U.S. national, regional, and state-specific socioeconomic factors correlate with child and adolescent ADHD diagnoses pre-COVID-19 pandemic

Abstract

Attention-deficit/hyperactivity disorder (ADHD), the most diagnosed emerging neurodevelopmental disorder in children, is a growing health crisis in the United States. Due to the potential increase in ADHD severity during and post the COVID-19 pandemic, we analyzed recent national and two state-specific ADHD data distribution among U.S. children and adolescents by investigating a broad range of socioeconomic status (SES) factors. Child and adolescent ADHD diagnosis and treatment data were parent-reported via National Survey of Children's Health (NSCH). The nationwide childhood prevalence of ADHD is 8.7%, and 62.1% of diagnosed children are taking medication. Louisiana (15.7%) has the highest percentage of children diagnosed with ADHD and California (5.6%) has the lowest, followed by Nevada (5.9%). Multiple correspondence analysis (MCA, n = 51,939) examining 30 factors highlights four areas of interest at the national and state level: race/ethnicity, financial status, family structure, and neighborhood characteristics. Positive correlations between ADHD diagnosis and unsafe school, unsafe neighborhood, and economic hardship are evident nationally and statewide, while the association between a lack of ADHD diagnosis and higher urban neighborhood amenities are evident nationally, but not in two opposing outlier states-Louisiana or Nevada. National and state-specific hierarchical analyses demonstrate significant correlations between the various SES factors and ADHD outcomes. Since the national analysis does not account for the demographic heterogeneity within regions or individual states, the U.S. should rely on comprehensive, county-specific, near real-time data reporting to effectively model and mitigate the ADHD epidemic and similar national health crises.

Figure 1

Distribution of parent-reported ADHD prevalence and medication throughout the United States. United States map showing percentages by state of (a) children aged 3–17 years currently diagnosed with ADHD and (b) children with ADHD currently taking ADHD medication. Darker shades indicate states with relatively higher percentages, and lighter shades indicate states with relatively lower percentages. California (5.6%) has the lowest percentage, Nevada (5.9%) has the second-lowest percentage, and Louisiana (15.7%) has the highest percentage of children who currently have ADHD. Nevada (32.2%) has the lowest percentage, Nebraska (81.76%) has the highest percentage, and Louisiana has the third-highest percentage (76.28%) of children with ADHD currently taking ADHD medication. Percentages of diagnosis and medication were mapped onto the U.S. states using the map function of the graph builder in JMP 14.3.0. (c) Compared to the Midwest (n = 11,946) and Northeast (n = 9,102), regional state averages of children diagnosed with ADHD are highest in the South (n = 17,889) and lowest in the West (n = 12,979). Red indicates children diagnosed with ADHD who are not receiving medication. Blue indicates children diagnosed with ADHD who receive medication. Darker shades of both gradients indicate children in older age brackets. (d) Cell plot of states ordered lowest (top) to highest (bottom) by region as ratios of the percent of children aged 3–17 years diagnosed with ADHD taking medication to the percent of all children aged 3–17 years diagnosed with ADHD. Darker shades of green indicate higher ratios relative to other states. There are 12 states in the Midwest, 9 states in the Northeast, 17 states in the South, and 13 states in the West. Data were collected from the National Survey of Children’s Health (NSCH) 2018–19 combined dataset.

Figure 2

Compared to estimates of (a) the national population (n = 51,916), (b–h) the represented states vary from 5.9 to 15.7% of children age 3–17 years diagnosed with ADHD and 32.2% to 76.3% of children taking ADHD medication among those diagnosed. From lowest to highest rate of diagnosis, the states represented are Nevada (n = 994), Nebraska (n = 953), South Dakota (n = 960), Kansas (n = 1,045), Massachusetts (n = 1,029), West Virginia (n = 1,082), and Louisiana (n = 1,080). Gray indicates children that are not currently diagnosed with ADHD and red indicates children that are currently diagnosed with ADHD. Blue indicates children that are currently diagnosed with ADHD and are taking ADHD medication, and orange indicates children that are currently diagnosed with ADHD but are not taking ADHD medication. (b) Nevada represents particularly low rates of diagnosis and medication, while (h) Louisiana represents particularly high rates of diagnosis and medication. Data were collected from the NSCH 2018–2019 combined dataset.

Figure 3

MCAs of ADHD diagnosis, SES factors, and demographics for samples (a) nationwide (n = 59,445), (b) from Louisiana (n = 1,255), and (c) from Nevada (n = 1,154). Data were collected from the NSCH 2018 and 2019 individual responses, and missing responses were imputed using the regularized iterative MCA algorithm of the missMDA R package. (a–c) MCA plots display associations between variables, with more closely associated variables displayed closer together. For instance, in panel A, ADHD_N and CBSA_Y are closely associated, but CBSA_N and Race_P are not. Positive diagnosis (ADHD_Y) and lack of diagnosis (ADHD_N) are shown in red. The percent variance retained is indicated for each dimension. (d) Table of all variables analyzed, with their possible responses and abbreviations.

Figure 4

Bar plots representing MCAs of ADHD diagnosis, SES factors, and demographics for samples (a) nationwide (n = 59,445), (b) from Louisiana (n = 1,255), and (c) from Nevada (n = 1,154). Data were collected from the NSCH 2018 and 2019 individual responses, and missing responses were imputed using the regularized iterative MCA algorithm of the missMDA R package. Bars show each variable’s proximity to either diagnosis variable, calculated as (distance to ADHD_N−distance to ADHD_Y)/(distance between ADHD_N and ADHD_Y). A value closer to 1.0 indicates a variable closer to the positive diagnosis and further from the lack of diagnosis, while a value closer to − 1.0 indicates a variable closer to the lack of diagnosis and further from the positive diagnosis.

Figure 4

Bar plots representing MCAs of ADHD diagnosis, SES factors, and demographics for samples (a) nationwide (n = 59,445), (b) from Louisiana (n = 1,255), and (c) from Nevada (n = 1,154). Data were collected from the NSCH 2018 and 2019 individual responses, and missing responses were imputed using the regularized iterative MCA algorithm of the missMDA R package. Bars show each variable’s proximity to either diagnosis variable, calculated as (distance to ADHD_N−distance to ADHD_Y)/(distance between ADHD_N and ADHD_Y). A value closer to 1.0 indicates a variable closer to the positive diagnosis and further from the lack of diagnosis, while a value closer to − 1.0 indicates a variable closer to the lack of diagnosis and further from the positive diagnosis.

Figure 5

Correlation matrices and hierarchical clustering dendrograms from chi-square test adjusted p-values between ADHD diagnosis, SES factors, and demographics (a) nationwide (n = 52,065), (b) in Louisiana only (n = 1,084), and (c) in Nevada (n = 999) only. In the (I) matrices, black indicates statistical nonsignificance (p > 0.05), darker colors indicate lesser significance, and yellow indicates the greatest statistical significance. “ + ” and “−” indicate positive and negative correlations, respectively. (II) Dendrograms reflect hierarchical clustering of examined variables according to p-values from pairwise inter-variable chi-square analyses. Variables are grouped according to similarities in p-values, and greater branch length along the x-axis indicates greater cluster dissimilarity.

Figure 5

Correlation matrices and hierarchical clustering dendrograms from chi-square test adjusted p-values between ADHD diagnosis, SES factors, and demographics (a) nationwide (n = 52,065), (b) in Louisiana only (n = 1,084), and (c) in Nevada (n = 999) only. In the (I) matrices, black indicates statistical nonsignificance (p > 0.05), darker colors indicate lesser significance, and yellow indicates the greatest statistical significance. “ + ” and “−” indicate positive and negative correlations, respectively. (II) Dendrograms reflect hierarchical clustering of examined variables according to p-values from pairwise inter-variable chi-square analyses. Variables are grouped according to similarities in p-values, and greater branch length along the x-axis indicates greater cluster dissimilarity.

Figure 6

Matrices of Cramér’s V values between ADHD diagnosis, SES factors, and demographics (a) nationwide (n = 52,065), (b) in Louisiana only (n = 1,084), and (c) in Nevada (n = 999) only. Darker colors indicate lower Cramér’s V effect sizes and yellow indicates the highest Cramér’s V effect sizes.