Health-related quality of life of children from low-income families,The New Patterns study

doi:10.21203/rs.3.rs-2956321/v1

Download PDF

Research Article

Health-related quality of life of children from low-income families,The New Patterns study

https://doi.org/10.21203/rs.3.rs-2956321/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 06 Dec, 2023

Read the published version in BMC Public Health →

You are reading this latest preprint version

Background

Child poverty has been gradually rising, so that about 12% of all Norwegian children are living in a state of relative poverty. This study was part of the New Patterns project, which recruits low-income families requiring long-term welfare services. Included families receive integrated welfare services, with the help of a family coordinator. The current study objectives were to describe health-related quality of life (HRQoL) in a sample of children and adolescents participating in the New Patterns project (baseline) and to explore the relations among age, background, participation in leisure activities, and HRQoL.

Methods

Participating families had children (N=214) aged 0–18 years, a three-year averaged household income below 60% of the equivalized median population income and needed long-term welfare services. HRQoL was measured using the KIDSCREEN-27 self-report instrument. Descriptive statistics, including means, standard deviations, and proportions, were calculated, and ordinary least squares regressions were performed, clustering standard errors at the family level.

Results

Compared with boys, girls reported lower HRQoL on all dimensions, and older children reported significantly lower HRQoL levels on the physical well-being and school environment dimensions than younger children. Children with immigrant backgrounds reported higher HRQoL than did children without immigrant backgrounds, even when controlling for gender, age, and participation in leisure activities.

Conclusions

Overall, the participating children reported good HRQoL. However, substantial variations within HRQoL dimensions were observed, indicating that some participants had lower HRQoL scores, especially on the physical and social support dimensions. Children with immigrant backgrounds reported significantly higher HRQoL than children without an immigrant background.

Health-related Quality of life

well-being

Low-income families

Child poverty

Immigrant.

In recent years, gradually rising child poverty has become a cause for concern [1]. Compared with other countries, Norway has a low poverty rate. However, an increasing number of Norwegian children are growing up in poverty, as the country’s child poverty rate has tripled since 2001. About 12% of all Norwegian children now live in a state of relative poverty [2]. Particularly concerning are families with children because the increased poverty rate is more prevalent among them compared with the general population, and because children’s consequences may be significant [3].

A longitudinal Norwegian study of intergenerational mobility demonstrated that children born into the lowest decile of the parental earnings rankings have fallen behind in several outcome dimensions [4]. This effect reflects employment, net household income, health (measured by disability program participation), family formation (for men), and mortality. Poverty affects the family as a whole and may have serious consequences for children, such as an increased risk of social marginalization and their own experience of poverty as adults [3]. Moreover, an extensive literature reports the negative health effects of poverty [5, 6]. For instance, a longitudinal study of adolescents in 34 countries indicated that those from low socioeconomic families are more affected by psychological and physical symptoms [7]. Poverty is also the most important social determinant of child health in high-income countries [8], and influences children’s health—largely indirectly, through adverse effects on their physical surroundings and psychosocial experiences [9, 10]. Accumulated poverty over time may result in adverse health outcomes [11, 12].

Studying health-related quality of life (HRQoL) is important as it may provide both a broad view of the impacts of poverty on child health [13], and information about their daily functioning and activities, psychosocial factors, how they feel and perceive themselves, and how they perceive their relationships with others [14–16]. HRQoL aims to assess various aspects of children’s well-being and has been described as “a multidimensional construct covering physical, emotional, social, and behavioral components of subjective well-being and functioning” [17]. The multidimensionality of the HRQoL scale provides researchers and practitioners with information about the multiple impacts of poverty and may serve as a framework for identifying and developing strategies to promote HRQoL[18]. The World Health Organization emphasizes well-being and HRQoL as public health goals and highlights child health and well-being as essential for healthier, more sustainable societies [1].

Relations between socioeconomic status (SES) and HRQoL are well established, with studies consistently demonstrating that low SES is related to low HRQoL [19–24]. Although less attention has been paid to children than to adults, this association is clearly also relevant for the former [23]. A recent study from the Netherlands found that poverty at birth and/or cumulative poverty is associated with a significantly increased risk of lower HRQoL in children [8]. Different factors linking SES to HRQoL have been discussed, including differences in access to material and social resources and reactions to family stress [19]. Nevertheless, few studies have explored the dimensions of HRQoL among children living in poverty. One reason for this is that compared with higher-income families, those living in poverty are less likely to participate in research and community programs [25].

The study described herein was part of the New Patterns project, which recruits low-income families requiring long-term welfare services. Participating families receive integrated services through a family coordinator across multiple sectors (culture, education, welfare, health, and social services), and volunteer organizations, supporting all family members for five years [26]. The New Patterns project thus provides a unique opportunity to study HRQoL in this population. Studying the HRQoL of children growing up in poverty can provide important information for practice and policymakers, especially for improving child well-being and health, and reducing social inequity. The objectives herein were to describe HRQoL among the children and adolescents participating in the baseline of the New Pattern study and to explore the relations between HRQoL and age, background, and participation in leisure activities.

Study sample

The New Patterns project details, including recruitment and methodology, have been previously described [26]. Briefly, eligible families have children aged 0–18 years, a three-year averaged household income below 60% of the equivalized population median and need long-term welfare services. Participating families live in both urban and rural municipalities in southwestern Norway. The current study included 214 children (from 123 families) aged eight years or older at project enrollment. Most of these participants (70%) enrolled in 2019 or 2020. Though project recruitment is ongoing, this study included only children who had enrolled by May 13, 2022.

Data description

Families participating in the New Patterns project undergo a mapping process with the family coordinator, in close collaboration with all family members. Both parents and children answer questionnaires. Though mapping is repeated yearly, this study included only baseline results (i.e., within the first year of enrollment). Mapping details have also been reported by Mølland et al. (2020).

Instruments

All background characteristics are measured at New Patterns project enrollment. Parental education is classified as the highest level of education completed by the mother and/or father at enrollment. Low education level means that the parent(s) only completed mandatory education (10 years in Norway). Children categorized as having an immigrant background had either themselves immigrated to Norway or were born in Norway to a parent or parents who had immigrated there. Children born in Norway whose parents did not have an immigrant background were defined as having a nonimmigrant background.

HRQoL was measured using the KIDSCREEN-27 instrument [14], which was completed by each participating child. The KIDSCREEN-27 assesses the 10 dimensions in the full 52-item version, merged into five dimensions: physical well-being (five items), psychological well-being (seven items), autonomy and parent relation (seven items), social support and peers (four items), and school environment (four items). Each item is rated on a five-point Likert scale referring to the past week, indicating either the intensity of an attitude or the frequency of a behavior or feeling. For example, the physical well-being dimension item “When thinking about the last week, have you been able to run well?” is rated using a scale from 1 (not at all) to 5 (extremely). Consistent with the KIDSCREEN manual, Rasch scores are computed for each subscale and transformed into t-values normed to a mean (standard deviation [SD]) of 50 (10), which can be compared with international t-values [27]. The Norwegian version has been shown to be valid and reliable [28].

Analysis

Data were analyzed using Stata 17 (Stata Corp. 2019, Stata Statistical Software: Release 16. College Station, TX, USA). Descriptive statistics, including means, SD, and fractions, were calculated. Differences in means were tested using standard t tests, assuming unequal variances. Ordinary least squares regressions were performed, clustering standard errors at the family level.

Ethics

Informed consent was obtained from all participants, and informed consent was obtained from the parents of children younger than age 16 years. The study was approved by the Norwegian Regional Ethics Committee West (reference no. 249507) and conducted according to recommendations from the Norwegian Data Protection Services (file no. 27435).

Table 1 presents the 214 participants’ descriptive characteristics, including age, sex, and parental sociodemographics. Among the overall sample, girls were slightly overrepresented (54%), the mean age was 12 years, 68% had an immigrant background, 42% had parents who were married or cohabiting, and 58% had parents with low education. A large proportion of participants had parents who were not currently participating in the workforce (74%).

Table 1

Sample characteristics (N = 214)
	N (%)	Mean (SD)	Total observations
Age (years)		12.03 (2.55)	214
Girls	115 (54%)		214
Immigrant background	145 (68%)		214
Born in Norway	126 (59%)		214
Married/cohabitant parents	87 (42%)		209
Parents with low education level	122 (58%)		209
Parents who participated in the workforce in the last 12 months	75 (36%)		209
Parents currently participating in the workforce	56 (26%)		209

< Insert Table 1 here >

Figure 1 shows a boxplot of the mean HRQoL scores for the five HRQoL domains for this sample, and European reference norms [27]. The plots indicate that the sample’s mean scores are close to European norms. The highest mean scores were for the domains of peer relationships and social support (mean 50.5, SD 12.3) and school environment (mean 52.4, SD 10.4), consistent with normative data. The lowest HRQoL score was for the physical well-being dimension. Although the mean scores were high for nearly all dimensions, there was a substantial range and variation within each.

< Insert Fig. 1 here >

Table 2 presents the mean HRQoL scores and mean differences for age groups and gender. Age was grouped into categories: 8–11 years and 12–18 years. Children in the older group reported significantly lower HRQoL levels on the dimensions of physical well-being (mean difference 6.9; 95% confidence interval [CI] 3.5, 10.5) and school environment (mean difference 4.2; 95% CI 1.2, 7.2). The lowest HRQoL scores were reported for children in the older age group, on physical well-being (mean 46.1, SD 12.38). Overall, girls reported lower HRQoL than boys on all dimensions.

Table 2

Mean self-reported health-related quality of life by age group and gender
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	12–18 years	8–11 years	Mean difference (Columns 2 and 1)	Cohen’s d	Girls	Boys	Mean difference (Columns 6 and 5)	Cohen’s d
	Mean (SD)	Mean (SD)	Mean [95% CI]		Mean (SD)	Mean (SD)	Mean [95% CI]
Physical	46.50	52.78	6.28***	0.53***	47.36	51.34	3.98*	0.33*
well-being	(11.97)	(11.37)	[3.13, 9.42]	[0.25, 0.82]	(12.09)	(11.82)	[0.77, 7.19]	[0.07, 0.59]
Psychological	51.83	52.62	0.79	0.073	51.11	53.37	2.25	0.21
well-being	(11.36)	(10.03)	[–2.18, 3.76]	[–0.18, 0.33]	(11.46)	(9.93)	[–0.70, 5.22]	[–0.062, 0.48]
Parents &	51.18	51.90	0.72	0.072	51.06	51.99	0.92	0.093
autonomy	(10.04)	(9.68)	[–1.95, 3.38]	[–0.191, 0.336]	(10.090)	(9.64)	[–1.72, 3.57]	[–0.19, 0.37]
Peers & social	50.19	50.92	0.72	0.058	50.38	50.65	0.27	0.022
support	(11.99)	(12.84)	[–2.65, 4.10]	[–0.237, 0.354]	(12.740)	(11.92)	[–3.032, 3.581]	[–0.25, 0.30]
School	50.89	54.48	3.59*	0.33*	52.19	52.71	0.52	0.047
environment	(11.59)	(9.64)	[0.74, 6.44]	[0.062, 0.60]	(11.20)	(10.62)	[–2.41, 3.44]	[–0.23, 0.32]
N	122	92	214	214	115	99	214	214
Notes: Column 1: Mean T-scores for children aged 12–18 years, with SD in parentheses. Column 2: Mean T-scores for children aged 8–11 years, with SD in parentheses. Column 3: Mean differences in T-scores between children aged 12–18 years and 8–11 years. 95% confidence intervals in brackets determined using a t test, assuming unequal variances. Column 4: Cohen’s d with bootstrapped confidence intervals. Column 5: Mean T-scores for girls, with SD in parentheses. Column 6: Mean T-scores for boys, with SD in parentheses. Column 7: Mean differences in T-scores between boys and girls. 95% confidence intervals in brackets determined using a t test, assuming unequal variances. Column 8: Cohen’s d with bootstrapped confidence intervals. * p < 0.05, p < 0.01, * p < 0.001.

< Insert Table 2 here>

Table 3 provides HRQoL information about children with and without an immigrant background and about participation in leisure activities. Children with an immigrant background reported higher HRQoL levels than children without an immigrant background on the dimensions: physical well-being (mean difference − 5.3; 95% CI − 8.9, − 1.9); psychological well-being (mean difference − 5.5; 95% CI − 8.6, − 2.3); parents and autonomy (mean difference − 3.9; 95% CI − 6.6, − 1.2); and school environment (mean difference − 4.8; 95% CI − 7.9, − 1.6).

Table 3

Mean self-reported health-related quality of life by immigrant status and participation in leisure activities
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Household immigrant	Household nonimmigrant	Mean difference (Columns 2 and 1)	Cohen’s d	Participate in leisure activities	Do not participate in leisure activities	Mean difference (Columns 6 and 5)	Cohen’s d
	Mean (SD)	Mean (SD)	Mean [95% CI]		Mean (SD)	Mean (SD)	Mean [95% CI]
Physical	50.93	45.57	–5.37**	–0.45***	51.70	45.96	–5.74***	–0.48***
well-being	(12.42)	(10.60)	[–8.58, − 2.15]	[–0.72, − 0.18]	(11.90)	(11.63)	[–8.92, − 2.56]	[–0.77, − 0.19]
Psychological	53.78	48.75	–5.03**	–0.47**	53.737	50.212	–3.53*	–0.33*
well-being	(10.86)	(9.96)	[–8.06, − 2.00]	[–0.78, − 0.16]	(11.10)	(10.17)	[–6.47, − 0.57]	[–0.59, − 0.067]
Parents and	52.60	49.16	–3.44*	–0.35**	52.657	49.978	–2.68*	–0.27*
autonomy	(10.31)	(8.47)	[–6.05, − 0.83]	[–0.60, − 0.09]	(9.83)	(9.76)	[–5.32, − 0.03]	[–0.54, − 0.01]
Peers and social	51.60	48.19	–3.41	–0.27*	51.785	48.849	–2.93	–0.24
support	(12.46)	(11.83)	[–6.86, 0.04]	[–0.53, − 0.02]	(11.73)	(12.96)	[–6.29, 0.42]	[–0.51, 0.03]
School	54.11	48.92	–5.19**	–0.49***	53.231	51.407	–1.82	–0.17
environment	(10.61)	(10.77)	[–8.27, − 2.12]	[–0.76, − 0.21]	(10.46)	(11.45)	[–4.80, 1.15]	[–0.43, 0.09]
N	145	69	214	214	121	93	214	214
Notes: Column 1: Mean scores for children with an immigrant background, with SD in parentheses. Column 2: Mean scores for children without an immigrant background, with SD in parentheses. Column 3: Mean differences in T-scores between children with and without immigrant backgrounds. 95% confidence intervals in brackets determined using a t test, assuming unequal variances. Column 4: Cohen’s d with bootstrapped confidence intervals. Column 5: Mean scores for children participating in leisure activities at the time of mapping, with SD in parentheses. Column 6: Mean scores for children who were not participating in leisure activities at the time of mapping, with SD in parentheses. Column 7: Mean differences in T-scores between children participating and not participating in leisure activities at the time of mapping. 95% confidence intervals in brackets determined using a t test, assuming unequal variances. Column 8: Cohen’s d with bootstrapped confidence intervals. * p < 0.05, p < 0.01, * p < 0.001.

Bivariate analysis indicated that children who participate in leisure activities reported higher levels of HRQoL on all dimensions, with significantly higher ratings on physical well-being (mean difference − 6.0; 95% CI − 9.0, − 2.5); psychological well-being (mean difference − 3.5; 95% CI − 6.5, − 0.58); parents and autonomy (mean difference − 2.6; 95% CI -5.3,-0.03); and school environment (mean difference − 1.82; 95%CI − 4.80, 1.15).

< Insert Table 3 here>

Regression analysis was conducted to describe the relations between the independent variables and the dependent variable (HRQoL) (Table 4). Models were adjusted for age, gender, participation in leisure activities, and immigrant background. Having an immigrant background was positively correlated with all five HRQoL dimensions (i.e., physical well-being, psychological well-being, parents and autonomy, peers and social support, and school environment)—indicating that children with immigrant backgrounds reported higher HRQoL than did children without an immigrant background, even when controlling for gender, age, and participation in leisure activities. Age was negatively correlated with the physical well-being and school environment dimensions.

Table 4

Regression results for health-related quality of life outcomes
	(1)	(2)	(3)	(4)	(5)
	Physical well-being	Psychological well-being	Parents and autonomy	Peers and social support	School environment
	β/SE	β/SE	β/SE	β/SE	β/SE
Household immigrant	5.14** (1.58)	5.00** (1.49)	3.46* (1.38)	3.48* (1.68)	5.17*** (1.49)
Age	–1.29*** (0.32)	–0.38 (0.31)	–0.10 (0.26)	–0.19 (0.32)	–1.05*** (0.27)
Male	3.32* (1.45)	1.66 (1.50)	0.39 (1.53)	–0.27 (1.62)	0.18 (1.50)
Participate in leisure activities	3.67* (1.84)	2.99 (1.69)	2.55 (1.58)	2.76 (1.81)	0.45 (1.59)
Constant	42.12*** (1.54)	46.38*** (1.58)	47.52*** (1.42)	46.71*** (1.92)	48.60*** (1.46)
Observations	214	201	214	214	214

These baseline HRQoL results from children and adolescents participating in an intervention targeting low-income families indicate that they have good overall HRQoL, with scores broadly in line with normative data [14]. However, there were age and gender differences, as well as effects of immigrant background status.

A main study finding was the difference in HRQoL scores between participants with and without an immigrant background. The former reported significantly higher HRQoL than the latter on the dimensions of physical and psychological well-being, parents and autonomy, and school environment. Some earlier studies have indicated that a migration background can be a risk factor for health problems and low HRQoL [18, 28]. However, results have varied in the literature, with some studies indicating more similarities, and even higher mental health scores and HRQoL, among immigrant children compared with their nonimmigrant peers [29–32]. This variation has also been documented across ethnic groups [32]. In a German study, young children with an immigrant background reported higher HRQoL scores than did nonimmigrant children [33]. Though it assessed both children and adults, one previous Norwegian study reported lower use of specialist mental service among immigrants compared with nonimmigrants [34]. Likewise, a recent study from Germany showed that children with low SES, low parental education, and a migrant status reported significantly lower HRQoL than did children without an immigrant background [35]. These inconsistencies suggest that such relations are highly complex.

The HRQoL differences between nonimmigrant and immigrant children from low-income families herein may be related to methodological challenges, including problems with survey completion. However, differences may also be related to levels of family complexity [36]. Previous research has indicated that poverty can last across generations and that family characteristics can affect children over time, such as poor-quality parenting related to economic stress or parental health problems [6]. Children with a nonimmigrant background who grow up in poor families may therefore inherit less favorable family environments and experience more stigma related to their family situations [37, 38]. It should be noted that most families participating in our study that have a nonimmigrant background also have at least one parent who is unable to work for different reason, such as health problems and disability. Moreover, Norway may be considered particularly welcoming to immigrants compared with the hostility they may face in many other countries. Another potential explanation for HRQoL differences may be that adolescents with an immigrant background experience less pressure from social referencing [39].

Herein, the HRQoL results are in line with those of a recent study of adolescents from the general population in the same region of Norway [40]. We also found that younger children reported higher HRQoL than did older children, consistent with earlier findings that HRQoL is associated with age (i.e., older children report lower HRQoL than younger children) and gender (boys report higher HRQoL than girls) [18, 23, 41]. In general, our study participants had HRQoL scores close to the normative data on most dimensions. However, some participants had lower HRQoL than the European norms—especially on the physical and social support dimensions. This is in line with a recent study from the Netherlands showing that children born into poverty have low physical HRQoL [42]).

Herein, the lowest HRQoL scores were reported by the older children (12–18 years) on the physical well-being dimension, which was also more prevalent in girls than in boys. The physical well-being dimension explores children’s perceptions of their physical activity, health, and vitality [14] so that low scores indicate more impaired physical functioning. Children from poor families are more likely to develop a variety of health conditions. Unstable, low family income can lead to increased stress in the family and a lack of predictability [38]. In a systematic review, different mechanisms relating child poverty to health and well-being highlighted the importance of access to both material and social resources and child reactions to stress-inducing conditions [38]. The chronic stress of living in poverty can cause toxic stress [43], to which children may respond when they experience the accumulated burdens of family economic difficulties without adequate support [37, 44]. Children may express stress as complaints like headache and feeling in poor health [18, 45]. This stress experience, over time, may partly explain their low physical dimension score herein.

Our results also show that children who participate in leisure activities report higher physical well-being dimension scores than those who do not. This corresponds with previous studies [46, 47], including a recent report on schoolchildren from a socioeconomically deprived area of England among whom there was a positive association between participation in leisure activities and HRQoL [46]. These findings support the positive link between leisure activity engagement and HRQoL, though causality remains unclear.

Strengths and limitations

The main study strength was its recruitment of children from low-income families who are less likely to participate in research and community programs, thereby describing an often-underrepresented population. We also recruited children with and without an immigrant background, using a validated HRQoL instrument. The findings should nevertheless be interpreted within the context of its limitations. First, its cross-sectional design disallows deducing causal relations, thus longitudinal follow-up will be valuable. Second, 2006 European KIDSCREEN reference norms may be dated.

Baseline results regarding HRQoL among children and adolescents participating in an intervention targeting low-income families indicate that they have overall good HRQoL, though some participants had low HRQoL scores, especially on the physical and social support dimensions. Children with an immigrant background report higher HRQoL than do children without an immigrant background. HRQoL information in children from low-income families is vital for clinical practice and policy, especially toward improving child well-being. Longitudinal studies of HRQoL among children from low-income families are also needed.

CI -Confidence interval

SD-Standard deviation

HRQoL- Health-related Quality of Life

Ethics approval and consent to participate.

The study was approved by the Norwegian Regional Ethics Committee West (reference no. 249507) and conducted according to recommendations from the Norwegian Data Protection Services (file no. 27435). Informed consent was obtained from all participants, and informed consent was obtained from the parents of children younger than age 16 years. All methods were carried out in accordance with relevant guidelines and regulations and in accordance with the World Medical Association Declaration of Helsinki.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and analyzed for this study are not publicly available, due to regulation by the Norwegian Data Protection Authority, but are available from the corresponding author upon reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

The study was supported by the Norwegian Research Council (Grant # 295686).

Authors’ contributions

EM, EA, and KLV initiated the New Patterns project. KLV was the project manager who implemented the New Patterns project. All authors (KH, EA, TB, KLV, PW, and EM) contributed to the study design and implementation. KH and EM wrote the first manuscript draft. All authors revised and approved the final version.

Acknowledgements

The authors thank the participating families, municipalities, and family coordinators for their commitment to the New Patterns project.

ORCID iDs:

Eirin Mølland https://orcid.org/0000-0002-2403 -936X

Kristin Haraldstad https://orcid.org/0000-0002 -7364-147X

Philip Wilson https://orcid.org/0000-0002-4123 -8248

UNICEF., Report card 10: Measuring child poverty. New league tables of child poverty in the world’s richest coutries. Florence, Italy: UNICEF Innocenti Research Center, 2012.
Bufdir. 2021. Bufdir.no. https://bufdir.no/Statistikk_og_analyse/Barnefattigdom/#/ Barnefattigdom/#/1001/omfang#barn-i-husholdninger-medvedvarende-lavinntekt-(2017)).
Fløtten T , S.G.A., Helhetlige tiltak mot barnefattigdom, En kunnskapsoppsummering, F. Report, Editor. 2014, FAFO Report: FAFO Report.
Markussen, S. and K. Røed, Economic Mobility Under Pressure. Journal of the European Economic Association, 2019. 18(4): p. 1844-1885.
Roos, L.L., E. Wall-Wieler, and J.B. Lee, Poverty and Early Childhood Outcomes. Pediatrics, 2019.
Kim L. Schmidt, S.M.M., Randip Gill, Gregory E. Miller, Anne M. Gadermann, Michael S. Kobor,, Society to cell: How child poverty gets “Under the Skin” to influence child development and lifelong health. Developmental Review, 2021. 61.
Elgar, F.J., et al., Socioeconomic inequalities in adolescent health 2002-2010: a time-series analysis of 34 countries participating in the Health Behaviour in School-aged Children study. Lancet, 2015. 385(9982): p. 2088-95.
Yang-Huang, J., et al., Changes in Family Poverty Status and Child Health. Pediatrics, 2021.
Dearing, E., Psychological costs of growing up poor. Ann N Y Acad Sci, 2008. 1136: p. 324-32.
Duncan, G.J., P.A. Morris, and C. Rodrigues, Does money really matter? Estimating impacts of family income on young children's achievement with data from random-assignment experiments. Dev Psychol, 2011. 47(5): p. 1263-79.
Benzeval, M., M.J. Green, and A.H. Leyland, Do social inequalities in health widen or converge with age? Longitudinal evidence from three cohorts in the West of Scotland. BMC Public Health, 2011. 11: p. 947.
Green, M.J., et al., Timing of poverty in childhood and adolescent health: Evidence from the US and UK. Soc Sci Med, 2018. 197: p. 136-143.
Lam, C.L.K., et al., Poverty and health-related quality of life of people living in Hong Kong: comparison of individuals from low-income families and the general population. J Public Health (Oxf), 2017. 39(2): p. 258-265.
Ravens-Sieberer, U., et al., The European KIDSCREEN approach to measure quality of life and well-being in children: development, current application, and future advances. Qual Life Res, 2014. 23(3): p. 791-803.
Ravens-Sieberer, U., et al., Subjective health, symptom load and quality of life of children and adolescents in Europe. Int J Public Health, 2009. 54 Suppl 2: p. 151-9.
Haverman, L., et al., Paediatric health-related quality of life: what is it and why should we measure it? Arch Dis Child, 2017. 102(5): p. 393-400.
Ravens-Sieberer, U., et al., Measuring subjective health in children and adolescents: results of the European KIDSCREEN/DISABKIDS Project. Psychosoc Med, 2007. 4: p. Doc08.
Otto, C., et al., Risk and protective factors of health-related quality of life in children and adolescents: Results of the longitudinal BELLA study. PLoS One, 2017. 12(12): p. e0190363.
von Rueden, U., et al., Socioeconomic determinants of health related quality of life in childhood and adolescence: results from a European study. J Epidemiol Community Health, 2006. 60(2): p. 130-5.
Drukker, M., F.J. Feron, and J. van Os, Income inequality at neighbourhood level and quality of life--a contextual analysis. Soc Psychiatry Psychiatr Epidemiol, 2004. 39(6): p. 457-63.
Pearce, A., H. Lewis, and C. Law, The role of poverty in explaining health variations in 7-year-old children from different family structures: findings from the UK Millennium Cohort Study. J Epidemiol Community Health, 2013. 67(2): p. 181-9.
Petersen-Ewert, C., M. Erhart, and U. Ravens-Sieberer, Assessing health-related quality of life in European children and adolescents. Neurosci Biobehav Rev, 2011. 35(8): p. 1752-6.
Reiss, F., et al., Socioeconomic status, stressful life situations and mental health problems in children and adolescents: Results of the German BELLA cohort-study. PLoS One, 2019. 14(3): p. e0213700.
Johansson, L.M., et al., Can physical activity compensate for low socioeconomic status with regard to poor self-rated health and low quality-of-life? Health Qual Life Outcomes, 2019. 17(1): p. 33.
De Weger, E., et al., 'They need to ask me first'. Community engagement with low-income citizens. A realist qualitative case-study. Health Expect, 2022. 25(2): p. 684-696.
Mølland, E., et al., The New Patterns study: coordinated measures to combat child poverty. Scand J Public Health, 2021. 49(5): p. 571-579.
The KIDSCREEN Group, E., The KIDSCREEN Questionnaires - Quality of life questionnaires for children and adolescents. Handbook. 2006: Lengerich: Pabst Science Publishers.
Andersen, J.R., et al., Psychometric properties of the Norwegian version of the Kidscreen-27 questionnaire. Health Qual Life Outcomes, 2016. 14: p. 58.
deKeyser, L., et al., Multi-informant reports of mental health in Swedish-born children of immigrants and children born to non-immigrants - the SESBiC-study. BMC Pediatr, 2014. 14: p. 95.
Belhadj Kouider, E., U. Koglin, and F. Petermann, Emotional and behavioral problems in migrant children and adolescents in Europe: a systematic review. Eur Child Adolesc Psychiatry, 2014. 23(6): p. 373-91.
Schmengler, H., et al., Socioeconomic inequalities in adolescent health behaviours across 32 different countries - The role of country-level social mobility. Soc Sci Med, 2022. 310: p. 115289.
Stevens, G.W. and W.A. Vollebergh, Mental health in migrant children. J Child Psychol Psychiatry, 2008. 49(3): p. 276-94.
Villalonga-Olives, E., et al., Health related quality of life of immigrant children: towards a new pattern in Germany? BMC Public Health, 2014. 14: p. 790.
Abebe, D.S., L. Lien, and J.I. Elstad, Immigrants' utilization of specialist mental healthcare according to age, country of origin, and migration history: a nation-wide register study in Norway. Soc Psychiatry Psychiatr Epidemiol, 2017. 52(6): p. 679-687.
Ravens-Sieberer, U., et al., Mental Health and Quality of Life in Children and Adolescents During the COVID-19 Pandemic-Results of the Copsy Study. Dtsch Arztebl Int, 2020. 117(48): p. 828-829.
Rijlaarsdam, J., et al., Economic Disadvantage and Young Children’s Emotional and Behavioral Problems: Mechanisms of Risk. Journal of Abnormal Child Psychology, 2013. 41(1): p. 125-137.
Garner, P.W., Children's emotional responsiveness and sociomoral understanding and associations with mothers' and fathers' socialization practices. Infant Ment Health J, 2012. 33(1): p. 95-106.
Hertzman, C. and T. Boyce, How experience gets under the skin to create gradients in developmental health. Annu Rev Public Health, 2010. 31: p. 329-47 3p following 347.
Högberg, B., M. Strandh, and C. Hagquist, Gender and secular trends in adolescent mental health over 24 years - The role of school-related stress. Soc Sci Med, 2020. 250: p. 112890.
Mikkelsen, H.T., et al., Health-related quality of life is strongly associated with self-efficacy, self-esteem, loneliness, and stress in 14-15-year-old adolescents: a cross-sectional study. Health Qual Life Outcomes, 2020. 18(1): p. 352.
Otto, C., et al., Mental health and well-being from childhood to adulthood: design, methods and results of the 11-year follow-up of the BELLA study. Eur Child Adolesc Psychiatry, 2021. 30(10): p. 1559-1577.
Yang-Huang, J., et al., Changes in Family Poverty Status and Child Health. Pediatrics, 2021. 147(4).
Santiago, C.D., et al., Predictors of responses to stress among families coping with poverty-related stress. Anxiety Stress Coping, 2012. 25(3): p. 239-58.
Shonkoff, J.P. and A.S. Garner, The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 2012. 129(1): p. e232-46.
Chen, E., Why Socioeconomic Status Affects the Health of Children:A Psychosocial Perspective. Current Directions in Psychological Science, 2004. 13(3): p. 112-115.
Basterfield, L., et al., The association between physical fitness, sports club participation and body mass index on health-related quality of life in primary school children from a socioeconomically deprived area of England. Prev Med Rep, 2021. 24: p. 101557.
Eddolls, W.T.B., et al., The association between physical activity, fitness and body mass index on mental well-being and quality of life in adolescents. Qual Life Res, 2018. 27(9): p. 2313-2320.

No competing interests reported.

Download PDF

Journal Publication

published 06 Dec, 2023

Read the published version in BMC Public Health →

Editorial decision: Major revision
25 Jul, 2023
Reviews received at journal
06 Jul, 2023
Reviewers agreed at journal
05 Jul, 2023
Reviewers agreed at journal
26 Jun, 2023
Reviewers invited by journal
08 Jun, 2023
Editor assigned by journal
08 Jun, 2023
Editor invited by journal
31 May, 2023
Submission checks completed at journal
31 May, 2023
First submitted to journal
19 May, 2023

You are reading this latest preprint version

Health-related quality of life of children from low-income families,The New Patterns study

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Study sample

Data description

Instruments

Analysis

Ethics

Results

Discussion

Conclusions

Abbreviations

Declarations

Ethics approval and consent to participate.

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors’ contributions

Acknowledgements

References

Additional Declarations

Status:

Journal Publication

Version 1