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Abstract
Objective
To estimate Toxocara infection rates by age, gender and ethnicity for US counties using data from the National Health and Nutrition Examination Survey (NHANES).
Methods
After initial analysis to account for missing data, a binary regression model is applied to obtain relative risks of Toxocara infection for 20,396 survey subjects. The regression incorporates interplay between demographic attributes (age, ethnicity and gender), family poverty and geographic context (region, metropolitan status). Prevalence estimates for counties are then made, distinguishing between subpopulations in poverty and not in poverty.
Results
Even after allowing for elevated infection risk associated with poverty, seropositivity is elevated among Black non-Hispanics and other ethnic groups. There are also distinct effects of region. When regression results are translated into county prevalence estimates, the main influences on variation in county rates are percentages of non-Hispanic Blacks and county poverty.
Conclusions
For targeting prevention it is important to assess implications of national survey data for small area prevalence. Using data from NHANES, the study confirms that both individual level risk factors and geographic contextual factors affect chances of Toxocara infection.
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Authors and Affiliations
Department of Geography, Center for Statistics, Queen Mary University of London, London, E1 4NS, UK
Peter Congdon
National Minority Quality Forum, 1200 New Hampshire Avenue Ste 575, Washington, DC, USA
Patsy Lloyd
Department of Epidemiology and Biostatistics, School of Public Health and Health Services, George Washington University, Washington, DC, USA
Patsy Lloyd
- Peter Congdon
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- Patsy Lloyd
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Corresponding author
Correspondence toPeter Congdon.
Appendix 1: Modeling missing data with particular regard to PIR
Appendix 1: Modeling missing data with particular regard to PIR
Missingness in the predictors is confined to the income to poverty ratio, conventionally described as the PIR. This variable is in fact a negative measure of poverty and a positive measure of socioeconomic status (SES). To account for missing PIR values, two extra variables are introduced. These are not used in the binary Toxocara risk model, but are additional measures of SES relevant to effectively modeling missing PIR values.
The extra variables are education (EDUC) of the household reference person, and Duncan’s socioeconomic index value (SEI) for the occupation of the household reference person. These variables are also subject to missingness, though for education under 1 in 200 values are missing (0.29% for males, 0.44% for females).
A marginal/conditional regression sequence is used (Ibrahim et al.1999) to model the joint distribution of EDUC, PIR and SEI conditional on the fully observed attributes (ethnicity, region, urbanity and age), denoted collectively asXobs. In this analysis, PIR is continuous, though in the subsequent prevalence model it is converted to a binary variable. The marginal regression involves a multinomial logit regression p(EDUC|Xobs) of education (four categories) on ethnicity, region and urbanity. The first conditional regression p(SEI|EDUC,Xobs) models SEI conditional on education, and known attributes. The second conditional regression p(PIR|SEI, EDUC,Xobs) then models PIR conditional on both education and SEI, and on known attributes.
For subjects with missing PIR values, the posterior means of the PIR from this conditional regression are substituted for missing values (the means are from the second half of a two-chain run of 10,000 iterations in WINBUGS). The results from this conditional regression are interesting in showing that income to poverty ratios increase (as might be anticipated) with SEI and education; in addition, PIR is lower for Black non-Hispanic, Hispanic and other ethnic groups. PIR is also higher in urban counties and for people living in the West region.
To allow for the possibility that missingness in PIR values is informative, there is in addition to the marginal/conditional regressions, a logit regression of the binary missingness indicator for PIR (R = 1 for PIR missing,R = 0 otherwise) on all three SES indicators and on observed demographic and geographic attributes. This regression produced a significant positive coefficient on the PIR value, so that the chance of missing PIR response is greater at higher PIR values.
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Congdon, P., Lloyd, P. Toxocara infection in the United States: the relevance of poverty, geography and demography as risk factors, and implications for estimating county prevalence.Int J Public Health56, 15–24 (2011). https://doi.org/10.1007/s00038-010-0143-6
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