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Öğe Bayesian network-based framework for exposure-response study design and interpretation(Bmc, 2019) Orak, Nur H.; Small, Mitchell J.; Druzdzel, Marek J.Conventional environmental-health risk-assessment methods are often limited in their ability to account for uncertainty in contaminant exposure, chemical toxicity and resulting human health risk. Exposure levels and toxicity are both subject to significant measurement errors, and many predicted risks are well below those distinguishable from background incident rates in target populations. To address these issues methods are needed to characterize uncertainties in observations and inferences, including the ability to interpret the influence of improved measurements and larger datasets. Here we develop a Bayesian network (BN) model to quantify the joint effects of measurement errors and different sample sizes on an illustrative exposure-response system. Categorical variables are included in the network to describe measurement accuracies, actual and measured exposures, actual and measured response, and the true strength of the exposure-response relationship. Network scenarios are developed by fixing combinations of the exposure-response strength of relationship (none, medium or strong) and the accuracy of exposure and response measurements (low, high, perfect). Multiple cases are simulated for each scenario, corresponding to a synthetic exposure response study sampled from the known scenario population. A learn-from-cases algorithm is then used to assimilate the synthetic observations into an uninformed prior network, yielding updated probabilities for the strength of relationship. Ten replicate studies are simulated for each scenario and sample size, and results are presented for individual trials and their mean prediction. The model as parameterized yields little-to-no convergence when low accuracy measurements are used, though progressively faster convergence when employing high accuracy or perfect measurements. The inferences from the model are particularly efficient when the true strength of relationship is none or strong with smaller sample sizes. The tool developed in this study can help in the screening and design of exposure-response studies to better anticipate where such outcomes can occur under different levels of measurement error. It may also serve to inform methods of analysis for other network models that consider multiple streams of evidence from multiple studies of cumulative exposure and effects.Öğe Coğrafi Sosyo-Ekonomik Mahrumiyetin ve Hava Kirleticilerinin Bebek Doğum Ağırlığı Üzerindeki Etkisi(2020) Orak, Nur H.Klasik çevresel sağlık risk tahmin yöntemleri, insan-çevre sistemlerinin döngüselliğine çözüm getirmede ve çevresel kirletici değişkenle-rinin insan sağlığı üzerindeki olası etkilerinin incelenmesinde yetersiz kalmaktadır. Bu çalışmada Bayesian yöntemleri kullanarak nitrojen dioksit (NO2) maruziyeti, partikül madde (PM), sosyoekonomik mahrumiyet ve bebek doğum ağırlığı arasındaki ilişkinin New York şehri örneği üzerinde araştırılması hedeflenmiştir. Epidemiyolojide NO2 ve PM2.5 maruziyetinin olası karıştırıcı etkenler (ör. vücut kitle indeksi) üzerindeki etkilerini göz önüne alabilmek için, ve NO2, PM2.5 ve değişkenlerin (ör. sosyoekonomik mahrumiyet) teorik etkileri arasında iki yönlü ilişkinin incelenmesi için Bayesian Ağ (BA) modeli geliştirilmiştir. Geliştirilen PM-NO2-BA etki modeli ile farklı türdeki bilgileri biraraya getirerek risk analizi gerçekleştirilmiştir. Bu makalede sunulan modelleme yaklaşımı halk sağlığına etki eden risk faktörleri ara-sındaki doğrusal olmayan ilişkinin değerlendirilmesi konusunda önemli katkıda bulunacaktır. Düşük Vücut Kitle Indeksine (VKI) sahip annelerin hava kirleticilerine maruziyeti ve buna bağlı olarak bebek doğum ağırlığı riskinin etkilendiği gözlenmiştir. Normal VKI’ne sahip annenin yüksek NO2 konsantrasyonuna maruziyeti sonucunda düşük doğum riski %7 iken düşük VKI’ne sahip annenin yüksek NO2 kirli-liğine maruz kalması sonucunda bu risk %36’ya çıkmaktadır. Farklı sosyo-ekonomik mahrumiyet indeksine (SMI) ait vakalar kirleticilere maruziyet sonucunda farklı risklerle karşılaşmaktadır. NO2 maruziyetine benzer olarak PM2.5 maruziyeti düşük SMI’da yüksek risk (%27) oluştururken daha yüksek indekse sahip grupta bu riskin azaldığı elde edilmiştir.Öğe A Hybrid Bayesian Network Framework for Risk Assessment of Arsenic Exposure and Adverse Reproductive Outcomes(Academic Press Inc Elsevier Science, 2020) Orak, Nur H.Arsenic contamination of drinking water affects more than 137 million people and has been linked to several adverse health effects. The traditional toxicological approach, dose-response graphs, are limited in their ability to unveil the relationships between potential risk factors of arsenic exposure for adverse human health outcomes, which are critically important to understanding the risk at low exposure levels of arsenic. Therefore, to provide insight on the potential interactions of different variables of the arsenic exposure network, this study characterizes the risk factors by developing a hybrid Bayesian Belief Network (BBN) model for health risk assessment. The results show that the low inorganic arsenic concentration increases the risk of low birth weight even for low gestational age scenarios. While increasing the mother's age does not increase the low birthweight risk, it affects the distribution between other categories of baby weight. For low MMA% ( < 4%) in the human body, increasing gestational age decreases the risk of having low birthweight. The proposed BBN model provides 82% sensitivity and 72% specificity in average for different states of birthweight.
