Impact of Income on Small Area Low Birth Weight Incidence Using Multiscale Models

Mehreteab Aregay; Andrew B. Lawson; Christel Faes; Russell S. Kirby; Rachel Carroll; Kevin Watjou; Mehreteab Aregay; Andrew B. Lawson; Christel Faes; Russell S. Kirby; Rachel Carroll; Kevin Watjou

doi:10.3934/publichealth.2015.4.667

AIMS Public Health

2015, Volume 2, Issue 4: 667-680. doi: 10.3934/publichealth.2015.4.667

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Impact of Income on Small Area Low Birth Weight Incidence Using Multiscale Models

1.
Department of Public Heath Sciences, Division of Biostatistics and Bioinformatics, MUSC, Charleston, USA;
2.
Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Hasselt, Belgium;
3.
Department of Community and Family Health, University of South Florida, Tampa, FL, USA

Received: 30 July 2015 Accepted: 23 September 2015 Published: 10 October 2015

Low birth weight (LBW) is an important public health issue in the US as well as worldwide. The two main causes of LBW are premature birth and fetal growth restriction. Socio-economic status, as measured by family income has been correlated with LBW incidence at both the individual and population levels. In this paper, we investigate the impact of household income on LBW incidence at di erent geographical levels. To show this, we choose to examine LBW incidences collected from the state of Georgia, in the US, at both the county and public health (PH) district. The data at the PH district are an aggregation of the data at the county level nested within the PH district. A spatial scaling effect is induced during data aggregation from the county to the PH level. To address the scaling effect issue, we applied a shared multiscale model that jointly models the data at two levels via a shared correlated random effect. To assess the bene t of using the shared multiscale model, we compare it with an independent multiscale model which ignores the scale effect. Applying the shared multiscale model for the Georgia LBW incidence, we have found that income has a negative impact at both the county and PH levels. On the other hand, the independent multiscale model shows that income has a negative impact only at the county level. Hence, if the scale effect is not properly accommodated in the model, a different interpretation of the ndings could result.
- Low birth weight (LBW),
- predictive accuracy,
- shared multiscale model,
- independent multiscale model,
- scaling effect,
- Bayesian multiscale model
Citation: Mehreteab Aregay, Andrew B. Lawson, Christel Faes, Russell S. Kirby, Rachel Carroll, Kevin Watjou. Impact of Income on Small Area Low Birth Weight Incidence Using Multiscale Models[J]. AIMS Public Health, 2015, 2(4): 667-680. doi: 10.3934/publichealth.2015.4.667

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Abstract

Low birth weight (LBW) is an important public health issue in the US as well as worldwide. The two main causes of LBW are premature birth and fetal growth restriction. Socio-economic status, as measured by family income has been correlated with LBW incidence at both the individual and population levels. In this paper, we investigate the impact of household income on LBW incidence at di erent geographical levels. To show this, we choose to examine LBW incidences collected from the state of Georgia, in the US, at both the county and public health (PH) district. The data at the PH district are an aggregation of the data at the county level nested within the PH district. A spatial scaling effect is induced during data aggregation from the county to the PH level. To address the scaling effect issue, we applied a shared multiscale model that jointly models the data at two levels via a shared correlated random effect. To assess the bene t of using the shared multiscale model, we compare it with an independent multiscale model which ignores the scale effect. Applying the shared multiscale model for the Georgia LBW incidence, we have found that income has a negative impact at both the county and PH levels. On the other hand, the independent multiscale model shows that income has a negative impact only at the county level. Hence, if the scale effect is not properly accommodated in the model, a different interpretation of the ndings could result.

References

[1]	World Health Organization (1992) International statistical classification of diseases and related health problems, tenth revision, Geneva.
[2]	Siva Subramanian KN (2014) Extremely Low Birth Weight Infant. Available from: http://emedicine.medscape.com/article/979717-overview.
[3]	Kowlessar NM, Jiang HJ, Steiner C (2011) Hospital Stays for Newborns. Available from: http://www.hcup-us.ahrq.gov/reports/statbriefs/sb163.jsp.
[4]	Hack M, Klein NK, Taylor HG (1995) Long-term developmental outcomes of low birth weight infants. Future Child, 5:176-96. doi: 10.2307/1602514
[5]	Stevens-Simon C, Orleans M (1999) Low-birth weight prevention programs: the enigma of failure. Birth, 26:184-91. doi: 10.1046/j.1523-536x.1999.00184.x
[6]	Ricketts SA, Murray EK, Schwalberg R (2005) Reducing low birthweight by resolving risks: Results from Colorados Prenatal Plus Program. Am J Public Health, 57:1952-1957.
[7]	Kleinman J, Kessel S (1987) Racial differences in low birth weight. N Engl J Med, 317:744-753.
[8]	Kirby RS, Liu J, Lawson AB et al. (2011) Spatio-temporal Patterning of Small Area Low Birth Weight Incidence and Its Correlates: A Latent Spatial Structure Approach. Spatial and Spatio-temporal Epidemiology, 4:265-271.
[9]	Aregay M, Lawson AB, Faes C et al. (2014) Bayesian Multiscale Modeling for Aggregated Disease Mapping Data. Statistical Methods in Medical Research, accepted.
[10]	Besag J, York J, Mollié A (1991) Bayesian image restoration with applications in spatial statistics (with discussion). Ann Inst Stat Math, 43:1-59. doi: 10.1007/BF00116466
[11]	Gelman A (2006) Prior distribution for variance parameters in hierarchical models. Bayesian Analysis, 3:515-533.
[12]	Spiegelhalter DJ, Best NG, Carlin BP et al. (2002) Bayesian measures of model complexity and fit. J R Stat Soc Series B, 64: 583-639. doi: 10.1111/1467-9868.00353
[13]	Gelman A, Carlin JB, Stern HS et al. (2004) Bayesian Data Analysis. New York: Chapman and Hall.
[14]	Lawson AB (2013) Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. 2nd ed. New York: Chapman and Hall/CRC Press.
[15]	Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences (with discussion). Stat Sci, 7:457-511. doi: 10.1214/ss/1177011136
[16]	Mathews TJ, MacDorman MF (2013) Infant Mortality Statistics from the 2009 Period Linked Birth/Infant Death Data Set. National Vital Statistics Reports, 61(8).
[17]	Martin JA, Hamilton BE, Sutton PD, et al. (2009) Births: Final data for 2006. National Vital Statistics Reports, 57(7).
[18]	World Health Organization: Feto-maternal nutrition and low birth weight. Available from: http://www.who.int/nutrition/topics/feto-maternal/en/. Retrieved 2015-07-23.
[19]	Borders AE, Grobman WA, Amsden LB, et al. (2007) Chronic stress and low birth weight neonates in a low-income population of women. Obstetrics and Gynecology, 109:331-338. doi: 10.1097/01.AOG.0000250535.97920.b5
[20]	Kolaczyk ED, Huang H (2001) Multiscale statistical models for hierarchical spatial aggre- gation. Geogr Anal, 33:95-118.
[21]	Louie MM, Kolaczyk ED (2006) A multiscale method for disease mapping in spatial epidemiology. Stat Med, 25:1287-1306. doi: 10.1002/sim.2276
[22]	Louie MM, Kolaczyk ED (2006) Multiscale detection of localized anomalous structure in aggregate disease incidence data. Stat Med, 25:787-810. doi: 10.1002/sim.2404
[23]	Louie MM, Kolaczyk ED (2004) On the covariance properties of certain multiscale spatial processes. Statist Probab Lett, 66:407-416. doi: 10.1016/j.spl.2003.11.004

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