There has been an increasing interest in urban agriculture (UA) practice and research in recent years. Scholars have already reported numerous beneficial and potential adverse impacts of UA on health-related outcomes. This scoping review aims to explore these impacts and identify knowledge gaps for future UA studies. A systematic search was conducted in seven electronic bibliographic databases to identify relevant peer-reviewed studies. Articles were screened and assessed for eligibility. From eligible studies, data were extracted to summarize, collate, appraise the quality and make a narrative account of the findings. A total of 101 articles (51 quantitative, 29 qualitative, and 21 mixed methods studies) were included in our final analysis. Among these articles, 38 and 37% reported findings from North America and Sub-Saharan Africa respectively. Quantitative studies revealed evidence of positive impacts of UA on food security, nutrition outcomes, physical and mental health outcomes, and social capital. The qualitative studies reported a wide range of perceived benefits and motivations of UA. The most frequently reported benefits include contributions to social capital, food security, health and/or wellbeing. However, the evidence must be interpreted with caution since the quality of most of the studies was assessed as weak to moderate. While no definitive conclusions can be drawn about the adverse impacts of UA on health, paying particular attention to contamination of UA soil is recommended. More peer-reviewed studies are needed in areas where UA is practiced such as Latin America and Caribbean. The inconsistency and the lack of strong quality in the methodology of the included studies are proof that more rigorous studies are also needed in future research. Nevertheless, the substantial existing evidence from this review corroborate that UA can influence different determinants of health such as food security, social capital, health and well-being in a variety of contexts.

This paper reviews studies of the validity of commercially available business (CAB) data on food establishments (“the foodscape”), offering a meta-analysis of characteristics associated with CAB quality and a case study evaluating the performance of commonly-used validity indicators describing the foodscape. Existing validation studies report a broad range in CAB data quality, although most studies conclude that CAB quality is “moderate” to “substantial”. We conclude that current studies may underestimate the quality of CAB data. We recommend that future validation studies use density-adjusted and exposure measures to offer a more meaningful characterization of the relationship of data error with spatial exposure.

Context and objectives Neighbourhood effect research on obesity took off in the early 2000s and was composed of mostly cross-sectional observational studies interested in various characteristics of the built environment and the socioeconomic environment. To limit biases related to self-selection and life course exposures, many researchers apply longitudinal designs in their studies. Until now, no review has specifically and exclusively examined longitudinal studies and the specific designs of these studies. In this review, we intend to answer the following research question: how are the temporal measurements of contextual exposure and obesity outcomes integrated into longitudinal studies that explore how neighbourhood-level built and socioeconomic environments impact adult obesity? Design A systematic search strategy was designed to address the research question. The search was performed in Embase, Web of Science and PubMed, targeting scientific papers published before 1 January 2018. The eligible studies reported results on adults, included exposure that was limited to neighbourhood characteristics at the submunicipal level, included an outcome limited to obesity proxies, and reported a design with at least two exposure measurements or two outcome measurements. Results This scoping review identified 66 studies that fit the eligibility criteria. A wide variety of neighbourhood characteristics were also measured, making it difficult to draw general conclusions about associations between neighbourhood exposure and obesity. We applied a typology that classified studies by whether exposure and outcome were measured as varying or fixed. Using this typology, we found that 32 studies reported both neighbourhood exposure and obesity outcomes that were varying in time; 28 reported varying outcomes but fixed exposures; and 6 had fixed outcomes and varying exposures. Conclusion Our typology illustrates the variety of longitudinal designs that were used in the selected studies. In the light of our results, we make recommendations on how to better report longitudinal designs and facilitate comparisons between studies.

Summary: Background: Health care administrative databases are increasingly used for health studies and public health surveillance. Cases of individuals with obesity are selected using case‐identification methods. However, the validity of these methods is fragmentary and particularly challenging for obesity case identification.Objective: The objectives of this systematic review are to (1) determine the case‐identification methods used to identify individuals with obesity in health care administrative databases and (2) to summarize the validity of these case‐identification methods when compared with a reference standard.Methods: A systematic literature search was conducted in six bibliographic databases for the period January 1980 to June 2019 for all studies evaluating obesity case‐identification methods compared with a reference standard.Results: Seventeen articles met the inclusion criteria. International Classification of Diseases (ICD) codes were the only case‐identification method utilized in selected articles. The performance of obesity‐identification methods varied widely across studies, with positive predictive value ranging from 19% to 100% while sensitivity ranged from 3% to 92%. The sensitivity of these methods was usually low while the specificity was higher.Conclusion: When obesity is reported in health care administrative databases, it is usually correctly reported; however, obesity tends to be highly underreported in databases. Therefore, case‐identification methods to monitor the prevalence and incidence of obesity within health care administrative databases are not reliable. In contrast, the use of these methods remains relevant for the selection of individuals with obesity for cohort studies, particularly when identifying cohorts of individuals with severe obesity or cohorts where obesity is associated with comorbidities.