{"doi":"10.1111/ppe.70009","title":"Looking Beyond the Individual: The Impact of Neighbourhood on Gestational Diabetes","abstract":"Gestational diabetes mellitus (GDM), characterised by insulin resistance during pregnancy, is one of the most common pregnancy complications, with its incidence increasing [1]. GDM is a well-established risk factor for adverse pregnancy outcomes, including hypertensive disorders of pregnancy, macrosomia, operative delivery, neonatal metabolic disturbances, and long-term cardiometabolic complications in both birthing people and their offspring [1]. While studies have demonstrated racial and ethnic disparities in the prevalence of GDM, the specific factors contributing to these health disparities remain poorly understood. Social determinants of health are non-medical, broader societal factors that shape health outcomes, including the conditions in which individuals are born, grow, work, live, play, and age [2]. Traditionally, investigations and interventions to improve health and health equity have focused on individual risk factors. However, a growing body of evidence highlights the critical role of neighbourhood-level exposures—such as housing quality, violence, access to healthy food, and poverty—in contributing to adverse health outcomes [3]. In this issue of Paediatric and Perinatal Epidemiology, Parra and colleagues [4] explore the relationship between neighbourhood deprivation and the risk of developing GDM. Outside of pregnancy, neighbourhood deprivation has been associated with poorer control of diabetes, but there is limited data on the pregnant population [5]. They conducted a population-based retrospective cohort study using data from the Arizona Prenatal Environmental and Reproductive Outcomes Study (AzPEARS), which merges birth certificate data with area-level exposure data from the US Census. For this analysis, data from over 480,000 births were merged with the Neighbourhood Deprivation Index (NDI), a composite measure that quantifies overall neighbourhood socioeconomic status. The NDI is scored on a scale from 0 to 1, with higher scores indicating greater deprivation. A multivariable log-binomial regression model calculated the risk of GDM across NDI quartiles, adjusting for maternal age, education, race/ethnicity, parity, rurality, and birth year. Additionally, a sensitivity analysis was conducted to account for body mass index, a known covariate, but one that may be on the causal pathway. The authors found that the overall incidence of GDM was consistent with existing literature, at 7.8%. However, there was considerable geographical variation, with incidence as high as 12% in communities with a high proportion of patients identifying as Native American/American Indian. Residents in the most deprived quartile were younger, less educated, had a higher prevalence of obesity, had smaller infants, and were more likely to have public insurance. The authors found a dose-dependent increase in GDM incidence with greater exposure to neighbourhood deprivation, which persisted in adjusted analyses. This finding is consistent with other literature demonstrating a dose-dependent relationship between developing GDM and increasing exposure to neighbourhood deprivation [6]. The authors utilised a large dataset that included almost half a million births, of which 37,636 were affected by GDM. The investigators used robust statistical methods to explore the association between neighbourhood deprivation and GDM. However, the study is limited by the absence of data on pregnancy outcomes beyond the incidence of GDM. Additional information regarding the impact of neighbourhood deprivation on glycaemic control, the need for pharmacologic treatment, hypertensive disorders, mode of delivery, and other neonatal complications would have provided a more comprehensive understanding of the broader impact of neighbourhood deprivation on maternal and neonatal health outcomes. This study found a high incidence of GDM in patients identifying as Native American/American Indian (almost 18%), which is more than double the overall incidence. 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Dolin","orcid":"0000-0002-6809-4141","position":0,"is_corresponding":true}],"reference_count":8,"raw_metadata":null,"created_at":"2026-07-19T02:52:05.227140Z","pmid":"40439271","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}