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• W2922192032 abstract "There is little known about geographic variability in the incidence of glomerular disease and its potential implications for care delivery. To evaluate this, we performed a population-level cohort study using a provincial renal pathology database (2000-2012) to capture all incident cases of glomerulonephritis in British Columbia, Canada. This included 401 patients with membranous nephropathy (MN), 824 patients with IgA nephropathy (IgAN), 385 patients with focal segmental glomerulosclerosis (FSGS), 397 patients with lupus nephritis (LN) and 399 patients with ANCA-related glomerulonephritis (ANCA-GN). Geographic clusters were identified using Bayesian spatial models to estimate the incidence of each disease in 74 regions compared to the mean incidence in the entire province (incidence rate ratio, [IRR]), adjusted for region-level age, sex and race. The proportion of overall variability in incidence attributed to inter-regional differences varied by disease: 18% in MN, 81% in IgAN, 18% in FSGS, 59% in ANCA-GN, and 89% in LN. Except for LN, clustering was not explained by demographics. All IgAN and LN clusters were in urban regions close to nephrology centers, whereas ANCA-GN, MN and FSGS clustered mainly in rural regions. All ANCA-GN clusters were rural with median population density 1.2 persons/km2 and driving distances of 10-676 km to the nearest nephrology center. Thus, we found significant geographic clustering in the incidence of different glomerular diseases. MN, FSGS and ANCA-GN clustered in sparsely populated regions with limited access to care, underscoring the importance of regional variability in glomerular diseases to inform health services delivery. There is little known about geographic variability in the incidence of glomerular disease and its potential implications for care delivery. To evaluate this, we performed a population-level cohort study using a provincial renal pathology database (2000-2012) to capture all incident cases of glomerulonephritis in British Columbia, Canada. This included 401 patients with membranous nephropathy (MN), 824 patients with IgA nephropathy (IgAN), 385 patients with focal segmental glomerulosclerosis (FSGS), 397 patients with lupus nephritis (LN) and 399 patients with ANCA-related glomerulonephritis (ANCA-GN). Geographic clusters were identified using Bayesian spatial models to estimate the incidence of each disease in 74 regions compared to the mean incidence in the entire province (incidence rate ratio, [IRR]), adjusted for region-level age, sex and race. The proportion of overall variability in incidence attributed to inter-regional differences varied by disease: 18% in MN, 81% in IgAN, 18% in FSGS, 59% in ANCA-GN, and 89% in LN. Except for LN, clustering was not explained by demographics. All IgAN and LN clusters were in urban regions close to nephrology centers, whereas ANCA-GN, MN and FSGS clustered mainly in rural regions. All ANCA-GN clusters were rural with median population density 1.2 persons/km2 and driving distances of 10-676 km to the nearest nephrology center. Thus, we found significant geographic clustering in the incidence of different glomerular diseases. MN, FSGS and ANCA-GN clustered in sparsely populated regions with limited access to care, underscoring the importance of regional variability in glomerular diseases to inform health services delivery. see commentary on page 277 see commentary on page 277 There is a paucity of literature describing the epidemiology of glomerular diseases. Prior studies have relied on local biopsy or end-stage kidney disease (ESKD) registries for which the source populations are unclear.1McGrogan A. Franssen C.F. de Vries C.S. The incidence of primary glomerulonephritis worldwide: a systematic review of the literature.Nephrol Dial Transplant. 2011; 26: 414-430Crossref PubMed Scopus (315) Google Scholar Thus, the true incidence of glomerular diseases at the time of onset, including geographic variation in disease, is largely unknown.2Cattran D.C. Toward quantitating the burden of glomerulonephritis in the United States.Kidney Int. 2016; 90: 732-734Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar The accurate measurement of disease incidence is important for several reasons. First, glomerular diseases are a significant public health challenge. Although individual diseases are considered to be rare, they collectively account for approximately 20% of the prevalent ESKD population in Canada.3Canadian Institute for Health InformationTreatment of end-stage organ failure in Canada, Canadian organ replacement register, 2007 to 2016: data tables, end-stage kidney disease and kidney transplants.https://www.cihi.ca/en/access-data-and-reportsDate accessed: January 21, 2019Google Scholar In addition to a high risk of disease progression, the burden of glomerular disease on individual patients, their families, and the health care system is considerable owing to toxicity of current treatments, increased hospitalization rates, loss of earning potential and high costs associated with novel immune therapies.4Lv J. Zhang H. Wong M.G. et al.Effect of oral methylprednisolone on clinical outcomes in patients with IgA nephropathy: the TESTING randomized clinical trial.JAMA. 2017; 318: 432-442Crossref PubMed Scopus (253) Google Scholar, 5Jefferson J.A. Complications of immunosuppression in glomerular disease.Clin J Am Soc Nephrol. 2018; 13: 1264-1275Crossref PubMed Scopus (27) Google Scholar, 6Wetmore J.B. Guo H. Liu J. et al.The incidence, prevalence, and outcomes of glomerulonephritis derived from a large retrospective analysis.Kidney Int. 2016; 90: 853-860Abstract Full Text Full Text PDF PubMed Scopus (24) Google Scholar, 7Barbour S. Lo C. Espino-Hernandez G. et al.The population-level costs of immunosuppression medications for the treatment of glomerulonephritis are increasing over time due to changing patterns of practice.Nephrol Dial Transplant. 2018; 33: 626-634Crossref PubMed Scopus (11) Google Scholar Understanding the implications of these factors for health care–system planning is not possible without an accurate assessment of the incidence of disease. Second, the management of glomerular disease is often complex, requiring highly specialized nephrology care and frequent monitoring. Understanding who is at risk of developing glomerular disease is therefore essential to ensure that care is optimal and resources can be delivered to those who need them. Finally, although recent studies have provided a granular understanding of the molecular basis of distinct disease subtypes,8Beck Jr., L.H. Bonegio R.G. Lambeau G. et al.M-type phospholipase A2 receptor as target antigen in idiopathic membranous nephropathy.N Engl J Med. 2009; 361: 11-21Crossref PubMed Scopus (1503) Google Scholar, 9Lyons P.A. Rayner T.F. Trivedi S. et al.Genetically distinct subsets within ANCA-associated vasculitis.N Engl J Med. 2012; 367: 214-223Crossref PubMed Scopus (678) Google Scholar, 10Stanescu H.C. Arcos-Burgos M. Medlar A. et al.Risk HLA-DQA1 and PLA(2)R1 alleles in idiopathic membranous nephropathy.N Engl J Med. 2011; 364: 616-626Crossref PubMed Scopus (385) Google Scholar, 11Gharavi A.G. Kiryluk K. Choi M. et al.Genome-wide association study identifies susceptibility loci for IgA nephropathy.Nat Genet. 2011; 43: 321-327Crossref PubMed Scopus (443) Google Scholar, 12Kiryluk K. Li Y. Sanna-Cherchi S. et al.Geographic differences in genetic susceptibility to IgA nephropathy: GWAS replication study and geospatial risk analysis.PLoS Genet. 2012; 8: e1002765Crossref PubMed Scopus (263) Google Scholar, 13Kiryluk K. Li Y. Scolari F. et al.Discovery of new risk loci for IgA nephropathy implicates genes involved in immunity against intestinal pathogens.Nat Genet. 2014; 46: 1187-1196Crossref PubMed Scopus (363) Google Scholar the factors that ultimately drive disease onset have not been delineated to the same degree. Well conducted epidemiologic studies in glomerular disease are needed to identify subpopulations at higher risk of disease and generate hypotheses regarding pathogenesis. Population-level disease surveillance can provide a better understanding of the incidence of disease, and its relationship to geography and access to care. For example, a well established finding is that certain disadvantaged populations have a higher prevalence of undifferentiated chronic kidney disease, especially those living in remote areas with poor access to health care resources.14Harasemiw O. Milks S. Oakley L. et al.Remote dwelling location is a risk factor for CKD among indigenous Canadians.Kidney Int Rep. 2018; 3: 825-832Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar, 15Komenda P. Lavallee B. Ferguson T.W. et al.The prevalence of CKD in rural Canadian indigenous peoples: results from the First Nations Community Based Screening to Improve Kidney Health and Prevent Dialysis (FINISHED) Screen, Triage, and Treat Program.Am J Kidney Dis. 2016; 68: 582-590Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar Such disparity would be particularly challenging in glomerular disease, the diagnosis and management of which requires timely access to a nephrologist and kidney biopsy, frequent clinical and laboratory monitoring, especially during periods of intense immunosuppression, and access to newer targeted therapies that are expensive and often require specific expertise. Therefore, the allocation and delivery of such specialized health care resources requires a detailed understanding of the population-level geographic distribution of disease. In the absence of good-quality epidemiologic data, whether underserviced populations are at higher risk of developing glomerular disease is unknown, along with whether they have the necessary access to optimal care for prevention of disease progression. We hypothesized that glomerular disease subtypes exhibit disease-specific geographic clustering of incident cases, with potential implications for the optimal delivery of specialized nephrology care. To investigate this possibility, we employed a Bayesian spatial model to identify geographic clusters of disease subtypes in a large and multiethnic Canadian province, using a centralized biopsy registry and demographic data from an accurately defined source population. A total of 2406 patients were included: 401 with membranous nephropathy (MN), 824 with IgA nephropathy (IgAN), 385 with focal segmental glomerulosclerosis (FSGS), 399 with antineutrophil cytoplasm antibody–related glomerulonephritis (ANCA-GN), and 397 with lupus nephritis (LN). Demographic and clinical characteristics of the specific glomerular disease cohorts are detailed in Table 1. As expected, patients with LN and IgAN tended to be younger and were more commonly of Asian origin, whereas patients with MN, ANCA-GN, and FSGS were most commonly Caucasian. A substantial proportion (17%) of ANCA-GN patients were Aboriginal. Patients with ANCA-GN had the lowest estimated glomerular filtration rate at the time of biopsy, and patients with MN had the greatest degree of proteinuria. A description of the population of British Columbia (BC) and distributions of age, sex, and race are provided in Supplementary Table S1. The overall crude incidence rate (per 100,000 person-years) was 1.79 (95% confidence interval [CI] 1.66–1.91) for IgAN, 0.87 (95% CI 0.78–0.95) for MN, 0.83 (95% CI 0.75–0.91) for FSGS, 0.86 (95% CI 0.78–0.95) for LN, and 0.86 (95% CI 0.78–0.95) for ANCA-GN.Table 1Patient characteristics for each glomerulonephritis subtype at the time of kidney biopsyCharacteristicMNIgANANCA-GNLNFSGSN401824399397385Age (yr)56 (16)44 (15)61 (17)35 (14)49 (20)Male sex5761451757Race Caucasian45.934.453.217.251 Black0.40.201.73 Asian24.947.716.559.521 South Asian22.311.58.613.114 Aboriginal4.73.517.93.86 Latin American0.41.20.81.11 Arabian1.31.20.82.13 Other00.32.31.41Creatinine (umol/l)90 [71–127]122 [ 90–193]273 [181–417]84 [61–130]150 [90–227]eGFR (ml/min per 1.73 m2)76 [46–96]54 [30–80]17 [10–29]93 [83–107]40 [22–69]MAP (mm Hg)97.4 (14.8)100.7 (15.3)94.8 (15)95.7 (16.8)100.7 (17.2)Albumin (g/l)26.2 (8)36.7 (6.5)31.7 (6.6)30.1 (8.2)33 (8.7)Proteinuria (g/d)5.8 [3.4–8.8]1.7 [0.9–3.2]1.2 [0.6–2.2]2.1 [1.1–3.9]3 [1.6–6.0]Progression to ESKD1328301435Incidence rate (per 100,000 person-years)0.87 (0.78–0.95)1.79 (1.66–1.91)0.86 (0.78–0.95)0.86 (0.78–0.95)0.83 (0.75–0.91)ANCA-GN, anti-neutrophil cytoplasm antibody–related glomerulonephritis; eGFR, estimated glomerular filtration rate; ESKD, end-stage kidney disease; FSGS, focal segmental glomerulosclerosis; IgAN, IgA nephropathy; LN, lupus nephritis; MAP, mean arterial pressure; MN, membranous nephropathy.Values given are mean (SD), median (interquartile range), or percentage, unless otherwise indicated.Laboratory data and blood pressure were taken as the closest values within 6 months of the biopsy from either the British Columbia Renal Agency or pathology databases.Daily protein excretion was measured by 24-hour urine collection, or estimated from urine albumin-to-creatinine and protein-to-creatinine ratios.GFR was estimated from provincially standardized creatinine measurements using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. Open table in a new tab ANCA-GN, anti-neutrophil cytoplasm antibody–related glomerulonephritis; eGFR, estimated glomerular filtration rate; ESKD, end-stage kidney disease; FSGS, focal segmental glomerulosclerosis; IgAN, IgA nephropathy; LN, lupus nephritis; MAP, mean arterial pressure; MN, membranous nephropathy. Values given are mean (SD), median (interquartile range), or percentage, unless otherwise indicated. Laboratory data and blood pressure were taken as the closest values within 6 months of the biopsy from either the British Columbia Renal Agency or pathology databases. Daily protein excretion was measured by 24-hour urine collection, or estimated from urine albumin-to-creatinine and protein-to-creatinine ratios. GFR was estimated from provincially standardized creatinine measurements using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. In a model with no covariates, the proportion of overall variability in the incidence of disease that was attributable to interregional differences varied substantially by type of glomerular disease: 18% in MN, 81% in IgAN, 18% in FSGS, 59% in ANCA-GN, and 89% in LN (Supplementary Table S2). Univariable analyses identified disease-specific measures of region-level age, sex, and race that conferred higher risk (Supplementary Table S3). Using these disease-specific covariates, we generated models for each type of glomerular disease that identified potential geographic clusters (Figure 1). Prominent geographic clusters were found for IgAN (incidence rate ratio [IRR] range 1.27–1.94), MN (IRR range 1.13–1.45), FSGS (IRR range 1.22–1.61), ANCA-GN (IRR range 1.19–3.53), and LN (IRR range 1.46–2.58). The location of clusters varied by type of disease. All the glomerular disease subtypes except ANCA-GN had different clusters in the southwestern corner of the province near the region’s major urban center (the city of Vancouver). MN, FSGS, and ANCA-GN had additional clusters in rural regions. This clustering was most prominent for ANCA-GN and FSGS, both of which had numerous clusters in the central and northern parts of the province. Figure 2 illustrates the population density (number of persons per square kilometer [km]) for each region, along with the driving distance (in km) to the nearest nephrology center. Large variability was found in both population density and driving distance among disease-specific clusters (Table 2). The IgAN and LN clusters were located exclusively in urban regions of high population density (range: 1304–6715 persons per km2) in close proximity to a nephrology center (driving distance range: 3–15 km). In contrast, ANCA-GN, MN, and FSGS clusters were predominantly in rural regions of low population density with potentially large driving distances to the nearest nephrology center. All ANCA-GN clusters were in sparsely populated regions (population density range: 0.4–68 persons per km2) located a median driving distance of 154 km (range: 10–676 km) from a nephrology center. Similarly, 88% of FSGS clusters were in rural regions with a median driving distance of 386 km (range: 7–950 km) to the nearest nephrology center.Table 2Population density (persons per km2) and driving distance (in km) to nephrology center for each region identified as a cluster, stratified by rural or urban statusVariableMNIgANFSGSANCA-GNLNRural clustersaRural regions are defined by a population density <400 persons per square kilometer.N (%) of all clusters6 (75)015 (88)25 (100)0Population density45.3 (0.9–334.7)n/a0.6 (0.1–334.7)1.2 (0.4–67.9)n/aDriving distance23 (7–130)n/a386 (7–950)154 (10–676)n/aUrban clustersN (%) of all clusters2 (25)8 (100)2 (12)06 (100)Population density1678.5bData for each cluster reported rather than median (range).4240.9 (1304.3–6714.6)1678.5bData for each cluster reported rather than median (range).n/a5079.7 (1304.3–6714.6)4677.2bData for each cluster reported rather than median (range).4677.2bData for each cluster reported rather than median (range).Driving distance7bData for each cluster reported rather than median (range).7 (3–15)7bData for each cluster reported rather than median (range).n/a7.5 (3–15)7bData for each cluster reported rather than median (range).7bData for each cluster reported rather than median (range).ANCA-GN, anti-neutrophil cytoplasm antibody–related glomerulonephritis; FSGS, focal segmental glomerulosclerosis; IgAN, IgA nephropathy; LN, lupus nephritis; MN, membranous nephropathy.Values are median (range), unless otherwise indicated.a Rural regions are defined by a population density <400 persons per square kilometer.b Data for each cluster reported rather than median (range). Open table in a new tab ANCA-GN, anti-neutrophil cytoplasm antibody–related glomerulonephritis; FSGS, focal segmental glomerulosclerosis; IgAN, IgA nephropathy; LN, lupus nephritis; MN, membranous nephropathy. Values are median (range), unless otherwise indicated. In order to examine whether the observed geographic clustering was independent of regional differences in age, sex, and race, we combined the identified clusters into larger “super-regions” that could be included in a multivariable model. For each glomerular disease subtype, contiguous geographic clusters with similar magnitudes of risk were aggregated into super-regions (Figure 3 and Supplementary Figure S1). Four super-regions were identified for MN, 4 for IgAN, 6 for FSGS, 9 for ANCA-GN, and 1 for LN. This condensed number of geographic clusters using super-regions accounted for the majority (67%–94%) of the interregional variability in disease incidence that was identified at the region level (Supplementary Table S2). Thus, we included these super-regions in disease-specific multivariable models, comparing the fixed effects in a model with super-regions to the fixed effects in a model with super-regions plus the region-level covariates age, sex, and race (Table 3). For MN, IgAN, FSGS, and ANCA-GN, the inclusion of age, sex, and race resulted in minimal changes to the point estimates for the IRR for each super-region. In LN, the geographic variability explained by the super-region was entirely accounted for by region-level demographics, as indicated by the complete attenuation of the super-region IRR in the multivariable model.Table 3The incidence rate ratio (95% credible interval) for each super-region before and after adjustment for region-level demographic risk factors (age, sex, and race)Super-region numberModel with super-regions onlyModel with super-regions and demographic covariatesaAdjusted for region-level age, sex, and race.Membranous nephropathy11.98 (1.07, 3.45)1.94 (0.96, 3.80)21.14 (0.85, 1.51)1.14 (0.84, 1.51)31.16 (1.04, 1.30)1.17 (1.02, 1.35)41.08 (0.96, 1.22)1.08 (0.94, 1.24)IgA nephropathy12.37 (1.49, 3.83)2.30 (1.27, 4.24)21.83 (1.13, 2.97)1.77 (1.05, 2.97)32.04 (1.14, 3.68)1.91 (0.86, 4.27)41.58 (0.96, 2.61)1.54 (0.92, 2.58)FSGS12.80 (1.86, 4.37)2.96 (1.79, 5.19)21.87 (1.00, 3.42)1.62 (0.79, 3.30)32.48 (0.83, 6.32)2.14 (0.68, 5.92)41.33 (0.69, 2.44)1.26 (0.47, 3.53)51.97 (1.30, 3.01)1.87 (0.85, 4.51)61.58 (0.87, 2.77)1.69 (0.63, 4.79)ANCA -GN11.98 (1.22, 3.25)1.99 (1.17, 3.43)21.15 (0.27, 3.58)1.17 (0.27, 3.74)32.00 (0.88, 4.12)1.91 (0.81, 4.14)43.07 (1.97, 5.40)3.15 (1.50, 6.33)51.84 (0.75, 3.99)1.77 (0.71, 4.01)62.13 (1.37, 3.25)2.09 (1.32, 3.27)72.07 (0.93, 4.17)1.95 (0.83, 4.22)81.57 (0.24, 3.23)1.53 (0.23, 6.30)92.70 (1.28, 5.45)2.42 (0.93, 6.08)Lupus nephritis12.07 (1.38, 3.10)1.01 (0.88, 1.95)ANCA-GN, anti-neutrophil cytoplasm antibody–related glomerulonephritis; FSGS, focal segmental glomerulosclerosis.In each case, the reference group consists of all regions not identified as clusters for that glomerulonephritis subtype.a Adjusted for region-level age, sex, and race. Open table in a new tab ANCA-GN, anti-neutrophil cytoplasm antibody–related glomerulonephritis; FSGS, focal segmental glomerulosclerosis. In each case, the reference group consists of all regions not identified as clusters for that glomerulonephritis subtype. Using population-level data, a centralized renal biopsy database, and a Bayesian spatial model, we provide the first robust estimates of the geographic variability in the incidence of glomerular diseases. We identified geographic clusters of disease that varied by disease and had observed incidence rates up to 3-fold higher than expected. Apart from LN, the clusters of increased disease incidence were not explained by regional differences in age, sex, and race. For MN, ANCA-GN, and FSGS, disease clusters were located in rural regions of low population density with considerable driving distances to the nearest nephrology center, and therefore represent disadvantaged subpopulations with a combination of high-risk kidney disease and limited access to necessary health care resources. To date, high-quality research on the incidence of various types of glomerular diseases has been limited. Most prior studies have been based on data from regional biopsy or ESKD registries.2Cattran D.C. Toward quantitating the burden of glomerulonephritis in the United States.Kidney Int. 2016; 90: 732-734Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar The former approach is subject to bias due to the absence of a clearly defined source population and is therefore unable to generate true incidence rates or investigate risk factors such as geography and demographics. Using ESKD registries identifies only those patients who have progressed to end-stage disease, rather than incident cases at the time of onset. A recent study from the United States used a population of insured patients to estimate the incidence of all-cause glomerulonephritis using administrative data.6Wetmore J.B. Guo H. Liu J. et al.The incidence, prevalence, and outcomes of glomerulonephritis derived from a large retrospective analysis.Kidney Int. 2016; 90: 853-860Abstract Full Text Full Text PDF PubMed Scopus (24) Google Scholar A significant limitation was the inability to describe incidence rates by specific glomerular disease subtypes. The absence of accurate data on the incidence of glomerular diseases is a glaring omission in a field dominated by high-quality basic science and translational research, and it limits the ability to advocate for glomerular disease–specific health care resources16Barbour S. Beaulieu M. Gill J. et al.An overview of the British Columbia glomerulonephritis network and registry: integrating knowledge generation and translation within a single framework.BMC Nephrol. 2013; 14: 236Crossref PubMed Scopus (21) Google Scholar and plan recruitment for clinical trials.17Leaf D.E. Appel G.B. Radhakrishnan J. Glomerular disease: Why is there a dearth of high quality clinical trials?.Kidney Int. 2010; 78: 337-342Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar To address this deficiency, we leveraged data from a unique provincial, centralized pathology database that records all patients with a biopsy-proven diagnosis of glomerular disease from a large, well defined, and geographically diverse source population. This approach allowed us to accurately define the at-risk population and adjust our analyses for population-level demographics. The relative rarity of individual diseases poses additional analytic challenges in attempts to examine regional variability in incidence. Areas that are less densely populated are expected to have low numbers of cases, and adjacent areas might be expected to have incidence rates that are more similar, compared with areas further away. Conventional frequentist approaches such as Poisson models do not reliably account for these 2 issues—over-dispersion and spatial autocorrelation—resulting in greater uncertainty in estimates. We therefore employed a dedicated Bayesian spatial model that accounts for these sources of error and has been used previously to define incidence patterns in other rare diseases such as multiple sclerosis18Torabi M. Green C. Yu N. et al.Application of three focused cluster detection methods to study geographic variation in the incidence of multiple sclerosis in Manitoba, Canada.Neuroepidemiology. 2014; 43: 38-48Crossref PubMed Scopus (11) Google Scholar and anti-glomerular basement membrane disease.19Canney M. O'Hara P.V. McEvoy C.M. et al.Spatial and temporal clustering of anti-glomerular basement membrane disease.Clin J Am Soc Nephrol. 2016; 11: 1392-1399Crossref PubMed Scopus (60) Google Scholar Our results identified geographic clusters with a disproportionately high incidence of glomerular disease that could not have been predicted from regional demographics and that represent subpopulations with a mismatch of high disease incidence and limited availability of health care resources. This mismatch is likely to place a significant travel burden on patients, reduce access to regular laboratory and clinical monitoring, and add limitations to the use of the potent immune therapy often required for management of glomerular disease. Rural disparities in access to complex and resource-intensive health care have been observed in other diseases and are equally likely to apply to glomerular disease. For example, a study of kidney disease patients found that those who resided more than 20 km from a dialysis center were less likely to receive an optimal vascular access for dialysis.20Miller L.M. Vercaigne L.M. Moist L. et al.The association between geographic proximity to a dialysis facility and use of dialysis catheters.BMC Nephrol. 2014; 15: 40Crossref PubMed Scopus (9) Google Scholar In the general population, individuals living in rural regions have been shown to have reduced access to cancer screening programs and specialist-delivered care.21Xu Y. Fu C. Onega T. et al.Disparities in geographic accessibility of National Cancer Institute cancer centers in the United States.J Med Syst. 2017; 41: 203Crossref PubMed Scopus (18) Google Scholar, 22Davis M.M. Renfro S. Pham R. et al.Geographic and population-level disparities in colorectal cancer testing: a multilevel analysis of Medicaid and commercial claims data.Prev Med. 2017; 101: 44-52Crossref PubMed Scopus (69) Google Scholar Those with large driving distances may have more restricted therapeutic options and are likely to wait longer to receive appropriate treatment.23Muralidhar V. Rose B.S. Chen Y.W. et al.Association between travel distance and choice of treatment for prostate cancer: Does geography reduce patient choice?.Int J Radiat Oncol Biol Phys. 2016; 96: 313-317Abstract Full Text Full Text PDF PubMed Scopus (36) Google Scholar, 24Johnson A. Shulman L. Kachajian J. et al.Access to care in Vermont: factors linked with time to chemotherapy for women with breast cancer—a retrospective cohort study.J Oncol Pract. 2016; 12: e848-e857Crossref PubMed Scopus (11) Google Scholar The culmination of these factors may contribute to worse cancer-related survival rates in rural subpopulations.25Markossian T.W. O'Neal C.M. Senkowski C. Geographic disparities in pancreatic cancer survival in a southeastern safety-net academic medical center.Aust J Rural Health. 2016; 24: 73-78Crossref PubMed Scopus (19) Google Scholar Although our results are specific to BC, the existence of region- and disease-specific subpopulations at high risk for glomerular disease but with reduced access to care is likely the case in other geographic areas, and it has significant implications for the appropriate planning and delivery of glomerular disease–related health services. Because regions with a disproportionately high incidence of disease cannot be identified based on population demographics alone, our results suggest the need for mandatory central reporting of incident glomerular diseases so that health systems can accurately define their glomerular disease population and allocate the necessary health care resources. Although the identification of specific underlying causes for the geographic clustering of different glomerular diseases is beyond the scope of this study, the widespread distribution of disease clusters suggests environmental rather than genetic factors. Previous literature has suggested a role of host–environment interactions in the development of IgAN,26Magistroni R. D'Agati V.D. Appel G.B. et al.New developments in the genetics, pathogenesis, and therapy of IgA nephropathy.Kidney Int. 2015; 88: 974-989Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar, 27Floege J. Feehally J. The mucosa-kidney axis in IgA nephropathy.Nat Rev Nephrol. 2016; 12: 1" @default.
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• W2922192032 title "Disease-specific incident glomerulonephritis displays geographic clustering in under-serviced rural areas of British Columbia, Canada" @default.
• W2922192032 cites W1918458784 @default.
• W2922192032 cites W1966416547 @default.
• W2922192032 cites W1970753995 @default.
• W2922192032 cites W1972063433 @default.
• W2922192032 cites W1990915963 @default.
• W2922192032 cites W1995850259 @default.
• W2922192032 cites W2015164579 @default.
• W2922192032 cites W2040613743 @default.
• W2922192032 cites W2042230146 @default.
• W2922192032 cites W2047487524 @default.
• W2922192032 cites W2061820416 @default.
• W2922192032 cites W2068622105 @default.
• W2922192032 cites W2079362912 @default.
• W2922192032 cites W2084708041 @default.
• W2922192032 cites W2098380343 @default.
• W2922192032 cites W2100076924 @default.
• W2922192032 cites W2101794801 @default.
• W2922192032 cites W2110444957 @default.
• W2922192032 cites W2111285312 @default.
• W2922192032 cites W2119900188 @default.
• W2922192032 cites W2122790621 @default.
• W2922192032 cites W2126762295 @default.
• W2922192032 cites W2148534890 @default.
• W2922192032 cites W2159681042 @default.
• W2922192032 cites W2162337442 @default.
• W2922192032 cites W2166822765 @default.
• W2922192032 cites W2206113961 @default.
• W2922192032 cites W2212745154 @default.
• W2922192032 cites W2330833821 @default.
• W2922192032 cites W2338205532 @default.
• W2922192032 cites W2407754058 @default.
• W2922192032 cites W2414059523 @default.
• W2922192032 cites W2466716896 @default.
• W2922192032 cites W2472545716 @default.
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