Table 2.
Characteristics of included papers.
| Author, Year | Country of Authors | Population | Aim of Study | SE Factor Modelled | Health Outcome Modelled | Characteristics of the Model | Validation and Utilisation of Model | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ML | D | St | FL | Sp | HtI | AI | EI | V | F | I | ||||||
| Almagor et al., 2021 | UK | Glasgow | To explore the potential impact of interventions on physical activity of children in an urban environment. | SEP divided into 4 levels representing a gradient of household income: AB-high, C1, C2, DE-low. | Minutes of moderate-to-vigorous physical activity/day. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ||
| Auchincloss and Garcia, 2015 | USA, Brazil | Abstract Space | Introduce guide for agent-based modelling and explore impact of urban segregation on inequalities in diet. | Urban segregation by household income—location and income of households (divided into low or high-income). | Average proportion of times the household shopped at a healthy food store (depends on household income, proximity to stores, and food preferences). | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ||||
| Benny et al., 2022 | Canada | Calgary | To simulate the effects of government transfers and increases to minimum wage on depression in mothers. | Individual Income categorised into CAD 39,999 or less, CAD 40,000 to 79,999, and CAD 80,000 or more. Education categorised into high school or less, some or completed university/college, and some or completed graduate school. | Depression measured using the Edinburgh Postnatal Depression Scale (EPDS). | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |||||
| Blok et al., 2015 | The Netherlands | Eindhoven | To explore the impact of 3 interventions (eliminating residential income segregation, reducing prices of healthy food, health education) aimed at reducing food consumption inequalities between low and high-income households. | Household Income divided into high (>USD 31,777/year) and low (<USD 31,777/year). | Average proportion of times a household visited a healthy food outlet. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |||
| Blok et al., 2018 | The Netherlands | Eindhoven | Explore impact of 5 interventions (health education, lowering prices of sports facilities, increasing availability of sports facilities, improving neighbourhood safety, combining all these interventions) on reducing income inequalities in sports. | Individual Income divided into low, middle, and high. | % of individuals participating in sport annually. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ||
| Chao et al., 2015 | Japan | Japan | Explore how socioeconomic disparity between and within gender groups affects changes in smoking prevalence. | Socioeconomic Status divided into 1–9 according to distribution of income. | % of each gender group who were smoking. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ||||
| Combs et al., 2020 | USA | Tobacco Town, Minnesota | Project the impact of menthol cigarette sales restrictions and retailer density reduction policies on tobacco sales for low income, African American and LGBTQ+ populations | Individual Income, divided into two groups: low-income (<USD 42,500) and high-income (>USD 42,500). | Costs to consumers per pack of cigarettes as proxy for tobacco consumption. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ||||
| Gostoli and Silverman, 2019 | UK | UK | Provide theoretical framework to understand drivers of unmet social care need and test policies. | Approximated Social Grade, a socioeconomic classification produced by the Office for National Statistics (six categories A, B, C1, C2, D, and E). | Health status and care need (weekly hours of care required); death (affected by agents’ level of unmet care need). | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |
| Gouri Suresh and Schauder, 2020 | USA | USA | Explore how income segregation impacts food access for poor, when preferences and knowledge of healthy foods are equal among different income groups. | Household Income randomly generated on the basis of 2016 income distribution reported by US census bureau. | Distance to nearest grocery store and whether healthy food was reliably available at nearest grocery store. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |||||
| Keyes et al., 2019 | USA | New York City | Estimate the impact of alcohol taxation on drinking, violence and homicide. | Household Income—stratified into 5 quintiles. | Average number of alcoholic drinks per day. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ||
| Langellier et al., 2017 | USA | Philadelphia | Evaluate impact of beverage tax and pre-kindergarten programme on children’s SSB consumption. | Household Income—categorised as low-income (≤100% of Federal Poverty Level) and modest-income (≤300% of FPL) households. | Sugar Sweetened Beverage consumption in drinks/week. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |||
| Langellier et al., 2021 | USA, Australia, Brazil | Mexico | Develop a simulation framework to assess how tax, nutrition warning and advertising impact ultra-processed food purchasing. | Individual Income, divided into low-income (<1890 pesos/week) and high-income (>1890 pesos/week). EA, divided into low- (less than high school education) and high-education (at least high school). | Ultra-processed food purchased, measured in kcal (energy intake) purchased per week. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |||
| Li et al., 2016 | USA | New York City | Simulate how mass media and nutrition education change fruit and vegetable consumption in NYC. | Educational Attainment, categorised by less than high school, high school, some college and college and above. | Proportion of the population in a given neighbourhood who consume on average >2 servings of fruit and vegetables per day. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |
| Nandi et al., 2016 | USA, India, UK | India | Estimate reduction in disease burden by scaling up home-based newborn care in rural India. | Wealth quintile. | Incidence cases of severe neonatal morbidity averted and deaths per 1000 live births averted. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ||||
| Picascia and Mitchell, 2022 | UK | Edinburgh, Dundee, Glasgow, Aberdeen | Investigate intra- and inter-city inequalities in Urban Green Spaces visiting by SES. | SES divided into 4 categories based on occupational grade: AB-high, C1, C2, DE-low. | Median number of visits to urban green space/year. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ||
| Salvo et al., 2022 | USA | Austin, Texas | To simulate the food environment and test the impact of different food access policies on vegetable consumption. | Annual Household Income categorised into: Under USD 25,000, USD 25,001–USD 45,000, USD 45,001–USD 65,000, and >USD 65,000, and educational attainment in four categories: <High school, High school or GED, Some college, and Full college or more. | Fruit and vegetable intake. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |||
| Shin and Bithell, 2019 | UK | Gangnam and Gwanak districts, Seoul | Understand cumulative effects of PM10 exposure on population vulnerability by education level and age. | Educational Attainment in 8 categories: primary-school dropout, primary-school graduate, middle-school dropout, middle-school graduate, high-school dropout, high-school graduate, college or university student, over a bachelor’s degree. | Health status: starts with 300 and drops when exposed to pollution; categorised into <100, 100–150, or 150–200. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |||
| Yang et al., 2015 | USA | US city | Explore how travel costs and educational interventions can alter income differentials in walking. | Household Income segregation—income divided into quintiles (1 to 5). | Proportion of trips to destinations made by walking. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |
| Yang et al., 2019 | USA | Abstract space | Investigate how transport interventions may affect depression in older adults. | Individual Income—divided into quintiles (1 to 5). | Depression status yes/no, where having depression is a score of >/=4 on CESD Scale-8. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |
| Yang et al., 2020 | USA, UK, The Netherlands | English city | Examine the impact of a free bus policy on public transit use and depression among older adults. | Individual Income—divided into quintiles (1 to 5). | Prevalence/ percentage of agents with depression. | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | |
ML—multi-level. D—dynamic. St—stochastic. FL—feedback loop. Sp—spatial. HtI—heterogeneous individuals. AI—agent–agent interactions. EI—agent–environment interactions. V—validation. F—framework. I—test an intervention.