Embed Size (px)
Citation preview
Sedative-hypnotic and Opioid Prescribing Patterns and Fall-Related Mortality, New
Mexico, 2007-2012
James W. Davis, M.A.1, Laura E. Tomedi, Ph.D., M.P.H.1, Luigi F. Garcia Saavedra, M.P.H.1,
Glenda Hubbard, M.P.H.1, Michael Landen, M.D. M.P.H.1
Author Affiliation:
1. Epidemiology and Response Division, New Mexico Department of Health, Santa Fe, New
Mexico
Conflict of interest: None of the authors have relevant financial disclosures or conflicts of
interest. All five authors conducted this work while employees of the New Mexico Department
of Health.
Address correspondence and reprint requests to:
James W. Davis, M.A.
1190 St. Francis Dr. N1105
PO Box 26110
Santa Fe, NM 87502
Telephone: 505-827-5424
E-mail: [email protected]
Running title: prescribing patterns and fall deaths
Key words: falls, opioids, benzodiazepines,
Word Count = 2,906; Tables = 3;
1
Abstract
Importance: Falls are the leading cause of injury death among adults ≥ 65 years. Opioid and
sedative-hypnotic use increases risk of fall death.
Objective: To determine the association between opioid and sedative-hypnotic prescribing
patterns and risk of fall death.
Design: Population-based matched case-control study.
Setting: New Mexico (NM) resident population, 2007-2012.
Participants: Cases were all fall deaths in NM, 2007-2012, age ≥ 65 years, who filled a
controlled substance prescription drug (CSPD) in the year prior to the fatal injury date (n=869).
For each case, 4 controls, age ≥ 65 years, who filled a CSPD in the year prior to the matched
case’s date of injury were randomly selected from the NM Prescription Monitoring Program
(PMP), which registers all CSPD prescriptions (n=3,476).
Exposure: Dose of opioids (Morphine Milligram Equivalent [MME]); dose of sedative-
hypnotics (Valium Milligram Equivalents [VME]); concurrent opioid/sedative-hypnotic
prescriptions; obtaining CSPD from multiple prescribers and/or pharmacies; chronic (≥ 90%
coverage for time period) use of opioids; and chronic use of sedative-hypnotics. Duration of use
was assessed at 1, 2, 6, and 12 months before injury.
Main Outcome and Measure: Fall injury death.
Results: In logistic regression analysis controlling for age and sex, the following patterns were
associated with increased risk of fall death: opioid use in the month preceding injury (>120 vs. 0
MME/day, odds ratio [OR], 4.13 [95% CI, 1.88-6.27]; P < 0.001), sedative-hypnotic use in the
month preceding injury (>20 vs. 0 VME/day, OR, 3.05 [95% CI, 1.92-4.84]; P < 0.001), and
filling CSPD at >3 pharmacies (OR, 3.83 [95% CI, 2.15-6.85]; P < 0.001). Chronic use of
2
opioids (OR, 0.44 [95% CI, 0.28-0.72]; P = 0.001) and sedative-hypnotics (OR, 0.58 [95% CI,
0.37-0.89]; P = 0.014) in the 6 months preceding injury was associated with a lower risk of fall
death. Concurrent prescriptions, multiple prescribers, and longer durations of use were no longer
associated after these terms were added to the model.
Conclusions and Relevance: Short duration use of opioids and sedative-hypnotics (especially at
high doses) and using >3 pharmacies are strongly associated with risk of fall death in older
patients. Judicious prescribing and increasing use of statewide PMPs for older patients may be
warranted.
3
Unintentional falls are the leading cause of injury death among people aged ≥ 65 years in the
United States and have increased by 38% from 2004 to 2013.1 Falls occur in 30-60% of older
adults each year and can result in fractures, lacerations, hospitalization, premature nursing home
placement, and death.2
Although falls among older adults are common, fall injuries and their sequelae are not a normal
part of aging. There are a number of best practices that providers can follow to prevent falls in
high risk patients, including minimization of medications.3 A number of medications, including
sedative-hypnotics (e.g. diazepam, alprazolam) and opioids (e.g. fentanyl, oxycodone) can
increase fracture risk in older adults.4-9 There is also limited evidence suggesting that concurrent
opioid and sedative-hypnotic use may be associated with risk of fracture among adults aged ≥
65.10 Opioids and sedative-hypnotics can cause sedation, dizziness, and impaired coordination,
increasing the risk of a fall.11,12 Chronic use of opioids may also interfere with bone and joint
health, increasing risk for osteoporosis and fracture in older adults.13
Healthcare providers have limited information with which to weigh the benefit of these
medications against the risk of serious harm resulting from drug side effects. Because
toxicological testing is seldom performed in cases of fall death, the extent to which prescription
drug use may have contributed to fall-related deaths cannot be derived from medical examiner
data alone.
4
In response to the sharp increase in prescription drug overdose deaths nationwide, 49 of 50 states
established prescription drug monitoring programs (PMP), which allow providers to review
patients’ prescription history. While PMPs are largely used to prevent prescription drug
overdose, reviewing patients’ prescription history may also prove useful for understanding and
preventing other controlled substance prescription drugs (CSPD)-related conditions, such as fall
death.
In 2009-2013, NM had one of the highest fall death rates among people aged ≥ 65 years (93.5
deaths per 100,000 people) in the nation, a rate approximately 70% higher than the national rate
of 53.8 deaths per 100,000 people. NM also has a centralized medical investigator system that
provides consistent data on injury death, and a mandatory PMP that includes all CSPD. The
combination of death data and prescription data provides a basis for investigating the association
between opioid and sedative-hypnotic usage patterns and fall-related death.
The objective of this study was to determine whether patterns of opioid and sedative-hypnotic
prescribing (long term use of opioids or sedative-hypnotics, high doses of opioids or sedative-
hypnotics, concurrent use of opioids and sedative-hypnotics, and obtaining CSPD from multiple
prescribers and/or pharmacies) were associated with fatal falls among older adults in NM. These
patterns among individuals with CSPD prescriptions who died from a fall (cases) were compared
to individuals with CSPD prescriptions who did not die of a fall (controls) using a population-
based case-control study among people aged ≥ 65 years.
5
Methods
Study Population and aggregation of prescription history
Death certificate data for 2007-2012 from the New Mexico Department of Health, Bureau of
Vital Records and Health Statistics was used to identify deaths resulting from falls among people
aged ≥ 65 years. Fall deaths were defined using underlying cause of death codes W00-W19 from
the 10th revision of the International Statistical Classification of Diseases and Related Health
Problems (ICD-10). Prescription history data was extracted from the New Mexico PMP for years
2006-2012. All controlled substance prescriptions (schedule II-IV) filled by outpatient
pharmacies are required to be reported to the PMP, which is a program of the New Mexico
Board of Pharmacy.
Death data were linked to PMP data using The Link King software14 using first name, last name,
middle initial, date of birth, gender, and zip code. Cases were defined as NM residents aged ≥ 65
years, who died of a fall injury, and who had at least one CSPD prescription in the 12 months
prior to the date of the fall injury. Four controls were randomly selected without replacement
from the PMP for each case. Each control was a NM resident, aged ≥ 65 years, with at least one
CSPD prescription in the 12 months prior to the date of injury of the matched case. No other
matching variables were used. Records with an age > 95 years were recoded to 95 years (n = 98).
The date of the fall injury that led to death, listed on the death certificate, was used as the index
date for analysis. Prescriptions filled before the date of fall injury listed on the death certificate
for cases were included, prescriptions filled after the date of injury on the death certificate were
excluded, and prescriptions that included the date of injury in their time span were prorated
based on the proportion of the span that was prior to the date of injury. Date of death was used as
6
a proxy when date of injury was not recorded on the death certificate. The date of injury of the
matched case was used for controls. For both cases and controls, a prescription history was
created that aggregated prescriptions by drug type (opioids and sedative-hypnotics) in time
periods of one, two, six, and twelve months prior to the date of injury. Sedative-hypnotics
included benzodiazepine-like Z-drugs (e.g., zolpidem) and benzodiazepines. Morphine milligram
equivalents (MME) were computed for each opioid prescription. Because opioids vary in
strength, a total dose in MME can be used to generate consistent and comparable estimates of the
total dose15 across opioids of different strengths. Similarly, we computed valium milligram
equivalents (VME) for sedative-hypnotic CSPD16. The number of prescribers and the number of
pharmacies providing any CSPD to patients in the year before the date of injury was also
determined.
Statistical methods
Multivariable logistic regression was used to determine the independent associations between
prescription patterns and fall death among patients receiving CSPD. The following prescription
patterns were assessed: opioid dose (MME per day covered by prescription [prescription day]);
sedative-hypnotic dose (VME/prescription day); concurrent opioid/sedative-hypnotic
prescription (% of days with an opioid prescription where patient also had a sedative-hypnotic
prescription); number of prescribers (number of health care providers who wrote a prescription
for CSPD that was filled); number of pharmacies (number of pharmacies where the patients has
filled prescriptions for CSPD); chronic use of opioids (patient had enough opioid prescriptions to
cover ≥ 90% of the time period); and chronic use of sedative-hypnotics (patient had enough
sedative-hypnotic prescriptions to cover ≥ 90% of the time period). Prescription patterns were
7
assessed in the following time periods: 1 month, 2 months, 6 months and 1 year. Locally
weighted regression was used to assess linearity between prescription indicators and probability
of death due to fall injury. We hypothesized that age and gender modified the relationship
between specific prescription patterns and fall death. Therefore cases and controls were not
matched on age or gender and assessed interaction terms for: 1) age by dose of opioids and
sedative-hypnotics, 2) gender by dose of opioids and sedative-hypnotics, and age by sex by
number of pharmacies.
Regression models were constructed by adding independent variables of interest (prescription
patterns) to a base model containing age and gender. Akaike's information criterion (AIC) was
used to determine if a model was an improvement on the prior model. When the difference
between categories of an independent variable was slight, categories were collapsed. Sensitivity
analyses were performed when collapsing categories by adjusting the values that defined the
categories. Data management and analysis were done in SAS 9.3 ®.
Results
In 2007-2012, there were a total of 1,513 fall deaths among NM residents aged ≥ 65 years, of
which 869 (57%) had controlled substance prescriptions filled in the year preceding their fall
injury. These 869 were selected as cases. Cases were more likely to be female than fall deaths
overall, but the age distribution of cases was not appreciably different from that of all fall deaths
(Table 1). A total of 3,476 controls were selected from 282,361 eligible people in the PMP.
Gender distributions for the cases and controls were identical, and controls were younger than
cases (Table 1).
8
Prescription patterns among cases and controls were assessed at one, two, six, and twelve months
prior to the date of injury (Table 2). Cases were more likely than controls to have had opioid
prescriptions filled in the month before their injury, and tended to have had higher doses
prescribed. Cases were 1.6 times as likely as controls to have filled low dose (40 MME/day)
opioid prescriptions and four times as likely to have very high dose (>200 MME/day)
prescriptions. Prescribing patterns for sedative-hypnotics were similar. Cases were 1.3 times
more likely than controls to have doses up to 20 VME/day, and 2.6 times as likely to have doses
over 60 VME/day. Cases were also more likely to have been prescribed sedative-hypnotics in
combination with opioids, to have had multiple prescribers, and to have had their prescriptions
filled at multiple pharmacies.
The final model contained terms for age, gender, average MME/day of opioids in the month
prior to injury, average VME/day of sedative-hypnotics in the month prior to injury, chronic use
of opioids in the six months prior to injury, chronic use of sedative-hypnotics in the six months
prior to injury, and number of pharmacies in the year prior to injury. The maximum-adjusted R-
square for the model was 0.41. The three interaction terms tested were non-significant.
Doses of opioids up to 120 MME/day increased the odds of a fall death by roughly 1.6 times
(OR=1.6), and higher doses by more than four times (OR=4.2) (Table 3). Sensitivity analyses
were conducted by collapsing dosage categories and no improvement in model fit was found for
values lower or higher than 120 MME/day. Similar to opioids, doses of sedative-hypnotics up to
20 VME/day increased the odds of a fall death by about 50% (OR=1.5) and higher doses by three
9
times (OR=3.1). Again, sensitivity analyses were conducted as categories were collapsed and a
cutoff of 20 VME/day provided the best model fit.
Chronic use of opioids in the six months prior to injury, defined as at least 90% of days having
prescription coverage (162/180 days), was approximately half as likely among cases as among
controls (OR=0.44) (Table 3). Similarly, chronic use of sedative-hypnotics in the six months
prior to injury was also about half as likely among cases as among controls (OR=0.58). In
contrast, new use, defined as use in the month prior to injury, but not in months 2-6 prior to
injury, did not have a strong effect. Also, chronic use for two months prior to injury did not have
a strong effect. Interaction terms between chronic use and other variables were tested but did not
contribute to the model.
Interaction terms between opioid doses and sedative-hypnotic doses were not statistically
significant. The effects of sedative-hypnotic doses on the risk of a fall-related death were similar
at all levels of opioid doses.
The odds of a fall death increased with the number of pharmacies used in the year prior to injury.
People who had filled controlled substance prescriptions at more than three pharmacies in the
past year had 3.8 times the odds of a fall death as those who had used only one pharmacy
(OR=3.8). The odds of a fall death did not increase with an increasing number of prescribers of
controlled substances.
10
Discussion
The burden of fall injuries among those aged ≥ 65 years is substantial and opportunities to
prevent these injuries, and subsequent deaths, may exist by changing prescribing practices. We
found that deaths from fall injuries among people aged ≥ 65 years were significantly associated
with high dose opioid prescriptions and high dose sedative-hypnotic prescriptions in the month
prior to the date of injury. For both, the association increased as the dose increased. Patients who
died of a fall injury were also significantly more likely to have filled prescriptions at multiple
pharmacies in the year prior to the date of injury than controls. These associations were assessed
using PMP data for a population receiving CSPDs and were independent of age and gender.
Other studies have found associations between fracture injuries and opioids11,17-19,
benzodiazepines12,20, and concomitant use of opioids and benzodiazepines.10 For example,
among adults aged ≥ 65 years, opioid doses ≥ 50 mg/day were associated with a twofold increase
in fracture risk19 and, among adults aged ≥ 65 years with arthritis, fracture risk was greater with
higher opioid dose17. We found a dose-response relationship between opioids and sedative-
hypnotics and fall death, a more severe health outcome than fall injury. The odds ratio increased
with dose, but was elevated even at lower, and relatively common, doses (>0-120 MME/day and
>0-20 VME/day). To place these estimates in the context of prescribing, a prescription for 10 mg
of oxycodone, 3 times per day, is equivalent to 45 MME/day and a prescription for 10 mg of
zolpidem, once per day, corresponds to 5 VME/day. Although people who died of fall injury had
significantly more average days with concurrent opioids and sedative-hypnotics prescriptions
than controls, the association was no longer significant after adjustment for total opioid and
11
sedative-hypnotics dose. This suggests that although both sedative-hypnotics and opioids may
separately contribute to risk of fall death, the two drugs do not seem to have an interactive effect
in fall death, such as is observed in cases of drug overdose death.21
Two mechanisms have been suggested for how opioids and sedative-hypnotics, particularly
opioids, may increase a person’s risk of fracture. The first is that the increased risk is caused by
acute central nervous system effects such as sedation and dizziness. The second potential
mechanism is that the increased risk is caused by osteoporosis arising from chronic opioid-
induced hypogonadism22. Studies have also suggested that newly initiated use of opioids was
more strongly associated with fall injury than chronic use, particularly in younger
populations.11,22 A meta-analysis of benzodiazepine use and fracture risk also found that short-
acting benzodiazepines were strongly associated with fracture risk.12 The current use of opioids
or sedative-hypnotics, versus chronic use, appears to be a key risk factor. This increases the
evidence that sedation and dizziness may play a greater role in fall risk.
Two measures are typically used to assess multiple provider episodes: number of health care
providers who prescribed CSPD and number of pharmacies where the prescriptions were filled.
We assessed both measures, however, only the number of pharmacies was significant in the final
model. Although the prevalence of multiple provider episodes is increasing, older adults have
lower rates of multiple provider episodes than younger patients.23 We found a dose-response
relationship between number of pharmacies and death from fall injury, even after adjustment for
opioid and sedative-hypnotic dose. Although people who died of a fall injury were significantly
more likely to have received prescriptions from multiple providers than other CSPD patients, the
12
relationship was not significant in the final model. Further study is needed to assess the nature of
this association. However, providers should be aware of prescriptions from other providers being
filled by their patients aged ≥ 65 years. Statewide PMPs can be an effective tool for providers to
ensure that a patient’s prescriptions are not being mismanaged.
There are several limitations that should be taken into consideration when reviewing the results
of this study. Although the PMP is a powerful database for tracking CSPD prescriptions, there is
no information on diagnosis or key demographic factors, such as race/ethnicity. Therefore, we
were neither able to control for underlying medical conditions, which may influence both fall
risk and controlled substance prescribing, nor for race/ethnicity. There are also no data on the
prior history of falls, nor is there information on functional status. The PMP contains only
controlled substance prescriptions, and there are a number of drugs that are not controlled
substances that have been implicated in falls.8,24,25 It was not possible to analyze the potential
interactions with those drugs. The PMP collects data on prescriptions filled; there is no
information about consumption or compliance with prescriber instructions. This was a
retrospective study, so causality cannot be inferred. Lastly, this study assessed deaths and CSPD
in New Mexico, and the results may not be generalizable to other states.
However, there are also a number of strengths that make this study an important contribution to
this field. PMPs have been used extensively in the study of drug overdose deaths, but this may be
the first time that PMP data have been used in the study of fall deaths. Both the PMP and the
state’s death records are large, population-based databases. This means that we were able to
assess every fall death that occurred in New Mexico during the time period, as opposed to a
13
sample of deaths. The quality of data included in PMPs varies from state to state but in New
Mexico data entry is mandated, enforced, updated weekly, and regularly reviewed by both the
Board of Pharmacy and the Department of Health. The use of the PMP ensured that prescriptions
were filled before the date of injury and limited recall bias.
Death from fall injury is a major health issue. Prescribers should carefully consider the risks and
benefits of the use of opioids and sedative-hypnotics in patients aged ≥ 65 years, and use them
with great care. Lower doses entail less risk than higher doses, but any dose level carries some
risk of fall-related injury or death. Assessing prescription history using statewide PMPs in
patients who are at risk for fall may be warranted. Our findings highlight the importance of
judicious prescribing practices and their association with deaths from fall injuries.
14
Acknowledgements
The authors thank the New Mexico Board of Pharmacy and the Bureau of Vital Records and
Health Statistics of the New Mexico Department of Health for providing the data for this study;
Toby Rosenblatt, MPA for reviewing the manuscript for content; and Mam Ibraheem, MD, PhD
for contributing to the initial design of the study.
Author contributions: Mr. Davis had full access to all the data in the study and takes full
responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Landen, Davis, Tomedi
Acquisition, analysis and interpretation of data: Davis, Tomedi, Garcia Saavedra
Drafting of the manuscript: Davis, Tomedi, Garcia Saavedra, Hubbard
Critical revision of the manuscript for important intellectual content: All Authors
Statistical analysis: Davis
Study supervision: Tomedi, Landen
Conflict of interest disclosures: None of the authors have conflicts of interest.
Funding/Support: No external funding was involved in this study.
15
References
1. Centers for Disease Control and Prevention WONDER Online Database. Underlying
Cause of Death 1999-2013 In. National Center for Health Statistics; 2015.
2. Rubenstein LZ. Falls in older people: epidemiology, risk factors and strategies for
prevention. Age and ageing 2006;35 Suppl 2:ii37-ii41.
3. Moncada LV. Management of falls in older persons: a prescription for prevention.
American family physician 2011;84:1267-76.
4. Agusti A, Pages E, Cuxart A, et al. Exposure to medicines among patients admitted for
hip fracture and the case-fatality rate at 1 year: a longitudinal study. European journal of clinical
pharmacology 2012;68:1525-31.
5. Finkle WD, Der JS, Greenland S, et al. Risk of fractures requiring hospitalization after an
initial prescription for zolpidem, alprazolam, lorazepam, or diazepam in older adults. Journal of
the American Geriatrics Society 2011;59:1883-90.
6. French DD, Campbell R, Spehar A, Cunningham F, Bulat T, Luther SL. Drugs and falls
in community-dwelling older people: a national veterans study. Clinical therapeutics
2006;28:619-30.
7. Huang AR, Mallet L, Rochefort CM, Eguale T, Buckeridge DL, Tamblyn R. Medication-
related falls in the elderly: causative factors and preventive strategies. Drugs & aging
2012;29:359-76.
8. Masud T, Frost M, Ryg J, et al. Central nervous system medications and falls risk in men
aged 60-75 years: the Study on Male Osteoporosis and Aging (SOMA). Age and ageing
2013;42:121-4.
16
9. Landi F, Onder G, Cesari M, et al. Psychotropic medications and risk for falls among
community-dwelling frail older people: an observational study. The journals of gerontology
Series A, Biological sciences and medical sciences 2005;60:622-6.
10. Nurminen J, Puustinen J, Piirtola M, Vahlberg T, Lyles A, Kivela SL. Opioids,
antiepileptic and anticholinergic drugs and the risk of fractures in patients 65 years of age and
older: a prospective population-based study. Age and ageing 2013;42:318-24.
11. Soderberg KC, Laflamme L, Moller J. Newly initiated opioid treatment and the risk of
fall-related injuries. A nationwide, register-based, case-crossover study in Sweden. CNS drugs
2013;27:155-61.
12. Xing D, Ma XL, Ma JX, Wang J, Yang Y, Chen Y. Association between use of
benzodiazepines and risk of fractures: a meta-analysis. Osteoporosis international : a journal
established as result of cooperation between the European Foundation for Osteoporosis and the
National Osteoporosis Foundation of the USA 2014;25:105-20.
13. Mattia C, Di Bussolo E, Coluzzi F. Non-analgesic effects of opioids: the interaction of
opioids with bone and joints. Current pharmaceutical design 2012;18:6005-9.
14. Campbell K, Deck D, Cox A, Broderick C. The Link King User Manual Version 5.2.
2005.
15. Von Korff M, Saunders K, Thomas Ray G, et al. De facto long-term opioid therapy for
noncancer pain. The Clinical journal of pain 2008;24:521-7.
16. Benzodiazepine Equivalence Table. 2001. (Accessed 10/22/12, 2012, at
http://www.benzo.org.uk/bzequiv.htm.)
17. Miller M, Sturmer T, Azrael D, Levin R, Solomon DH. Opioid analgesics and the risk of
fractures in older adults with arthritis. Journal of the American Geriatrics Society 2011;59:430-8.
17
18. Carbone LD, Chin AS, Lee TA, et al. The association of opioid use with incident lower
extremity fractures in spinal cord injury. The journal of spinal cord medicine 2013;36:91-6.
19. Saunders KW, Dunn KM, Merrill JO, et al. Relationship of opioid use and dosage levels
to fractures in older chronic pain patients. Journal of general internal medicine 2010;25:310-5.
20. Ham AC, Swart KM, Enneman AW, et al. Medication-related fall incidents in an older,
ambulant population: the B-PROOF study. Drugs & aging 2014;31:917-27.
21. Jann M, Kennedy WK, Lopez G. Benzodiazepines: a major component in unintentional
prescription drug overdoses with opioid analgesics. Journal of pharmacy practice 2014;27:5-16.
22. Li L, Setoguchi S, Cabral H, Jick S. Opioid use for noncancer pain and risk of fracture in
adults: a nested case-control study using the general practice research database. American
journal of epidemiology 2013;178:559-69.
23. Han H, Kass PH, Wilsey BL, Li CS. Increasing trends in Schedule II opioid use and
doctor shopping during 1999-2007 in California. Pharmacoepidemiology and drug safety
2014;23:26-35.
24. Coutinho EdSF, da Silva SD. [Medication as a risk factor for falls resulting in severe
fractures in the elderly]. Cad Saúde Pública 2002;18:8.
25. Huang AR, Mallet L, Rochefort CM, Eguale T, Buckeridge DL, Tamblyn R. Medication-
related falls in the elderly: Causative factors and Preventive strategies. Drugs Aging 2012;29:19.
18
Table 1. Age and gender distribution among people who had at least one controlled substance prescription filled in the year preceding a fatal fall injury (cases) and matched controls, New Mexico, 2007-2012
Characteristic Cases Controls
P Value (n = 869) (n = 3,476) Age Group, N (%) <0.001 65-69 41 (4.7%) 1,001 (28.8%) 70-74 67 (7.7%) 790 (22.7%) 75-79 108 (12.4%) 641 (18.4%) 80-84 172 (19.8%) 519 (14.9%) 85-89 229 (26.4%) 338 (9.7%) 90-94 172 (19.8%) 132 (3.8%) 95+ 80 (9.2%) 55 (1.6%) Gender, N (%) 1.000 Female 532 (61.2%) 2,128 (61.2%) Male 337 (38.8%) 1,348 (38.8%)
19
Table 2. Prescription history distributions for fall deaths among people aged ≥ 65 years who had at least one controlled substance prescription filled in the year preceding a fatal fall injury (cases, n = 869) and matched controls (n = 3,476), New Mexico, 2007-2012
Opioid Dose (MME/Prescription Day)
1 Month b 2 Months b 6 Months b 1 Year a
Category Cases Controls Cases Controls Cases Controls Cases Controls 0 528 (60.8%) 2770 (79.7%) 443 (51.0%) 2419 (69.6%) 280 (32.2%) 1570 (45.2%) 153 (17.6%) 787 (22.6%) >0-40 195 (22.4%) 471 (13.6%) 236 (27.2%) 686 (19.7%) 327 (37.6%) 1182 (34.0%) 419 (48.2%) 1659 (47.7%) >40-120 97 (11.2%) 195 (5.6%) 145 (16.7%) 307 (8.8%) 213 (24.5%) 604 (17.4%) 243 (28.0%) 851 (24.5%) >120-200 35 (4.0%) 25 (0.7%) 29 (3.3%) 36 (1.0%) 31 (3.6%) 71 (2.0%) 35 (4.0%) 114 (3.3%) >200 14 (1.6%) 15 (0.4%) 16 (1.8%) 28 (0.8%) 18 (2.1%) 49 (1.4%) 19 (2.2%) 65 (1.9%)
Sedative-hypnotic Dose (VME/Prescription Day)
1 Month b 2 Months b 6 Months b 1 Year b
Category Cases Controls Cases Controls Cases Controls Cases Controls 0 569 (65.5%) 2665 (76.7%) 524 (60.3%) 2488 (71.6%) 453 (52.1%) 2117 (60.9%) 388 (44.7%) 1806 (52.0%) >0-20 233 (26.8%) 719 (20.7%) 254 (29.2%) 845 (24.3%) 306 (35.2%) 1117 (32.1%) 351 (40.4%) 1345 (38.7%) >20-60 56 (6.4%) 76 (2.2%) 80 (9.2%) 124 (3.6%) 97 (11.2%) 217 (6.2%) 120 (13.8%) 287 (8.3%) >60 11 (1.3%) 16 (0.5%) 11 (1.3%) 19 (0.6%) 13 (1.5%) 25 (0.7%) 10 (1.2%) 38 (1.1%)
Percent of Opioid Days with Concurrent Sedative-hypnotics
1 Month b 2 Months b 6 Months b 1 Year b
Category Cases Controls Cases Controls Cases Controls Cases Controls None 753 (86.7%) 3249 (93.5%) 726 (83.5%) 3177 (91.4%) 663 (76.3%) 2987 (85.9%) 606 (69.7%) 2769 (79.7%) Up to 50% 39 (4.5%) 55 (1.6%) 55 (6.3%) 83 (2.4%) 87 (10.0%) 177 (5.1%) 128 (14.7%) 298 (8.6%) Over 50% 77 (8.9%) 172 (5.0%) 88 (10.1%) 216 (6.2%) 119 (13.7%) 312 (9.0%) 135 (15.5%) 409 (11.8%)
Number of Prescribers in Time Span
1 Month b 2 Months b 6 Months b 1 Year b
Number Cases Controls Cases Controls Cases Controls Cases Controls 0 400 (46.0%) 2328 (67.0%) 286 (32.9%) 1851 (53.3%) 119 (13.7%) 829 (23.9%) 0 (0.0%) 0 (0.0%) 1 378 (43.5%) 1020 (29.3%) 421 (48.5%) 1353 (38.9%) 416 (47.9%) 1830 (52.7%) 408 (47.0%) 2039 (58.7%) 2 71 (8.2%) 112 (3.2%) 115 (13.2%) 217 (6.2%) 205 (23.6%) 567 (16.3%) 226 (26.0%) 874 (25.1%) 3 16 (1.8%) 15 (0.4%) 32 (3.7%) 40 (1.2%) 74 (8.5%) 166 (4.8%) 125 (14.4%) 315 (9.1%) >3 4 (0.5%) 1 (0.0%) 15 (1.7%) 15 (0.4%) 55 (6.3%) 84 (2.4%) 110 (12.7%) 248 (7.1%)
Number of Pharmacies in Time Span
1 Month b 2 Months b 6 Months b 1 Year b
20
Category Cases Controls Cases Controls Cases Controls Cases Controls 0 400 (46.0%) 2328 (67.0%) 286 (32.9%) 1851 (53.3%) 119 (13.7%) 829 (23.9%) 0 (0.0%) 0 (0.0%) 1 405 (46.6%) 1087 (31.3%) 467 (53.7%) 1492 (42.9%) 508 (58.5%) 2231 (64.2%) 538 (61.9%) 2711 (78.0%) 2 52 (6.0%) 59 (1.7%) 92 (10.6%) 124 (3.6%) 165 (19.0%) 345 (9.9%) 199 (22.9%) 578 (16.6%) 3 12 (1.4%) 2 (0.1%) 22 (2.5%) 8 (0.2%) 62 (7.1%) 64 (1.8%) 91 (10.5%) 152 (4.4%) >3 0 (0.0%) 0 (0.0%) 2 (0.2%) 1 (0.0%) 15 (1.7%) 7 (0.2%) 41 (4.7%) 35 (1.0%)
Chronic Use of Opioids (At Least 90% of the Time Span)
1 Month a 2 Months a 6 Months 1 Year
Category Cases Controls Cases Controls Cases Controls Cases Controls No 772 (88.8%) 3209 (92.3%) 803 (92.4%) 3281 (94.4%) 833 (95.9%) 3341 (96.1%) 837 (96.3%) 3368 (96.9%) Yes 97 (11.2%) 267 (7.7%) 66 (7.6%) 195 (5.6%) 36 (4.1%) 135 (3.9%) 32 (3.7%) 108 (3.1%)
Chronic Use of Sedative-hypnotics (At Least 90% of the Time Span)
1 Month 2 Months 6 Months 1 Year
Category Cases Controls Cases Controls Cases Controls Cases Controls No 766 (88.2%) 3081 (88.6%) 794 (91.4%) 3165 (91.1%) 829 (95.4%) 3277 (94.3%) 836 (96.2%) 3311 (95.3%) Yes 103 (11.9%) 395 (11.4%) 75 (8.6%) 311 (9.0%) 40 (4.6%) 199 (5.7%) 33 (3.8%) 165 (4.8%) MME, Morphine Milligram Equivalent; VME, Valium Milligram Equivalent a P < .05 b P < .0001
21
Table 3. Adjusted odds ratios for fall deaths among people aged ≥ 65 years who had at least one controlled substance prescription filled in the year preceding their injury (cases) and matched controls, New Mexico, 2007-2012
Variable P-value Level Odds Ratio
Lower CI
Upper CI
Age in years <.0001 N/A 1.15 1.13 1.16
Sex 0.005 Male 1.31 1.08 1.58
Female ref Average MME/day, 1 month <.0001 >120 4.13 1.88 6.27
>0-120 1.61 1.25 2.07
0 ref Average VME/day, 1 month <.0001 >20 3.05 1.92 4.84
>0-20 1.54 1.22 1.95
0 ref Number of pharmacies, 1 year <.0001 >3 3.83 2.15 6.85
3 2.08 1.48 2.91
2 1.30 1.03 1.63
1 ref Chronic a use of opioids, 6 months 0.001 Yes 0.44 0.28 0.72
No ref
Chronic a use of sedative-hypnotics, 6 months 0.014 Yes 0.58 0.37 0.89
No ref
MME, Morphine Milligram Equivalent; VME, Valium Milligram Equivalent a Chronic use is defined as obtaining prescriptions that will cover ≥ 90% of time span
22
