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. 2023 Jun 22;18(6):e0286552. doi: 10.1371/journal.pone.0286552

Healthcare costs and outcomes associated with laboratory-confirmed Lyme disease in Ontario, Canada: A population-based cohort study

Stephen Mac 1,2,*, Gerald Evans 3,4, Eleanor Pullenayegum 5,6, Samir N Patel 7,8, Beate Sander 1,2,3,7
Editor: Shuo-Yan Gau9
PMCID: PMC10286989  PMID: 37347742

Abstract

Background

The objective of this study was to estimate the economic burden attributable to laboratory-confirmed Lyme disease (LD) in Ontario, Canada and assess health outcomes associated with LD.

Method

We conducted a cohort study using laboratory-confirmed LD cases accrued between 2006 and 2018. The exposed cohort was matched 1:3 to the unexposed cohort using a combination of hard and propensity score matching. We used phase-of-care costing methods to calculate attributable costs for four phases of illness: pre-diagnosis, acute care, post-acute care, and continuing care in 2018 Canadian dollars. We used ICD-10-CA and OHIP billing codes to identify emergency department visits, physician billings and hospitalizations related to LD sequelae to assess health outcomes.

Results

A total of 2,808 cases were identified with a mean age of 46.5 (20.7) years and 44% female. Within 30-days, 404 (14.3%) cases required an ED visit and 63 (2.4%) cases required hospitalization. The mean (95% CI) total costs for LD cases in pre-diagnosis, acute, and post-acute care phases were $209 ($181, 238), $1,084 ($956, $1,212), and $1,714 ($1,499, $1,927), respectively. The highest mean attributable 10-day cost was $275 ($231, $319) during acute care. At 1-year post-infection, LD increased the relative risk of nerve palsies by 62 (20, 197), and polyneuropathy by 24 (3.0, 190). LD resulted in 16 Lyme meningitis events vs. 0 events in the unexposed.

Conclusion

Individuals with laboratory-confirmed LD have increased healthcare resource use pre-diagnosis and up to six months post-diagnosis, and were more likely to seek healthcare services related to LD sequelae.

Introduction

Lyme disease (LD) is the most commonly reported vector-borne disease in North America, caused by the spirochetal bacterium, Borrelia burgdorferi, which is transmitted from black-legged ticks [1]. In Canada, the annual number of LD cases has increased from 144 in 2009 to 2,636 in 2019 (18-fold increase) [2], and in Canada’s most populous province (Ontario), the incidence rate has increased from 0.7 per 100,000 in 2010 to 7.9 per 100,000 persons in 2019 [3]. LD diagnoses in Ontario, Canada can be made clinically or through laboratory confirmation [3], and cases are reported to the integrated Public Health Information System (iPHIS).

The economic impact of LD in endemic parts of Europe and the United States are considerable compared to other vector-borne diseases. In a 2015 US study, LD resulted in an increase of $2,968 healthcare costs over 12-months compared to the controls [4]. Annual economic burden from a societal perspective in various countries was estimated to be between $0.14 to $786M US dollars [5]. In Canada, economic burden has not been well studied other than a recent study in 2019 by our colleagues estimating attributable LD costs to be $832 (2014 Canadian dollars) over one year [6]. Limited burden studies are partially due to the geospatial differences in risk of LD, and the relative novelty of the vector-borne disease in many provinces, resulting in the inability to recruit for prospective or retrospective cohort studies until recently. The long-term sequelae of LD are highly inconsistent across different populations, ages, and jurisdictions; and remain without consensus [7].

This study’s objective was to estimate attributable healthcare costs in various LD phases-of-care, and health outcomes associated with laboratory-confirmed LD using a larger dataset, to further the understanding of economic and health burden to support the Federal Framework on Lyme Disease Act in Canada [8].

Methods

Study design and participants

We conducted an incidence-based, population-based matched cohort study using laboratory-confirmed LD cases in Ontario, Canada from the healthcare payer (Ministry of Health and Long-Term Care) perspective. Ontario has a publicly-funded universal healthcare system through the Ontario Health Insurance Plan (OHIP). We used person-level health administrative data housed at ICES. ICES is an independent, non-profit research institute whose legal status under Ontario’s health information privacy law allows it to collect and analyze health care and demographic data, without consent, for health system evaluation and improvement [9]. This project has been approved by the Research Ethics Board at the Ontario Agency for Health Protection and Promotion (Public Health Ontario) and University Health Network. Data analyses were performed using SAS 9.4 (SAS Institute, Cary, NC).

We identified incident laboratory-confirmed LD cases between January 1, 2006, and December 31, 2018, using linked laboratory and reportable disease datasets, and LD case definitions (S1 File) [10]. Provincial reportable disease data is collected through iPHIS and serological testing results are collected through Public Health Ontario (PHO) [11], which is the only approved LD serological testing laboratory in Ontario. These datasets were linked using unique encoded identifiers and analyzed at ICES. Individuals were excluded if they were missing data related to age, sex, birth date, did not live in Ontario, ineligible for OHIP, or over 110 years of age at index date.

Index date was defined as the earlier of the positive PHO results date or iPHIS reportable disease date. Actual infection or disease onset date could not be identified and may have been before or on the index date depending on time of tick bite, timing of LD manifestations, and whether or not LD testing was considered [12]. We defined phase-of-care lengths by plotting mean total daily costs from 30 days prior to the index date to 365 days post-index date for all individuals and used joinpoint analysis to observe changes in healthcare costs suggestive of disease onset and progression [13, 14]. Through joinpoint regression analysis and advice from clinical experts (GE, SP), we defined five phases: pre-diagnosis (10-days prior to index), acute care (30-days post-index), post-acute care (31–180 days post-index), predeath (90-days prior to death date), and continuing care (time between end of post-acute care and predeath phase) (Fig 1).

Fig 1. Phase-of-care for Lyme disease.

Fig 1

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Propensity score and hard matching

We used a combination of propensity score matching and hard matching. We calculated propensity scores (i.e., likelihood for persons to be infected with LD) using a logistic regression on socioeconomic status, rurality, residing public health unit (PHU), and comorbidities. We used the Johns Hopkins ACG® System Aggregated Diagnosis Groups Version 10 to generate comorbidity profiles two years prior to index date [15]. The ACG is a person-focused diagnosis based method of categorizing patients’ illnesses using a combination of ICD-9 and ICD-10 codes and assigning to one of 32 diagnosis clusters. We used neighbourhood income quintiles as a proxy for socioeconomic status, rurality (Y/N) derived from Statistics Canada, and PHU as a proxy for risk of being an endemic area based on residence (S1 Table).

Individuals neither infected with LD nor had a PHO serologic test record were selected from the Registered Persons Database (RPDB) for matching. We excluded individuals with negative PHO test results to avoid potential confounding healthcare seeking behaviours. Each lab-confirmed LD case (exposed) was matched with up to three uninfected (unexposed) persons using a nearest neighbour, without replacement approach on sex, age ± 5 years, index year ± 1, and the logit of the propensity score using a caliper width of 0.2 standard deviations of the logit of the propensity score [16]. Weighted standardized differences were calculated to assess the balance using a threshold of 0.10 [17]. Since our accrued cohort spans over two decades from 2006 to 2018, there is a possibility of time-dependent changes to LD awareness and clinical management (e.g., cost of diagnosis, treatment), hence we included index year in matching.

To examine the effect of LD on costs before death, exposed individuals who died were matched with up to three unexposed persons (controls) who died during the observation period at 90 days prior to death. Exposed individuals were matched on sex, age ± 5 years, death date ± 90 days, and the logit of the propensity score.

Outcomes and analysis

Costs were calculated following person-level costing methods established at ICES [18]. A summary of administrative datasets and brief descriptions are in S2 Table. We calculated and reported all mean and standardized 10-day costs in 2018 Canadian dollars. We estimated net attributable healthcare costs of laboratory-confirmed LD costs between exposed and unexposed individuals for each phase-of-care and cost categories using generalized estimating equation (GEE) analyses with the cost being the dependent variable, LD status as the independent variable, and matched sets treated as clusters. Each GEE analysis used a gamma distribution to inform variance parameterization and an identity link function [19, 20]. We used bootstrapping to resample matched sets with 1,000 replications to calculate 95% confidence intervals (95% CI).

We reported healthcare resource use and all-cause mortality for exposed individuals. ED visits and hospitalizations pre-index and post-index were determined using ICD-10 codes for LD and its related sequelae (S3 Table). Health outcomes associated with LD were estimated through healthcare seeking services (ED visits, physician billings, and hospitalizations) for conditions similar to known LD sequelae using the National Ambulatory Care Reporting System (NACRS), Discharge Abstract Database (DAD) and OHIP datasets (S2 Table), and a mix of ICD-10-CA and OHIP billing codes (S3 Table). Since there are no OHIP billing codes for LD; we used OHIP billing codes for conditions indicative of LD sequelae identified from a systematic review [7], and confirmed through clinical expert guidance (GE, SP). These sequelae included arthritis, carditis, cognitive (depression, delay in development), dermatologic, Lyme meningitis, polyneuropathy, and physical (headaches, nausea skin rashes) sequelae. We used a look back period of 1-year pre-index date to exclude sequelae that were also reported prior to confirmed LD infection (i.e., sequelae were reported pre-and post-index date were not considered attributable). We reported the proportion of the matched cohort developing sequelae and estimated relative risks (RR) using a GEE (poisson distribution, log link) to account for clustering.

All results are reported following the RECORD statement for observational studies (S4 Table) [21].

Results

LD cohort

A cohort of 2,808 individuals with laboratory-confirmed LD were included, mean (SD) age of 46.5 (20.7) years, and 44% female. Approximately 28% lived in rural areas, 54% were average to high socioeconomic status (i.e., neighbourhood income quintiles 3 to 5), and 61% of cases resided in four PHUs (Table 1). The age groups with most cumulative LD cases were 50 to 59, and 60 to 69 years. In age groups < 80 years, there were more male cases (S5 Table).

Table 1. Baseline characteristics of matched cohort at index date.

Before matching After matching
Variables Exposed individuals (n = 2,808) Unexposed individuals (n = 369,250) Exposed individuals (n = 2,772) Unexposed individuals (n = 8,217) Weighted Standardized Differences
Age
    Mean ± SD 46.50 ± 20.65 40.35 ± 22.59 46.51 ± 20.58 46.37 ± 20.57 0
    Median (IQR) 51 (31–62) 40 (22–57) 51 (31–62) 51 (31–62) 0
Sex
    Female 1,245 (44.3%) 186,323 (50.5%) 1,231 (44.4%) 3,657 (44.5%) 0
    Male 1,563 (55.7%) 182,927 (49.5%) 1,541 (55.6%) 4,560 (55.5%) 0
Neighbourhood Income Quintile
    1 (lowest) 325 (11.6%) 74,020 (20.1%) 325 (11.7%) 985 (11.9%) 0.00
    2 466 (16.6%) 72,803 (19.8%) 464 (16.7%) 1,298 (15.7%) 0.02
    3 606 (21.6%) 73,093 (19.9%) 600 (21.6%) 1,829 (22.1%) 0.01
    4 589 (21.0%) 74,203 (20.2%) 586 (21.1%) 1,769 (21.3%) 0.01
    5 (highest) 819 (29.2%) 73,912 (20.1%) 797 (28.8%) 2,410 (29.1%) 0.02
    Missing 0 (0.0%) 93 (0.0%) - - -
Rural
    No 2,014 (71.8%) 330,525 (89.7%) 2,005 (72.3%) 6,039 (72.8%) 0.00
    Yes 792 (28.2%) 37,992 (10.3%) 767 (27.7%) 2,252 (27.2%) 0.00
Public Health Unit
    Algoma 6 (0.2%) 3,087 (0.8%) 6 (0.2%) 12 (0.1%) 0.00
    Brant 12 (0.4%) 4,034 (1.1%) 12 (0.4%) 37 (0.4%) 0.00
    Durham 94 (3.3%) 17,318 (4.7%) 92 (3.3%) 248 (3.0%) 0.00
    Elgin-St Thomas 7 (0.2%) 714 (0.2%) 7 (0.3%) 23 (0.3%) 0.00
    Bruce-Grey-Owen Sound 11 (0.4%) 4,248 (1.2%) 11 (0.4%) 39 (0.5%) 0.02
    Haldimand-Norfolk 36 (1.3%) 2,914 (0.8%) 36 (1.3%) 125 (1.5%) 0.00
    Haliburton-Kawartha-Pine Ridge 47 (1.7%) 4,786 (1.3%) 47 (1.7%) 157 (1.9%) 0.00
    Halton 59 (2.1%) 14,605 (4.0%) 59 (2.1%) 188 (2.3%) 0.01
    Hamilton-Wentworth 41 (1.5%) 14,759 (4.0%) 41 (1.5%) 116 (1.4%) 0.02
    Hastings and Prince Edward 151 (5.4%) 4,365 (1.2%) 151 (5.4%) 485 (5.8%) 0.01
    Huron <6 (<0.2%) 1,578 (0.4%) <6 (<0.2%) 6 (0.1%) 0.00
    Kent-Chatham 13 (0.5%) 2,718 (0.7%) 12 (0.4%) 35 (0.4%) 0.00
    Kingston-Frontenac-Lennox and Addington PHU 445 (15.9%) 5,043 (1.4%) 441 (15.9%) 1,345 (16.2%) 0.00
    Lambton <6 (<0.2%) 3,511 (1.0%) <6 (<0.2%) 13 (0.2%) 0.01
    Leeds-Grenville-Lanark 505 (18.0%) 4,339 (1.2%) 478 (17.2%) 1,403 (16.9%) 0.00
    Middlesex-London 34 (1.2%) 12,333 (3.3%) 34 (1.2%) 112 (1.4%) 0.01
    Niagara 58 (2.1%) 12,062 (3.3%) 58 (2.1%) 176 (2.1%) 0.02
    North Bay <6 (<0.2%) 3,576 (1.0%) <6 (<0.2%) 15 (0.2%) 0.01
    Northwestern 23 (0.8%) 2,293 (0.6%) 23 (0.8%) 71 (0.9%) 0.02
    Ottawa Carleton 461 (16.4%) 25,762 (7.0%) 461 (16.6%) 1,285 (15.5%) 0.01
    Oxford 10 (0.4%) 799 (0.2%) 10 (0.4%) 29 (0.3%) 0.01
    Peel 60 (2.1%) 38,554 (10.5%) 60 (2.2%) 201 (2.4%) 0.01
    Perth 8 (0.3%) 2,081 (0.6%) 8 (0.3%) 24 (0.3%) 0.02
    Peterborough 26 (0.9%) 3,825 (1.0%) 26 (0.9%) 89 (1.1%) 0.01
    Porcupine <6 (<0.2%) 2,325 (0.6%) <6 (<0.2%) 14 (0.2%) 0.02
    Renfrew 19 (0.7%) 2,727 (0.7%) 19 (0.7%) 57 (0.7%) 0.00
    Eastern Ontario 130 (4.6%) 5,544 (1.5%) 130 (4.7%) 361 (4.4%) 0.02
    Simcoe 56 (2.0%) 14,604 (4.0%) 56 (2.0%) 179 (2.2%) 0.00
    Sudbury <6 (<0.2%) 5,205 (1.4%) <6 (<0.2%) 21 (0.3%) 0.03
    Thunder Bay 6 (0.2%) 4,256 (1.2%) 6 (0.2%) 16 (0.2%) 0.00
    Timiskaming <6 (<0.2%) 952 (0.3%) <6 (<0.2%) 6 (0.1%) 0.00
    Waterloo 50 (1.8%) 14,297 (3.9%) 50 (1.8%) 132 (1.6%) 0.00
    Wellington-Dufferin-Guelph 27 (1.0%) 7,624 (2.1%) 27 (1.0%) 90 (1.1%) 0.01
    Windsor-Essex 25 (0.9%) 11,198 (3.0%) 25 (0.9%) 88 (1.1%) 0.01
    York 78 (2.8%) 30,152 (8.2%) 78 (2.8%) 216 (2.6%) 0.00
    City of Toronto 288 (10.3%) 76,544 (20.8%) 288 (10.4%) 877 (10.6%) 0.00
Co-morbidities
    Time Limited: Minor
        No 1,816 (64.7%) 284,858 (77.1%) 1,807 (65.2%) 5,482 (66.7%) 0.02
        Yes 992 (35.3%) 84,392 (22.9%) 965 (34.8%) 2,735 (33.3%) 0.02
    Time Limited: Minor-Primary Infections
        No 1,628 (58.0%) 208,304 (56.4%) 1,620 (58.4%) 4,931 (60.0%) 0.03
        Yes 1,180 (42.0%) 160,946 (43.6%) 1,152 (41.6%) 3,286 (40.0%) 0.03
    Time Limited: Major
        No 2,631 (93.7%) 348,587 (94.4%) 2,598 (93.7%) 7,707 (93.8%) 0
        Yes 177 (6.3%) 20,663 (5.6%) 174 (6.3%) 510 (6.2%) 0
    Time Limited: Major-Primary Infections
        No 2,247 (80.0%) 337,223 (91.3%) 2,243 (80.9%) 6,720 (81.8%) 0.01
        Yes 561 (20.0%) 32,027 (8.7%) 529 (19.1%) 1,497 (18.2%) 0.01
    Allergies
        No 2,638 (93.9%) 344,475 (93.3%) 2,607 (94.0%) 7,719 (93.9%) 0.01
        Yes 170 (6.1%) 24,775 (6.7%) 165 (6.0%) 498 (6.1%) 0.01
    Asthma
        No 2,679 (95.4%) 349,850 (94.7%) 2,643 (95.3%) 7,842 (95.4%) 0.01
        Yes 129 (4.6%) 19,400 (5.3%) 129 (4.7%) 375 (4.6%) 0.01
    Likely to Recur: Discrete
        No 1,878 (66.9%) 261,394 (70.8%) 1,859 (67.1%) 5,589 (68.0%) 0.01
        Yes 930 (33.1%) 107,856 (29.2%) 913 (32.9%) 2,628 (32.0%) 0.01
    Likely to Recur: Discrete-Infections
        No 2,319 (82.6%) 296,619 (80.3%) 2,290 (82.6%) 6,917 (84.2%) 0.04
        Yes 489 (17.4%) 72,631 (19.7%) 482 (17.4%) 1,300 (15.8%) 0.04
    Likely to Recur: Progressive
        No 2,748 (97.9%) 360,985 (97.8%) 2,714 (97.9%) 8,046 (97.9%) 0
        Yes 60 (2.1%) 8,265 (2.2%) 58 (2.1%) 171 (2.1%) 0
    Chronic Medical: Stable
    No 1,785 (63.6%) 248,173 (67.2%) 1,764 (63.6%) 4,898 (59.6%) 0.09
    Yes 1,023 (36.4%) 121,077 (32.8%) 1,008 (36.4%) 3,319 (40.4%) 0.09
    Chronic Medical: Unstable
        No 2,298 (81.8%) 310,477 (84.1%) 2,270 (81.9%) 6,621 (80.6%) 0.03
        Yes 510 (18.2%) 58,773 (15.9%) 502 (18.1%) 1,596 (19.4%) 0.03
    Chronic Specialty: Stable-Orthopedic
        No 2,712 (96.6%) 361,174 (97.8%) 2,676 (96.5%) 7,921 (96.4%) 0.01
        Yes 96 (3.4%) 8,076 (2.2%) 96 (3.5%) 296 (3.6%) 0.01
    Chronic Specialty: Stable-Ear, Nose, Throat
    No 2,735 (97.4%) 361,681 (98.0%) 2,702 (97.5%) 8,000 (97.4%) 0.01
    Yes 73 (2.6%) 7,569 (2.0%) 70 (2.5%) 217 (2.6%) 0.01
    Chronic Specialty: Stable-Eye
        No 2,636 (93.9%) 352,278 (95.4%) 2,603 (93.9%) 7,719 (93.9%) 0
        Yes 172 (6.1%) 16,972 (4.6%) 169 (6.1%) 498 (6.1%) 0
    Chronic Specialty: Unstable-Orthopedic
        No 2,765 (98.5%) 363,359 (98.4%) 2,729 (98.4%) 8,058 (98.1%) 0.03
        Yes 43 (1.5%) 5,891 (1.6%) 43 (1.6%) 159 (1.9%) 0.03
    Chronic Specialty: Unstable-Eye
        No 2,622 (93.4%) 347,805 (94.2%) ≥ 99.8%* ≥ 99.9%* 0
        Yes 186 (6.6%) 21,445 (5.8%) NR NR 0
    Dermatologic
        No 2,271 (80.9%) 319,310 (86.5%) 2,588 (93.4%) 7,674 (93.4%) 0.01
        Yes 537 (19.1%) 49,940 (13.5%) 184 (6.6%) 543 (6.6%) 0.01
    Injuries/Adverse Effects: Minor
        No 1,980 (70.5%) 286,652 (77.6%) 2,248 (81.1%) 6,714 (81.7%) 0
        Yes 828 (29.5%) 82,598 (22.4%) 524 (18.9%) 1,503 (18.3%) 0
    Injuries/Adverse Effects: Major
        No 2,247 (80.0%) 305,865 (82.8%) 1,965 (70.9%) 5,859 (71.3%) 0
        Yes 561 (20.0%) 63,385 (17.2%) 807 (29.1%) 2,358 (28.7%) 0
    Psychosocial: Time Limited, Minor
        No 2,700 (96.2%) 354,306 (96.0%) 2,219 (80.1%) 6,606 (80.4%) 0.02
        Yes 108 (3.8%) 14,944 (4.0%) 553 (19.9%) 1,611 (19.6%) 0.02
    Psychosocial: Recurrent or Persistent, Stable
        No 2,184 (77.8%) 293,454 (79.5%) 2,665 (96.1%) 7,877 (95.9%) 0.02
        Yes 624 (22.2%) 75,796 (20.5%) 107 (3.9%) 340 (4.1%) 0.02
    Psychosocial: Recurrent or Persistent, Unstable
        No 2,714 (96.7%) 351,916 (95.3%) 2,156 (77.8%) 6,341 (77.2%) 0.02
        Yes 94 (3.3%) 17,334 (4.7%) 616 (22.2%) 1,876 (22.8%) 0.02
    Signs/Symptoms: Minor
        No 1,770 (63.0%) 244,578 (66.2%) 2,679 (96.6%) 7,969 (97.0%) 0.02
        Yes 1,038 (37.0%) 124,672 (33.8%) 93 (3.4%) 248 (3.0%) 0.02
    Signs/Symptoms: Uncertain
        No 1,323 (47.1%) 201,714 (54.6%) 1,756 (63.3%) 5,310 (64.6%) 0.01
        Yes 1,485 (52.9%) 167,536 (45.4%) 1,016 (36.7%) 2,907 (35.4%) 0.01
    Signs/Symptoms: Major
        No 1,999 (71.2%) 274,961 (74.5%) 1,316 (47.5%) 3,880 (47.2%) 0.02
        Yes 809 (28.8%) 94,289 (25.5%) 1,456 (52.5%) 4,337 (52.8%) 0.02
    Discretionary
        No 2,319 (82.6%) 317,193 (85.9%) 1,978 (71.4%) 5,926 (72.1%) 0.01
        Yes 489 (17.4%) 52,057 (14.1%) 794 (28.6%) 2,291 (27.9%) 0.01
    See and Reassure
        No 2,709 (96.5%) 358,851 (97.2%) 2,292 (82.7%) 6,773 (82.4%) 0.02
        Yes 99 (3.5%) 10,399 (2.8%) 480 (17.3%) 1,444 (17.6%) 0.02
    Prevention/Administrative
        No 1,857 (66.1%) 247,107 (66.9%) 2,676 (96.5%) 7,900 (96.1%) 0.03
        Yes 951 (33.9%) 122,143 (33.1%) 96 (3.5%) 317 (3.9%) 0.03
    Malignancy
        No 2,578 (91.8%) 345,125 (93.5%) 1,838 (66.3%) 5,570 (67.8%) 0.03
        Yes 230 (8.2%) 24,125 (6.5%) 934 (33.7%) 2,647 (32.2%) 0.03
    Pregnancy
        No 2,748 (97.9%) 358,134 (97.0%) 2,546 (91.8%) 7,480 (91.0%) 0.05
        Yes 60 (2.1%) 11,116 (3.0%) 226 (8.2%) 737 (9.0%) 0.05
    Dental
        No 2,770 (98.6%) 362,489 (98.2%) 2,712 (97.8%) 8,095 (98.5%) 0.02
        Yes 38 (1.4%) 6,761 (1.8%) 60 (2.2%) 122 (1.5%) 0.02
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IQR, interquartile range; NR, not report due to small cell; SD, standard deviation

* Cannot be exact due to small cells

Note: Due to small cells, the unmatched individuals were not displayed in this table, but are described in summary in the Results.

The annual incidence increased between 2006 and 2017 with males making up a majority in 2012 and onwards (Fig 2).

Fig 2. Laboratory-confirmed LD cases (n = 2,808) by index year and sex.

Fig 2

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The mean (SD) and range of the follow-up time was 4.83 (3.06) years, and 0.15–14.29 years, respectively. Within 30-days of index date, 404 (14.3%) individuals required an ED visit and 63 (2.4%) were hospitalized with LD as the main responsible diagnosis. The mean (SD) length of stay per hospitalization was 5.5 (4.2) days. Within the exposed cohort, 40 (1.4%) individuals died of all-cause mortality at a mean (SD) time of 4.14 (3.01) years post-diagnosis (Table 2).

Table 2. Healthcare resource use for exposed cohort (n = 2,808).

Frequency (percentage of total cases N = 2,808)
Time period New ED visits* Hospitalizations* with LD (A69.2) Hospitalizations* with LD (A69.2) as MRDX
Pre-diagnosis 30 (1.1%) < 6 (< 0.2%) < 6 (< 0.2%)
1-10d post-diagnosis 181 (6.4%) 43 (1.6%) 27 (1.0%)
11-30d post-diagnosis 223 (7.9%) 52 (1.8%) 39 (1.4%)
31-90d post-diagnosis 115 (4.1%) 33 (1.2%) 19 (0.7%)
>90d post-diagnosis 28 (1.0%) 10 (0.3%) < 6 (< 0.2%)
Mean (SD) LOS 3.21 (2.91) hours 7.32 (9.11) days 5.52 (4.19) days
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*Number of cases requiring ED visits or hospitalization, regardless of number of visits.

ED, emergency department; LD, Lyme disease; LOS, length of stay; MRDX, most responsible diagnosis; SD, standard deviation

Matched cohorts

After a combination of propensity score and hard matching at index date, 2,772 LD cases were matched to 8,217 individuals without LD (98.7%). Balance was assessed for all covariates and all weighted standardized differences were < 0.10, indicating good balance (Table 1). There were 34 cases who we were unable to match; these cases were more likely male (59%), resided in a single PHU (84%), resided in rural areas (75%), and in the highest neighbourhood income quintile (68%).

Among infected individuals who died, 38 (95%) were re-matched. The mean (SD) age at death for the re-matched exposed individuals was 72.2 (10.7) years. Nearly all weighted standardized differences were < 0.10, indicating a good balance (S6 Table).

Healthcare costs

The mean (95% CI) costs for LD exposed individuals in the pre-diagnosis, acute, post-acute, and continuing care phases were $209 ($181, 238), $1,084 ($956, $1,212), $1,714 ($1,499, $1,927), and $11,013 ($9,854, $12,172), respectively while the mean (95% CI) costs for unexposed individuals were $96 ($81, $112), $260 ($226, $294), $1,339 ($1,221, $1,457) and $13,414 ($12,622, $14,208), respectively (S7 Table). Over a time period including the 10 days pre-diagnosis and 6-months post-diagnosis, the mean (95% CI) attributable cost of a laboratory-confirmed LD case was $1,312 ($1,166, $1,458). In 2019, where the LD incidence in Ontario was 7.9 per 100,000 persons (1,159 cases) [22], the estimated attributable 6-month cost to the healthcare system would have been approximately $1,520,608.

Mean (95% CI) attributable 10-day costs was highest in the acute care phase at $275 ($231, $319), with hospitalization being the largest component at $160 ($127, $192). Mean (95% CI) attributable 10-day costs for the pre-diagnosis phase was $113 ($81, $144) with hospitalizations and ED visit costs contributing most of the costs at $46 ($26, $67), and $39 ($33, $45), respectively. Fig 3 illustrates the total mean 10-day costs for the exposed and unexposed cohorts in each of the four phases of care, broken down by cost types (e.g., hospitalization costs, physician costs). Laboratory and drug costs were also highest in the pre-diagnosis and acute care phases of LD, but nearly negligible in the later LD phases of care (Table 3).

Fig 3. Mean 10-day standardized costs for Lyme disease phase-of-care between pre-diagnosis to continuing care.

Fig 3

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Table 3. Standardized 10-day mean attributable costs by LD stagea.

Mean 10-day attributable costs (2018 CAD)
Type Pre-diagnosis Acute Post-acute Continuing Predeath
Hospitalization costs b $46 ($26, $67) $160 ($127, $192) $15 ($4, $26) -$2 (-$9, $6) $479 (-$1,163, $2,122)
Emergency visit costs $39 ($33, $45) $39 ($35, $42) $2 ($1, $2) $0 (-$1, $0) -$8 (-$36, $21)
Laboratory costs $12 ($10, $13) $6 ($6, $7) $1 ($1, $1) $0 ($0, $0) -$1 (-$4, $2)
Physician costs b $38 ($30, $45) $62 ($55, $69) $10 ($7, $13) $0 (-$2, $1) -$55 (-$186, $76)
Drug costs -$7 (-$13, -$2) $2 (-$1, $5) -$2 (-$5, $1) -$7 (-$9, -$4) $61 (-$28, $150)
Other costs b -$15 (-$21, -$9) $6 (-$3, $16) -$6 (-$13, $1) -$12 (-$17, -$8) -$87 (-$411, $237)
Total costs $113 ($81, $144) $275 ($231, $319) $20 ($2, $37) -$22 (-$33, -$10) $391 (-$1,152, $1,934)
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a Pre-diagnosis phase: 10 days prior to index date; acute phase: index to 30 day post-index date; post-acute phase: 31 to 180 days (6 months) post index date; continuing care phase: Remaining observation time between 6 months post-index and end of follow-up or predeath phase if individual died; predeath phase: 90 days prior to all-cause death.

b Hospitalization costs include only salaried physician services; physician services provided to inpatients are included in physician costs.

c Other costs include: long-term care, continuing care, rehabilitation, mental health, dialysis, cancer, and home care

Mean (95% CI) attributable 10-day costs for the post-acute care, and continuing care phase was $20 ($2, $37), and -$22 ($-33, -$10), respectively suggesting that LD generally does not result in additional long-term healthcare costs. In the predeath care phase, the mean (95% CI) attributable 10-day cost of all-cause mortality post-LD infection was $391 (-$1,152, $1,934).

Health outcomes

Within 6-months post-diagnosis, 4.4% of infected individuals sought healthcare services for these conditions similar to known LD sequelae compared to 0.4% in the general population (Table 4). A small proportion of cases (0.7%) required healthcare services for multiple conditions similar to known LD sequelae.

Table 4. Conditions similar to known LD sequelae at different follow-up post-index dates stratified by age and sex.

6 months follow-up post-index 1-year follow-up post-index 3-years follow-up post-index
Type of condition Unexposed (n = 8,217) Exposed (n = 2,772) RR (95% CI) Unexposed (n = 8,217) Exposed (n = 2,772) RR (95% CI) Unexposed (n = 8,217) Exposed (n = 2,772) RR (95% CI)
Arthritis 19 (0.2%) 54 (1.9%) 8.39 (5.03, 13.9)* 42 (0.5%) 63 (2.3%) 4.42 (3.02, 6.47)* 336 (4.1%) 178 (6.4%) 1.57 (1.31, 1.87)*
Cardiac < 6 (< 0.1%) 9 (0.3%) 6.67 (2.06, 21.6)* 21 (0.3%) 15 (0.5%) 2.12 (1.09, 4.11)* 338 (4.1%) 128 (4.6%) 1.12 (0.92, 1.36)
Cognitive NA NA NA 7 (0.1%) < 6 (< 0.1%) 1.69 (0.50, 5.79) 218 (2.7%) 91 (3.3%) 1.24 (0.98, 1.57)
Dermatologic < 6 (< 0.1%) 11 (0.4%) 10.88 (3.04, 38.9)* 9 (0.1%) 17 (0.6%) 5.60 (2.50, 12.55)* 185 (2.3%) 123 (4.4%) 1.97 (1.59, 2.45)*
Meningitis 0 (0%) 13 (0.5%) NR* 0 (0%) 16 (0.6%) NR* 0 (0%) 27 (1.0%) NR*
Nerve palsies < 6 (< 0.1%) 58 (2.1%) 171.84 (23.96 1,232.5)* < 6 (< 0.1%) 58 (2.1%) 62.04 (19.57, 196.64)* 9 (0.1%) 86 (3.1%) 28.32 (14.27, 56.20)*
Physical 9 (0.1%) 14 (0.5%) 4.61 (2.00, 10.6)* 56 (0.7%) 38 (1.4%) 2.01 (1.34, 3.02)* 1,015 (12.4%) 411 (14.8%) 1.20 (1.08, 1.32)*
Polyneuropathy 0 (0.0%) 6 (0.2%) NR* < 6 (< 0.1%) 8 (0.3%) 23.72 (2.98, 188.92)* 45 (0.5%) 36 (1.3%) 2.36 (1.52, 3.65)*
Sequelae count
Single (n = 1) 32 (0.4%) 122 (4.4%) - 112 (1.4%) 159 (5.7%) - 1,196 (14.6%) 533 (19.2%) -
Multiple (n ≥ 2) < 6 (< 0.1%) 20 (0.7%) - 17 (0.1%) 30 (1.1%) - 433 (5.2%) 245 (8.7%) -
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*p-value < 0.05

CI, confidence interval; LD, Lyme disease; NA, not available to be calculated due to zero counts; RR, relative risk

Within 1-year post-diagnosis, exposed individuals were still more likely to seek healthcare services, and 1% of cases developed Lyme meningitis. The RR (95% CI) for arthritis, cardiac abnormalities, cognitive sequelae, dermatologic sequelae, nerve palsies, physical sequelae and polyneuropathy was 4.42 (3.02, 6.47), 2.12 (1.09, 4.11), 1.69 (0.50, 5.79), 5.60 (2.50, 12.55), 62.04 (19.57, 196.64), 2.01 (1.34, 3.02), and 23.72 (2.98, 188.92), respectively. There were 16 cases of Lyme meningitis in infected individuals vs. 0 cases in uninfected individuals. The RR could not be calculated (Table 4).

Infected children and adolescents ≤ 18 years of age (n = 378) compared to unexposed (n = 1,119) had increased risks for healthcare visits relating to arthritis (RR 11.70, 95% CI: 5.99–22.87), cardiac abnormalities (RR 3.46, 95% CI: 1.17–10.25), nerve palsies (RR 26.65, 95% CI: 6.22–114.1), physical sequelae (RR 2.22, 95% CI: 1.66–3.08), and Lyme meningitis (11 vs. 0 events). Infected adults (>18 years) (n = 2,394) compared to unexposed (n = 7,098) were at increased risk for healthcare visits relating to arthritis (RR 1.26, 95% CI: 1.05–1.53), nerve palsies (RR 28.81, 95% CI: 13.25–62.65), physical sequelae (RR 1.12, 95% CI: 1.01–1.25), dermatologic conditions (RR 1.97, 95% CI: 1.58–2.47), polyneuropathy (RR 2.36, 95% CI: 1.52–3.65), and Lyme meningitis (16 vs. 0 events).

Discussion

Laboratory-confirmed LD resulted in increased healthcare costs 10-days prior to diagnosis, and up to 6 months post-infection. Mean attributable costs were highest 30-days post-infection and in the 10 days prior to diagnosis. In the acute care phase (30 days post-diagnosis), approximately 2% of cases were hospitalized with a mean length of stay of 5 days, but 58% of attributable costs were related to hospitalization, suggesting that while uncommon, hospitalizations contribute substantially to LD economic burden. In a recent Lyme borreliosis costing study from Poland [23], hospitalizations contributed to 67% of total LD costs, similar to the 58% from our study. Physician and ED visit costs were the next highest costs in the acute care phase. Drug costs were low from a healthcare payer perspective because the dataset only captures drug costs for individuals in outpatient settings who are ODB eligible (i.e., ≥ 65 years of age) [24].

In the post-acute care phase (2 to 6 months post-diagnosis), healthcare costs attributable to LD were potentially driven by those who develop conditions similar to known LD sequelae, albeit a fraction of the costs incurred during the acute care phase. Within 6-months post-diagnosis, there was increased risk for individuals infected with LD to seek healthcare services for conditions similar to known LD sequelae: arthritis, cardiac sequelae, nerve palsies, dermatologic sequelae, physical sequelae (headaches, ataxia, muscle pain, arthralgia), Lyme meningitis, and polyneuropathy. Due to unavailable billing codes specific to LD, our results can only be interpreted to suggest an increased attributable risk for healthcare visits related to LD sequelae as reported in the literature, but are not distinct reports of LD sequelae (e.g., Lyme arthritis, Lyme carditis). We excluded individuals who had healthcare visits for similar LD-related sequalae 1-year prior to their LD infection in order to increase the likelihood of these healthcare visits being attributable to the LD-infection.

In the continuing care phase, there were no costs attributable to LD, suggesting that LD may not generally result in long-term use of healthcare resources. Even though we identified a balanced matched cohort, there may be remaining unmeasurable confounders or high-cost users in the control group. Another hypothesis is that individuals who were infected with LD still have unobserved social or physical confounders that contribute to their risk of LD infection (e.g., increased leisure time, affinity to outdoors) and an inherently healthier profile given risk of LD infection is related to time spent outdoors where vectors carrying B. burgdorferi are present to transmit the bacteria [25]. Lastly, patients infected with LD may be seeking alternative care outside of the healthcare system, which would not have been captured in this analysis [26, 27].

In comparison to the earlier study from our colleagues using LD cases from 2006 to 2013 [6], the mean age and proportion of male cases were lower, hospitalizations within 30-days post-index was slightly lower (3.4% vs. 4.7%), and attributable 10-day costs for LD in the 30-days post index were similar ($275 vs. $277). Our study estimated increased healthcare resource use prior to diagnosis with an attributable cost of $113 in those 10 days due to diagnostic workup.

Our study is subject to several limitations. Our analysis used a cohort of individuals with LD from a linked laboratory and reportable disease dataset. We were unable to report costs based on LD stage (i.e., early localized vs. disseminated LD) due to limited data collected on LD stage at diagnosis, symptoms experienced, or time from initial symptom onset. These datasets are vulnerable to underreporting. The use of a PHO test results may inadvertently introduce selection and sampling bias as out-of-province testing, false negatives, or lack of diagnostic confirmation were excluded from the analysis. Those who are typically able to receive test results are more likely to receive timely and adequate healthcare and thus, the exclusion of those without positive test results may underestimate the overall economic burden of the disease. However, by using this LD case definition, we ensured consistent diagnosis across all cases to provide best available evidence to accurately estimate LD burden.

The lack of LD billing code limits our ability to identify those with PTLDS, and may underestimate the risk of LD sequelae. Despite these limitations, we used billing codes reviewed by clinical microbiologists and infectious disease specialists to report relative risks for conditions similar to known LD sequelae in a Canadian context. Lastly, the attributable economic burden of LD may be underreported due to dataset limitations. While the non-medical costs of LD have been recently studied in Belgium and the United States [28, 29], this analysis on economic burden only focuses on the healthcare costs and does not capture societal costs that may arise due to long-term sequelae and PTLDS such as caregiver costs, wellbeing costs, out-of-pocket costs, and productivity loss.

Despite these limitations, our study provides comprehensive estimates of the economic and health burden of laboratory-confirmed LD using a cohort of over 2,500 cases over 13 years in a province with the second highest Canadian incidence rates. We used propensity score matching to reduce potential observable confounders. These cost estimates can further the understanding on LD burden, use of healthcare resources throughout different phases of the disease, and facilitate cost-effectiveness analysis on interventions (vaccination programs) in high-risk areas.

Conclusion

Direct healthcare costs attributable to lab-confirmed LD are highest in the 30 days post-diagnosis, and 10 days prior to diagnosis. Post-diagnosis, LD increased individuals’ use of healthcare services for conditions similar to known LD sequelae. This study highlighted the importance of being able to identify LD infections, and can support future decision-making around public health interventions.

Supporting information

S1 File. Case definitions for Lyme disease in Ontario.

(DOCX)

Click here for additional data file. (28.7KB, docx)
S1 Table. Variables included in propensity score regression and matching.

(DOCX)

Click here for additional data file. (29.9KB, docx)
S2 Table. Cost variable definitions and source of data.

(DOCX)

Click here for additional data file. (29.5KB, docx)
S3 Table. ICD-10 and OHIP codes to identify Lyme disease and potentially related sequelae.

(DOCX)

Click here for additional data file. (34.2KB, docx)
S4 Table. RECORD checklist.

(DOCX)

Click here for additional data file. (37.8KB, docx)
S5 Table. Laboratory-confirmed LD cases by age and sex (n = 2,808).

(DOCX)

Click here for additional data file. (32.6KB, docx)
S6 Table. Baseline characteristics of matched cohort at predeath.

(DOCX)

Click here for additional data file. (44.5KB, docx)
S7 Table. Mean phase-of-care costs for matched exposed and unexposed individuals.

(DOCX)

Click here for additional data file. (33.2KB, docx)

Acknowledgments

Parts of this material are based on data and information compiled and provided by MOH, and CIHI. The analyses, conclusions, opinions and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred. Parts of this material are based on data and/or information compiled and provided by CIHI. However, the analyses, conclusions, opinions and statements expressed in the material are those of the author(s), and not necessarily those of CIHI. The analyses, conclusions, views, opinions and statements expressed in this article are those of the author(s) and do not necessarily represent those of, or reflect, the official position of Public Health Ontario.

We would like to acknowledge Andrew Mendlowitz, Li Bai, James Jung, Alexander Kopp, and Alex Marchand-Austin for guidance on the analyses.

Data Availability

The dataset from this study is held securely in coded form at ICES. While legal data sharing agreements between ICES and data providers (e.g., healthcare organizations and government) prohibit ICES from making the dataset publicly available, access may be granted to those who meet pre-specified criteria for confidential access, available at www.ices.on.ca/DAS (email: das@ices.on.ca). The full dataset creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification.

Funding Statement

This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). This study also received funding from Canadian Institutes of Health Research (CIHR) project grant PJT149087 held by Beate Sander. Stephen Mac was supported by a CIHR Frederick Banting and Charles Best Canada Graduate Scholarship Doctoral Award GSD-159274. BS is supported by a Canada Research Chair in Economics of Infectious Diseases (CRC-950-232429). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Auwaerter PG. Lyme Disease: Knowing Good Evidence to Help Inform Practice. Infect Dis Clin North Am. Elsevier; 2015;29: xi–xvi. doi: 10.1016/j.idc.2015.03.001 [DOI] [PubMed] [Google Scholar]
  • 2.Government of Canada. Surveillance of Lyme disease [Internet]. 2021 [cited 25 Sep 2021]. Available: https://www.canada.ca/en/public-health/services/diseases/lyme-disease/surveillance-lyme-disease.html
  • 3.Government of Canada. Lyme disease 2016 case definition [Internet]. 2020 [cited 5 Jan 2021]. Available: https://www.canada.ca/en/public-health/services/diseases/lyme-disease/surveillance-lyme-disease/case-definition.html
  • 4.Adrion ER, Aucott J, Lemke KW, Weiner JP, Stanek G, Wormser G, et al. Health Care Costs, Utilization and Patterns of Care following Lyme Disease. Brissette CA, editor. PLoS One. Public Library of Science; 2015;10: e0116767. doi: 10.1371/journal.pone.0116767 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Mac S, da Silva SR, Sander B. The economic burden of lyme disease and the cost-effectiveness of lyme disease interventions: A scoping review. PLoS One. 2019;14: e0210280. doi: 10.1371/journal.pone.0210280 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Shing E, Wang J, Khoo E, Evans GA, Moore S, Nelder MP, et al. Estimating direct healthcare costs attributable to laboratory-confirmed Lyme disease in Ontario, Canada: A population-based matched cohort study using health administrative data. Zoonoses Public Health. Wiley; 2019; doi: 10.1111/zph.12560 [DOI] [PubMed] [Google Scholar]
  • 7.Mac S, Bahia S, Simbulan F, Pullenayegum EM, Evans GA, Patel SN, et al. Long-Term Sequelae and Health-Related Quality of Life Associated With Lyme Disease: A Systematic Review. Clin Infect Dis. 2020;71: 440–452. doi: 10.1093/cid/ciz1158 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Government of Canada. Federal Framework on Lyme Disease Act. 2017.
  • 9.ICES. Privacy [Internet]. [cited 12 Mar 2021]. Available: https://ssl.ices.on.ca/AboutICES/PrivacyOffice/Pages/,DanaInfo=.aioulhjFpkn2K00Nrq,SSL+Privacy.aspx
  • 10.Ministry of Health and Long-Term Care. Appendix B: Provincial Case Definitions for Diseases of Public Health Significance [Internet]. 2019. Available: https://www.health.gov.on.ca/en/pro/programs/publichealth/oph_standards/docs/lyme_disease_cd.pdf
  • 11.Public Health Ontario. Used with permission. 2020.
  • 12.Wormser GP, Dattwyler RJ, Shapiro ED, Halperin JJ, Steere AC, Klempner MS, et al. The Clinical Assessment, Treatment, and Prevention of Lyme Disease, Human Granulocytic Anaplasmosis, and Babesiosis: Clinical Practice Guidelines by the Infectious Diseases Society of America. Clin Infect Dis. 2006;43: 1089–134. doi: 10.1086/508667 [DOI] [PubMed] [Google Scholar]
  • 13.Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19: 335–351. doi: [DOI] [PubMed] [Google Scholar]
  • 14.Yabroff KR, Lamont EB, Mariotto A, Warren JL, Topor M, Meekins A, et al. Cost of care for elderly cancer patients in the United States. J Natl Cancer Inst Health Services and Economics Branch/Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Executive Plaza North, Rm 4005, 6130 Executive Blvd, MSC 7344, Bethesda, MD 20892–7344, USA. yabroffr@mail.nih.go; 2008;100: 630–41. doi: 10.1093/jnci/djn103 [DOI] [PubMed] [Google Scholar]
  • 15.John Hopkins University. Johns Hopkins ACG® System [Internet]. 2013 [cited 25 Sep 2021]. Available: https://www.hopkinsacg.org/
  • 16.Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res. 2011;46: 399–424. Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3144483&tool=pmcentrez&rendertype=abstract doi: 10.1080/00273171.2011.568786 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Austin PC. A comparison of 12 algorithms for matching on the propensity score. Stat Med. 2014;33: 1057–1069. doi: 10.1002/sim.6004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Wodchis WP, Bushmeneva K, Nikitovic M, Mckillop I. Guidelines on Person-Level Costing Using Administrative Databases in Ontario. 2013. [Google Scholar]
  • 19.Ballinger GA. Using Generalized Estimating Equations for Longitudinal Data Analysis. Organ Res Methods. 2004;7: 127–150. doi: 10.1177/1094428104263672 [DOI] [Google Scholar]
  • 20.Manning WG, Basu A, Mullahy J. Generalized modeling approaches to risk adjustment of skewed outcomes data. J Health Econ. 2005;24: 465–488. doi: 10.1016/j.jhealeco.2004.09.011 [DOI] [PubMed] [Google Scholar]
  • 21.Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement. PLOS Med. Public Library of Science; 2015;12: e1001885. doi: 10.1371/journal.pmed.1001885 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Public Health Ontario. Infectious Disease Trends in Ontario [Internet]. 2021 [cited 5 Jan 2021]. Available: https://www.publichealthontario.ca/data-and-analysis/commonly-used-products/reportable-disease-trends-annually#/34
  • 23.Rogalska AM, Pawełczyk O, Solarz K, Holecki T. What Are the Costs of Diagnostics and Treatment of Lyme Borreliosis in Poland? Front Public Heal. 2021;8: 6–11. doi: 10.3389/fpubh.2020.599239 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Government of Ontario. Get coverage for prescription drugs [Internet]. 2022 [cited 12 Jan 2022]. Available: https://www.ontario.ca/page/get-coverage-prescription-drugs#section-1
  • 25.Berry K, Bayham J, Meyer SR, Fenichel EP. The Allocation of Time and Risk of Lyme: A Case of Ecosystem Service Income and Substitution Effects. Environ Resour Econ. Springer Netherlands; 2017;70: 1–20. doi: 10.1007/s10640-017-0142-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Ali A, Vitulano L, Lee R, Weiss TR, Colson ER. Experiences of patients identifying with chronic Lyme disease in the healthcare system: A qualitative study. BMC Fam Pract. 2014;15: 1–8. doi: 10.1186/1471-2296-15-79 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Boudreau CR, Lloyd VK, Gould ON. Motivations and Experiences of Canadians Seeking Treatment for Lyme Disease Outside of the Conventional Canadian Health-Care System. J Patient Exp. 2018;5: 120–126. doi: 10.1177/2374373517736385 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Hook SA, Jeon S, Niesobecki SA, Hansen AJP, Meek JI, Bjork JKH, et al. Economic Burden of Reported Lyme Disease in High-Incidence Areas, United States, 2014–2016. Emerg Infect Dis. Emerg Infect Dis; 2022;28: 1170–1179. doi: 10.3201/eid2806.211335 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Geebelen L, Devleesschauwer B, Lernout T, Tersago K, Parmentier Y, Van Oyen H, et al. Lyme borreliosis in Belgium: a cost-of-illness analysis. BMC Public Health. BioMed Central Ltd; 2022;22: 1–11. doi: 10.1186/S12889-022-14380-6/TABLES/4 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Shuo-Yan Gau

6 Apr 2023

PONE-D-23-05674Healthcare Costs and Outcomes Associated with Laboratory-confirmed Lyme disease in Ontario, Canada: A Population-based Cohort Study

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

********** 

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

********** 

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I appreciate that this study has provided the associations between the health burden of LD and its utilization of healthcare resources in Ontario. This manuscript is well organized, and the topic has good merit to discuss. However, I am curious why the study period is from 2006 to 2018. Additionally, Figure 3 appears a bit cluttered and visually confusing.

Reviewer #2: The manuscript provided rigorous and extensive analyses on the direct medical costs of Lyme disease. However, I would like to suggest the authors to provide some clarifications

- Page 6 line 108-109: Please provide more details about how the unexposed participants were idenified and deemed as uninfected (ie. laboratory-confirmed uninfected participants).

- The costs should be accounted for the staging of Lyme disease, namely erythema migrans and disseminated lyme disease.

- I strongly recommend the authors include non-medical costs of Lyme disease, such as absenteeism and presenteeism. Examples of recent studies including non-medical costs of Lyme disease are as follows; BMC Public Health. 2022 Nov 28;22(1):2194. doi: 10.1186/s12889-022-14380-6. and Emerg Infect Dis. 2022 Jun;28(6):1170-1179. doi: 10.3201/eid2806.211335. However, I completely leave the decision whether to perform non-medical cost analyses to the authors.

- There were several "Error! Reference source not found" in the manuscript. Please correct them.

********** 

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2023 Jun 22;18(6):e0286552. doi: 10.1371/journal.pone.0286552.r002

Author response to Decision Letter 0

21 Apr 2023

Stephen Mac

Institute of Health Policy, Management, and Evaluation

University of Toronto

155 College St., 4th floor

Toronto, ON M5T 3M6

Email: sm.mac@mail.utoronto.ca

To: Dr. Shuo-Yan Gau

Academic Editor

PLOS ONE

April 21, 2023

RE: PONE-D-23-05674 “Healthcare Costs and Outcomes Associated with Laboratory-confirmed Lyme disease in Ontario, Canada: A Population-based Cohort Study”

Dear Dr. Gau,

Thank you for taking the time to review and move forward with our manuscript. We have addressed all comments and suggestions in this resubmission to PLOS ONE. Please find enclosed our revised manuscript. We have addressed the additional requirements per your request and ensured that our manuscript meets PLOS ONE’s style requirements.

Regarding data availability, we outline this in the Footnotes under Data Availability Statement that is standard for all studies conducted at ICES: “The dataset from this study is held securely in coded form at ICES. While legal data sharing agreements between ICES and data providers (e.g., healthcare organizations and government) prohibit ICES from making the dataset publicly available, access may be granted to those who meet pre-specified criteria for confidential access, available at www.ices.on.ca/DAS (email: das@ices.on.ca). The full dataset creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification.”

Following this letter, we included our detailed response (with page numbers corresponding to the tracked manuscript) to all comments for your review.

Thank you for your time and consideration. We look forward to the final decision.

Sincerely,

Stephen Mac

On behalf of all authors below

Stephen Mac PhD

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada

Toronto Health Economics and Technology Assessment (THETA) Collaborative, University Health Network, Toronto, Canada

Gerald A. Evans MD FRCPC

Department of Medicine, Queen’s University, Kingston, Canada

Samir N. Patel PhD FCCM

Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada

Public Health Ontario, Toronto, Canada

Eleanor M. Pullenayegum PhD

Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

The Hospital for Sick Children, Toronto, Canada

Beate Sander PhD

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada

Toronto Health Economics and Technology Assessment (THETA) Collaborative, University Health Network, Toronto, Canada

ICES, Toronto, Canada

Public Health Ontario, Toronto, Canada

 

REVIEWER #1 COMMENTS

Comment: I appreciate that this study has provided the associations between the health burden of LD

and its utilization of healthcare resources in Ontario. This manuscript is well organized, and the topic has

good merit to discuss. However, I am curious why the study period is from 2006 to 2018.

Response: Thank you for this comment. The study period is limited from 2006 to 2018 as this was the duration of data that was available at the time this study was initiated in 2020. Since the laboratory data comes from Public Health Ontario, there are various timely processes required to de-identify, clean, and transfer the data to ICES in Ontario for linkage to administrative data and for subsequent analysis. Unfortunately, this data cut cannot be updated for this study at this point due to lack of resources. However, we are confident that this cohort, which includes 13 years of patients, provides an appropriate sample size to provide an estimate of the health outcomes and economic burden associated with lab-confirmed Lyme disease in Ontario, Canada.

Comment: Additionally, Figure 3 appears a bit cluttered and visually confusing.

Response: We agree that this figure may feel clustered and so we have focused on the four phases: pre-diagnosis, acute, post-acute and continuing care. We have also included a description of Figure 3 in the manuscript and how it can be interpreted on page 15 (lines 220-222). The Figure 3 caption has been updated to “Figure 3. Mean 10-day standardized costs for Lyme disease phase-of-care between pre-diagnosis to continuing care” on page 15 (lines 226-227).

REVIEWER #2 COMMENTS

Comment: Page 6 line 108-109: Please provide more details about how the unexposed participants were identified

and deemed as uninfected (i.e., laboratory-confirmed uninfected participants).

Response: We described in detail how unexposed individuals were identified in the manuscript on page 5 (lines 93-95): “Individuals neither infected with LD nor had a PHO serologic test record were selected from the Registered Persons Database (RPDB) for matching. We excluded individuals with negative PHO test results to avoid potential confounding healthcare seeking behaviours.” This description in the original manuscript seems out of place and we have taken your suggestion and moved the details to page 6 (lines 118-120) to describe the unexposed individual selection before describing the matching technique.

Comment: The costs should be accounted for the staging of Lyme disease, namely erythema migrans and

disseminated Lyme disease.

Response: This is a very good comment. We would have also preferred to present the analysis based on the staging of Lyme disease (i.e., early localized, early disseminated, late disseminated). However, the variables collected within the laboratory (Public Health Ontario) and reportable disease dataset (iPHIS) do not identify the LD stage, symptoms experienced, or time from symptom onset. Therefore, we elected to present these findings as costs within standardized phases post-diagnosis. We have acknowledged this limitation in the Discussion section on page 20 (lines 308-310).

Comment: I strongly recommend the authors include non-medical costs of Lyme disease, such as absenteeism and

presenteeism. Examples of recent studies including non-medical costs of Lyme disease are as follows; BMC Public Health. 2022 Nov 28;22(1):2194. doi: 10.1186/s12889-022-14380-6. and Emerg Infect Dis. 2022 Jun;28(6):1170-1179. doi: 10.3201/eid2806.211335. However, I completely leave the decision whether to perform non-medical cost analyses to the authors.

Response: Thanks for the comment. The societal costs of Lyme disease are important to understand and indeed missing due to the objective of this study, which was to understand the real-world economic burden of Lyme disease to the healthcare system using administrative data. Unfortunately, societal costs are unable to be estimated with health administrative data that we have access to in Canada, as the data collected is solely from the Minister of Health’s perspective and does not capture time off, indirect costs, caregiver burden, transportation used, etc. These costs would need to be estimated via other data sources or more appropriate study designs such as a cross-sectional survey or prospective cohort study, along with applying a human capital or friction cost method. We have highlighted this in our Discussion and further expanded on the recent studies you shared that were completed in Belgium and the United States on page 21 (lines 322-323).

Comment: There were several "Error! Reference source not found" in the manuscript. Please correct them.

Response: Apologies for these formatting errors in the manuscript. We have identified all the cross-reference errors and have corrected them all.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (241.7KB, docx)

Decision Letter 1

Shuo-Yan Gau

18 May 2023

Healthcare Costs and Outcomes Associated with Laboratory-confirmed Lyme disease in Ontario, Canada: A Population-based Cohort Study

PONE-D-23-05674R1

Dear Dr. Mac,

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.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. 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.

Kind regards,

Shuo-Yan Gau

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?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

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?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

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: No

Reviewer #2: No

**********

Acceptance letter

Shuo-Yan Gau

13 Jun 2023

PONE-D-23-05674R1

Healthcare Costs and Outcomes Associated with Laboratory-confirmed Lyme disease in Ontario, Canada: A Population-based Cohort Study

Dear Dr. Mac:

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

Mr. Shuo-Yan Gau

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 File. Case definitions for Lyme disease in Ontario.

    (DOCX)

    Click here for additional data file. (28.7KB, docx)
    S1 Table. Variables included in propensity score regression and matching.

    (DOCX)

    Click here for additional data file. (29.9KB, docx)
    S2 Table. Cost variable definitions and source of data.

    (DOCX)

    Click here for additional data file. (29.5KB, docx)
    S3 Table. ICD-10 and OHIP codes to identify Lyme disease and potentially related sequelae.

    (DOCX)

    Click here for additional data file. (34.2KB, docx)
    S4 Table. RECORD checklist.

    (DOCX)

    Click here for additional data file. (37.8KB, docx)
    S5 Table. Laboratory-confirmed LD cases by age and sex (n = 2,808).

    (DOCX)

    Click here for additional data file. (32.6KB, docx)
    S6 Table. Baseline characteristics of matched cohort at predeath.

    (DOCX)

    Click here for additional data file. (44.5KB, docx)
    S7 Table. Mean phase-of-care costs for matched exposed and unexposed individuals.

    (DOCX)

    Click here for additional data file. (33.2KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (241.7KB, docx)

    Data Availability Statement

    The dataset from this study is held securely in coded form at ICES. While legal data sharing agreements between ICES and data providers (e.g., healthcare organizations and government) prohibit ICES from making the dataset publicly available, access may be granted to those who meet pre-specified criteria for confidential access, available at www.ices.on.ca/DAS (email: das@ices.on.ca). The full dataset creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification.

    Articles from PLOS ONE are provided here courtesy of PLOS

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