Socioeconomic disadvantages and vulnerability to the pandemic among children and youth: A macro-level investigation of American counties.

This study intends to reveal the underlying structural inequity in vulnerability to infection of the novel coronavirus disease pandemic among children and youth. Using multi-source data from New York Times novel coronavirus disease tracking project and County Health Rankings & Roadmap Program, this study shows that children and youth in socioeconomically disadvantaged status are faced with disproportionate risk of infection in this pandemic. On the county level, socioeconomic disadvantages (i.e., single parent family, low birthweight, severe housing problems) contribute to the confirmed cases and death cases of the novel coronavirus disease. Policymakers should pay more attention to this vulnerable group to implement more targeted and effective epidemic prevention and control.

Introduction

The novel coronavirus disease pandemic has become an unprecedented global crisis. As of January 2021, >101 million people have been infected with this disease, and >2 million people have died of it worldwide (World Health Organization., 2021). However, there is very likely a social gradient in health risks of this pandemic that runs from top to bottom of the socioeconomic spectrum (McNeely, Schintler & Stable, 2020). Certain groups of people are more vulnerable to infection out of differences in resources, income, and race, etc (Ahu, Dobe, & Yadav, 2020). The infection rate of novel coronavirus disease is shown much higher among socially-disadvantaged and underserved groups (McNeely et al., 2020). Children and youth with the low socioeconomic status (e.g., living in poverty, homeless, or from a single-parent family) confront with severer situations, such as a crowded living environment (Wirth, 2006), the low level of education (Waitzberg et al., 2020), and the lack of resources (O'Sullivan & Phillips, 2019), etc. These children and youth often suffer from the inequity in health risks of a pandemic, due to the structural and huge difference in socioeconomic status (Stansfield, Williams, & Parker, 2017).

The sharp rise in family unemployment, the closure of social service agencies and other factors resulting from “social distancing” during the pandemic intensify the health risk of infection to children and youth (Silva & Smith, 2020). The underlying structural inequity in vulnerability to the health risk of this pandemic has not been fully explored by current research and needs to be further analyzed in depth. With the pandemic raging through the world, poor children and youth lack social support and they need the protection of public health policies. However, public health policies could hardly take benefits of all vulnerable groups into account (Silva & Smith, 2020), and as a result, magnify the original gap between groups of people in different socioeconomic status (Cardoso et al., 2020). Until now, there are>2.2 billion children in the world, accounting for about 28% of the world’s population, and about 16% of the world’s population are between the age of 10 and 19 (Singh et al., 2020). As a huge vulnerable population, children and youth in socio-economically disadvantaged status may bear a greater risk of infection, and their welfare needs attention of policymakers in the pandemic.

To fill in this research gap, this study will make a fine-grained analysis of structural inequity in vulnerability to infection of this pandemic among children and youth by investigating the effect of socioeconomic disadvantages on the confirmed cases among children and youth in this pandemic. In the next sections this study first describes in detail the phenomenon of health inequity among children and youth, and then makes a discussion of structural inequity, resulting from socioeconomic disadvantages, in infection risk of the novel coronavirus disease pandemic among children and youth. In the method section, this study uses the county-level data to test the associations between socioeconomic disadvantages and confirmed cases among children and youth. Effects of three kinds of socioeconomic disadvantages including single parent family, low birthweight and severe housing problems are examined. Finally, this study discusses the empirical results and provides policy implications to help cope well with urgent reality of this pandemic.

This study hypothesizes that single parent family, low birthweight, and severe housing problems could contribute to confirmed cases and death cases of the novel coronavirus disease. Growing up in a single-parent family seems to have disadvantages in terms of socioeconomic circumstances (e.g., McLanahan and Percheski, 2008, Weitoft et al., 2003). Children of single parents are vulnerable to economic deprivation, poverty, and lack of resources (e.g., Brady and Burroway, 2012, Eggebeen and Lichter, 1991, McLanahan and Percheski, 2008, Weitoft et al., 2003). Due to limited household resources, single-parent families have difficulties offering their children sufficient personal protection equipment in this pandemic (Zhang, 2020). Moreover, the absence of parental role in single-parent families could bring their children increased health risks, such as severe morbidity (Weitoft et al., 2003). As such, single-parent families could be accompanied with the higher infection rate among their children in this pandemic.

Low birthweight (normally refers to<2500 g), as an important marker of maternal and fetal health, can predict mortality, stunting, and adult-onset chronic conditions (Blencowe et al., 2019, Lethbridge and Phipps, 2005). The higher risk of low birthweight is associated with economic deprivation, racial discrimination, and social trauma, etc. that their patents experience (Mustillo et al., 2004, Yuan et al., 2020), and related with nutritional and health status of women commence pregnancy as well (Wardlaw, Blanc, Zupan, & Ahman, 2004). Low birthweight also has direct associations with neighborhood socio-economic status, since women living in socially and economically deprived neighborhoods (such as with higher rates of unemployment, housing violations and per capita crime, and lower rates of average wealth and per capita income), benefit less from prenatal care than those living in socially and economically sound neighborhoods (O’Campo et al., 1997, Yuan et al., 2020). During this pandemic, we suppose that the innately poor health condition of children born with low birthweight is more likely to increase their risks of infection.

Severe housing problems are inextricably linked with deterioration in health status. Specific health hazards associated with housing include lack of safe drinking water and proper sewage disposal, moisture and fungi (mold), rodent and insect pests, pesticide residues, and indoor air pollution, etc (Matte & Jacobs, 2000). It is also suggested that an increase in housing costs can weaken health benefits (Thomson & Petticrew, 2007). Hence, severe housing problem may expose children and youth to higher risks of infection during this pandemic, by increasing the possibility of virus transmission and decreasing the strength of disease protection. In conclusion, socioeconomic disadvantages including single parent family, low birthweight and severe housing problems could bring children and youth multifaceted risks of susceptibility in this pandemic.

Literature review

Structural inequity and the health crisis among children and youth

The unprecedented pandemic has exposed the ever-existing social inequity. A disproportionately higher health risk of this pandemic among specific groups of people reflects the political, economic, and social disparity of all dimensions (Karabag, 2020). The health inequity between socioeconomic groups can result from unequal intangible endowments (e.g., knowledge, power, prestige, or benign social relationships; Clouston et al., 2016, McNeely et al., 2020), consequences of colonialism (Gilmour et al., 2020), the income gap (Bor et al., 2017), gender difference (Gutierrezl & Bertozzi, 2020), unbalanced education development (Selden & Berdahl, 2020), or the disparity in access to timely and effective treatment (Blumenshine et al., 2008), etc.

The policy intervention has long been insufficient to protect the health of children and youth in underdeveloped areas (Li, Yuan & Zeng, 2020a). During the period of cholera, the rich had a lower risk of getting infected due to having more access to healthy drinking water (Clouston et al., 2016). In an outbreak of Ebola, women living in resource-poor areas suffered from additional sexual and reproductive health risks. During the H1N1 flu epidemic, the social issue of racial discrimination led to the higher rate of infection and morbidity among blacks and Hispanics (Quinn et al., 2011). In the novel coronavirus disease pandemic, grassroots workers are at higher risk of susceptibility to the virus due to certain occupational factors (Yaya et al., 2020). The underserved population are also more vulnerable to this pandemic given the absence of charity support, having a higher risk of homelessness and a higher likelihood of suffering mental illness.

Children have weak resilience, social and structural fragility in the face of health stress, and often need social and family support (Li et al., 2020b, Qi and Wu, 2020, Yuan and Li, 2020). But the socio-economically disadvantaged status of families of origin exhausts material, emotional and social resources of parents. Children and adolescents in these families may face higher health risks resulting from improper parenting (Kwon & Wickrama, 2014), such as stunting, unhealthy diet, obesity, and drug abuse (Hanson & Chen, 2007). Child neglect and domestic violence have become leading causes of morbidity and mortality among 14-year-old children (Smith & Ashiabi, 2007).

Children and adolescents living in impoverished communities are faced with health risks in the absence of public resources (Yuan et al., 2020). Such communities are often characterized by high unemployment, high crime rates, and drug proliferation (Strom & MacDonald, 2007), which may increase the incidence of unhealthy behavior among children and adolescents (Kwon & Wickrama, 2014). Besides, the unsafe resident environment can put children and youth under threat of beating and murder (Strom & MacDonald, 2007), which can then increase their stress, anxiety, and depression greatly (Huang, Edwards, & Laurel-Wilson, 2020). Moreover, children and adolescents whose parents work at night and living in light- or noise-polluted communities are associated with “late sleep” (Muller, Paine, Wu & Signal, 2020) and obesity (Xiu et al., 2020). In Jamaica, it is found that children living near a local battery smelter have high level of blood lead which is related to the high incidence of epilepsy, coma, anemia, and lead poisoning (Brooks-Gunn & Duncan, 1997). All these cases could increase the health risks of children and youth.

The health crisis among children and youth during the novel coronavirus disease pandemic

Socio-economically-disadvantaged children and youth are more likely to be marginalized and discriminated against in a pandemic (Ramaswamy & Seshadri, 2020), which may put them in high-risk activities (Juneja, Singh, & Sultan, 2020). Families in socio-economically disadvantaged status cannot offer children effective protection resources, instead bring multifaceted risks of susceptibility. These families often cut down their spending on personal protection equipment (Christoffel, Gomes, Souza, & Ciuffo, 2020), sanitation and disinfection water (Baggett et al., 2013), or alcohol, soap, and other anti-bacterial items (O'Sullivan & Phillips, 2019), etc. This may reduce sanitary protection of their children and increase the risk of infection in this pandemic accordingly (Jesus et al., 2020). The decline in family income and the rise in unemployment in the pandemic lead to the accumulation of family pressure (Lawson, Piel, & Simon, 2020). In some underdeveloped areas, children and youth in families lacking sources of income may be forced to make a living as child laborers during the pandemic (Zahed, Chehrehrazi, & Talemi, 2020), which could expose them to high infectious risk.

Besides, children and youth from socio-economically disadvantaged families live mostly in small and unventilated space and densely-populated communities. Bad resident environment may often be exposed to diseases, pests and environmental pollutants, allowing the spread of the novel coronavirus (Waitzberg, Davidovitch, Leibner, Penn, & Brammli-Greenberg, 2020). Crowding housing conditions may also be exposed to high level of ambient air pollution which could increase the risk of infection (e.g., Deng Q., Deng L., Lu, Li, & Norbäck, 2018). Given they often live on the fringe of cities, the high dependence on crowded public transportation can increase their contact with other people and the resulting higher risk of exposure to the virus (Waitzberg et al., 2020). In addition, their parents may become carriers of the virus because of contact with grassroots jobs and occupations, making them at risk of infection (O'Sullivan & Phillips, 2019).

Moreover, medical support for children and youth living in these families is insufficient. They can only access limited protection and treatment drugs in medical service institutions (Stevenson et al., 2009), and can only be placed in areas crowded and with a serious shortage of beds and nurses due to lack of financial ability (Waitzberg et al., 2020). Also, they have not obtained effective monitoring and early warning systems (Sotomayor & Barrero, 2020), and have lacked diagnostic testing equipment (Jesus et al., 2020). These greatly reduce the ability of children and youth to protect themselves against the virus. Further, during this pandemic, social assistance institutions (e.g., charities) are almost closed and unable to provide social services (Silva & Smith, 2020), leading to some of impoverished children suffering from being homeless and wandering on the street. This will expose them directly to the environment of virus infection and lack of medical support for prevention and treatment (Jesus et al., 2020).

Finally, it is worth noting that prevention measures in the novel coronavirus disease pandemic are mainly spread through digital media. However, the lack of communication facilities may make it difficult for poor children and youth to get timely relevant information on social media (Waitzberg et al., 2020). Even in some very poor areas of the world, the extreme lack of education can cause language barriers, limiting the availability of medical prevention and treatment services (Waitzberg et al., 2020). As such, children and youth in socioeconomically disadvantaged status are at higher risk of exposure to infection and adverse health outcomes during this pandemic.

Method and materials

Description of data

Multi-source data are combined and matched for analysis. The data of confirmed cases of the novel coronavirus disease are from the New York Times novel coronavirus disease tracking project that releases daily real-time and county-level confirmed cases in the United States (https://github.com/nytimes/covid-19-data). The data of children and youth’s characteristics and relevant socioeconomic factors on county level come from the County Health Rankings & Roadmap Program (CHRRP, 2020). The CHRRP program aims at improving health equity and helps to propose health promotion policy and practice. This program is implemented and made public by Robert Wood Johnson Foundation and University of Wisconsin, (https://www.countyhealthrankings.org/explore-health-rankings). The matched dataset is applied for regression analysis.

Variables

Several cross-sections (May. 28th, 2020; Jun. 28th, 2020; Jul. 28th, 2020; Aug. 28th, 202; Sep. 28th, 2020; Oct. 28th, 2020) are used to examine the relation between socioeconomic disadvantages related to children and the confirmed cases (and death cases) of novel coronavirus disease on county level.

Outcome variables include confirmed cases and death cases of novel coronavirus disease (May. 28th, 2020; Jun. 28th, 2020; Jul. 28th, 2020; Aug. 28th, 202; Sep. 28th, 2020; Oct. 28th, 2020) in the US on county level.

Independent variables are shown as following.

“Single parent family” is measured by the percentage of children living in single parent households in respective county (Mean = 32.77, SD = 10.30).

“Low birthweight” is measured by the percentage of live births with low birthweight in respective county (Mean = 8.05, SD = 2.20).

“Severe housing problems” is measured by the percentage of households with at least one of the following housing problems, including overcrowding, high housing costs, or lack of kitchen or plumbing facilities in respective county (Mean = 13.95, SD = 4.34).

The aspect of regional level of health care service is used as covariates and has been controlled in the regression analysis. Specifically, it includes the following proxies

“Physicians” is measured by the primary care physicians per 100,000 population (Mean = 53.78, SD = 34.52).

“Dentist” is measured by the ratio of population to dentists (Mean = 45.33, SD = 31.39).

“% Vaccinated” is measured by the percentage of fee-for-service Medicare enrollees that have an annual flu vaccination (Mean = 42.27, SD = 9.54).

“Life expectancy” is measured by the average number of years that people born at the same time can expect to live if current age-specific mortality rates remain unchanged (Mean = 76.83, SD = 7.43). This variable is used to reflect population health and health care service level in certain areas in normal time.

Besides, the aspect of characteristics of living area is used as covariates and has been controlled. Specifically, it includes the following variables

“PM 2.5” is measured by the average daily density of fine particulate matter in micrograms per cubic meter (PM 2.5) in respective county (Mean = 9.10, SD = 2.03).

“Food environment” is measured by the index of healthy food environment, from 0 (worst) to 10 (best) in respective county (Mean = 7.46, SD = 1.23).

“% Limited access to healthy food” is measured by the percentage of population who are low-income and do not live close to a grocery store in respective county (Mean = 7.91, SD = 7.19).

Further, we control the influence of demographic structure of each region.

“Age > 65” is measured by the percent of the population with age > 65 years in respective county (Mean = 19.02, SD = 4.51).

“Age < 18” is measured by the percent of the population with age < 18 years in respective county (Mean = 22.11, SD = 3.36).

More details about variable description have been provided in Table 1 . Also, the spatial distribution of socioeconomic factors and confirmed cases (and death cases) across counties are respectively illustrated in Fig. 1, Fig. 2, Fig. 3 .Table 2. Table 3.

Table 1

Description of variables.

Variable Name	Description	Mean	S.D.
Confirmed case	The number of confirmed cases of novel coronavirus disease.
(May 28th, 2020)		571.59	3128.59
(Jun 28th, 2020)		776.35	3542.30
(Jul 28th, 2020)		1273.65	5796.57
(Aug 28th, 2020)		1834.50	8726.93
(Sep 28th, 2020)		2214.43	9753.18
(Oct 28th, 2020)		2754.94	11074.14
Death case	The number of death cases of novel coronavirus disease.
(May 28th, 2020)		32.47	212.43
(Jun 28th, 2020)		34.99	184.09
(Jul 28th, 2020)		40.19	199.99
(Aug 28th, 2020)		57.60	477.94
(Sep 28th, 2020)		64.80	489.79
(Oct 28th, 2020)		71.94	500.26
Socioeconomic disadvantages related to children
% Single parent family	Percentage of children living in single parent households.	32.77	10.30
% Low birthweight	Percentage of live births with a low birthweight	8.05	2.20
% Severe housing problems	Percentage of households with at least one of four housing problems: overcrowding, high housing costs, lack of kitchen or plumbing facilities.	13.95	4.34
Health care service level
Physicians	Primary care physicians per 100,000 population	53.78	34.52
Dentist	Ratio of population to dentists	45.33	31.39
% Vaccinated	Percentage of fee-for-service Medicare enrollees that had an annual flu vaccination.	42.27	9.54
Life expectancy	The average number of years that people born at the same time can expect to live if current age-specific mortality rates remain unchanged	76.83	7.43
Characteristics of living area
PM 2.5	Average daily density of fine particulate matter in micrograms per cubic meter (PM2.5).	9.10	2.03
Food environment	Index of healthy food environment, from 0 (worst) to 10 (best).	7.46	1.23
% Limited access to healthy food	Percentage of population who are low-income and do not live close to a grocery store.	7.91	7.19
Demographic structure
% Age > 65	Percent of the population with age > 65	19.02	4.51
% Age < 18	Percent of the population with age < 18	22.11	3.36

Fig. 1

The spatial distribution of socioeconomic disadvantages: single-parent families (%) (1A), low birth weight (%) (1B), severe housing problems (%) (1C).

Fig. 2

The spatial distribution of confirmed cases on May. 28th, 2020 (2A), Jun. 28th, 2020 (2B), Jul. 28th, 2020 (2C), Aug. 28th, 2020 (2D), Sep. 28th, 2020 (2E), Oct. 28th, 2020 (2F).

Fig. 3

The spatial distribution of death cases on May. 28th, 2020 (3A), Jun. 28th, 2020 (3B), Jul. 28th, 2020 (3C), Aug. 28th, 2020 (3D), Sep. 28th, 2020 (3E), Oct. 28th, 2020 (3F).

Table 2

The influence of socioeconomic disadvantages on confirmed cases of novel coronavirus disease in the United States (on county level).

	Dependent variable: ln [1 + confirmed cases]
	May 28th		Jun 28th		Jul 28th		Aug 28th		Sep 28th		Oct 28th
	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.
Socioeconomic disadvantages related to children
% Single parent family	0.025 **	0.004	0.022 **	0.004	0.018 **	0.004	0.015 **	0.003	0.012 **	0.003	0.009 **	0.003
% Low birthweight	0.058 **	0.016	0.079 **	0.016	0.087 **	0.015	0.085 **	0.014	0.069 **	0.013	0.051 **	0.013
% Severe housing problems	0.115 **	0.011	0.122 **	0.011	0.128 **	0.011	0.120 **	0.010	0.106 **	0.009	0.087 **	0.008
Health care service level
Physicians	0.007 **	0.002	0.006 **	0.001	0.006 **	0.001	0.006 **	0.001	0.006 **	0.001	0.006 **	0.001
Dentist	0.006 *	0.003	0.007 **	0.002	0.006 **	0.002	0.005 **	0.002	0.006 **	0.002	0.006 **	0.002
% Vaccinated	0.048 **	0.003	0.047 **	0.003	0.041 **	0.003	0.037 **	0.003	0.035 **	0.003	0.033 **	0.003
Life expectancy	0.014 **	0.005	0.016 **	0.005	0.019 **	0.004	0.018 **	0.004	0.020 **	0.003	0.020 **	0.003
Characteristics of living area
PM 2.5	0.239 **	0.016	0.243 **	0.015	0.259 **	0.014	0.271 **	0.014	0.260 **	0.013	0.234 **	0.013
Food environment	0.345 **	0.050	0.296 **	0.044	0.252 **	0.038	0.230 **	0.035	0.215 **	0.034	0.201 **	0.035
% Limited access to healthy food	0.020 **	0.007	0.020 **	0.006	0.018 **	0.005	0.017 **	0.005	0.017 **	0.005	0.017 **	0.005
Demographic structure
% Age > 65	−0.122 **	0.011	−0.119 **	0.011	−0.110 **	0.011	−0.106 **	0.010	−0.104 **	0.010	−0.097 **	0.009
% Age < 18	−0.022	0.013	0.007	0.012	0.020	0.012	0.020	0.010	0.018	0.010	0.025 **	0.009
Intercept	−4.818 **	0.892	−4.860 **	0.839	−4.200 **	0.757	−3.250 **	0.689	−2.243 **	0.634	−1.284 *	0.602
Num. of obs.	2950		2944		2944		2945		2945		2945
R-square	0.5089		0.5389		0.5640		0.5813		0.5799		0.5651
F-statistics	200.18		226.39		254.91		276.19		270.93		239.49
P-value	[0.000]		[0.000]		[0.000]		[0.000]		[0.000]		[0.000]

Notes: Robust standard errors are reported. *p < 0.05, **p < 0.01.

Table 3

The influence of socioeconomic disadvantages on death cases of novel coronavirus disease in the United States (on county level).

	Dependent variable: ln [1 + death cases]
	May 28th		Jun 28th		Jul 28th		Aug 28th		Sep 28th		Oct 28th
	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.
Socioeconomic disadvantages related to children
% Single parent family	0.012 **	0.003	0.015 **	0.003	0.016 **	0.004	0.019 **	0.004	0.019 **	0.003	0.019 **	0.003
% Low birthweight	0.047 **	0.013	0.060 **	0.013	0.074 **	0.014	0.087 **	0.014	0.094 **	0.014	0.079 **	0.014
% Severe housing problems	0.097 **	0.010	0.096 **	0.009	0.108 **	0.010	0.114 **	0.010	0.112 **	0.010	0.099 **	0.010
Health care service level
Physicians	0.006 **	0.002	0.006 **	0.002	0.007 **	0.001	0.006 **	0.001	0.006 **	0.001	0.006 **	0.001
Dentist	0.006 *	0.002	0.006 *	0.003	0.005 *	0.002	0.005 *	0.002	0.004	0.002	0.004	0.002
% Vaccinated	0.025 **	0.003	0.029 **	0.003	0.030 **	0.003	0.032 **	0.003	0.032 **	0.003	0.032 **	0.003
Life expectancy	0.004	0.004	0.004	0.004	0.005	0.005	0.006	0.004	0.006	0.004	0.008 *	0.004
Characteristics of living area
PM 2.5	0.196 **	0.014	0.218 **	0.015	0.232 **	0.014	0.258 **	0.014	0.269 **	0.014	0.263 **	0.014
Food environment	0.274 **	0.039	0.271 **	0.039	0.255 **	0.040	0.249 **	0.039	0.247 **	0.037	0.246 **	0.035
% Limited access to healthy food	0.025 **	0.005	0.027 **	0.005	0.028 **	0.005	0.028 **	0.005	0.029 **	0.005	0.027 **	0.005
Demographic structure
% Age > 65	−0.058 **	0.009	−0.061 **	0.009	−0.062 **	0.009	−0.059 **	0.010	−0.059 **	0.010	−0.057 **	0.009
% Age < 18	−0.022 *	0.010	−0.011	0.010	0.006	0.011	0.018	0.011	0.019	0.011	0.026 *	0.010
Intercept	−5.267 **	0.692	−5.769 **	0.711	−6.309 **	0.728	−6.790 **	0.724	−6.686 **	0.700	−6.435 **	0.658
Num. of obs.	2950		2944		2944		2945		2945		2945
R-square	0.3652		0.3833		0.4145		0.4474		0.4604		0.4597
F-statistics	101.29		123.18		152.07		190.34		203.77		189.55
P-value	[0.000]		[0.000]		[0.000]		[0.000]		[0.000]		[0.000]

Notes: Robust standard errors are reported. *p < 0.05, **p < 0.01.

Analytical strategy

The linear regressions that have been adjusted with robust standard errors on individual level are implemented to investigate the influences of socioeconomic disadvantages (i.e., single parent family, low birthweight, severe housing problem) on the confirmed cases and death cases of novel coronavirus disease pandemic. The robust standard errors adjustment is applied to overcome the weakness of individual heterogeneity resulting from potential non-randomized variations in independent variables and the accompanied violation of independent and identical distribution. Stata 16.0 (Stata Corp. LLC., College Station, TX, USA) is applied in the analysis, and the regression is shown as below.

Ln [1 + confirmed cases] = β0 + β1 Single parent family (%) + β2 Low birthweight (%) + β3 Severe housing problems (%) + β4 Health care service level + β5 Characteristics of living area + β6 Demographic structure + ε

Ln [1 + death cases] = β0 + β1 Single parent family (%) + β2 Low birthweight (%) + β3 Severe housing problems (%) + β4 Health care service level + β5 Characteristics of living area + β6 Demographic structure + ε

Empirical results

Empirical results show that socioeconomic disadvantages positively affect confirmed cases of novel coronavirus disease. More specifically, single parent family is significantly and positively associated with confirmed cases (May 28th, 0.025; Jun 28th, 0.022; Jul 28th, 0.018; Aug 28th, 0.015; Sep 28th, 0.012; Oct 28th, 0.009; all coefficients with p < 0.01), which indicates that a county in the US with a higher proportion of children from single-parent families is at a higher risk of confirmed cases. Low birthweight significantly and positively predicts confirmed cases (May 28th, 0.058; Jun 28th, 0.079; Jul 28th, 0.087; Aug 28th, 0.085; Sep 28th, 0.069; Oct 28th, 0.051; all coefficients with p < 0.01), which shows that a county in the US with a higher proportion of children born with low birthweight faces higher risks of confirmed cases. Severe housing problems is significantly and positively associated with confirmed cases (May 28th, 0.115; Jun 28th, 0.122; Jul 28th, 0.128; Aug 28th, 0.120; Sep 28th, 0.106; Oct 28th, 0.087; all coefficients with p < 0.01), which demonstrates that a county in the US with a higher proportion of households suffering severe housing problems is at a higher risk of confirmed cases.

Similar results are also found in the aspect of death cases, where the association between social disadvantage and death cases can be more outstanding than that between social disadvantage and confirmed cases (in the aspect of statistical significance). More specifically, single parent family is significantly and positively associated with death cases (May 28th, 0.012; Jun 28th, 0.015; Jul 28th, 0.016; Aug 28th, 0.019; Sep 28th, 0.019; Oct 28th, 0.019; all coefficients with p < 0.01). Low birthweight significantly and positively predicts death cases (May 28th, 0.047; Jun 28th, 0.060; Jul 28th, 0.074; Aug 28th, 0.087; Sep 28th, 0.094; Oct 28th, 0.079; all coefficients with p < 0.01). Severe housing problems significantly and positively predicts death cases (May 28th, 0.097; Jun 28th, 0.096; Jul 28th, 0.108; Aug 28th, 0.114; Sep 28th, 0.112; Oct 28th, 0.099; all coefficients with p < 0.01).

Discussion

This study demonstrates the relationship between socioeconomic disadvantages (i.e., single-parent family, low birthweight, severe housing problems) and the risk of infection and mortality among children and adolescents in this pandemic from a macro-level perspective. Those in socio-economically disadvantaged status are faced with a disproportionate risk of infection and mortality in this pandemic.

Single-parent families, due to limited material resources, lack necessary personal protection equipment such as sanitation and disinfection water, or alcohol, soap, and other anti-bacterial items during this pandemic. Meanwhile, the absence of parental role in single-parent families may manifested more as lack of parents’ guidance on epidemic prevention knowledge and awareness and health-behavior practices, including hand hygiene, sneezing/coughing into their elbows, conscious avoidance of unnecessary contact with people while ill, adherence to wearing a face mask, and keeping away from tobacco smoking that is viewed as a risk factor leading to complicated novel coronavirus (Gambaryan & Drapkina, 2020), etc. These may increase risks of infection among children and adolescents in single-parent families during this pandemic. In response, improvement of family situation with the support of social policies is an important measure to reduce the infection rate among children from single-parent families during this pandemic.

Moreover, children and adolescents born with low birthweight may face the higher risks of infection in this pandemic. The greater prevalence of poor health conditions in them, such as stunting and morbidities, could reduce intrinsic capacity and resilience and hinder the fight against infections. Low birthweight, often accompanied with socioeconomic disadvantages such as household economic deprivation or neighborhood socio-economically disadvantaged status, affects the innate health of children and adolescents. These socioeconomic disadvantages could be aggravated in this pandemic, with the sharp rise in family unemployment, the decline in family income and wealth, and the increase in social instability factors. This pandemic is thus found to pose a threat to the health of pregnant women and, accordingly, exacerbate the incidence of low-birthweight babies (Smith et al., 2020). As such, improved prenatal care of healthcare providers is urgently needed to decrease the risk of low birthweight during this pandemic.

This study also finds that severe housing problems, such as overcrowding, high housing costs, or lack of facilities, are associated with higher infection rate among children and youth in this pandemic. Crowded housing condition could allow the spread of novel coronavirus, by making the air quality worse and making it difficult to keep physical distance (Kaiser & Stathopoulos, 2020). The lack of hygiene facilities, testing equipment, and clean drinking water, etc. in communities weakens disease prevention. Therefore, it is urgent to provide necessary facilities and resources for communities having severe housing problems to promote the protection of children and youth.

Policy makers should be more concerned about children and youth in socio-economically disadvantaged status to reduce their marginalized treatment (Li & Yuan, 2019). By making up for the deficiency in protective supplies, healthcare services, and other public services among children and youth in socio-economically disadvantaged status, social inequity in infection risk of this pandemic would be alleviated. And this action can promote fairness in health policies and help curb the spread of the pandemic in times of crisis.

This study is still not free from limitations. First, the development of the public health crisis is dynamic and volatile, however, the socioeconomic disadvantages of children and youth are relatively stable due to gradually shaped by the social change and transition in a long term. Therefore, the magnitude of influence of socioeconomic disadvantages in this study might be relatively constant. Future research can explore other dynamic triggers for infection of this pandemic. Besides, future research can discuss the roles of unpredictable external shock in the development of this pandemic, such as the development of effective vaccines, or the strong governmental policy intervention to cut off the transmission chain, etc. Moreover, this study focuses on a single country investigation. The contribution of socioeconomic disparities to the disproportionate risks for vulnerable populations may also apply to the cross-national situation. As such, the cross-national investigation is needed in future to further explore and strengthen the generalizability of the conclusion.

Author disclosure statement

Funding: This study does not receive external funding.

Competing interest: Authors of this study have no competing interest to declare.

Ethical approval and informed consent: The ethical approval and informed consent are not required, as this study uses publicly available data source and authors have no contact to human related materials. More specifically, the data applied in this study are publicly available and unrestricted re-use is permitted via an open license (CC BY 4.0 license).

Data availability declaration: The novel coronavirus disease data come from the New York Times novel coronavirus disease tracking project (). The data of social-economic factors related to children and youth on county level are collected from the County Health Rankings & Roadmap Program (2020) ().

Consent for publication: Consent for publication is not required since there are no personal identifying materials included in this manuscript.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.