Geospatial hotspots and neighbourhood deprivation associated with unintentional home injuries in British Columbia, Canada

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Giles, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6325654/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background Despite existing prevention initiatives, preventable unintentional home injuries remain a significant public health concern in Canada, and are often influenced by the social determinants of health. This study identified dissemination-area-level hotspots of unintentional home injuries resulting in hospitalizations across British Columbia (B.C.), Canada, from 2015 to 2019, and it’s examined their relationship with multiple deprivation indexes. Methods Unintentional home injury hospitalization data from B.C., Canada, (2015–2019) were obtained from the Discharged Abstract Database. These data were then linked with dissemination area (DA) level data and the Canadian Index of Multiple Deprivation (CIMD) for B.C. Spatial autocorrelation was assessed using Moran's I, and hotspot analysis was performed using the Getis-Ord Gi* statistic. Crude injury rates for each DA were calculated. Geospatial and bivariate analysis were examined using ArcGIS Pro. Results Between 2015 and 2019, the annual rate of unintentional home injuries leading to hospitalization in B.C. was 256.5 per 100,000 population. Unintentional home injuries leading to hospitalizations in B.C. were significantly clustered (Moran’s I = 0.05, z-score = 38.53, and p-value = 0.000). A total of 1,183 hotspots and 3,130 cold spots across DAs in B.C. were identified. Significant hotspots (99% CI, z-score > 2.58) were found in the southern B.C. region, especially across Thompson-Okanagan region and Vancouver Island, indicating that higher unintentional home injury rates were clustered in urban areas and larger population centres. In urban hotspots, bivariate analysis showed a positive relationship between unintentional home injury rates and economic dependency, residential instability, and situational vulnerability, and an inverse relationship with ethnocultural composition. Conclusion This geospatial analysis identified urban clusters in B.C. with higher unintentional home injury rates, particularly in areas of socioeconomic deprivation. These findings provide valuable insights into high-risk areas for implementing tailored injury prevention programs and policies. Health sciences/Risk factors Earth and environmental sciences/Environmental social sciences/Socioeconomic scenarios Health sciences/Medical research/Epidemiology Geospatial analysis home household unintentional injuries neighbourhood deprivation socio-economic disparities Figures Figure 1 Figure 2 Background Unintentional injuries, those for which the intent cannot be predetermined, represent a significant burden on Canada’s healthcare system and society. Unintentional injuries constitute the majority of injury cases associated with deaths (75%), hospitalisations (89%), emergency department visits (95%), and disabilities (90%) ( 1 ). In 2018, unintentional injuries accounted for 86% of total injury costs, amounting to $ 25.3 billion ( 2 ). A significant proportion of these unintentional injuries occur in or around home that include but are not limited to falls, burns, poisonings, ingestion of foreign objects, smoke inhalation, drowning, cuts, collisions with objects, as well as injuries resulting from structural collapse ( 3 ). Given these statistics, unintentional injuries represent a significant public health issue that demands critical investigation. The occurrence of these injuries varies significantly across age groups and between men and women, with unintentional home injuries disproportionately affecting children and older adults due to their unique vulnerabilities and increased time spent at home, which amplifies their exposure to potential hazards in the home environment ( 3 , 4 ). Every year, over 20,000 children visit emergency departments across Canada due to unintentional injuries sustained at home ( 5 ). In particular, children under five years of age are rapidly developing physically and cognitively, but lack mature motor skills and risk awareness, which increase their risk of sustaining unintentional injuries within home environment ( 6 ). Based on the hospitalization records from 2017–2018, approximately 52% of falls leading to hospital admissions among adults aged 65 and older occurred their household residence ( 7 ). Older adults face an increased risk of falls due to a combination of age-related physical limitations, chronic health conditions, medication side effects, and environmental hazards in homes not adapted for aging populations ( 8 ). Specifically, in British Columbia (B.C.), 84% of hospital admissions for injuries that occurred at home between 2015 and 2019 were unintentional ( 9 ). The same study found that neighbourhood socioeconomic deprivations influence the rates of unintentional home injury hospitalizations in B.C. Socioeconomic factors can affect unintentional home injury risks through multiple pathways, including the household income, housing conditions, access to resources for injury prevention, and overall community well-being ( 10 ). In addition, people's exposure to home hazards and safety risks in B.C. is influenced by structural determinants of health, which shape the distribution of resources across the province ( 11 ). To address this increasing trend of unintentional home injuries, numerous multi-level initiatives, including social marketing campaigns ( 12 ), injury surveillance systems ( 13 ), and development of policy relevant indicators for evaluating interventions ( 14 ), have been implemented in B.C. over the last two decades to reduce unintentional home injuries, yet preventable injuries continue to occur, highlighting the need for sophisticated methodologies to better understand unintentional home injury risks. As such, spatial epidemiological studies have been proposed to facilitate injury prevention strategies ( 15 ). Schuurman et al. emphasized the importance of considering spatial scale in injury analysis and illustrated how injury patterns can vary significantly when examined at different geographic levels ( 16 ). Public health researchers use geospatial methods to link individual health data with the physical space and social community in which an individual resides in order to map injury locations in relation to other geographical and social attributes ( 17 ). For example, Kureshi and colleagues mapped the spatial distribution of traumatic brain injuries by major injury causes at the smallest geographical unit across Nova Scotia, Canada, and identified the various pathways through which deprivation influences the risk of differing traumatic brain injury mechanisms ( 18 ). Despite a growing body of research on the geographic distribution of injuries, the literature on spatial epidemiology of unintentional home injuries in B.C. is limited. Geospatial methods have been used to investigate regional differences in injury rates and identify hotspots for various types of injuries ( 19 ). However there has been no injury type assessment and no measurement of injury rates that integrate measures of multiple deprivations. The use of large geographic units to represent injury hotspots and their association with deprivation may be useful for identifying broader patterns but does not provide the level of spatial resolution necessary to identify small-scale variations within neighborhoods ( 16 ). However, the broad patterns identified through this approach can serve as a starting point for more targeted analyses using smaller geographic units. Understanding the spatial patterns of unintentional home injury hospitalizations in relation to neighborhood deprivation has important implications for public health practice and policy ( 15 ). By identifying vulnerable populations and high-risk areas, this research can inform the development and implementation of targeted, evidence-based injury prevention interventions. Moreover, this research can contribute to broader discussions on health equity and the social determinants of health, highlighting the need for comprehensive approaches that address both individual and community-level factors in injury prevention. As such, this study aimed to identify the geographic hotspots of unintentional home injuries that resulted in hospitalization by dissemination area across B.C., Canada, between 2015 and 2019, and explore the relationship of hotspots with deprivation. Notably, while this study focused on unintentional home injury hospitalizations, the analysis is based on the dissemination area where the injury occurred, as the data are geocoded by the place of occurrence, specifically the home. Although this study used the patient's residential address for DA information, the injury location may be the same as the residential address but not necessarily the patient's home. Methods Study area B.C. is the westernmost province in Canada, located on the Pacific Coast. It covers a total area of 944,735 km² and had a population of 5,722,318 as of January 1, 2025 ( 20 ). The province's largest city, Vancouver, has a population of 678,984 in 2024, while the Metro Vancouver area is home to approximately 2.48 million people, representing about 44% of the province's total population. In our study, we adopted the classification approach developed by Statistics Canada to categorize communities in B.C. based on their remoteness ( 21 ). Using the remoteness index dataset, we classified census subdivisions into five distinct categories: easily accessible, accessible, less accessible, remote, and very remote areas (Supplementary 1). This nuanced classification allowed us to capture the varying degrees of remoteness across B.C., providing a more comprehensive understanding of the geographic distribution of population and resources than traditional urban-rural dichotomies. Data sources and characteristics The data for this study were obtained by the B.C. Injury Research and Prevention Unit based on reported incidents through the Discharge Abstract Database (DAD), maintained by the B.C. Ministry of Health ( 22 ). The dataset comprised de-identified hospitalization records for all separations from a hospital following a home injury for the years 2015 through 2019, inclusive. This study included only the primary cause of admission (i.e., the most responsible diagnosis coded in the DAD) to identify unintentional home injury hospitalizations. The dataset included administrative, sociodemographic, and diagnostic variables. The sociodemographic variables were sex, age, and residential dissemination area (DA). The diagnostic variables included external causes of injury (e.g., falls, drowning, or burns) classified according to the International Statistical Classification of Diseases and Related Health Problems Canadian version 10 (ICD-10-CA) ( 23 ). ICD-10 CA codes associated with unintentional home injury and DAs associated with B.C. were retained for the purpose of this analysis (Supplementary 2). In summary, there was a total of 74,985 home injury hospitalizations, with 84.2% of them classified as unintentional in B.C. between 2015 and 2019. Females constituted 60.1% (n = 37,988) of these unintentional home injury hospitalizations. Those aged 65 years and over accounted for 72.9% (n = 46,060) of unintentional home injury hospitalizations, while the 45–64 age group represented 15.9% (n = 10,018). Falls were the primary cause of unintentional home injury hospitalizations accounting for 82.4% (n = 52,064) followed by poisoning at 7.2% (n = 4,559). Moreover, 4.7% (n = 3,000) of these unintentional home injuries resulted in deaths. In Canada, a Dissemination Area (DA) is the smallest standard geographic area for which all census data are disseminated by Statistics Canada. DAs generally have a population of between 400 and 700 persons, with an average of about 500 people. DAs are designed to remain consistent over time, allowing for comparisons between censuses and for analyses of demographic changes. Scholars have used DAs widely in spatial and socioeconomic studies in Canada ( 17 ) as DAs provide a fine geographic scale for analysing local patterns and variations that may be obscured at larger geographic levels. The population data for each DA were obtained from the Statistics Canada’s 2016 Census Profiles ( 24 ). The cartographic boundary data were obtained from Statistics Canada (2021), which depicts the extent of the geographic areas across Canada ( 25 ). Notably, some spatial areas within our study region may not contain any resident population due to uninhabited status or other factors. Consequently, these areas will lack analytical units for analysis, resulting in their exclusion from calculations involving population-based rates and spatial statistics. Socioeconomic factors and unintentional home injury rates The Canadian Index of Multiple Deprivation (CIMD) for B.C. was used to map the spatial relationship between neighbourhood deprivation and unintentional home injuries in B.C., Canada ( 26 ). The CIMD for B.C. is a provincial-level index that measures four distinct dimensions of deprivation and marginalization at the DA level using population census data from 2016. The dimensions are ethnocultural composition, situational vulnerability, economic dependency, and residential instability, each consisting of various indicators that are aggregated and combined through factor analysis ( 26 ). A detailed description of various indicators that comprise the CIMD for B.C. is provided in Supplementary 3. Identified cases of unintentional home injury, DA descriptives, and CIMD categorization quintiles were merged using DA unique identifiers. Bivariate maps were created to visualize the relationships between neighbourhood socioeconomic factors and unintentional home injury rates. This method allows for the visualization of two variables, such as unintentional home injury hospitalization rates and multiple deprivation, on a single map based on the intersection of data values for both variables. Statistical analysis Unintentional home injuries were aggregated into DAs across B.C. Crude injury rates for each DA were calculated using the count of unintentional home injuries within each DA as the numerator and the 2016 DA population from the Canadian census profile as the denominator. Data processing and analysis was conducted in the ArcGIS Pro version 3.3 ( 27 ). Spatial autocorrelation Spatial autocorrelation is a statistical concept that measures the degree to which a variable is correlated to itself across geographic space. It is based on the principle that observations geographically closer to each other tend to be more similar than those farther apart, which is often summarized by Tobler's first law of geography: "everything is related to everything else, but near things are more related than distant things." ( 16 , p. 236) To determine the overall spatial autocorrelation of unintentional home injury hospitalization by neighbourhood across B.C., the Global Moran's I statistic was employed. The mathematical and statistical calculations of Global Moran’s I are explained elsewhere ( 29 ). The null hypothesis assumes that there is no spatial pattern among unintentional home injuries across British Columbia, indicating complete spatial randomness. A positive Moran’s I indicates clustering of high and low unintentional home injury rates, while a negative Moran’s I reflects a dispersed pattern. Hotspot analysis The Getis-Ord-Gi* statistic was used to identify area-based hotspots of unintentional home injury rates ( 30 ). In this study, a hotspot was defined as an area with a higher concentration of unintentional home injury events compared with the expected number assuming a random distribution of injuries. The use of Getis-Ord-Gi* statistic helps to identify areas where unintentional home injuries with either high or low values cluster spatially, by examining each DA within the context of its neighbouring DAs. The Getis-Ord Gi* statistic is calculated using the following formula: $$\:\frac{G\begin{array}{c}*\\\:i\end{array}={\sum\:}_{j=1}^{n}wi,\:jxj-\:\stackrel{-}{X\:}{\sum\:}_{j=1}^{n}wi,j}{\begin{array}{c}S\sqrt{{\sum\:}_{j=1}^{n}{w}_{i,j}^{2}}-\left({\sum\:}_{j=1}^{n}{w}_{i,j}\right)2\\\:n=1\end{array}}$$ In this formula, \(\:xj\:\) represents the attribute value for feature \(\:j\) , which in our case is the unintentional home injury rate for each dissemination area. The term \(\:{w}_{i,j\:}\) denotes the spatial weight between features i and j , reflecting the spatial relationship between different areas. The variable \(\:n\) represents the total number of features, which is DAs in our study. X̄ is the mean of the attribute values across all areas, while \(\:S\) represents the standard deviation of these values. This formula yields a z-score, where positive values indicate clustering of high unintentional home injury rates (hot spots), and negative values suggest clustering of low rates (cold spots), allowing us to identify areas of significant spatial concentration of injuries. Results Global spatial autocorrelation Global spatial autocorrelation analysis showed a statistically significant clustering pattern of unintentional home injuries in B.C. (Moran’s I = 0.05). In other words, areas with high unintentional home injury rates tended to be located near other areas with high rates of such injuries, and vice versa. The statistical significance of this clustering was confirmed by a z-score of 38.53 and a p-value of 0.000. This demonstrated that the observed spatial patterns of unintentional home injuries in B.C. are highly unlikely to have occurred randomly and justified further local exploration. Hotspot analysis Between 2015 and 2019, the annual rate of unintentional home injuries resulting in hospitalization in B.C. was 256.5 per 100,000 population. The Getis-Ord Gi* analysis identified 1,183 hotspots and 3,130 cold spots across DAs in B.C. A cluster of statistically significant hotspots (99% CI, z-score > 2.58) was identified in the southernmost region of B.C., particularly across Thompson-Okanagan region (Fig. 1 ). There were also significant hotspots on Vancouver Island. In other words, unintentional home injury rates leading to hospitalization in B.C. were predominantly clustered in accessible areas, with higher concentration observed in large and medium-sized population centres. Sociodemographic profiles of hotspots The bivariate maps revealed distinct spatial patterns in the relationships between unintentional home injury rates and various socioeconomic factors across B.C. (Figs. 2 ). Economic dependency showed notable spatial heterogeneity across B.C. and southern regions presented more complex interaction patterns. The Thompson-Okanagan region demonstrated a direct relationship, with higher economic dependency corresponding to increased unintentional home injury rates. Ethnocultural composition exhibited an inverse relationship with unintentional home injury rates, as regions with high injury rates and low ethnocultural diversity were dispersed throughout the province, particularly along the south coast. Situational vulnerability showed a consistent positive relationship with unintentional home injury rates with areas of high situational vulnerability and high injury rates concentrated primarily in the Thompson-Okanagan region and southern areas of Vancouver Island. Similarly, residential instability demonstrated a clustered pattern, wherein areas with high unintentional home injury rates corresponded with higher residential instability both provincially and across the Thompson-Okanagan region and southern Vancouver Island areas . Discussion This study identified significant spatial clustering of unintentional home injuries leading to hospitalizations in B.C., indicating that areas with high injury rates tend to be geographically proximal to other high-rate areas. The unintentional home injuries hotspots were largely concentrated in urban areas of B.C. The bivariate analysis of unintentional home injuries resulting in hospitalizations within hotspots revealed a direct relationship with economic dependency, situational vulnerability, and residential instability, while an inverse relationship was observed with ethnocultural diversity. The concentration of unintentional home injury hotspots in B.C.’s accessible areas, particularly across Thompson-Okanagan region and on the southern and central Vancouver Island, reflect the complex interplay between population density and injury risk factors. Although most unintentional home injuries in our study occurred in urban areas, previous research has shown that injury rates increase along the rural-urban continuum, with higher rates in more rural areas ( 31 ). However, rural areas in Canada are often more hazardous than urban settings due to residents’ frequent exposure to farming equipment and heavy machinery, increasing the risk of severe crush injuries, which are primarily classified as occupational rather than home injuries ( 31 ). Notably, urban environments in Canada may present unique hazards, such as more complex home structures and a higher prevalence of older housing stock with associated structural risks, which may increase the risk of unintentional home injuries ( 32 ). In addition, increased stress in urban areas, particularly from working multiple jobs often driven by financial circumstances, can lead to unhealthy coping mechanisms and risk-taking behaviours, increasing the likelihood of unintentioal injuries, including those that occur at home ( 33 , 34 ). The geographic hotspot identified in our study should be prioritized for in-depth investigations to understand local risk factors and for the implementation of tailored injury prevention programs. Between 2015 to 2019, falls among older adults were the leading cause of unintentional home injuries in B.C. ( 9 ). The highest proportion of older adults aged 65 and over live in urban areas, such as parts of Vancouver Island ( 35 ). This concentration of seniors in urban areas, especially on Vancouver Island, may explain the increased rates of fall-related home injuries in these regions. This is consistent with a study in Nova Scotia that identified high concentration of fall hotspots in urban areas ( 18 ). In addition, previous studies have found that older adults living alone, a situation more prevalent in urban areas, have two times the likelihood of falling compared to those living with others ( 36 – 38 ). However, older adults living in urban areas may have better access to healthcare services, fall prevention programs, and community support compared to other regions. Home-based targeted interventions, such as zero-step entries, single-floor living, and wide doorways are crucial for enabling older adults to navigate their homes safely and independently ( 39 ). Furthermore, the risk of falls increases with age, and B.C. has seen a significant growth in the population aged 85 and older, which has increased by 24% over the past decade ( 35 ). This demographic shift emphasizes the need for implementing effective fall prevention strategies and consulting with older adults in designing the built environment. The intersection of economic dependency, residential instability, and situational vulnerability creates a multifaceted cycle of disadvantage that increases the risk of injuries in and around the home environment ( 40 – 42 ). Our findings underscore the importance of addressing socioeconomic determinants in injury prevention strategies. For example, policies aimed at improving economic conditions, housing stability, and community support systems in vulnerable neighbourhoods may have significant potential to reduce unintentional home injuries ( 43 ), and subsequent hospitalizations. Public health initiatives could focus on communities with high levels of economic dependency and residential instability, implementing injury prevention programs that address both immediate injury risks and underlying socioeconomic factors ( 44 ). Future injury prevention programs should adopt an intersectional approach, considering how various socioeconomic factors interact to create unique risk profiles for different population subgroups. Furthermore, our findings emphasize the importance of longitudinal studies to better understand how changes in economic stability and housing security affect unintentional home injury rates over extended periods. The inverse relationship between the ethnocultural dimension and unintentional home injury hospitalization rates provides a unique understanding of unintentional home injury risk factors. This protective effect of ethnic diversity aligns with some previous research suggesting that culturally diverse neighbourhoods may benefit from stronger social networks and community support systems, which could contribute to home safety practices and injury prevention ( 45 ). The observed low unintentional home injury hospitalization rates among recent immigrants could be attributed to underutilization of emergency services due to barriers in navigating healthcare system and cultural factors, such as language difficulties, lack of familiarity with the healthcare system, concerns about costs, and cultural perceptions of emergencies ( 46 ). However, a study examining unintentional injury hospitalization rates among immigrant children and youth in Ontario, Canada, found that refugee children experienced 20% more injuries compared to their non-refugee immigrant counterparts ( 47 ). Given differential injury risk exposure among immigrant population, it is critical to collect and analyse disaggregated data by ethnocultural groups to identify specific risk and protective factors within each community ( 48 ). Furthermore, our findings emphasize the need for culturally sensitive injury prevention programs that account for the diverse practices, beliefs, and living conditions of various ethnocultural groups ( 49 ). The protective effect associated with ethnocultural diversity suggests that fostering inclusive, culturally diverse communities and leveraging existing community strengths could be beneficial in injury prevention efforts. Limitations While this study provides valuable spatial insights related to unintentional home injury hospitalizations in B.C., several limitations should be acknowledged. The authors inability to collect non-hospitalization data for unintentional home injuries presents a limitation in our study, and the findings of this study should be interpreted considering this limitation. Injury surveillance often depends on administrative claims data created for billing purposes in hospital emergency departments and inpatient care ( 13 ). Incorporating data from multiple sources, including outpatient settings and community surveys, could provide a more complete picture of unintentional home injuries across the severity spectrum. Moreover, we used data aggregated at the DA level, which may not accurately represent individual-level relationships between deprivation and unintentional home injuries. Multi-level modelling techniques could be used for future analysis to examine individual and area-level factors simultaneously ( 50 ). However, the use of geographic information system and spatial analysis techniques allowed the identification of geographic hotspots across B.C. In addition, highlighting the potential relationship between area-level multiple deprivation and unintentional home injuries can inform future research on social determinants of injury. Conclusion This geospatial analysis identified geographic clusters with significantly higher rates of unintentional home injuries in urban regions of B.C., particularly in areas characterized by higher levels of multiple deprivation. By focusing on these identified high-risk urban areas and leveraging community strengths, public health initiatives can be more effectively designed and implemented to address the specific factors contributing to higher rates of unintentional home injury. Developing partnerships between public health, housing, social services, and community organizations is crucial for addressing the complex factors contributing to unintentional home injuries, as they may enable more integrated and effective approaches to preventing these injuries. Abbreviations BC British Columbia DA Dissemination Area CIMD Canadian Index of Multiple Deprivation DAD Discharge Abstract Database RI Remoteness Index Declarations Ethics approval: This study was approved by University of British Columbia UB.C. Children’s and Women’s C&W Research Ethics Board (certificate # H22-01297). Consent for publication. Not applicable. Availability of data and material: The data that supported the findings of this study are openly available through B.C. Injury Research and Prevention Unit at https://data.injuryresearch.B.C..ca/DataTools/hospitalization.aspx and Statistics Canada at https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/prof/details/download-telecharger/comp/page_dl-tc.cfm?Lang=E Competing interest: The authors declare that they have no competing interests. Funding: This work was supported by the B.C. Injury Research and Prevention Unit, B.C. Children’s Hospital Research Institute and the Mitacs Accelerate Program in partnership with Pacific Public Health Foundation. Author contributions: Umerdad Khudadad, Ian Pike, and Audrey R. Giles contributed to the study conception and design. Data curation and analysis were performed by Umerdad Khudadad, Tomoko McGaughey, and Alex Zheng. The first draft of the manuscript was written by Umerdad Khudadad and all authors commented on the manuscript. All authors read and approved the final manuscript. Acknowledgements: Not applicable. References Parachute. 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Available from: https://open.canada.ca/data/en/dataset/5c670585-97ed-4e6a-a607-30fab940ff88 ESRI. ArcGIS Pro Version 3.3. [Internet]. 2024. Available from: https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview Tobler WR. A Computer Movie Simulating Urban Growth in the Detroit Region. Econ Geogr [Internet]. 1970 Jun [cited 2025 Feb 26];46:234. Available from: https://www.jstor.org/stable/143141?origin=crossref Li H, Calder CA, Cressie N. Beyond Moran’s I : Testing for Spatial Dependence Based on the Spatial Autoregressive Model. Geogr Anal [Internet]. 2007 Oct [cited 2025 Feb 25];39(4):357–75. Available from: https://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.2007.00708.x Fischer MM, Getis A,. Handbook of applied spatial analysis: software tools, methods and applications. [Internet]. Springer Berlin Heidelberg; 2010. Available from: https://link.springer.com/book/10.1007/978-3-642-03647-7 Bang F, McFaull S, Cheesman J, Do MT. The rural–urban gap: differences in injury characteristics. Health Promot Chronic Dis Prev Can [Internet]. 2019 Dec [cited 2025 Feb 26];39(12):317–22. Available from: https://www.canada.ca/en/public-health/services/reports-publications/health-promotion-chronic-disease-prevention-canada-research-policy-practice/vol-39-no-12-2019/original-quantitative-research-rural-urban-gap-injury-characteristics.html McCormack GR, Cabaj J, Orpana H, Lukic R, Blackstaffe A, Goopy S, et al. A scoping review on the relations between urban form and health: a focus on Canadian quantitative evidence. Health Promot Chronic Dis Prev Can [Internet]. 2019 May [cited 2025 Mar 2];39(5):187–200. Available from: https://www.canada.ca/en/public-health/services/reports-publications/health-promotion-chronic-disease-prevention-canada-research-policy-practice/vol-39-no-5-2019/relations-between-urban-form-and-health-focus-on-canadian-quantitative-evidence.html Heikkilä K, Fransson EI, Nyberg ST, Zins M, Westerlund H, Westerholm P, et al. Job Strain and Health-Related Lifestyle: Findings From an Individual-Participant Meta-Analysis of 118 000 Working Adults. Am J Public Health [Internet]. 2013 Nov [cited 2025 Feb 26];103(11):2090–7. Available from: https://ajph.aphapublications.org/doi/full/10.2105/AJPH.2012.301090 Peng S, Yang T, Rockett IRH. Life stress and uncertainty stress: which is more associated with unintentional injury? Psychol Health Med [Internet]. 2020 Jul 2 [cited 2025 Feb 26];25(6):774–80. Available from: https://www.tandfonline.com/doi/full/10.1080/13548506.2019.1687913 Wister A, Kim B, Qui S. Fact Book on Aging in British Columbia and Canada [Internet]. Simon Fraser University, Gerontology Research Centre; 2024. Available from: https://www.sfu.ca/content/dam/sfu/grc/research/projects/fact-book-on-aging-archive/Fact%20Book%20on%20Aging,%208th%20edition.pdf Dal Bello-Haas VPM, O’Connell ME, Ursenbach J. Comparison across age groups of causes, circumstances, and consequences of falls among individuals living in Canada: A cross-sectional analysis of participants aged 45 to 85 years from the Canadian Longitudinal Study on Aging. Sakurai R, editor. PLOS ONE [Internet]. 2024 Mar 14 [cited 2025 Mar 2];19(3):e0300026. Available from: https://dx.plos.org/10.1371/journal.pone.0300026 Fallon LF, Awosika-Olumo A, Fulks JS. Factors related to accidents and falls among older individuals. Traumatology [Internet]. 2002 Dec [cited 2025 Mar 2];8(4):205–10. Available from: https://doi.apa.org/doi/10.1177/153476560200800403 Pirrie M, Saini G, Angeles R, Marzanek F, Parascandalo J, Agarwal G. Risk of falls and fear of falling in older adults residing in public housing in Ontario, Canada: findings from a multisite observational study. BMC Geriatr [Internet]. 2020 Dec [cited 2025 Mar 2];20(1):11. Available from: https://bmcgeriatr.biomedcentral.com/articles/10.1186/s12877-019-1399-1 Katyarmal S, Wyndham-West M, Khan S. Architectural, interior design, and technological interventions to facilitate rehabilitation and aging in place. Innov Aging [Internet]. 2023 Dec 21 [cited 2025 Mar 24];7(Supplement_1):989–989. Available from: https://academic.oup.com/innovateage/article/7/Supplement_1/989/7490589 Zandy M, Zhang LR, Kao D, Rajabali F, Turcotte K, Zheng A, et al. Area-based socioeconomic disparities in mortality due to unintentional injury and youth suicide in British Columbia, 2009–2013. Health Promot Chronic Dis Prev Can [Internet]. 2019 Feb [cited 2025 Feb 27];39(2):35–44. Available from: https://www.canada.ca/en/public-health/services/reports-publications/health-promotion-chronic-disease-prevention-canada-research-policy-practice/vol-39-no-2-2019/disparities-mortality-injury-youth-suicide-british-columbia-2009-2013.html Gadermann AM, Karim ME, Norena M, Emerson SD, Hubley AM, Russell LB, et al. The Association of Residential Instability and Hospitalizations among Homeless and Vulnerably Housed Individuals: Results from a Prospective Cohort Study. J Urban Health [Internet]. 2020 Apr [cited 2025 Feb 26];97(2):239–49. Available from: http://link.springer.com/10.1007/s11524-019-00406-9 Liao C, Varcoe C, Brown H, Pike I. Beyond individual factors: a critical ethnographic account of urban residential fire risks, experiences, and responses in single-room occupancy (SRO) housing. BMC Public Health [Internet]. 2024 Aug 28 [cited 2025 Feb 26];24(1):2343. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-024-19866-z Moore M, Conrick KM, Fuentes M, Rowhani-Rahbar A, Graves JM, Patil D, et al. Research on Injury Disparities: A Scoping Review. Health Equity [Internet]. 2019 Oct 1 [cited 2025 Feb 26];3(1):504–11. Available from: https://www.liebertpub.com/doi/10.1089/heq.2019.0044 O’Mara-Eves A, Brunton G, Oliver S, Kavanagh J, Jamal F, Thomas J. The effectiveness of community engagement in public health interventions for disadvantaged groups: a meta-analysis. BMC Public Health [Internet]. 2015 Dec [cited 2025 Mar 24];15(1):129. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-015-1352-y Saunders NR, Macpherson A, Guan J, Sheng L, Guttmann A. The shrinking health advantage: unintentional injuries among children and youth from immigrant families. BMC Public Health [Internet]. 2018 Dec [cited 2025 Feb 27];18(1):73. Available from: http://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-017-4612-1 Kamran H, Hassan H, Ali MUN, Ali D, Taj M, Mir Z, et al. Scoping review: barriers to primary care access experienced by immigrants and refugees in English-speaking countries. Qual Res J [Internet]. 2022 Jul 13 [cited 2025 Feb 26];22(3):401–14. Available from: https://www.emerald.com/insight/content/doi/10.1108/QRJ-02-2022-0028/full/html Saunders NR, Macpherson A, Guan J, Guttmann A. Unintentional injuries among refugee and immigrant children and youth in Ontario, Canada: a population-based cross-sectional study. Inj Prev [Internet]. 2018 Oct [cited 2025 Mar 2];24(5):337–43. Available from: https://injuryprevention.bmj.com/lookup/doi/10.1136/injuryprev-2016-042276 Kauh TJ, Read JG, Scheitler AJ. The Critical Role of Racial/Ethnic Data Disaggregation for Health Equity. Popul Res Policy Rev [Internet]. 2021 Feb [cited 2025 Mar 24];40(1):1–7. Available from: http://link.springer.com/10.1007/s11113-020-09631-6 Giles AR, Hognestad S, Brooks LA. The Need for Cultural Safety in Injury Prevention. Public Health Nurs [Internet]. 2015 Sep [cited 2025 Mar 24];32(5):543–9. Available from: https://onlinelibrary.wiley.com/doi/10.1111/phn.12210 Leyland AH, Groenewegen PP. What Is Multilevel Modelling? In: Multilevel Modelling for Public Health and Health Services Research [Internet]. Cham: Springer International Publishing; 2020 [cited 2025 Mar 24]. p. 29–48. Available from: https://link.springer.com/10.1007/978-3-030-34801-4_3 Additional Declarations No competing interests reported. Supplementary Files Suppl1Figure.docx Suppl2Table.docx Suppl3Table.docx Cite Share Download PDF Status: Published Journal Publication published 29 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Jun, 2025 Reviews received at journal 30 May, 2025 Reviewers agreed at journal 27 May, 2025 Reviews received at journal 18 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 08 May, 2025 Editor assigned by journal 08 May, 2025 Editor invited by journal 25 Apr, 2025 Submission checks completed at journal 24 Apr, 2025 First submitted to journal 28 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6325654","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":454147778,"identity":"1f87977e-fdf6-4021-9e35-783db0e7ad08","order_by":0,"name":"Umerdad Khudadad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACHh4gNvgnx0+ilooDxpINDIwNB4jXcuZA4oYDxGoxOHP2mMTbtjuMm28kH3/8oeawPAP74Qf4tZztS5Oc2/aM2exGWmLDgWOHDRt40gzwaznPYybN28bMZnYjx7DhANttxgYJBuK08BjPAGn5d9u+QYL9AwGH9ZhJ85w5LGEgAdRysO12YoMED35bJM+cMbacU5FmIHHmWeKMs33/k9t4cgrwauE7k2N4442BTX1/e/KBDxXf0mz72Y9vwKsFE7CRqH4UjIJRMApGARYAABNATYRoth4dAAAAAElFTkSuQmCC","orcid":"","institution":"University of Ottawa","correspondingAuthor":true,"prefix":"","firstName":"Umerdad","middleName":"","lastName":"Khudadad","suffix":""},{"id":454147779,"identity":"2c0ccf4b-af6a-41eb-9fcb-1d9083b19364","order_by":1,"name":"Tomoko McGaughey","email":"","orcid":"","institution":"Carleton University","correspondingAuthor":false,"prefix":"","firstName":"Tomoko","middleName":"","lastName":"McGaughey","suffix":""},{"id":454147780,"identity":"91b00531-5176-44e9-837c-016ca4f837fe","order_by":2,"name":"Alex Zheng","email":"","orcid":"","institution":"British Columbia Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Zheng","suffix":""},{"id":454147781,"identity":"a2deaf7a-aeed-4e9b-a3c5-eddfd18fadae","order_by":3,"name":"Audrey R. Giles","email":"","orcid":"","institution":"University of Ottawa","correspondingAuthor":false,"prefix":"","firstName":"Audrey","middleName":"R.","lastName":"Giles","suffix":""},{"id":454147782,"identity":"5f21db11-ae81-40ed-b417-1ff3416c45e7","order_by":4,"name":"Ian Pike","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"","lastName":"Pike","suffix":""}],"badges":[],"createdAt":"2025-03-28 07:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6325654/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6325654/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-37031-x","type":"published","date":"2026-01-29T15:59:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82595826,"identity":"ece42951-ec54-4233-aace-53c598f4eb47","added_by":"auto","created_at":"2025-05-13 08:45:37","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":341809,"visible":true,"origin":"","legend":"\u003cp\u003eHot and cold spots for unintentional home injury rates. The main map is of the province of British Columbia and the inset is of areas with major hotspots in the urban population centres. Values represent z-scores from the Getis Ord Gi* analysis. Blank areas are those for which no unintentional home injury was reported during study period.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6325654/v1/8d3610af99653d6d31aede3e.jpeg"},{"id":82595828,"identity":"f4c35758-b861-4c13-a03d-62e5b693fcd5","added_by":"auto","created_at":"2025-05-13 08:45:37","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1270876,"visible":true,"origin":"","legend":"\u003cp\u003eMap of bivariate relationship of unintentional home injury rates and Canadian index of multiple deprivation for B.C. The insets are from hotspot areas.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6325654/v1/16f03bf8f73cb57037d6015c.jpeg"},{"id":101690776,"identity":"df298fa2-2f80-4b8e-8835-05e153e6ecaf","added_by":"auto","created_at":"2026-02-02 16:08:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2246503,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6325654/v1/5b6e5612-72b7-4377-b77a-b98e28bd0abb.pdf"},{"id":82595820,"identity":"9f5e1490-18fd-4c91-a17c-a094a8091e7f","added_by":"auto","created_at":"2025-05-13 08:45:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":280821,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl1Figure.docx","url":"https://assets-eu.researchsquare.com/files/rs-6325654/v1/597341211f919e14b707ba90.docx"},{"id":82595830,"identity":"501e23df-5657-4a8f-a466-f68a187a25ea","added_by":"auto","created_at":"2025-05-13 08:45:37","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15204,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl2Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-6325654/v1/fdfacfce2cfccdd87f4a3019.docx"},{"id":82595822,"identity":"4617d392-dcf9-4b8a-a3cd-47e4a0c44209","added_by":"auto","created_at":"2025-05-13 08:45:36","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15484,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl3Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-6325654/v1/079b9b582824089ac4e400b1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geospatial hotspots and neighbourhood deprivation associated with unintentional home injuries in British Columbia, Canada","fulltext":[{"header":"Background","content":"\u003cp\u003eUnintentional injuries, those for which the intent cannot be predetermined, represent a significant burden on Canada\u0026rsquo;s healthcare system and society. Unintentional injuries constitute the majority of injury cases associated with deaths (75%), hospitalisations (89%), emergency department visits (95%), and disabilities (90%) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In 2018, unintentional injuries accounted for 86% of total injury costs, amounting to \u003cspan\u003e$\u003c/span\u003e25.3\u0026nbsp;billion (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). A significant proportion of these unintentional injuries occur in or around home that include but are not limited to falls, burns, poisonings, ingestion of foreign objects, smoke inhalation, drowning, cuts, collisions with objects, as well as injuries resulting from structural collapse (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Given these statistics, unintentional injuries represent a significant public health issue that demands critical investigation.\u003c/p\u003e \u003cp\u003eThe occurrence of these injuries varies significantly across age groups and between men and women, with unintentional home injuries disproportionately affecting children and older adults due to their unique vulnerabilities and increased time spent at home, which amplifies their exposure to potential hazards in the home environment (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Every year, over 20,000 children visit emergency departments across Canada due to unintentional injuries sustained at home (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In particular, children under five years of age are rapidly developing physically and cognitively, but lack mature motor skills and risk awareness, which increase their risk of sustaining unintentional injuries within home environment (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Based on the hospitalization records from 2017\u0026ndash;2018, approximately 52% of falls leading to hospital admissions among adults aged 65 and older occurred their household residence (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Older adults face an increased risk of falls due to a combination of age-related physical limitations, chronic health conditions, medication side effects, and environmental hazards in homes not adapted for aging populations (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpecifically, in British Columbia (B.C.), 84% of hospital admissions for injuries that occurred at home between 2015 and 2019 were unintentional (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The same study found that neighbourhood socioeconomic deprivations influence the rates of unintentional home injury hospitalizations in B.C. Socioeconomic factors can affect unintentional home injury risks through multiple pathways, including the household income, housing conditions, access to resources for injury prevention, and overall community well-being (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In addition, people's exposure to home hazards and safety risks in B.C. is influenced by structural determinants of health, which shape the distribution of resources across the province (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address this increasing trend of unintentional home injuries, numerous multi-level initiatives, including social marketing campaigns (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), injury surveillance systems (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and development of policy relevant indicators for evaluating interventions (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), have been implemented in B.C. over the last two decades to reduce unintentional home injuries, yet preventable injuries continue to occur, highlighting the need for sophisticated methodologies to better understand unintentional home injury risks. As such, spatial epidemiological studies have been proposed to facilitate injury prevention strategies (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Schuurman et al. emphasized the importance of considering spatial scale in injury analysis and illustrated how injury patterns can vary significantly when examined at different geographic levels (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Public health researchers use geospatial methods to link individual health data with the physical space and social community in which an individual resides in order to map injury locations in relation to other geographical and social attributes (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). For example, Kureshi and colleagues mapped the spatial distribution of traumatic brain injuries by major injury causes at the smallest geographical unit across Nova Scotia, Canada, and identified the various pathways through which deprivation influences the risk of differing traumatic brain injury mechanisms (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite a growing body of research on the geographic distribution of injuries, the literature on spatial epidemiology of unintentional home injuries in B.C. is limited. Geospatial methods have been used to investigate regional differences in injury rates and identify hotspots for various types of injuries (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However there has been no injury type assessment and no measurement of injury rates that integrate measures of multiple deprivations. The use of large geographic units to represent injury hotspots and their association with deprivation may be useful for identifying broader patterns but does not provide the level of spatial resolution necessary to identify small-scale variations within neighborhoods (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, the broad patterns identified through this approach can serve as a starting point for more targeted analyses using smaller geographic units.\u003c/p\u003e \u003cp\u003eUnderstanding the spatial patterns of unintentional home injury hospitalizations in relation to neighborhood deprivation has important implications for public health practice and policy (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). By identifying vulnerable populations and high-risk areas, this research can inform the development and implementation of targeted, evidence-based injury prevention interventions. Moreover, this research can contribute to broader discussions on health equity and the social determinants of health, highlighting the need for comprehensive approaches that address both individual and community-level factors in injury prevention. As such, this study aimed to identify the geographic hotspots of unintentional home injuries that resulted in hospitalization by dissemination area across B.C., Canada, between 2015 and 2019, and explore the relationship of hotspots with deprivation. Notably, while this study focused on unintentional home injury hospitalizations, the analysis is based on the dissemination area where the injury occurred, as the data are geocoded by the place of occurrence, specifically the home. Although this study used the patient's residential address for DA information, the injury location may be the same as the residential address but not necessarily the patient's home.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eB.C. is the westernmost province in Canada, located on the Pacific Coast. It covers a total area of 944,735 km\u0026sup2; and had a population of 5,722,318 as of January 1, 2025 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The province's largest city, Vancouver, has a population of 678,984 in 2024, while the Metro Vancouver area is home to approximately 2.48\u0026nbsp;million people, representing about 44% of the province's total population. In our study, we adopted the classification approach developed by Statistics Canada to categorize communities in B.C. based on their remoteness (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Using the remoteness index dataset, we classified census subdivisions into five distinct categories: easily accessible, accessible, less accessible, remote, and very remote areas (Supplementary 1). This nuanced classification allowed us to capture the varying degrees of remoteness across B.C., providing a more comprehensive understanding of the geographic distribution of population and resources than traditional urban-rural dichotomies.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData sources and characteristics\u003c/h3\u003e\n\u003cp\u003eThe data for this study were obtained by the B.C. Injury Research and Prevention Unit based on reported incidents through the Discharge Abstract Database (DAD), maintained by the B.C. Ministry of Health (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The dataset comprised de-identified hospitalization records for all separations from a hospital following a home injury for the years 2015 through 2019, inclusive. This study included only the primary cause of admission (i.e., the most responsible diagnosis coded in the DAD) to identify unintentional home injury hospitalizations. The dataset included administrative, sociodemographic, and diagnostic variables. The sociodemographic variables were sex, age, and residential dissemination area (DA). The diagnostic variables included external causes of injury (e.g., falls, drowning, or burns) classified according to the \u003cem\u003eInternational Statistical Classification of Diseases and Related Health Problems Canadian\u003c/em\u003e version 10 (ICD-10-CA) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). ICD-10 CA codes associated with unintentional home injury and DAs associated with B.C. were retained for the purpose of this analysis (Supplementary 2).\u003c/p\u003e \u003cp\u003eIn summary, there was a total of 74,985 home injury hospitalizations, with 84.2% of them classified as unintentional in B.C. between 2015 and 2019. Females constituted 60.1% (n\u0026thinsp;=\u0026thinsp;37,988) of these unintentional home injury hospitalizations. Those aged 65 years and over accounted for 72.9% (n\u0026thinsp;=\u0026thinsp;46,060) of unintentional home injury hospitalizations, while the 45\u0026ndash;64 age group represented 15.9% (n\u0026thinsp;=\u0026thinsp;10,018). Falls were the primary cause of unintentional home injury hospitalizations accounting for 82.4% (n\u0026thinsp;=\u0026thinsp;52,064) followed by poisoning at 7.2% (n\u0026thinsp;=\u0026thinsp;4,559). Moreover, 4.7% (n\u0026thinsp;=\u0026thinsp;3,000) of these unintentional home injuries resulted in deaths.\u003c/p\u003e \u003cp\u003eIn Canada, a Dissemination Area (DA) is the smallest standard geographic area for which all census data are disseminated by Statistics Canada. DAs generally have a population of between 400 and 700 persons, with an average of about 500 people. DAs are designed to remain consistent over time, allowing for comparisons between censuses and for analyses of demographic changes. Scholars have used DAs widely in spatial and socioeconomic studies in Canada (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) as DAs provide a fine geographic scale for analysing local patterns and variations that may be obscured at larger geographic levels. The population data for each DA were obtained from the Statistics Canada\u0026rsquo;s 2016 Census Profiles (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The cartographic boundary data were obtained from Statistics Canada (2021), which depicts the extent of the geographic areas across Canada (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Notably, some spatial areas within our study region may not contain any resident population due to uninhabited status or other factors. Consequently, these areas will lack analytical units for analysis, resulting in their exclusion from calculations involving population-based rates and spatial statistics.\u003c/p\u003e\n\u003ch3\u003eSocioeconomic factors and unintentional home injury rates\u003c/h3\u003e\n\u003cp\u003eThe Canadian Index of Multiple Deprivation (CIMD) for B.C. was used to map the spatial relationship between neighbourhood deprivation and unintentional home injuries in B.C., Canada (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The CIMD for B.C. is a provincial-level index that measures four distinct dimensions of deprivation and marginalization at the DA level using population census data from 2016. The dimensions are ethnocultural composition, situational vulnerability, economic dependency, and residential instability, each consisting of various indicators that are aggregated and combined through factor analysis (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). A detailed description of various indicators that comprise the CIMD for B.C. is provided in Supplementary 3. Identified cases of unintentional home injury, DA descriptives, and CIMD categorization quintiles were merged using DA unique identifiers. Bivariate maps were created to visualize the relationships between neighbourhood socioeconomic factors and unintentional home injury rates. This method allows for the visualization of two variables, such as unintentional home injury hospitalization rates and multiple deprivation, on a single map based on the intersection of data values for both variables.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eUnintentional home injuries were aggregated into DAs across B.C. Crude injury rates for each DA were calculated using the count of unintentional home injuries within each DA as the numerator and the 2016 DA population from the Canadian census profile as the denominator. Data processing and analysis was conducted in the ArcGIS Pro version 3.3 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpatial autocorrelation\u003c/h3\u003e\n\u003cp\u003eSpatial autocorrelation is a statistical concept that measures the degree to which a variable is correlated to itself across geographic space. It is based on the principle that observations geographically closer to each other tend to be more similar than those farther apart, which is often summarized by Tobler's first law of geography: \"everything is related to everything else, but near things are more related than distant things.\" (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, p. 236)\u003c/p\u003e \u003cp\u003eTo determine the overall spatial autocorrelation of unintentional home injury hospitalization by neighbourhood across B.C., the Global Moran's I statistic was employed. The mathematical and statistical calculations of Global Moran\u0026rsquo;s I are explained elsewhere (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The null hypothesis assumes that there is no spatial pattern among unintentional home injuries across British Columbia, indicating complete spatial randomness. A positive Moran\u0026rsquo;s I indicates clustering of high and low unintentional home injury rates, while a negative Moran\u0026rsquo;s I reflects a dispersed pattern.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHotspot analysis\u003c/h2\u003e \u003cp\u003eThe Getis-Ord-Gi* statistic was used to identify area-based hotspots of unintentional home injury rates (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In this study, a hotspot was defined as an area with a higher concentration of unintentional home injury events compared with the expected number assuming a random distribution of injuries. The use of Getis-Ord-Gi* statistic helps to identify areas where unintentional home injuries with either high or low values cluster spatially, by examining each DA within the context of its neighbouring DAs. The Getis-Ord Gi* statistic is calculated using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\frac{G\\begin{array}{c}*\\\\\\:i\\end{array}={\\sum\\:}_{j=1}^{n}wi,\\:jxj-\\:\\stackrel{-}{X\\:}{\\sum\\:}_{j=1}^{n}wi,j}{\\begin{array}{c}S\\sqrt{{\\sum\\:}_{j=1}^{n}{w}_{i,j}^{2}}-\\left({\\sum\\:}_{j=1}^{n}{w}_{i,j}\\right)2\\\\\\:n=1\\end{array}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this formula, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:xj\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the attribute value for feature \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e, which in our case is the unintentional home injury rate for each dissemination area. The term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{i,j\\:}\\)\u003c/span\u003e\u003c/span\u003edenotes the spatial weight between features \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, reflecting the spatial relationship between different areas. The variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e represents the total number of features, which is DAs in our study. \u003cem\u003eX̄\u003c/em\u003e is the mean of the attribute values across all areas, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S\\)\u003c/span\u003e\u003c/span\u003e represents the standard deviation of these values. This formula yields a z-score, where positive values indicate clustering of high unintentional home injury rates (hot spots), and negative values suggest clustering of low rates (cold spots), allowing us to identify areas of significant spatial concentration of injuries.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGlobal spatial autocorrelation\u003c/h2\u003e \u003cp\u003eGlobal spatial autocorrelation analysis showed a statistically significant clustering pattern of unintentional home injuries in B.C. (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.05). In other words, areas with high unintentional home injury rates tended to be located near other areas with high rates of such injuries, and vice versa. The statistical significance of this clustering was confirmed by a z-score of 38.53 and a p-value of 0.000. This demonstrated that the observed spatial patterns of unintentional home injuries in B.C. are highly unlikely to have occurred randomly and justified further local exploration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHotspot analysis\u003c/h2\u003e \u003cp\u003eBetween 2015 and 2019, the annual rate of unintentional home injuries resulting in hospitalization in B.C. was 256.5 per 100,000 population. The Getis-Ord Gi* analysis identified 1,183 hotspots and 3,130 cold spots across DAs in B.C. A cluster of statistically significant hotspots (99% CI, z-score\u0026thinsp;\u0026gt;\u0026thinsp;2.58) was identified in the southernmost region of B.C., particularly across Thompson-Okanagan region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were also significant hotspots on Vancouver Island. In other words, unintentional home injury rates leading to hospitalization in B.C. were predominantly clustered in accessible areas, with higher concentration observed in large and medium-sized population centres.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic profiles of hotspots\u003c/h2\u003e \u003cp\u003eThe bivariate maps revealed distinct spatial patterns in the relationships between unintentional home injury rates and various socioeconomic factors across B.C. (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Economic dependency showed notable spatial heterogeneity across B.C. and southern regions presented more complex interaction patterns. The Thompson-Okanagan region demonstrated a direct relationship, with higher economic dependency corresponding to increased unintentional home injury rates. Ethnocultural composition exhibited an inverse relationship with unintentional home injury rates, as regions with high injury rates and low ethnocultural diversity were dispersed throughout the province, particularly along the south coast. Situational vulnerability showed a consistent positive relationship with unintentional home injury rates with areas of high situational vulnerability and high injury rates concentrated primarily in the Thompson-Okanagan region and southern areas of Vancouver Island. Similarly, residential instability demonstrated a clustered pattern, wherein areas with high unintentional home injury rates corresponded with higher residential instability both provincially and across the Thompson-Okanagan region and southern Vancouver Island areas .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified significant spatial clustering of unintentional home injuries leading to hospitalizations in B.C., indicating that areas with high injury rates tend to be geographically proximal to other high-rate areas. The unintentional home injuries hotspots were largely concentrated in urban areas of B.C. The bivariate analysis of unintentional home injuries resulting in hospitalizations within hotspots revealed a direct relationship with economic dependency, situational vulnerability, and residential instability, while an inverse relationship was observed with ethnocultural diversity.\u003c/p\u003e \u003cp\u003eThe concentration of unintentional home injury hotspots in B.C.\u0026rsquo;s accessible areas, particularly across Thompson-Okanagan region and on the southern and central Vancouver Island, reflect the complex interplay between population density and injury risk factors. Although most unintentional home injuries in our study occurred in urban areas, previous research has shown that injury rates increase along the rural-urban continuum, with higher rates in more rural areas (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). However, rural areas in Canada are often more hazardous than urban settings due to residents\u0026rsquo; frequent exposure to farming equipment and heavy machinery, increasing the risk of severe crush injuries, which are primarily classified as occupational rather than home injuries (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Notably, urban environments in Canada may present unique hazards, such as more complex home structures and a higher prevalence of older housing stock with associated structural risks, which may increase the risk of unintentional home injuries (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In addition, increased stress in urban areas, particularly from working multiple jobs often driven by financial circumstances, can lead to unhealthy coping mechanisms and risk-taking behaviours, increasing the likelihood of unintentioal injuries, including those that occur at home (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The geographic hotspot identified in our study should be prioritized for in-depth investigations to understand local risk factors and for the implementation of tailored injury prevention programs.\u003c/p\u003e \u003cp\u003eBetween 2015 to 2019, falls among older adults were the leading cause of unintentional home injuries in B.C. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The highest proportion of older adults aged 65 and over live in urban areas, such as parts of Vancouver Island (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This concentration of seniors in urban areas, especially on Vancouver Island, may explain the increased rates of fall-related home injuries in these regions. This is consistent with a study in Nova Scotia that identified high concentration of fall hotspots in urban areas (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In addition, previous studies have found that older adults living alone, a situation more prevalent in urban areas, have two times the likelihood of falling compared to those living with others (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). However, older adults living in urban areas may have better access to healthcare services, fall prevention programs, and community support compared to other regions. Home-based targeted interventions, such as zero-step entries, single-floor living, and wide doorways are crucial for enabling older adults to navigate their homes safely and independently (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Furthermore, the risk of falls increases with age, and B.C. has seen a significant growth in the population aged 85 and older, which has increased by 24% over the past decade (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This demographic shift emphasizes the need for implementing effective fall prevention strategies and consulting with older adults in designing the built environment.\u003c/p\u003e \u003cp\u003eThe intersection of economic dependency, residential instability, and situational vulnerability creates a multifaceted cycle of disadvantage that increases the risk of injuries in and around the home environment (\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Our findings underscore the importance of addressing socioeconomic determinants in injury prevention strategies. For example, policies aimed at improving economic conditions, housing stability, and community support systems in vulnerable neighbourhoods may have significant potential to reduce unintentional home injuries (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), and subsequent hospitalizations. Public health initiatives could focus on communities with high levels of economic dependency and residential instability, implementing injury prevention programs that address both immediate injury risks and underlying socioeconomic factors (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Future injury prevention programs should adopt an intersectional approach, considering how various socioeconomic factors interact to create unique risk profiles for different population subgroups. Furthermore, our findings emphasize the importance of longitudinal studies to better understand how changes in economic stability and housing security affect unintentional home injury rates over extended periods.\u003c/p\u003e \u003cp\u003eThe inverse relationship between the ethnocultural dimension and unintentional home injury hospitalization rates provides a unique understanding of unintentional home injury risk factors. This protective effect of ethnic diversity aligns with some previous research suggesting that culturally diverse neighbourhoods may benefit from stronger social networks and community support systems, which could contribute to home safety practices and injury prevention (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The observed low unintentional home injury hospitalization rates among recent immigrants could be attributed to underutilization of emergency services due to barriers in navigating healthcare system and cultural factors, such as language difficulties, lack of familiarity with the healthcare system, concerns about costs, and cultural perceptions of emergencies (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). However, a study examining unintentional injury hospitalization rates among immigrant children and youth in Ontario, Canada, found that refugee children experienced 20% more injuries compared to their non-refugee immigrant counterparts (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Given differential injury risk exposure among immigrant population, it is critical to collect and analyse disaggregated data by ethnocultural groups to identify specific risk and protective factors within each community (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Furthermore, our findings emphasize the need for culturally sensitive injury prevention programs that account for the diverse practices, beliefs, and living conditions of various ethnocultural groups (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). The protective effect associated with ethnocultural diversity suggests that fostering inclusive, culturally diverse communities and leveraging existing community strengths could be beneficial in injury prevention efforts.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile this study provides valuable spatial insights related to unintentional home injury hospitalizations in B.C., several limitations should be acknowledged. The authors inability to collect non-hospitalization data for unintentional home injuries presents a limitation in our study, and the findings of this study should be interpreted considering this limitation. Injury surveillance often depends on administrative claims data created for billing purposes in hospital emergency departments and inpatient care (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Incorporating data from multiple sources, including outpatient settings and community surveys, could provide a more complete picture of unintentional home injuries across the severity spectrum. Moreover, we used data aggregated at the DA level, which may not accurately represent individual-level relationships between deprivation and unintentional home injuries. Multi-level modelling techniques could be used for future analysis to examine individual and area-level factors simultaneously (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). However, the use of geographic information system and spatial analysis techniques allowed the identification of geographic hotspots across B.C. In addition, highlighting the potential relationship between area-level multiple deprivation and unintentional home injuries can inform future research on social determinants of injury.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis geospatial analysis identified geographic clusters with significantly higher rates of unintentional home injuries in urban regions of B.C., particularly in areas characterized by higher levels of multiple deprivation. By focusing on these identified high-risk urban areas and leveraging community strengths, public health initiatives can be more effectively designed and implemented to address the specific factors contributing to higher rates of unintentional home injury. Developing partnerships between public health, housing, social services, and community organizations is crucial for addressing the complex factors contributing to unintentional home injuries, as they may enable more integrated and effective approaches to preventing these injuries.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBritish Columbia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDissemination Area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCanadian Index of Multiple Deprivation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDischarge Abstract Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRemoteness Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis study was approved by University of British Columbia UB.C. Children\u0026rsquo;s and Women\u0026rsquo;s C\u0026amp;W Research Ethics Board (certificate # H22-01297).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication.\u0026nbsp;\u003c/strong\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eThe data that supported the findings of this study are openly available through B.C. Injury Research and Prevention Unit at https://data.injuryresearch.B.C..ca/DataTools/hospitalization.aspx\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand Statistics Canada at https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/prof/details/download-telecharger/comp/page_dl-tc.cfm?Lang=E\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the B.C. Injury Research and Prevention Unit, B.C. Children\u0026rsquo;s Hospital Research Institute and the Mitacs Accelerate Program in partnership with Pacific Public Health Foundation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eUmerdad Khudadad, Ian Pike, and Audrey R. Giles contributed to the study conception and design. Data curation and analysis were performed by Umerdad Khudadad, Tomoko McGaughey, and Alex Zheng. The first draft of the manuscript was written by Umerdad Khudadad and all authors commented on the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNot applicable.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eParachute. Case and rates of injury in Canada, 2018. [Internet]. 2018. Available from: https://parachute.ca/en/professional-resource/cost-of-injury-in-canada/the-human-cost-of-injury/\u003c/li\u003e\n\u003cli\u003eParachute. Cost of Injury in Canada [Internet]. 2018. Available from: https://parachute.ca/en/professional-resource/cost-of-injury-in-canada/costs-to-the-health-system-and-society/\u003c/li\u003e\n\u003cli\u003ePike I, Richmond S, Rothman L, Macpherson A. Canadian Injury Prevention Resource [Internet]. 2015. Available from: https://parachute.ca/wp-content/uploads/2019/08/Canadian-Injury-Prevention-Resource.pdf\u003c/li\u003e\n\u003cli\u003ePublic Health Agency of Canada. Inequalities in unintentional injury mortality in Canada. [Internet]. 2022. Available from: https://www.canada.ca/en/public-health/services/publications/science-research-data/inequalities-unintentional-injury-mortality-infographic.html\u003c/li\u003e\n\u003cli\u003eParachute. Home safety [Internet]. 2023. Available from: https://parachute.ca/en/injury-topic/home-safety/\u003c/li\u003e\n\u003cli\u003eParachute. Unintentional Injury Trends for Canadian Children [Internet]. 2016. Available from: https://parachute.ca/wp-content/uploads/2019/06/SKW-Trend-Report.pdf\u003c/li\u003e\n\u003cli\u003ePublic Health Agency of Canada. Surveillance report on falls among older adults in Canada [Internet]. 2022. Available from: https://www.canada.ca/en/public-health/services/publications/healthy-living/surveillance-report-falls-older-adults-canada.html\u003c/li\u003e\n\u003cli\u003eDo MT, Chang VC, Kuran N, Thompson W. Fall-related injuries among Canadian seniors, 2005\u0026ndash;2013: an analysis of the Canadian Community Health Survey. Health Promot Chronic Dis Prev Can [Internet]. 2015 Sep [cited 2025 Mar 22];35(7):99\u0026ndash;108. Available from: https://www.canada.ca/en/public-health/services/reports-publications/health-promotion-chronic-disease-prevention-canada-research-policy-practice/vol-35-no-7-2015/fall-related-injuries-among-canadian-seniors-2005-2013-analysis-canadian-community-health-survey.html\u003c/li\u003e\n\u003cli\u003eKhudadad U, Karbakhsh M, Yau A, Rajabali F, Zheng A, Giles AR, et al. Home injuries in British Columbia: patterns across the deprivation spectrum. Int J Inj Contr Saf Promot [Internet]. 2024 Oct [cited 2025 Feb 26];31(4):556\u0026ndash;67. 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Available from: https://link.springer.com/10.1007/978-3-030-34801-4_3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Geospatial analysis, home, household, unintentional injuries, neighbourhood deprivation, socio-economic disparities","lastPublishedDoi":"10.21203/rs.3.rs-6325654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6325654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite existing prevention initiatives, preventable unintentional home injuries remain a significant public health concern in Canada, and are often influenced by the social determinants of health. This study identified dissemination-area-level hotspots of unintentional home injuries resulting in hospitalizations across British Columbia (B.C.), Canada, from 2015 to 2019, and it\u0026rsquo;s examined their relationship with multiple deprivation indexes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUnintentional home injury hospitalization data from B.C., Canada, (2015\u0026ndash;2019) were obtained from the Discharged Abstract Database. These data were then linked with dissemination area (DA) level data and the Canadian Index of Multiple Deprivation (CIMD) for B.C. Spatial autocorrelation was assessed using Moran's I, and hotspot analysis was performed using the Getis-Ord Gi* statistic. Crude injury rates for each DA were calculated. Geospatial and bivariate analysis were examined using ArcGIS Pro.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBetween 2015 and 2019, the annual rate of unintentional home injuries leading to hospitalization in B.C. was 256.5 per 100,000 population. Unintentional home injuries leading to hospitalizations in B.C. were significantly clustered (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.05, z-score\u0026thinsp;=\u0026thinsp;38.53, and p-value\u0026thinsp;=\u0026thinsp;0.000). A total of 1,183 hotspots and 3,130 cold spots across DAs in B.C. were identified. Significant hotspots (99% CI, z-score\u0026thinsp;\u0026gt;\u0026thinsp;2.58) were found in the southern B.C. region, especially across Thompson-Okanagan region and Vancouver Island, indicating that higher unintentional home injury rates were clustered in urban areas and larger population centres. In urban hotspots, bivariate analysis showed a positive relationship between unintentional home injury rates and economic dependency, residential instability, and situational vulnerability, and an inverse relationship with ethnocultural composition.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis geospatial analysis identified urban clusters in B.C. with higher unintentional home injury rates, particularly in areas of socioeconomic deprivation. These findings provide valuable insights into high-risk areas for implementing tailored injury prevention programs and policies.\u003c/p\u003e","manuscriptTitle":"Geospatial hotspots and neighbourhood deprivation associated with unintentional home injuries in British Columbia, Canada","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 08:45:31","doi":"10.21203/rs.3.rs-6325654/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-02T07:05:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-30T21:45:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333572674195071751440464649859347953688","date":"2025-05-27T16:28:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-18T17:16:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254987580543246952654589986348954846584","date":"2025-05-08T15:51:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-08T12:31:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-08T12:29:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-25T10:54:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T01:21:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-28T07:02:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23ad61e4-2d4b-4a06-9aa5-153196ac41f0","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48292444,"name":"Health sciences/Risk factors"},{"id":48292445,"name":"Earth and environmental sciences/Environmental social sciences/Socioeconomic scenarios"},{"id":48292446,"name":"Health sciences/Medical research/Epidemiology"}],"tags":[],"updatedAt":"2026-02-02T16:05:05+00:00","versionOfRecord":{"articleIdentity":"rs-6325654","link":"https://doi.org/10.1038/s41598-026-37031-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-29 15:59:13","publishedOnDateReadable":"January 29th, 2026"},"versionCreatedAt":"2025-05-13 08:45:31","video":"","vorDoi":"10.1038/s41598-026-37031-x","vorDoiUrl":"https://doi.org/10.1038/s41598-026-37031-x","workflowStages":[]},"version":"v1","identity":"rs-6325654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6325654","identity":"rs-6325654","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}