A population-based cohort study of mitochondrial disease and mental health conditions in Ontario, Canada

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However, a lack of population-level studies examining the relationship between mitochondrial disease and mental health has resulted in an evidence gap and creates a challenge for identifying and addressing care needs for the mitochondrial disease population. Using multiple linked population health databases in a single-payer health system that covers the full population, this study aimed to investigate the prevalence of mood disorders and other mental health conditions in patients with mitochondrial disease and to examine the joint impact of mitochondrial disease and mental health conditions on healthcare use and health system costs. To contextualize these findings, a clinical comparator cohort of multiple sclerosis (MS) patients was analyzed. Results Overall, co-prevalent mental health conditions are common in the mitochondrial popualtion. Double the proportion of patients in the mitochondrial disease cohort had a co-prevalent mental health illness as compared to the MS population (18% vs 9%). Healthcare utilization was highest among patients with co-prevalent mitochondrial disease and mental illness, with 49% hospitalized within 1 year prior to cohort entry (compared to 12% of MS patients with no mental health condition). Costs were likewise highest among mitochondrial disease patients with mental health conditions. Conclusions This study presents the first comprehensive, population-wide cohort study of mitochondrial disease and co-prevalent mental health conditions. Our findings demonstrate a high burden of mental health conditions among mitochondrial disease patients, with high associated health care needs. We also find that patients with concurrent mental illness and mitochondrial disease represent a high-burden, high-cost population in a single-payer health insurance setting. Mitochondrial disease Mental health epidemiology health care utilization health care costs Figures Figure 1 Background Mitochondrial dysfunction, caused by mutations in nuclear or mitochondrial DNA, can result in a group of disorders known as mitochondrial disease [ 1 ]. It can present itself during childhood or adulthood and there are over 250 genes that have been found to be implicated in the disease [ 2 ]. Within Canada, there exists limited work on the epidemiology of mitochondrial disease derived from population data sources. Epidemiologic research has demonstrated that the prevalence of this disease is 1 in 4000, and suggests that mitochondrial disease presents a large economic burden on the healthcare system and patients [ 3 , 4 ]. Additionally, mitochondrial dysfunction has been linked with mental health disorder from clinical cases [ 5 , 6 ] and postmortem studies [ 7 ]. However, population-based studies examining the relationship between mitochondrial disease and mental health are lacking. Given the heterogenous nature of this disease and the missing data on the epidemiology of these conditions in Canada, it is challenging to quantify and address the care needs for those affected by mitochondrial disease and co-occurring illness. Mitochondrial disease presents individual heterogeneity and although genetic targets exist, diagnosing this disease can be challenging [ 2 ]. Furthermore, mitochondrial disease has also been shown to present with a wide range of comorbidities, such as diabetes and Parkinson's disease [ 8 ]. This is due to the high abundance of mitochondria in almost all cells of the body. Dysfunction of the mitochondria have profound effects on neurotransmission and may contribute to changes in neuronal circuits in the brain that are associated with cognition, memory, and other forms of neuronal plasticity [ 9 ]. Thus, illnesses in which mitochondria are dysfunctional may cause disruption that has been implicated in several psychiatric conditions. For example, impaired neurotransmission is evident in patients with bipolar disorder [ 10 ], which is likely linked to specific neurodevelopmental abnormalities [ 11 ]. Because neurons depend on energy, mitochondrial dysfunction during neurodevelopment is expected to impact neurotransmission, with potentially crucial implications for the development of psychiatric conditions [ 12 ]. However, despite preliminary evidence for these associations [ 7 , 13 ], few population-based epidemiological studies have examined the association between mitochondrial dysfunction and specific psychiatric conditions. Building on previous population-based epidemiological studies [ 3 ] and using multiple sources of population health databases, the objectives of this study were to characterize the epidemiology of mitochondrial disease and its co-occurrence with mental health conditions in Ontario. Specifically, we sought to examine the association between mood disorders and other mental health conditions in patients with mitochondrial disease as well as examine the joint impact of mental health conditions on healthcare use and health system costs. To contextualize these findings, a clinical comparator cohort of multiple sclerosis (MS) patients was also used. Results Study population A flow chart depicting the cohort creation for both mitochondrial disease and MS cohorts can be shown in Fig. 1 . These datasets were linked using unique encoded identifiers and analyzed at ICES. There were 3,123 unique individuals who had one or more hospitalizations with a diagnostic code indicating mitochondrial disease and could be linked to health insurance data in Ontario. There were 3,069 individuals remaining after excluding incomplete Ontario Health Insurance Program (OHIP) eligibility or missing demographic information. Since a 3-year lookback window was required to capture mental health conditions and the first date to capture mental health data is April 1st, 2002, any individual with their first hospitalization indicating mitochondrial disease before April 1st, 2005 was excluded. This left a final cohort of 1,495 individuals with mitochondrial disease. There were 20,782 unique individuals who had one or more hospitalizations with a diagnostic code indicating MS and could be linked to a valid RPDB record. After excluding hospitalizations from before April 1st, 2005, a final cohort of 8,482 individuals with MS was obtained. Cohort characteristics Table 1 shows the demographic characteristics of the cohort, including sex, age at cohort entry (first hospitalization for mitochondrial disease or MS), and neighbourhood-level material deprivation quintile. Material deprivation refers to the inability of individuals or households to afford necessary goods and services [ 14 ]. Table 1 Study cohort characteristics. Mitochondrial disease patients (N = 1,495) Multiple Sclerosis patients (N = 8,482) N % N % Sex M 710 47.5 2,558 30.2 F 785 52.5 5,924 69.8 Age at cohort entry 0–9 290 19.4 6 0.1 19–20 111 7.4 128 1.5 20–29 70 4.7 620 7.3 30–39 91 6.1 1,362 16.1 40–49 156 10.4 1,675 19.7 50–59 247 16.5 2,045 24.1 60–69 260 17.4 1,576 18.6 70–79 167 11.2 765 9 80+ 103 6.9 305 3.6 Neighbourhood deprivation quintile 1 254 17 1,666 19.6 2 257 17.2 1,614 19 3 279 18.7 1,561 18.4 4 294 19.7 1,699 20 5 393 26.3 1,860 21.9 When examining the baseline characteristics of the mitochondrial disease study population, there were no major sex differences. This is distinct from a larger proportion of the MS population was made up of females (69.8%), which was expected given the epidemiology of MS. In terms of age at cohort entry for the mitochondrial disease population, first hospitalization was most common between ages 0–9, with 19.4 percent of the study population entering the cohort before age 10. A substantial number of cohort entries also occurred in older ages, with 33.9 percent entering the cohort between ages 50 and 69. For those with MS, age at cohort entry was most common between ages 40 and 69, with 62.4% of the study population entering the cohort after age 40. Overall, a greater proportion of mitochondrial disease patients were living in lower socioeconomic neighbourhoods, such that 26.3 percent of those in the mitochondrial disease cohort resided in the lowest socioeconomic status neighbourhoods (highest deprivation quintile), compared to 21.9 percent of the MS patients. Mental health Overall, a much larger proportion of the mitochondrial disease cohort had a co-prevalent mental health illness as compared to the MS population. For those with mitochondrial disease, 18.3% (n = 274) had one or more mental illnesses. Conversely, only 8.6% (n = 728) of those with MS also had a co-prevalent mental health illness. Health care utilization Healthcare utilization for one-year periods prior to and following the index hospitalization is shown in Table 2 . Healthcare utilization, both pre-and post-index hospitalization, was higher for those with one or more mental health conditions, especially for mitochondrial disease patients who also had one or more mental health conditions. For example, 48.9 percent of mitochondrial disease patients with one or more mental health conditions had at least 1 hospitalization, whereas only 39.1 percent of mitochondrial disease patients with no mental health conditions had at least 1 hospitalization. The proportion of MS patients with 1 or more hospitalizations was lower, with 12.3 percent of MS patients with no mental health conditions and 22.9 percent of MS patients with one or more mental health conditions being hospitalized at least once. This trend was similar across pre- and post- index hospitalization for other factors of health care utilization, including intensive care unit (ICU) admissions and emergency department visits. However, it was found that health care utilization for outpatient care, including primary and specialist care visits, was slightly higher for MS patients. For example, MS patients had the greatest utilization of specialist visits, where 80.7 percent of MS patients with no mental health conditions and 82.0 percent of MS patients with one or more mental health conditions had 1 or more specialist visits, as compared to 73.4 percent of mitochondrial disease patients. Table 2 Health care utilization prior to and after cohort entry, mitochondrial disease and multiple sclerosis patients. Health care utilization Mitochondrial disease patients (n = 1,495) Multiple Sclerosis patients (n = 8,482) No Mental Health Conditions ≥ 1 Mental health condition No Mental Health Conditions ≥ 1 Mental health condition 1 year prior to index date Hospitalization episodes of care Total hospitalizations, No. 1061 368 1407 283 ≥ 1 hospitalization, No. (%) 478 (39.1) 134 (48.9) 956 (12.3) 167 (22.9) Episodes of care per person, No. 1 (1–3) 2 (1–4) 1 (1–1) 1 (1–2) Length of stay, d 5 (3–10) 5 (3–9) 5 (3–9) 6 (3–10) ICU visits Total visits, No. 97 42 158 26 ≥ 1 ICU admission, No. (%) 77 (6.3) 31 (11.3) 124 (1.6) 24 (3.3) Visits per person, No. 1 (1–1) 1 (1–2) 1 (1–1) 1 (1–1) Length of stay, h 9 (5–17) 5.5 (4–12) 7 (4–13) 7.5 (4–15) Emergency department visits Total visits, No. 2572 1470 9886 2451 ≥ 1 visit, No. (%) 845 (69.2) 238 (86.9) 4592 (59.2) 593 (81.5) Visits per person, No. 2 (1–4) 3 (2–7) 1 (1–3) 3 (2–5) Primary care visits Total visits, No. 8485 2101 47453 6450 ≥ 1 visit, No. (%) 1096 (89.8) 241 (88.0) 6976 (90.0) 666 (91.5) Visits per person, No. 6 (3–10) 6 (3–12) 5 (3–8) 7 (3–12) Specialist visits Total visits, No. 6347 1373 39036 4055 ≥ 1 visit, No. (%) 896 (73.4) 201 (73.4) 6255 (80.7) 597 (82.0) Visits per person, No. 5 (2–10) 4 (2–8) 4 (2–8) 4 (2–9) 1 year following index date Hospitalization episodes of care Total hospitalizations, No. 1189 439 3642 545 ≥ 1 hospitalization, No. (%) 504 (41.3) 114 (52.6) 2157 (27.8) 275 (37.9) Episodes of care per person, No. 2 (1–3) 2 (1-3.5) 1 (1–2) 1 (1–2) Length of stay, d 5 (3–11) 5 (3–10) 6 (3–12) 6 (4–11) ICU visits Total visits, No. 135 62 435 57 ≥ 1 ICU admission, No. (%) 113 (9.3) 47 (17.2) 332 (4.3) 45 (6.2) Visits per person, No. 1 (1–1) 1 (1–1) 1 (1–1) 1 (1–1) Length of stay, h 10 (5–20) 9 (3–6) 10 (5–20) 8 (5–15) Emergency department visits Total visits, No. 2572 1470 9886 2451 ≥ 1 visit, No. (%) 845 (69.2) 238 (86.9) 4592 (59.2) 593 (81.5) Visits per person, No. 2 (1–4) 3 (2–7) 3 (2–5) Primary care visits Total visits, No. 9299 1963 55351 6495 ≥ 1 visit, No. (%) 1085 (88.9) 229 (83.6) 6936 (89.5) 667 (91.6) Visits per person, No. 6 (3–11) 6 (3–11) 5 (3–9) 7 (3–12) Specialist visits Total visits, No. 6752 1376 43416 4949 ≥ 1 visit, No. (%) 952 (78.0) 213 (77.7) 6889 (88.8) 678 (93.1) Visits per person, No. 5 (2–10) 5 (2–8) 5 (3–8) 5 (3–10) Health care costs Healthcare costs incurred by members of the study population before and after their index hospitalization are shown in Table 3 . Overall, healthcare costs incurred by those with mitochondrial disease and MS were highest for those with concurrent mental health conditions. For example, the mean cost incurred in the 12 months prior to hospitalization for mitochondrial disease patients with no mental health condition was $ 6859, and the mean cost incurred in 12 months post-discharge for this cohort was $ 21,257. Conversely, the same costs pre- and post-index date for mitochondrial disease patients with ≥ 1 or more mental health conditions were $ 10,132 and $ 33,363, respectively. Similarly, the pre- and post-index median costs of health care for MS patients without any mental health conditions were $ 3984.5 and $ 7754, respectively, whereas these same costs for MS patients with one or more mental health conditions were $ 7178 and $ 27314.5. Thus, costs incurred by this study population were high in both cohorts but particularly high in mitochondrial disease patients and even higher for mitochondrial disease patients with one or more mental health conditions. This trend was maintained for almost all other factors part of health care costs incurred by these populations including inpatient hospitalization, emergency department, and physician billing costs where mitochondrial disease patients with one or more health conditions incurred the highest health care costs within this study population. Table 3 Health care costs prior to and following cohort entry, mitochondrial disease and multiple sclerosis patients. Health care costs Mitochondrial disease patients (n = 1,495) Multiple Sclerosis patients (n = 8,482) No Mental Health Conditions ≥ 1 Mental health condition No Mental Health Conditions ≥ 1 Mental health condition 1 year prior to index date Any health care cost, n (%) 1202 (98.4) 272 (99.3) 7706 (99.4) 727 (99.9) Total cost, median (IQR) 6859 (1803–22157) 10132 (3017–33077) 3984.5 (1729–11425) 7178 (2732.5–18859) Any inpatient hospitalization cost, n (%) 479 (39.2) 134 (48.9) 958 (12.4) 168 (23.1) Inpatient hospitalization cost (median (IQR)) 9212 (3847–23409) 11441.5 (5234–24346) 4769 (3343–11897) 7842 (4018.5–15730.5) Any outpatient hospital cost (clinic visits), n (%) 744 (60.9) 124 (45.3) 4064 (52.4) 331 (45.5) Outpatient hospital clinic visit costs (median (IQR)) 1246 (630–2267.5) 982.5 (328.5–1951) 681 (338–1433) 655 (327–1293) Any emergency department costs, n (%) 851 (69.7) 238 (86.9) 4629 (59.7) 601 (82.6) Emergency department costs (median (IQR)) 811 (429–1431) 1363 (710–2698) 577 (317–933) 931 (477–1651) Any physician billings (GP and specialist), n (%) 1193 (97.7) 267 (97.5) 7596 (98.0) 723 (99.3) Physician billings costs (median (IQR)) 1676 (602–3912) 2227 (891–5174) 1105 (539–2005) 1699 (792–3183) 1 year following index date Any health care cost, n (%) 1221 (100.0) 274 (100.0) 7754 (100.0) 728 (100.0) Total cost (median (IQR)) 21257 (9914–61684) 33363 (14820–68131) 19539.5 (9380–44956) 27314.5 (12523.5–51715.5) Any inpatient hospitalization cost, n (%) 1221 (100.0) 274 (100.0) 7754 (100.0) 728 (100.0) Inpatient hospitalization cost (median (IQR)) 9709 (4354–30374) 14624 (5819–29682) 6534 (4506–14290) 8123.5 (4729–18586.5) Any outpatient hospital cost (clinic visits), n (%) 961 (78.7) 170 (62.0) 5763 (74.3) 477 (65.5) Outpatient hospital clinic visit costs (median (IQR)) 1250 (636–2614) 954 (343–1636) 950 (363–1621) 660 (330–1310) Any emergency department costs, n (%) 1003 (82.2) 248 (90.5) 5778 (74.5) 646 (88.7) Emergency department costs (median (IQR)) 823 (520–1481) 1977.5 (889.5–3206.5) 723 (479–1166) 1034 (616–1832) Any physician billings (GP and specialist), n (%) 1221 (100.0) 274 (100.0) 7754 (100.0) 728 (100.0) Physician billings costs (median (IQR)) 3639 (1852–7152) 5154.5 (2600–9078) 2865 (1799.5–4829.5) 3586 (2119.5–5942.5) 1 year following index date (excluding index hospitalization) Any health care cost, n (%) 1171 (95.9) 265 (96.7) 7606 (98.1) 725 (99.6) Total cost (median (IQR)) 9011 (2027–24511) 20591 (5974–51946) 9869.5 (2743–31530) 17866.5 (5659–39297.5) Any inpatient hospitalization cost, n (%) 517 (42.3) 152 (55.5) 2220 (28.6) 290 (39.8) Inpatient hospitalization cost (median (IQR)) 13462 (5044–33096) 13398.5 (7735–32440) 9058.5 (4565–21636.5) 10702 (5074–20843) Any outpatient hospital cost (clinic visits), n (%) 837 (68.6) 175 (63.9) 5576 (77.9) 560 (76.9) Outpatient hospital clinic visit costs (median (IQR)) 1249 (636–2582) 998 (622–1872) 966 (622–1637) 982 (630–1933) Any emergency department costs, n (%) 715 (58.6) 217 (79.2) 3606 (46.5) 527 (72.4) Emergency department costs (median (IQR)) 674 (332–1387) 1709 (909–3017) 552.5 (269–1040) 857 (442–1684) Any physician billings (GP and specialist), n (%) 1154 (94.5) 265 (96.7) 7541 (97.3) 722 (99.2) Physician billings costs (median (IQR)) 1960.5 (770–4488) 3621 (1284–7207) 1527 (766–2947) 2369 (1196–4077) Discussion Key findings Overall, we identified 1,495 individuals hospitalized with mitochondrial disease and 8,482 individuals hospitalized with MS in Ontario, Canada, between 2005 and 2019. The prevalence of mental health conditions was significant and higher in the mitochondrial disease cohort compared to the MS cohort. The greatest health care utilization was among mitochondrial disease patients with a co-prevalent mental health illness, with 49% hospitalized compared to 12–39% in other groups. Conversely, those with MS and no mental health conditions had the lowest healthcare utilization within the study population. In terms of healthcare costs, individuals with a co-prevalent mental health illness in the mitochondrial cohort incurred the highest healthcare costs, likely due to the high co-prevlant conditions. Our findings suggest that the mitochondrial disease and MS populations have important healthcare and more complex healthcare needs. For comparability, previous literature has studied health system users in Ontario between 2009 and 2011, finding a median health care cost of $ 333 per user, with only 5% of health system users incurring $ 7,961 or more of health care costs per year [ 15 ]. Based on this finding, a majority of our study cohort would be considered among the highest users of healthcare within health system. The findings of generally high healthcare costs incurred by the mitochondrial disease population are also consistent with findings from a study done with the mitochondrial disease population in the United States [ 16 ]. This paper found that this demographic incurred over $ 113 million of healthcare expenditures. This expenditure went towards the direct medical costs associated with treating those with mitochondrial disease. The utilization and cost of health care increase in those with co-prevalent mental health illnesses. Healthcare costs and utilization were higher following mitochondrial disease-related hospitalization. Since the hospitalization from that main visit was excluded from these calculations, this increase cannot be attributed to the cost of hospitalization itself. However, it can be related to post-hospitalization care, such as rehabilitation or the cost of a specialist follow-up. Our findings are consistent with other studies about health costs associated with mental illness, which suggest that patients with high mental health costs incur over 30% more costs than other high-cost patients [ 17 ]. To our knowledge, no previous study has investigated the health burden and cost associated with mental illness in a mitochondrial disease or MS patient population; our findings demonstrate that the burdens associated with these chronic conditions are exacerbated by co-prevalent mental illness. Limitations The cohort used in this population-based study is limited to hospitalized cases of mitochondrial disease because of diagnostic coding limitations in Ontario's administrative health data. Outpatient physician billings in Ontario (OHIP data) are based on a modified ICD-9 coding system and do not capture enough detail to identify rare diseases, including mitochondrial disease accurately. Patients with less acute symptoms may receive a diagnosis in Ontario (e.g. from an outpatient clinic) but would not be captured by our disease cohort. We thus consider our study population to represent a high-acuity patient population; for this reason, we have chosen a high-specificity, low-sensitivity algorithm for identifying our comparator MS cases. Another limitation of this approach is that the date of cohort entry is based on the first hospitalization and may not reflect when a patient first becomes symptomatic or aware of their diagnosis. For this reason, pre- and post-index date periods used for assessing healthcare utilization and costs do not necessarily correspond to pre- and post-diagnostic windows. Similarly, we cannot definitively assess the onset timing of mental health conditions compared to mitochondrial disease or MS. Conclusion This study presents a comprehensive, population-wide cohort study of mitochondrial disease and co-prevalent mental health conditions in a large single-payer health system. Our findings demonstrate that the prevalence of mental health conditions is substantially elevated among mitochondrial disease patients. We also demonstrate that patients with concurrent mental illness and mitochondrial disease contribute to complexity and healthcare needs. Methods Data sources ICES is an independent, non-profit research institute funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). As a prescribed entity under Ontario’s privacy legislation, ICES is authorized to collect and use health care data for the purposes of health system analysis, evaluation and decision support. Secure access to these data is governed by policies and procedures that are approved by the Information and Privacy Commissioner of Ontario. Basic demographic information, including sex and age, was gathered from the Registered Persons Database (RPDB), which has stored data for persons registered under the OHIP at any time since 1992. Mitochondrial disease and MS cases were identified using hospitalization records from the Discharge Abstract Database (DAD), which captures acute care hospitalizations (inpatient stays). Mental health conditions were identified through emergency department or hospital visits. Hospitalizations were captured through DAD and the Ontario Mental Health Reporting System (OMHRS). Emergency department visits were captured through the National Ambulatory Care Reporting System (NACRS) database. Healthcare utilization was captured using data from DAD (inpatient hospitalizations and ICU stays), NACRS (emergency department visits), and physician billings from OHIP data (primary care visits and specialist visits). Healthcare costs were based on these datasets and several others, including drug payments, home care and long-term care; a full list of databases used by the costing algorithm is available in the published guidelines [ 18 ]. These datasets were linked using unique encoded identifiers and analyzed at ICES. Cohort identification We identified cases of mitochondrial disease as any individual hospitalized one or more times with a diagnostic code indicating mitochondrial disease in the discharge record. ICD-10 code G71.3 was used to identify diagnoses of mitochondrial disease. We also used a comparison cohort of patients with MS; MS has been used as a comparator climical cohort in other cohort-based studies of mitochondrial disease [ 12 ]. We likewise captured individuals hospitalized one or more times in the study period, with a diagnostic code indicating MS (ICD-10 code G35). A validation study in Ontario found this algorithm to be low-sensitivity for general MS cases [ 19 ]. However, we believe it is appropriate for our study to capture high-acuity cases only to best align with the mitochondrial disease cohort. Cases were excluded from either cohort if they could not be linked to a valid record in RPDB or were not OHIP eligible for at least 12 months pre- and post-admission. The eligibility exclusion was to ensure that individuals were receiving most of their health care in Ontario before and after hospitalization. Sex and age were identified based on individuals' health card records at the time of first hospital admission. In order to determine mental health conditions prior to their first hospital admission, a 3-year lookback window was used. Since the first available mental health data is from April 2002, any person who had their first hospital admission for mitochondrial disease before April 2005 was removed from the cohort for this analysis. Mental health conditions ICD codes indicating mental health-related care were chosen based on a framework developed by the Mental Health and Addictions Program at ICES, formally known as the Mental Health and Addictions Scorecard and Evaluation Framework (MHASEF) [ 20 ]. This framework was developed using guidelines from the Canadian Institute for Health Information for capturing specific clinical mental health conditions from emergency department or hospital visits [ 21 ], and includes minor modifications based on Ontario-specific coding practices. MHASEF includes ICD-10-CA codes used in DAD and NACRS, and ICD-9-CM codes based on the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) used in OMHRS. Health care utilization Healthcare utilization was captured for all mitochondrial disease and MS patients for one year prior to and following the index hospitalization. The index hospitalization was excluded from all health care utilization measures. A hospitalization episode was defined as the period from admission to an acute inpatient setting to final discharge from acute care, allowing for transfers between inpatient hospitals. Length of stay was based on the full episode, which may be made up of multiple individual hospital discharge records. For example, if Hospital A transfers a patient who was discharged from acute care to Hospital B, then the episode length is from admission to Hospital A to discharge from Hospital B. Emergency department visits were limited to one visit per patient per day. Primary care and specialist physician visits were captured using physician billings and were limited to one claim per patient per physician per day. Physician specialties were based on the submitted claim and were classified as either primary care (family practice and general practice, pediatrics, and community medicine) or specialist (all other specialties). Health care costs Health care costs were assessed using health administrative data from across ICES data holdings based on a costing algorithm that has been described in detail elsewhere [ 18 ]. Briefly, person-level costs are derived by combining health care utilization records with the Ministry of Health and Long-Term Care cost information. We used this algorithm to capture costs for mitochondrial disease and MS patients for one year before and after index hospitalization. As with healthcare utilization measures, costs incurred during the index hospitalization were excluded. We measured health care costs overall and for the following specific cost categories: inpatient hospitalization, outpatient hospital clinic visits, emergency department visits, and physician billings. Abbreviations DAD Discharge Abstract Database ICU Intensive care unit MHASEF Mental Health and Addictions Scorecard and Evaluation Framework MS Multiple sclerosis NACRS National Ambulatory Care Reporting System OHIP Ontario Health Insurance Program Declarations Ethics approval and consent to participate This study was approved by the University of Toronto Health Sciences Research Ethics Board. Availability of data and materials ICES is a prescribed entity under Ontario's Personal Health Information Protection Act (PHIPA). PHIPA authorizes ICES to collect personal health information, without consent, for the purpose of analysis or compiling statistical information with respect to the management of, evaluation or monitoring of, the allocation of resources to or planning for all or part of the health system. Legal data sharing agreements between and data providers (e.g., healthcare organizations and government) prohibit ICES from making the dataset publicly available, as the data contain sensitive and potentially identifying health information. However, access may be granted to those who meet pre-specified criteria for confidential access, available at www.ices.on.ca/DAS (email: [email protected] ). 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. Competing interests The authors declare that they have no competing interests. Funding This research is part of the Mitochondrial Innovation Initiative, which receives funding from the University of Toronto, Thomas Zachos Chair and MitoCanada Foundation. 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). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 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 (MLTCThe 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. Authors' contributions LCR, ACA, and LTY conceptualized the study. LCR oversaw the statistical analysis and writing of the manuscript. EB and MH conducted the analysis. EB, MH, and TS contributed to the interpretation of the data and writing of the manuscript. All authors read and approved the final manuscript. Acknowledgements 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. We thank IQVIA Solutions Canada Inc. for use of their Drug Information File. Parts of this material are based on data and information compiled and provided by the Ontario Ministry of Health. 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. Consent for Publication declarations Not applicable. References Ng YS, Turnbull DM. 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Behav Brain Funct. 2012;8(1):9. Andreazza AC, Duong A, Young LT. Bipolar Disorder as a Mitochondrial Disease. Biol Psychiatry. 2018;83(9):720–1. Catalán-García M, García-García FJ, Moreno-Lozano PJ, Alcarraz-Vizán G, Tort-Merino A, Milisenda JC, Cantó-Santos J, Barcos-Rodríguez T, Cardellach F, Lladó A et al. Mitochondrial Dysfunction: A Common Hallmark Underlying Comorbidity between sIBM and Other Degenerative and Age-Related Diseases. J Clin Med 2020, 9(5). Chen H, Chan DCJC. Critical dependence of neurons on mitochondrial dynamics. 2006, 18(4):453–9. Kato T, Kato N. Mitochondrial dysfunction in bipolar disorder. Bipolar Disord. 2000;2(3 Pt 1):180–90. Young LT. Is bipolar disorder a mitochondrial disease? J psychiatry neuroscience: JPN. 2007;32(3):160–1. Daniels TE, Olsen EM, Tyrka AR. Stress and Psychiatric Disorders: The Role of Mitochondria. Ann Rev Clin Psychol. 2020;16:165–86. Fattal O, Budur K, Vaughan AJ, Franco K. Review of the literature on major mental disorders in adult patients with mitochondrial diseases. Psychosomatics. 2006;47(1):1–7. Matheson FI, Dunn JR, Smith KL, Moineddin R, Glazier RHJCJPHRCDSP. Development of the Canadian Marginalization Index: a new tool for the study of inequality. 2012:S12-S16. Wodchis WP, Austin PC, Henry DA. A 3-year study of high-cost users of health care. 2016, 188(3):182–8. McCormack SE, Xiao R, Kilbaugh TJ, Karlsson M, Ganetzky RD, Cunningham ZZ, Goldstein A, Falk MJ, Damrauer SM. Hospitalizations for mitochondrial disease across the lifespan in the U.S. Mol Genet Metab. 2017;121(2):119–26. de Oliveira C, Cheng J, Vigod S, Rehm J, Kurdyak PJHA. Patients with high mental health costs incur over 30 percent more costs than other high-cost patients. 2016, 35(1):36–43. Wodchis WP, Bushmeneva K, Nikitovic M, McKillop I. Guidelines on person-level costing using administrative databases in Ontario. 2013. Widdifield J, Ivers NM, Young J, Green D, Jaakkimainen L, Butt DA, O'Connor P, Hollands S, Tu K. Development and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada. Mult Scler. 2015;21(8):1045–54. MHASEF Research Team. Mental Health and Addictions System Performance in Ontario: A Baseline Scorecard. In. Toronto, ON: Institute for Clinical Evaluative Sciences; 2018. Mental Illness Hospitalization [indicator] [ http://indicatorlibrary.cihi.ca/display/HSPIL/Mental+Illness+Hospitalization ]. Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2025 Read the published version in Orphanet Journal of Rare Diseases → Version 1 posted Reviewers agreed at journal 28 May, 2024 Reviewers invited by journal 28 May, 2024 Editor assigned by journal 09 Apr, 2024 First submitted to journal 29 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3990108","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307690849,"identity":"b9622edb-7e6d-4931-aeaa-7c7398a16239","order_by":0,"name":"Laura Rosella","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACPgSTufEAQwURWtgQTMaGAwxnGCTA7ANEa2FsI0YL+xmzDz8Y7snJtzc2HPg573Adv/ThY48/VDDI8zfg0MKTYzyzh6HY2ODMwYaDvdsOS0j2paUbHDjDYDgDh1VsDDnGDDwMCYkbJBIbDvACtRic4TGTONjGkIDLdWz8b4wZ/wC1zJ//sOHg3zlIWuRxaZHIMWYG2dJwg7HhMG8DkhYDnFqeFTPLGCQA/ZLYcFjmWLrkzB62NIkzZyQMN+LQws+fvJnxTUUCMMQOH3z4psaan5+H+ZhERYWNvBwOLRBggCkkgU/9KBgFo2AUjAICAACRvFd4/6FeawAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4867-869X","institution":"Unviersity of Toronto","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"","lastName":"Rosella","suffix":""},{"id":307690850,"identity":"a79021c3-a9df-4b1a-a6c0-7fd0fadce248","order_by":1,"name":"Mackenzie Hurst","email":"","orcid":"","institution":"University of Toronto Dalla Lana School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Mackenzie","middleName":"","lastName":"Hurst","suffix":""},{"id":307690851,"identity":"a97b07b7-799b-4915-845c-0d5a1bc86cdd","order_by":2,"name":"Emmalin Buajitti","email":"","orcid":"","institution":"University of Toronto Dalla Lana School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Emmalin","middleName":"","lastName":"Buajitti","suffix":""},{"id":307690852,"identity":"1c65709f-7da4-4247-a3ec-d2e80d306c6e","order_by":3,"name":"Thomas Samson","email":"","orcid":"","institution":"University of Toronto Dalla Lana School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Samson","suffix":""},{"id":307690853,"identity":"9901778f-f03d-428d-b691-fead3c59f3d5","order_by":4,"name":"L. Trevor Young","email":"","orcid":"","institution":"University of Toronto Temerty Faculty of Medicine","correspondingAuthor":false,"prefix":"","firstName":"L.","middleName":"Trevor","lastName":"Young","suffix":""},{"id":307690854,"identity":"f806a574-694e-46e9-83ee-84b5729cb8f7","order_by":5,"name":"Ana C. Andreazza","email":"","orcid":"","institution":"University of Toronto Temerty Faculty of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"C.","lastName":"Andreazza","suffix":""}],"badges":[],"createdAt":"2024-02-26 06:06:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3990108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3990108/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13023-025-03688-2","type":"published","date":"2025-04-14T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58229333,"identity":"3cba3d7c-e7a1-43df-a654-51b3a1445f14","added_by":"auto","created_at":"2024-06-12 19:14:55","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":439226,"visible":true,"origin":"","legend":"\u003cp\u003eStudy inclusion flow chart.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3990108/v1/b3cd9caf8b0d4a8c57a0c31e.jpeg"},{"id":81051061,"identity":"5c47217e-56b7-4288-9df4-c2bee9570a15","added_by":"auto","created_at":"2025-04-21 16:10:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1652596,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3990108/v1/b1225170-44b9-4cb0-9aab-83c2eedb5e98.pdf"}],"financialInterests":"","formattedTitle":"A population-based cohort study of mitochondrial disease and mental health conditions in Ontario, Canada","fulltext":[{"header":"Background","content":"\u003cp\u003eMitochondrial dysfunction, caused by mutations in nuclear or mitochondrial DNA, can result in a group of disorders known as mitochondrial disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It can present itself during childhood or adulthood and there are over 250 genes that have been found to be implicated in the disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Within Canada, there exists limited work on the epidemiology of mitochondrial disease derived from population data sources. Epidemiologic research has demonstrated that the prevalence of this disease is 1 in 4000, and suggests that mitochondrial disease presents a large economic burden on the healthcare system and patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additionally, mitochondrial dysfunction has been linked with mental health disorder from clinical cases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and postmortem studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, population-based studies examining the relationship between mitochondrial disease and mental health are lacking. Given the heterogenous nature of this disease and the missing data on the epidemiology of these conditions in Canada, it is challenging to quantify and address the care needs for those affected by mitochondrial disease and co-occurring illness.\u003c/p\u003e \u003cp\u003eMitochondrial disease presents individual heterogeneity and although genetic targets exist, diagnosing this disease can be challenging [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, mitochondrial disease has also been shown to present with a wide range of comorbidities, such as diabetes and Parkinson's disease [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This is due to the high abundance of mitochondria in almost all cells of the body. Dysfunction of the mitochondria have profound effects on neurotransmission and may contribute to changes in neuronal circuits in the brain that are associated with cognition, memory, and other forms of neuronal plasticity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Thus, illnesses in which mitochondria are dysfunctional may cause disruption that has been implicated in several psychiatric conditions. For example, impaired neurotransmission is evident in patients with bipolar disorder [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which is likely linked to specific neurodevelopmental abnormalities [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Because neurons depend on energy, mitochondrial dysfunction during neurodevelopment is expected to impact neurotransmission, with potentially crucial implications for the development of psychiatric conditions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, despite preliminary evidence for these associations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], few population-based epidemiological studies have examined the association between mitochondrial dysfunction and specific psychiatric conditions.\u003c/p\u003e \u003cp\u003eBuilding on previous population-based epidemiological studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and using multiple sources of population health databases, the objectives of this study were to characterize the epidemiology of mitochondrial disease and its co-occurrence with mental health conditions in Ontario. Specifically, we sought to examine the association between mood disorders and other mental health conditions in patients with mitochondrial disease as well as examine the joint impact of mental health conditions on healthcare use and health system costs. To contextualize these findings, a clinical comparator cohort of multiple sclerosis (MS) patients was also used.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eA flow chart depicting the cohort creation for both mitochondrial disease and MS cohorts can be shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These datasets were linked using unique encoded identifiers and analyzed at ICES. There were 3,123 unique individuals who had one or more hospitalizations with a diagnostic code indicating mitochondrial disease and could be linked to health insurance data in Ontario. There were 3,069 individuals remaining after excluding incomplete Ontario Health Insurance Program (OHIP) eligibility or missing demographic information. Since a 3-year lookback window was required to capture mental health conditions and the first date to capture mental health data is April 1st, 2002, any individual with their first hospitalization indicating mitochondrial disease before April 1st, 2005 was excluded. This left a final cohort of 1,495 individuals with mitochondrial disease. There were 20,782 unique individuals who had one or more hospitalizations with a diagnostic code indicating MS and could be linked to a valid RPDB record. After excluding hospitalizations from before April 1st, 2005, a final cohort of 8,482 individuals with MS was obtained.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCohort characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic characteristics of the cohort, including sex, age at cohort entry (first hospitalization for mitochondrial disease or MS), and neighbourhood-level material deprivation quintile. Material deprivation refers to the inability of individuals or households to afford necessary goods and services [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy cohort characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMitochondrial disease patients (N\u0026thinsp;=\u0026thinsp;1,495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultiple Sclerosis patients (N\u0026thinsp;=\u0026thinsp;8,482)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eAge at cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eNeighbourhood deprivation quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen examining the baseline characteristics of the mitochondrial disease study population, there were no major sex differences. This is distinct from a larger proportion of the MS population was made up of females (69.8%), which was expected given the epidemiology of MS. In terms of age at cohort entry for the mitochondrial disease population, first hospitalization was most common between ages 0\u0026ndash;9, with 19.4 percent of the study population entering the cohort before age 10. A substantial number of cohort entries also occurred in older ages, with 33.9 percent entering the cohort between ages 50 and 69. For those with MS, age at cohort entry was most common between ages 40 and 69, with 62.4% of the study population entering the cohort after age 40. Overall, a greater proportion of mitochondrial disease patients were living in lower socioeconomic neighbourhoods, such that 26.3 percent of those in the mitochondrial disease cohort resided in the lowest socioeconomic status neighbourhoods (highest deprivation quintile), compared to 21.9 percent of the MS patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMental health\u003c/h2\u003e \u003cp\u003eOverall, a much larger proportion of the mitochondrial disease cohort had a co-prevalent mental health illness as compared to the MS population. For those with mitochondrial disease, 18.3% (n\u0026thinsp;=\u0026thinsp;274) had one or more mental illnesses. Conversely, only 8.6% (n\u0026thinsp;=\u0026thinsp;728) of those with MS also had a co-prevalent mental health illness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHealth care utilization\u003c/h2\u003e \u003cp\u003eHealthcare utilization for one-year periods prior to and following the index hospitalization is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Healthcare utilization, both pre-and post-index hospitalization, was higher for those with one or more mental health conditions, especially for mitochondrial disease patients who also had one or more mental health conditions. For example, 48.9 percent of mitochondrial disease patients with one or more mental health conditions had at least 1 hospitalization, whereas only 39.1 percent of mitochondrial disease patients with no mental health conditions had at least 1 hospitalization. The proportion of MS patients with 1 or more hospitalizations was lower, with 12.3 percent of MS patients with no mental health conditions and 22.9 percent of MS patients with one or more mental health conditions being hospitalized at least once. This trend was similar across pre- and post- index hospitalization for other factors of health care utilization, including intensive care unit (ICU) admissions and emergency department visits. However, it was found that health care utilization for outpatient care, including primary and specialist care visits, was slightly higher for MS patients. For example, MS patients had the greatest utilization of specialist visits, where 80.7 percent of MS patients with no mental health conditions and 82.0 percent of MS patients with one or more mental health conditions had 1 or more specialist visits, as compared to 73.4 percent of mitochondrial disease patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHealth care utilization prior to and after cohort entry, mitochondrial disease and multiple sclerosis patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHealth care utilization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMitochondrial disease patients (n\u0026thinsp;=\u0026thinsp;1,495)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultiple Sclerosis patients (n\u0026thinsp;=\u0026thinsp;8,482)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Mental Health Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 Mental health condition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Mental Health Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 Mental health condition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1 year prior to index date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHospitalization episodes of care\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal hospitalizations, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 hospitalization, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e478 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e956 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e167 (22.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpisodes of care per person, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay, d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (3\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eICU visits\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal visits, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 ICU admission, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisits per person, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay, h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (5\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5 (4\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (4\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.5 (4\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEmergency department visits\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal visits, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 visit, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e845 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (86.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4592 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e593 (81.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisits per person, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrimary care visits\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal visits, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 visit, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1096 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241 (88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6976 (90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e666 (91.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisits per person, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (3\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpecialist visits\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal visits, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 visit, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e896 (73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201 (73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6255 (80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e597 (82.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisits per person, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (2\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (2\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1 year following index date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHospitalization episodes of care\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal hospitalizations, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 hospitalization, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e504 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2157 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e275 (37.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpisodes of care per person, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1-3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay, d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (4\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eICU visits\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal visits, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 ICU admission, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e332 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisits per person, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay, h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (5\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (3\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (5\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (5\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEmergency department visits\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal visits, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 visit, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e845 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (86.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4592 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e593 (81.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisits per person, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrimary care visits\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal visits, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 visit, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1085 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229 (83.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6936 (89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e667 (91.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisits per person, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (3\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpecialist visits\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal visits, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 visit, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e952 (78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6889 (88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e678 (93.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisits per person, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (3\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHealth care costs\u003c/h2\u003e \u003cp\u003eHealthcare costs incurred by members of the study population before and after their index hospitalization are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Overall, healthcare costs incurred by those with mitochondrial disease and MS were highest for those with concurrent mental health conditions. For example, the mean cost incurred in the 12 months prior to hospitalization for mitochondrial disease patients with no mental health condition was \u003cspan\u003e$\u003c/span\u003e6859, and the mean cost incurred in 12 months post-discharge for this cohort was \u003cspan\u003e$\u003c/span\u003e21,257. Conversely, the same costs pre- and post-index date for mitochondrial disease patients with \u0026ge;\u0026thinsp;1 or more mental health conditions were \u003cspan\u003e$\u003c/span\u003e10,132 and \u003cspan\u003e$\u003c/span\u003e33,363, respectively. Similarly, the pre- and post-index median costs of health care for MS patients without any mental health conditions were \u003cspan\u003e$\u003c/span\u003e3984.5 and \u003cspan\u003e$\u003c/span\u003e7754, respectively, whereas these same costs for MS patients with one or more mental health conditions were \u003cspan\u003e$\u003c/span\u003e7178 and \u003cspan\u003e$\u003c/span\u003e27314.5. Thus, costs incurred by this study population were high in both cohorts but particularly high in mitochondrial disease patients and even higher for mitochondrial disease patients with one or more mental health conditions. This trend was maintained for almost all other factors part of health care costs incurred by these populations including inpatient hospitalization, emergency department, and physician billing costs where mitochondrial disease patients with one or more health conditions incurred the highest health care costs within this study population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHealth care costs prior to and following cohort entry, mitochondrial disease and multiple sclerosis patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHealth care costs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMitochondrial disease patients (n\u0026thinsp;=\u0026thinsp;1,495)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultiple Sclerosis patients (n\u0026thinsp;=\u0026thinsp;8,482)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Mental Health Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 Mental health condition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Mental Health Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 Mental health condition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1 year prior to index date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny health care cost, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1202 (98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272 (99.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7706 (99.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e727 (99.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cost, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6859 (1803\u0026ndash;22157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10132 (3017\u0026ndash;33077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3984.5 (1729\u0026ndash;11425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7178 (2732.5\u0026ndash;18859)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny inpatient hospitalization cost, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e479 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e958 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e168 (23.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient hospitalization cost (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9212 (3847\u0026ndash;23409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11441.5 (5234\u0026ndash;24346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4769 (3343\u0026ndash;11897)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7842 (4018.5\u0026ndash;15730.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny outpatient hospital cost (clinic visits), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e744 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4064 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e331 (45.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient hospital clinic visit costs (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1246 (630\u0026ndash;2267.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e982.5 (328.5\u0026ndash;1951)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e681 (338\u0026ndash;1433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e655 (327\u0026ndash;1293)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny emergency department costs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e851 (69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (86.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4629 (59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e601 (82.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency department costs (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e811 (429\u0026ndash;1431)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1363 (710\u0026ndash;2698)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e577 (317\u0026ndash;933)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e931 (477\u0026ndash;1651)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny physician billings (GP and specialist), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1193 (97.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267 (97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7596 (98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e723 (99.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician billings costs (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1676 (602\u0026ndash;3912)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2227 (891\u0026ndash;5174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1105 (539\u0026ndash;2005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1699 (792\u0026ndash;3183)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1 year following index date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny health care cost, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1221 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7754 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e728 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cost (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21257 (9914\u0026ndash;61684)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33363 (14820\u0026ndash;68131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19539.5 (9380\u0026ndash;44956)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27314.5 (12523.5\u0026ndash;51715.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny inpatient hospitalization cost, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1221 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7754 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e728 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient hospitalization cost (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9709 (4354\u0026ndash;30374)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14624 (5819\u0026ndash;29682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6534 (4506\u0026ndash;14290)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8123.5 (4729\u0026ndash;18586.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny outpatient hospital cost (clinic visits), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e961 (78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5763 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e477 (65.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient hospital clinic visit costs (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1250 (636\u0026ndash;2614)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e954 (343\u0026ndash;1636)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e950 (363\u0026ndash;1621)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e660 (330\u0026ndash;1310)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny emergency department costs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1003 (82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5778 (74.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e646 (88.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency department costs (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e823 (520\u0026ndash;1481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1977.5 (889.5\u0026ndash;3206.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e723 (479\u0026ndash;1166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1034 (616\u0026ndash;1832)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny physician billings (GP and specialist), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1221 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7754 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e728 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician billings costs (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3639 (1852\u0026ndash;7152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5154.5 (2600\u0026ndash;9078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2865 (1799.5\u0026ndash;4829.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3586 (2119.5\u0026ndash;5942.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1 year following index date (excluding index hospitalization)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny health care cost, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1171 (95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265 (96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7606 (98.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e725 (99.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cost (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9011 (2027\u0026ndash;24511)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20591 (5974\u0026ndash;51946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9869.5 (2743\u0026ndash;31530)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17866.5 (5659\u0026ndash;39297.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny inpatient hospitalization cost, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e517 (42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2220 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e290 (39.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient hospitalization cost (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13462 (5044\u0026ndash;33096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13398.5 (7735\u0026ndash;32440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9058.5 (4565\u0026ndash;21636.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10702 (5074\u0026ndash;20843)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny outpatient hospital cost (clinic visits), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e837 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5576 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e560 (76.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient hospital clinic visit costs (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1249 (636\u0026ndash;2582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e998 (622\u0026ndash;1872)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e966 (622\u0026ndash;1637)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e982 (630\u0026ndash;1933)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny emergency department costs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e715 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217 (79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3606 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e527 (72.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency department costs (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e674 (332\u0026ndash;1387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1709 (909\u0026ndash;3017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e552.5 (269\u0026ndash;1040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e857 (442\u0026ndash;1684)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny physician billings (GP and specialist), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1154 (94.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265 (96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7541 (97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e722 (99.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician billings costs (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1960.5 (770\u0026ndash;4488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3621 (1284\u0026ndash;7207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1527 (766\u0026ndash;2947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2369 (1196\u0026ndash;4077)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eKey findings\u003c/h2\u003e \u003cp\u003eOverall, we identified 1,495 individuals hospitalized with mitochondrial disease and 8,482 individuals hospitalized with MS in Ontario, Canada, between 2005 and 2019. The prevalence of mental health conditions was significant and higher in the mitochondrial disease cohort compared to the MS cohort. The greatest health care utilization was among mitochondrial disease patients with a co-prevalent mental health illness, with 49% hospitalized compared to 12\u0026ndash;39% in other groups. Conversely, those with MS and no mental health conditions had the lowest healthcare utilization within the study population. In terms of healthcare costs, individuals with a co-prevalent mental health illness in the mitochondrial cohort incurred the highest healthcare costs, likely due to the high co-prevlant conditions.\u003c/p\u003e \u003cp\u003eOur findings suggest that the mitochondrial disease and MS populations have important healthcare and more complex healthcare needs. For comparability, previous literature has studied health system users in Ontario between 2009 and 2011, finding a median health care cost of \u003cspan\u003e$\u003c/span\u003e333 per user, with only 5% of health system users incurring \u003cspan\u003e$\u003c/span\u003e7,961 or more of health care costs per year [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Based on this finding, a majority of our study cohort would be considered among the highest users of healthcare within health system. The findings of generally high healthcare costs incurred by the mitochondrial disease population are also consistent with findings from a study done with the mitochondrial disease population in the United States [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This paper found that this demographic incurred over \u003cspan\u003e$\u003c/span\u003e113\u0026nbsp;million of healthcare expenditures. This expenditure went towards the direct medical costs associated with treating those with mitochondrial disease.\u003c/p\u003e \u003cp\u003eThe utilization and cost of health care increase in those with co-prevalent mental health illnesses. Healthcare costs and utilization were higher following mitochondrial disease-related hospitalization. Since the hospitalization from that main visit was excluded from these calculations, this increase cannot be attributed to the cost of hospitalization itself. However, it can be related to post-hospitalization care, such as rehabilitation or the cost of a specialist follow-up. Our findings are consistent with other studies about health costs associated with mental illness, which suggest that patients with high mental health costs incur over 30% more costs than other high-cost patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To our knowledge, no previous study has investigated the health burden and cost associated with mental illness in a mitochondrial disease or MS patient population; our findings demonstrate that the burdens associated with these chronic conditions are exacerbated by co-prevalent mental illness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe cohort used in this population-based study is limited to hospitalized cases of mitochondrial disease because of diagnostic coding limitations in Ontario's administrative health data. Outpatient physician billings in Ontario (OHIP data) are based on a modified ICD-9 coding system and do not capture enough detail to identify rare diseases, including mitochondrial disease accurately. Patients with less acute symptoms may receive a diagnosis in Ontario (e.g. from an outpatient clinic) but would not be captured by our disease cohort. We thus consider our study population to represent a high-acuity patient population; for this reason, we have chosen a high-specificity, low-sensitivity algorithm for identifying our comparator MS cases.\u003c/p\u003e \u003cp\u003eAnother limitation of this approach is that the date of cohort entry is based on the first hospitalization and may not reflect when a patient first becomes symptomatic or aware of their diagnosis. For this reason, pre- and post-index date periods used for assessing healthcare utilization and costs do not necessarily correspond to pre- and post-diagnostic windows. Similarly, we cannot definitively assess the onset timing of mental health conditions compared to mitochondrial disease or MS.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents a comprehensive, population-wide cohort study of mitochondrial disease and co-prevalent mental health conditions in a large single-payer health system. Our findings demonstrate that the prevalence of mental health conditions is substantially elevated among mitochondrial disease patients. We also demonstrate that patients with concurrent mental illness and mitochondrial disease contribute to complexity and healthcare needs.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003e ICES is an independent, non-profit research institute funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). As a prescribed entity under Ontario\u0026rsquo;s privacy legislation, ICES is authorized to collect and use health care data for the purposes of health system analysis, evaluation and decision support. Secure access to these data is governed by policies and procedures that are approved by the Information and Privacy Commissioner of Ontario. Basic demographic information, including sex and age, was gathered from the Registered Persons Database (RPDB), which has stored data for persons registered under the OHIP at any time since 1992.\u003c/p\u003e \u003cp\u003eMitochondrial disease and MS cases were identified using hospitalization records from the Discharge Abstract Database (DAD), which captures acute care hospitalizations (inpatient stays). Mental health conditions were identified through emergency department or hospital visits. Hospitalizations were captured through DAD and the Ontario Mental Health Reporting System (OMHRS). Emergency department visits were captured through the National Ambulatory Care Reporting System (NACRS) database.\u003c/p\u003e \u003cp\u003eHealthcare utilization was captured using data from DAD (inpatient hospitalizations and ICU stays), NACRS (emergency department visits), and physician billings from OHIP data (primary care visits and specialist visits). Healthcare costs were based on these datasets and several others, including drug payments, home care and long-term care; a full list of databases used by the costing algorithm is available in the published guidelines [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese datasets were linked using unique encoded identifiers and analyzed at ICES.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCohort identification\u003c/h2\u003e \u003cp\u003eWe identified cases of mitochondrial disease as any individual hospitalized one or more times with a diagnostic code indicating mitochondrial disease in the discharge record. ICD-10 code G71.3 was used to identify diagnoses of mitochondrial disease.\u003c/p\u003e \u003cp\u003eWe also used a comparison cohort of patients with MS; MS has been used as a comparator climical cohort in other cohort-based studies of mitochondrial disease [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We likewise captured individuals hospitalized one or more times in the study period, with a diagnostic code indicating MS (ICD-10 code G35). A validation study in Ontario found this algorithm to be low-sensitivity for general MS cases [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, we believe it is appropriate for our study to capture high-acuity cases only to best align with the mitochondrial disease cohort.\u003c/p\u003e \u003cp\u003eCases were excluded from either cohort if they could not be linked to a valid record in RPDB or were not OHIP eligible for at least 12 months pre- and post-admission. The eligibility exclusion was to ensure that individuals were receiving most of their health care in Ontario before and after hospitalization. Sex and age were identified based on individuals' health card records at the time of first hospital admission.\u003c/p\u003e \u003cp\u003eIn order to determine mental health conditions prior to their first hospital admission, a 3-year lookback window was used. Since the first available mental health data is from April 2002, any person who had their first hospital admission for mitochondrial disease before April 2005 was removed from the cohort for this analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMental health conditions\u003c/h2\u003e \u003cp\u003eICD codes indicating mental health-related care were chosen based on a framework developed by the Mental Health and Addictions Program at ICES, formally known as the Mental Health and Addictions Scorecard and Evaluation Framework (MHASEF) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This framework was developed using guidelines from the Canadian Institute for Health Information for capturing specific clinical mental health conditions from emergency department or hospital visits [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and includes minor modifications based on Ontario-specific coding practices. MHASEF includes ICD-10-CA codes used in DAD and NACRS, and ICD-9-CM codes based on the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) used in OMHRS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eHealth care utilization\u003c/h2\u003e \u003cp\u003eHealthcare utilization was captured for all mitochondrial disease and MS patients for one year prior to and following the index hospitalization. The index hospitalization was excluded from all health care utilization measures.\u003c/p\u003e \u003cp\u003eA hospitalization episode was defined as the period from admission to an acute inpatient setting to final discharge from acute care, allowing for transfers between inpatient hospitals. Length of stay was based on the full episode, which may be made up of multiple individual hospital discharge records. For example, if Hospital A transfers a patient who was discharged from acute care to Hospital B, then the episode length is from admission to Hospital A to discharge from Hospital B. Emergency department visits were limited to one visit per patient per day.\u003c/p\u003e \u003cp\u003ePrimary care and specialist physician visits were captured using physician billings and were limited to one claim per patient per physician per day. Physician specialties were based on the submitted claim and were classified as either primary care (family practice and general practice, pediatrics, and community medicine) or specialist (all other specialties).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eHealth care costs\u003c/h2\u003e \u003cp\u003eHealth care costs were assessed using health administrative data from across ICES data holdings based on a costing algorithm that has been described in detail elsewhere [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Briefly, person-level costs are derived by combining health care utilization records with the Ministry of Health and Long-Term Care cost information. We used this algorithm to capture costs for mitochondrial disease and MS patients for one year before and after index hospitalization. As with healthcare utilization measures, costs incurred during the index hospitalization were excluded.\u003c/p\u003e \u003cp\u003eWe measured health care costs overall and for the following specific cost categories: inpatient hospitalization, outpatient hospital clinic visits, emergency department visits, and physician billings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDAD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Discharge Abstract Database\u003c/p\u003e\n\u003cp\u003eICU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intensive care unit\u003c/p\u003e\n\u003cp\u003eMHASEF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mental Health and Addictions Scorecard and Evaluation Framework\u003c/p\u003e\n\u003cp\u003eMS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Multiple sclerosis\u003c/p\u003e\n\u003cp\u003eNACRS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;National Ambulatory Care Reporting System\u003c/p\u003e\n\u003cp\u003eOHIP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ontario Health Insurance Program\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the University of Toronto Health Sciences Research Ethics Board.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eICES is a prescribed entity under Ontario\u0026apos;s Personal Health Information Protection Act (PHIPA). PHIPA authorizes ICES to collect personal health information, without consent, for the purpose of analysis or compiling statistical information with respect to the management of, evaluation or monitoring of, the allocation of resources to or planning for all or part of the health system. Legal data sharing agreements between and data providers (e.g., healthcare organizations and government) prohibit ICES from making the dataset publicly available, as the data contain sensitive and potentially identifying health information. However, access may be granted to those who meet pre-specified criteria for confidential access, available at www.ices.on.ca/DAS (email: [email protected]). 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.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research is part of the Mitochondrial Innovation Initiative, which receives funding from the University of Toronto, Thomas Zachos Chair and MitoCanada Foundation. 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). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 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 (MLTCThe 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLCR, ACA, and LTY conceptualized the study. LCR oversaw the statistical analysis and writing of the manuscript. EB and MH conducted the analysis. EB, MH, and TS contributed to the interpretation of the data and writing of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParts 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.\u0026nbsp;We thank IQVIA Solutions Canada Inc. for use of their Drug Information File. Parts of this material are based on data and information compiled and provided by the Ontario Ministry of Health. 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.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for Publication declarations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNg YS, Turnbull DM. Mitochondrial disease: genetics and management. J Neurol. 2016;263(1):179\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlston CL, Rocha MC, Lax NZ, Turnbull DM, Taylor RW. The genetics and pathology of mitochondrial disease. J Pathol. 2017;241(2):236\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuajitti E, Rosella LC, Zabzuni E, Young LT, Andreazza AC. Prevalence and health care costs of mitochondrial disease in Ontario, Canada: A population-based cohort study. PLoS ONE. 2022;17(4):e0265744.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGorman GS, Schaefer AM, Ng Y, Gomez N, Blakely EL, Alston CL, Feeney C, Horvath R, Yu-Wai-Man P, Chinnery PF, et al. Prevalence of nuclear and mitochondrial DNA mutations related to adult mitochondrial disease. Ann Neurol. 2015;77(5):753\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato T, Kunugi H, Nanko S, Kato N. Association of bipolar disorder with the 5178 polymorphism in mitochondrial DNA. Am J Med Genet. 2000;96(2):182\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInczedy-Farkas G, Remenyi V, Gal A, Varga Z, Balla P, Udvardy-Meszaros A, Bereznai B, Molnar MJ. Psychiatric symptoms of patients with primary mitochondrial DNA disorders. Behav Brain Funct. 2012;8(1):9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndreazza AC, Duong A, Young LT. Bipolar Disorder as a Mitochondrial Disease. Biol Psychiatry. 2018;83(9):720\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatal\u0026aacute;n-Garc\u0026iacute;a M, Garc\u0026iacute;a-Garc\u0026iacute;a FJ, Moreno-Lozano PJ, Alcarraz-Viz\u0026aacute;n G, Tort-Merino A, Milisenda JC, Cant\u0026oacute;-Santos J, Barcos-Rodr\u0026iacute;guez T, Cardellach F, Llad\u0026oacute; A et al. Mitochondrial Dysfunction: A Common Hallmark Underlying Comorbidity between sIBM and Other Degenerative and Age-Related Diseases. J Clin Med 2020, 9(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, Chan DCJC. Critical dependence of neurons on mitochondrial dynamics. 2006, 18(4):453\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato T, Kato N. Mitochondrial dysfunction in bipolar disorder. Bipolar Disord. 2000;2(3 Pt 1):180\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung LT. Is bipolar disorder a mitochondrial disease? J psychiatry neuroscience: JPN. 2007;32(3):160\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaniels TE, Olsen EM, Tyrka AR. Stress and Psychiatric Disorders: The Role of Mitochondria. Ann Rev Clin Psychol. 2020;16:165\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFattal O, Budur K, Vaughan AJ, Franco K. Review of the literature on major mental disorders in adult patients with mitochondrial diseases. Psychosomatics. 2006;47(1):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatheson FI, Dunn JR, Smith KL, Moineddin R, Glazier RHJCJPHRCDSP. Development of the Canadian Marginalization Index: a new tool for the study of inequality. 2012:S12-S16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWodchis WP, Austin PC, Henry DA. A 3-year study of high-cost users of health care. 2016, 188(3):182\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCormack SE, Xiao R, Kilbaugh TJ, Karlsson M, Ganetzky RD, Cunningham ZZ, Goldstein A, Falk MJ, Damrauer SM. Hospitalizations for mitochondrial disease across the lifespan in the U.S. Mol Genet Metab. 2017;121(2):119\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Oliveira C, Cheng J, Vigod S, Rehm J, Kurdyak PJHA. Patients with high mental health costs incur over 30 percent more costs than other high-cost patients. 2016, 35(1):36\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWodchis WP, Bushmeneva K, Nikitovic M, McKillop I. Guidelines on person-level costing using administrative databases in Ontario. 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiddifield J, Ivers NM, Young J, Green D, Jaakkimainen L, Butt DA, O'Connor P, Hollands S, Tu K. Development and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada. Mult Scler. 2015;21(8):1045\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMHASEF Research Team. Mental Health and Addictions System Performance in Ontario: A Baseline Scorecard. In. Toronto, ON: Institute for Clinical Evaluative Sciences; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMental Illness Hospitalization [indicator] [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://indicatorlibrary.cihi.ca/display/HSPIL/Mental+Illness+Hospitalization\u003c/span\u003e\u003cspan address=\"http://indicatorlibrary.cihi.ca/display/HSPIL/Mental+Illness+Hospitalization\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ].\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mitochondrial disease, Mental health, epidemiology, health care utilization, health care costs","lastPublishedDoi":"10.21203/rs.3.rs-3990108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3990108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background\n\nMitochondrial disease has been linked to mental health disorder in clinical cohorts and post-mortem studies. However, a lack of population-level studies examining the relationship between mitochondrial disease and mental health has resulted in an evidence gap and creates a challenge for identifying and addressing care needs for the mitochondrial disease population.\nUsing multiple linked population health databases in a single-payer health system that covers the full population, this study aimed to investigate the prevalence of mood disorders and other mental health conditions in patients with mitochondrial disease and to examine the joint impact of mitochondrial disease and mental health conditions on healthcare use and health system costs. To contextualize these findings, a clinical comparator cohort of multiple sclerosis (MS) patients was analyzed.\n\nResults\n\nOverall, co-prevalent mental health conditions are common in the mitochondrial popualtion. Double the proportion of patients in the mitochondrial disease cohort had a co-prevalent mental health illness as compared to the MS population (18% vs 9%). Healthcare utilization was highest among patients with co-prevalent mitochondrial disease and mental illness, with 49% hospitalized within 1 year prior to cohort entry (compared to 12% of MS patients with no mental health condition). Costs were likewise highest among mitochondrial disease patients with mental health conditions.\n\nConclusions\n\nThis study presents the first comprehensive, population-wide cohort study of mitochondrial disease and co-prevalent mental health conditions. Our findings demonstrate a high burden of mental health conditions among mitochondrial disease patients, with high associated health care needs. We also find that patients with concurrent mental illness and mitochondrial disease represent a high-burden, high-cost population in a single-payer health insurance setting.","manuscriptTitle":"A population-based cohort study of mitochondrial disease and mental health conditions in Ontario, Canada","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 19:14:50","doi":"10.21203/rs.3.rs-3990108/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-05-28T13:38:55+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-28T11:28:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-09T06:55:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Orphanet Journal of Rare Diseases","date":"2024-03-29T10:10:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cdb62439-a2f7-4234-aa94-d61fcb600e94","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T16:06:07+00:00","versionOfRecord":{"articleIdentity":"rs-3990108","link":"https://doi.org/10.1186/s13023-025-03688-2","journal":{"identity":"orphanet-journal-of-rare-diseases","isVorOnly":false,"title":"Orphanet Journal of Rare Diseases"},"publishedOn":"2025-04-14 15:57:41","publishedOnDateReadable":"April 14th, 2025"},"versionCreatedAt":"2024-06-12 19:14:50","video":"","vorDoi":"10.1186/s13023-025-03688-2","vorDoiUrl":"https://doi.org/10.1186/s13023-025-03688-2","workflowStages":[]},"version":"v1","identity":"rs-3990108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3990108","identity":"rs-3990108","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}