Identifying Racial and Geographic Disparities in Paediatric Asthma: Medical Case
Received: 01-May-2022, Manuscript No. ejbi-22-64682; Editor assigned: 02-May-2022, Pre QC No. ejbi-22-64682(PQ); Reviewed: 16-May-2022 QC No. ejbi-22-64682; Revised: 19-May-2022, Manuscript No. ejbi-22-64682(R); Published: 26-May-2022, DOI: 10.24105/ejbi.2022.18.5.42-43
Citation: Becker E (2022). Identifying Racial and Geographic Disparities in Paediatric Asthma: Medical Case. EJBI. 18(5):42-43.
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Abstract
Pediatric asthma is disproportionately prevalent in black and other socioeconomic disadvantaged kids. It’s challenging to identify vulnerable individuals, localities, or immediate exposures that may lead to juvenile exacerbation of asthma due to interurban diversity in environmental risk variables and limited access to the highest health data. This paper describes a new multidisciplinary health disparities intervention and treatment strategy for paediatric asthma. The prevalence of asthma encounters is first mapped at a high spatial and temporal resolution using addresslevel health records from such a major children’s hospital. The Environmental Protection Screening Method indicators for census tracts are then established to look for patterns in public health threats and hazards that may contributing to the prevalence of asthma in various areas. EJSM indicators and paediatric asthma incidence rates throughout the region, assisting in the identification of demographic features and associated risk factors with both elevated/low rates of paediatric asthma. The EJSM scanning activity and BPR analysis show that each community has its own set of risks and vulnerabilities that can influence the rate of acute paediatric asthma acute care visits, suggesting research and intervention targets. However, it is obvious that many forms of social poverty are contributing to high incidence of paediatric asthma, which is linked to unequal developments and racial social inequality. The findings serve as a foundation for developing placebased population health research and interventions.
Introduction
Pediatric asthma is one of the most prevalent and dangerous diseases affecting children worldwide, with rates rising among moderate, Black, and other economically disadvantaged groups. The disease's expansion in the region has followed the same pattern as the overall trend. This has a huge financial impact on the families, communities, and health-care systems affected, with prices for asthma-related hospitals in the region increasing. Additionally, the demographics distribution of paediatric asthma incidence rates reveals a significant racial difference in the region: although making up a small percentage of the population, Black persons accounted for the majority of all asthma-related ED visits [1].
Given proper information, individualised asthma management plans, and medication adherence, many adolescents can effectively manage their asthma symptoms. Exposure to complicated mixtures of pharmacological and non-chemical stressors, environmental or physical exposures, and social determinants, that are often associated with one another, can trigger or exacerbate asthma symptoms. Sociologically disadvantaged kids, like those with a low economic background, may be more susceptible to chronic stress and are more susceptible to physical or chemical stressors such as local pollution and bad housing. Furthermore, evidence showed that exposure to both environmental and social risks can have a combined influence on child nutrition, and that socioeconomic disadvantages can affect the health implications of exposure to environmental risks [2].
Persistent racial residential segregation has impacted the distribution of health risk, susceptibility, and exposure to environmental hazards in the city, as it has in most metropolitan areas around the country. Racial residential segregation, codified through ‘redlining,' reinforced a bias in local planning and zoning toward suburban expansion, resulting in urban disinvestment - the process of unequal development. According to research, segregation and uneven development are major causes of asthma disparities [3].
Such uneven development has consequences for research and clinical methods aimed at identifying and reducing racial disparities in paediatric asthma; not only is burden of paediatric asthma likely to differ significantly at the local level, but so are contributory risk factors. Indicators of population health risk, on the other hand, are frequently assessed using state or national samples and rarely provided below the county level, obscuring racial and spatial health inequities. The inability to capture local variation in the risk of paediatric asthma stymies efforts to identify relevant research and intervention targets, as well as to gain knowledge what mixture of risk factor and structural conditions might contribute to a higher disease rates or impact a patient's health outcomes [4].
Alternatively, increased health data enables exploratory analysis of population health markers at various spatial agglomerations, such as precincts, which improves on commonly released indicators by federal, state, and municipal public health organisations. High-resolution analyses employing geography like census tracts also enable research into community risk variables that really can, for example, increase to the probability of paediatric asthma readmission [5].
Given what is known about paediatric asthma behaviour and the context of unequal development, racial disparities in asthma are likely to represent a complex mix of place- and individual risks and vulnerabilities. This study uses address-level health records to apply novel machine learning, geographical information system ( gis, and statistical modeling tools to investigate the distribution of paediatric asthma in the region and determine what might be causing disparities across towns and patients.
Conclusion
The EJSM and BPR cluster analyses, when used together, provide a transparent way to investigate patterns in the link between population health and various risk and vulnerability combinations. However, although there are specific risk screen names that will matter to each society and serve as intervention targets in each context, effective solutions to structural inequalities caused by ancient separation and uneven development are still needed to eliminate health inequities like paediatric asthma. This concept design offers insight into the issue and informs how future research and intervention initiatives to reduce the burden of juvenile asthma in vulnerable populations should be designed. Future projects should use CBPR to 'anchor' these analytical activities with local knowledge.
REFERENCES
- Vanderweil SG, Tsai CL, Pelletier AJ, et al. Inappropriate use of antibiotics for acute asthma in United States emergency departments. J Acad Emerg Med. 2008;15(8):736-43.
- Gonzalez PC, Gauvreau K, Demone JA, et al. Regional racial and ethnic differences in mortality for congenital heart surgery in children may reflect unequal access to care. Pediatr Cardiol. 2003;24(2):103-108.
- Shields AE, Comstock C, Weiss KB. Variations in asthma care by race/ethnicity among children enrolled in a state Medicaid program. Pediatr. 2004;113(3):496-504.
- Kim Y, Pirritano M, Parrish KM. Determinants of racial and ethnic disparities in utilization of hospital-based care for asthma among Medi-Cal children in Los Angeles. J Asthma. 2021;13:1-10.
- Andrew Aligne C, Auinger P, Byrd RS, et al. Risk factors for pediatric asthma: contributions of poverty, race, and urban residence. Am J Respir Crit Care Med. 2000;162(3):873-877.
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