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Life-course socioeconomic status and obesity: a scoping review protocol
  1. Habila Adamou1,2,
  2. Dener François1,
  3. Alexandre Lebel1,2,
  4. Marie-Claude Paquette3,4
  1. 1Graduate School of Land Management and Urban Planning, Laval University, Quebec, Québec, Canada
  2. 2Institut universitaire de cardiologie et de pneumologie de Quebec, Evaluation Platform on Obesity Prevention, Laval University, Quebec, Québec, Canada
  3. 3Institut national de santé publique du Québec, Quebec, Québec, Canada
  4. 4Department of Nutrition, Université de Montréal, Montreal, Quebec, Canada
  1. Correspondence to Habila Adamou; habila.adamou-djibo.1{at}


Objective We aim to explore the literature that studies the links between life-course socioeconomic status and weight status and characterize the life-course approach used.

Introduction Obesogenic environments are increasing rapidly in deprived environments, and cross-sectional studies have shown limitations in explaining the links between these environments and obesity. The life-course approach has been proposed recently to better understand the links between socioeconomic status and weight status.

Inclusion criteria Studies that identify life-course socioeconomic status and longitudinal built environment indicators and associate them with body weight indicators between January 2000 and January 2023.

Methods Studies in French or English were searched in Medline (PubMed), Web of Science and GeoBase (Embase) according to the strategies formulated for each database. The selected studies were exported to Covidence for evaluation according to the inclusion/exclusion criteria.

Results The main results retained are the association between longitudinal socioeconomic indicators and weight measures; longitudinal built environment indicators and the measures of weight.

  • Obesity
  • Health Equity

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  • Our study aimed to explore the literature on the links between life-course socioeconomic indicators and weight status.

  • A scoping review will examine the state-of-the-art life-course approach studies to investigate the effects of socioeconomic and built environments on obesity.

  • The review includes a comprehensive research strategy to identify studies that analyse the complexity of longitudinal exposure across the three models of the life-course approach: the sensitive period, social mobility and accumulation risk.

  • Our scoping review will help to identify life-course approach indicators construction methods and the associations between these indicators and obesity.

  • Our scoping review does not use exposure to environments around schools for young people, which is one of the limitations.


The environment where people live is evaluated according to the neighbourhood’s socioeconomic characteristics and the characteristics of the built environment.1–3 Body mass index is the primary indicator for weight-related health problems and is used to investigate the neighbourhood socioeconomic and built environment impacts on health.1 2 4–6 However, we had some indicators to measure neighbourhood socioeconomic characteristics and the built environment. For example, neighbourhood design, neighbourhood walkability,7 or land use mix for the built environment1 6; social diversity, social inclusion, unemployment, poverty, material deprivation, social disadvantage, income for the neighbourhood socioeconomic characteristics1; food availability density, food accessibility or food quality by distance or proximity to the neighbourhood for the food environment.1

However, some studies on neighbourhood socioeconomics and built environments are cross-sectional.1 3 6 This limits the ability to establish causal relationships between neighbourhood characteristics and weight status. At times, findings between neighbourhood and weight status are mixed, and the mechanisms of action remain unclear.3 5 8 For example, the links between the food environment and weight status are mixed9 10 despite solid growth in obesogenic environments in disadvantaged settings.11 12 Studies that consider the duration of exposure to neighbourhood socioeconomic and built environment are more recent.13

Cross-sectional approaches that analyse the association between neighbourhood characteristics and weight status as a snapshot are limited. Longitudinal approaches considering the duration of exposure and the dynamics of exposure to the living environment could fill the current gaps. The life-course approach that analyses neighbourhood socioeconomic trajectories and built environment trajectories provides a novel perspective to studying these influences. To our knowledge, no scoping review has been conducted using this approach. To do this, we use the PCC (Population, Concept, and Context) method to define the key points (table 1).

Table 1

Definition of the objectives and the inclusion criteria using the PCC method to incorporate life-course and study designs

Definition of the key points (PCC)


This scoping review explores the literature that studies the links between life-course socioeconomic status and weight status and characterize the life-course approach used. It will look at the state of knowledge in the scientific literature of the relationships between life-course socioeconomic indicators or longitudinal built environment indicators and weight status. The underlying hypothesis is that longitudinal exposure to a deprived socioeconomic or built environment is associated with the odds of living with overweight or with obesity. To achieve these objectives, we define inclusion and exclusion criteria (table 2) and a non-exhaustive list of concepts and keywords (table 3). Table 4 gives the results of the search strategy used in Medline (PubMed) and GeoBase (Embase).

Table 2

Inclusion and exclusion criteria to achieve the study objectives

Table 3

Non-exhaustive list of concepts and keywords used in the study

Table 4

Search strategy

  1. Characterise the array of evidentiary sources within public health and social epidemiology domains.

  2. Scrutinize the methodologies used to characterize life-course socioeconomic status.

  3. Discern salient attributes or variables associated with a conceptual framework.

  4. Identify and scrutinize lacunae in existing knowledge in life-course socioeconomic status studies.

Inclusion/exclusion criteria.

Keywords to formulate database search strategy.

Selection of studies and PRISMA diagram

The inclusion and exclusion criteria guide the study selection process. Based on these criteria, the above descriptors with Boolean operators allow us to formulate search strategies in Medline (PubMed), Web of Science and GeoBase (Embase) databases. The choice of these databases is justified because our topic is population health, which involves social, economic, environmental, medical and demographic issues. Each of these databases has particularities requiring a specific and adapted search strategy. We will identify a few benchmark articles in our preliminary readings. These articles correspond perfectly to our inclusion and exclusion criteria; they are the standard articles we seek. These benchmark articles allow us to know if our different search strategies in the databases are well formulated to capture the essence of the scientific literature. Thus, each search strategy should contain all or part of the selected benchmark articles.


The results obtained in the databases are exported in the Covidence software. After merging the results from the different databases, duplicates will be identified and deleted. Two independent reviewers evaluated the database of studies and thus obtained an invitation from Covidence to join the working group.

A first evaluation is based on the title, constituting an intermediate step. After that, the reviewers will decide which studies are retained for abstract reading. Each reviewer selects a list of studies for reading the full text from this second group. The reviewers then agree on the list of studies to be read in full.


The list of studies retained for our scoping review is thus obtained by consensus among the reviewers. Each reviewer’s choice is objectively justified, considering the previously established objectives and inclusion criteria. The PRISMA diagram (figure 1) below illustrates this systematic approach to selecting and retaining studies. In a conflict, a third reviewer will act as a judge.

Figure 1

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart (2020).

Data extraction and presentation of results

Data extraction is performed using the Covidence software. This step is made possible following the reviewers' study selection process. Thus, the following information is collected in the data extraction tab in Covidence.

Identification and study population

The identification section allows us to situate the study in geographical and temporal contexts and contributes to a better understanding of the different studies.

We will extract information on the study’s authors, the year of publication, the origin of the data used and the study populations. Authors' choice is justified trivially for copyright, recognition and identifying the article’s authors. The date of publication is important because it allows the study to be situated in a time frame and contributes to a better interpretation of the results of the study; the origin of the data, the location of the participants or the country of the participants helps to place the study in a political, geographic, social, economic, environmental and historical context. All these contextual elements are essential in understanding and interpreting the results. Socioeconomic and built environments are often specific to political, socioeconomic and historical contexts. Differences in wealth, inequality or services between and within countries require some caution when reconciling or comparing studies from different countries or contexts.

Objectives and methodologies

Identifying the objectives of the included studies is of importance for our scoping review. Indeed, this will allow us to classify the studies according to their type of environmental trajectories.

In terms of methodological aspects, the first step is to identify the statistical approach used to create the trajectories; we have indicated in our inclusion criteria that the studies must identify longitudinal socioeconomic or built environment indicators, so we wish to establish an exhaustive list of approaches, in addition to latent class analysis and sequence analysis, which are the most used methods in the scientific literature. The second step of the methodological aspects consists of determining the statistical method(s) used to calculate the associations between the trajectories thus created and the weight indicators in the case.

Results and highlights

The main results sought are measures of association between life-course socioeconomic or built environment longitudinal indicators and weight indicators.

Ethics statements

Patient consent for publication



  • Twitter @Habilacogite

  • Contributors HA, DF, AL and MCP determined the idea and research topic for the scoping review. HA and DF designed the protocol. HA wrote the manuscript. MCP corrected the manuscript.

  • Funding This research received funding from the Fond de Recherche du Québec-Santé (31022,35784) and the Quebec Population Health Research Network (QPHRN).

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.