and third, we present each subgroup's share of total MMEs. These estimates allow us to identify subgroups with disproportionately high, or low, shares of opioid use and provide information on the concentration of opioid use in the non-elderly and elderly adult populations. All differences mentioned in the text are significant at the p<.05 level, or better.²

Findings

Demographic characteristics
Overall and by sex
Overall in 2015–2016, the 197.6 million non-elderly adults (ages 18–64) in the U.S. civilian noninstitutionalized population obtained an annual average of 376 MMEs of outpatient prescription opioids commonly used to treat chronic and acute pain (figure 1a). There was no significant difference in average annual MMEs between men and women.

The 50.5 million elderly adults (age 65 and older) in the U.S. civilian noninstitutionalized population acquired an annual average of 774 MMEs of outpatient opioids (figure 1b) with no significant difference in average annual MMEs between men and women.

Age
Among non-elderly adults in 2015–2016, average annual MMEs increased in a step-by-step fashion across age categories from ages 18–26 (49 MMEs), to ages 27–34 (128), to ages 35–44 (321), to ages 45–54 (524), and finally to ages 55–64 (794) (figure 2a). Together, the two oldest age categories, 45–54 and 55–64, comprised a little more than two-fifths (42.5 percent) of the non-elderly adult population, but acquired nearly three-quarters (74.3 percent) of total MMEs. Conversely, the two youngest age categories, 18–26 and 27–34, comprised 37.2 percent of the population but obtained only 8.5 percent of total MMEs.

There were no significant differences in average annual MMEs among elderly adults age 65–74, 75–84, and 85 and older (figure 2b).

Race/Ethnicity
Among non-elderly adults, non-Hispanic whites (490) acquired more MMEs of outpatient prescription opioids, on average, than any other racial-ethnic group (figure 3a). Non-Hispanic blacks (327) had higher average annual MMEs than Hispanics (135) and persons of other racial-ethnic groups (170). As a result, the share of total opioids acquired by non-Hispanic whites (78.5 percent), was significantly larger than their population share (60.3 percent). Conversely, the share of opioids obtained by Hispanics and persons of other racial-ethnic groups (6.4 and 4.3 percent, respectively) was smaller than their respective shares of the non-elderly adult population (17.8 and 9.4 percent).

In 2015–2016, elderly non-Hispanic whites obtained more MMEs, on average, than Hispanics and persons of other racial-ethnic groups (figure 3b). The share of total MMEs obtained by non-Hispanic whites (83.0 percent) was larger than their share of the population (76.3 percent) while MME shares were smaller than the population shares for Hispanics (5.1 versus 8.2 percent) and persons of other racial-ethnic groups (3.3 versus 6.6 percent) (figure 3b).

Marital status
There were no significant differences by marital status in average annual MMEs of outpatient opioids acquired by non-elderly adults in 2015–2016 (figure 4a).

Among elderly adults, those who were not married had higher average annual MMEs (979) and obtained a share of total MMEs (55.2 percent) that was larger than their share of the population (43.6 percent) (figure 4b). Married elderly adults had lower average annual MMEs (615) and obtained a share of total MMEs (44.8 percent) that was lower than their share of the population (56.4 percent).

Socioeconomic status
Poverty status
Among non-elderly adults in 2015–2016, average annual MMEs were highest for those with family incomes below the federal poverty line (895) and fell as levels of income increased to low income (600), middle income (304), and high income (206) (figure 5a). Accordingly, shares of total MMEs were larger than population shares for those with lower incomes (28.2 versus 11.9 percent and 24.5 versus 15.4 percent, respectively, for poor and low income non-elderly adults). MME shares were smaller than population shares for those with higher incomes (23.3 versus 28.9 percent and 24.0 versus 43.9 percent, respectively, for middle and high-income non-elderly adults).

Among elderly adults, those with high incomes had lower average annual MMEs (477) than those who were poor (1,000), low income (1,221), or middle income (785) (figure 5b). High-income elderly adults comprised 41.2 percent of the population and acquired about one-quarter (25.4 percent) of total MMEs, while those who were low income comprised 21.9 percent of the population and acquired a little more than one-third (34.6 percent) of total MMEs.

Insurance coverage
This report uses four mutually exclusive categories of insurance for non-elderly adults: any private insurance, public coverage due to a disability ("public disability-related"), public coverage based on other factors ("public other"), and uninsured. In 2015–2016, non-elderly adults with public disability-related coverage had average annual MMEs (3,755) that were about 7 to 24 times higher than the average for the other insurance groups. As a result, non-elderly adults with public disability-related coverage accounted for a share of total MMEs (39 percent) that was about 10 times their share of the population (3.9 percent) (figure 6a). Those with public other coverage (508) had higher average MMEs than the privately insured (211) and the uninsured (155). The privately insured comprised almost three-quarters (73.5 percent) of the population, but accounted for only 41.1 percent of total MMEs, and the uninsured comprised 11.3 percent of the population but accounted for only 4.7 percent of total MMEs.

Among elderly adults in 2015–2016, those with Medicare and other public coverage had higher average annual MMEs than those with Medicare and private coverage (1,188 versus 672) (figure 6b). Those with Medicare and other public coverage comprised 11.1 percent of the population and obtained 16.8 percent of total MMEs, while those with Medicare and private coverage comprised 54.6 percent of the population and obtained 46.7 percent of total MMEs.

Employment status
In 2015–2016, among non-elderly adults, those who were not employed had average annual MMEs (1,134) that were about 9 times the average for those who were employed (120) (figure 7a). Those who were not employed comprised about one-quarter (25.3 percent) of the non-elderly adult population and obtained about three-quarters (76.2 percent) of total MMEs. The situation was reversed for non-elderly adults who were employed as they comprised about three-quarters of the population (74.7 percent) and obtained a little less than one-quarter (23.8 percent) of total MMEs.

Among elderly adults, those who were not employed had higher average annual MMEs, comprised 81.3 percent of the population, and acquired 91.9 percent of total MMEs (figure 7b). Those who were employed had lower average annual MMEs, comprised 18.7 percent of the population, and acquired 8.1 percent of MMEs.

Education
In 2015–2016, non-elderly adults with a high-school diploma had higher average annual MMEs (481) than those with at least some college (299) (figure 8a). The share of MMEs was larger than the population share for those with a high-school diploma (37.6 versus 29.3 percent) while the share of MMEs was smaller than the population share for those with some college (45.7 versus 57.4 percent).

There were no significant differences by level of education in average annual MMEs of outpatient opioids obtained by elderly adults in 2015–2016 (figure 8b).

Health status
Perceived health status
In 2015–2016, average MMEs obtained by non-elderly adults increased in a step-by-step fashion as health status declined from excellent (35), to very good (96), to good (378), to fair (1,591), and finally to poor (3,865) (figure 9a). Together, individuals in fair and poor health accounted for a little more than three-fifths (61.4 percent) of total MMEs, but comprised only 11.1 percent of the population. Conversely, non-elderly adults in very good and excellent health comprised a little more than three-fifths (61.7 percent) of the population, but accounted for only 11.3 percent of total MMEs.

Among elderly adults in 2015–2016, there was no significant difference in average annual MMEs between those reporting excellent (150) and very good (236) health, but MMEs increased dramatically as perceived health status declined to good (731), to fair (1,639), and finally to poor (3,309) (figure 9b). Together, elderly adults in very good and excellent health comprised almost half (47.7 percent) of the population and obtained only 12.6 percent of total MMEs, while those in fair and poor health comprised a little more than one-fifth (21.3 percent) of the population and obtained almost three-fifths (58.2 percent) of total MMEs.

Perceived mental health status
Among non-elderly adults in 2015–2016, those in poor (2,351) and fair (1,758) mental health had the highest average annual MMEs, and use declined as mental health status improved from good (558), to very good (195), to excellent (131) (figure 10a). Together, those in fair and poor mental health comprised 7.0 percent of the non-elderly adult population, but accounted for about one-third (34.8 percent) of total MMEs. Conversely, those in very good or excellent health comprised more than two-thirds (68.7 percent) of the population and obtained 29.0 percent of total MMEs.

Among elderly adults in 2015–2016, those in excellent (344) and very good (422) mental health had the lowest average annual MMEs, and average MMEs increased as mental health status declined from good (1,056), to fair (1,547), to poor (3,035) (figure 10b). The share of total MMEs was larger than the population share for those in poor (14.3 versus 3.7 percent), fair (17.8 versus 8.9 percent), and good (39.1 versus 28.8 percent) mental health. Conversely, the share of total MMEs acquired by those in excellent (12.8 percent) and very good (16.0 percent) mental health was smaller than their respective shares of the elderly population (29.0 and 29.5 percent).

Geography
Census region
Among non-elderly adults, there were no significant differences in 2015–2016 in average annual MMEs by Census region (figure 11a). Accordingly, MME shares were similar to population shares in each region.

In 2015–2016, elderly adults living in the Northeast Census region had lower average annual MMEs (392) than those living in the Midwest (828), South (878), and West (871) (figure 11b). Those living in the Northeast comprised a share of the elderly population (18.8 percent) that was almost twice as large as their share of total MMEs (9.5 percent).

Metropolitan Statistical Area (MSA) status
Among non-elderly adults in 2015–2016, average annual MMEs were more than twice as high for those living in non-MSAs (683) as for those living in MSAs (330) (figure 12a). Non-elderly adults living in non-MSAs comprised 13.2 percent of population and accounted for about one-quarter (24.0 percent) of total MMEs while those living in MSAs comprised 86.8 percent of the population and accounted for about three-quarters (76.0 percent) of total MMEs.

Similarly, average annual MMEs in 2015–2016, were nearly twice as high for elderly adults living in non-MSAs (1,259) than for those living in MSAs (670) (figure 12b). Elderly adults living in non-MSAs comprised 17.6 percent of the population and acquired 28.6 percent of total MMEs, while those living in MSAs comprised 82.4 percent of the population and acquired 71.4 percent of MMEs.

Data Source

This Research Findings report uses pooled data from the 2015 and 2016 MEPS Full-Year Consolidated Data Files (HC-181 and HC-192) and non-public versions of the 2015 and 2016 Prescribed Medicines Files (HC-178A and HC-188A).

Definitions

Opioids
In this Research Findings report, we calculate average annual MMEs for outpatient prescription opioids that are commonly used to treat pain. These opioids are identified using generic drug names for narcotic analgesics and narcotic analgesic combinations in the Multum Lexicon database from Cerner Multum, Inc. We identify slightly more opioids commonly used for pain than one would find in the MEPS public use files due to methods used to preserve the confidentiality of sample members. Opioids that are excluded from our analysis include respiratory agents, antitussives, and drugs commonly used in Medication Assisted Treatment.

Because of methodological and definitional differences, MEPS estimates of opioid use, and of total MMEs acquired, may differ from estimates based on other data sources. The MEPS includes only prescribed drugs purchased in outpatient settings. Prescription medicines administered in an inpatient setting or in a clinic or physician's office are excluded. MEPS data are household reported, and one respondent reports for the entire household. MEPS uses computer assisted personal interviewing, and questions are asked using a recall period of 3 to 6 months. Finally, this Research Findings report examines opioid use among non-elderly adults (ages 18–64) and elderly adults (age 65 and older).

Average annual MMEs
MMEs for each prescription fill are calculated using this formula: (number of units) x (strength per unit) x (MME conversion factor), where strength per unit is given in milligrams or micrograms and the MME conversion factor relates the potency of 1 milligram of each opioid to one milligram of morphine. Note that the MME conversion factor is intended only for analytic purposes and does not constitute clinical guidance or recommendations for moving patients from one opioid to another. (For additional information on MMEs, including a list of conversion factors, see https://www.cms.gov/Medicare/Prescription-Drug-Coverage/PrescriptionDrugCovContra/Downloads/Oral-MME-CFs-vFeb-2018.pdf).

After calculating total MMEs for each prescription, we sum total MMEs for each person-year observation. We calculate average annual MMEs including individuals with no opioid use (i.e., 0 MMEs).

Share of total MMEs
The share of total MMEs for a given subgroup is equal to {(total MMEs obtained by subgroup) / (total MMEs for population)} x 100.

Age
Respondents were asked to report the age of each family member as of the date of each interview for each round of data collection. The age variable used to create these estimates is based on the sample person's age as of the end of the year. If data were not collected at the end of the year because the sample person was out of scope (e.g., deceased or institutionalized), then age at the time of the previous round was used.

Non-elderly adults
Individuals ages 18–64.

Elderly adults
Individuals age 65 and older.

Race/Ethnicity
Classification by race/ethnicity was based on information reported for each family member. First, respondents were asked if the person's main national origin or ancestry was Puerto Rican; Cuban; Mexican, Mexican-American, or Chicano; other Latin American; or other Spanish. All persons whose main national origin or ancestry was reported in one of these Hispanic groups, regardless of racial background, were classified as Hispanic. All other persons were classified according to their reported race. For this analysis, the following classification by race and ethnicity was used: Hispanic, non-Hispanic black, non-Hispanic white, and non-Hispanic other. The "other" category includes American Indian, Alaska Native, Asian or Pacific Islander, other race, and multiple races.

Marital status
Marital status is based on the person's marital status at the end of the year. If missing, the most recent non-missing marital status variable is used. A small percentage of persons (less than 2 percent) had a missing value for marital status.

Poverty status
Each sampled person was classified according to the total annual income of his or her family. Possible sources of income included annual earnings from wages, salaries, bonuses, tips, and commissions; business and farm gains and losses; unemployment and Worker's Compensation; interest and dividends; alimony, child support, and other private cash transfers; private pensions, individual retirement account withdrawals, Social Security, and Department of Veterans Affairs payments; Supplemental Security Income (SSI) and cash welfare payments from public assistance, Aid to Families with Dependent Children and Aid to Dependent Children; gains or losses from estates, trusts, partnerships, S corporations, rent, and royalties; and a small amount of "other" income. Poverty status is the ratio of family income to the corresponding federal poverty thresholds, which control for family size and age of the head of family. Categories are defined as follows:

Poor: Family income below the federal poverty line
Low income: Family income over the poverty line through 200 percent of the poverty line
Middle income: Family income over 200 percent to 400 percent of the poverty line
High income: Family income over 400 percent of the poverty line

Insurance coverage for non-elderly adults

Any private: Non-elderly adults were classified as having any private health insurance coverage if they had private insurance that provided coverage for hospital and physician care (including Medigap coverage and TRICARE [Armed Forces-related coverage]) at some point during the year.
Public disability-related: Non-elderly adults were considered to have public disability-related health insurance coverage if they were not covered by private insurance or TRICARE and were covered by Medicare at some point during the year, or they received SSI and were covered by Medicaid or other public hospital and physician coverage at some point during the year. Most, but not all, non-elderly adults with Medicare coverage were dually eligible for Medicaid or other public coverage.
Public other: Non-elderly adults were considered to have public other health insurance coverage if they were not covered by private insurance, TRICARE, or Medicare; did not receive SSI; and were covered by Medicaid or other public hospital and physician coverage at some point during the year.
Uninsured: Non-elderly adults who did not have health insurance coverage for the entire calendar year were classified as uninsured. The uninsured were defined as people not covered by Medicaid, Medicare, TRICARE, other public hospital/physician programs, private hospital/physician insurance (including Medigap coverage), or insurance purchased through health insurance Marketplaces. People covered only by non-comprehensive state-specific programs (e.g., Maryland Kidney Disease Program) or private single service plans such as coverage for dental or vision care only, or coverage for accidents or specific diseases, were considered uninsured.

Insurance coverage for elderly adults

Medicare and private: Elderly adults were classified as having Medicare and any private health insurance coverage if they were covered by Medicare and had private insurance that provided coverage for hospital and physician care (including Medigap coverage and TRICARE) at some point during the year.
Medicare and public only: Elderly adults were classified as having Medicare and public only insurance if they were covered by Medicare, they were not covered by private insurance or TRICARE at any point during the year, and they were covered by Medicaid or other public hospital and physician coverage at some point during the year.
Medicare only: Elderly adults were classified as having Medicare only if they were covered by Medicare at some point during the year, but they were not covered by private insurance, TRICARE, Medicaid, or other public hospital and physician coverage at any point during the year.
No Medicare: Elderly adults who reported no Medicare coverage during the year comprised less than 2 percent of the sample and were excluded from the analysis.

Employment status
Employment status is based on the person's employment status at the end of the year. If missing, the most recent non-missing employment status variable is used. A small percentage of persons (less than 2 percent) had a missing response for employment status.

Education
Education for each person is based on the highest education level completed when entering MEPS. A small percentage of persons (less than 2 percent) had a missing response for education.

Perceived physical and mental health status
The MEPS household respondent was asked to rate the physical and mental health status of each person in the family at the time of the interview according to the following categories: excellent, very good, good, fair, and poor. For persons with missing health status in a round, the response for health status at the previous round was used, if available. A small percentage of persons (less than 1 percent) had a missing response for perceived physical and mental health status.

Census region
The Census region variable is based on the location of the household at the end of the year. If missing, the most recent location available was used.

Northeast: Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York, New Jersey, and Pennsylvania
Midwest: Ohio, Indiana, Illinois, Michigan, Wisconsin, Minnesota, Iowa, Missouri, North Dakota, South Dakota, Nebraska, and Kansas
South: Delaware, Maryland, District of Columbia, Virginia, West Virginia, North Carolina, South Carolina, Georgia, Florida, Kentucky, Tennessee, Alabama, Mississippi, Arkansas, Louisiana, Oklahoma, and Texas
West: Montana, Idaho, Wyoming, Colorado, New Mexico, Arizona, Utah, Nevada, Washington, Oregon, California, Alaska, and Hawaii

Metropolitan Statistical Area (MSA)
The MSA variable is based on the location of the household at the end of the year and reflects the most recent delineations of MSAs established by Office of Management and Budget.

About MEPS-HC

The Medical Expenditure Panel Survey Household Component (MEPS-HC) collects nationally representative data on health care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian noninstitutionalized population. The MEPS-HC is cosponsored by the Agency for Healthcare Research and Quality (AHRQ) and the National Center for Health Statistics (NCHS). More information about MEPS-HC can be found on the MEPS website at https://meps.ahrq.gov/.

References

For a detailed description of the MEPS-HC survey design, sample design, and methods used to minimize sources of nonsampling error, see the following publications:

Cohen, J. Design and Methods of the Medical Expenditure Panel Survey Household Component. MEPS Methodology Report No. 1. AHCPR Pub. No. 97-0026. Rockville, MD: Agency for Health Care Policy and Research, 1997. https://meps.ahrq.gov/data_files/publications/mr1/mr1.shtml
Cohen, S. Sample Design of the 1996 Medical Expenditure Panel Survey Household Component. MEPS Methodology Report No. 2. AHCPR Pub. No. 97-0027. Rockville, MD: Agency for Health Care Policy and Research, 1997. https://meps.ahrq.gov/data_files/publications/mr2/mr2.shtml

Footnotes

¹For example, see Statistical Briefs #515 and #516 which examine the average annual percentages of adults with 1 or more prescription fills and with four or more prescription fills in the elderly and non-elderly adult populations, respectively. https://meps.ahrq.gov/data_files/publications/st515/stat515.shtml, https://meps.ahrq.gov/data_files/publications/st516/stat516.shtml
²The figures in this report show 95 percent confidence intervals (CIs) for average annual MMEs per person. Note that if the estimated averages for two groups have non-overlapping CIs, the difference is necessarily significant at the p<.05 level, or better. However, if the estimates have overlapping CIs, it is not necessarily true that they are not significantly different at p<.05. For an explanation, see Knezevic, A. "Overlapping confidence intervals and statistical significance." StatNews: Cornell University Statistical Consulting Unit 73, no. 1 (2008).

Suggested Citation

Moriya, A.S. and Miller, G.E. Average Annual Morphine Milligram Equivalents (MMEs) of Outpatient Prescription Opioids and Share of Total MMEs among Elderly and Non-Elderly Adults in 2015–2016 . Research Findings #45. November 2019. Agency for Healthcare Research and Quality, Rockville, MD. https://meps.ahrq.gov/mepsweb/data_files/publications/rf45/rf45.shtml

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AHRQ welcomes questions and comments from readers of this publication who are interested in obtaining more information about access, cost, use, financing, and quality of healthcare in the United States. We also invite you to tell us how you are using this Research Findings report and other MEPS data and tools and to share suggestions on how MEPS products might be enhanced to further meet your needs. Please email us at MEPSProjectDirector@ahrq.hhs.gov or send a letter to the address below:

Joel W. Cohen, PhD, Director
Center for Financing, Access, and Cost Trends
Agency for Healthcare Research and Quality
5600 Fishers Lane, Mailstop 07W41A
Rockville, MD 20857

An accessible data table follows this Figure 1a.

Figure 1a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs, overall and by sex, among non-elderly adults in 2015‑2016
Overall and by sex	Annual MMEs per capita (95% CI)	Share of population²	Share of MMEs²
Overall	376 (322, 431)
Male	335 (264, 406)	49.2%	43.9%
Female	416 (342, 491)	50.8%	56.1%

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
² The difference between the share of population and the share of MME is not statistically significant at the 5% level for any of the categories.

An accessible data table follows this Figure 1b.

Figure 1b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs, overall and by sex, among elderly adults in 2015‑2016
Overall and by sex	Annual MMEs per capita (95% CI)	Share of population²	Share of MMEs²
Overall	774 (644, 904)
Male	850 (628, 1,072)	44.7%	49.1%
Female	712 (571, 854)	55.3%	50.9%

An accessible data table follows this Figure 2a image.

Figure 2a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by age, among non-elderly adults in 2015‑2016
Age group	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
18-26	49 (28, 70)	19.9%	2.6%*
27-34	128 (71, 184)	17.3%	5.9%*
35-44	321 (231, 411)	20.2%	17.2%
45-54	524 (411, 637)	21.5%	29.9%*
55-64	794 (627, 960)	21.0%	44.4%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 2b.

Figure 2b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by age, among elderly adults in 2015‑2016
Age group	Annual MMEs per capita (95% CI)	Share of population²	Share of MMEs²
65-74	856 (668, 1,043)	57.7%	63.8%
75-84	691 (489, 892)	30.7%	27.4%
85+	587 (315, 859)	11.6%	8.8%

An accessible data table follows this Figure 3a.

Figure 3a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by race/ethnicity, among non-elderly adults in 2015‑2016
Race/Ethnicity	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Non-Hispanic white	490 (402, 578)	60.3%	78.5%*
Non-Hispanic black	327 (242, 411)	12.6%	10.9%
Hispanic	135 (90, 180)	17.8%	6.4%*
Other	170 (97, 244)	9.4%	4.3%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 3b.

Figure 3b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by race/ethnicity, among elderly adults in 2015‑2016
Race/Ethnicity	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Non-Hispanic white	842 (684, 999)	76.3%	83.0%*
Non-Hispanic black	748 (401, 1,095)	8.8%	8.6%
Hispanic	484✝ (195, 772)	8.2%	5.1%
Other	386 (198, 573)	6.6%	3.3%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.
✝ Unreliable estimate (relative standard error is greater than 0.30).

An accessible data table follows this Figure 4a.

Figure 4a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by marital status, among non-elderly adults in 2015‑2016
Marital status	Annual MMEs per capita (95% CI)	Share of population²	Share of MMEs²
Married	382 (307, 457)	51.8%	52.6%
Not married	370 (302, 439)	48.2%	47.4%

An accessible data table follows this Figure 4b.

Figure 4b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by marital status, among elderly adults in 2015‑2016
Marital status	Annual MMEs per capita (95% CI)	Share of population²	Share of MMEs²
Married	615 (461, 768)	56.4%	44.8%*
Not married	979 (766, 1,192)	43.6%	55.2%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 5a.

Figure 5a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by poverty status, among non-elderly adults in 2015‑2016
Poverty status	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Poor	895 (657, 1,133)	11.9%	28.2%*
Low income	600 (457, 744)	15.4%	24.5%*
Middle income	304 (235, 372)	28.9%	23.3%*
High income	206 (140, 272)	43.9%	24.0%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 5b.

Figure 5b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by poverty status, among elderly adults in 2015‑2016
Poverty status	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Poor	1,000 (590, 1,411)	9.4%	12.2%
Low income	1,221 (875, 1,567)	21.9%	34.6%*
Middle income	785 (533, 1,037)	27.4%	27.8%
High income	477 (318, 636)	41.2%	25.4%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 6a.

Figure 6a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by insurance coverage, among non-elderly adults in 2015‑2016
Insurance coverage	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Any private	211 (173, 248)	73.5%	41.1%*
Public disability-related	3,755 (2,901, 4,608)	3.9%	39.0%*
Public other	508 (350, 666)	11.2%	15.2%
Uninsured	155 (77, 232)	11.3%	4.7%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 6b.

Figure 6b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by insurance coverage, among elderly adults in 2015‑2016
Insurance coverage	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Medicare only	833 (620, 1,046)	34.3%	36.4%
Medicare and private	672 (512, 832)	54.6%	46.7%*
Medicare and other public	1,188 (792, 1,584)	11.1%	16.8%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 7a.

Figure 7a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by employment status, among non-elderly adults in 2015‑2016
Employment status	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Employed	120 (99, 141)	74.7%	23.8%*
Not employed	1,134 (947, 1,321)	25.3%	76.2%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 7b.

Figure 7b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by employment status, among elderly adults in 2015‑2016
Employment status	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Employed	334✝ (123, 545)	18.7%	8.1%*
Not employed	876 (721, 1,032)	81.3%	91.9%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.
✝ Unreliable estimate (relative standard error is greater than 0.30).

An accessible data table follows this Figure 8a.

Figure 8a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by educational attainment, among non-elderly adults in 2015‑2016
Educational attainment	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Less than HS	474 (299, 649)	13.3%	16.8%
HS diploma	481 (380, 583)	29.3%	37.6%*
Some college or more	299 (236, 362)	57.4%	45.7%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 8b.

Figure 8b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by educational attainment, among elderly adults in 2015‑2016
Educational attainment	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Less than HS	1,038 (735, 1,341)	17.4%	23.2%
HS diploma	786 (569, 1,003)	30.6%	31.0%
Some college or more	685 (483, 887)	52.0%	45.8%

An accessible data table follows this Figure 9a.

Figure 9a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by perceived health status, among non-elderly adults in 2015‑2016
Perceived health status	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Excellent	35 (25, 46)	27.6%	2.6%*
Very good	96 (69, 123)	34.1%	8.7%*
Good	378 (296, 460)	27.2%	27.3%
Fair	1,591 (1,254, 1,928)	8.7%	36.8%*
Poor	3,865 (2,882, 4,847)	2.4%	24.6%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 9b.

Figure 9b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by perceived health status, among elderly adults in 2015‑2016
Perceived health status	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Excellent	150 (66, 233)	16.7%	3.2%*
Very good	236 (167, 305)	31.0%	9.4%*
Good	731 (535, 926)	31.0%	29.1%
Fair	1,639 (1,167, 2,111)	15.0%	31.6%*
Poor	3,309 (2,282, 4,335)	6.3%	26.6%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 10a.

Figure 10a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by perceived mental health status, among non-elderly adults in 2015‑2016
Perceived mental health status	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Excellent	131 (99, 163)	38.4%	13.4%*
Very good	195 (152, 237)	30.3%	15.6%*
Good	558 (431, 685)	24.4%	36.1%*
Fair	1,758 (1,253, 2,264)	5.6%	26.3%*
Poor	2,351 (1,269, 3,434)	1.4%	8.5%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 10b.

Figure 10b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by perceived mental health status, among elderly adults in 2015‑2016
Perceived mental health status	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Excellent	344 (228, 460)	29.0%	12.8%*
Very good	422 (294, 550)	29.5%	16.0%*
Good	1,056 (789, 1,323)	28.8%	39.1%*
Fair	1,547 (845, 2,248)	8.9%	17.8%*
Poor	3,035 (1,625, 4,444)	3.7%	14.3%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 11a.

Figure 11a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by census region, among non-elderly adults in 2015‑2016
Census region	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Northeast	330 (185, 475)	17.7%	15.5%
Midwest	460 (316, 603)	20.8%	25.4%
South	377 (306, 448)	37.5%	37.6%
West	338 (222, 453)	24.0%	21.5%

An accessible data table follows this Figure 11b.

Figure 11b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by census region, among elderly adults in 2015‑2016
Census region	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
Northeast	392 (220, 563)	18.8%	9.5%*
Midwest	828 (588, 1,068)	21.7%	23.2%
South	878 (614, 1,141)	37.2%	42.2%
West	871 (611, 1,131)	22.3%	25.1%

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 12a.

Figure 12a: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by MSA status, among non-elderly adults in 2015‑2016
MSA status	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
MSA	330 (274, 385)	86.8%	76.0%*
non-MSA	683 (497, 870)	13.2%	24.0%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

An accessible data table follows this Figure 12b.

Figure 12b: Annual Morphine Milligram Equivalents (MMEs) of outpatient prescription opioids¹: MME per capita, share of population and share of MMEs by MSA status, among elderly adults in 2015‑2016
MSA status	Annual MMEs per capita (95% CI)	Share of population	Share of MMEs
MSA	670 (537, 803)	82.4%	71.4%*
non-MSA	1,259 (843, 1,674)	17.6%	28.6%*

Source: Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey, Household Component, 2015–2016.
¹ Comprised of all opioids commonly used to treat pain.
* The difference between the share of population and the share of MME is statistically significant at the 5% level.

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RESEARCH FINDINGS #45: Average Annual Morphine Milligram Equivalents (MMEs) of Outpatient Prescription Opioids and Share of Total MMEs among Elderly and Non-Elderly Adults in 2015–2016

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Introduction

Findings

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About MEPS-HC

References

Footnotes

Suggested Citation

RESEARCH FINDINGS #45:
Average Annual Morphine Milligram Equivalents (MMEs) of Outpatient Prescription Opioids and Share of Total MMEs among Elderly and Non-Elderly Adults in 2015–2016