|Year : 2016 | Volume
| Issue : 3 | Page : 86-91
Quantifying the arthritis pyramid for Ontario by using comprehensive community health data
Tanveer Towheed1, Shikha Gupta2, Shari Glustein3, Vic Sahai4
1 Department of Medicine, Queen's University, Kingston, Ontario, Canada
2 School of Rehabilitation Therapy, Queen's University, Kingston, Ontario, Canada
3 Hotel Dieu Hospital Research Institute, Kingston, Ontario, Canada
4 Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada
|Date of Submission||08-Jul-2016|
|Date of Acceptance||23-Aug-2016|
|Date of Web Publication||2-Nov-2016|
Hotel Dieu Hospital, 166 Brock Street, Kingston, Ontario K7L 5G2
Source of Support: None, Conflict of Interest: None
Introduction: Arthritis is a leading cause of functional impairment and health care utilization in Canada and in the Western world. The aim of this investigation is to quantify the frequency, severity, and magnitude of arthritis in Ontario, Canada, using recognized databases supplemented with comprehensive, population-based survey data to facilitate informed, evidence-based planning. Materials and Methods: Data from Vital Statistics (2011, mortality), Canadian Institute for Health Information (2013, Discharge Abstract Database), Census (2011, demographic information), National Ambulatory Care Reporting System (2013, emergency department visits), and the Canadian Community Health Survey (2011/12) were used to construct an arthritis pyramid for residents of Ontario aged 15 years and older. Results and Discussion: Although arthritis is not a common cause of death, it is an important reason for hospitalizations and emergency room visits. Its greatest impact lies in the prevalence of individuals who are affected; approximately 1.8 million individuals in Ontario, and the resulting negative impact on functional ability, health care utilization, and health-care costs. The impact on society is immense and is expected to worsen as the population ages. Conclusion: A nationwide health care strategy to prevent and manage all forms of arthritis is crucial. In order to do this, we must first understand its prevalence and impact on society. This study provides a detailed information on the iceberg effect for arthritis and offers valid information for regional planning, provincial comparisons and an illustration for similar analyses nationally and internationally.
Keywords: Arthritis, epidemiology, incidence, policy making, prevalence, public health
|How to cite this article:|
Towheed T, Gupta S, Glustein S, Sahai V. Quantifying the arthritis pyramid for Ontario by using comprehensive community health data. J Health Res Rev 2016;3:86-91
|How to cite this URL:|
Towheed T, Gupta S, Glustein S, Sahai V. Quantifying the arthritis pyramid for Ontario by using comprehensive community health data. J Health Res Rev [serial online] 2016 [cited 2021 Dec 4];3:86-91. Available from: https://www.jhrr.org/text.asp?2016/3/3/86/193188
| Introduction|| |
Musculoskeletal conditions, including arthritis, have become a greater burden to health-care resources over the past century due to progress made against communicable diseases and increased longevity. In particular, from 1996 to 2010, the number of individuals in Ontario living with rheumatoid arthritis (RA) has more than doubled. In a global context, the prevalence of RA and osteoarthritis (OA) is relatively higher in Ontario than other provinces in Canada and higher in Western than Eastern countries. Within Canada, prevalence rates are higher in the provinces than the territories (predominantly aboriginal populations).
The burden on the health-care system of individuals with arthritis is especially heavy with respect to primary care physicians and ambulatory care clinics. User rates for inpatient services, day surgeries, and emergency departments are also significant. Further, the burden of OA (along with other arthritic conditions) in Ontario is associated with higher health-care costs, lower health-related quality of life, and functional impairment. OA, in particular, is correlated with comorbidities associated with poor Western lifestyle habits including obesity, cardiovascular disease, and diabetes, leading to rising prevalence rates and cost to society. The functional limitations of OA tend to exacerbate associated conditions, leading to a self-perpetuating cycle of disease and disability. Estimating the burden of arthritis on the Ontario population is an important first step in understanding the current and future impacts it has on public health as well as the health-care system.
There are a few recent studies that have attempted to estimate the prevalence of arthritis in Ontario. The aim of this paper is to quantify the magnitude of arthritis at different levels of severity ranging from functional impairments to hospitalizations and mortality in Ontario, Canada. Quantifying an arthritis health effect in this way will facilitate evidence-based decision-making.
| Materials and Methods|| |
The health effect pyramid
Canadian epidemiologist Last first proposed the iceberg effect in 1963. Last suggested that the “tip of the iceberg” is the detected or diagnosed disease, resulting in documented mortality and hospitalization. However, at the base of the submerged iceberg, there exists a large undetected burden of disease. In this paper, we have stratified the tip of the iceberg using a health effect pyramid paradigm [Figure 1]. The top level of the pyramid shows the number of deaths due to arthritis and the second level indicates the number of hospitalizations due to arthritis. The middle level depicts the emergency room visits of people with arthritis. The fourth level shows the number of people who state that they are functionally impaired due to their arthritis. The base reflects the number of people who report being diagnosed with arthritis.
Known databases supplemented with comprehensive, population-based survey data are used to create the pyramid. Formal approval was taken from intelliHEALTH ONTARIO at the Ontario Ministry of Health and Long-Term Care for using the administrative databases.
Mortality data (Vital Statistics) and hospital use data from the Canadian Institute for Health Information (CIHI) were available for the apex of the pyramid. However, for accurate assessment of the other components of the pyramid, the Canadian Community Health Survey (CCHS) was used. The most recent data available from the following data sources, detailed below, were used.
Data were collected by Statistics Canada in the 2011 Census. These data were used to provide a demographic profile.
Vital Statistics (2012)
Death records (by calendar year) were used to calculate the mortality rates and numbers. Records of deaths are collected and compiled by the Office of the Registrar General. Statistics Canada performs final editing of the data. Data are based on a “Statement of Death” and a “Medical Certificate of Death” which are completed with a division registrar before the issuance of a “Burial Permit.” Data were obtained through the Ontario Ministry of Health and Long-Term Care: IntelliHEALTH ONTARIO.
Canadian institute for health information
In this report, hospital admission/discharge records (fiscal year (April 1, 2013−March 31, 2014)), collected by CIHI, were used as a proxy indicator of morbidity. The variables derived from arthritis diagnosis are coded in accordance with the International Classification of Disease, 10th revision (ICD-10). Data were obtained through the Ontario Ministry of Health and Long-Term Care: IntelliHEALTH ONTARIO. Arthritis was considered to be the main cause of hospitalization.
National Ambulatory Care Record System (2013/2014)
The National Ambulatory Care Record System data represent Ontario emergency room visits collected by the CIHI. Fiscal year (2013/2014) has been reported. Data were obtained through IntelliHEALTH ONTARIO.
Canadian community health survey (2011/2012)
This survey provides data on Canadians' health status, arthritis and other risk factors, and health care use. The CCHS is a Canada-wide population survey that was administered to households throughout Canada. Data at the provincial level can be extracted with a great deal of power. People living in institutions, on native reserves and in extremely remote locations, were excluded from the survey. A total of 20,017 individual responses were collected in Ontario and represented a population of 11.5 million people. Individuals aged 12 and over participated in this survey; however, only individuals aged 15 and over were analyzed for this study. Guidelines for public release of data from the CCHS state that all estimates must be rounded to the nearest 100, thus numbers have been rounded accordingly. Participants were asked, “Do you have any of the following long-term conditions that have been diagnosed by a health-care professional?” and “arthritis” was one of the choices. As the study was based on the open administrative databases, no ethical clearance was required.
Arthritis was defined using the ICD-10 classification. The etiology of arthritis was analyzed using ICD-10 codes: M00-M003, M05-M19, M22-M25, M45-M47, M48-M48.2, M48-M48.9, M65-M68, M70-M71, M75-M77, and M79-M99. To conform to the CCHS data, only individuals aged 15 and over were used in the analyses. Only individuals who resided in Ontario were included in the analysis of the mortality, hospitalization, and emergency department data.
The CCHS  is a household, population-based, cross-sectional survey, and the smallest sampling unit is the public health unit area. The CCHS survey design is far more complex than the simple random sample and therefore, the sample means will not be unbiased estimates of population means. Therefore, techniques to calculate sampling variance must account for design of the survey and we must, therefore, use weighted analysis and bootstrapping technique to estimate the variance of the sample estimate.
Indirect standardization, using the standardized morbidity ratio (SMR), was used to compare each local health integration network (LHIN) geographic region to the province using the weighted average of the age-specific rates. Data were analyzed using SPSS (2012) statistics for Windows, version 21.0 (IBM Corp., Armonk, NY, USA). The standard error was calculated by the square root of the observed number of events divided by the expected number.
| Results and Discussion|| |
Occurrences of mortality, hospitalizations, and emergency department visits related to arthritis are summarized below. The prevalence of arthritis and its relationship with functional impairment are also presented [Figure 2].
|Figure 2: Ontario arthritis health effects pyramid. Pyramid describes the occurrences of mortality, hospitalizations, emergency department visits related to arthritis, and functional impairment and prevalence of arthritis in Ontario. Abbreviations: M: Males F: Females. *All ratio comparisons are to the number of deaths|
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There were 77,969 fatalities, due to all causes, in Ontario in 2009. Only 342 of these fatalities were directly attributable to arthritis.
There were 1,128,746 hospitalizations (discharges) with 6,568,098 total length of stay (LOS) days in the province of Ontario in 2013/2014. About 49,736 of these hospitalizations and 208,715 total LOS days were directly attributable to arthritis as the primary cause for admission. This represented 4.4% of all Ontario hospital discharges and 3.2% of all LOS days.
Emergency department visits
There were 5,809,895 emergency department visits recorded in Ontario in 2013/2014. About 191,614 of these emergency department visits were directly attributable to arthritis. This represented 3.3% of all Ontario emergency department visits.
Visits to family physicians and specialists
In 2011–2012, 88.7% of those having arthritis visited their family physician compared to 75.3% of individuals who did not have arthritis. The mean number of visits to family physicians for individuals with arthritis was 4.38 (95% confidence interval [CI]: 4.16–4.60), while their mean number of consultations with specialists was 1.92 (95% CI: 1.61–2.22). The mean number of visits for those who did not have arthritis was 2.42 (95% CI: 2.26–2.57) to family physicians and 1.71 (95% CI: 1.07–1.27) to specialists [Table 1]. Further, half of the individuals (49.7%) with arthritis visited specialists as compared to 28% of those who did not have arthritis. These visits may or may not be related to arthritis but suggest that individuals with arthritis visited family physicians and specialists more often than individuals without arthritis.
|Table 1: Mean number of visits of individuals with and without arthritis to family physicians and specialists (2011-2012)*|
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Based on the CCHS, only 37.3% of those diagnosed with arthritis rated their health as “excellent” or “very good” compared to 65.4% of their counterparts not diagnosed with arthritis. CCHS estimates that 31.1% (572,000 of 1,839,000) of all individuals diagnosed with arthritis had activity limitations. This is compared with 6.9% (643,000 of 9,371,000) of those individuals who were not diagnosed with arthritis. Based on the Health Utilities Index of Pain and Discomfort, 20.6% (379,000 of 1,839,000) of those diagnosed with arthritis said that they usually felt severe pain compared to 9.7% (909,000 of 9,371,000) of those not diagnosed with arthritis.
In Ontario (2011/2012), the population aged 15 and over who reported that they have been diagnosed by a health-care professional as having arthritis was 1,839,098 (16%). This included 694,500 males and 1,144,600 females which represent 12.7% (95% CI: 10.1–15.8%) and 20% (95% CI: 17.5–22.7%) of the male and female populations, respectively.
Age and sex distribution
Greater than 1.8 million individuals in Ontario report having arthritis, and the majority of those affected are females (694,539 males and 1,144,557 females) [Table 2]. Crude frequencies related to arthritis are higher for older people and for females than for younger people and males [Table 2]. This trend is also present for deaths, hospital discharges, and diagnosis. The only exception is for emergency department visits. Although more number of older women than men visited the emergency room for reasons related to arthritis, younger men had slightly more arthritis-related visits relative to younger women.
|Table 2: Frequency of severity of arthritis by age and sex* in Ontario (2012-2014)|
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[Table 3] contains the prevalence data and SMRs for the LHINs. The North East (NE) LHIN had the highest crude prevalence rate (24.9%) and an SMR of 1.33 (95% CI: 1.32–1.34). This suggests that the NE LHIN had an adjusted (age and sex) rate that was 34% higher than that of the province. The South East LHIN had the second highest crude prevalence rate (23.4%) and an SMR of 1.24 (95% CI: 1.23–1.24), indicating a 24% higher adjusted rate relative to the province. The Hamilton-Niagara-Haldimand-Brant LHIN had the third highest crude prevalence rate (20.5%) with an SMR of 1.15 (95% CI: 1.14–1.15). This highlights that prevalence of arthritis is not uniform across regions in the province.
|Table 3: Prevalence rate of arthritis by local health integration networks geographical region* in Ontario (2013-2014)|
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The pyramid reveals that arthritis is not a common cause of death; however, it is a frequent cause of hospitalizations and emergency room visits in Ontario. Its greatest impact lies in the large prevalence of individuals that are affected and the resulting negative impact this has on functional status, health care utilization, and health-care costs. Greater than 1.8 million individuals in Ontario report having arthritis and the majority of those affected are females.
We found that >600,000 individuals in Ontario reported having functional impairment because of arthritis and that 33.1% of those with arthritis reported having activity limitations, compared to only 7.6% of those individuals who were not diagnosed with arthritis. These results confirm the great impact of arthritis on an individual's functional status. Further data estimates from CCHS (2011–2012) suggest that there was a difference of 13.4% points between the two groups in frequency of visits to the family physicians.
It is likely that an even larger population lies at the base of the pyramid that remains undetected. This is partially due to less reliable documentation procedures in health-care settings other than hospitals where individuals might first present with arthritis-related symptoms, such as physiotherapy and chiropractors' clinics. In addition, the data collected for the base of the pyramid are from sample-based surveys.
Some studies have measured the burden of various forms of arthritis in Canada or its provinces. Widdifield et al. estimated the burden of RA in Ontario from 1996 to 2010 by age, sex, and health planning region. The authors found that the number of RA patients increased over time, from 42,734 Ontarians (0.5%) in 1996 to 97,499 (0.9%) in 2010. Matching our results, it was found that the burden was higher among females (1.3%) than males (0.5%) and increased with age, with almost half of all RA patients aged 65 years and older. Further, the burden was higher in Northern communities (1.0%) than in Southern urban areas (0.7%). Similar to our findings, Birtwhistle et al. in their retrospective cohort study estimated the prevalence of OA in patients aged 30 years and older in 2012, using electronic medical records in Canadian primary care patients. They found that the prevalence of diagnosed OA was 14.2% (15.6% among women, 12.4% among men).
The population prevalence of arthritis is expected to increase dramatically given the aging of the Canadian population. For example, using data from the 1991 General Social Survey and the 1994 National Population Health Survey, Badley and Wang  projected that the prevalence of arthritis diagnosed by a health-care professional as a long-term condition in Canada will increase over the time period of 1991–2031 from 10.7% to 15.7%, an increase of 46.7%, and that the number of people with arthritis will increase from 2.9 million to 6.5 million, an increase of 124%. Disability attributed to arthritis is also projected to increase from a prevalence of 2.3% in 1991 to 3.3% in 2031.
A nationwide health-care strategy to prevent and manage all forms of arthritis is crucial to stem the long-term increase in prevalence. For OA, one arm of the strategy should be aimed at reduction of obesity, known to be associated with OA due to the extra load on weight-bearing joints, especially the lumbar spine and knee. A second arm of a nationwide health-care strategy to prevent OA should focus on the prevention of joint injury. For RA, given its strong genetic component, one arm of a prevention strategy should focus on the early identification of at-risk individuals. A second arm of prevention for RA should focus on modifiable risk factors such as smoking, alcohol intake, and calcium and Vitamin D supplementation.
Limitations and suggestions for future research
In this study, we used various national administrative health information databases as a key source to estimate the burden of arthritis in Ontario and Canada. As data were obtained through a number of sources, data from the same years were not always available. Moreover, given that all databases used in this study have their own definitions, a degree of misclassification of the disease can be expected. Despite these limitations, these data provide valuable information for estimating disease burden and planning health services, and are being increasingly used for epidemiologic and outcomes research, health care quality measurement and management, and health services' population-based research. Widdifield et al. demonstrated in their study that administrative data can be used to identify RA patients with a high degree of accuracy. RA diagnosis date and disease duration are fairly well estimated from administrative data in jurisdictions of universal health-care insurance.
Data for those <15 years of age were excluded from the analysis. Given the relatively lower impact of arthritis as a health issue in people aged 0–14, we do not expect that this will have a great effect on the overall results.
The accuracy of self-reports of arthritis as a clinical diagnosis was not confirmed by a clinician, thus there may have been some confusion with another related musculoskeletal disorder. However, a study by Rasooly et al. found that in a rheumatology outpatient population, most patients reported a diagnosis that was compatible with the original clinical diagnosis. This would tend to support the accuracy of self-reported diagnoses of arthritis received from health-care professionals.
It is likely that an even larger population lies at the base of the pyramid that would represent individuals who have chronic joint symptoms that may also be due to arthritis, but that may not yet have been diagnosed by a health-care professional. We were not able to determine this number based on our analysis.
Finally, the CCHS did not include homeless people, people living in institutions, or living on reserves.
Future research is required to develop formal systems to estimate the true burden of disease, improve knowledge about the most cost-effective strategies to prevent and reduce disease burden in different populations, and developing policy relevant forecasts of likely health, economic, and social costs of continuing to ignore this burden. This will inform making policy level interventions to deal with arthritis effectively.
| Conclusion|| |
To implement an effective prevention strategy for arthritis, we must first understand its prevalence and impact on society. Despite its limitations, this study is the first comprehensive illustration and examination of an Ontario arthritis pyramid. It provides detailed evidence of the iceberg effect for arthritis and provides valid information for regional prevention planning, provincial comparisons, and similar analyses nationally and internationally. Although educating and recruiting more rheumatologists can be a short-term strategy to address the burden of arthritis on the Canadian health-care system, long-term prevention methods are required to combat the increasing numbers in the levels of the arthritis in Canada.
We would like to acknowledge the intelliHEALTH Ontario Support Team at the Ontario Ministry of Health and Long-Term Care for their support in accessing the databases.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]