Network analysis of anxiety and depression symptoms among adults with chronic pain: based on 2019 National Health Interview Survey

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Methods We analyzed 7,021 adults reporting chronic pain from the 2019 National Health Interview Survey (NHIS). Anxiety and depression were assessed using the 7-item Generalized Anxiety Disorder scale (GAD-7) and the 8-item Patient Health Questionnaire (PHQ-8). Network analysis was conducted to identify central symptoms and b ridge symptoms linking the anxiety and depression communities, and the Network Comparison Test (NCT) was used to examine gender differences. Results Among U.S. adults with chronic pain, anxiety and depression symptoms formed a highly interconnected network with prominent cross-domain associations. “Uncontrollable worry” (G2) and “excessive worry” (G3) emerged as the most central symptoms, with “sadness” (P2) and “worthlessness” (P6) served as key bridge symptoms linking anxiety and depression. Additionally, Gender did not significantly affect the network structure. Conclusion The most central symptoms (“uncontrollable worry” and “excessive worry”), and the strongest bridge symptoms linking anxiety and depression (“sadness” and “worthlessness”) may represent potential targets for interventions aimed at reducing co-occurring anxiety and depression in individuals with chronic pain. Anxiety Depression Chronic Pain Network analysis Figures Figure 1 Figure 2 Figure 3 1. Introduction Chronic pain affects more than 20% of U.S. adults and represents a major public health challenge due to its substantial contributions to disability, healthcare utilization, and lost productivity[ 1 , 2 ]. Among individuals with chronic pain, anxiety and depression are highly prevalent and exert powerful bidirectional influences on pain severity, functional impairment, and treatment outcomes[ 3 – 5 ]. These emotional conditions not only amplify pain perception through cognitive processes such as catastrophizing and hypervigilance but also contribute to the maintenance of chronic pain via sleep disturbance and reduced behavioral engagement[ 5 , 6 ]. Therefore, examine new features and mechanisms of comorbidity between depression and anxiety symptoms is critical for advancing clinical management. Despite the clinical significance of this comorbidity, the mechanisms underlying the interplay between specific anxiety and depression symptoms in chronic pain populations remain incompletely understood. Although anxiety and depression frequently co-occur in individuals with chronic pain, growing evidence suggests that this comorbidity is not merely the sum of two independent diagnostic categories[ 7 ]. Network theory conceptualizes mental disorders as systems of mutually reinforcing symptoms. Symptoms, such as worry, fatigue, anhedonia, or concentration problems, can activate each other through psychological, behavioral, and physiological pathways, creating self-sustaining feedback loops that contribute to chronicity[ 8 , 9 ]. Recent studies have begun to apply network analysis to chronic pain populations[ 10 – 14 ]. However, most existing studies have focused on older adults or patients with specific medical conditions. Epidemiological evidence consistently shows sex disparities in chronic pain prevalence, emotional comorbidity, coping patterns, and healthcare-seeking behaviors[ 15 – 19 ]. To examine new features and mechanisms of comorbidity between depression and anxiety symptoms, our study leverages data from the 2019 NHIS, a nationally representative dataset, to estimate a network. Using this large and diverse chronic pain cohort, we (1) estimated an item-level network of anxiety and depression symptoms, (2) identified central and bridge symptoms that may serve as key drivers of emotional comorbidity, (3) compared symptom network structures between men and women using permutation-based network comparison tests. Identifying central or bridge symptoms may inform personalized treatment approaches[ 20 ]. Network-derived insights may also guide clinicians toward symptom-focused monitoring strategies that capture early warning signs of escalating distress[ 21 ]. From a public health perspective, mapping symptom pathways in a nationally representative chronic pain cohort enables population-based screening and rational resource allocation, thereby facilitating the development of more equitable, evidence-based and integrated mental health management strategies. From a methodological standpoint, network analysis offers several advantages well-suited to the aims of this study. Network models capture the unique and conditional associations between individual symptoms[ 22 , 23 ], allowing for the identification of direct pathways linking anxiety and depression within chronic pain. Furthermore, the use of a Gaussian Graphical Model (GGM) with regularization enables robust estimation of symptom interactions in large datasets[ 24 ]. 2. Methods 2.1Data Source This study was a secondary analysis of data from the 2019 NHIS, a nationally representative, cross-sectional survey conducted annually by the National Center for Health Statistics (NCHS). The NHIS employs a complex multistage probability sampling design, incorporating stratification, communitying, and weighting procedures to represent the civilian, non-institutionalized U.S. adult population. The 2019 Adult Sample file was used for all analyses. All analyses accounted for the NHIS complex sampling design by incorporating sampling weights, strata, and primary sampling units to ensure nationally representative estimates. Because the NHIS data are publicly available and fully de-identified, this secondary analysis was exempt from institutional review board approval. 2.2Participants Participants were drawn from the 2019 NHIS adult sample. Individuals were included if they met the following criteria: 1. Aged 18 years or older; 2. Reported experiencing pain “almost every day” or “most days” during the past three months[ 25 ], consistent with established definitions of chronic pain in NHIS-based research; 3. Had complete data on all items of the anxiety and depression symptom measures. Participants were excluded if they had missing, refused, or “not ascertained” responses on key variables, including demographic characteristics and symptom items. Responses coded as 7 (refused), 8 (not ascertained), or 9 (don’t know) were treated as missing and excluded prior to analysis. The Fig. 1 provides more information on how the instances with missing values were further removed. A final sample of 7021 respondents formed for analysis. 2.3Measures 2.3.1Anxiety Symptoms Anxiety symptoms were assessed using the GAD-7 scale[ 26 ], which consists of seven items evaluating the frequency of core anxiety symptoms over the past two weeks. Each item is rated on a four-point Likert scale ranging from 1 (not at all) to 4 (nearly every day). Item-level responses were used for network analyses to preserve symptom-specific information. Higher scores indicate greater symptom severity. Total scores were calculated to describe overall anxiety severity in the sample but were not used for network estimation. 2.3.2Depressive Symptoms Depressive symptoms were measured using the PHQ-8, an eight-item instrument assessing depressive symptom frequency over the past two weeks[ 27 , 28 ] using the same four-point response scale as the GAD-7. Consistent with prior research, item-level PHQ-8 responses were included as individual nodes in the symptom network. Total scores were calculated to describe overall depression severity in the sample but were not used for network estimation. 2.4Statistical Analysis 2.4.1 Descriptive Statistics All descriptive analyses incorporated NHIS sampling weights to produce nationally representative estimates[ 29 ]. Continuous variables were assessed for normality using distributional diagnostics. Normally distributed variables were summarized using means and standard deviations, whereas non-normally distributed variables were reported as medians with interquartile ranges (IQRs)[ 30 ]. Categorical variables were summarized as weighted frequencies and percentages. 2.4.2 Network Analysis A joint anxiety–depression symptom network comprising 15 nodes, seven GAD-7 items and eight PHQ-8 items, was estimated using a Gaussian Graphical Model (GGM)[ 31 ]. In this framework, edges represent regularized partial correlations, reflecting unique associations between pairs of symptoms after controlling for all other symptoms in the network. Network estimation was conducted using the graphical least absolute shrinkage and selection operator (graphical LASSO) combined with extended Bayesian information criterion (EBIC) model selection[ 32 – 34 ]. The EBIC hyperparameter γ was set to 0.5, a conservative value commonly recommended to balance sensitivity and specificity and to reduce the risk of spurious edges in high-dimensional data[ 35 ]. All network analyses incorporated NHIS sampling weights to account for the complex survey design. To identify clinically important symptoms within the network, centrality indices were computed, including: Strength , reflecting the sum of absolute edge weights connected to a node; Expected Influence (EI) , which accounts for both positive and negative associations and is particularly suitable for psychopathology networks[ 36 – 38 ]. To examine symptom connectivity between anxiety and depression domains, bridge centrality measures were calculated, including: Bridge Strength , quantifying the extent to which a symptom connects nodes across symptom communities; Bridge Expected Influence (BEI) , capturing directional cross-community influence[ 39 ]. Symptoms with high central or bridge centrality were interpreted as potential key intervention targets within the chronic pain–related emotional symptom network[ 40 ]. 2.4.3 Network Comparison Test To formally evaluate gender differences in the symptom networks, we employed the NCT implemented in the R package NetworkComparisonTest [ 41 ]. The NCT is a permutation-based procedure that assesses whether two independent networks differ in terms of global strength, overall structure, and individual edge weights. For each test, we performed 5,000 permutations. In each permutation, participants were randomly reassigned to the two groups while preserving the original sample sizes, and networks were re-estimated using the same EBICglasso procedure ( γ = 0.5) used in the primary network estimation[ 42 ]. Two-tailed p-values were obtained by comparing the observed statistics with the permutation-based null distributions. Three invariance tests were conducted: (1) network structure invariance , assessing the maximum difference in edge weights ( \(\:M\) ); (2) global strength invariance , examining differences in overall connectivity defined as the sum of absolute edge weights ( \(\:S\) ); (3) edge strength invariance , identifying specific edges that differ between groups ( \(\:E\) )[ 41 ]. A random seed (123) was set prior to the permutation procedure to ensure reproducibility. 2.4.4Network Stability and Accuracy The accuracy and stability of the estimated network were evaluated using nonparametric bootstrap procedures[ 43 ]. Edge-weight accuracy was assessed by constructing 95% confidence intervals around estimated edge weights using bootstrapped samples[ 38 ]. Centrality stability was examined using case-dropping bootstrap analyses, and the correlation stability coefficient (CS-coefficient) was calculated. CS values exceeding 0.25 were considered acceptable, and values above 0.50 were interpreted as indicating good stability[ 42 , 44 ]. 2.5Software All statistical analyses were performed using Python (version 3.9.7) and R (version 4.4.1). Data preprocessing, descriptive statistics, network estimation, and visualization were conducted in Python with the pandas, numpy, scipy, NetworkX, scikit‑learn, matplotlib, and seaborn libraries. Gender‑specific subgroup analyses, including NCT and associated visualizations, were carried out in R using the bootnet, qgraph, reshape and NetworkComparisonTest packages. The analysis code is available at: https://github.com/SentaW/Code-for-network-analysis-of-anxiety-and-depression-symptoms-among-adults-with-chronic-pain/tree/main 3. Results 3.1Participants’ Characteristics After applying sampling weights to account for the complex survey design of the NHIS, participant characteristics are presented in Table 1 and reflect U.S. adults with chronic pain at the national level. The mean age was 58.65 years (SD = 16.35), and 56.97% of participants were female. Most participants were non-Hispanic White (76.39%). Approximately two-fifths of participants reported an annual household income below $35,000, while nearly one-fifth reported an income of $100,000 or greater. Most had at least a high school education, and 92.66% were covered by health insurance. Participants were distributed across all four U.S. census regions, with the largest proportion residing in the South. Table 1 . Participant characteristics Variable Category n (%) Age(years), Mean ± SD 58.65±16.35 Family income($), Mean ± SD 57,257±49,387 Gender male 3,021 (43.03) female 4,000 (56.97) Race/Ethnicity Hispanic 591 (8.42) NH White only 5,363 (76.39) NH Black/African American only 733 (10.44) NH Asian only 107 (1.52) NH AIAN only 64 (0.91) NH AIAN and any other group 100 (1.42) Other single and multiple races 63 (0.90) Educational level Never attended/kindergarten only 16 (0.23) Grade 1-11 726 (10.34) 12th grade, no diploma 109 (1.55) GED or equivalent 297 (4.23) High School Graduate 1,742 (24.81) Some college, no degree 1,302 (18.54) Associate degree: occupational, technical, or vocational program 357 (5.08) Associate degree: academic program 685 (9.76) Bachelor's degree (Example: BA, AB, BS, BBA) 1,130 (16.09) Master's degree (Example: MA, MS, MEng, MEd, MBA) 461 (6.57) Professional School degree (Example: MD, DDS, DVM, JD) 63 (0.90) Doctoral degree (Example: PhD, EdD) 92 (1.31) Marital status Married 3,022 (43.04) Living with a partner together as an unmarried couple 434 (6.18) Neither 3,524 (50.19) Insurance Not covered 507 (7.22) covered 6,506 (92.66) Region Northeast 1,070 (15.24) Midwest 1,714 (24.41) South 2,597 (36.99) West 1,640 (23.36) Income level $0 to $34,999 2940 (41.87) $35,000 to $49,999 975 (13.89) $50,000 to $74,999 1146 (16.32) $75,000 to $99,999 699 (9.96) $100,000 or greater 1261 (17.96) Abbreviations: NH , non-Hispanic; AIAN , American Indian or Alaska Native; GED , General Educational Development. Hispanic refers to Hispanic/Latino ethnicity regardless of race. NH-White-only, NH-Black/African American-only, NH-Asian-only, and NH-AIAN-only denote non-Hispanic individuals identifying with a single racial group; NH-AIAN and any other group denotes AIAN in combination with one or more other racial groups. GED or equivalent indicates a high school equivalency credential. 3.2 Descriptive Statistics of Symptom Measures Descriptive statistics for overall anxiety and depression severity, as well as item-level symptoms from the GAD-7 and PHQ-8, are summarized in Table 2 , along with node predictability estimates. The median severity for both anxiety and depression was 1.00 ((Interquartile Range, IQR: 1.00–2.00). Among GAD-7 items, mean scores ranged from 1.38 to 1.68, with “excessive worry” and “nervousness” showing the highest average severity, and “fearfulness” and “restlessness” the lowest. For PHQ-8 items, mean scores ranged from 1.25 to 2.20; “fatigue” and “sleep disturbance” had the highest mean severity, whereas “psychomotor symptoms” and “worthlessness” were among the lowest. All item-level symptom data were used in subsequent network analyses. Table 2 . Descriptive statistics for anxiety and depression symptom measures Variable Value Predictability( R 2 ) Mean ± SD Median [IQR] Overall Severity Anxiety Severity 1.00 [1.00, 2.00] Depression Severity 1.00 [1.00, 2.00] GAD-7 Items G1 (Nervousness) 1.65 ± 0.97 0.532382641 G2 (Uncontrollable worry) 1.58 ± 0.99 0.564843946 G3 (Excessive worry) 1.68 ± 1.02 0.547731503 G4 (Trouble relaxing) 1.63 ± 1.01 0.51963307 G5 (Restlessness) 1.41 ± 0.86 0.436164868 G6 (Irritability) 1.62 ± 0.94 0.45048257 G7 (Fearfulness) 1.38 ± 0.81 0.467074448 PHQ-8 Items P1 (Anhedonia) 1.56 ± 0.93 0.460070182 P2 (Sadness) 1.54 ± 0.89 0.522736271 P3 (Sleep disturbance) 2.04 ± 1.19 0.382733633 P4 (Fatigue) 2.20 ± 1.17 0.431799107 P5 (Appetite changes) 1.61 ± 1.02 0.414854129 P6 (Worthlessness) 1.42 ± 0.86 0.498646165 P7 (Concentration problems) 1.42 ± 0.87 0.452095219 P8 (Psychomotor symptoms) 1.25 ± 0.69 0.38646709 Note. Predictability ( R ² ) reflects the proportion of variance in each symptom explained by its connections with other symptoms in the network. 3.3 Estimated Symptom Network Structure The estimated regularized partial correlation network consisted of 15 nodes, seven from GAD-7 and eight from PHQ-8, representing anxiety and depression symptoms, and is illustrated in Figure 2. Nodes were color-coded by scale, and edges reflected partial correlations, with green indicating positive and red indicating negative associations. The network showed dense connectivity, with predominantly positive edges; only six weak negative edges were observed, all with absolute weights below 0.018. The strongest connections within the anxiety community were observed between “uncontrollable worry” and “excessive worry” (r = 0.435), and between “trouble relaxing” and “restlessness” (r = 0.283). Within the depression community, the strongest edges were between “anhedonia” and “sadness” (r = 0.315), and between “sleep disturbance” and “fatigue” (r = 0.248). The five strongest edges are summarized in Table 3 . Complete edge weights for all pairwise connections are provided in Supplementary table S1. Table 3 . Top 5 strongest edges within and between symptom communities Node 1 Node 2 Correlation ( r ) Strongest Within-Community Edges G2 G3 0.435 P1 P2 0.315 P3 P4 0.248 G4 G5 0.283 P6 P2 0.206 Strongest Between-Community (Bridge) Edges G5 P8 0.169 P7 P8 0.170 G1 P2 0.144 G4 P3 0.130 G6 P4 0.090 Note. r = partial correlation coefficient. All edges listed are positive associations. Bridge edges are defined as connections between a GAD-7 node and a PHQ-8 node. 3.4 Centrality and Bridge Symptom Analysis Node centrality was assessed using EI, which reflects a node's total connectivity within the network. The symptoms with the highest EI, indicating greatest overall influence, were predominantly anxiety-related: uncontrollable worry (G2; EI = 1.125), excessive worry (G3; EI = 1.113), trouble relaxing (G4; EI = 1.047), and nervousness (G1; EI = 1.028). Among depressive symptoms, sadness (P2; EI = 1.058) and worthlessness (P6; EI = 0.930) also exhibited relatively high EI values. These findings suggest that worry-related experiences and core mood disturbances play a central role in the overall symptom network. BEI quantifies a node's propensity to connect the anxiety and depression communities. The strongest bridge symptoms were sadness (P2; BEI = 0.421), worthlessness (P6; BEI = 0.409), and nervousness (G1; BEI = 0.396). Notably, sadness ranked highly on both EI and BEI metrics. Centrality indices for each node are reported in Supplementary table S2. The distributions of EI and BEI are displayed in Figure 3, with the corresponding numerical values provided in Supplementary table S3. 3.5 Network Comparison The NCT was conducted to examine potential gender differences in the symptom network. The test statistic for network structure invariance was M = 0.083 ( p = 0.444), indicating that the difference in overall network structure between the female and male networks was 0.083. The global strength of the female network was 6.717, whereas that of the male network was 6.748, yielding a difference of 0.031 ( p = 0.637). At the edge level, 8 of 105 possible connections showed nominally significant differences, a number only slightly exceeding chance expectation (~5 edges)[41, 45]. The most pronounced differences involved connections between anxiety symptoms (G1–G4, G3–G4) and between anxiety and depression symptoms (G1–P3, G1–P4, G4–P6). However, none of these edge-level differences remained significant after applying false discovery rate correction (all q > 0.05). Detailed edge-level statistics are provided in Supplementary table S4. 4. Discussion Using a nationally representative sample of U.S. adults with chronic pain, the present study estimated an item-level network linking anxiety and depression symptoms. Several symptoms demonstrated prominent central and bridge properties, revealing a densely connected symptom architecture with distinct anxiety and depression communities. By identifying specific symptom–symptom interactions underlying the co-occurrence of anxiety and depression, these findings extend prior categorical and dimensional research and provide a more fine-grained understanding of emotional distress in individuals with chronic pain. The estimated network exhibited a high level of connectivity, with the vast majority of edges being positive. The strongest connections occurred within the anxiety and depression communities, indicating that symptoms within each disorder tend to reinforce one another. Within the anxiety community, the most prominent connection linked “uncontrollable worry” and “excessive worry,” consistent with cognitive models of generalized anxiety in which persistent and uncontrollable worry constitutes a core process. Within the depression community, the strongest edges connected “loss of interest” with “sadness,” and “sleep disturbance” with “fatigue.” These symptom pairings reflect well-recognized depressive symptom clusters involving core affective disturbances and neurovegetative complaints. Although cross-community connections were generally weaker than the strongest within-community associations, numerous bridge edges connected anxiety and depression symptoms. The most prominent cross-community association was observed between anxiety-related “restlessness” and depression-related “psychomotor symptoms.” This linkage may reflect a shared manifestation of psychomotor activation that bridges the experiential domains of anxiety and depression, highlighting potential pathways through which symptoms of one disorder may activate or exacerbate symptoms of the other[46]. Centrality analysis further identified several highly connected nodes within the network. Symptoms such as “uncontrollable worry” and “sadness” exhibited particularly high expected influence values, indicating that they maintained strong connections with many other symptoms in the network. Highly central symptoms may play an important role in sustaining overall symptom activation and may therefore represent influential points within the broader emotional symptom system. Beyond overall connectivity, bridge centrality analysis identified symptoms that most strongly connected the anxiety and depression communities. From the depression side, “sadness” and “feelings of worthlessness” emerged as the most prominent bridge symptoms, whereas “nervousness” and “trouble relaxing” represented key bridges from the anxiety domain. These findings have potential clinical implications. Symptoms that connect distinct symptom clusters may serve as conduits through which comorbidity develops and persists. Interventions that effectively target such bridging symptoms may therefore have cascading effects across the broader symptom network, potentially weakening the mutual reinforcement between anxiety and depression[47–49]. Network comparison analyses indicated that the overall symptom network was largely comparable between males and females. Neither global network strength nor overall network structure differed significantly across gender groups. Although several edges showed nominal differences at the uncorrected level, none remained significant after correction for multiple comparisons. These findings suggest that the structural organization of symptom interrelations linking anxiety and depression may be broadly similar across genders among individuals experiencing chronic pain, despite well-documented gender differences in the prevalence of emotional disorders. The robustness of the estimated network was supported by the stability analyses. The relatively high correlation stability coefficient and narrow bootstrap confidence intervals around the edge weights indicate that the estimated network structure was stable and interpretable within the present sample. Several limitations should be considered when interpreting these findings. First, the cross-sectional design precludes causal inference regarding the directionality of symptom interactions. Consequently, it cannot be determined whether central or bridge symptoms function as causes or consequences of network activation. Longitudinal or experience-sampling designs would be necessary to clarify temporal dynamics and potential causal pathways. Second, all measures were based on self-reported symptoms and may therefore be influenced by recall bias or reporting tendencies. Third, although the sample was nationally representative of U.S. adults with chronic pain, the findings may not generalizeto other cultural contexts, clinical populations, or specific pain conditions. Finally, the network was limited to symptoms assessed by the GAD-7 and PHQ-8 scales. Incorporating symptoms from additional domains relevant to chronic pain such as sleep disturbance, pain catastrophizing, or fatigue-related constructs may provide a more comprehensive understanding of the broader psychopathological landscape. Future research should aim to replicate the present findings in independent samples and examine the clinical utility of targeting highly connected symptoms within intervention studies. Additionally, investigating how contextual factors such as trauma exposure, socioeconomic disadvantage, or pain duration shape the structure of symptom networks may further advance our understanding of emotional distress in chronic pain populations. 5. Conclusion This network analysis provides a fine-grained map of the symptom-level architecture linking anxiety and depression in adults with chronic pain. Several symptoms including uncontrollable worry, excessive worry, sadness, and worthlessness emerged as highly central or bridging symptoms, suggesting that they may play a key role in the whole estimated network. These findings highlight the value of a symptom-level, transdiagnostic perspective. Targeting such highly central and bridge symptoms may offer a promising direction for developing more precise interventions aimed at reducing the co-occurring burden of anxiety and depression in chronic pain populations. Abbreviations BEI Bridge Expected Influence EI Expected Influence GAD-7 Generalized Anxiety Disorder-7 NHIS National Health Interview Survey NCT Network Comparison Test PHQ-8 Patient Health Questionnaire-8 Declarations Ethics approval and consent to participate The National Health Interview Survey (NHIS) protocol was approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. All participants provided written informed consent. This study used de‑identified, publicly available secondary data; therefore, additional institutional review board approval was not required. The research was conducted in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The datasets analyzed during the current study are publicly available from the NHIS database at: https://www.cdc.gov/nchs/nhis Competing interests The authors declare that they have no competing interests. Funding This research supported by the Science and Technology Popularization Project of Hunan Provincial Department of Science and Technology (Grant No. 2025ZK4044). Authors' contributions Shuntao Wang and Caixia Sun conceived and designed the study, conducted the statistical analyses and drafted the manuscript. Xiaoshan Li and Huiru Zhang contributed to data cleaning and analysis. Xinyue Liu and Manyi Wang assisted with literature review and manuscript revision. Zuyue Zeng, Jing Zhou and Jing He provided clinical expertise and supervised the study. Yujiao Li and Yating Zhan contributed to data interpretation and critical revision of the manuscript. Xiuhong Lei and Guqing Zeng supervised the project, and approved the final manuscript. All authors read and approved the final version of the manuscript. Acknowledgements The authors would like to acknowledge the National Center for Health Statistics for providing access to the publicly available data used in this study. We would like to express our sincere gratitude to the Science and Technology Popularization Project of Hunan Provincial Department of Science and Technology for the financial support of this work. References US Pain Foundation. Licensed to treat, unprepared for pain - U.S. pain foundation. 2025. https://uspainfoundation.org/news/licensed-to-treat-unprepared-for-pain/ . Accessed 19 Jan 2026. Yong RJ, Mullins PM, Bhattacharyya N. Prevalence of chronic pain among adults in the United States. Pain. 2022;163:e328–32. https://doi.org/10.1097/j.pain.0000000000002291 . Aaron RV, Ravyts SG, Carnahan ND, Bhattiprolu K, Harte N, McCaulley CC, et al. Prevalence of depression and anxiety among adults with chronic pain: a systematic review and meta-analysis. 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A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23:617–34. https://doi.org/10.1037/met0000167 . Kang Y, Trewern L, Jackman J, McCartney D, Soni A. Chronic pain: definitions and diagnosis. BMJ. 2023;381. https://doi.org/10.1136/bmj-2023-076036 . Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166:1092. https://doi.org/10.1001/archinte.166.10.1092 . Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–13. https://doi.org/10.1046/j.1525-1497.2001.016009606.x . Kroenke K, Strine TW, Spitzer RL, Williams JBW, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114:163–73. https://doi.org/10.1016/j.jad.2008.06.026 . NHIS methods. National Health Interview Survey. 2024. https://www.cdc.gov/nchs/nhis/about/nhis-methods.html . Accessed 24 Jan 2026. Altman DG, Bland JM. Statistics notes: the normal distribution. BMJ (Clin res ed,). 1995;310:298. https://doi.org/10.1136/bmj.310.6975.298 Epskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23:617–34. https://doi.org/10.1037/met0000167 . Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2008;9:432–41. https://doi.org/10.1093/biostatistics/kxm045 . Chen J, Chen Z. Extended bayesian information criteria for model selection with large model spaces. Biometrika. 2008;95:759–71. https://doi.org/10.1093/biomet/asn034 . Peterson C, Stingo FC, Vannucci M. Bayesian inference of multiple gaussian graphical models. J Am Stat Assoc. 2015;110:159–74. https://doi.org/10.1080/01621459.2014.896806 . Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: network visualizations of relationships in psychometric data. 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Comparing network structures on three aspects: a permutation test. Psychol Methods. 2023;28:1273–85. https://doi.org/10.1037/met0000476 . Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: a tutorial paper. Behav Res Methods. 2018;50:195–212. https://doi.org/10.3758/s13428-017-0862-1 . Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: a tutorial paper. Behav Res Methods. 2018;50:195–212. https://doi.org/10.3758/s13428-017-0862-1 . Zu T, Qin Y. Local bootstrap for network data. Biometrika. 2025;112. https://doi.org/10.1093/biomet/asae046 . Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc B. 1995;57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x . Rodrigues AR, Castro D, Cardoso J, Ferreira F, Serrão C, Coelho CM, et al. A network approach to emotion regulation and symptom activation in depression and anxiety. Front Public Health. 2024;12:1362148. https://doi.org/10.3389/fpubh.2024.1362148 . Zhang C, Li R, Zhang W, Tao Y, Liu X, Lv Y. A simulation-based network analysis of intervention targets for comorbid symptoms of depression and anxiety in chinese healthcare workers in the post-dynamic zero-COVID policy era. BMC Psychiatry. 2025;25:457. https://doi.org/10.1186/s12888-025-06931-z . Zhang Y, Cui Y, Li Y, Lu H, Huang H, Sui J, et al. Network analysis of depressive and anxiety symptoms in older Chinese adults with diabetes mellitus. Front Psychiatry. 2024;15:1328857. https://doi.org/10.3389/fpsyt.2024.1328857 . Luo J, Bei D-L, Zheng C, Jin J, Yao C, Zhao J, et al. The comorbid network characteristics of anxiety and depressive symptoms among Chinese college freshmen. BMC Psychiatry. 2024;24:297. https://doi.org/10.1186/s12888-024-05733-z . Additional Declarations No competing interests reported. Supplementary Files S1.Networkedgeinformationweightssignsandtypelabels.docx S2.Centralityindicesforeachsymptomnodeintheestimatednetwork.csv S3.EIandBEIforeachsymptomnodeintheestimatednetwork.csv S4.NCTresultsfornetworkstructureglobalstrengthandedgeinvarianceintheestimatednetwork.csv Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 May, 2026 Reviews received at journal 05 May, 2026 Reviews received at journal 05 May, 2026 Reviews received at journal 27 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviews received at journal 25 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 01 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 28 Mar, 2026 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. 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cohort.\u003c/p\u003e","description":"","filename":"flowchartofsamplescreening.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9251999/v1/962e756616104a7b22aee46d.jpg"},{"id":108492903,"identity":"b2ce6b2d-331e-440f-844b-2e40762213ec","added_by":"auto","created_at":"2026-05-05 09:58:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1918883,"visible":true,"origin":"","legend":"\u003cp\u003eRegularized partial correlation network of anxiety and depression symptoms among adults with chronic pain. Green edges depict positive regularized partial correlations; red edges depict negative correlations. Edge thickness corresponds to the absolute magnitude of the association.\u003c/p\u003e","description":"","filename":"networkvisualizationwithsign.png","url":"https://assets-eu.researchsquare.com/files/rs-9251999/v1/c74be35688f39db5502252e0.png"},{"id":108492911,"identity":"43128b32-5282-4dac-a905-0ab89443724a","added_by":"auto","created_at":"2026-05-05 09:58:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":444380,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork centrality plot of symptoms. (a) and (b) respectively depicted the expected influence and bridge expected influence of variables selected in the present network (z score).\u003c/p\u003e","description":"","filename":"EIBEI.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9251999/v1/621ddf1f8fb2dafabf05d8c9.jpg"},{"id":108804327,"identity":"230dcd1c-0c6c-4e39-8d7b-9f0ffde13663","added_by":"auto","created_at":"2026-05-08 15:19:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2472154,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9251999/v1/be706951-7b1f-4f4b-b1ae-125d9e47d9b5.pdf"},{"id":108385155,"identity":"b6baa08f-ff8c-417a-b912-a9ab379c7057","added_by":"auto","created_at":"2026-05-04 06:01:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17108,"visible":true,"origin":"","legend":"","description":"","filename":"S1.Networkedgeinformationweightssignsandtypelabels.docx","url":"https://assets-eu.researchsquare.com/files/rs-9251999/v1/14103a2736b1a699cba66687.docx"},{"id":108493088,"identity":"92981a47-6491-4a50-97b1-4aa633a703a7","added_by":"auto","created_at":"2026-05-05 09:59:22","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6156,"visible":true,"origin":"","legend":"","description":"","filename":"S2.Centralityindicesforeachsymptomnodeintheestimatednetwork.csv","url":"https://assets-eu.researchsquare.com/files/rs-9251999/v1/bb6dc5216724b6b102be488d.csv"},{"id":108385159,"identity":"0abd91f0-fc3f-49d0-b109-49cdd7e30eb3","added_by":"auto","created_at":"2026-05-04 06:01:35","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"S3.EIandBEIforeachsymptomnodeintheestimatednetwork.csv","url":"https://assets-eu.researchsquare.com/files/rs-9251999/v1/ebbd108fe1963a624929acd1.csv"},{"id":108492343,"identity":"efe74212-1a4f-4385-81f5-850e6bcef98e","added_by":"auto","created_at":"2026-05-05 09:57:31","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4750,"visible":true,"origin":"","legend":"","description":"","filename":"S4.NCTresultsfornetworkstructureglobalstrengthandedgeinvarianceintheestimatednetwork.csv","url":"https://assets-eu.researchsquare.com/files/rs-9251999/v1/e4ac274a3aa383241673615e.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network analysis of anxiety and depression symptoms among adults with chronic pain: based on 2019 National Health Interview Survey","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic pain affects more than 20% of U.S. adults and represents a major public health challenge due to its substantial contributions to disability, healthcare utilization, and lost productivity[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among individuals with chronic pain, anxiety and depression are highly prevalent and exert powerful bidirectional influences on pain severity, functional impairment, and treatment outcomes[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These emotional conditions not only amplify pain perception through cognitive processes such as catastrophizing and hypervigilance but also contribute to the maintenance of chronic pain via sleep disturbance and reduced behavioral engagement[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, examine new features and mechanisms of comorbidity between depression and anxiety symptoms is critical for advancing clinical management. Despite the clinical significance of this comorbidity, the mechanisms underlying the interplay between specific anxiety and depression symptoms in chronic pain populations remain incompletely understood.\u003c/p\u003e \u003cp\u003eAlthough anxiety and depression frequently co-occur in individuals with chronic pain, growing evidence suggests that this comorbidity is not merely the sum of two independent diagnostic categories[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Network theory conceptualizes mental disorders as systems of mutually reinforcing symptoms. Symptoms, such as worry, fatigue, anhedonia, or concentration problems, can activate each other through psychological, behavioral, and physiological pathways, creating self-sustaining feedback loops that contribute to chronicity[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recent studies have begun to apply network analysis to chronic pain populations[\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, most existing studies have focused on older adults or patients with specific medical conditions. Epidemiological evidence consistently shows sex disparities in chronic pain prevalence, emotional comorbidity, coping patterns, and healthcare-seeking behaviors[\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo examine new features and mechanisms of comorbidity between depression and anxiety symptoms, our study leverages data from the 2019 NHIS, a nationally representative dataset, to estimate a network. Using this large and diverse chronic pain cohort, we (1) estimated an item-level network of anxiety and depression symptoms, (2) identified central and bridge symptoms that may serve as key drivers of emotional comorbidity, (3) compared symptom network structures between men and women using permutation-based network comparison tests.\u003c/p\u003e \u003cp\u003eIdentifying central or bridge symptoms may inform personalized treatment approaches[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Network-derived insights may also guide clinicians toward symptom-focused monitoring strategies that capture early warning signs of escalating distress[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. From a public health perspective, mapping symptom pathways in a nationally representative chronic pain cohort enables population-based screening and rational resource allocation, thereby facilitating the development of more equitable, evidence-based and integrated mental health management strategies.\u003c/p\u003e \u003cp\u003eFrom a methodological standpoint, network analysis offers several advantages well-suited to the aims of this study. Network models capture the unique and conditional associations between individual symptoms[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], allowing for the identification of direct pathways linking anxiety and depression within chronic pain. Furthermore, the use of a Gaussian Graphical Model (GGM) with regularization enables robust estimation of symptom interactions in large datasets[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1Data Source\u003c/h2\u003e \u003cp\u003eThis study was a secondary analysis of data from the 2019 NHIS, a nationally representative, cross-sectional survey conducted annually by the National Center for Health Statistics (NCHS). The NHIS employs a complex multistage probability sampling design, incorporating stratification, communitying, and weighting procedures to represent the civilian, non-institutionalized U.S. adult population. The 2019 Adult Sample file was used for all analyses.\u003c/p\u003e \u003cp\u003eAll analyses accounted for the NHIS complex sampling design by incorporating sampling weights, strata, and primary sampling units to ensure nationally representative estimates.\u003c/p\u003e \u003cp\u003eBecause the NHIS data are publicly available and fully de-identified, this secondary analysis was exempt from institutional review board approval.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2Participants\u003c/h2\u003e \u003cp\u003eParticipants were drawn from the 2019 NHIS adult sample. Individuals were included if they met the following criteria:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e1. Aged 18 years or older;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e2. Reported experiencing pain \u0026ldquo;almost every day\u0026rdquo; or \u0026ldquo;most days\u0026rdquo; during the past three months[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], consistent with established definitions of chronic pain in NHIS-based research;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e3. Had complete data on all items of the anxiety and depression symptom measures.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eParticipants were excluded if they had missing, refused, or \u0026ldquo;not ascertained\u0026rdquo; responses on key variables, including demographic characteristics and symptom items. Responses coded as 7 (refused), 8 (not ascertained), or 9 (don\u0026rsquo;t know) were treated as missing and excluded prior to analysis. The Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides more information on how the instances with missing values were further removed. A final sample of 7021 respondents formed for analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3Measures\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1Anxiety Symptoms\u003c/h2\u003e \u003cp\u003eAnxiety symptoms were assessed using the GAD-7 scale[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which consists of seven items evaluating the frequency of core anxiety symptoms over the past two weeks. Each item is rated on a four-point Likert scale ranging from 1 (not at all) to 4 (nearly every day). Item-level responses were used for network analyses to preserve symptom-specific information. Higher scores indicate greater symptom severity.\u003c/p\u003e \u003cp\u003eTotal scores were calculated to describe overall anxiety severity in the sample but were not used for network estimation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2Depressive Symptoms\u003c/h2\u003e \u003cp\u003eDepressive symptoms were measured using the PHQ-8, an eight-item instrument assessing depressive symptom frequency over the past two weeks[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] using the same four-point response scale as the GAD-7. Consistent with prior research, item-level PHQ-8 responses were included as individual nodes in the symptom network.\u003c/p\u003e \u003cp\u003eTotal scores were calculated to describe overall depression severity in the sample but were not used for network estimation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4Statistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eAll descriptive analyses incorporated NHIS sampling weights to produce nationally representative estimates[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Continuous variables were assessed for normality using distributional diagnostics. Normally distributed variables were summarized using means and standard deviations, whereas non-normally distributed variables were reported as medians with interquartile ranges (IQRs)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Categorical variables were summarized as weighted frequencies and percentages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Network Analysis\u003c/h2\u003e \u003cp\u003eA joint anxiety\u0026ndash;depression symptom network comprising 15 nodes, seven GAD-7 items and eight PHQ-8 items, was estimated using a Gaussian Graphical Model (GGM)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In this framework, edges represent regularized partial correlations, reflecting unique associations between pairs of symptoms after controlling for all other symptoms in the network.\u003c/p\u003e \u003cp\u003eNetwork estimation was conducted using the graphical least absolute shrinkage and selection operator (graphical LASSO) combined with extended Bayesian information criterion (EBIC) model selection[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The EBIC hyperparameter \u003cem\u003eγ\u003c/em\u003e was set to 0.5, a conservative value commonly recommended to balance sensitivity and specificity and to reduce the risk of spurious edges in high-dimensional data[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll network analyses incorporated NHIS sampling weights to account for the complex survey design.\u003c/p\u003e \u003cp\u003eTo identify clinically important symptoms within the network, centrality indices were computed, including: \u003cb\u003eStrength\u003c/b\u003e, reflecting the sum of absolute edge weights connected to a node; \u003cb\u003eExpected Influence (EI)\u003c/b\u003e, which accounts for both positive and negative associations and is particularly suitable for psychopathology networks[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo examine symptom connectivity between anxiety and depression domains, bridge centrality measures were calculated, including: \u003cb\u003eBridge Strength\u003c/b\u003e, quantifying the extent to which a symptom connects nodes across symptom communities; \u003cb\u003eBridge Expected Influence (BEI)\u003c/b\u003e, capturing directional cross-community influence[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSymptoms with high central or bridge centrality were interpreted as potential key intervention targets within the chronic pain\u0026ndash;related emotional symptom network[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Network Comparison Test\u003c/h2\u003e \u003cp\u003eTo formally evaluate gender differences in the symptom networks, we employed the NCT implemented in the R package \u003cb\u003eNetworkComparisonTest\u003c/b\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The NCT is a permutation-based procedure that assesses whether two independent networks differ in terms of global strength, overall structure, and individual edge weights.\u003c/p\u003e \u003cp\u003eFor each test, we performed 5,000 permutations. In each permutation, participants were randomly reassigned to the two groups while preserving the original sample sizes, and networks were re-estimated using the same EBICglasso procedure (\u003cem\u003eγ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5) used in the primary network estimation[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Two-tailed p-values were obtained by comparing the observed statistics with the permutation-based null distributions.\u003c/p\u003e \u003cp\u003eThree invariance tests were conducted: (1) \u003cb\u003enetwork structure invariance\u003c/b\u003e, assessing the maximum difference in edge weights (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:M\\)\u003c/span\u003e\u003c/span\u003e); (2) \u003cb\u003eglobal strength invariance\u003c/b\u003e, examining differences in overall connectivity defined as the sum of absolute edge weights (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S\\)\u003c/span\u003e\u003c/span\u003e); (3) \u003cb\u003eedge strength invariance\u003c/b\u003e, identifying specific edges that differ between groups (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\)\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A random seed (123) was set prior to the permutation procedure to ensure reproducibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4Network Stability and Accuracy\u003c/h2\u003e \u003cp\u003eThe accuracy and stability of the estimated network were evaluated using nonparametric bootstrap procedures[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Edge-weight accuracy was assessed by constructing 95% confidence intervals around estimated edge weights using bootstrapped samples[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCentrality stability was examined using case-dropping bootstrap analyses, and the correlation stability coefficient (CS-coefficient) was calculated. CS values exceeding 0.25 were considered acceptable, and values above 0.50 were interpreted as indicating good stability[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5Software\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using Python (version 3.9.7) and R (version 4.4.1). Data preprocessing, descriptive statistics, network estimation, and visualization were conducted in Python with the pandas, numpy, scipy, NetworkX, scikit‑learn, matplotlib, and seaborn libraries. Gender‑specific subgroup analyses, including NCT and associated visualizations, were carried out in R using the bootnet, qgraph, reshape and NetworkComparisonTest packages.\u003c/p\u003e \u003cp\u003eThe analysis code is available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/SentaW/Code-for-network-analysis-of-anxiety-and-depression-symptoms-among-adults-with-chronic-pain/tree/main\u003c/span\u003e\u003cspan address=\"https://github.com/SentaW/Code-for-network-analysis-of-anxiety-and-depression-symptoms-among-adults-with-chronic-pain/tree/main\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1Participants\u0026rsquo; Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter applying sampling weights to account for the complex survey design of the NHIS, participant characteristics are presented in \u003cstrong\u003eTable 1\u003c/strong\u003e and reflect U.S. adults with chronic pain at the national level. The mean age was 58.65 years (SD = 16.35), and 56.97% of participants were female. Most participants were non-Hispanic White (76.39%).\u003c/p\u003e\n\u003cp\u003eApproximately two-fifths of participants reported an annual household income below $35,000, while nearly one-fifth reported an income of $100,000 or greater. Most had at least a high school education, and 92.66% were covered by health insurance. Participants were distributed across all four U.S. census regions, with the largest proportion residing in the South.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eParticipant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(years),\u0026nbsp;\u003c/strong\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.65\u0026plusmn;16.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily income($),\u003c/strong\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57,257\u0026plusmn;49,387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,021 (43.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4,000 (56.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHispanic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e591 (8.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNH White only\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5,363 (76.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNH Black/African American only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e733 (10.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNH Asian only\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107 (1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNH AIAN only\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64 (0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNH AIAN and any other group\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100 (1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOther single and multiple races\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63 (0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNever attended/kindergarten only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGrade 1-11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e726 (10.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12th grade, no diploma\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e109 (1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGED or equivalent\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e297 (4.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHigh School Graduate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,742 (24.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSome college, no degree\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,302 (18.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAssociate degree: occupational, technical, or vocational program\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e357 (5.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAssociate degree: academic program\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e685 (9.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBachelor\u0026apos;s degree (Example: BA, AB, BS, BBA)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,130 (16.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree (Example: MA, MS, MEng, MEd, MBA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e461 (6.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eProfessional School degree (Example: MD, DDS, DVM, JD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63 (0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDoctoral degree (Example: PhD, EdD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92 (1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,022 (43.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLiving with a partner together as an unmarried couple\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e434 (6.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNeither\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,524 (50.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInsurance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNot covered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e507 (7.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ecovered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6,506 (92.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNortheast\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,070 (15.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMidwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,714 (24.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,597 (36.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,640 (23.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIncome level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e$0 to $34,999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2940 (41.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e$35,000 to $49,999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e975 (13.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e$50,000 to $74,999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1146 (16.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e$75,000 to $99,999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e699 (9.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e$100,000 or greater\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1261 (17.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e \u003cstrong\u003eNH\u003c/strong\u003e, non-Hispanic; \u003cstrong\u003eAIAN\u003c/strong\u003e, American Indian or Alaska Native; \u003cstrong\u003eGED\u003c/strong\u003e, General Educational Development. \u003cstrong\u003eHispanic\u003c/strong\u003e refers to Hispanic/Latino ethnicity regardless of race. \u003cstrong\u003eNH-White-only, NH-Black/African American-only, NH-Asian-only, and NH-AIAN-only\u003c/strong\u003e denote non-Hispanic individuals identifying with a single racial group; \u003cstrong\u003eNH-AIAN\u003c/strong\u003e and any other group denotes AIAN in combination with one or more other racial groups. \u003cstrong\u003eGED or equivalent\u003c/strong\u003e indicates a high school equivalency credential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Descriptive Statistics of Symptom Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics for overall anxiety and depression severity, as well as item-level symptoms from the GAD-7 and PHQ-8, are summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e, along with node predictability estimates. The median severity for both anxiety and depression was 1.00 ((Interquartile Range, IQR: 1.00\u0026ndash;2.00).\u003c/p\u003e\n\u003cp\u003eAmong GAD-7 items, mean scores ranged from 1.38 to 1.68, with \u0026ldquo;excessive worry\u0026rdquo; and \u0026ldquo;nervousness\u0026rdquo; showing the highest average severity, and \u0026ldquo;fearfulness\u0026rdquo; and \u0026ldquo;restlessness\u0026rdquo; the lowest. For PHQ-8 items, mean scores ranged from 1.25 to 2.20; \u0026ldquo;fatigue\u0026rdquo; and \u0026ldquo;sleep disturbance\u0026rdquo; had the highest mean severity, whereas \u0026ldquo;psychomotor symptoms\u0026rdquo; and \u0026ldquo;worthlessness\u0026rdquo; were among the lowest. All item-level symptom data were used in subsequent network analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDescriptive statistics for anxiety and depression symptom measures\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"94%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictability(\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Severity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eAnxiety Severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.00 [1.00, 2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eDepression Severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.00 [1.00, 2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAD-7 Items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eG1 (Nervousness)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.65 \u0026plusmn; 0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.532382641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eG2 (Uncontrollable worry)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.58 \u0026plusmn; 0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.564843946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eG3 (Excessive worry)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.68 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.547731503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eG4 (Trouble relaxing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.63 \u0026plusmn; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.51963307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eG5 (Restlessness)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.41 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.436164868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eG6 (Irritability)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.62 \u0026plusmn; 0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.45048257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eG7 (Fearfulness)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.38 \u0026plusmn; 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.467074448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePHQ-8 Items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eP1 (Anhedonia)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.56 \u0026plusmn; 0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.460070182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eP2 (Sadness)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.54 \u0026plusmn; 0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.522736271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eP3 (Sleep disturbance)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e2.04 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.382733633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eP4 (Fatigue)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e2.20 \u0026plusmn; 1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.431799107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eP5 (Appetite changes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.61 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.414854129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eP6 (Worthlessness)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.42 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.498646165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eP7 (Concentration problems)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.42 \u0026plusmn; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.452095219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eP8 (Psychomotor symptoms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.25 \u0026plusmn; 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.38646709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. Predictability (\u003cem\u003eR\u003c/em\u003e\u003cem\u003e\u0026sup2;\u003c/em\u003e) reflects the proportion of variance in each symptom explained by its connections with other symptoms in the network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e \u003cstrong\u003eEstimated Symptom Network Structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe estimated regularized partial correlation network consisted of 15 nodes, seven from GAD-7 and eight from PHQ-8, representing anxiety and depression symptoms, and is illustrated in Figure 2. Nodes were color-coded by scale, and edges reflected partial correlations, with green indicating positive and red indicating negative associations. The network showed dense connectivity, with predominantly positive edges; only six weak negative edges were observed, all with absolute weights below 0.018.\u003c/p\u003e\n\u003cp\u003eThe strongest connections within the anxiety community were observed between \u0026ldquo;uncontrollable worry\u0026rdquo; and \u0026ldquo;excessive worry\u0026rdquo; (r = 0.435), and between \u0026ldquo;trouble relaxing\u0026rdquo; and \u0026ldquo;restlessness\u0026rdquo; (r = 0.283). Within the depression community, the strongest edges were between \u0026ldquo;anhedonia\u0026rdquo; and \u0026ldquo;sadness\u0026rdquo; (r = 0.315), and between \u0026ldquo;sleep disturbance\u0026rdquo; and \u0026ldquo;fatigue\u0026rdquo; (r = 0.248). The five strongest edges are summarized in \u003cstrong\u003eTable 3\u003c/strong\u003e.\u0026nbsp;Complete edge weights for all pairwise connections are provided in\u0026nbsp;Supplementary table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. Top 5 strongest edges within and between symptom communities\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNode 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNode 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation (\u003cem\u003er\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrongest Within-Community Edges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eG5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrongest Between-Community (Bridge) Edges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eG5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eP7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eG6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. \u003cem\u003er\u003c/em\u003e = partial correlation coefficient. All edges listed are positive associations. Bridge edges are defined as connections between a GAD-7 node and a PHQ-8 node.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Centrality and Bridge Symptom Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNode centrality was assessed using EI, which reflects a node\u0026apos;s total connectivity within the network. The symptoms with the highest EI, indicating greatest overall influence, were predominantly anxiety-related: \u003cstrong\u003euncontrollable worry\u003c/strong\u003e (G2; EI = 1.125), \u003cstrong\u003eexcessive worry\u003c/strong\u003e (G3; EI = 1.113), \u003cstrong\u003etrouble relaxing\u003c/strong\u003e (G4; EI = 1.047), and \u003cstrong\u003enervousness\u003c/strong\u003e (G1; EI = 1.028). Among depressive symptoms, \u003cstrong\u003esadness\u003c/strong\u003e (P2; EI = 1.058) and \u003cstrong\u003eworthlessness\u003c/strong\u003e (P6; EI = 0.930) also exhibited relatively high EI values. These findings suggest that worry-related experiences and core mood disturbances play a central role in the overall symptom network.\u003c/p\u003e\n\u003cp\u003eBEI quantifies a node\u0026apos;s propensity to connect the anxiety and depression communities. The strongest bridge symptoms were \u003cstrong\u003esadness\u003c/strong\u003e (P2; BEI = 0.421), \u003cstrong\u003eworthlessness\u003c/strong\u003e (P6; BEI = 0.409), and \u003cstrong\u003enervousness\u003c/strong\u003e (G1; BEI = 0.396). Notably, \u003cstrong\u003esadness\u003c/strong\u003e ranked highly on both EI and BEI metrics. Centrality indices for each node are reported in Supplementary table S2. The distributions of EI and BEI are displayed in Figure 3, with the corresponding numerical values provided in Supplementary table S3.\u003c/p\u003e\n\u003cp\u003e3.5\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNetwork Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NCT was conducted to examine potential gender differences in the symptom network. The test statistic for network structure invariance was \u003cem\u003eM\u003c/em\u003e = 0.083 (\u003cem\u003ep\u003c/em\u003e = 0.444), indicating that the difference in overall network structure between the female and male networks was 0.083. The global strength of the female network was 6.717, whereas that of the male network was 6.748, yielding a difference of 0.031 (\u003cem\u003ep\u003c/em\u003e = 0.637).\u003c/p\u003e\n\u003cp\u003eAt the edge level, 8 of 105 possible connections showed nominally significant differences, a number only slightly exceeding chance expectation (~5 edges)[41, 45]. The most pronounced differences involved connections between anxiety symptoms (G1\u0026ndash;G4, G3\u0026ndash;G4) and between anxiety and depression symptoms (G1\u0026ndash;P3, G1\u0026ndash;P4, G4\u0026ndash;P6). However, none of these edge-level differences remained significant after applying false discovery rate correction (all \u003cem\u003eq\u003c/em\u003e \u0026gt; 0.05). Detailed edge-level statistics are provided in Supplementary table S4.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUsing a nationally representative sample of U.S. adults with chronic pain, the present study estimated an item-level network linking anxiety and depression symptoms. Several symptoms demonstrated prominent central and bridge properties, revealing a densely connected symptom architecture with distinct anxiety and depression communities. By identifying specific symptom–symptom interactions underlying the co-occurrence of anxiety and depression, these findings extend prior categorical and dimensional research and provide a more fine-grained understanding of emotional distress in individuals with chronic pain.\u003c/p\u003e\n\u003cp\u003eThe estimated network exhibited a high level of connectivity, with the vast majority of edges being positive. The strongest connections occurred within the anxiety and depression communities, indicating that symptoms within each disorder tend to reinforce one another. Within the anxiety community, the most prominent connection linked “uncontrollable worry” and “excessive worry,” consistent with cognitive models of generalized anxiety in which persistent and uncontrollable worry constitutes a core process. Within the depression community, the strongest edges connected “loss of interest” with “sadness,” and “sleep disturbance” with “fatigue.” These symptom pairings reflect well-recognized depressive symptom clusters involving core affective disturbances and neurovegetative complaints.\u003c/p\u003e\n\u003cp\u003eAlthough cross-community connections were generally weaker than the strongest within-community associations, numerous bridge edges connected anxiety and depression symptoms. The most prominent cross-community association was observed between anxiety-related “restlessness” and depression-related “psychomotor symptoms.” This linkage may reflect a shared manifestation of psychomotor activation that bridges the experiential domains of anxiety and depression, highlighting potential pathways through which symptoms of one disorder may activate or exacerbate symptoms of the other[46].\u003c/p\u003e\n\u003cp\u003eCentrality analysis further identified several highly connected nodes within the network. Symptoms such as “uncontrollable worry” and “sadness” exhibited particularly high expected influence values, indicating that they maintained strong connections with many other symptoms in the network. Highly central symptoms may play an important role in sustaining overall symptom activation and may therefore represent influential points within the broader emotional symptom system.\u003c/p\u003e\n\u003cp\u003eBeyond overall connectivity, bridge centrality analysis identified symptoms that most strongly connected the anxiety and depression communities. From the depression side, “sadness” and “feelings of worthlessness” emerged as the most prominent bridge symptoms, whereas “nervousness” and “trouble relaxing” represented key bridges from the anxiety domain. These findings have potential clinical implications. Symptoms that connect distinct symptom clusters may serve as conduits through which comorbidity develops and persists. Interventions that effectively target such bridging symptoms may therefore have cascading effects across the broader symptom network, potentially weakening the mutual reinforcement between anxiety and depression[47–49].\u003c/p\u003e\n\u003cp\u003eNetwork comparison analyses indicated that the overall symptom network was largely comparable between males and females. Neither global network strength nor overall network structure differed significantly across gender groups. Although several edges showed nominal differences at the uncorrected level, none remained significant after correction for multiple comparisons. These findings suggest that the structural organization of symptom interrelations linking anxiety and depression may be broadly similar across genders among individuals experiencing chronic pain, despite well-documented gender differences in the prevalence of emotional disorders.\u003c/p\u003e\n\u003cp\u003eThe robustness of the estimated network was supported by the stability analyses. The relatively high correlation stability coefficient and narrow bootstrap confidence intervals around the edge weights indicate that the estimated network structure was stable and interpretable within the present sample.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be considered when interpreting these findings. First, the cross-sectional design precludes causal inference regarding the directionality of symptom interactions. Consequently, it cannot be determined whether central or bridge symptoms function as causes or consequences of network activation. Longitudinal or experience-sampling designs would be necessary to clarify temporal dynamics and potential causal pathways. Second, all measures were based on self-reported symptoms and may therefore be influenced by recall bias or reporting tendencies. Third, although the sample was nationally representative of U.S. adults with chronic pain, the findings may not generalizeto other cultural contexts, clinical populations, or specific pain conditions. Finally, the network was limited to symptoms assessed by the GAD-7 and PHQ-8 scales. Incorporating symptoms from additional domains relevant to chronic pain such as sleep disturbance, pain catastrophizing, or fatigue-related constructs may provide a more comprehensive understanding of the broader psychopathological landscape.\u003c/p\u003e\n\u003cp\u003eFuture research should aim to replicate the present findings in independent samples and examine the clinical utility of targeting highly connected symptoms within intervention studies. Additionally, investigating how contextual factors such as trauma exposure, socioeconomic disadvantage, or pain duration shape the structure of symptom networks may further advance our understanding of emotional distress in chronic pain populations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis network analysis provides a fine-grained map of the symptom-level architecture linking anxiety and depression in adults with chronic pain. Several symptoms including uncontrollable worry, excessive worry, sadness, and worthlessness emerged as highly central or bridging symptoms, suggesting that they may play a key role in the whole estimated network. These findings highlight the value of a symptom-level, transdiagnostic perspective. Targeting such highly central and bridge symptoms may offer a promising direction for developing more precise interventions aimed at reducing the co-occurring burden of anxiety and depression in chronic pain populations.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBEI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBridge Expected Influence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExpected Influence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAD-7\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Anxiety Disorder-7\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health Interview Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNetwork Comparison Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHQ-8\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatient Health Questionnaire-8\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Health Interview Survey (NHIS) protocol was approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. All participants provided written informed consent. This study used de‑identified, publicly available secondary data; therefore, additional institutional review board approval was not required. The research was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are publicly available from the NHIS database at: https://www.cdc.gov/nchs/nhis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research supported by the Science and Technology Popularization Project of Hunan Provincial Department of Science and Technology (Grant No. 2025ZK4044).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShuntao Wang and Caixia Sun conceived and designed the study, conducted the statistical analyses and drafted the manuscript.\u003c/p\u003e\n\u003cp\u003eXiaoshan Li and Huiru Zhang contributed to data cleaning and analysis.\u003c/p\u003e\n\u003cp\u003eXinyue Liu and Manyi Wang assisted with literature review and manuscript revision.\u003c/p\u003e\n\u003cp\u003eZuyue Zeng, Jing Zhou and Jing He provided clinical expertise and supervised the study.\u003c/p\u003e\n\u003cp\u003eYujiao Li and Yating Zhan contributed to data interpretation and critical revision of the manuscript.\u003c/p\u003e\n\u003cp\u003eXiuhong Lei and Guqing Zeng supervised the project, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the National Center for Health Statistics for providing access to the publicly available data used in this study.\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to the Science and Technology Popularization Project of Hunan Provincial Department of Science and Technology for the financial support of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUS Pain Foundation. 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The comorbid network characteristics of anxiety and depressive symptoms among Chinese college freshmen. BMC Psychiatry. 2024;24:297. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12888-024-05733-z\u003c/span\u003e\u003cspan address=\"10.1186/s12888-024-05733-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anxiety, Depression, Chronic Pain, Network analysis","lastPublishedDoi":"10.21203/rs.3.rs-9251999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9251999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo determine the network structure and interaction patterns of anxiety and depression symptoms in patients with chronic pain.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed 7,021 adults reporting chronic pain from the 2019 National Health Interview Survey (NHIS). Anxiety and depression were assessed using the 7-item Generalized Anxiety Disorder scale (GAD-7) and the 8-item Patient Health Questionnaire (PHQ-8). Network analysis was conducted to identify central symptoms and b ridge symptoms linking the anxiety and depression communities, and the Network Comparison Test (NCT) was used to examine gender differences.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong U.S. adults with chronic pain, anxiety and depression symptoms formed a highly interconnected network with prominent cross-domain associations. \u0026ldquo;Uncontrollable worry\u0026rdquo; (G2) and \u0026ldquo;excessive worry\u0026rdquo; (G3) emerged as the most central symptoms, with \u0026ldquo;sadness\u0026rdquo; (P2) and \u0026ldquo;worthlessness\u0026rdquo; (P6) served as key bridge symptoms linking anxiety and depression. Additionally, Gender did not significantly affect the network structure.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe most central symptoms (\u0026ldquo;uncontrollable worry\u0026rdquo; and \u0026ldquo;excessive worry\u0026rdquo;), and the strongest bridge symptoms linking anxiety and depression (\u0026ldquo;sadness\u0026rdquo; and \u0026ldquo;worthlessness\u0026rdquo;) may represent potential targets for interventions aimed at reducing co-occurring anxiety and depression in individuals with chronic pain.\u003c/p\u003e","manuscriptTitle":"Network analysis of anxiety and depression symptoms among adults with chronic pain: based on 2019 National Health Interview Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 06:01:30","doi":"10.21203/rs.3.rs-9251999/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-09T17:34:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T16:27:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T06:35:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T22:49:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T08:42:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T09:24:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204995188703321917332270898129291589528","date":"2026-04-24T06:03:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205957581773708896984238816485404747642","date":"2026-04-23T08:43:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166719424748347197876722949162517366924","date":"2026-04-23T00:56:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285224924064452655043588924094707182640","date":"2026-04-22T02:41:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289565499380590645137817276308016324501","date":"2026-04-22T00:15:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235393944020087266390439285477321325334","date":"2026-04-21T20:44:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307591786047675918857501862121320971073","date":"2026-04-21T09:46:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T08:37:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-01T13:19:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T22:17:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T22:17:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-03-28T10:28:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35bfa84b-86b5-4570-9632-95cee4de717e","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-09T17:34:23+00:00","index":57,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T16:27:49+00:00","index":54,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T06:35:03+00:00","index":53,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T06:01:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 06:01:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9251999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9251999","identity":"rs-9251999","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}