Keywords
Income inequality, child health, poverty
Income inequality, child health, poverty
We improved the first draft of this protocol after receiving the insightful feedback by two reviewers. We edited the phrasing of the aims for clarity. We edited the introduction to improve our contextualising of income inequality, that there are many income data sources, that there are many variations of income (e.g. household), and we included a definition of lower, middle, and high income countries. We updated the inclusion and exclusion criteria for clarity that this piece of work focuses on less than 18 years of age only and study designs excluded e.g. ecological studies. We updated the data extraction to explain the level of data collection. We updated the limitations section that excluded certain literature types and that we did not study socioeconomic status (SES), nor socioeconomic position (SEP). We used the term socioeconomic for consistency throughout the protocol paper.
See the authors' detailed response to the review by Richard M. Duffy
See the authors' detailed response to the review by Emily Lowthian
Income inequality demonstrates continual income gap between households that are either predisposed to or result in further social deprivation, evolving mental disorders, social injustice, poor education, poor employment attainment and lower life expectancy (Buttrick et al., 2017; Chen et al., 2007; Coburn, 2000; Kawachi & Kennedy, 1997; Lillard et al., 2015; Lynch et al., 2000). Public and social policies (e.g. taxes, welfare benefits) may influence actual income level, perceived income level within a neighbourhood or effective income level on quality of life (Kawachi & Kennedy, 1997; Kondo et al., 2009; Morrissey et al., 2020). Furthermore, addressing income inequality may benefit child and adolescent outcomes (Engle et al., 2011). Income inequality influences the mortality and health outcomes of children and their trajectories (longstanding illnesses, psychosocial wellbeing and obesity) into adolescence and adulthood (Vallejo-Torres et al., 2014). The effect of income inequality on child health outcomes differs throughout the life course, e.g. study findings differ as to the effect of income inequality on mortality based on age (Lynch et al., 2001; McIsaac & Wilkinson, 1997). Higher per person family income is associated with better outcomes in relation to physical activity, psychological symptoms and overall life satisfaction in adolescents. (Elgar et al., 2015). Moreover, there is evidence that those individuals in a higher income bracket are healthier and that addressing income inequality by raising the incomes of the poorest can improve health outcomes and health inequalities (Lynch et al., 2004)
Measuring inequality is a subject where a significant proportion of time is spent on conceptualizing inequality and the meaning of terms. The development of inequality within a region or the efficacy of a certain social reform can be documented differently depending on the instrument used to measure inequality (De Maio, 2007). Therefore, due consideration must be given by researchers and policy-makers as to the metric of income inequality being utilised, in order to make accurate and well-informed associations with health outcomes. This includes the variables needed to calculate the income inequality instrument. Income inequality instruments (i.e. quantitative metrics of income inequality) include the Lorenz curve, the Gini coefficient, decile ratios, the Palma ratio, the Theil Index and others (Trapeznikova, 2019). Each instrument provides insight into different aspects of income inequality. A policy-maker interested in the effect of a policy on the most socioeconomically deprived in a society may use the Palma ratio as an alternative to the Gini coefficient as their inequality instrument and concentrate on consumption instead of income data (De Maio, 2007; Kawachi & Kennedy, 1999; Świgost, 2017). Moreover, inequality instruments across countries may vary, and differences exist in data sources and definitions (e.g. New-world bank classification of lower-middle, upper-middle, and high income) (World Bank Country and Lending Groups, 2021). For example, measures of income inequality (e.g. data on income) are usually collected from household surveys and as such may not suit studying inequality at the top end of the income distribution as high-income respondents may be less likely to disclose all their wealth. In studies that focus on child outcome, each income inequality instrument provides additional information on the construct of socioeconomic inequality being investigated in relation to the measured child and adolescent outcome. The income data source used in child and adolescent studies is often at the household/family income level rather than individual level (Choi et al., 2017). Martikainen et al., demonstrates that caution must be used in making associations between income types (e.g. household, individual, disposable) in mortality research and there is a need to better understand the role of individual income, household income and disposable income in understanding the association of income inequality and childhood outcomes (Martikainen et al., 2009). Moreover, income inequality instruments are not identical. A preliminary search of PubMed and Embase database did not yield any systematic reviews investigating the frequency of quantitative income inequality instruments used in outcome studies. Moreover, this illustrates that this review will be the first to report the frequency of use of each quantitative income inequality instrument in studies of children and adolescents.
The objectives of this systematic review protocol are to (a) determine the frequency of use of each quantitative (objective) income inequality instrument within studies investigating child and adolescent outcomes, (b) to ascertain if the frequency of quantitative income inequality instruments varies depending on characteristics (e.g. country, health outcome etc), (c) to determine the difference wealth sources (e.g. survey data, taxation data) used to calculate each income inequality instrument, (d) to discuss possible advantages and disadvantages of each income inequality instrument.
- The types of quantitative instrument of income inequality used in studies among children and adolescents is not well quantified.
- There is no consensus as to the most appropriate quantitative instrument of income inequality that should be used in studies among children and adolescents.
- The advantages and disadvantages of each method of defining and assessing quantitative income inequality instruments in studies of children and adolescents are not well understood.
The systematic review protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42021259114 on 10/10/21). The following databases will be included in the search process: PubMed, Embase, and PsycINFO from 2010 up till January 2021. This is to capture countries of varying economic levels. The following key terms will be used in the search: “child/ren” or “adolescent/s” and “socioeconomic” or “poverty” or “social inequality” or “income”. (Figure 1) A sample search strategy is available (see extended data).
Inclusion criteria: quantitative study designs (cross-sectional, case-control, prospective, longitudinal, ecological) focusing on income inequality as the primary (main) study question in children and adolescents will be included (i.e. less than 18 years of age only). Articles published in the English language will be included and if published in any other language will be translated. Only published studies in peer-reviewed journals will be included.
Exclusion criteria: The following study designs (qualitative, case studies, randomised control trials, reviews, disucssions, ecological and commentaries) will be excluded as our interest is in the use of income measures in studies of child health outcomes. Grey literature and conference abstracts will be excluded. (Table 1)
Selected articles will be stored and managed using Mendeley 1.19 Reference Manager Library. This will be used to facilitate sharing and collaboration between reviewers during the screening of abstracts and titles, data extraction and quality appraisal stages.
The following procedure will be used for data selection and data extraction. The titles and abstracts will be screened by at least two reviewers independently. A third reviewer will arbitrate if there are disagreements between the two reviewers. Abstracts that do not fulfil the inclusion criteria will be excluded. The abstract must contain and demonstrate that income inequality is a primary exposure of the study within the article. If there is uncertainty in terms of inclusion criteria, the article will be retained for the next stage of screening. Subsequently, full-text articles will be screened to ensure adherence to inclusion and exclusion criteria. This will be conducted by two independent reviewers. A third reviewer will arbitrate if there are disagreements between the two reviewers. Data extraction of selected studies will be done independently by two reviewers. Data extraction will include first author name, final author name, year of publication, journal name, origin of study location, study location (i.e. low, medium, high income), study setting (including if the instrument reflects national, regional or local level data), income inequality instrument(s), aim of study, sample population, age of population, sample size, frequency of income inequality measurement (e.g. one recording of salary or multiple recordings of salary), data collection method, analytical approach, statistical test, study design, primary outcome measured, and type of association reported (Table 2). The primary outcome measured will be coded based on health outcome. A third reviewer will ensure accuracy of data extraction. References of included articles will be checked for any other potential eligible studies. The extracted data will be populated, categorised and stored in Microsoft Excel. A data abstraction form will be used (Table 2). To improve the reliability of data abstraction by the reviewers, a pilot test will be performed of the data abstraction form on a small, random sample and if needed the form will be adjusted.
This review will apply the following critical appraisal tools based on study design (e.g. longitudinal) to assess the methodological quality of selected studies using the Newcastle-Ottawa Scale (Stang, 2010). This systematic review will identify the frequency and type of income inequality instruments used. As such, the quality of the study is important, albeit the focus of quality assessment will be on whether a study explained the rationale for using a particular income inequality instrument. Moreover, three additional questions will be asked: (1) Does the study clearly state the income inequality instrument used? (2) Does the study explain the rationale for using that income inequality instrument? (3) Does the study explain how the income/consumption data was collected (e.g. survey, administrative data /taxation records) for that income inequality instrument? (Table 3) This may facilitate further ability to categorise a studies explanation for using a particular income inequality instrument. Two reviewers will independently appraise the quality of the selected studies. Any discrepancies between reviewers will be discussed and resolved. A third reviewer will arbitrate if no consensus achieved. The consistency of the appraisal tool (Newcastle-Ottawa Scale) will be determined by calculating Cohen’s Kappa inter-rater reliability statistic (Cohen, 1960). Studies will not be excluded based on quality of evidence, moreover it will be reflected in the narrative synthesis.
A qualitative meta-summary will be used to synthesise the descriptive findings from the quantitative studies. This is to apply a mixed research method synthesis that will aggregate and integrate the findings from the included studies.
To the best of the authors’ knowledge, this review will be the first to systematically determine the prevalence of quantitative income inequality instruments in studies of the child and adolescent population. The methodological approach (e.g. extraction of data with a narrative synthesis) may provide a broader understanding to provide a comprehensive exploration of the topic. This exploration may highlight if researchers are using a particular income inequality instrument with specific health outcomes and if there is a rationale for same. The use of the Newcastle-Ottawa Scale in appraising the quality of the overall body of evidence will assist future readers in determining which quantitative income inequality instrument to utilise in their research when investigating outcomes in a child and adolescent population.
The authors anticipate that our research will have limitations. It is possible that relevant studies may not be found. The search time-period is limited and as such, it will not reflect the use of income inequality metrics prior to the inclusion dates. This research focuses on objective income inequality instruments, it does not include other important aspects of poverty including socioeconomic position (SEP), other environmental factors (e.g. neighbourhood poverty) or other scales (e.g. Family Affluence Scale). Grey literature, government reports and/or conference abstracts are not included. This review will focus on studies that include income inequality as a primary exposure. Income inequality measures investigated as a secondary exposure will not be included. This is to ensure the review is practical, achievable, and relevant.
This protocol describes the methodological steps that will be taken in conducting a systematic review to identify and describe the quantitative instruments of income inequality. The thorough methodology to searching the literature, selecting studies, data extraction and appraisal, will better inform current and future research findings. The findings from this review will be valuable to stakeholders who are investigating or designing studies of income inequality or the effects of social inequality on child and adolescent outcomes. It may highlight additional varied instruments available to researchers and policy makers. Moreover, it may highlight the need for a cross-disciplinary discussion towards developing a standard conceptual framework for quantitative research on income inequality.
Open Science Framework: “A systematic review protocol of quantitative instruments of income inequality in studies of children and adolescents”, https://doi.org/10.17605/OSF.IO/ABQ39 (Driscoll (2021)).
This project contains the following extended data:
Open Science Framework: PRISMA-P checklist for “A systematic review protocol of quantitative instruments of income inequality in studies of children and adolescents”, https://doi.org/10.17605/OSF.IO/ABQ39 (Driscoll (2021)).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Inequalities in child and adolescent health, adverse childhood experiences, parental behaviours, child wellbeing.
Is the rationale for, and objectives of, the study clearly described?
Yes
Is the study design appropriate for the research question?
Yes
Are sufficient details of the methods provided to allow replication by others?
Yes
Are the datasets clearly presented in a useable and accessible format?
Not applicable
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Inequalities in child and adolescent health, adverse childhood experiences, parental behaviours, child wellbeing.
Is the rationale for, and objectives of, the study clearly described?
Yes
Is the study design appropriate for the research question?
Yes
Are sufficient details of the methods provided to allow replication by others?
Yes
Are the datasets clearly presented in a useable and accessible format?
Not applicable
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: The majority of my current work is on mental health law, but in the recent past I have researched subjective wellbeing variations during the recession, health outcomes in minority groups (asylum seekers and migrant populations).
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