Childrens Environmental Health (CEH) Systems Model
Cohen Hubal et al. described a conceptual system-of systems modeling approach to assessing children’s environmental health. [11]Based on Little, et. al. [3]. A supreme orienter is a person who has a goal to manage complex socioenvironmental problems. To help identify the supreme orienter, basic orienters are defined, which are contributors to achieving the goal. Operational orienters are more specific and quantifiable than basic orienters, which can be abstract and broad. Finally, indicators are quantifiable data that can be used to compare the operational orienters in order to evaluate progress. Fig. 1 shows the system-of-systems model that was used to assess children’s environmental health.
Systems thinking in childrens’ environmental health (Cohen Hubal, et al. 2020.
Systems thinking framework to the CEH flowchart on right.
Each level is affected by the physical environment, social environment and other factors.
The essential components of a healthy environment for children’s health are discussed in this article on child’s health
Child development and the relative indicators for environmental health for each
The basic orienters
This model aims to achieve optimal children’s environmental health in North Carolina. It does this by incorporating the following basic orienters. Clean air, clean water, and safe products are all operational orienters that contribute to a healthy physical environment. The social and economic resources that are available to children affect the quality of a child’s social environment. A healthy child is one who realizes their full potential and develops in a normal way. To determine the current state of these operational orientationers, key indicators can be identified and measured. These indicators include chemical occurrence in their physical environment orienter or income level in their social environment orienter. These indicators allow for evaluation of the effects of environmental health decisions and actions upon the orienters as well as the complex system that governs children’s environmental health. To demonstrate this approach, a set of examples indicators were chosen for each of the basic orientations. They are described in detail below and again in Fig. S1.
Physical environment
The three indicators that represent chemicals in the North Carolina physical environment were (1) number of Brownfield sites, (2) number Superfund sites and (3) percentage homes built before 1979. The North Carolina Department of Environmental Quality provided data on Brownfield locations within each county. [12]They were manually recorded in an Excel spreadsheet. Only one location was recorded in counties that reported duplicate Brownfield locations. The Environmental Protection Agency’s National Priorities List provided data about the locations and number of Superfund sites. [13]. Data were filtered to North Carolina addresses and manually matched locations to the appropriate counties. Each county was summed with the number of Superfund sites and Brownfield locations. PolicyMap contains data about the percent of homes built prior to 1979 in each county. [14].
Social environment
The three indicators that represent the North Carolina’s social environment were (1) percentage of North Carolina residents under 18 living in poverty, (2) percent of residents without health insurance and (3) percentage head of household not holding a high school diploma. These data were all gathered from the Kids Count database by the Annie E. Casey Foundation [15]. Data on the percentage of household heads without a high-school diploma were downloaded as 5-year averages between 2010 and 2014. The five-year average of percent uninsured minors and percent of minors living in poverty in each county was calculated for the years 2011 through 2015.
The 2019 American Community Survey through Social Explorer provided data on the population size of children ages 0-4, 5-9, and 10-14 for each county. [16]. This database also included a total population size of each county, including children and adults of all ages. The percentage of children below 5 years old in each county was calculated using the total population (children and adults) divided by the population of children 0-4.
Health outcomes
The three indicators used to measure children’s health in North Carolina counties were (1) the percentage of low birthweight babies and (2) the percentage of children under 2 with elevated levels of blood lead (5g), and (3) percentages of children under 15 who were hospitalized for asthma-related reasons. These data were gathered from the Kids Count Database through the Annie E. Casey Foundation [15]. The blood lead data for each county were downloaded as 5-year averages from 2014 to 2018. The asthma discharge data of each county were downloaded in 5-year averages for years 2010-2014. They were then divided by the total number of children (under 19) in each county. For years 2011-2015, the percentage of low birthweights was downloaded in each county and was calculated as an average over five years.
ANOVA analysis
ANOVA (analysis of variance) tests are often used to determine which predictor variables best explain the variability in response data. ANOVA is used to explore potential drivers of variability within children’s health outcomes data. It can be used from social environment factors, physical environment factors, or other health outcomes. QQ-plots confirmed the normality assumption for all of the data above. One-way ANOVAs of each health outcome variable were performed against the rest. If the p values were less than 0.05, they were considered significant. The ANOVA tests returned the p-values for each of the health outcome variables (social, physical, or health) in relation to one child’s health outcome. This indicates how likely it is that these outcomes will be correlated. All calculations in this and previous sections were done using R (version 4.0.3).
ToxPi Analysis
To show how integrating disparate information streams and weighing indicator for complex systems can help inform environmental health decisions, ToxPi was used to perform comparative analyses to extend univariate ANOVA. All the above-described data on health, physical environment, and social environment were loaded into ToxPi Graphical User Interface. [17]And analyzed within the programs unique statistical framework [18]. This framework produced a dimensionless index score (ToxPi) for each North Carolina county. It is the cumulative representation and vulnerability of each county based on the individual vulnerability metrics. Each slice is represented as a unit circle divided into different colored slices. Distance from the origin of each slice is proportional the normalized values of the data points that make up that slice. The width, on the other hand, indicates the relative weight that slice contributes to the overall ToxPi calculation. [18]. All data metrics were weighted equally in this study. Each slice was given an equivalent radial width.
ToxPis ranking and visualization of each county allowed for knowledge-driven exploration. ToxPi outputs were imported into R. K=means cluster plots of the data (Fig. S3 were used to determine the optimal number groups for North Carolina counties. ToxPi (Fig. S4 was used to confirm the use of 5 groups of counties and to perform the grouping. The grouped county data were exported from ToxPi to be mapped via the ToxPi*GIS web application (https://toxpi.org/gis/webapp/Using the latitude-longitude coordinates of the center of each North Carolina County, you can calculate the following: To visualize the relationship of vulnerability indicators (physical and social environments, and health outcomes), and the distribution in children in North Carolina, ToxPi data were plotted over a baseline map showing the percentage of the population below age 5. The base map was created in ArcMap Online, then transferred into ToxPi*GIS. To visualize trends in the state’s social, physical, and health environments, the hierarchical clusters were overlayed onto the base map.