Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods

Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods Drew Walker, Swati Rajwal, Sudeshna Das, Snigdha Peddireddy, Abeed Sarker This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7096343/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Social isolation and loneliness, which have been increasing in recent years strongly contribute toward suicide rates. Although social isolation and loneliness are not currently recorded within the US National Violent Death Reporting System’s (NVDRS) structured variables, natural language processing (NLP) techniques can be used to identify these constructs in law enforcement and coroner/medical examiner narratives. Using topic modeling, lexicon development and supervised learning classifiers, we developed high-quality classifiers (average F1: .86, accuracy: .82). Evaluating over 300,000 suicides from 2002 to 2020, we identified 1,198 mentioning chronic social isolation. Decedents had higher odds of chronic social isolation classification if they were men (OR = 1.44; CI: 1.24, 1.69, p < .0001), gay (OR = 3.68; 1.97, 6.33, p < .0001), or were divorced (OR = 3.34; 2.68, 4.19, p < .0001). We found significant predictors for other social isolation topics of recent or impending divorce, child custody loss, eviction or recent move, and break-up. Our methods can improve surveillance and prevention of social isolation and loneliness in the United States. Social science/Sociology Social science/Psychology/Human behaviour Social science/Language and linguistics Figures Figure 1 Figure 2 Teaser Natural language processing identified trends and predictors of social isolation and loneliness within a comprehensive dataset of US suicide records. Introduction Social isolation is a critical factor that contributes to the United States’ mental health and suicide crisis. In 2023, the US Surgeon General released an advisory highlighting the epidemic of loneliness and social isolation and proposing a call to action for intervention frameworks and policy priorities. 1 He emphasized the Interpersonal Theory of Suicide’s assertion that social isolation is the most important contributing factor to suicide across all age and demographic groups. 1 , 2 Social isolation and its intense, well-documented negative effects, may be experienced more acutely during specific critical life events, and these experiences may vary by race, gender, or other demographic factors. 3 , 4 Despite mounting evidence on the detrimental effects of social isolation, identification and measurement of this phenomenon, and its impacts on mortality and morbidity at an epidemiological scale are difficult to measure, given existing survey methods. 5 , 6 Recent reviews have called for public health researchers to pursue novel methods to understand evolving trends in how social isolation contributes to suicide and mental health, and how these trends differ across populations and demographic factors. 5 , 7 , 8 The National Violent Death Reporting System (NVDRS) has historically been one of the most comprehensive US data sources on suicide and violent deaths. 9 NVDRS pools over 600 data elements, abstracted from coroner medical examiner and law enforcement narratives, toxicology reports, and death certificates. Current research on NVDRS has used the dataset to explore contexts surrounding suicide, in an attempt to understand common circumstances and co-occurring alcohol and substance use. 10 NVDRS collects data on demographics and a wide variety of related circumstances, ranging from “relationship problems; mental health conditions and treatment; toxicology results; and life stressors, including recent money- or work-related or physical health problems.” 11 Still, there are limits to structured data, including information about circumstances related to social isolation and related events. 10 , 12 The NVDRS contains extensive text data from law enforcement (LE) and coroner medical examiner (CME) incident narratives, including information on circumstances, other suspects or individuals on scene, and additional details not captured in structured data. These text fields may vary in the level of detail but often provide additional information not captured within structured variables. Despite the potential insights from LE/CME narratives, there is significant cost in time and effort required by abstractors to manually review a large range of concepts within this volume of highly variable and lengthy text data. 12 Advances in natural language processing have enabled researchers in recent years to apply computational methods to mine information from narratives in NVDRS, illuminating important trends related to mental health, violent death, and suicide. 12 – 14 The objective of this study was to explore circumstances related to the intersection of social isolation and suicide. To achieve this objective, we developed and applied natural language processing techniques to identify social isolation-related circumstances and events from free-text LE/CME narratives, recorded within the NVDRS for suicide decedents identified from 2002 to 2020. After development of identification methods, we applied models to assess longitudinal trends and associations with relevant demographic factors to identify at-risk groups for social isolation-related suicide. Results Table 1 provides descriptive statistics for the 306,817 decedents who died by suicide recorded in the NVDRS dataset from 2002 to 2020. Most decedents in this sample were male (78.1%), with an average age of 46.3 years, and largely were classified as White (82.1%), Black (6.5%), or Hispanic (6.3%) decedents. BERTopic Topic Modeling BERTopic topic modeling generated 64 topics within the highest performance coherence model, which was trained upon the shorter text fields of LE/CME suicide circumstance summaries. LE circumstance summaries (n = 20,731, 6.8% of decedents) and CME circumstance summaries (n = 30,584, 10.0%) provide shorter text summarizations (40–50 characters) of relevant contexts related to the suicides mentioned within the corresponding full LE/CME narratives. The shorter text within these fields allowed for more easily understood and coherent topic modeling results, which could then be identified by subject matter experts, and used to construct lexicons of words and phrases to search across a larger sample of full-length LE/CME narratives. Shorter text circumstance summary fields were used for topic modeling due to the high amount of noise assessed in longer-length full LE/CME narratives. After reviewing topics, we arrived at six distinct narrative topics to apply towards classifying the full LE/CME narratives related to social isolation: 1) chronic social isolation, 2) recent or impending divorce, 3) eviction or move, 4) break-up, 5) child custody loss, and 6) pet loss. These classifier labels were chosen due to their relevance to social isolation and value added to identifying phenomena not currently captured by existing NVDRS structured variables. Regex search and annotation process Table 2 displays results of regular expression search matches, along with the percentage of the sample which were annotated as related to each topic, interrater reliability for the first 50 samples, and an example sentence (edited to provide anonymity) from positively classified narrative matches for each topic. When matching with the regular expression lexicons, we had the highest frequencies of matches with 1) recent eviction or move, (N = 29,913 decedent narrative matches; 9.76% of sample narratives), 2) recent or impending divorce or separation (15,263; 4.98%), and 3) recent break-up (12,279; 4.0%). Lower frequencies of matches were found among the topics of child custody loss (5,322, 1.73% of sample), recent pet loss (1,355, 0.44%), and chronic social isolation (1,198, 0.39%). Following manual annotation to assess relevance to topics of interest, nearly all 100 samples with break-up/social isolation were found to be relevant to the topic, while divorce (66%), child custody loss (50%), eviction/move (36%), and loss of pet (28%) resulted in fewer narratives that actually contained references to the topic of interest. Interrater annotator agreement was relatively high across all topics, ranging from 72 to 98%, (kappa range: .24, .85). Sample Annotation Results Supplemental Table 1 describes social isolation and related event classifier performance results, following the annotation of 100 samples of each of the 6 topics. Overall, across all models, best results were achieved from logistic regression and naive bayes models. Of all the topics, we achieved highest performance results in social isolation, child custody loss, break-up, and divorce, with macro F1 performance ranging from .87 to 1, accuracy from .66 to 1, recall from .75 to 1, and positive-class precision from .75 to 1. Recent eviction or move performed slightly worse, with the pet loss classifier performing the worst, only predicting negative classes. For this reason, the pet loss topic was dropped from the predictive analyses. Predictions, Distributions on entire Dataset Supplemental Table 2 describes the frequencies of regular expression matches and refined classifier predictions across the entire dataset, normalized per rate of 1000 suicides in the overall dataset. The most frequently predicted topics included 1) recent or impending divorce (50.0 per 1000 decedents), 2) recent breakup (40.1 per 1000 decedents), and recent eviction or move (30.9 per 1000 decedents). The least frequently predicted classes were child custody loss (4.0 per 1000 decedents), and chronic social isolation (3.9 per 1000 decedents). Figure 1 displays trends in suicide events by topic from 2002 to 2020. Over this time, across all topics, we observed a gradual increase in rates over time, with several peaks in topic classifications per year. Recent breakup, recent/impending divorce, and recent eviction/move were the most frequently observed topics over time, despite decreases by 2020. We observed peaks in suicide narratives mentioning recent/impending divorces in 2010; and recent evictions/moves in 2016 through 2017. Chronic social isolation and child custody loss peaked in 2020 and 2019, respectively, though both topics had relatively much lower frequencies than others. Demographic predictors of social isolation-related event narrative classification Decedents flagged with social isolation in their LE/CME narrative, on average, were 1.28 years younger (95% CI: -0.23, -2.32, p = .016) than those without. For divorce, decedents were on average 1.44 years younger (95% CI: -1.74, -1.14, p < .0001). Eviction classifications were associated with being younger (95% CI: -1.43, − .67, p < .0001). Larger differences in age were found among decedents with child custody loss classifications, where decedents were 9.28 years younger on average (95% CI: -10.31, -8.25, p < .0001), and those with breakup classifications, where decedents were on average 15.11 years younger (95% CI: -15.44, -14.79, p < .0001). We next examined non-continuous demographic variables and whether the decedent had a social isolation-classified LE/CME narrative (Table 3 ). Chronic Social Isolation Men had higher odds of chronic social isolation classification (Odds Ratio (OR) = 1.44, 95% CI: 1.24, 1.69, p < .0001); decedents identified as gay had higher odds (OR = 3.68, 95% CI: 1.97, 6.33, p < .0001) compared with heterosexual decedents; and those who were divorced had higher odds (OR = 3.34, 95% CI: 2.68, 4.19, p < .0001) compared with decedents who were married. The odds of having chronic social isolation classification was higher for married but separated decedents (OR = 3.43, 95% CI: 2.24, 5.09, p < .0001), for widowed decedents (OR = 3.04, 95% CI: 2.21, 4.14, p < .0001), for never married decedents (OR = 5.81, 95% CI: 4.78, 7.12, p < .0001), and for decedents identified as not currently in a relationship (OR = 6.97, 95% CI: 5.61, 8.69, p < .0001). Decedents identified as non-Hispanic Black or African American had lower odds of chronic social isolation classification (OR = 0.62, 95% CI: .46, .81, p = .001) compared with non-Hispanic White decedents. Recent or Impending Divorce Compared to married decedents, those identified as married but separated had higher odds of having recent or impending divorce narrative classification (OR = 6.61, 95% CI: 6.27, 6.96, p < .0001); compared with decedents in a relationship, those not currently in a relationship had higher odds of having the recent or impending divorce classification (OR = 1.79, 95%CI: 1.69, 1.89, p < .0001); men had higher odds of having recent or impending divorce classification than women (OR = 1.31, 95% CI: 1.26, 1.37, p < .0001). Additionally, several factors showed decreased odds of impending or recent divorce classification. Compared to decedents identified as White, most other racial groups had reduced odds of having recent or impending divorce classification. Decedents identified as gay had lower odds of having recent or impending divorce classification (OR = 0.28, 95% CI: .19, .41, p < .0001) compared to heterosexual decedents; decedents identified as transgender had lower odds of having recent or impending divorce classification (OR = 0.24, 95% CI: .10, .48, p < .0001). Decedents who were identified as divorced had lower odds of having recent or impending divorce narrative classification (OR = 0.68, 95% CI: .65, .71, p < .0001); never married, single, or widowed all much lower odds of having recent or impending divorce classification (OR range: .04, .10, all p < .0001). Decedents identified as homeless had lower odds of recent or impending divorce narrative classification (OR = 0.73, 95% CI: .61, .87, p < .0001); decedents identified with a physical health problem had lower odds of recent or impending divorce narrative classification (OR = 0.54, 95% CI: .51, .57, p < .0001). Eviction or recent move Decedents identified as Hispanic had higher odds of eviction classification (OR = 1.18, 95% CI: 1.09, 1.28, p < .0001) compared to non-Hispanic White decedents. Decedents with marital statuses including divorce had higher odds of eviction or recent move (OR = 1.33, 95% CI: 1.25, 1.40, p < .0001); decedents identified as married but separated had higher odds of eviction classification compared to married decedents (OR = 2.34, 95% CI: 2.02, 2.48, p < .0001). Decedents who were not currently in a relationship had higher odds of eviction or recent move classification, compared to those currently in a relationship (OR = 1.58, 95% CI: 1.47, 1.70, p < .0001). Decedents identified as homeless had higher odds of eviction or recent move narrative classification (OR = 1.42, 95% CI: 1.20, 1.66, p < .0001). Finally, men had lower odds of having eviction or recent move classification compared to women (OR = 0.88, 95%: .84, .92, p < .0001). Break-up Men had higher odds of having breakup classification in narratives than women (OR = 1.13, 95% CI: 1.08, 1.18, p < .0001). Decedents identified as American Indian or Alaska Native, non-Hispanic had higher odds of having a break-up narrative classification compared to decedents identified as White (OR = 1.46, 95% CI: 1.27, 1.68, p < .0001); decedents identified as Hispanic had higher odds of having break-up narrative classification compared to decedents identified as White (OR = 1.61, 95% CI: 1.51, 1.72, p < .0001); decedents identified as two or more races, non-Hispanic had higher odds of having break-up narrative classifications compared to decedents identified as White (OR = 1.74, 95% CI: 1.52, 2.00, p < .0001). Sexual orientation was also a significant predictor of break-up classification, where decedents identified as gay had higher odds of having a break-up narrative classification compared to heterosexual decedents (OR = 1.90, 95% CI: 1.55, 2.31, p < .0001); decedents identified as lesbian had higher odds of having a break-up narrative classification compared to heterosexual decedents (OR = 3.26, 95% CI: 2.57, 4.08, p < .0001). Across marital status, all decedents that were not indicated as married had increased odds, further detailed in Table 3 . Not currently being in a relationship was associated with higher odds of having break-up narrative classification (OR = 11.42, 95% CI: 10.79 12.08, p < .0001). Out of all demographic predictors, only having a physical health problem predicted reduced odds of break-up classification (OR = 0.27, 95% CI: .25, .29, p < .0001). Child custody loss Compared with decedents who were identified White, decedents identified as American Indian/Alaska Native, non-Hispanic had higher odds of having a child custody loss classification (OR = 3.02, 95% CI: 2.20, 4.04, p < .0001); decedents who were identified as Hispanic had higher odds of having a child custody loss classification (OR = 1.68, 95% CI: 1.38, 2.02, p < .0001). Marital status also predicted classification of child custody loss within narratives. Compared to those married, divorced marital status was associated with higher odds of child custody loss classification, (OR = 2.06, 95% CI: 1.75, 2.42, p < .0001); married but separated was associated with higher odds of child custody loss classification (OR = 2.65, 95% CI: 1.94, 3.53, p < .0001); never married was associated with higher odds of child custody loss classification (OR = 1.75, 95% CI: 1.51, 2.03, p < .0001). Homelessness was associated with higher odds of having child custody loss classification, compared with decedents with housing (OR = 2.08, 95% CI: 1.41, 2.94, p < .0001). Number of substances was associated with increased odds of having child custody loss classification, for each additional substance identified on scene (OR = 1.03, 95% CI: 1.01, 1.04, p < .0001). Sex identified in NVDRS was predictive of child custody loss narrative classification, where men had reduced odds compared with women (OR = 0.57, 95% CI: .51, .65, p < .0001). Finally, decedents with physical health problems also had reduced odds of child custody loss classifications compared with decedents without physical health problems (OR = 0.51, 95% CI: .43, .61, p < .0001). Discussion This study used NLP topic modeling to develop supervised classifiers of circumstances related to social isolation among LE/CME narratives for suicide decedents. Logistic regressions of classifiers applied to narratives revealed several demographic predictors with important associations that could be important to suicide surveillance and prevention efforts. Our NLP classifiers demonstrated validity with existing NVDRS variables and can be utilized by public health investigators to better examine predictors of social isolation that were difficult to identify via existing NVDRS variables alone. Our methodology provides a general framework for identifying other topics associated with suicide narratives and can better inform prevention efforts. Logistic regressions of narrative topics with demographic predictors revealed several associations that may be important to suicide surveillance efforts. Our findings that male decedents had higher rates of social isolation narrative classification is in line with previous research suggesting that men experience higher rates of loneliness overall. 15 This effect was even higher for decedents identified as gay. This may be due to a variety of societal and systematic factors in which gay decedents may feel ostracized or stigmatized by their immediate social surroundings. 16 Research suggests that for many LGBT decedents, the spaces that seek to offer social support and connection, such as sports clubs and religious community organizations, may instead offer the opposite, with decedents experiencing rejection and stigma. 17 Results showing lack of/fractured intimate relationships are significant predictors of chronic social isolation are unsurprising, and support the validity of the classifier. Our classifier for impending or recent divorce can likely pick up on a more nuanced relationship status than existing structural NVDRS variables. Results show that divorced decedents are less likely to have this classification, which suggests that the current “divorced” status variable may be capturing divorce that occurred in the more distant past relative to suicide. Instead, our impending/recent divorce classification is more closely related to the “Married, Civil/Domestic Partnership but separated” existing variable, and may be able to detect more recent changes. This is important to note, given the higher impact that recent divorces have been documented to have on suicides than more temporally distal divorces. 18 All non-White racial groups had lower odds of impending or recent divorce within suicide narratives, which falls in line with research suggesting that these groups have overall lower suicide risk following divorce, particularly among decedents with larger family support networks. 19 Reduced odds of classification of divorce among decedents identified as gay may also be related to same sex marriage not being broadly legal in the US until 2015. Narrative classifiers indicating recent break-ups showed high associations with single and not in relationship statuses. Among other demographic factors, we found greater likelihood of break-up classifications among gay and lesbian decedents. Within LGBT communities, break-ups may also result in more significant ruptures to other social community bonds than heterosexual decedents, causing the feelings of social isolation post-breakup to be more acute or severe. 20 Despite being a rare narrative classification, child custody loss was highest among American Indians/Alaska Native persons and is particularly troubling. Among many other factors, this categorization appearing in suicide LE/CME narratives may be related to recent nationwide challenges surrounding navigating the intersecting jurisdictions of the Adoption and Safe Families Act and Indian Child Welfare Act, where, in states such as South Dakota, Indian children are “11 times more likely to be removed from their families and placed in foster care than non-Indian children.” 21 Child custody loss classification is also high among decedents who are Hispanic. Although immigration status was not clearly documented within the structured NVDRS data, this may be related to well-documented increased incidents of child custody loss among Hispanic populations resulting from forced separations incurred while trying to navigate the US immigration system. 22 These findings may reflect trends in downstream parental mental health consequences of child custody loss and family separations known to be prevalent within these US populations. Taken together, these classifiers may serve as valuable tools for monitoring trends in social isolation over time and across diverse demographic groups. These data and insights can be instrumental in developing more effective policies and interventions aimed at enhancing access to social support and addressing other specific needs of at-risk individuals and disproportionately affected populations. This could involve increasing access to mental health services, creating community support programs, or implementing policies that promote inclusivity and reduce disparities. Additionally, raising awareness about the health implications of social isolation can help empower individuals to seek out social connections and support. By integrating these strategies, we can not only identify and address existing disparities but also proactively work towards reducing inequalities linked to social isolation. This approach underscores the importance of integrating data-driven insights into policy and intervention, ensuring a comprehensive and impactful response. The CDC’s Suicide Prevention Resource for Action outlines comprehensive, evidence-based strategies for suicide prevention that communities and public health organizations can adopt to remedy existing health inequities, including social isolation and loneliness, and consequently reduce the risk of suicide and suicidal behaviors. 23 While much has been learned from the NVDRS, researchers have largely used the structured variables; traditional qualitative methods are too labor intensive to use at scale. Despite the potential benefits to uncovering additional nuance and insights within LE/CME text narratives, these data are only beginning to be examined. 24 Recent studies have applied similar NLP-based methods within the NVDRS for identifying intimate partner violence and assessing relevant predictors, 25 , 26 characterizing circumstantial antecedents for fire-arm related suicides among women, 27 and identifying social determinants of health such as economic, interpersonal, health and job-related problems. 28 , 29 NLP-related methodologies have also been used to improve overall classification of suicide and reduce potential data quality issues and racial biases in classification trends. 30 , 31 Our work stands with others who are working to leverage state of the art NLP methods to maximize the potential of LE/CME narratives to illuminate many aspects of violent death, from proximate correlates to nuanced context. While this study offers several important contributions to the study of social isolation and suicide, it is important to consider key limitations. Classification frequencies observed in this study for chronic social isolation likely underrepresent actual incidence rates, given that the most socially isolated decedents are likely to lack evidence sources frequently referenced in the LE/CME narratives, which are often decedent next of kin, friends, spouses, or neighbors. Classification models are likely influenced by many positive classes within most annotation samples. To create more generalizable models, it may be advantageous to annotate additional training data, or ensure that the training data are more balanced across positive and negative examples. Although we have taken every measure to ensure annotators agree in their labeling mechanism through collaborative development of coding ontologies, annotating the social isolation circumstances is still a challenging task due to its conceptually complex nature. The coding task may also be subject to annotator bias. 32 Additionally, while topic modeling on shorter-length summary fields was advantageous to initially identify more coherent topics, it may not fully capture the variability of language reflected in the full-length narratives. Lexicons developed in this study from summary fields may be able to be expanded in future iterations using word embeddings models trained on NVDRS narrative data. Finally, it should be noted that although the NVDRS is one of the most comprehensive US data sources on violent deaths and suicide, many states changed or adopted data collection efforts only as of 2010, and thus we expect that there are differences in missingness based on decedent region before and after this time. Additionally, increases in prevalence may also reflect improvements in documentation and data collection. Other studies have demonstrated that the prevalence of LE/CME narratives within NVDRS have increased over time as well. 12 Furthermore, our study period ends in 2020, and likely does not capture the full effect of the COVID-19 pandemic on increased social isolation, and potentially related suicides. Methods Experimental Design In this observational study we utilized a dataset from the National Violent Death Reporting System (NVDRS), which comprises 306,817 suicide incidents documented between 2002 and 2020, to apply NLP techniques to identify social-isolation related circumstances mentioned within suicide narratives which are not captured in current structured variables. The NVDRS consolidates comprehensive information on violent deaths from a variety of sources, including detailed narratives from law enforcement (LE) and coroner/medical examiner (CME) offices. Of the 306,817 decedents recorded, 216,237 (70.5%) of these had narrative information in at least one LE/CME free-text field. Statistical Analysis Our NLP methodological framework is guided by Computational Grounded Theory, which emphasizes an iterative process of text data mining with the critical inclusion of human expertise. 33 Our approach combines advanced unsupervised and supervised learning techniques with human expert annotations to examine the circumstances surrounding suicides, specifically focusing on social isolation-related events and factors. The analytic process, as illustrated in Figure 2, includes: 1) topic modeling of suicide circumstance summaries guided by expert selection, 2) development of regular expression lexicons to search full LE/CME narratives, 3) annotation of these matches for topic relevance to train and evaluate supervised learning classifiers, and application of these classifiers across the entire NVDRS dataset. 1. BERTopic Topic Modeling Topic modeling began with the selection of cases from the NVDRS database that included text fields from LE/CME circumstance summaries, which occurred less frequently than full-length LE/CME narratives. LE circumstance summaries (n = 20,731, 6.8%) and CME circumstance summaries (n=30,584, 10.0%) provide shorter text summarizations (40-50 characters) of relevant contexts related to the suicides mentioned within the corresponding full LE/CME narratives. These circumstance summary texts were used for BERTopic topic modeling, an unsupervised machine learning approach that applies the BERT framework to derive meaningful topics from extensive textual data. 34 The shorter text within circumstance summaries resulted in less noise in topic model results than conducting the modeling on the full-length narratives. Summary topic model results could therefore be more easily understood and relevant topics identified by subject matter experts before identifying topics within the larger sample of full narrative texts. Further refining our BERTopic implementation, we conducted a grid search across 54 combinations of hyperparameters to optimize the topic clustering process prior to topic interpretation. This process is further detailed in the Supplemental Materials. The highest coherence model resulted in a total of 64 topics, including one uncategorized topic. Next, the team conferred to select any topics identified in the topic model which were related to social isolation, conceptualized as either chronic social isolation, or events which could trigger intense social isolation. This method surfaced six social isolation-related topics: chronic social isolation, child custody loss, divorce/separation, breakup, loss of pet, and eviction/moving. These topics were used in the next step to identify key words and phrases which could be used to search through the full-length text narratives, which were longer, but far more prevalent than the summaries. 2. Regular Expression Lexicon Development and Matching Following the unsupervised BERTopic modeling, we identified relevant key n-grams (from unigrams to trigrams) within the top 50 most frequent terms and phrases that were present in circumstance summaries categorized in each specific social isolation-related topic. Our team of annotators identified terms that were uniquely related to each topic under consideration. This step was essential to pinpoint terms related to social isolation or related events, which we subsequently used to conduct regex searches across the corpus of decedents’ full-length text LE/CME narratives. At this point, we focused our analyses on the full-length narratives. To ensure the validity of these topics' association with suicide, an additional layer of expert annotation was performed on a sample of 100 full-length narratives matched with each of the six topics. This annotation process focused on confirming the relevance of the topics to the suicides reported in the NVDRS database. We conducted interrater agreement for the first 50 coded narratives for each narrative. 3.Supervised Learning Classifier Training, Evaluation, and Prediction Each set of 100 annotated narratives were used to train a suite of supervised learning classifiers for each social isolation-related topic more precisely beyond keyword matching alone. These classifiers included Naive Bayes, Logistic Regression, Random Forest, and RoBERTa, with an 80/20 training/test split. Hyperparameter optimization was conducted across discrete values relevant to each model, described further in the Supplemental Materials. Finally, we evaluated classifiers, prioritizing high macro-F1 and positive-class recall rates, and saved those models which were used to predict a binary (relevant or non-relevant) classifier for each topic. After identifying the best performing models for each topic, we generated predictions for the 100 samples and conducted 1000 iterations of bootstrapping random slices of 80% of the dataset to generate confidence intervals for each performance metric. Declarations Acknowledgments The National Violent Death Reporting System (NVDRS) is administered by the Centers for Disease Control and Prevention (CDC) by participating NVDRS states. The findings and conclusions of this study are those of the authors alone and do not necessarily represent the official position of the CDC or of participating NVDRS states. Funding: Include all funding sources, including grant numbers, complete funding agency names, and recipient’s initials. Each funding source should be listed in a separate paragraph such as: National Institute on Drug Abuse R01DA057599 (AS, SD) National Institute on Drug Abuse T32 DA0505552 (DW, SP) Author contributions: Each author’s contribution(s) to the paper should be listed (we suggest following the CRediT model with each CRediT role given its own line. No punctuation in the initials. Conceptualization: DW, AS, SD Methodology: DW, SR, SD, SP, AS Investigation: DW, SR, SD, SP, AS Visualization: DW Supervision: AS, SS Writing—original draft: DW, AS Writing—review & editing: DW, SR, SD, SP, AS Competing interests: Authors declare that they have no competing interests Data and materials availability: Access to the NVDRS Restricted Access Database requires approval from the NVDRS RAD review committee, consisting of scientific and data analysis experts within CDC's National Center for Injury Prevention and Control. More information on application procedures can be found here: https://www.cdc.gov/nvdrs/about/nvdrs-data-access.html Code for analysis used in this study can be found at the following GitHub repository: https://github.com/drew-walkerr/nvdrs-social-isolation-classification.git References Office of the Surgeon General (OSG). Our Epidemic of Loneliness and Isolation: The U.S. Surgeon General’s Advisory on the Healing Effects of Social Connection and Community. US Department of Health and Human Services; 2023. Accessed February 21, 2024. http://www.ncbi.nlm.nih.gov/books/NBK595227/ Van Orden KA, Witte TK, Cukrowicz KC, Braithwaite S, Selby EA, Joiner TE. The Interpersonal Theory of Suicide. Psychol Rev. 2010;117(2):575–600. doi: 10.1037/a0018697 Umberson D, Lin Z, Cha H. Gender and Social Isolation across the Life Course. J Health Soc Behav. 2022;63(3):319–335. doi: 10.1177/00221465221109634 Calati R, Ferrari C, Brittner M, et al. Suicidal thoughts and behaviors and social isolation: A narrative review of the literature. J Affect Disord. 2019;245:653–667. doi: 10.1016/j.jad.2018.11.022 Leigh-Hunt N, Bagguley D, Bash K, et al. An overview of systematic reviews on the public health consequences of social isolation and loneliness. Public Health. 2017;152:157–171. doi: 10.1016/j.puhe.2017.07.035 Valtorta NK, Kanaan M, Gilbody S, Hanratty B. Loneliness, social isolation and social relationships: what are we measuring? A novel framework for classifying and comparing tools. BMJ Open. 2016;6(4):e010799. doi: 10.1136/bmjopen-2015-010799 Smith RW, Holt-Lunstad J, Kawachi I. Benchmarking Social Isolation, Loneliness, and Smoking: Challenges and Opportunities for Public Health. Am J Epidemiol. 2023;192(8):1238–1242. doi: 10.1093/aje/kwad121 Loneliness and Social Isolation Detection Using Passive Sensing Techniques: Scoping Review. JMIR Preprints. Accessed April 25, 2024. https://preprints.jmir.org/preprint/34638 Sheats KJ. Surveillance for Violent Deaths — National Violent Death Reporting System, 39 States, the District of Columbia, and Puerto Rico, 2018. MMWR Surveill Summ. 2022;71. doi: 10.15585/mmwr.ss7103a1 Nazarov O, Guan J, Chihuri S, Li G. Research utility of the National Violent Death Reporting System: a scoping review. Inj Epidemiol. 2019;6(1):18. doi: 10.1186/s40621-019-0196-9 National Violent Death Reporting System|NVDRS|Violence Prevention|Injury Center|CDC. Published May 23, 2023. Accessed February 21, 2024. https://www.cdc.gov/nvdrs/about/index.html Dang LN, Kahsay ET, James LN, Johns LJ, Rios IE, Mezuk B. Research utility and limitations of textual data in the National Violent Death Reporting System: a scoping review and recommendations. Inj Epidemiol. 2023;10(1):23. doi: 10.1186/s40621-023-00433-w Arseniev-Koehler A, Cochran SD, Mays VM, Chang KW, Foster JG. Integrating topic modeling and word embedding to characterize violent deaths. Proc Natl Acad Sci. 2022;119(10):e2108801119. doi: 10.1073/pnas.2108801119 Somé NH, Noormohammadpour P, Lange S. The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review. Front Psychiatry. 2024;15. doi: 10.3389/fpsyt.2024.1291362 Ernst M, Klein EM, Beutel ME, Brähler E. Gender-specific associations of loneliness and suicidal ideation in a representative population sample: Young, lonely men are particularly at risk. J Affect Disord. 2021;294:63–70. doi: 10.1016/j.jad.2021.06.085 Mink MD, Lindley LL, Weinstein AA. Stress, Stigma, and Sexual Minority Status: The Intersectional Ecology Model of LGBTQ Health. J Gay Lesbian Soc Serv. 2014;26(4):502–521. doi: 10.1080/10538720.2014.953660 Garcia J, Vargas N, Clark JL, Magaña Álvarez M, Nelons DA, Parker RG. Social isolation and connectedness as determinants of well-being: Global evidence mapping focused on LGBTQ youth. Glob Public Health. 2020;15(4):497–519. doi: 10.1080/17441692.2019.1682028 Stack S, Scourfield J. Recency of Divorce, Depression, and Suicide Risk. J Fam Issues. 2015;36(6):695–715. doi: 10.1177/0192513X13494824 Denney JT, Rogers RG, Krueger PM, Wadsworth T. Adult Suicide Mortality in the United States: Marital Status, Family Size, Socioeconomic Status, and Differences by Sex. Soc Sci Q. 2009;90(5):1167–1185. doi: 10.1111/j.1540-6237.2009.00652.x Monk JK, Ogolsky BG, Oswald RF. Coming Out and Getting Back In: Relationship Cycling and Distress in Same- and Different-Sex Relationships. Fam Relat. 2018;67(4):523–538. doi: 10.1111/fare.12336 Eagle JL Agnel Philip,Jaida Grey. Native American Families Are Being Broken Up in Spite of a Law Meant to Keep Children With Their Parents. ProPublica. Published June 15, 2023. Accessed March 28, 2024. https://www.propublica.org/article/native-american-parental-rights-termination-icwa-scotus Dreby J. U.S. immigration policy and family separation: The consequences for children’s well-being. Soc Sci Med. 2015;132:245–251. doi: 10.1016/j.socscimed.2014.08.041 CDC. (2022). Suicide Prevention Resource for Action: A Compilation of the Best Available Evidence. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention. Available at https://www.cdc.gov/suicide/pdf/preventionresource.pdf Lindley LC, Policastro CN, Dosch B, Ortiz Baco JG, Cao CQ. Artificial Intelligence and the National Violent Death Reporting System: A Rapid Review. Comput Inform Nurs CIN. Published online March 26, 2024. doi: 10.1097/CIN.0000000000001124 Kafka JM, Fliss MD, Trangenstein PJ, Reyes LM, Pence BW, Moracco KE. Detecting intimate partner violence circumstance for suicide: development and validation of a tool using natural language processing and supervised machine learning in the National Violent Death Reporting System. Inj Prev. 2023;29(2):134–141. doi: 10.1136/ip-2022-044662 Kafka JM, Moracco KE, Pence BW, Trangenstein PJ, Fliss MD, McNaughton Reyes L. Intimate partner violence and suicide mortality: a cross-sectional study using machine learning and natural language processing of suicide data from 43 states. Inj Prev J Int Soc Child Adolesc Inj Prev. 2024;30(2):125–131. doi: 10.1136/ip-2023-044976 Goldstein EV, Mooney SJ, Takagi-Stewart J, et al. Characterizing Female Firearm Suicide Circumstances: A Natural Language Processing and Machine Learning Approach. Am J Prev Med. 2023;65(2):278–285. doi: 10.1016/j.amepre.2023.01.030 Wang S, Dang Y, Sun Z, et al. An NLP approach to identify SDoH-related circumstance and suicide crisis from death investigation narratives. J Am Med Inform Assoc JAMIA. 2023;30(8):1408–1417. doi: 10.1093/jamia/ocad068 Kim K, Ye GY, Haddad AM, Kos N, Zisook S, Davidson JE. Thematic analysis and natural language processing of job-related problems prior to physician suicide in 2003–2018. Suicide Life Threat Behav. 2022;52(5):1002–1011. doi: 10.1111/sltb.12896 Wang S, Zhou Y, Han Z, et al. Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes. Published online March 28, 2024. doi: 10.48550/arXiv.2403.19432 Rahman N, Mozer R, McHugh RK, Rockett IRH, Chow CM, Vaughan G. Using natural language processing to improve suicide classification requires consideration of race. Suicide Life Threat Behav. 2022;52(4):782–791. doi: 10.1111/sltb.12862 Hovy D, Prabhumoye S. Five sources of bias in natural language processing. Lang Linguist Compass. 2021;15(8):e12432. doi: 10.1111/lnc3.12432 Nelson LK. Computational Grounded Theory: A Methodological Framework. Sociol Methods Res. 2020;49(1):3–42. doi: 10.1177/0049124117729703 Grootendorst M. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. Published online March 11, 2022. doi: 10.48550/arXiv.2203.05794 Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12:2825–2830. McInnes L, Healy J, Saul N, Großberger L. UMAP: Uniform Manifold Approximation and Projection. J Open Source Softw. 2018;3(29):861. doi: 10.21105/joss.00861 Řehůřek R, Sojka P. Software Framework for Topic Modelling with Large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. ELRA; 2010:45–50. Röder M, Both A, Hinneburg A. Exploring the Space of Topic Coherence Measures. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. WSDM ’15. Association for Computing Machinery; 2015:399–408. doi: 10.1145/2684822.2685324 Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations There is NO Competing Interest. Supplementary Files supplementarymaterialnvdrssocialisolationnaturehumanbehavior.docx Supplementary Material Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7096343","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":487344318,"identity":"c424d269-9a59-430f-904e-b94ce3062cf1","order_by":0,"name":"Drew Walker","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDCCAyCiAIjZ2YBERQKxWgyAmBmk5QzJWhjbiNDCd/vwMQmglsT+ZrbEx5Xz0vIY2Hsfv8CnRfJcWhpYy4zDbIcNz27LKWbgOW5mgU+LwRkesxtALbkNh9nbJBu3VSQ2SKSxGeDXwv8NrGX+Yfb2n41ziNLCwwbWsuEw2zHGxoYckBbmB3j9cobN/EeCgUT9xsNsyZINx9KK2XiOseHTwcB3hvmxwYcKG2O5422GHxtqkvP42duYP+DVAwIJDBIINtAKNgk8irFpB8YqYVtGwSgYBaNgJAEAWYZGPu70gCcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4216-2396","institution":"Kaiser Permanente School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Drew","middleName":"","lastName":"Walker","suffix":""},{"id":487344319,"identity":"e78e9d9c-f3af-4cf2-93ed-0c32c7607ea2","order_by":1,"name":"Swati Rajwal","email":"","orcid":"https://orcid.org/0000-0002-3826-5069","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Swati","middleName":"","lastName":"Rajwal","suffix":""},{"id":487344320,"identity":"e596d002-c5ef-4133-b280-3d1e3a90f289","order_by":2,"name":"Sudeshna Das","email":"","orcid":"https://orcid.org/0000-0002-2112-6986","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Sudeshna","middleName":"","lastName":"Das","suffix":""},{"id":487344321,"identity":"a5e9b948-ee29-4f89-b524-975b3cfbbbe5","order_by":3,"name":"Snigdha Peddireddy","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Snigdha","middleName":"","lastName":"Peddireddy","suffix":""},{"id":487344322,"identity":"029e5bd1-5232-48dd-88ed-da649cc6fac0","order_by":4,"name":"Abeed Sarker","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Abeed","middleName":"","lastName":"Sarker","suffix":""}],"badges":[],"createdAt":"2025-07-10 22:50:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7096343/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7096343/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87554272,"identity":"1ec9ea23-ac23-4f22-a104-7a3429749b04","added_by":"auto","created_at":"2025-07-25 06:40:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48695,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSocial isolation-related suicide events over time, per 1000 suicides per year\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7096343/v1/6840ad65091f4b45a7829a96.png"},{"id":87554705,"identity":"1cbe867a-908c-4091-8e04-690b19aca0fc","added_by":"auto","created_at":"2025-07-25 06:48:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":114884,"visible":true,"origin":"","legend":"\u003cp\u003eSocial isolation-related suicide circumstance identification analysis pipeline\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7096343/v1/5c734fb7e9f29a4cdd297294.png"},{"id":87554707,"identity":"908ca67b-47cf-4c2b-a818-6817c9e51591","added_by":"auto","created_at":"2025-07-25 06:48:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":809346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7096343/v1/e97e4064-3e8a-42c8-82ff-7338e0fc914d.pdf"},{"id":87554275,"identity":"ceacef88-f7f4-41fc-a69d-6fd1f9b7ee85","added_by":"auto","created_at":"2025-07-25 06:40:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45170,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"supplementarymaterialnvdrssocialisolationnaturehumanbehavior.docx","url":"https://assets-eu.researchsquare.com/files/rs-7096343/v1/b438e3c5f001257b170b24fd.docx"},{"id":87554273,"identity":"5ab544f9-8023-4918-ad50-cabfaeb33a0d","added_by":"auto","created_at":"2025-07-25 06:40:24","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28578,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7096343/v1/f5f91acc79b74034c7fad203.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods","fulltext":[{"header":"Teaser","content":"\u003cp\u003eNatural language processing identified trends and predictors of social isolation and loneliness within a comprehensive dataset of US suicide records.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eSocial isolation is a critical factor that contributes to the United States\u0026rsquo; mental health and suicide crisis. In 2023, the US Surgeon General released an advisory highlighting the epidemic of loneliness and social isolation and proposing a call to action for intervention frameworks and policy priorities.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e He emphasized the Interpersonal Theory of Suicide\u0026rsquo;s assertion that social isolation is the most important contributing factor to suicide across all age and demographic groups.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Social isolation and its intense, well-documented negative effects, may be experienced more acutely during specific critical life events, and these experiences may vary by race, gender, or other demographic factors.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Despite mounting evidence on the detrimental effects of social isolation, identification and measurement of this phenomenon, and its impacts on mortality and morbidity at an epidemiological scale are difficult to measure, given existing survey methods.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Recent reviews have called for public health researchers to pursue novel methods to understand evolving trends in how social isolation contributes to suicide and mental health, and how these trends differ across populations and demographic factors.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e The National Violent Death Reporting System (NVDRS) has historically been one of the most comprehensive US data sources on suicide and violent deaths.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e NVDRS pools over 600 data elements, abstracted from coroner medical examiner and law enforcement narratives, toxicology reports, and death certificates. Current research on NVDRS has used the dataset to explore contexts surrounding suicide, in an attempt to understand common circumstances and co-occurring alcohol and substance use.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e NVDRS collects data on demographics and a wide variety of related circumstances, ranging from \u0026ldquo;relationship problems; mental health conditions and treatment; toxicology results; and life stressors, including recent money- or work-related or physical health problems.\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Still, there are limits to structured data, including information about circumstances related to social isolation and related events.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e The NVDRS contains extensive text data from law enforcement (LE) and coroner medical examiner (CME) incident narratives, including information on circumstances, other suspects or individuals on scene, and additional details not captured in structured data. These text fields may vary in the level of detail but often provide additional information not captured within structured variables. Despite the potential insights from LE/CME narratives, there is significant cost in time and effort required by abstractors to manually review a large range of concepts within this volume of highly variable and lengthy text data.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003eAdvances in natural language processing have enabled researchers in recent years to apply computational methods to mine information from narratives in NVDRS, illuminating important trends related to mental health, violent death, and suicide.\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe objective of this study was to explore circumstances related to the intersection of social isolation and suicide. To achieve this objective, we developed and applied natural language processing techniques to identify social isolation-related circumstances and events from free-text LE/CME narratives, recorded within the NVDRS for suicide decedents identified from 2002 to 2020. After development of identification methods, we applied models to assess longitudinal trends and associations with relevant demographic factors to identify at-risk groups for social isolation-related suicide.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e provides descriptive statistics for the 306,817 decedents who died by suicide recorded in the NVDRS dataset from 2002 to 2020. Most decedents in this sample were male (78.1%), with an average age of 46.3 years, and largely were classified as White (82.1%), Black (6.5%), or Hispanic (6.3%) decedents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBERTopic Topic Modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBERTopic topic modeling generated 64 topics within the highest performance coherence model, which was trained upon the shorter text fields of LE/CME suicide circumstance summaries. LE circumstance summaries (n\u0026thinsp;=\u0026thinsp;20,731, 6.8% of decedents) and CME circumstance summaries (n\u0026thinsp;=\u0026thinsp;30,584, 10.0%) provide shorter text summarizations (40\u0026ndash;50 characters) of relevant contexts related to the suicides mentioned within the corresponding full LE/CME narratives. The shorter text within these fields allowed for more easily understood and coherent topic modeling results, which could then be identified by subject matter experts, and used to construct lexicons of words and phrases to search across a larger sample of full-length LE/CME narratives.\u003c/p\u003e\n\u003cp\u003eShorter text circumstance summary fields were used for topic modeling due to the high amount of noise assessed in longer-length full LE/CME narratives. After reviewing topics, we arrived at six distinct narrative topics to apply towards classifying the full LE/CME narratives related to social isolation: 1) chronic social isolation, 2) recent or impending divorce, 3) eviction or move, 4) break-up, 5) child custody loss, and 6) pet loss. These classifier labels were chosen due to their relevance to social isolation and value added to identifying phenomena not currently captured by existing NVDRS structured variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegex search and annotation process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e displays results of regular expression search matches, along with the percentage of the sample which were annotated as related to each topic, interrater reliability for the first 50 samples, and an example sentence (edited to provide anonymity) from positively classified narrative matches for each topic. When matching with the regular expression lexicons, we had the highest frequencies of matches with 1) recent eviction or move, (N\u0026thinsp;=\u0026thinsp;29,913 decedent narrative matches; 9.76% of sample narratives), 2) recent or impending divorce or separation (15,263; 4.98%), and 3) recent break-up (12,279; 4.0%). Lower frequencies of matches were found among the topics of child custody loss (5,322, 1.73% of sample), recent pet loss (1,355, 0.44%), and chronic social isolation (1,198, 0.39%).\u003c/p\u003e\n\u003cp\u003eFollowing manual annotation to assess relevance to topics of interest, nearly all 100 samples with break-up/social isolation were found to be relevant to the topic, while divorce (66%), child custody loss (50%), eviction/move (36%), and loss of pet (28%) resulted in fewer narratives that actually contained references to the topic of interest. Interrater annotator agreement was relatively high across all topics, ranging from 72 to 98%, (kappa range: .24, .85).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Annotation Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplemental Table\u0026nbsp;1 describes social isolation and related event classifier performance results, following the annotation of 100 samples of each of the 6 topics. Overall, across all models, best results were achieved from logistic regression and naive bayes models. Of all the topics, we achieved highest performance results in social isolation, child custody loss, break-up, and divorce, with macro F1 performance ranging from .87 to 1, accuracy from .66 to 1, recall from .75 to 1, and positive-class precision from .75 to 1. Recent eviction or move performed slightly worse, with the pet loss classifier performing the worst, only predicting negative classes. For this reason, the pet loss topic was dropped from the predictive analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictions, Distributions on entire Dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplemental Table\u0026nbsp;2 describes the frequencies of regular expression matches and refined classifier predictions across the entire dataset, normalized per rate of 1000 suicides in the overall dataset. The most frequently predicted topics included 1) recent or impending divorce (50.0 per 1000 decedents), 2) recent breakup (40.1 per 1000 decedents), and recent eviction or move (30.9 per 1000 decedents). The least frequently predicted classes were child custody loss (4.0 per 1000 decedents), and chronic social isolation (3.9 per 1000 decedents).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e displays trends in suicide events by topic from 2002 to 2020. Over this time, across all topics, we observed a gradual increase in rates over time, with several peaks in topic classifications per year. Recent breakup, recent/impending divorce, and recent eviction/move were the most frequently observed topics over time, despite decreases by 2020. We observed peaks in suicide narratives mentioning recent/impending divorces in 2010; and recent evictions/moves in 2016 through 2017. Chronic social isolation and child custody loss peaked in 2020 and 2019, respectively, though both topics had relatively much lower frequencies than others.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic predictors of social isolation-related event narrative classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDecedents flagged with social isolation in their LE/CME narrative, on average, were 1.28 years younger (95% CI: -0.23, -2.32, p\u0026thinsp;=\u0026thinsp;.016) than those without. For divorce, decedents were on average 1.44 years younger (95% CI: -1.74, -1.14, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Eviction classifications were associated with being younger (95% CI: -1.43, \u0026minus;\u0026thinsp;.67, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Larger differences in age were found among decedents with child custody loss classifications, where decedents were 9.28 years younger on average (95% CI: -10.31, -8.25, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001), and those with breakup classifications, where decedents were on average 15.11 years younger (95% CI: -15.44, -14.79, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). We next examined non-continuous demographic variables and whether the decedent had a social isolation-classified LE/CME narrative (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChronic Social Isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMen had higher odds of chronic social isolation classification (Odds Ratio (OR)\u0026thinsp;=\u0026thinsp;1.44, 95% CI: 1.24, 1.69, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); decedents identified as gay had higher odds (OR\u0026thinsp;=\u0026thinsp;3.68, 95% CI: 1.97, 6.33, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001) compared with heterosexual decedents; and those who were divorced had higher odds (OR\u0026thinsp;=\u0026thinsp;3.34, 95% CI: 2.68, 4.19, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001) compared with decedents who were married. The odds of having chronic social isolation classification was higher for married but separated decedents (OR\u0026thinsp;=\u0026thinsp;3.43, 95% CI: 2.24, 5.09, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001), for widowed decedents (OR\u0026thinsp;=\u0026thinsp;3.04, 95% CI: 2.21, 4.14, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001), for never married decedents (OR\u0026thinsp;=\u0026thinsp;5.81, 95% CI: 4.78, 7.12, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001), and for decedents identified as not currently in a relationship (OR\u0026thinsp;=\u0026thinsp;6.97, 95% CI: 5.61, 8.69, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Decedents identified as non-Hispanic Black or African American had lower odds of chronic social isolation classification (OR\u0026thinsp;=\u0026thinsp;0.62, 95% CI: .46, .81, p\u0026thinsp;=\u0026thinsp;.001) compared with non-Hispanic White decedents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecent or Impending Divorce\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to married decedents, those identified as married but separated had higher odds of having recent or impending divorce narrative classification (OR\u0026thinsp;=\u0026thinsp;6.61, 95% CI: 6.27, 6.96, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); compared with decedents in a relationship, those not currently in a relationship had higher odds of having the recent or impending divorce classification (OR\u0026thinsp;=\u0026thinsp;1.79, 95%CI: 1.69, 1.89, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); men had higher odds of having recent or impending divorce classification than women (OR\u0026thinsp;=\u0026thinsp;1.31, 95% CI: 1.26, 1.37, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001).\u003c/p\u003e\n\u003cp\u003eAdditionally, several factors showed decreased odds of impending or recent divorce classification. Compared to decedents identified as White, most other racial groups had reduced odds of having recent or impending divorce classification. Decedents identified as gay had lower odds of having recent or impending divorce classification (OR\u0026thinsp;=\u0026thinsp;0.28, 95% CI: .19, .41, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001) compared to heterosexual decedents; decedents identified as transgender had lower odds of having recent or impending divorce classification (OR\u0026thinsp;=\u0026thinsp;0.24, 95% CI: .10, .48, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Decedents who were identified as divorced had lower odds of having recent or impending divorce narrative classification (OR\u0026thinsp;=\u0026thinsp;0.68, 95% CI: .65, .71, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); never married, single, or widowed all much lower odds of having recent or impending divorce classification (OR range: .04, .10, all p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Decedents identified as homeless had lower odds of recent or impending divorce narrative classification (OR\u0026thinsp;=\u0026thinsp;0.73, 95% CI: .61, .87, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); decedents identified with a physical health problem had lower odds of recent or impending divorce narrative classification (OR\u0026thinsp;=\u0026thinsp;0.54, 95% CI: .51, .57, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEviction or recent move\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDecedents identified as Hispanic had higher odds of eviction classification (OR\u0026thinsp;=\u0026thinsp;1.18, 95% CI: 1.09, 1.28, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001) compared to non-Hispanic White decedents. Decedents with marital statuses including divorce had higher odds of eviction or recent move (OR\u0026thinsp;=\u0026thinsp;1.33, 95% CI: 1.25, 1.40, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); decedents identified as married but separated had higher odds of eviction classification compared to married decedents (OR\u0026thinsp;=\u0026thinsp;2.34, 95% CI: 2.02, 2.48, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Decedents who were not currently in a relationship had higher odds of eviction or recent move classification, compared to those currently in a relationship (OR\u0026thinsp;=\u0026thinsp;1.58, 95% CI: 1.47, 1.70, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Decedents identified as homeless had higher odds of eviction or recent move narrative classification (OR\u0026thinsp;=\u0026thinsp;1.42, 95% CI: 1.20, 1.66, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Finally, men had lower odds of having eviction or recent move classification compared to women (OR\u0026thinsp;=\u0026thinsp;0.88, 95%: .84, .92, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBreak-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMen had higher odds of having breakup classification in narratives than women (OR\u0026thinsp;=\u0026thinsp;1.13, 95% CI: 1.08, 1.18, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Decedents identified as American Indian or Alaska Native, non-Hispanic had higher odds of having a break-up narrative classification compared to decedents identified as White (OR\u0026thinsp;=\u0026thinsp;1.46, 95% CI: 1.27, 1.68, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); decedents identified as Hispanic had higher odds of having break-up narrative classification compared to decedents identified as White (OR\u0026thinsp;=\u0026thinsp;1.61, 95% CI: 1.51, 1.72, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); decedents identified as two or more races, non-Hispanic had higher odds of having break-up narrative classifications compared to decedents identified as White (OR\u0026thinsp;=\u0026thinsp;1.74, 95% CI: 1.52, 2.00, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Sexual orientation was also a significant predictor of break-up classification, where decedents identified as gay had higher odds of having a break-up narrative classification compared to heterosexual decedents (OR\u0026thinsp;=\u0026thinsp;1.90, 95% CI: 1.55, 2.31, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); decedents identified as lesbian had higher odds of having a break-up narrative classification compared to heterosexual decedents (OR\u0026thinsp;=\u0026thinsp;3.26, 95% CI: 2.57, 4.08, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Across marital status, all decedents that were not indicated as married had increased odds, further detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Not currently being in a relationship was associated with higher odds of having break-up narrative classification (OR\u0026thinsp;=\u0026thinsp;11.42, 95% CI: 10.79 12.08, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Out of all demographic predictors, only having a physical health problem predicted reduced odds of break-up classification (OR\u0026thinsp;=\u0026thinsp;0.27, 95% CI: .25, .29, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChild custody loss\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared with decedents who were identified White, decedents identified as American Indian/Alaska Native, non-Hispanic had higher odds of having a child custody loss classification (OR\u0026thinsp;=\u0026thinsp;3.02, 95% CI: 2.20, 4.04, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); decedents who were identified as Hispanic had higher odds of having a child custody loss classification (OR\u0026thinsp;=\u0026thinsp;1.68, 95% CI: 1.38, 2.02, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Marital status also predicted classification of child custody loss within narratives. Compared to those married, divorced marital status was associated with higher odds of child custody loss classification, (OR\u0026thinsp;=\u0026thinsp;2.06, 95% CI: 1.75, 2.42, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); married but separated was associated with higher odds of child custody loss classification (OR\u0026thinsp;=\u0026thinsp;2.65, 95% CI: 1.94, 3.53, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001); never married was associated with higher odds of child custody loss classification (OR\u0026thinsp;=\u0026thinsp;1.75, 95% CI: 1.51, 2.03, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Homelessness was associated with higher odds of having child custody loss classification, compared with decedents with housing (OR\u0026thinsp;=\u0026thinsp;2.08, 95% CI: 1.41, 2.94, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Number of substances was associated with increased odds of having child custody loss classification, for each additional substance identified on scene (OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.01, 1.04, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Sex identified in NVDRS was predictive of child custody loss narrative classification, where men had reduced odds compared with women (OR\u0026thinsp;=\u0026thinsp;0.57, 95% CI: .51, .65, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Finally, decedents with physical health problems also had reduced odds of child custody loss classifications compared with decedents without physical health problems (OR\u0026thinsp;=\u0026thinsp;0.51, 95% CI: .43, .61, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study used NLP topic modeling to develop supervised classifiers of circumstances related to social isolation among LE/CME narratives for suicide decedents. Logistic regressions of classifiers applied to narratives revealed several demographic predictors with important associations that could be important to suicide surveillance and prevention efforts. Our NLP classifiers demonstrated validity with existing NVDRS variables and can be utilized by public health investigators to better examine predictors of social isolation that were difficult to identify via existing NVDRS variables alone. Our methodology provides a general framework for identifying other topics associated with suicide narratives and can better inform prevention efforts.\u003c/p\u003e\u003cp\u003eLogistic regressions of narrative topics with demographic predictors revealed several associations that may be important to suicide surveillance efforts. Our findings that male decedents had higher rates of social isolation narrative classification is in line with previous research suggesting that men experience higher rates of loneliness overall.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e This effect was even higher for decedents identified as gay. This may be due to a variety of societal and systematic factors in which gay decedents may feel ostracized or stigmatized by their immediate social surroundings.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Research suggests that for many LGBT decedents, the spaces that seek to offer social support and connection, such as sports clubs and religious community organizations, may instead offer the opposite, with decedents experiencing rejection and stigma.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Results showing lack of/fractured intimate relationships are significant predictors of chronic social isolation are unsurprising, and support the validity of the classifier.\u003c/p\u003e\u003cp\u003eOur classifier for impending or recent divorce can likely pick up on a more nuanced relationship status than existing structural NVDRS variables. Results show that divorced decedents are less likely to have this classification, which suggests that the current \u0026ldquo;divorced\u0026rdquo; status variable may be capturing divorce that occurred in the more distant past relative to suicide. Instead, our impending/recent divorce classification is more closely related to the \u0026ldquo;Married, Civil/Domestic Partnership but separated\u0026rdquo; existing variable, and may be able to detect more recent changes. This is important to note, given the higher impact that recent divorces have been documented to have on suicides than more temporally distal divorces.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e All non-White racial groups had lower odds of impending or recent divorce within suicide narratives, which falls in line with research suggesting that these groups have overall lower suicide risk following divorce, particularly among decedents with larger family support networks.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Reduced odds of classification of divorce among decedents identified as gay may also be related to same sex marriage not being broadly legal in the US until 2015. Narrative classifiers indicating recent break-ups showed high associations with single and not in relationship statuses. Among other demographic factors, we found greater likelihood of break-up classifications among gay and lesbian decedents. Within LGBT communities, break-ups may also result in more significant ruptures to other social community bonds than heterosexual decedents, causing the feelings of social isolation post-breakup to be more acute or severe.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDespite being a rare narrative classification, child custody loss was highest among American Indians/Alaska Native persons and is particularly troubling. Among many other factors, this categorization appearing in suicide LE/CME narratives may be related to recent nationwide challenges surrounding navigating the intersecting jurisdictions of the Adoption and Safe Families Act and Indian Child Welfare Act, where, in states such as South Dakota, Indian children are \u0026ldquo;11 times more likely to be removed from their families and placed in foster care than non-Indian children.\u0026rdquo; \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Child custody loss classification is also high among decedents who are Hispanic. Although immigration status was not clearly documented within the structured NVDRS data, this may be related to well-documented increased incidents of child custody loss among Hispanic populations resulting from forced separations incurred while trying to navigate the US immigration system.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e These findings may reflect trends in downstream parental mental health consequences of child custody loss and family separations known to be prevalent within these US populations.\u003c/p\u003e\u003cp\u003eTaken together, these classifiers may serve as valuable tools for monitoring trends in social isolation over time and across diverse demographic groups. These data and insights can be instrumental in developing more effective policies and interventions aimed at enhancing access to social support and addressing other specific needs of at-risk individuals and disproportionately affected populations. This could involve increasing access to mental health services, creating community support programs, or implementing policies that promote inclusivity and reduce disparities. Additionally, raising awareness about the health implications of social isolation can help empower individuals to seek out social connections and support.\u003c/p\u003e\u003cp\u003eBy integrating these strategies, we can not only identify and address existing disparities but also proactively work towards reducing inequalities linked to social isolation. This approach underscores the importance of integrating data-driven insights into policy and intervention, ensuring a comprehensive and impactful response. The CDC\u0026rsquo;s Suicide Prevention Resource for Action outlines comprehensive, evidence-based strategies for suicide prevention that communities and public health organizations can adopt to remedy existing health inequities, including social isolation and loneliness, and consequently reduce the risk of suicide and suicidal behaviors.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eWhile much has been learned from the NVDRS, researchers have largely used the structured variables; traditional qualitative methods are too labor intensive to use at scale. Despite the potential benefits to uncovering additional nuance and insights within LE/CME text narratives, these data are only beginning to be examined.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Recent studies have applied similar NLP-based methods within the NVDRS for identifying intimate partner violence and assessing relevant predictors,\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e characterizing circumstantial antecedents for fire-arm related suicides among women,\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and identifying social determinants of health such as economic, interpersonal, health and job-related problems.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e NLP-related methodologies have also been used to improve overall classification of suicide and reduce potential data quality issues and racial biases in classification trends.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Our work stands with others who are working to leverage state of the art NLP methods to maximize the potential of LE/CME narratives to illuminate many aspects of violent death, from proximate correlates to nuanced context.\u003c/p\u003e\u003cp\u003eWhile this study offers several important contributions to the study of social isolation and suicide, it is important to consider key limitations. Classification frequencies observed in this study for chronic social isolation likely underrepresent actual incidence rates, given that the most socially isolated decedents are likely to lack evidence sources frequently referenced in the LE/CME narratives, which are often decedent next of kin, friends, spouses, or neighbors.\u003c/p\u003e\u003cp\u003eClassification models are likely influenced by many positive classes within most annotation samples. To create more generalizable models, it may be advantageous to annotate additional training data, or ensure that the training data are more balanced across positive and negative examples. Although we have taken every measure to ensure annotators agree in their labeling mechanism through collaborative development of coding ontologies, annotating the social isolation circumstances is still a challenging task due to its conceptually complex nature. The coding task may also be subject to annotator bias.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Additionally, while topic modeling on shorter-length summary fields was advantageous to initially identify more coherent topics, it may not fully capture the variability of language reflected in the full-length narratives. Lexicons developed in this study from summary fields may be able to be expanded in future iterations using word embeddings models trained on NVDRS narrative data.\u003c/p\u003e\u003cp\u003eFinally, it should be noted that although the NVDRS is one of the most comprehensive US data sources on violent deaths and suicide, many states changed or adopted data collection efforts only as of 2010, and thus we expect that there are differences in missingness based on decedent region before and after this time. Additionally, increases in prevalence may also reflect improvements in documentation and data collection.\u003c/p\u003e\u003cp\u003eOther studies have demonstrated that the prevalence of LE/CME narratives within NVDRS have increased over time as well.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Furthermore, our study period ends in 2020, and likely does not capture the full effect of the COVID-19 pandemic on increased social isolation, and potentially related suicides.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExperimental Design\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this observational study we utilized a dataset from the National Violent Death Reporting System (NVDRS), which comprises 306,817 suicide incidents documented between 2002 and 2020, to apply NLP techniques to identify social-isolation related circumstances mentioned within suicide narratives which are not captured in current structured variables. The NVDRS consolidates comprehensive information on violent deaths from a variety of sources, including detailed narratives from law enforcement (LE) and coroner/medical examiner (CME) offices. Of the 306,817 decedents recorded, 216,237 (70.5%) of these had narrative information in at least one LE/CME free-text field.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur NLP methodological framework is guided by Computational Grounded Theory, which emphasizes an iterative process of text data mining with the critical inclusion of human expertise.\u003csup\u003e33\u003c/sup\u003e Our approach combines advanced unsupervised and supervised learning techniques with human expert annotations to examine the circumstances surrounding suicides, specifically focusing on social isolation-related events and factors. The analytic process, as illustrated in Figure 2, includes: 1) topic modeling of suicide circumstance summaries guided by expert selection, 2) development of regular expression lexicons to search full LE/CME narratives, 3) annotation of these matches for topic relevance to train and evaluate supervised learning classifiers, and application of these classifiers across the entire NVDRS dataset.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1. BERTopic Topic Modeling\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTopic modeling began with the selection of cases from the NVDRS database that included text fields from LE/CME circumstance summaries, which occurred less frequently than full-length LE/CME narratives. LE circumstance summaries (n = 20,731, 6.8%) and CME circumstance summaries (n=30,584, 10.0%) provide shorter text summarizations (40-50 characters) of relevant contexts related to the suicides mentioned within the corresponding full LE/CME narratives. These circumstance summary texts were used for BERTopic topic modeling, an unsupervised machine learning approach that applies the BERT framework to derive meaningful topics from extensive textual data.\u003csup\u003e34\u003c/sup\u003e The shorter text within circumstance summaries resulted in less noise in topic model results than conducting the modeling on the full-length narratives. Summary topic model results could therefore be more easily understood and relevant topics identified by subject matter experts before identifying topics within the larger sample of full narrative texts. Further refining our BERTopic implementation, we conducted a grid search across 54 combinations of hyperparameters to optimize the topic clustering process prior to topic interpretation. This process is further detailed in the Supplemental Materials. The highest coherence model resulted in a total of 64 topics, including one uncategorized topic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, the team conferred to select any topics identified in the topic model which were related to social isolation, conceptualized as either chronic social isolation, or events which could trigger intense social isolation. This method surfaced six social isolation-related topics: chronic social isolation, child custody loss, divorce/separation, breakup, loss of pet, and eviction/moving. These topics were used in the next step to identify key words and phrases which could be used to search through the full-length text narratives, which were longer, but far more prevalent than the summaries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2. Regular Expression Lexicon Development and Matching\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the unsupervised BERTopic modeling, we identified relevant key n-grams (from unigrams to trigrams) within the top 50 most frequent terms and phrases that were present in circumstance summaries categorized in each specific social isolation-related topic. Our team of annotators identified terms that were uniquely related to each topic under consideration. This step was essential to pinpoint terms related to social isolation or related events, which we subsequently used to conduct regex searches across the corpus of decedents\u0026rsquo; full-length text LE/CME narratives. At this point, we focused our analyses on the full-length narratives. To ensure the validity of these topics\u0026apos; association with suicide, an additional layer of expert annotation was performed on a sample of 100 full-length narratives matched with each of the six topics. This annotation process focused on confirming the relevance of the topics to the suicides reported in the NVDRS database. We conducted interrater agreement for the first 50 coded narratives for each narrative.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.Supervised Learning Classifier Training, Evaluation, and Prediction\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach set of 100 annotated narratives were used to train a suite of supervised learning classifiers for each social isolation-related topic more precisely beyond keyword matching alone. These classifiers included Naive Bayes, Logistic Regression, Random Forest, and RoBERTa, with an 80/20 training/test split. Hyperparameter optimization was conducted across discrete values relevant to each model, described further in the Supplemental Materials. Finally, we evaluated classifiers, prioritizing high macro-F1 and positive-class recall rates, and saved those models which were used to predict a binary (relevant or non-relevant) classifier for each topic. After identifying the best performing models for each topic, we generated predictions for the 100 samples and conducted 1000 iterations of bootstrapping random slices of 80% of the dataset to generate confidence intervals for each performance metric.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Violent Death Reporting System (NVDRS) is administered by the Centers for Disease Control and Prevention (CDC) by participating NVDRS states. The findings and conclusions of this study are those of the authors alone and do not necessarily represent the official position of the CDC or of participating NVDRS states.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInclude all funding sources, including grant numbers, complete funding agency names, and recipient\u0026rsquo;s initials. Each funding source should be listed in a separate paragraph such as:\u003c/p\u003e\n\u003cp\u003eNational Institute on Drug Abuse R01DA057599 (AS, SD)\u003c/p\u003e\n\u003cp\u003eNational Institute on Drug Abuse T32 DA0505552 (DW, SP)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach author\u0026rsquo;s contribution(s) to the paper should be listed (we suggest following the CRediT model with each CRediT role given its own line. No punctuation in the initials.\u003c/p\u003e\n\u003cp\u003eConceptualization: DW, AS, SD\u003c/p\u003e\n\u003cp\u003eMethodology: DW, SR, SD, SP, AS\u003c/p\u003e\n\u003cp\u003eInvestigation: DW, SR, SD, SP, AS\u003c/p\u003e\n\u003cp\u003eVisualization: DW\u003c/p\u003e\n\u003cp\u003eSupervision: AS, SS\u003c/p\u003e\n\u003cp\u003eWriting\u0026mdash;original draft: DW, AS\u003c/p\u003e\n\u003cp\u003eWriting\u0026mdash;review \u0026amp; editing: DW, SR, SD, SP, AS\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccess to the NVDRS Restricted Access Database requires approval from the NVDRS RAD review committee, consisting of scientific and data analysis experts within CDC\u0026apos;s National Center for Injury Prevention and Control. More information on application procedures can be found here: https://www.cdc.gov/nvdrs/about/nvdrs-data-access.html Code for analysis used in this study can be found at the following GitHub repository: https://github.com/drew-walkerr/nvdrs-social-isolation-classification.git\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOffice of the Surgeon General (OSG). Our Epidemic of Loneliness and Isolation: The U.S. Surgeon General\u0026rsquo;s Advisory on the Healing Effects of Social Connection and Community. US Department of Health and Human Services; 2023. Accessed February 21, 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/books/NBK595227/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/books/NBK595227/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Orden KA, Witte TK, Cukrowicz KC, Braithwaite S, Selby EA, Joiner TE. The Interpersonal Theory of Suicide. Psychol Rev. 2010;117(2):575\u0026ndash;600. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/a0018697\u003c/span\u003e\u003cspan address=\"10.1037/a0018697\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUmberson D, Lin Z, Cha H. Gender and Social Isolation across the Life Course. J Health Soc Behav. 2022;63(3):319\u0026ndash;335. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/00221465221109634\u003c/span\u003e\u003cspan address=\"10.1177/00221465221109634\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalati R, Ferrari C, Brittner M, et al. Suicidal thoughts and behaviors and social isolation: A narrative review of the literature. J Affect Disord. 2019;245:653\u0026ndash;667. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2018.11.022\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2018.11.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeigh-Hunt N, Bagguley D, Bash K, et al. An overview of systematic reviews on the public health consequences of social isolation and loneliness. Public Health. 2017;152:157\u0026ndash;171. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.puhe.2017.07.035\u003c/span\u003e\u003cspan address=\"10.1016/j.puhe.2017.07.035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValtorta NK, Kanaan M, Gilbody S, Hanratty B. Loneliness, social isolation and social relationships: what are we measuring? A novel framework for classifying and comparing tools. BMJ Open. 2016;6(4):e010799. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2015-010799\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2015-010799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith RW, Holt-Lunstad J, Kawachi I. Benchmarking Social Isolation, Loneliness, and Smoking: Challenges and Opportunities for Public Health. Am J Epidemiol. 2023;192(8):1238\u0026ndash;1242. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/kwad121\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwad121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoneliness and Social Isolation Detection Using Passive Sensing Techniques: Scoping Review. JMIR Preprints. Accessed April 25, 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://preprints.jmir.org/preprint/34638\u003c/span\u003e\u003cspan address=\"https://preprints.jmir.org/preprint/34638\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheats KJ. Surveillance for Violent Deaths \u0026mdash; National Violent Death Reporting System, 39 States, the District of Columbia, and Puerto Rico, 2018. MMWR Surveill Summ. 2022;71. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15585/mmwr.ss7103a1\u003c/span\u003e\u003cspan address=\"10.15585/mmwr.ss7103a1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNazarov O, Guan J, Chihuri S, Li G. Research utility of the National Violent Death Reporting System: a scoping review. Inj Epidemiol. 2019;6(1):18. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40621-019-0196-9\u003c/span\u003e\u003cspan address=\"10.1186/s40621-019-0196-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Violent Death Reporting System|NVDRS|Violence Prevention|Injury Center|CDC. Published May 23, 2023. Accessed February 21, 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nvdrs/about/index.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nvdrs/about/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDang LN, Kahsay ET, James LN, Johns LJ, Rios IE, Mezuk B. Research utility and limitations of textual data in the National Violent Death Reporting System: a scoping review and recommendations. Inj Epidemiol. 2023;10(1):23. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40621-023-00433-w\u003c/span\u003e\u003cspan address=\"10.1186/s40621-023-00433-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArseniev-Koehler A, Cochran SD, Mays VM, Chang KW, Foster JG. Integrating topic modeling and word embedding to characterize violent deaths. Proc Natl Acad Sci. 2022;119(10):e2108801119. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.2108801119\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2108801119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSom\u0026eacute; NH, Noormohammadpour P, Lange S. The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review. Front Psychiatry. 2024;15. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyt.2024.1291362\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2024.1291362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErnst M, Klein EM, Beutel ME, Br\u0026auml;hler E. Gender-specific associations of loneliness and suicidal ideation in a representative population sample: Young, lonely men are particularly at risk. J Affect Disord. 2021;294:63\u0026ndash;70. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2021.06.085\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2021.06.085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMink MD, Lindley LL, Weinstein AA. Stress, Stigma, and Sexual Minority Status: The Intersectional Ecology Model of LGBTQ Health. J Gay Lesbian Soc Serv. 2014;26(4):502\u0026ndash;521. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10538720.2014.953660\u003c/span\u003e\u003cspan address=\"10.1080/10538720.2014.953660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarcia J, Vargas N, Clark JL, Maga\u0026ntilde;a \u0026Aacute;lvarez M, Nelons DA, Parker RG. Social isolation and connectedness as determinants of well-being: Global evidence mapping focused on LGBTQ youth. Glob Public Health. 2020;15(4):497\u0026ndash;519. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/17441692.2019.1682028\u003c/span\u003e\u003cspan address=\"10.1080/17441692.2019.1682028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStack S, Scourfield J. Recency of Divorce, Depression, and Suicide Risk. J Fam Issues. 2015;36(6):695\u0026ndash;715. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0192513X13494824\u003c/span\u003e\u003cspan address=\"10.1177/0192513X13494824\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDenney JT, Rogers RG, Krueger PM, Wadsworth T. Adult Suicide Mortality in the United States: Marital Status, Family Size, Socioeconomic Status, and Differences by Sex. Soc Sci Q. 2009;90(5):1167\u0026ndash;1185. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1540-6237.2009.00652.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1540-6237.2009.00652.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMonk JK, Ogolsky BG, Oswald RF. Coming Out and Getting Back In: Relationship Cycling and Distress in Same- and Different-Sex Relationships. Fam Relat. 2018;67(4):523\u0026ndash;538. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/fare.12336\u003c/span\u003e\u003cspan address=\"10.1111/fare.12336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEagle JL Agnel Philip,Jaida Grey. Native American Families Are Being Broken Up in Spite of a Law Meant to Keep Children With Their Parents. ProPublica. Published June 15, 2023. Accessed March 28, 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.propublica.org/article/native-american-parental-rights-termination-icwa-scotus\u003c/span\u003e\u003cspan address=\"https://www.propublica.org/article/native-american-parental-rights-termination-icwa-scotus\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDreby J. U.S. immigration policy and family separation: The consequences for children\u0026rsquo;s well-being. Soc Sci Med. 2015;132:245\u0026ndash;251. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.socscimed.2014.08.041\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2014.08.041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCDC. (2022). Suicide Prevention Resource for Action: A Compilation of the Best Available Evidence. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention. Available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/suicide/pdf/preventionresource.pdf\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/suicide/pdf/preventionresource.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindley LC, Policastro CN, Dosch B, Ortiz Baco JG, Cao CQ. Artificial Intelligence and the National Violent Death Reporting System: A Rapid Review. Comput Inform Nurs CIN. Published online March 26, 2024. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/CIN.0000000000001124\u003c/span\u003e\u003cspan address=\"10.1097/CIN.0000000000001124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKafka JM, Fliss MD, Trangenstein PJ, Reyes LM, Pence BW, Moracco KE. Detecting intimate partner violence circumstance for suicide: development and validation of a tool using natural language processing and supervised machine learning in the National Violent Death Reporting System. Inj Prev. 2023;29(2):134\u0026ndash;141. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/ip-2022-044662\u003c/span\u003e\u003cspan address=\"10.1136/ip-2022-044662\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKafka JM, Moracco KE, Pence BW, Trangenstein PJ, Fliss MD, McNaughton Reyes L. Intimate partner violence and suicide mortality: a cross-sectional study using machine learning and natural language processing of suicide data from 43 states. Inj Prev J Int Soc Child Adolesc Inj Prev. 2024;30(2):125\u0026ndash;131. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/ip-2023-044976\u003c/span\u003e\u003cspan address=\"10.1136/ip-2023-044976\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoldstein EV, Mooney SJ, Takagi-Stewart J, et al. Characterizing Female Firearm Suicide Circumstances: A Natural Language Processing and Machine Learning Approach. Am J Prev Med. 2023;65(2):278\u0026ndash;285. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amepre.2023.01.030\u003c/span\u003e\u003cspan address=\"10.1016/j.amepre.2023.01.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang S, Dang Y, Sun Z, et al. An NLP approach to identify SDoH-related circumstance and suicide crisis from death investigation narratives. J Am Med Inform Assoc JAMIA. 2023;30(8):1408\u0026ndash;1417. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jamia/ocad068\u003c/span\u003e\u003cspan address=\"10.1093/jamia/ocad068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim K, Ye GY, Haddad AM, Kos N, Zisook S, Davidson JE. Thematic analysis and natural language processing of job-related problems prior to physician suicide in 2003\u0026ndash;2018. Suicide Life Threat Behav. 2022;52(5):1002\u0026ndash;1011. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/sltb.12896\u003c/span\u003e\u003cspan address=\"10.1111/sltb.12896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang S, Zhou Y, Han Z, et al. Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes. Published online March 28, 2024. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48550/arXiv.2403.19432\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.2403.19432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahman N, Mozer R, McHugh RK, Rockett IRH, Chow CM, Vaughan G. Using natural language processing to improve suicide classification requires consideration of race. Suicide Life Threat Behav. 2022;52(4):782\u0026ndash;791. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/sltb.12862\u003c/span\u003e\u003cspan address=\"10.1111/sltb.12862\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHovy D, Prabhumoye S. Five sources of bias in natural language processing. Lang Linguist Compass. 2021;15(8):e12432. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/lnc3.12432\u003c/span\u003e\u003cspan address=\"10.1111/lnc3.12432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNelson LK. Computational Grounded Theory: A Methodological Framework. Sociol Methods Res. 2020;49(1):3\u0026ndash;42. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0049124117729703\u003c/span\u003e\u003cspan address=\"10.1177/0049124117729703\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrootendorst M. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. Published online March 11, 2022. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48550/arXiv.2203.05794\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.2203.05794\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12:2825\u0026ndash;2830.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcInnes L, Healy J, Saul N, Gro\u0026szlig;berger L. UMAP: Uniform Manifold Approximation and Projection. J Open Source Softw. 2018;3(29):861. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21105/joss.00861\u003c/span\u003e\u003cspan address=\"10.21105/joss.00861\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eŘehůřek R, Sojka P. Software Framework for Topic Modelling with Large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. ELRA; 2010:45\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR\u0026ouml;der M, Both A, Hinneburg A. Exploring the Space of Topic Coherence Measures. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. WSDM \u0026rsquo;15. Association for Computing Machinery; 2015:399\u0026ndash;408. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1145/2684822.2685324\u003c/span\u003e\u003cspan address=\"10.1145/2684822.2685324\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7096343/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7096343/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocial isolation and loneliness, which have been increasing in recent years strongly contribute toward suicide rates. Although social isolation and loneliness are not currently recorded within the US National Violent Death Reporting System’s (NVDRS) structured variables, natural language processing (NLP) techniques can be used to identify these constructs in law enforcement and coroner/medical examiner narratives. Using topic modeling, lexicon development and supervised learning classifiers, we developed high-quality classifiers (average F1: .86, accuracy: .82). Evaluating over 300,000 suicides from 2002 to 2020, we identified 1,198 mentioning chronic social isolation. Decedents had higher odds of chronic social isolation classification if they were men (OR = 1.44; CI: 1.24, 1.69, p \u0026lt; .0001), gay (OR = 3.68; 1.97, 6.33, p \u0026lt; .0001), or were divorced (OR = 3.34; 2.68, 4.19, p \u0026lt; .0001). We found significant predictors for other social isolation topics of recent or impending divorce, child custody loss, eviction or recent move, and break-up. Our methods can improve surveillance and prevention of social isolation and loneliness in the United States.\u003c/p\u003e","manuscriptTitle":"Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 06:40:19","doi":"10.21203/rs.3.rs-7096343/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-psychology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commspsychol","sideBox":"Learn more about [Communications Psychology](http://www.nature.com/commspsychol/)","snPcode":"44271","submissionUrl":"https://mts-commspsychol.nature.com/cgi-bin/main.plex","title":"Communications Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3685d8dd-7686-4d7c-a195-2c6a19a0a5e0","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51740740,"name":"Social science/Sociology"},{"id":51740741,"name":"Social science/Psychology/Human behaviour"},{"id":51740742,"name":"Social science/Language and linguistics"}],"tags":[],"updatedAt":"2025-07-25T06:40:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 06:40:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7096343","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7096343","identity":"rs-7096343","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}