Article

Helmick CG, Felson DT, Lawrence RC, et al. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States: part I

CDC, Atlanta, Georgia 30341-3717, USA.
Arthritis & Rheumatology (Impact Factor: 7.76). 01/2008; 58(1):15-25. DOI: 10.1002/art.23177
Source: PubMed

ABSTRACT

To provide a single source for the best available estimates of the US prevalence of and number of individuals affected by arthritis overall, rheumatoid arthritis, juvenile arthritis, the spondylarthritides, systemic lupus erythematosus, systemic sclerosis, and Sjögren's syndrome. A companion article (part II) addresses additional conditions.
The National Arthritis Data Workgroup reviewed published analyses from available national surveys, such as the National Health and Nutrition Examination Survey and the National Health Interview Survey (NHIS). For analysis of overall arthritis, we used the NHIS. Because data based on national population samples are unavailable for most specific rheumatic conditions, we derived estimates from published studies of smaller, defined populations. For specific conditions, the best available prevalence estimates were applied to the corresponding 2005 US population estimates from the Census Bureau, to estimate the number affected with each condition.
More than 21% of US adults (46.4 million persons) were found to have self-reported doctor-diagnosed arthritis. We estimated that rheumatoid arthritis affects 1.3 million adults (down from the estimate of 2.1 million for 1995), juvenile arthritis affects 294,000 children, spondylarthritides affect from 0.6 million to 2.4 million adults, systemic lupus erythematosus affects from 161,000 to 322,000 adults, systemic sclerosis affects 49,000 adults, and primary Sjögren's syndrome affects from 0.4 million to 3.1 million adults.
Arthritis and other rheumatic conditions continue to be a large and growing public health problem. Estimates for many specific rheumatic conditions rely on a few, small studies of uncertain generalizability to the US population. This report provides the best available prevalence estimates for the US, but for most specific conditions, more studies generalizable to the US or addressing understudied populations are needed.

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Available from: Hilal Maradit Kremers, Nov 26, 2014
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    • "Rheumatic arthritis is the main cause of impairment and pain in developed countries, which makes them a critical social, health and economical problem. In the United states, the number of adults affected by this condition is 21% [1]. Ultrasounds (US) can be a powerful and reasonably cheap diagnostic tool for rheumatology, enabling the screening of these diseases in their early stages [2]. "
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    DESCRIPTION: Rheumatic arthritis (RA) is an autoimmune disease that causes irreversible damage to joints and other physiological structures. The Metacarpophalangeal (MCP) joint is one of the first regions to suffer alterations. These alterations are visible with high frequency ultrasound devices, which are used to quantify inflammatory activity in the MCP due to RA. The accurate segmentation of the bone surface and the identification of the MCP capsule region remains a challenge in ultrasound image processing. In this article we aim to make a contribution to this problem by incorporating prior knowledge of the bone and joint regions anatomy into our segmentation algorithm. The log Gabor filter is used for speckle noise reduction and to extract ridge-like structures from the images, while the phase is left unchanged. After thresholding, scores are generated, based on the intensities and areas of the resulting regions, enabling the selection of the structure that best matches the bone. Finally, segmented joint bones are processed to calculate the initial seeds of joint capsule region. Experimental results demonstrate the accuracy of the proposed segmentation algorithm. The mean pixel error between the automatic segmentation and the reference images were 4.4 pixel. The bone regions not segmented were, on average, 5.4%.
    Full-text · Research · Jan 2016
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    • "One of the most pervasive threats to mobility in elderly is knee osteoarthritis (OA). With the aging of the population and the increasing incidence of obesity (Lawrence et al., 2008; Murphy et al., 2008), the prevalence of knee OA, and consequently burden on the society is rising. Among adults in western populations, knee OA is one of the most frequent causes of pain, loss of function and disability (Carmona et al., 2001; Van Saase et al., 1989). "
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    ABSTRACT: Previously, we reported reduced time-averaged knee local stability, in the unaffected, but not the affected leg of elderly with knee osteoarthritis OA compared to controls. Since stability may show phase-related changes, we reanalyzed the dataset reported previously using time-dependent local stability, λ(t), and also calculated time-averaged local stability, λs, for comparison. We studied treadmill walking at increasing speeds, focusing on sagittal plane knee movements. 16 patients, 12 healthy peers and 15 young subjects were measured. We found a clear maximum in λ(t) (i.e. minimum in stability) at around 60% of the stride cycle (StanceMax λ(t)), a second clear maximum (SwingMax λ(t)) at around 95% followed by a minimum between 70% and 100% (SwingMin λ(t)). StanceMax λ(t) of both legs was significantly higher in the OA than the young control group. Values for healthy elderly fell between those of the other groups, were significantly higher than in young adults, but there was only a trend towards a significant difference with the StanceMax λ(t) of the OA group׳s affected side. Time-averaged and time-dependent stability measures within one leg were uncorrelated, while time-dependent stability measures at the affected side were inversely correlated with λs at the unaffected side. The results indicate that time-dependent local dynamic stability might provide a more detailed insight into the problems of gait stability in OA than conventional averaged local dynamic stability measures and support the notion that the paradoxical decline in unaffected side time-averaged local stability may be caused by a trade-off between affected and unaffected side stability.
    Full-text · Article · Nov 2015 · Journal of Biomechanics
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    • "Rheumatic arthritis is the main cause of impairment and pain in developed countries, which makes them a critical social, health and economical problem. In the United states, the number of adults affected by this condition is 21% [1]. Ultrasounds (US) can be a powerful and reasonably cheap diagnostic tool for rheumatology, enabling the screening of these diseases in their early stages [2]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Rheumatic arthritis (RA) is an autoimmune disease that causes irreversible damage to joints and other physiological structures. The Metacarpophalangeal (MCP) joint is one of the first regions to suffer alterations. These alterations are visible with high frequency ultrasound devices, which are used to quantify inflammatory activity in the MCP due to RA. The accurate segmentation of the bone surface and the identification of the MCP capsule region remains a challenge in ultrasound image processing. In this article we aim to make a contribution to this problem by incorporating prior knowledge of the bone and joint regions anatomy into our segmentation algorithm. The log Gabor filter is used for speckle noise reduction and to extract ridge-like structures from the images, while the phase is left unchanged. After thresholding, scores are generated, based on the intensities and areas of the resulting regions, enabling the selection of the structure that best matches the bone. Finally, segmented joint bones are processed to calculate the initial seeds of joint capsule region. Experimental results demonstrate the accuracy of the proposed segmentation algorithm. The mean pixel error between the automatic segmentation and the reference images were 4.4 pixel. The bone regions not segmented were, on average, 5.4%.
    Full-text · Conference Paper · Aug 2015
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