{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T16:47:46Z","timestamp":1759942066674},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2004,2,18]],"date-time":"2004-02-18T00:00:00Z","timestamp":1077062400000},"content-version":"vor","delay-in-days":17,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Risk Analysis"],"published-print":{"date-parts":[[2004,2]]},"abstract":"<jats:p>Regional estimates of cryptosporidiosis risks from drinking water exposure were developed and validated, accounting for AIDS status and age. We constructed a model with probability distributions and point estimates representing <jats:italic>Cryptosporidium<\/jats:italic> in tap water, tap water consumed per day (exposure characterization); dose response, illness given infection, prolonged illness given illness; and three conditional probabilities describing the likelihood of case detection by active surveillance (health effects characterization). The model predictions were combined with population data to derive expected case numbers and incidence rates per 100,000 population, by age and AIDS status, borough specific and for New York City overall in 2000 (risk characterization). They were compared with same\u2010year surveillance data to evaluate predictive ability, assumed to represent true incidence of waterborne cryptosporidiosis. The predicted mean risks, similar to previously published estimates for this region, overpredicted observed incidence\u2014most extensively when accounting for AIDS status. The results suggest that overprediction may be due to conservative parameters applied to both non\u2010AIDS and AIDS populations, and that biological differences for children need to be incorporated. Interpretations are limited by the unknown accuracy of available surveillance data, in addition to variability and uncertainty of model predictions. The model appears sensitive to geographical differences in AIDS prevalence. The use of surveillance data for validation and model parameters pertinent to susceptibility are discussed.<\/jats:p>","DOI":"10.1111\/j.0272-4332.2004.00424.x","type":"journal-article","created":{"date-parts":[[2004,2,18]],"date-time":"2004-02-18T20:12:15Z","timestamp":1077135135000},"page":"209-220","source":"Crossref","is-referenced-by-count":9,"title":["Cryptosporidiosis Susceptibility and Risk: A Case Study"],"prefix":"10.1111","volume":"24","author":[{"family":"Anna Makri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza","family":"Modarres","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rebecca","family":"Parkin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2004,2,18]]},"reference":[{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.2307\/3435000"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/0272-4332.205055"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.0272-4332.2004.00423.x"},{"key":"e_1_2_8_5_2","volume-title":"Cryptosporidiosis Risk in New York City: A Case Study of Incorporating Susceptibility into Microbial Risk Assessment","author":"Makri A.","year":"2002"},{"key":"e_1_2_8_6_2","volume-title":"Cryptosporidium in Drinking Water: Evaluation of the ILSI\/RSI Quantitative Risk Assessment Framework.","author":"Teunis P. 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