Population-based trends in hospitalizations due to injection drug use-related serious bacterial infections, Oregon, 2008 to 2018

Jeffrey Capizzi; Judith Leahy; Haven Wheelock; Jonathan Garcia; Luke Strnad; Monica Sikka; Honora Englander; Ann Thomas; P Todd Korthuis; Timothy William Menza

doi:10.1371/journal.pone.0242165

. 2020 Nov 9;15(11):e0242165. doi: 10.1371/journal.pone.0242165

Population-based trends in hospitalizations due to injection drug use-related serious bacterial infections, Oregon, 2008 to 2018

Jeffrey Capizzi ¹, Judith Leahy ¹, Haven Wheelock ², Jonathan Garcia ³, Luke Strnad ⁴, Monica Sikka ⁴, Honora Englander ⁴, Ann Thomas ¹, P Todd Korthuis ^1,^4,^#, Timothy William Menza ^1,^4,^*,^#

Editor: Nickolas D Zaller⁵

PMCID: PMC7652306 PMID: 33166363

Abstract

Background

Injection drug use has far-reaching social, economic, and health consequences. Serious bacterial infections, including skin/soft tissue infections, osteomyelitis, bacteremia, and endocarditis, are particularly morbid and mortal consequences of injection drug use.

Methods

We conducted a population-based retrospective cohort analysis of hospitalizations among patients with a diagnosis code for substance use and a serious bacterial infection during the same hospital admission using Oregon Hospital Discharge Data. We examined trends in hospitalizations and costs of hospitalizations attributable to injection drug use-related serious bacterial infections from January 1, 2008 through December 31, 2018.

Results

From 2008 to 2018, Oregon hospital discharge data included 4,084,743 hospitalizations among 2,090,359 patients. During the study period, hospitalizations for injection drug use-related serious bacterial infection increased from 980 to 6,265 per year, or from 0.26% to 1.68% of all hospitalizations (P<0.001). The number of unique patients with an injection drug use-related serious bacterial infection increased from 839 to 5,055, or from 2.52% to 8.46% of all patients (P<0.001). While hospitalizations for all injection drug use-related serious bacterial infections increased over the study period, bacteremia/sepsis hospitalizations rose most rapidly with an 18-fold increase. Opioid use diagnoses accounted for the largest percentage of hospitalizations for injection drug use-related serious bacterial infections, but hospitalizations for amphetamine-type stimulant-related serious bacterial infections rose most rapidly with a 15-fold increase. People living with HIV and HCV experienced increases in hospitalizations for injection drug use-related serious bacterial infection during the study period. Overall, the total cost of hospitalizations for injection drug use-related serious bacterial infections increased from $16,305,129 in 2008 to $150,879,237 in 2018 (P<0.001).

Conclusions

In Oregon, hospitalizations for injection drug use-related serious bacterial infections increased dramatically and exacted a substantial cost on the health care system from 2008 to 2018. This increase in hospitalizations represents an opportunity to initiate substance use disorder treatment and harm reduction services to improve outcomes for people who inject drugs.

Introduction

The substance use disorder (SUD) epidemic has long been a major public health challenge in Oregon, which led the nation in prescription opioid prescribing in 2006, and experienced increases in opioid overdose early in the epidemic [1]. Oregon experienced a substantial increase in the rate of opioid use diagnoses among the state’s Medicaid population from 101 cases per 100,000 population in 2005 to 506 cases per 100,000 population in 2015 (Personal communication, John W. McIlveen, Ph.D., State Opioid Treatment Authority, Health Systems Division, Oregon Health Authority, September 12, 2017). While deaths due to prescription opioids have declined in the setting of reductions in provider prescribing, non-pharmaceutical fentanyl deaths have increased from 0.4 per 100,000 population in 2009 to 1.8 per 100,000 population in 2018 [2]. Concurrently, and consistent with national data, methamphetamine use, alone and in combination with opioids, has increased significantly statewide [3–5]. Methamphetamine-related deaths increased from 0.5 per 100,000 population in 2006–2008 to 3.4 per 100,000 population in 2015–2017 [2].

The substance use disorder (SUD) epidemic has led to increased infectious complications of injection drug use (IDU) [6], including serious bacterial infections (SBIs), such as skin and soft tissue infections (SSTI), bacteremia or sepsis, osteomyelitis, infectious endocarditis, as well as hepatitis B virus (HBV), hepatitis C virus (HCV), and HIV infections [7–10]. IDU-related SBIs are associated with high morbidity and mortality [11] and may be an important marker of SUD severity; those with an IDU-related SBI experienced a more than fifty-fold increase in overdose death compared to those without an IDU-related SBI [12]. Hospitalization rates and hospitalization costs associated with IDU-related SBIs are important measures of the social, economic, and public health burden of IDU. These data highlight the critical need for SUD screening, harm reduction services, and patient engagement–all interventions that can and should happen at both the hospital- and community-level [13].

Despite recent reports of increased hospitalizations for SBIs [8, 10, 14, 15], few studies provide population-based estimates of hospitalization trends and costs among people who inject drugs (PWID). A recent statewide assessment of endocarditis hospitalizations in North Carolina documented a more than twelve-fold increase in hospitalizations for heart valve replacement among patients with diagnoses of drug use and endocarditis between 2007 and 2017 [10]. Such estimates of hospitalizations for IDU-related SBI and their costs are urgently needed to optimize resource allocation to clinical and public health interventions best suited to limiting the infectious disease consequences of IDU.

In addition, increases in infectious endocarditis and acute HCV diagnoses may indicate new or emerging IDU networks, especially in areas where IDU has not been a previously established problem [14, 16]. These emerging networks may experience greater vulnerability to HIV and HCV transmission [16]. In Oregon, the proportion of new HIV infections attributable to IDU increased from 11% in 2012 to 26% in 2018 [17]. Concurrent increases in chronic HCV infection among those younger than 30 years of age reflect more recent infection likely acquired in the context of IDU [18]. Thus, SBI associated with IDU may indicate concurrent or future increases in HIV and HCV infection.

In the context of the trends in Oregon SUD metrics and the increasing infectious complications related to IDU in other jurisdictions and their implications for HIV and HCV transmission, we sought to 1) describe statewide trends in IDU-related SBI hospitalizations overall and by SBI type and drug use diagnosis, 2) assess IDU-related SBI diagnoses among individuals living with HIV and HCV, and 3) and determine the annual costs of IDU-related SBI overall and by SBI type.

Methods

Study design and population

We conducted a population-based retrospective cohort study of people hospitalized in Oregon with IDU-related SBIs from January 1, 2008 to December 31, 2018. These hospitalizations included residents of other states receiving care in Oregon.

Data source and setting

We used the Oregon Hospital Discharge Dataset (HDD) containing all inpatient hospitalizations from 60 Oregon hospitals, excluding only the state’s two Veteran’s Affairs hospitals and one long-term acute care facility [19]. Thirty-three hospitals (55%) were located in rural or frontier areas. We accessed the Oregon HDD through a data use agreement with the Office of Health Analytics of the Oregon Health Authority (OHA). Data were de-identified after matching to Oregon HIV and HCV surveillance data (defined below) but before any further analysis. Representatives of the OHA Science and Epidemiology Council deemed this work public health practice and exempt from institutional review board review based on Oregon Administrative Rule 333-019-0005 [20].

Case classification

We defined an IDU-related SBI hospitalization as any hospital stay in which a patient received a diagnosis of substance use associated with opioids, cocaine, amphetamine-type stimulants (ATS), sedatives or other drugs and at least one of the following infections: endocarditis, bacteremia/sepsis, osteomyelitis, or SSTI. We vetted and modified the algorithm based on International Classification of Diseases, 9^th and 10^th Revisions (ICD-9 and ICD-10), codes through a key informant process that included consultation from the Centers for Disease Control and Prevention, wound clinic and emergency department doctors, and a medical information specialist. The algorithm excluded diagnostic codes not specific for drug use or infections associated with IDU, such as codes reflecting drug-use remission or poisoning. We expected this algorithm to be highly specific and less sensitive relative to the actual number of hospitalizations among PWID because of incomplete coding of drug use [21]. Between January 1, 2008 and September 30, 2015 we used ICD-9 diagnostic codes to classify cases. Subsequent to September 30, 2015 HDD implemented ICD-10 codes which we used for case classification. S1 Data includes diagnosis codes used for inclusion in and, exclusion from, the case classification.

To capture the full extent of the impact of IDU-related SBI, we did not restrict our analysis by age. During the study period, there were 34 patients less than twelve years of age included in the dataset; 22 were neonates and twelve were one to eleven years of age. Together, they represented 0.1% (34/34,404) unique patients in the dataset. While SBI hospitalizations in these age groups are not necessarily the result of IDU directly, they may be the indirect result of caregiver IDU [22].

To identify patients living with either HIV or HCV, we matched patients in the HDD to Oregon HIV and HCV surveillance data using both deterministic (SPSS, IBM, Armonk, NY) and probabilistic (LinkPlus, Centers for Disease Control and Prevention, Atlanta, GA) methods based on first, middle, last name, alias names, and date of birth. Ninety percent (4,093/4,527) of persons in the HDD diagnosed with HIV matched to reported HIV cases, and 75% (17,127/22,795) of persons diagnosed with HCV matched to reported HCV cases. Patients in the HDD with HIV who were not matched to the HIV surveillance system were individuals who never resided in Oregon and were, thus, never reported as confirmed cases in Oregon. Matching of HDD patients with HCV to the surveillance system was less successful since HCV surveillance in Oregon is collected passively by laboratory reporting and does not document aliases.

Cost determination

We determined cost in 2018 dollars by adjusting the charged amount by the cost-to-charge ratios published by the OHA Office of Health Analytics [23]. There was a different adjustment for each hospital and year. For each year, we calculated the total costs and the median and interquartile range (IQR) of costs per IDU-related SBI hospitalization overall and for each infection type: endocarditis, bacteremia/sepsis, osteomyelitis, or SSTI.

Statistical analysis

We summarized demographic characteristics of gender, age, race/ethnicity, HIV and HCV status, Oregon residence, and geography (urban, rural/frontier defined by zip code [24]) overall, by SBI type, and by drug class used. We calculated the annual number of hospitalizations for SBIs for each year. For hospitalizations coded with more than one SBI, we prioritized the condition of greatest severity in order to prevent duplicating cost or counts across SBIs [21]. We prioritized endocarditis, then osteomyelitis (both potential complications of bacteremia) followed by bacteremia/sepsis, and SSTI. For example, we consider a patient diagnosed with endocarditis and a SSTI during the same admission as a single hospitalization. As the care delivered during such a hospitalization is likely driven by the endocarditis diagnosis, we attribute the cost of the hospitalization to endocarditis rather than the SSTI (based on the severity hierarchy) and not to both diagnoses (which would result in cost duplication). For hospitalizations coded with more than one drug class, we created an additional category to distinguish polysubstance use from single-class drug use. We then calculated the total IDU-related SBI hospitalization costs and median and IQR of costs per IDU-related SBI hospitalization by year and SBI type. For comparison, we calculated the total costs and the median and IQR of costs per SBI hospitalization per year among patients without a concurrent drug use diagnosis code and who never had a drug use diagnosis code during the follow-up period.

We used generalized linear models with a log link, a Poisson distribution and robust standard errors to perform tests-of-trend in IDU-related SBI hospitalizations and costs over time. We defined statistical significance at the P<0.05 level and used SPSS for all analyses (IBM, Armonk, NY).

Results

Oregon discharge data for the period of January 1, 2008 through December 31, 2018 included 4,084,743 hospitalizations among 2,090,359 unique patients. Ninety-three percent of hospitalizations reflected care to Oregon residents.

Demographics of patients hospitalized for injection drug use-related serious bacterial infections

Our algorithm identified 34,404 individuals hospitalized at least once for an IDU-related SBI between January 1, 2008 and December 31, 2018. Fifty-four percent were men, 49% were over the age of 50, 84% were white, 3% identified as Hispanic, 72% lived in urban areas, and 95% were Oregon residents (Table 1). Over half had a diagnosis code of opioid-only use, one-quarter had a diagnosis code of ATS-only use, and 16% had diagnosis codes of polysubstance use, most commonly combination opioid and ATS use. Seven percent were living with HIV and 38% were living with HCV.

Table 1. Sociodemographic characteristics of patients hospitalized with an injection drug use-related serious bacterial infection, Hospital Discharge Data, Oregon, 2008–20018.

		N = 34,404, n (%)
Sex	Female	15,865 (46)
Age, years	0–11	34 (<1)
	12–19	207 (1)
	20–29	4,329 (13)
	30–39	6,271 (18)
	40–49	6,618 (19)
	50–59	7,906 (23)
	60–69	5274 (15)
	70–79	2358 (7)
	80 and older	1407 (4)
Race/ethnicity	Hispanic/Latinx	1,112 (3)
	Black	1,186 (3)
	White	28,869 (84)
	Other^a	1,993 (6)
	Refused/Unknown	1,244 (4)
HIV status	HIV case	2,360 (7)
HCV status	HCV case	12,902 (38)
Drug use diagnosis	Opioid-only	17,807 (52)
	Amphetamine-type stimulants-only	8,497 (25)
	Cocaine-only	548 (2)
	Sedative-only	304 (1)
	Other drug	1,827 (5)
	More than one class	5,421 (16)
Oregon residence	Oregon resident	32,720 (95)
Geography	Rural/Frontier	8,098 (25)
	Urban	24,622 (75)

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^a Other includes American Indian/Alaska Native, Asian, and Native Hawaiian/Pacific Islander

Patients hospitalized for SSTI and osteomyelitis were more likely to be men than women (Table 2). Thirty-one percent of those hospitalized for bacteremia or sepsis and thirty-eight percent of those hospitalized for endocarditis were aged 60 years or older. Forty-seven percent of those with SSTI and 41% of those with osteomyelitis were living with HCV infection. The percentage of patients with a diagnosis of opioid-only use was greatest among those with osteomyelitis, while the percentage of patients diagnosed with ATS-only use was highest among those hospitalized for SSTI.

Table 2. Sociodemographic characteristics by type of injection drug-use related serious bacterial infection, Hospital Discharge Data, Oregon, 2008–2018.

		SSTI (N = 11,515)	Bacteremia (N = 16,166)	Osteomyelitis (N = 2,196)	Endocarditis (N = 4,527)
		n (%)	n (%)	n (%)	n (%)
Sex	Female	4,701 (41)	8,010 (50)	787 (36)	2,367 (52)
	Male	6,814 (59)	8,156 (50)	1,409 (64)	2,160 (48)
Age, years	0–11	4 (<1)	28 (<1)	1 (<1)	1 (<1)
	12–19	87 (1)	99 (1)	6 (<1)	15 (<1)
	20–29	1,694 (15)	1,918 (12)	144 (7)	573 (13)
	30–39	2,639 (23)	2,543 (16)	347 (16)	742 (16)
	40–49	2,761 (24)	2,753 (17)	481 (22)	623 (14)
	50–59	2,643 (23)	3,755 (23)	676 (31)	832 (18)
	60–69	1163 (10)	2963 (18)	381 (17)	767 (17)
	70–79	355 (3)	1376 (9)	126 (6)	501 (11)
	80 and older	169 (1)	731 (5)	34 (2)	473 (10)
Race/ethnicity	Hispanic/Latinx	305 (3)	580 (4)	89 (4)	138 (3)
	Black	339 (3)	562 (3)	59 (3)	226 (5)
	White	9,477 (82)	13,743 (85)	1,871 (85)	3,778 (83)
	Other^a	862 (7)	806 (5)	104 (5)	221 (5)
	Refused/Unknown	532 (5)	475 (3)	73 (3)	164 (4)
HIV case	Not HIV case	10,727 (93)	15,138 (94)	2,024 (92)	4,155 (92)
	HIV case	788 (7)	1,028 (6)	172 (8)	372 (8)
HCV case	Not HCV case	6,132 (53)	11,037 (68)	1,295 (59)	3,038 (67)
	HCV case	5,383 (47)	5,129 (32)	901 (41)	1,489 (33)
Drug use	Opioid-only	5,700 (50)	8,640 (53)	1,211 (55)	2,256 (50)
	Amphetamine-type stimulants-only	2,968 (26)	3,968 (25)	487 (22)	1,074 (24)
	Cocaine-only	167 (1)	239 (1)	32 (1)	110 (2)
	Sedative-only	40 (<1)	192 (1)	11 (<1)	61 (1)
	Other drug	727 (6)	761 (5)	143 (7)	196 (4)
	More than one class	1,913 (17)	2,366 (15)	312 (14)	830 (18)
Geography	Rural/Frontier	2,301 (21)	4,009 (26)	568 (27)	1,220 (28)
	Urban	8,626 (79)	11,404 (74)	1,528 (73)	3,064 (72)

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^a Other includes American Indian/Alaska Native, Asian, and Native Hawaiian/Pacific Islander

Sixty-four percent and 65% of those hospitalized for an IDU-related SBI with a diagnosis of ATS-only use and cocaine-only use, respectively, were men (Table 3). Thirty-nine percent and 62% of patients hospitalized for an IDU-related SBI in the context of a diagnosis of opioid-only use and sedative-only use, respectively, were aged 60 years or older. Thirty-nine percent of those hospitalized for an IDU-related SBI with a diagnosis of cocaine-only use were Black. Eleven percent and 59% of patients hospitalized with an IDU-related SBI and a diagnosis of polysubstance use were living with HIV and living with HCV, respectively. Thirty-three percent of those with an IDU-related SBI hospitalization and an ATS-only use diagnosis were living in rural/frontier areas.

Table 3. Sociodemographic characteristics of patients hospitalized for injection drug use-related serious bacterial infection by substance use diagnosis, Hospital Discharge Data, Oregon, 2008–2018.

		Opioid-only (N = 17,807)	Amphetamine-type stimulants- only (N = 8,497)	Cocaine-only (N = 548)	Sedative-only (N = 304)	Other drug (N = 1,827)	More than one class (N = 5,421)
		n (%)	n (%)	n (%)	n (%)	n (%)	n (%)
Sex	Female	9,128 (51)	3,039 (36)	193 (35)	198 (65)	827 (45)	2,480 (46)
	Male	8,679 (49)	5,458 (64)	355 (65)	106 (35)	1,000 (55)	2,941 (54)
Age, years	0–11	29 (<1)	0	0	2 (<1)	1 (<1)	2 (<1)
	12–19	44 (<1)	84 (1)	6 (1)	1 (<1)	19 (1)	53 (1)
	20–29	1,640 (9)	993 (1)	65 (12)	15 (5)	241 (13)	1,375 (25)
	30–39	2,514 (14)	1,748 (21)	76 (14)	11 (4)	347 (19)	1,575 (29)
	40–49	2,641 (15)	2,328 (27)	120 (22)	30 (10)	414 (23)	1,085 (20)
	50–59	3,989 (22)	2,351 (28)	167 (30)	57 (19)	424 (23)	918 (17)
	60–69	3664 (21)	886 (10)	96 (18)	73 (24)	220 (12)	335 (6)
	70–79	2047 (11)	99 (1)	15 (3)	45 (15)	95 (5)	57 (1)
	80 and older	1239 (7)	8 (<1)	3 (1)	70 (23)	66 (4)	21 (<1)
Race/ethnicity	Hispanic/Latinx	491 (3)	324 (4)	22 (4)	10 (3)	73 (4)	192 (4)
	Black	473 (3)	215 (3)	216 (39)	2 (1)	61 (3)	219 (4)
	White	15,135 (85)	7,254 (85)	215 (39)	276 (91)	1,492 (82)	4,497 (83)
	Other^a	1,037 (6)	460 (5)	63 (11)	9 (3)	115 (6)	309 (6)
	Refused/Unknown	671 (4)	244 (3)	32 (6)	7 (2)	86 (5)	204 (4)
HIV status	Not HIV case	16,850 (95)	7,803 (92)	511 (93)	300 (99)	1,729 (95)	4,851 (89)
	HIV case	957 (5)	694 (8)	37 (7)	4 (1)	98 (5)	570 (11)
HCV status	Not HCV case	11,838 (66)	5,558 (65)	416 (76)	280 (92)	1,200 (66)	2,210 (41)
	HCV case	5,969 (34)	2,939 (25)	132 (24)	24 (8)	627 (34)	3,211 (59)
Geography	Rural/Frontier	3,838 (23)	2,662 (33)	39 (7)	86 (30)	532 (31)	941 (18)
	Urban	13,175 (77)	5,391 (67)	486 (93)	204 (70)	1,206 (69)	4,160 (82)

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^a Other includes American Indian/Alaska Native, Asian, and Native Hawaiian/Pacific Islander

Overall time trends in injection drug use-related serious bacterial infections

IDU-related SBI hospitalizations increased over six-fold, from 980 to 6,265 hospitalizations, or 0.26% to 1.68% of all hospitalizations from 2008 to 2018 (P<0.001; Fig 1). The number of unique persons with an IDU-related SBI increased from 839 to 5,055, or from 2.52% to 8.46% of all unique persons hospitalized during this period (P<0.001).

Trends in injection drug use-related serious bacterial infection by infection type and drug use

IDU-related bacteremia/sepsis hospitalizations increased 18-fold, from 189 to 3,345, or from 0.05% to 0.9% of all hospitalizations from 2008 to 2018 (P<0.001; Fig 1). Endocarditis hospitalizations increased eight-fold, from 112 to 929, or from 0.03% to 0.5% of all hospitalizations (P<0.001). Osteomyelitis hospitalizations increased six-fold, from 59 to 371, or from 0.02% to 0.1% of all hospitalizations (P<0.001) while SSTI hospitalizations increased three-fold, from 620 to 1620, or from 0.16% to 0.43% of all hospitalizations (P<0.001).

From 2008 to 2018, opioids were the most common drug class associated with hospitalization for IDU-related SBI (52%), followed by ATS (25%), multiple drugs (16%), cocaine (2%), and sedatives (1%). The largest increase in IDU-related SBI between 2008 and 2018 occurred among people diagnosed with ATS-only use (a 15-fold increase, P<0.001). The next largest increases occurred among those with polysubstance use (13-fold increase, P<0.001) and using sedative-only use diagnoses (12-fold increase, P<0.001). In comparison, IDU-related SBI hospitalizations among those with an opioid-only use diagnosis showed a five-fold increase (P<0.001, Fig 2).

Trends in injection drug use-related serious bacterial infections among those living with HIV and HCV

IDU-related SBI hospitalizations among persons with HIV increased five-fold, from 18 to 174, or an increase from 1.7% to 13.0% of all hospitalizations among people living with HIV (P<0.001; Fig 3). IDU-related SBI hospitalizations among persons with HCV also increased five-fold, from 105 to 910, or an increase from 3.7% to 17.0% of all hospitalizations among people living with HCV (P<0.001).

Cost associated with injection drug use-related serious bacterial infections

Overall, total costs of IDU-related SBI hospitalizations increased nine-fold from $16,305,129 in 2008 to $150,879,237 in 2018 (P<0.001; Table 4). The median cost per IDU-related SBI hospitalization increased from $9,525 (IQR: $5,814, $18,441) in 2008 to $13,000 (IQR: $7,809, $24,671) in 2018. Increases in total cost were greatest among bacteremia/sepsis hospitalizations (a 15-fold increase from $5,622,041 to $83,845,107, P<0.001) followed by endocarditis (a 14-fold increased from $2,229,222 to $30,366,521, P<0.001), osteomyelitis (an eight-fold increase from $1,701,881 to $13,015,821, P<0.001), and SSTI (a 3.5-fold increase from $6,751,985 to $23,651,787, P<0.001, Tables 5 and 6).

Table 4. Total costs of injection drug use-related serious bacterial infections and median cost per hospitalization for serious bacterial infections, by injection drug use, Hospital Discharge Data, Oregon, 2008–2018^a.

	SBI, IDU-related				SBI, not IDU-related
Year	Total	Median	25%ile	75%ile	Total	Median	25%ile	75%ile
2008	$16,305,129	$9,525	$5,814	$18,441	$697,939,223	$11,210	$6,352	$23,121
2009	$17,689,550	$9,857	$6,005	$18,537	$773,443,861	$11,950	$6,732	$24,905
2010	$18,426,648	$9,982	$6,117	$17,280	$821,897,961	$12,305	$6,916	$25,348
2011	$20,888,954	$10,123	$6,295	$16,962	$855,136,477	$12,471	$7,043	$25,460
2012	$23,130,549	$9,885	$6,128	$16,089	$878,329,637	$12,366	$7,021	$24,926
2013	$33,692,406	$10,409	$6,496	$17,348	$928,546,762	$12,642	$7,313	$25,121
2014	$58,821,754	$10,725	$6,840	$18,777	$1,042,552,439	$12,487	$7,298	$24,819
2015	$121,564,949	$12,991	$7,818	$24,784	$1,159,360,623	$12,735	$7,527	$25,262
2016	$142,651,621	$13,629	$8,315	$27,083	$1,192,774,951	$13,510	$7,876	$26,560
2017	$147,094,529	$13,937	$8,356	$26,978	$1,251,412,186	$13,663	$8,013	$27,167
2018	$150,879,237	$13,000	$7,809	$24,671	$1,192,496,100	$12,914	$7,675	$24,704

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^aCosts adjusted by charge-to-cost ratio (by year and hospital) and adjusted to 2018 US dollars

Table 5. Total costs of IDU-related skin/soft tissue infection and bacteremia and median cost per hospitalization for skin/soft tissue infection and bacteremia, by injection drug use, Hospital Discharge Data, Oregon, 2008–2018^a.

	SSTI, IDU-related				SSTI, not IDU-related				Bacteremia, IDU-related				Bacteremia, not IDU-related
Year	Total	Median	25%ile	75%ile	Total	Median	25%ile	75%ile	Total	Median	25%ile	75%ile	Total	Median	25%ile	75%ile
2008	$6,751,985	$7,804	$5,116	$12,235	$116,174,137	$7,933	$4,873	$14,353	$5,622,041	$19,868	$10,782	$33,968	$343,451,334	$15,319	$8,181	$33,928
2009	$6,953,456	$8,238	$5,278	$12,455	$122,461,812	$8,355	$5,099	$15,041	$6,974,869	$16,476	$9,433	$33,775	$398,393,335	$15,808	$8,422	$34,001
2010	$7,439,488	$8,183	$5,295	$13,214	$123,166,582	$8,488	$5,172	$14,767	$6,636,342	$14,418	$7,746	$23,376	$440,862,732	$15,828	$8,759	$32,729
2011	$7,758,188	$7,894	$5,317	$12,166	$117,533,145	$8,487	$5,225	$15,444	$8,777,363	$13,407	$8,465	$22,718	$460,225,911	$15,114	$8,433	$30,916
2012	$8,629,763	$8,263	$5,474	$13,135	$115,605,435	$8,723	$5,385	$15,439	$8,802,159	$11,724	$7,149	$18,197	$489,908,831	$13,799	$7,967	$27,639
2013	$10,596,151	$8,483	$5,499	$12,838	$115,500,784	$8,932	$5,594	$15,710	$15,843,281	$12,396	$7,519	$19,187	$534,438,487	$13,662	$8,058	$26,601
2014	$14,479,263	$8,513	$5,678	$12,913	$117,626,589	$8,675	$5,567	$15,245	$29,790,498	$11,703	$7,561	$20,402	$607,854,320	$13,213	$7,834	$25,508
2015	$19,310,106	$9,220	$5,950	$14,751	$116,446,173	$8,776	$5,596	$15,465	$65,503,907	$14,216	$8,505	$26,540	$609,513,162	$12,859	$7,805	$24,773
2016	$19,676,208	$9,493	$6,011	$15,131	$118,161,184	$9,414	$5,830	$16,772	$80,432,942	$15,174	$9,330	$29,083	$639,658,241	$13,548	$8,139	$25,793
2017	$21,202,658	$9,811	$6,386	$16,040	$123,953,721	$9,588	$6,018	$17,167	$81,144,765	$14,857	$9,046	$28,678	$680,094,936	$13,504	$8,169	$25,624
2018	$23,651,787	$9,194	$5,962	$15,836	$121,319,280	$9,414	$5,841	$16,745	$83,845,107	$13,798	$8,415	$24,891	$660,489,907	$12,703	$7,781	$23,418

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^aCosts adjusted by charge-to-cost ratio (by year and hospital) and adjusted to 2018 US dollars

Table 6. Sum of total costs of IDU-related osteomyelitis and endocarditis and median cost per hospitalization for osteomyelitis and endocarditis, by injection drug use, Hospital Discharge Data, Oregon, 2008–2018^a.

	Osteomyelitis, IDU-related				Osteomyelitis, not IDU-related				Endocarditis, IDU-related				Endocarditis, not IDU-related
Year	Total	Median	25%ile	75%ile	Total	Median	25%ile	75%ile	Total	Median	25%ile	75%ile	Total	Median	25%ile	75%ile
2008	$1,701,881	$19,302	$11,199	$32,220	$39,242,787	$15,886	$9,240	$28,099	$2,229,222	$11,329	$6,362	$23,497	$199,070,966	$10,756	$6,309	$22,092
2009	$1,287,714	$16,257	$9,631	$29,579	$43,937,442	$15,993	$9,743	$28,531	$2,473,510	$13,205	$6,112	$33,352	$208,651,271	$11,513	$6,559	$24,366
2010	$2,027,157	$19,470	$11,044	$39,966	$45,757,096	$17,754	$10,337	$30,456	$2,323,662	$13,329	$6,922	$26,031	$212,111,551	$11,727	$6,569	$25,485
2011	$2,186,453	$21,036	$11,845	$37,818	$53,913,613	$17,645	$10,368	$31,965	$2,166,949	$16,261	$8,140	$24,798	$223,463,808	$12,020	$6,850	$25,945
2012	$2,568,475	$16,109	$9,534	$33,693	$52,795,599	$17,792	$10,652	$30,607	$3,130,153	$13,133	$7,086	$22,269	$220,019,772	$12,728	$6,865	$29,635
2013	$3,274,987	$18,830	$11,163	$34,254	$57,048,009	$17,645	$10,901	$29,703	$3,977,986	$13,977	$7,560	$24,237	$221,559,482	$13,264	$7,124	$32,668
2014	$6,326,910	$18,435	$10,444	$29,011	$58,908,996	$17,559	$10,784	$29,485	$8,225,082	$13,551	$8,231	$27,513	$258,162,535	$13,820	$7,495	$32,600
2015	$13,542,693	$20,673	$12,689	$39,435	$61,451,050	$18,381	$11,273	$31,035	$23,208,243	$16,576	$8,977	$38,005	$371,950,238	$14,979	$8,253	$33,473
2016	$11,436,419	$24,558	$12,066	$44,108	$43,668,130	$18,885	$11,926	$33,056	$31,106,052	$17,839	$9,400	$39,494	$391,287,396	$15,789	$8,691	$35,289
2017	$13,249,918	$22,182	$13,824	$45,977	$53,574,358	$19,646	$11,520	$33,457	$31,497,189	$16,972	$9,832	$38,536	$393,789,172	$16,687	$8,955	$38,416
2018	$13,015,821	$21,609	$12,193	$42,091	$54,978,748	$17,708	$11,278	$30,646	$30,366,521	$17,557	$9,638	$38,462	$355,708,164	$15,575	$8,521	$35,085

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^aCosts adjusted by charge-to-cost ratio (by year and hospital) and adjusted to 2018 US dollars

Total costs of SBI hospitalizations that were not IDU-related were greater than IDU-related SBI hospitalizations due to the greater number of SBI hospitalizations that were not IDU-related (Tables 5 and 6). In 2018, there were 53,497 SBI hospitalizations that were not IDU-related compared to 6,265 IDU-related SBI hospitalizations. However, there was only a 1.7-fold increase in total costs of SBI hospitalizations that were not IDU-related over the study period; the largest increase was an almost 2-fold increase in total costs of non-IDU-related bacteremia hospitalizations. In 2018, the median cost per IDU-related SBI hospitalization was similar to the median cost of non-IDU-related SBI hospitalizations. Also, the median cost per hospitalization for IDU-related SSTI was similar to the median cost per hospitalization for SSTI that was not IDU-related. In contrast, the median cost per hospitalization for IDU-related bacteremia, osteomyelitis, and endocarditis was greater than the median cost per hospitalization for bacteremia, osteomyelitis, and endocarditis that was not IDU-related. Osteomyelitis accounted for the greatest difference in median cost per IDU-related ($21,609) versus non-IDU-related ($17,708) SBI hospitalization.

Discussion

Our study demonstrates increasing hospitalizations and costs for IDU-related SBI, underscoring the substantial impact of the SUD epidemic on healthcare and public health systems.

IDU-related bacteremia/sepsis increased dramatically during the study period, ultimately comprising the largest proportion of hospitalizations and incurring the largest total costs in subsequent years. Consistent with data from North Carolina, we also observed a significant increase in endocarditis diagnoses [10]. However, the magnitude of IDU-related cases was greater in Oregon and, in 2018, the 929 cases of IDU-related endocarditis cost the healthcare system over $30 million dollars. Increases in endocarditis may signal emerging IDU networks in need of harm reduction services [14]. Our data suggest, however, that bacteremia may be a more sensitive indicator of emerging IDU networks given its greater incidence compared to endocarditis. Use of bacteremia as such an indicator may also be more useful in rural areas with lower population density. In contrast, osteomyelitis and endocarditis may be markers of more severe, long-standing SUD characterized by more frequent use and riskier injection practices [12, 25]. The increasing proportion of hospitalizations for IDU-related SBI among people living with HIV and HCV likely reflects an increase in IDU-related risk behavior that may indicate ongoing community-level transmission of HCV and HIV.

While opioid-only use diagnoses accounted for the greatest proportion of IDU-related SBI overall during the study period, we observed the largest increase in IDU-related SBI among people with ATS-only use diagnoses. This increase in SBI mirrors state-level data indicating an increasing rate of both fatal and non-fatal overdoses related to methamphetamine use during the same time period [2], increased ATS-related hospitalizations and costs [3], and increased treatment admissions for concomitant methamphetamine and opioid use disorder in Oregon and nationally [4, 5]. Among PWID participating in the Centers for Disease Control and Prevention’s National Behavioral Surveillance (NHBS) survey in Portland, OR, combination methamphetamine and opioid use was associated with more frequent injection, syringe sharing, and decreased use of syringe exchange, factors which may increase the risk of SBI (personal communication, Timothy Menza, Principal Investigator, Portland NHBS, July 2, 2020). In addition to SBI, methamphetamine use is associated with recent increases in HIV infection, early syphilis, and HCV among PWID [18, 26, 27]. Thus, inpatient admissions for SBI also represent key opportunities for HIV, STI, and HCV screening, prevention and linkage to care [28]. Our findings also emphasize a critical need for the incorporation of addiction medicine training into the fields of infectious diseases and hospital medicine [29, 30].

Our study has several limitations. First, the study relies on administrative claims data which may not accurately capture diagnoses and other demographics (e.g. race, ethnicity). The algorithm we used to identify IDU-related SBIs required a patient to have both a drug use and serious bacterial infection diagnosis coded during the same stay. This conservative definition may underestimate the true burden and costs of IDU-related SBI as patients had multiple visits with a drug use code or infection before having both during the same hospitalization. This pattern implies that the algorithm was more specific and less sensitive, and that many patients with only an SBI diagnosis may have a drug use disorder that was not coded during the hospitalization. We suspect that the algorithm also underestimated the proportion of SBI associated with polysubstance use. Among PWID participating in NHBS in Portland, OR, 70% of respondents reported using both ATS and opioids (personal communication, Timothy Menza, Principal Investigator, NHBS Portland, October 7, 2020). In contrast, only 16% of SBI hospitalizations in the current study were associated with more than one drug use code. Second, the study period bridged the transition from the use of ICD-9 to ICD-10 diagnosis codes. Trends among diagnoses were consistent across this transition but demonstrated more variation as providers started using ICD-10. Third, these data reflect the socio-demographic composition, patterns of drug use and hospital care in Oregon all of which may differ from other regions of the United States. Our study sample, like the population of Oregon, was predominantly white (75.1% of the population of Oregon identifies as white), which makes detecting differences in IDU-related SBI hospitalizations by race and ethnicity challenging. However, we observed that 5% of endocarditis hospitalizations occurred among Black patients, but Black individuals comprise only 2% of the Oregon population. Thus, compared to their representation in the general population, Black patients are likely over-represented among those diagnosed with IDU-related endocarditis in Oregon. The observed age distribution of our sample is also a potential source of bias. A quarter of our sample was over the age of 60. Older patients may use prescription medications that fall within the categories of the drug codes of interest and may be more likely to experience SBIs [31, 32]. Thus, older patients may be more likely to be misclassified as having an IDU-related SBI compared to younger patients. Furthermore, hospitalizations among older patients may be more costly than hospitalizations experienced by younger patients [33].

Despite these limitations, our study has important implications for health systems, payers, and policy-makers. Increases in hospitalizations underscores the opportunity to initiate substance use disorder treatment and harm reduction services during hospitalization. While many people may not seek addictions care in the community, hospitalization can present a reachable moment to engage non-treatment seeking adults in care [34]. Hospital-based initiation of buprenorphine and methadone are acceptable to patients and providers [13, 35]. Hospital-based SUD treatment can reduce post-hospital substance use [36], increase engagement in post-hospital SUD treatment [13], and improve care quality [37, 38]. Evidence is mixed as to effects of hospital-based addictions care on readmissions for SBI [13, 34, 39–41].

From 2008 to 2018, we observed greater increases in total costs associated with hospitalizations for IDU-related SBI, overall and for each SBI type, compared to SBI hospitalizations that were not IDU-related. In 2018, the median cost per hospitalization for bacteremia, osteomyelitis, and endocarditis that were IDU-related was greater than the median cost per hospitalization for these infections that were not IDU-related. The reason for the greater cost of hospitalization for these infection types among PWID may be three-fold. First, providers may not consider PWID candidates for outpatient parenteral antibiotic therapy (OPAT), thus, prolonging hospital lengths of stay for infections that may require 2–6 weeks of intravenous antimicrobials. Although emerging data indicate that injection drug use is not a contraindication to OPAT and that PWID can be successfully and safely treated in the outpatient setting, these data had likely not yet impacted practice in our study period [42, 43]. Second, due to fear of discrimination and judgment by healthcare providers, PWID with SBI may present to care later than people who do not inject drugs [44]. The resulting delay may result in more complicated presentations requiring higher levels of care and surgical intervention for source control. Finally, PWID may be more likely to experience infections with methicillin-resistant Staphylococcus aureus (MRSA) [45] which often has more severe clinical presentations that require the use of more costly therapeutics [46].

The cost of increased hospitalizations represents a growing preventable burden that should be of major concern to healthcare payers. Keeshin and colleagues noted that an admission for infectious endocarditis could cost more than the start-up costs of a syringe exchange program [14] and Schranz and colleagues estimated that an admission for endocarditis exceeds the cost of a year’s worth of medication for opioid use disorder [10]; our data are consistent with these findings. Presentations of our data to local public health authorities, health systems, federally qualified health centers, law enforcement, community members with lived and professional experience with PWID, and other stakeholders have led to the adoption of syringe service programs in several high-needs Oregon counties. Other public health entities looking to expand access to sterile syringes and injection supplies may consider leveraging their own data on IDU-related SBI to promote community-based harm reduction for PWID. Payers might consider funding individual- and community-level preventive strategies and response measures, such as health and human service provider trainings on IDU, sterile injection patient education, increased access to sterile equipment, syringes and naloxone, adult HAV/HBV vaccinations, biomedical HIV prevention, and substance abuse treatment programs could be established or strengthened to decrease IDU-associated infections. Our study shows that payers already have skin in the game; they might consider upstream, preventive efforts to improve health and reduce costs.

While opioid use accounted for over half of IDU-related SBIs during the study period, the greatest increases in hospitalizations and costs for IDU-related SBI were associated with ATS and polysubstance use. The rising rates of hospitalizations and costs related to ATS and polysubstance use suggest that the singular “opioid epidemic” narrative which has driven the public health response to the SUD crisis may miss the mark [4, 5, 18, 47]. Instead, healthcare delivery systems, policy-makers, and researchers must broaden their scope and take into account the importance of polysubstance use across communities.

Conclusion

IDU-related SBI hospitalizations and costs increased in Oregon between 2008 and 2018. Most IDU-related SBI hospitalizations were associated with opioid-only use diagnoses, but we observed substantial increases in IDU-related SBI associated with ATS-only and polysubstance use diagnoses. Additionally, IDU-related SBI hospitalizations increased among people living with HIV and HCV. Our findings suggest an urgent need to expand community-based addiction treatment and harm reduction services to prevent SBI, and to expand hospital-based addictions care to engage people with SUD during hospitalization.

Supporting information

S1 Data. ICD-9 and ICD-10 code equivalents for Hospital Discharge Data: Injection drug use-related serious bacterial infection hospitalizations.

(DOCX)

Click here for additional data file.^{(19.1KB, docx)}

Acknowledgments

We thank the following people for their contributions to creating the algorithm to define injection drug use-related serious bacterial infections: Dr. Tanya Page (Providence Medical Group, Oregon), Dr. Bill Walter (Lane County Health Department, Oregon), Dr. Svetla Slavova (Associate Professor, University of Kentucky), Theresa Garvin (Director, St Claire Medical Center, Kentucky), and Dr. Jon Zibell (Senior Public Health Analyst at Research Triangle International [RTI] and previously with the Division of Viral Hepatitis, CDC).

Data Availability

The data underlying the results presented in the study are available from the Office of Health Analytics of the Oregon Health Authority, https://www.oregon.gov/oha/HPA/ANALYTICS/Pages/Hospital-Reporting.aspx.

Funding Statement

This work was supported by grants from the NIH National Institute on Drug Abuse (UH3DA044831, U01TR002631, UG1DA015815) to PTK. The URL for the National Institute on Drug Abuse is drugabuse.gov. The funder had no role in study design, implementation, analysis, or manuscript review.

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PLoS One. doi: 10.1371/journal.pone.0242165.r001

Decision Letter 0

Nickolas D Zaller

10 Sep 2020

PONE-D-20-23516

Population-based trends in hospitalizations due to injection drug use-related serious bacterial infections, Oregon, 2008 to 2018

PLOS ONE

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Reviewer #1: Yes

Reviewer #2: Partly

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #2: No

**********

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Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: The manuscript is clear and succinct in laying out the extent to which serious bacterial infections have increased and placed an increasing burden on hospitals in Oregon. The changes over the 11-year study period are a compelling reminder of the severity of the illicit drug use problem in the US and especially compelling since the population of Oregon is so overwhelmingly White. The changes in drug use patterns is reflected somewhat in the increasing number of infections among individuals whose drug use include amphetamine-type stimulants, and the authors are wise to point this out.

There are some elements of the manuscript that need greater attention from the authors. One important area is the results on costs. Given the increase in cases, the total costs have skyrocketed, but some of the increase appears to be related to the cost for each episode over the 11-year period. It would be instructive if the authors could compare the increasing cost per episode over time to cost per episode for similar infections in which the discharge codes did not include evidence substance use and for the cost per episode for hospitalization costs in general. This context would benefit those in state agencies, insurance companies, and hospital systems have a better understanding of the role of inflationary hospital costs and simultaneously the implications of a failure to prevent increases in serious bacterial infections among people who inject drugs.

The authors can dispense with Figure 1, since it is the cumulative total of the data presented in Figure 2, simply by adding the data on the annual total to Figure 2.

I take issue with the statement in the Conclusion that “SBI hospitalizations and costs increase[s]…were associated with amphetamine and polysubstance use diagnoses and increased hospitalizations among people living with HIV and HCV.” The majority of cases continued to involve opioids, so not mentioning opioids in the conclusion is an unfortunate omission.

There are a number of minor edits that would improve the text. These are referred to in the list below by line number and suggested edits are capitalized in many instances.

Line 50, Abstract: This would be clearer if the text read: “During the study period, hospitalizations…increased from 980 to 6,265 PER YEAR, or from 0.26% to 1.68%...”

Line 76, Introduction: “Methamphetamine-related deaths increasED from 0.5 per 100,000…”

Lines 81-82, Introduction reads: “IDU-related SBIs are associated with high morbidity and mortality with a more than fifty-fold increase in death in some studies.” Increased relative to what?

Line 84-85, Introduction: The sentence that begins, “They also highlight critical opportunities for SUD screening, harm reduction services, and patient engagement…” does not read clearly because the antecedents for “They” is “Hospitalization rates and hospitalization costs”, which aren’t really opportunities. I suggest the sentence be rephrased to read, “They also highlight THE critical NEED for SUD screening, harm reduction services, and patient engagement…”

Line 88, Introduction: This is the first time that the acronym PWID is used and so it should be written out.

Line 116-117, Methods: “We accessed these data…” The previous sentence describes the percentage of hospitals in rural/frontier areas. It is possible to construe that the “these” in the next sentence refers only to these hospitals and not all the other hospitals. Please rephrase for clarity.

Line 125, Methods: I suggest using the term amphetamine-type stimulants rather than amphetamines to describe this class of drugs. The authors might consider then using the acronym ATS for subsequent appearances. This is particularly pertinent since the most common illicit ATS would be methamphetamine,

Line 136, Methods: No need for the commas.

Line 187, Results: Should read “OPIOID and amphetamine use”, not heroin use. Nowhere else in the methods or results are opioids broken down into different compounds, nor do the ICD-9 or ICD-10 codes provide such specificity.

Line 340-341, Discussion: The text refers to “the “opioid epidemic” narrative which has driven the public health response”. It is unclear to me what narrative they are referring to, so the authors need to be much more specific.

Reviewer #2: The submitted article offers interesting insight into the consequences of SBIs among PWID. A particular strength is the use statewide data. With that, my comments are as follows:

Minor comments:

1. Please ensure you define each acronym when it is first used.

2. With the exception of stratifying by age category, you have quite a large sample. Chi-square is very sensitive to large sample sizes. Because of this, the p-values may be meaningless.

3. The cost analysis appears to be very general. While interesting, a deeper look into excess cost attributable to IDU would be far more impactful.

Major comments:

1. In the Statistical Analysis section of the Methods, the explanation for prioritizing SBIs of greatest severity to prevent duplicating cost or counts is not clear. Hospitalizations with multiple SBI would be obscured intentionally then?

2. For your age category variable, why set the cut off at 60? This age group represents a sizable proportion of your sample (26%). It would be good to know what the breakdown is within this category.

3. The last sentence of the second paragraph in the Case Classification section ("While SBI hospitalizations.."), are there any published articles to support this?

4. You indicate that you did not restrict your analysis by age to "capture the full extent of the impact of IDU-related SBI." Further, you offer some deeper justification for including young individuals. However, you do not mention the other end of the age spectrum. We know that older individuals also can be impacted by SBIs unrelated to IDU, and many may have non-illicit, medicinal dependencies. It is possible, if not probable, that a considerable number of older individuals in your sample are misclassified as PWID, yet there is not a single mention of this potential bias. Compounding the issue is that older individuals comprise a large proportion of your sample (26% 60 and older), yet we can't tell if these are mostly people in their 60s, 70s, 80s, etc. All of these issues culminate in a potentially biased cost analysis that likely includes a considerable number of misclassified, older individuals who may incur greater costs in the first place due to infirmity and/or longer lengths of stay.

**********

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Reviewer #1: No

Reviewer #2: Yes: Michael Cima

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PLoS One. 2020 Nov 9;15(11):e0242165. doi: 10.1371/journal.pone.0242165.r002

Author response to Decision Letter 0

10 Oct 2020

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

We have now formatted the manuscript to PLOS ONE’s style requirements.

2. Thank you for stating the following in the Financial Disclosure section:

"This work was supported by grants from the NIH National Institute on Drug Abuse

(UH3DA044831, U01TR002631, UG1DA015815) to PTK. The URL for the National

Institute on Drug Abuse is drugabuse.gov. The funder had no role in study design,

implementation, analysis, or manuscript review."

We note that one or more of the authors are employed by a commercial company: "Outside In"

Please also include the following statement within your amended Funding Statement.

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

OutsideIn is a federally-qualified health center (FQHC) and non-profit organization, not a commercial company. OutsideIn is publicly funded and provides safety net services to patients without insurance with a focus on youth and people affected by substance use and houselessness. HW, the author affiliated with Outside In, is the director of OutsideIn’s drug user health program. We now indicate in the author affiliations that OutsideIn is a federally-qualified health center. We do not feel that this affiliation represents a funding source nor a competing interest in the way that a consulting, pharmaceutical or biotechnology company does. Knowing this information, if PLOS ONE still requires amendments to the Funding Statement and Competing Interests Statement, we would be happy to do so.

Thank you. We now include the captions for supporting information files at the end of the manuscript.

Comments to the Author

review as an attachment if it exceeds 20,000 characters)

We now present the cost data for those who did not have diagnosis codes indicating substance use. We have updated the methods, results, and discussion to reflect the inclusion of these data.

The authors can dispense with Figure 1, since it is the cumulative total of the data presented in Figure 2, simply by adding the data on the annual total to Figure 2.

We’ve now combined Figures 1 and 2.

We’ve addressed this statement in the Conclusion.

There are a number of minor edits that would improve the text. These are referred to in the list below by line number and suggested edits are capitalized in many instances.

Line 50, Abstract: This would be clearer if the text read: “During the study period, hospitalizations…increased from 980 to 6,265 PER YEAR, or from 0.26% to 1.68%...”

Edit incorporated.

Line 76, Introduction: “Methamphetamine-related deaths increasED from 0.5 per 100,000…”

Edit incorporated.

Lines 81-82, Introduction reads: “IDU-related SBIs are associated with high morbidity and mortality with a more than fifty-fold increase in death in some studies.” Increased relative to what?

Thanks for catching this. The clause now reads: “…those with an IDU-related SBI experienced a more than fifty-fold increase in overdose death compared to those without an IDU-related SBI [12].” We have also revised the text to state that IDU-related SBI may be a marker of severe SUD as a reason for the association with overdose death.

The sentence now reads: “These data highlight the critical need for SUD screening, harm reduction services, and patient engagement – all interventions that can and should happen at both the hospital- and community-level [13].”

Line 88, Introduction: This is the first time that the acronym PWID is used and so it should be written out.

Edit incorporated.

We’ve replaced “these data” with “Oregon HDD” to clarify that we are accessing all of the hospital discharge data and not just the rural data.

Edit incorporated.

Line 136, Methods: No need for the commas.

Commas deleted.

Good catch. We’ve now edited it to read opioid rather than heroin.

We’ve clarified this statement in the discussion.

Reviewer #2: The submitted article offers interesting insight into the consequences of SBIs among PWID. A particular strength is the use statewide data. With that, my comments are as follows:

Minor comments:

1. Please ensure you define each acronym when it is first used.

Thank you, we have made changes to define acronyms the first time they are used.

2. With the exception of stratifying by age category, you have quite a large sample. Chi-square is very sensitive to large sample sizes. Because of this, the p-values may be meaningless.

We have removed the P-values from the tables.

3. The cost analysis appears to be very general. While interesting, a deeper look into excess cost attributable to IDU would be far more impactful.

We now present the cost data for SBI among those without drug use diagnosis codes. We’ve included revisions to the methods, results, and discussion detailing these data.

Major comments:

We now provide a reference for this practice of prioritizing SBIs and a statement to clarify the methods to prevent count and cost duplication.

2. For your age category variable, why set the cut off at 60? This age group represents a sizable proportion of your sample (26%). It would be good to know what the breakdown is within this category.

We now provide further breakdown of those 60 or greater, including those who are 60-69, 70-79, and 80 and older.

3. The last sentence of the second paragraph in the Case Classification section ("While SBI hospitalizations.."), are there any published articles to support this?

We now provide a reference for this statement.

We know provide further breakdown of those 60 or greater, including those who are 60-69, 70-79, and 80 and older. We now introduce the potential for bias among older patients in the limitations of our manuscript.

PLoS One. doi: 10.1371/journal.pone.0242165.r003

Decision Letter 1

Nickolas D Zaller

28 Oct 2020

Population-based trends in hospitalizations due to injection drug use-related serious bacterial infections, Oregon, 2008 to 2018

PONE-D-20-23516R1

Dear Dr. Menza,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Nickolas D. Zaller

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: Thank you for addressing the comments from the previous submission. This is an important and interesting article, and I appreciate the opportunity to review it.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Robert Heimer

Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0242165.r004

Acceptance letter

Nickolas D Zaller

29 Oct 2020

PONE-D-20-23516R1

Population-based trends in hospitalizations due to injection drug use-related serious bacterial infections, Oregon, 2008 to 2018

Dear Dr. Menza:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Nickolas D. Zaller

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Data. ICD-9 and ICD-10 code equivalents for Hospital Discharge Data: Injection drug use-related serious bacterial infection hospitalizations.

(DOCX)

Click here for additional data file.^{(19.1KB, docx)}

Data Availability Statement

[pone.0242165.ref001] 1.National Institute on Drug Abuse. Oregon: Opioid-Involved Deaths and Related Harms. 2020. Available at: https://www.drugabuse.gov/opioid-summaries-by-state/oregon-opioid-involved-deaths-related-harms. Accessed 3 June 2020.

[pone.0242165.ref002] 2.Oregon Health Authority: Prescribing and Overdose Data for Oregon: Opioid Overdose and Misuse: State of Oregon. Available at: https://www.oregon.gov/oha/ph/preventionwellness/substanceuse/opioids/pages/data.aspx. Accessed 3 June 2020.

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[pone.0242165.ref005] 5.Lopez A. The Co-Use of Methamphetamine and Opioids Among Patients in Treatment in Oregon, USA. 2019. Available at: https://ndews.umd.edu/sites/ndews.umd.edu/files/pubs/NDEWS-Hotspot-Serenity-Lane-Sept2019-FINAL.pdf. Accessed 16 December 2019.

[pone.0242165.ref006] 6.Springer SA, Korthuis PT, del Rio C. Integrating Treatment at the Intersection of Opioid Use Disorder and Infectious Disease Epidemics in Medical Settings: A Call for Action After a National Academies of Sciences, Engineering, and Medicine Workshop. Ann Intern Med 2018; 169:335–336. 10.7326/M18-1203 [DOI] [PMC free article] [PubMed] [Google Scholar]

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[pone.0242165.ref010] 10.J. Schranz A, Fleischauer A, H. Chu V, Wu L-T, L. Rosen D. Trends in Drug Use–Associated Infective Endocarditis and Heart Valve Surgery, 2007 to 2017. Ann Intern Med 2018; Available at: https://www.acpjournals.org/doi/abs/10.7326/M18-2124. Accessed 3 June 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Population-based trends in hospitalizations due to injection drug use-related serious bacterial infections, Oregon, 2008 to 2018

Jeffrey Capizzi

Judith Leahy

Haven Wheelock

Jonathan Garcia

Luke Strnad

Monica Sikka

Honora Englander

Ann Thomas

P Todd Korthuis

Timothy William Menza

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Study design and population

Data source and setting

Case classification

Cost determination

Statistical analysis

Results

Demographics of patients hospitalized for injection drug use-related serious bacterial infections

Table 1. Sociodemographic characteristics of patients hospitalized with an injection drug use-related serious bacterial infection, Hospital Discharge Data, Oregon, 2008–20018.

Table 2. Sociodemographic characteristics by type of injection drug-use related serious bacterial infection, Hospital Discharge Data, Oregon, 2008–2018.

Table 3. Sociodemographic characteristics of patients hospitalized for injection drug use-related serious bacterial infection by substance use diagnosis, Hospital Discharge Data, Oregon, 2008–2018.

Overall time trends in injection drug use-related serious bacterial infections

Fig 1. Injection drug use-related SBI hospitalizations, overall and by SBI type, as a percentage of all hospitalizations, Hospital Discharge Data, Oregon, 2008–2018.

Trends in injection drug use-related serious bacterial infection by infection type and drug use

Fig 2. Injection drug use-related SBI hospitalizations, by drug type, as a percentage of all hospitalizations, Hospital Discharge Data, Oregon, 2008–2018.

Trends in injection drug use-related serious bacterial infections among those living with HIV and HCV

Fig 3. Injection drug use-related SBI hospitalizations among people living with HIV and HCV as a percentage of all hospitalizations among people living with HIV and HCV, respectively, Hospital Discharge Data, Oregon, 2008–2018.

Cost associated with injection drug use-related serious bacterial infections

Table 4. Total costs of injection drug use-related serious bacterial infections and median cost per hospitalization for serious bacterial infections, by injection drug use, Hospital Discharge Data, Oregon, 2008–2018a.

Table 5. Total costs of IDU-related skin/soft tissue infection and bacteremia and median cost per hospitalization for skin/soft tissue infection and bacteremia, by injection drug use, Hospital Discharge Data, Oregon, 2008–2018a.

Table 6. Sum of total costs of IDU-related osteomyelitis and endocarditis and median cost per hospitalization for osteomyelitis and endocarditis, by injection drug use, Hospital Discharge Data, Oregon, 2008–2018a.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Nickolas D Zaller

Roles

Author response to Decision Letter 0

Decision Letter 1

Nickolas D Zaller

Roles

Acceptance letter

Nickolas D Zaller

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 4. Total costs of injection drug use-related serious bacterial infections and median cost per hospitalization for serious bacterial infections, by injection drug use, Hospital Discharge Data, Oregon, 2008–2018^a.

Table 5. Total costs of IDU-related skin/soft tissue infection and bacteremia and median cost per hospitalization for skin/soft tissue infection and bacteremia, by injection drug use, Hospital Discharge Data, Oregon, 2008–2018^a.

Table 6. Sum of total costs of IDU-related osteomyelitis and endocarditis and median cost per hospitalization for osteomyelitis and endocarditis, by injection drug use, Hospital Discharge Data, Oregon, 2008–2018^a.