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BackgroundTaking a representative sample to determine prevalence of variables such as disease is difficult when little is known about the target population. Several methods have been proposed that apply cluster sampling techniques to Primary Sampling Units (PSUs). The PSUs are typically towns or census tracts. Some methods are based on random walks within towns, e.g., the original World Health Organization’s Extended Program on Immunization (‘EPI’) surveys and variants, including sampling from four quadrants of each town (‘Quad’). Several major international surveys take random samples from small areas (‘SA’) such as census tracts. Another method uses satellite images and Global Positioning Systems to randomly sample within PSUs from squares in a superimposed grid (‘Square’). We used computer simulations to compare these sampling methods and simple random sampling within towns (‘SRS’) in virtual populations. SRS was our standard, even though it is impractical in low-information settings.MethodsWe constructed 50 virtual populations with varying characteristics, each comprising about a million people spread over 300 towns. The risk of disease for each person varied within and between towns. We created a binary exposure variable and allocated disease statuses to individuals assuming four relative risks (RRs) from exposure. We added three populations with equal risk of disease for every person in the population.For each population, each of the sampling methods – EPI, Quad, SA, Square, and SRS - and each of three sample sizes per PSU (7, 15, and 30), we simulated 1000 samples. For each simulation we estimated the prevalence and RRs. We used the bias and variance of the estimates to calculate the Root Mean Squared Error (RMSE) of these estimates. We ranked the RMSEs of each method and computed the ratio of each method’s RMSE to that of SRS for each population. We computed the mean ranks and ratios across the 50 populations.ResultsApart from SRS, Square had the lowest mean rank of RMSEs for all samples sizes when estimating prevalence. When estimating RR, Square had the lowest mean ranks for samples sizes of 15 and 30 per PSU; for n=7 per PSU, the Quad mean rank was the lowest.The results for the mean ratios of RMSEs showed the same pattern; Square had the lowest values for all sample sizes when estimating prevalence and the lowest values for the two larger sample sizes when estimating RRs. Notably, when estimating prevalence, the ratios increased with sample size per PSU for SA, Quad, and EPI, suggesting those methods did not benefit as much from the larger sample sizes as would be expected from statistical theory.ConclusionsOf several methods that are practical in an imperfectly known population, the Square method was mostly the best, especially for the larger sample sizes. The methods that sample within small areas (Quad, SA, and EPI) do not gain as much statistical benefit as expected from larger sample sizes per PSU, because of some clustering within the areas.

Background:Taking a representative sample to determine prevalence of variables like disease is difficult when little is known about the target population. Several methods have been proposed, including a recent revision of the World Health Organization’s Extended Program on Immunization (EPI) surveys. The original EPI method samples towns as Primary Sampling Units (PSUs) with probability proportional to size and uses a nearest neighbour approach to sample households within PSUs. The new version samples from smaller PSUs and conducts a probability sample of households within those PSUs. Other techniques use satellite images and Global Positioning Systems to sample within towns from circles around randomly identified points (‘Circle’ method) or from randomly sampled squares in a superimposed grid (‘Square’ method). We compared these sampling methods in multiple virtual populations using computer simulation.Methods:We constructed 50 virtual populations with varying characteristics. Populations comprised about a million people across 300 towns. We created three populations with different prevalences of disease but with uniform characteristics across each population. We created a binary exposure variable and allocated disease statuses to individuals assuming different relative risks (RRs) of exposure. We simulated thirteen methods of sampling: simple random sampling; the original EPI method and variants; the Square and Circle methods; and the new EPI method. For each population, each sampling method, and each of three sample sizes per PSU (7, 15, and 30), we simulated 1,000 samples. For most sampling methods, the PSUs were towns. We conducted simulations using the same 30 PSUs and using a freshly-chosen set of PSUs. For each simulation we estimated prevalence and RRs and combined the bias and variance of the 1,000 samples to compute the Root Mean Squared Error (RMSE).Results: The Circle and Square methods produced almost identical results, so we report only the Square method results. Apart from simple random sampling, the RMSE for the Square method was almost universally best for estimating prevalence, and generally best when estimating relative risks. The revised EPI approach was worse, but generally better than the original EPI. Conclusions:The Square method is recommended as statistically optimal, unless practical considerations favour another approach.

The volume of bone erosions in the metacarpophalangeal joints is a radiological feature that can be used to track the progression of rheumatoid arthritis. We introduce a hybrid segmentation algorithm that combines region growing and level-set segmentation algorithms to semiautomatically measure the volume of bone erosions in magnetic resonance images. A total of 40 rheumatoid arthritis patients were included in the study. The scans of eight patients were used for training, whereas the remaining 32 scans were used to determine the accuracy, precision, and speed of the technique. The reproducibility of the semiautomated technique and that of manual segmentation was defined in terms of intraclass correlation coefficients. Both techniques were equally precise with intraclass correlation coefficient values greater than 0.9. The hybrid algorithm was highly accurate: the least squares fit between the semiautomated segmentations to those manually traced by a musculoskeletal radiologist resulted in a slope of 1.030 with an x-intercept of 1.385 mm(3) and an R(2) value of 0.923. The semiautomated technique was significantly faster than manual segmentation, which took two to four times longer to complete. Our hybrid algorithm shows promise in the quantitative assessment of radiological features of rheumatoid arthritis in a clinical setting.

Objective The principal aim of this study was to create a segmentation program, to be used by nonmusculoskeletal or junior fellows, that defines the bones in the metacarpophalangeal joint in a dynamic 3-dimensional image that will lead to higher inter-reader agreement of bone erosion scores. Methods The second to fifth metacarpal head and phalangeal bases of 15 participants were rated according to the Rheumatoid Arthritis Magnetic Resonance Imaging Scoring system by one trained and one untrained reader. Two comparisons were made. The first comparison was between the 2 readers using only the traditional 2-dimensional magnetic resonance image set. The second comparison was between the 2 readers, with the untrained reader using a custom segmentation program with traditional 2-dimensional magnetic resonance image set. Results The software marginally increased inter-reader reliability with the exception of the second metacarpal head, for which reliability was increased substantially. Future work will concentrate on improving image acquisition, better delineate erosions from surrounding bone oedema, and address methods to directly determine erosion volumes. Conclusions Software designed to display dynamic 3-dimensional images enables a relatively untrained user to score the metacarpophalangeal joints in the hand for erosions equivalent to that produced by an expert using the manual methods.

Objective.Assess the reliability of early erosions in rheumatoid arthritis (EERA) software for quantifying erosive damage to the metacarpophalangeal joints of patients with rheumatoid arthritis (RA).Methods.One hundred magnetic resonance image sets from 68 patients with early referral RA were evaluated. Reliability was assessed using 95% limits of agreement and intraclass correlation coefficient (ICC) with 95% CI.Results.Limits of agreement linearly depended on erosion volume: 0.44× between readers and 0.19× within readers. Interrater ICC was 0.976 (95% CI 0.965–0.984) and intrarater ICC was 0.996 (95% CI 0.994–0.997).Conclusion.EERA is highly reproducible for quantifying erosions in patients with early RA.

SummaryObjectivesTo determine the ability of radiographic bone texture (BTX) parameters to quantify subchondral tibia sclerosis and to examine clinical relevance for assessing osteoarthritis (OA) progression. We examined the relationship between BTX parameters and each of (1) location-specific joint space width (JSW) [JSW(x)] and minimum JSW (mJSW) of the affected compartment, and (2) knee alignment (KA) angle in knee radiographs of participants undergoing total knee arthroplasty (TKA).DesignDigitized fixed-flexion knee radiographs were analyzed for run-length and topological BTX parameters in a subchondral region using an algorithm. Medial JSW(x) was computed at x=0.200, 0.225, 0.250 and 0.275 according to a coordinate system defined by anatomic landmarks. mJSW was determined for medial and lateral compartment lesions. KA angles were determined from radiographs using an anatomic landmark-guided algorithm. JSW measures and the magnitude of knee malalignment were each correlated with BTX parameters. Reproducibility of BTX parameters was measured by root-mean square coefficients of variation (RMSCV%).ResultsRun-length BTX parameters were highly reproducible (RMSCV%5%). In TKA participants (17 women, 13 men; age: 66±9 years; body mass index (BMI): 31±6kgm−2; WOMAC: 41.5±16.1; Kellgren–Lawrence score mode: 4), reduced trabecular spacing (Tb.Sp) and increased free ends (FE) were correlated with decreased JSW after accounting for BMI, gender and knee malalignment. These relationships were dependent on site of JSW measurement.ConclusionHigh reproducibility in quantifying bone sclerosis using Tb.Sp and its significant relationship with JSW demonstrated potential for assessing OA progression. Increased trabecular FE and reduced porosity observed with smaller JSW suggest collapsing subchondral bone or trabecular plate perforation in advanced knee OA.

236 – Table I. Correlation between trabecular space (TrSp) & JSW(x=20.0%) and TrSp & JSW(x=27.5%) JSW(x=20.0%) JSW(x=27.5%) Covariate Adjustment β P-value Lower CI Upper CI β P-value Lower CI Upper CI Unadjusted 0.217 0.218 0.000 0.000 0.243 0.166 0.000 0.000 Gender 0.262 0.023 0.000 0.000 0.246 0.033 0.000 0.000 Gender & BMI 0.313 0.008 0.000 0.000 0.304 0.011 0.000 0.000 Abstract 236 – Table II. Correlation between free ends (FE) & JSW(x=20.0%) and FE & JSW(x=27.5%).236 – Table II. Correlation between free ends (FE) & JSW(x=20.0%) and FE & JSW(x=27.5%). JSW(x=20.0%) JSW(x=27.5%) Covariate Adjustment β P-value Lower CI Upper CI β P-value Lower CI Upper CI Unadjusted -0.249 0.156 -0.012 0.002 -0.252 0.151 -0.009 0.001 BMI -0.370 0.027 -0.014 -0.001 -0.397 0.018 -0.011 -0.001 Age & BMI -0.400 0.021 -0.011 -0.001 -0.400 0.021 -0.011 -0.001 Figure 1. Placement of locater-rule endpoints on anatomic landmarks in knee radiograph for identification of subchondral regions of interest. of significance were identified between the JSW(x=20.0%) and JSW(x=27.5%) sites. No significant correlations between either JSW(x=20.0%) or JSW(x=27.5%) and each of BsTv, BvTv, CI, NL and TrTh were revealed (p>0.05). Conclusions: In the subchondral bone of the tibia, lower JSW(x) measures appear to be associated with larger values in trabecular FE and lower amounts of TrSp after accounting for gender, age and BMI. We anticipated that BMI and age would explain some variability in FE since older individuals tend to have higher BMI and more severe JSW narrowing. On the other hand, smaller JSW(x) correlating with lower TrSp after gender and BMI adjustment supports the proposition that severely OA patients with more JSW narrowing may have higher subchondral bone density. The influence of BMI may be similar to that formerly explained, while gender effects may be due to reduced estrogen levels in postmenopausal women. While minimum JSW may act as a surrogate measure for cartilage thickness, we further propose that digital assessment of JSW(x) could allow estimation of subchondral bone texture properties.