PMID- 30634518
OWN - NLM
STAT- MEDLINE
DCOM- 20190529
LR  - 20231005
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 16
IP  - 2
DP  - 2019 Jan 9
TI  - Modeling Schistosoma japonicum Infection under Pure Specification Bias: Impact of 
      Environmental Drivers of Infection.
LID - 10.3390/ijerph16020176 [doi]
LID - 176
AB  - Uncertainties in spatial modeling studies of schistosomiasis (SCH) are relevant 
      for the reliable identification of at-risk populations. Ecological fallacy occurs 
      when ecological or group-level analyses, such as spatial aggregations at a 
      specific administrative level, are carried out for an individual-level inference. 
      This could lead to the unreliable identification of at-risk populations, and 
      consequently to fallacies in the drugs' allocation strategies and their 
      cost-effectiveness. A specific form of ecological fallacy is pure specification 
      bias. The present research aims to quantify its effect on the parameter estimates 
      of various environmental covariates used as drivers for SCH infection. This is 
      done by (i) using a spatial convolution model that removes pure specification 
      bias, (ii) estimating group and individual-level covariate regression parameters, 
      and (iii) quantifying the difference between the parameter estimates and the 
      predicted disease outcomes from the convolution and ecological models. We modeled 
      the prevalence of Schistosoma japonicum using group-level health outcome data, 
      and city-level environmental data as a proxy for individual-level exposure. We 
      included environmental data such as water and vegetation indexes, distance to 
      water bodies, day and night land surface temperature, and elevation. We estimated 
      and compared the convolution and ecological model parameter estimates using 
      Bayesian statistics. Covariate parameter estimates from the convolution and 
      ecological models differed between 0.03 for the nearest distance to water bodies 
      (NDWB), and 0.28 for the normalized difference water index (NDWI). The 
      convolution model presented lower uncertainties in most of the parameter 
      estimates, except for NDWB. High differences in uncertainty were found in night 
      land surface temperature (0.23) and elevation (0.13). No significant differences 
      were found between the predicted values and their uncertainties from both models. 
      The proposed convolution model is able to correct for a pure specification bias 
      by presenting less uncertain parameter estimates. It shows a good predictive 
      performance for the mean prevalence values and for a positive number of infected 
      people. Further research is needed to better understand the spatial extent and 
      support of analysis to reliably explore the role of environmental variables.
FAU - Araujo Navas, Andrea L
AU  - Araujo Navas AL
AD  - Faculty of Geo-information Science and Earth Observation (ITC), University of 
      Twente, PO Box 217, 7500 AE Enschede, The Netherlands. 
      a.l.araujonavas@utwente.nl.
FAU - Osei, Frank
AU  - Osei F
AUID- ORCID: 0000-0003-2206-3662
AD  - Faculty of Geo-information Science and Earth Observation (ITC), University of 
      Twente, PO Box 217, 7500 AE Enschede, The Netherlands. f.b.osei@utwente.nl.
FAU - Leonardo, Lydia R
AU  - Leonardo LR
AD  - Faculty of Geo-information Science and Earth Observation (ITC), University of 
      Twente, PO Box 217, 7500 AE Enschede, The Netherlands. lrleonardo@up.edu.ph.
FAU - Soares Magalhaes, Ricardo J
AU  - Soares Magalhaes RJ
AD  - UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University 
      of Queensland, Gatton 4343 QLD, Australia. r.magalhaes@uq.edu.au.
AD  - Child Health and Environment Program, Child Health Research Centre, The 
      University of Queensland, South Brisbane 4101 QLD, Australia. 
      r.magalhaes@uq.edu.au.
FAU - Stein, Alfred
AU  - Stein A
AUID- ORCID: 0000-0002-9456-1233
AD  - Faculty of Geo-information Science and Earth Observation (ITC), University of 
      Twente, PO Box 217, 7500 AE Enschede, The Netherlands. a.stein@utwente.nl.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190109
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
SB  - IM
MH  - Animals
MH  - Bayes Theorem
MH  - Bias
MH  - *Environment
MH  - Humans
MH  - *Models, Theoretical
MH  - Philippines/epidemiology
MH  - Prevalence
MH  - Schistosoma japonicum/*isolation & purification
MH  - Schistosomiasis japonica/*epidemiology
PMC - PMC6351909
COIS- The authors declare no conflict of interest.
EDAT- 2019/01/13 06:00
MHDA- 2019/05/30 06:00
PMCR- 2019/01/01
CRDT- 2019/01/13 06:00
PHST- 2018/11/14 00:00 [received]
PHST- 2018/12/18 00:00 [revised]
PHST- 2019/01/03 00:00 [accepted]
PHST- 2019/01/13 06:00 [entrez]
PHST- 2019/01/13 06:00 [pubmed]
PHST- 2019/05/30 06:00 [medline]
PHST- 2019/01/01 00:00 [pmc-release]
AID - ijerph16020176 [pii]
AID - ijerph-16-00176 [pii]
AID - 10.3390/ijerph16020176 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2019 Jan 9;16(2):176. doi: 
      10.3390/ijerph16020176.