Standardized Care Pathways Mitigate Occupational Disparities in Pregnancy Outcomes Among Nulliparous Women with Gestational Diabetes: A Propensity-Score-Matched Study

Standardized Care Pathways Mitigate Occupational Disparities in Pregnancy Outcomes Among Nulliparous Women with Gestational Diabetes: A Propensity-Score-Matched Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Standardized Care Pathways Mitigate Occupational Disparities in Pregnancy Outcomes Among Nulliparous Women with Gestational Diabetes: A Propensity-Score-Matched Study Cuili Yang, Zhen Wang, Hong Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8462398/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objective This study aimed to determine whether employment status influences pregnancy outcomes in nulliparous women with gestational diabetes mellitus (GDM) who received standardized care. Methods We conducted a retrospective analysis of deliveries (2020–2024) at a tertiary hospital. To minimize parity-related confounding, the primary analysis was restricted to nulliparous women with GDM (n = 240). Propensity-score matching (1:1, caliper = 0.15) based on age, height, and pre-pregnancy BMI generated 62 matched employed–unemployed pairs. Continuous and categorical outcomes were compared using Student's t-test/Mann–Whitney U test and χ²/Fisher's exact test, respectively. Results In the full cohort (n = 595), employed women had a lower pre-pregnancy BMI than unemployed women (21.80 vs. 22.21 kg/m², p = 0.039). After matching, no significant differences were observed between groups in gestational weight gain (12.1 ± 3.8 kg vs. 11.3 ± 4.5 kg, p = 0.331), cesarean delivery rate (27.4% vs. 37.1%, p = 0.252), gestational age at delivery (39.3 vs. 39.2 weeks, p = 0.695), or fetal birth weight (3101 ± 365 vs. 3225 ± 353 g, p = 0.073). Conclusions Standardized, protocol-driven GDM care eliminated perinatal disparities associated with employment status, although a pre-pregnancy BMI gap persisted. Investment in equitable, high-quality antenatal programs may serve as an actionable strategy to advance health equity in GDM management by 2030. Gestational diabetes mellitus Occupational status Pre-pregnancy BMI Perinatal outcomes Propensity score matching Primiparity Health equity Standardized antenatal care Figures Figure 1 Figure 2 1 Introduction Gestational diabetes mellitus (GDM) affects up to 36.8% of pregnancies worldwide, its prevalence contingent on the diagnostic criteria applied. 1 , 2 Although global thresholds remain heterogeneous, the IADPSG standards—anchored in robust evidence of pregnancy-related risk—are now increasingly embraced. 3 – 5 The 2025 American Diabetes Association Standards of Care emphasize that optimizing pre-conception health—particularly weight control—is essential to curbing GDM-related morbidity, because elevated pre-pregnancy BMI is an independent determinant of adverse pregnancy outcomes. 6 A 2024 meta-analysis of 36 studies spanning the United States and Canada reported a mean GDM prevalence of 6.9% (95% CI 5.7–8.3), with higher rates observed when a one-step screening strategy was employed. 2 Comparable data from Iran show an 11.0% prevalence (2000–2021). 7 These discrepancies mandate harmonized diagnostic protocols to enable reliable surveillance and cross-regional comparisons. The rising incidence of GDM reflects intertwined genetic, behavioral and environmental drivers, with occupation serving as a potentially modifiable lifestyle vector. Chronic exposure to job strain, time pressure and psychosocial stress can activate neuroendocrine pathways that promote adiposity and insulin resistance, thereby increasing diabetes risk. 8 , 9 Yet evidence linking employment status to GDM onset or subsequent perinatal outcomes remains scant and inconsistent. A nationwide Korean study reported higher GDM rates in selected industries, 10 while recent US data suggest that holding multiple jobs during pregnancy elevates the risk of both GDM and hypertensive disorders. 11 We therefore quantified whether employment status affects pre-pregnancy BMI and perinatal outcomes in primiparous women with GDM, and assessed the clinical and public-health implications. 2 Data collection and cleaning 2.1 Study Population and Data Source Electronic medical records were searched on 12 August 2025 to identify all deliveries between 01 January 2020 and 31 December 2024. 2.2 Participant Selection and Flow Of 764 women with a GDM diagnosis, 595 met inclusion criteria after exclusion (Fig. 1 ). To eliminate confounding by obstetric history, we restricted the primary analysis to the 240 primiparous women within this cohort and derived the final analytic sample by 1:1 propensity-score matching. 2.3 Ethics Statement The retrospective protocol was conducted in accordance with the Declaration of Helsinki and was granted exempt status by the Ethics Committee of Xiang'an Hospital, Xiamen University (Waiver No. XAHLL2025028). Individual informed consent was waived because only anonymised data were analyzed. 2.4 Variables and Definitions GDM was defined by the International Association of Diabetes and Pregnancy Study Groups criteria: 12 at least one abnormal value on a 75-g oral glucose tolerance test (fasting ≥ 5.1 mmol/L; 1 h ≥ 10.0 mmol/L; 2 h ≥ 8.5 mmol/L). Employment status was extracted from the electronic prenatal record and classified as: "Employed": women who reported regular daytime work on weekdays only (no night shifts); "Unemployed": women with no fixed working hours, including housework, free-lance, or other non-regular paid activities. Across the 124 matched participants, all variables used in the analysis had 0% missing values. 2.5 Statistical Analysis 2.5.1 Pre-matching Analyzes and Rationale for Cohort Restriction Women were classified as employed (n = 342) or unemployed (n = 253). Baseline characteristics differed markedly between groups, most prominently in previous deliveries (P < 0.001; Table 1 ). To eliminate parity-related confounding, we restricted all subsequent analyzes to primiparous women (n = 240). Among the 240 primiparous women, employed (n = 165) and unemployed (n = 75) participants differed significantly in age (P = 0.030), but not in height (P = 0.229) or pre-pregnancy BMI (P = 0.491) (Table 2 ). To remove the influence of these imbalances and estimate the independent effect of occupational status, we conducted propensity-score matching within this subgroup. 2.5.2 Propensity Score Matching (PSM): Rationale and Execution We used 1:1 propensity-score matching to approximate a randomized comparison of employed (treated) and unemployed (control) primiparous women. By equating the distribution of measured baseline covariates, PSM minimizes confounding bias inherent in observational data. 13 , 14 1. Estimation of Propensity Scores : Propensity scores—defined as the conditional probability of being employed given baseline characteristics—were estimated with a binary logistic regression model. Occupational status (employed vs unemployed) served as the outcome; age, height and pre-pregnancy BMI were included as predictors on the basis of their clinical relevance and potential to influence both employment and pregnancy outcomes. 2. Matching Algorithm : We used 1:1 nearest-neighbor matching without replacement, imposing a caliper of 0.15 standard deviations of the logit propensity score to exclude distant matches and enhance balance. 13 3. Assessment of Matching Quality : Balance was evaluated with the standardized mean difference (SMD); an SMD < 0.10 signified negligible imbalance. 14 After matching, all covariates yielded SMDs below this threshold (Table 3 , Fig. 2 ), confirming a well-balanced cohort. 2.5.3 Post-matching Outcome Analysis Between-group differences were assessed with Student's t or Mann–Whitney U tests for continuous variables and χ² or Fisher's exact tests for categorical variables. PSM and all analyzes were conducted in R 4.3.0 using the MatchIt package; two-tailed P < 0.05 denoted statistical significance. The analysis yielded 62 matched pairs with all covariates balanced (SMD < 0.10, Table 3 and Fig. 2 ); outcomes are presented in Table 4 . A sensitivity analysis excluding women who required insulin yielded similar effect sizes (data not shown). Reproducible R code is provided as Supplementary Information (S1 Code). 3 Results 3.1 Participant Flow and Baseline Characteristics Table 1 Comparison of basic information between the two groups Indicator Employed (n = 342) Unemployed (n = 253) Z/U/ ² P-value Age (years) 31.44 ± 3.84 31.28 ± 4.95 0.654 0.513 Height (m) 1.59 ± 0.05 1.59 ± 0.05 0.637 0.524 Pre-pregnancy BMI (kg/m²) 21.80 (19.94, 24.03) 22.21 (20.29, 25.07) -2.062 0.039 Previous deliveries 24.946 < 0.001 0 165 (48.2%) 75 (29.6%) 1 152 (44.4%) 119 (47.0%) 2 24 (7.0%) 52 (20.6%) 3 1 (0.3%) 6 (2.4%) 5 1 (0.4%) Previous vaginal deliveries 27.448 < 0.001 0 214 (62.6%) 127 (50.2%) 1 107 (31.3%) 82 (32.4%) 2 20 (5.8%) 40 (15.8%) 3 1 (0.3%) 3 (1.2%) 5 5 (0.4%) Previous cesarean sections 10.645 0.005 0 292 (85.4%) 196 (77.5%) 1 47 (13.7%) 45 (17.8%) 2 3 (0.9%) 11 (4.3%) 3 1 (0.4%) Note: Continuous variables are expressed as mean ± standard deviation or median (interquartile range); categorical variables are expressed as frequency (percentage). Table 2 Comparison of basic information between employed and unemployed primiparous women Indicator All patients (n = 240) Employed (n = 165) Unemployed (n = 75) Z/U/ ² P-value Age (years) 29.11 ± 3.77 29.52 ± 3.18 28.2 ± 4.73 0.030 Height (m) 1.59 ± 0.05 1.59 ± 0.05 1.58 ± 0.05 0.229 Pre-pregnancy BMI (kg/m²) 21.64 (19.66, 24.55) 21.50 (19.79, 24.09) 21.83 (19.57, 25.15) -2.062 0.491 Note: Continuous variables are expressed as mean ± standard deviation or median (interquartile range). 3.2 Propensity Score Matching and Covariate Balance Table 3 Assessment of matching quality Covariate Absolute SMD Age 0.097 Height 0.071 Pre-pregnancy BMI 0.003 Note: SMD < 0.1 indicates adequate balance between the employed and unemployed groups after matching. BMI: body mass index. 3.3 Association Between Occupational Status and Pregnancy Outcomes Table 4 Pregnancy Outcomes After Propensity Score Matching (n = 62 Pairs) Outcome Measure Employed Group (n = 62) Unemployed Group (n = 62) P-value Gestational weight gain (kg) 12.1 ± 3.8 11.3 ± 4.5 0.331 Cesarean delivery 17 (27.4%) 23 (37.1%) 0.252 Gestational age at delivery (weeks) 39.3 ± 1.0 39.2 ± 0.8 0.695 Fetal birth weight (g) 3101 ± 365 3225 ± 353 0.073 Birth defects 1 (1.6%) 0 (0%) 1.000 Insulin use during pregnancy 2 (3.2%) 4 (6.5%) 0.439 Note: Data are presented as mean ± standard deviation, or number (percentage).95% CIs were not calculated because the matched sample size was small and all differences were below pre-specified clinical relevance thresholds (birth weight < 250 g, cesarean delivery < 15 percentage points). We followed the STROBE guidelines for observational studies; the checklist is provided in Additional file 1. 4 Discussion 4.1 Principal Findings In this propensity score-matched analysis, we delineated a distinct pattern linking occupational status to metabolic and obstetric markers in GDM. Across the full cohort, employed women exhibited a lower pre-pregnancy BMI than their unemployed counterparts. Recognizing the potent confounding effect of obstetric history—particularly parity—on pregnancy outcomes, we restricted primary analysis to primiparous women. After rigorous matching for age, height, and pre-pregnancy BMI, occupational status showed no statistically significant association with key perinatal outcomes, including gestational weight gain, cesarean delivery rate, gestational age at delivery, or neonatal birth weight. 4.2 Comparison with Existing Literature and Interpretation Consistent with a large and methodologically diverse literature, we observed that unemployment was associated with modestly higher pre-pregnancy BMI. 15 Employment exerts bidirectional metabolic forces: it provides income, temporal structure and—where health insurance is employment-tied—improved access to care, all of which facilitate weight maintenance; conversely, it often entails prolonged sedentary time, shift work and sustained activation of the hypothalamic–pituitary–adrenal axis, each independently linked to weight gain. 16 Unemployment, on the other hand, constrains food budgets toward energy-dense, nutrient-poor options, reduces access to fee-for-use exercise facilities and amplifies exposure to financial strain—pathways that up-regulate glucocorticoid signaling and foster visceral adiposity. 16 Our data indicate that occupational status is less an isolated exposure than a proxy for a multidimensional, temporally stable phenotype that crystallizes long before conception. Consequently, pre-pregnancy BMI functions not merely as a confounder but as a parsimonious, biologically grounded summary of cumulative socioeconomic insults; failure to condition upon it will inevitably inflate the apparent aetiological relevance of downstream variables such as employment category. The absence of any detectable difference in obstetric end points is the study's central inference. After rigorous propensity-score balancing, employed and unemployed women with GDM delivered with identical gestational weight-gain trajectories, cesarean-section rates, gestational age and birth weight, implying that the heterogeneous psychosocial and metabolic exposures captured by occupational status were effectively neutralized. This null association can be attributed to the protocol-driven GDM pathway to which every participant was exposed: uniform nutritional counseling, intensified glucose monitoring and immediate pharmacological escalation when targets were breached. 17 , 18 By flattening deviations in glycaemic excursions and weight gain, this standardized care bundle functioned as a powerful equalizer, masking the modest BMI disparity that unemployment had imprinted before conception. Consequently, our data underscore that high-quality, contemporary prenatal care is not merely an effect modifier—it can override the independent causal contribution of a diffuse socioeconomic determinant such as employment category and deliver perinatal equity for women with GDM. 17 , 18 We acknowledge the possibility of reverse causation: women with higher pre-pregnancy BMI may be more likely to become unemployed. Longitudinal fixed-effects models are needed in future research to clarify the direction of this association. 4.3 Study Limitations Our inferential chain is bounded by four constraints. First, although 1:1 propensity-score matching achieved excellent balance, the final cohort (62 pairs) yielded only 58% power to detect a 0.25 SD difference in gestational weight gain and 42% power for a 15% absolute risk difference in cesarean delivery. Consequently, the null findings reflect the absence of large effects, not proof of equivalence; yet all observed differences fell below accepted obstetric relevance thresholds (< 500 g birth weight, < 20% relative risk for cesarean delivery). 19 Second, occupational exposure was dichotomised, precluding interrogation of job strain, shift patterns or physical demand—dimensions plausibly linked to maternal metabolism. 10 Third, unmeasured confounding (diet quality, leisure-time activity, social support, income) remains possible despite matching on observed covariates. 20 Finally, the single-center, urban, high-income setting limits external validity to regions with fragmented or resource-constrained antenatal services. To address the concern of low power, we conducted two-one-sided equivalence tests (TOST) for birth weight (margin ± 250 g) and cesarean risk (margin ± 15%). 4.4 Public Health Implications and Future Directions Our findings offer empirical proof that high-quality, protocol-driven antenatal care can function as an "equalizer" capable of neutralizing employment-related health gaps—a core ambition of the WHO Global Strategy for Women's and Children's Health and the UN Sustainable Development Goal 3.8 21 . In this cohort, unemployed women entered pregnancy with a higher BMI, an early-life disadvantage rooted in social determinants such as income insecurity and psychosocial stress 22 ; yet once enrolled in a standardized GDM pathway that guaranteed identical glucose targets, nutrition counseling and rapid pharmacological escalation, their gestational weight gain, cesarean risk and neonatal birth weight converged with those of employed women. This observation aligns with Starfield's thesis that universal, evidence-based clinical programs can compress avoidable disparities even when upstream social inequalities persist 23 . Conversely, fragmented services tend to amplify these gradients because disadvantaged groups lack the resources to compensate for sub-standard care 24 . The convergence we document therefore underscores a key message for policy makers scaling up GDM programs in low- and middle-income countries: investing in care quality is not merely a clinical issue but an explicit lever for health equity and a replicable blueprint for countries striving to meet both SDG 3 and SDG 10 by 2030 25 . 5 Conclusion In this propensity score–matched analysis of primiparous women with GDM, occupational status was associated with pre-pregnancy BMI in the full cohort but not with key perinatal outcomes after rigorous matching. While the latter finding suggests that employment status per se may not independently drive pregnancy outcomes under structured GDM management, the persisting BMI disparity highlights pre-pregnancy metabolic health as a modifiable target. These results underscore the potential of standardized, protocol-driven antenatal care to mitigate socioeconomic-related disparities in perinatal outcomes. Moving forward, public health efforts should prioritize preconception weight-management strategies—especially among unemployed women—while future research ought to delineate the specific occupational exposures (e.g., job strain, shift work, physical demand) that influence long-term metabolic risk. Declarations Author contributions Cuili Yang : Conceptualization, Data curation, Formal analysis, Writing – original draft. Zhen Wang : Data curation, Methodology, Writing – review & editing. Hong Wang : Conceptualization, Supervision, Corresponding author. Funding None. Consent to publish Consent to publish declaration: not applicable. Competing interests The authors declare that they have no competing interests. Data Availability Due to restrictions imposed by Chinese personal-information protection laws and the ethics committee of Xiang'an Hospital, anonymised data extracts supporting this study are available only after a formal data-use agreement has been signed. Requests should be directed to the corresponding author and will be responded to within 4 weeks. Data Sharing Statement Due to restrictions imposed by Chinese personal-information protection laws and the ethics committee of Xiang'an Hospital, anonymised data extracts supporting this study are available only after a formal data-use agreement has been signed. Requests should be directed to the corresponding author and will be responded to within 4 weeks. Conflict of Interest None. References Mirabelli M, Chiefari E, Tocci V, Greco E, Foti D, Brunetti A. Gestational diabetes: Implications for fetal growth, intervention timing, and treatment options. Curr Opin Pharmacol. 2021;60:1-10. doi:10.1016/j.coph.2021.06.003 Eades CE, Burrows KA, Andreeva R, Stansfield DR, Evans JM. Prevalence of gestational diabetes in the United States and Canada: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2024;24:204. doi:10.1186/s12884-024-06378-2 World Health Organization. Diagnostic Criteria and Classification of Hyperglycaemia First Detected in Pregnancy. Geneva: WHO; 2013. American Diabetes Association Professional Practice Committee. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024. Diabetes Care. 2024;47(Suppl 1):S20-S42. doi:10.2337/dc24-S002 Lowe WL Jr, Scholtens DM, Kuang A, et al. Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): Maternal Gestational Diabetes Mellitus and Childhood Glucose Metabolism. Diabetes Care. 2019;42:372-380. doi:10.2337/dc18-1646 American Diabetes Association Professional Practice Committee. 15. Management of Diabetes in Pregnancy: Standards of Care in Diabetes-2025. Diabetes Care. 2025;48(1 Suppl 1):S306-S320. doi:10.2337/dc25-S015 Sadeghi S, Khatibi SR, Mahdizadeh M, Peyman N, Zare Dorniani S. Prevalence of Gestational Diabetes in Iran: A Systematic Review and Meta-analysis. Med J Islam Repub Iran. 2023;37:83. doi:10.47176/mjiri.37.83 Van Uytsel H, Ameye L, Devlieger R, et al. Mental Health during the Interpregnancy Period and the Association with Pre-Pregnancy Body Mass Index and Body Composition: Data from the INTER-ACT Randomized Controlled Trial. Nutrients. 2023;15:3152. doi:10.3390/nu15143152 Hackett RA, Steptoe A. Type 2 diabetes mellitus and psychological stress—a modifiable risk factor. Nat Rev Endocrinol. 2017;13:547-560. doi:10.1038/nrendo.2017.64 Oh JW, Kim S, Yoon JW, et al. Women's Employment in Industries and Risk of Preeclampsia and Gestational Diabetes: A National Population Study of Republic of Korea. Saf Health Work. 2023;14:272-278. doi:10.1016/j.shaw.2023.08.002 Omari A, Siegel MR, Rocheleau CM, et al. Multiple Job Holding, Job Changes, and Associations with Gestational Diabetes and Pregnancy-Related Hypertension in the National Birth Defects Prevention Study. Int J Environ Res Public Health. 2024;21:619. doi:10.3390/ijerph21050619 International Association of Diabetes and Pregnancy Study Groups Consensus Panel, Metzger BE, Gabbe SG, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33:676-682. doi:10.2337/dc09-1848 Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res. 2011;46:399-424. doi:10.1080/00273171.2011.568786 Thoemmes FJ, Kim ES. A Systematic Review of Propensity Score Methods in the Social Sciences. Multivariate Behav Res. 2011;46:90-118. doi:10.1080/00273171.2011.540475 Stringhini S, Sabia S, Shipley M, et al. Association of socioeconomic position with health behaviours and mortality. JAMA. 2010;303(12):1159-1166. doi:10.1001/jama.2010.315 Block JP, He Y, Zaslavsky AM, Ding L, Ayanian JZ. Psychosocial stress and change in weight among US adults. Am J Epidemiol. 2009;170(2):181-192. doi:10.1093/aje/kwp104 Landon MB, Spong CY, Thom E, et al. A multicenter, randomized trial of treatment for mild gestational diabetes. N Engl J Med. 2009;361(14):1339-1348. doi:10.1056/NEJMoa0902430 Crowther CA, Hiller JE, Moss JR, McPhee AJ, Jeffries WS, Robinson JS. Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. N Engl J Med. 2005;352(24):2477-2486. doi:10.1056/NEJMoa042973 American College of Obstetricians and Gynecologists. ACOG Practice Bulletin No. 205: Vaginal Birth After Cesarean Delivery. Obstet Gynecol. 2019;133:e110-e127. doi:10.1097/AOG.0000000000003078 Stuart EA. Matching methods for causal inference: A review and a look forward. Stat Sci. 2010;25:1-21. doi:10.1214/09-STS313 Solar O, Irwin A. A conceptual framework for action on the social determinants of health. Geneva: World Health Organization; 2010. WHO. Global strategy for women’s, children’s and adolescents’ health (2016-2030). Geneva: WHO; 2015. Starfield B, Shi L, Macinko J. Contribution of primary care to health systems and health. Milbank Q. 2005;83(3):457-502. doi:10.1111/j.1468-0009.2005.00409.x McIntyre D, Thiede M, Dahlgren G, Whitehead M. What are the economic consequences for households of illness and of paying for health care in low- and middle-income country contexts? Soc Sci Med. 2006;62(4):858-865. doi:10.1016/j.socscimed.2005.07.012 WHO. Quality of care for maternal and newborn health: a monitoring framework for network countries. Geneva: WHO; 2023. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1STROBEchecklist.docx S1Code.zip.zip ReproducibilityCheck.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 26 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor invited by journal 01 Jan, 2026 Editor assigned by journal 29 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 27 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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09:40:37","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87969,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8462398/v1/713d32adafdeb2d2722e71f8.html"},{"id":100996489,"identity":"d7ed97b8-2a7a-4229-b59e-c0a63fda22c6","added_by":"auto","created_at":"2026-01-23 15:28:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1536193,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow diagram\u003c/p\u003e","description":"","filename":"Fugure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8462398/v1/8854280b2500fb8d0a6c0fe2.png"},{"id":100996484,"identity":"3b57dfe7-ed0f-4981-8223-faf128c26a37","added_by":"auto","created_at":"2026-01-23 15:28:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":317362,"visible":true,"origin":"","legend":"\u003cp\u003eCovariate balance before and after PSM\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8462398/v1/8a995487f1a62fa564e6145b.png"},{"id":101207745,"identity":"78a0e4d0-3963-4efe-9ae1-2c2e8d0cb1fd","added_by":"auto","created_at":"2026-01-27 10:06:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2570240,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8462398/v1/1f0424e7-8246-4350-a832-41f55211ecf7.pdf"},{"id":100996486,"identity":"618be839-919f-451e-af80-b35d96497850","added_by":"auto","created_at":"2026-01-23 15:28:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":37434,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1STROBEchecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-8462398/v1/85f4da4e8cbbcc7db3bef660.docx"},{"id":100996485,"identity":"5cfb27d3-a0c2-4bc9-beb3-75c1663f6d2b","added_by":"auto","created_at":"2026-01-23 15:28:07","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39406,"visible":true,"origin":"","legend":"","description":"","filename":"S1Code.zip.zip","url":"https://assets-eu.researchsquare.com/files/rs-8462398/v1/956a5b3b2048b46c47fb09bb.zip"},{"id":100996496,"identity":"858c2924-04ce-4293-af73-8bd1657c6689","added_by":"auto","created_at":"2026-01-23 15:28:08","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":361019,"visible":true,"origin":"","legend":"","description":"","filename":"ReproducibilityCheck.zip","url":"https://assets-eu.researchsquare.com/files/rs-8462398/v1/2208f88739a0925d531da04c.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Standardized Care Pathways Mitigate Occupational Disparities in Pregnancy Outcomes Among Nulliparous Women with Gestational Diabetes: A Propensity-Score-Matched Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGestational diabetes mellitus (GDM) affects up to 36.8% of pregnancies worldwide, its prevalence contingent on the diagnostic criteria applied.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Although global thresholds remain heterogeneous, the IADPSG standards\u0026mdash;anchored in robust evidence of pregnancy-related risk\u0026mdash;are now increasingly embraced.\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe 2025 American Diabetes Association Standards of Care emphasize that optimizing pre-conception health\u0026mdash;particularly weight control\u0026mdash;is essential to curbing GDM-related morbidity, because elevated pre-pregnancy BMI is an independent determinant of adverse pregnancy outcomes.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e A 2024 meta-analysis of 36 studies spanning the United States and Canada reported a mean GDM prevalence of 6.9% (95% CI 5.7\u0026ndash;8.3), with higher rates observed when a one-step screening strategy was employed.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Comparable data from Iran show an 11.0% prevalence (2000\u0026ndash;2021).\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e These discrepancies mandate harmonized diagnostic protocols to enable reliable surveillance and cross-regional comparisons.\u003c/p\u003e \u003cp\u003eThe rising incidence of GDM reflects intertwined genetic, behavioral and environmental drivers, with occupation serving as a potentially modifiable lifestyle vector. Chronic exposure to job strain, time pressure and psychosocial stress can activate neuroendocrine pathways that promote adiposity and insulin resistance, thereby increasing diabetes risk.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Yet evidence linking employment status to GDM onset or subsequent perinatal outcomes remains scant and inconsistent. A nationwide Korean study reported higher GDM rates in selected industries,\u003csup\u003e10\u003c/sup\u003e while recent US data suggest that holding multiple jobs during pregnancy elevates the risk of both GDM and hypertensive disorders.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e We therefore quantified whether employment status affects pre-pregnancy BMI and perinatal outcomes in primiparous women with GDM, and assessed the clinical and public-health implications.\u003c/p\u003e"},{"header":"2 Data collection and cleaning","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population and Data Source\u003c/h2\u003e \u003cp\u003eElectronic medical records were searched on 12 August 2025 to identify all deliveries between 01 January 2020 and 31 December 2024.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Participant Selection and Flow\u003c/h2\u003e \u003cp\u003eOf 764 women with a GDM diagnosis, 595 met inclusion criteria after exclusion (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To eliminate confounding by obstetric history, we restricted the primary analysis to the 240 primiparous women within this cohort and derived the final analytic sample by 1:1 propensity-score matching.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Ethics Statement\u003c/h2\u003e \u003cp\u003e The retrospective protocol was conducted in accordance with the Declaration of Helsinki and was granted exempt status by the Ethics Committee of Xiang'an Hospital, Xiamen University (Waiver No. XAHLL2025028). Individual informed consent was waived because only anonymised data were analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Variables and Definitions\u003c/h2\u003e \u003cp\u003eGDM was defined by the International Association of Diabetes and Pregnancy Study Groups criteria:\u003csup\u003e12\u003c/sup\u003e at least one abnormal value on a 75-g oral glucose tolerance test (fasting\u0026thinsp;\u0026ge;\u0026thinsp;5.1 mmol/L; 1 h\u0026thinsp;\u0026ge;\u0026thinsp;10.0 mmol/L; 2 h\u0026thinsp;\u0026ge;\u0026thinsp;8.5 mmol/L). Employment status was extracted from the electronic prenatal record and classified as: \"Employed\": women who reported regular daytime work on weekdays only (no night shifts); \"Unemployed\": women with no fixed working hours, including housework, free-lance, or other non-regular paid activities. Across the 124 matched participants, all variables used in the analysis had 0% missing values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Pre-matching Analyzes and Rationale for Cohort Restriction\u003c/h2\u003e \u003cp\u003eWomen were classified as employed (n\u0026thinsp;=\u0026thinsp;342) or unemployed (n\u0026thinsp;=\u0026thinsp;253). Baseline characteristics differed markedly between groups, most prominently in previous deliveries (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To eliminate parity-related confounding, we restricted all subsequent analyzes to primiparous women (n\u0026thinsp;=\u0026thinsp;240).\u003c/p\u003e \u003cp\u003eAmong the 240 primiparous women, employed (n\u0026thinsp;=\u0026thinsp;165) and unemployed (n\u0026thinsp;=\u0026thinsp;75) participants differed significantly in age (P\u0026thinsp;=\u0026thinsp;0.030), but not in height (P\u0026thinsp;=\u0026thinsp;0.229) or pre-pregnancy BMI (P\u0026thinsp;=\u0026thinsp;0.491) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To remove the influence of these imbalances and estimate the independent effect of occupational status, we conducted propensity-score matching within this subgroup.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Propensity Score Matching (PSM): Rationale and Execution\u003c/h2\u003e \u003cp\u003eWe used 1:1 propensity-score matching to approximate a randomized comparison of employed (treated) and unemployed (control) primiparous women. By equating the distribution of measured baseline covariates, PSM minimizes confounding bias inherent in observational data.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e1. Estimation of Propensity Scores\u003c/b\u003e: Propensity scores\u0026mdash;defined as the conditional probability of being employed given baseline characteristics\u0026mdash;were estimated with a binary logistic regression model. Occupational status (employed vs unemployed) served as the outcome; age, height and pre-pregnancy BMI were included as predictors on the basis of their clinical relevance and potential to influence both employment and pregnancy outcomes.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Matching Algorithm\u003c/b\u003e: We used 1:1 nearest-neighbor matching without replacement, imposing a caliper of 0.15 standard deviations of the logit propensity score to exclude distant matches and enhance balance.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Assessment of Matching Quality\u003c/b\u003e: Balance was evaluated with the standardized mean difference (SMD); an SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.10 signified negligible imbalance.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e After matching, all covariates yielded SMDs below this threshold (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), confirming a well-balanced cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Post-matching Outcome Analysis\u003c/h2\u003e \u003cp\u003eBetween-group differences were assessed with Student's t or Mann\u0026ndash;Whitney U tests for continuous variables and χ\u0026sup2; or Fisher's exact tests for categorical variables. PSM and all analyzes were conducted in R 4.3.0 using the \u003cem\u003eMatchIt\u003c/em\u003e package; two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 denoted statistical significance. The analysis yielded 62 matched pairs with all covariates balanced (SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.10, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e); outcomes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. A sensitivity analysis excluding women who required insulin yielded similar effect sizes (data not shown). Reproducible R code is provided as Supplementary Information (S1 Code).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participant Flow and Baseline Characteristics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of basic information between the two groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;342)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;253)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ/U/ \u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.28\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy BMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.80 (19.94, 24.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.21 (20.29, 25.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious deliveries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165 (48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (47.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious vaginal deliveries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214 (62.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (50.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious cesarean sections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292 (85.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (77.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range); categorical variables are expressed as frequency (percentage).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of basic information between employed and unemployed primiparous women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;240)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ/U/ \u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy BMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.64 (19.66, 24.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.50 (19.79, 24.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.83 (19.57, 25.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Propensity Score Matching and Covariate Balance\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssessment of matching quality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsolute SMD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote: SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.1 indicates adequate balance between the employed and unemployed groups after matching. BMI: body mass index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association Between Occupational Status and Pregnancy Outcomes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePregnancy Outcomes After Propensity Score Matching (n\u0026thinsp;=\u0026thinsp;62 Pairs)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome Measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed Group (n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnemployed Group (n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational weight gain (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCesarean delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age at delivery (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFetal birth weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3101\u0026thinsp;\u0026plusmn;\u0026thinsp;365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3225\u0026thinsp;\u0026plusmn;\u0026thinsp;353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth defects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin use during pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, or number (percentage).95% CIs were not calculated because the matched sample size was small and all differences were below pre-specified clinical relevance thresholds (birth weight\u0026thinsp;\u0026lt;\u0026thinsp;250 g, cesarean delivery\u0026thinsp;\u0026lt;\u0026thinsp;15 percentage points).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e We followed the STROBE guidelines for observational studies; the checklist is provided in Additional file 1.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Principal Findings\u003c/h2\u003e \u003cp\u003eIn this propensity score-matched analysis, we delineated a distinct pattern linking occupational status to metabolic and obstetric markers in GDM. Across the full cohort, employed women exhibited a lower pre-pregnancy BMI than their unemployed counterparts. Recognizing the potent confounding effect of obstetric history\u0026mdash;particularly parity\u0026mdash;on pregnancy outcomes, we restricted primary analysis to primiparous women. After rigorous matching for age, height, and pre-pregnancy BMI, occupational status showed no statistically significant association with key perinatal outcomes, including gestational weight gain, cesarean delivery rate, gestational age at delivery, or neonatal birth weight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Comparison with Existing Literature and Interpretation\u003c/h2\u003e \u003cp\u003eConsistent with a large and methodologically diverse literature, we observed that unemployment was associated with modestly higher pre-pregnancy BMI.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Employment exerts bidirectional metabolic forces: it provides income, temporal structure and\u0026mdash;where health insurance is employment-tied\u0026mdash;improved access to care, all of which facilitate weight maintenance; conversely, it often entails prolonged sedentary time, shift work and sustained activation of the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis, each independently linked to weight gain.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Unemployment, on the other hand, constrains food budgets toward energy-dense, nutrient-poor options, reduces access to fee-for-use exercise facilities and amplifies exposure to financial strain\u0026mdash;pathways that up-regulate glucocorticoid signaling and foster visceral adiposity.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Our data indicate that occupational status is less an isolated exposure than a proxy for a multidimensional, temporally stable phenotype that crystallizes long before conception. Consequently, pre-pregnancy BMI functions not merely as a confounder but as a parsimonious, biologically grounded summary of cumulative socioeconomic insults; failure to condition upon it will inevitably inflate the apparent aetiological relevance of downstream variables such as employment category.\u003c/p\u003e \u003cp\u003eThe absence of any detectable difference in obstetric end points is the study's central inference. After rigorous propensity-score balancing, employed and unemployed women with GDM delivered with identical gestational weight-gain trajectories, cesarean-section rates, gestational age and birth weight, implying that the heterogeneous psychosocial and metabolic exposures captured by occupational status were effectively neutralized. This null association can be attributed to the protocol-driven GDM pathway to which every participant was exposed: uniform nutritional counseling, intensified glucose monitoring and immediate pharmacological escalation when targets were breached.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e By flattening deviations in glycaemic excursions and weight gain, this standardized care bundle functioned as a powerful equalizer, masking the modest BMI disparity that unemployment had imprinted before conception. Consequently, our data underscore that high-quality, contemporary prenatal care is not merely an effect modifier\u0026mdash;it can override the independent causal contribution of a diffuse socioeconomic determinant such as employment category and deliver perinatal equity for women with GDM.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eWe acknowledge the possibility of reverse causation: women with higher pre-pregnancy BMI may be more likely to become unemployed. Longitudinal fixed-effects models are needed in future research to clarify the direction of this association.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Study Limitations\u003c/h2\u003e \u003cp\u003eOur inferential chain is bounded by four constraints. First, although 1:1 propensity-score matching achieved excellent balance, the final cohort (62 pairs) yielded only 58% power to detect a 0.25 SD difference in gestational weight gain and 42% power for a 15% absolute risk difference in cesarean delivery. Consequently, the null findings reflect the absence of large effects, not proof of equivalence; yet all observed differences fell below accepted obstetric relevance thresholds (\u0026lt;\u0026thinsp;500 g birth weight, \u0026lt;\u0026thinsp;20% relative risk for cesarean delivery).\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Second, occupational exposure was dichotomised, precluding interrogation of job strain, shift patterns or physical demand\u0026mdash;dimensions plausibly linked to maternal metabolism.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Third, unmeasured confounding (diet quality, leisure-time activity, social support, income) remains possible despite matching on observed covariates.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Finally, the single-center, urban, high-income setting limits external validity to regions with fragmented or resource-constrained antenatal services. To address the concern of low power, we conducted two-one-sided equivalence tests (TOST) for birth weight (margin\u0026thinsp;\u0026plusmn;\u0026thinsp;250 g) and cesarean risk (margin\u0026thinsp;\u0026plusmn;\u0026thinsp;15%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Public Health Implications and Future Directions\u003c/h2\u003e \u003cp\u003eOur findings offer empirical proof that high-quality, protocol-driven antenatal care can function as an \"equalizer\" capable of neutralizing employment-related health gaps\u0026mdash;a core ambition of the WHO Global Strategy for Women's and Children's Health and the UN Sustainable Development Goal 3.8\u003csup\u003e21\u003c/sup\u003e. In this cohort, unemployed women entered pregnancy with a higher BMI, an early-life disadvantage rooted in social determinants such as income insecurity and psychosocial stress\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e; yet once enrolled in a standardized GDM pathway that guaranteed identical glucose targets, nutrition counseling and rapid pharmacological escalation, their gestational weight gain, cesarean risk and neonatal birth weight converged with those of employed women. This observation aligns with Starfield's thesis that universal, evidence-based clinical programs can compress avoidable disparities even when upstream social inequalities persist\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Conversely, fragmented services tend to amplify these gradients because disadvantaged groups lack the resources to compensate for sub-standard care\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The convergence we document therefore underscores a key message for policy makers scaling up GDM programs in low- and middle-income countries: investing in care quality is not merely a clinical issue but an explicit lever for health equity and a replicable blueprint for countries striving to meet both SDG 3 and SDG 10 by 2030\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn this propensity score\u0026ndash;matched analysis of primiparous women with GDM, occupational status was associated with pre-pregnancy BMI in the full cohort but not with key perinatal outcomes after rigorous matching. While the latter finding suggests that employment status per se may not independently drive pregnancy outcomes under structured GDM management, the persisting BMI disparity highlights pre-pregnancy metabolic health as a modifiable target. These results underscore the potential of standardized, protocol-driven antenatal care to mitigate socioeconomic-related disparities in perinatal outcomes. Moving forward, public health efforts should prioritize preconception weight-management strategies\u0026mdash;especially among unemployed women\u0026mdash;while future research ought to delineate the specific occupational exposures (e.g., job strain, shift work, physical demand) that influence long-term metabolic risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCuili Yang\u003c/strong\u003e:\u0026nbsp;Conceptualization, Data curation, Formal analysis, Writing \u0026ndash; original draft.\u003cbr\u003e \u0026nbsp;\u003cstrong\u003eZhen Wang\u003c/strong\u003e:\u0026nbsp;Data curation, Methodology, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e \u003cstrong\u003eHong Wang\u003c/strong\u003e:\u0026nbsp;Conceptualization, Supervision, Corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to publish declaration: not applicable.\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\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eDue to restrictions imposed by Chinese personal-information protection laws and the ethics committee of Xiang'an Hospital, anonymised data extracts supporting this study are available only after a formal data-use agreement has been signed. Requests should be directed to the corresponding author and will be responded to within 4 weeks.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Sharing Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to restrictions imposed by Chinese personal-information protection laws and the ethics committee of Xiang\u0026apos;an Hospital, anonymised data extracts supporting this study are available only after a formal data-use agreement has been signed. Requests should be directed to the corresponding author and will be responded to within 4 weeks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMirabelli M, Chiefari E, Tocci V, Greco E, Foti D, Brunetti A. Gestational diabetes: Implications for fetal growth, intervention timing, and treatment options. Curr Opin Pharmacol. 2021;60:1-10. doi:10.1016/j.coph.2021.06.003\u003c/li\u003e\n \u003cli\u003eEades CE, Burrows KA, Andreeva R, Stansfield DR, Evans JM. Prevalence of gestational diabetes in the United States and Canada: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2024;24:204. doi:10.1186/s12884-024-06378-2\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. Diagnostic Criteria and Classification of Hyperglycaemia First Detected in Pregnancy. Geneva: WHO; 2013.\u003c/li\u003e\n \u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024. Diabetes Care. 2024;47(Suppl 1):S20-S42. doi:10.2337/dc24-S002\u003c/li\u003e\n \u003cli\u003eLowe WL Jr, Scholtens DM, Kuang A, et al. Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): Maternal Gestational Diabetes Mellitus and Childhood Glucose Metabolism. Diabetes Care. 2019;42:372-380. doi:10.2337/dc18-1646\u003c/li\u003e\n \u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 15. Management of Diabetes in Pregnancy: Standards of Care in Diabetes-2025. Diabetes Care. 2025;48(1 Suppl 1):S306-S320. doi:10.2337/dc25-S015\u003c/li\u003e\n \u003cli\u003eSadeghi S, Khatibi SR, Mahdizadeh M, Peyman N, Zare Dorniani S. Prevalence of Gestational Diabetes in Iran: A Systematic Review and Meta-analysis. Med J Islam Repub Iran. 2023;37:83. doi:10.47176/mjiri.37.83\u003c/li\u003e\n \u003cli\u003eVan Uytsel H, Ameye L, Devlieger R, et al. Mental Health during the Interpregnancy Period and the Association with Pre-Pregnancy Body Mass Index and Body Composition: Data from the INTER-ACT Randomized Controlled Trial. 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A conceptual framework for action on the social determinants of health. Geneva: World Health Organization; 2010.\u003c/li\u003e\n \u003cli\u003eWHO. Global strategy for women\u0026rsquo;s, children\u0026rsquo;s and adolescents\u0026rsquo; health (2016-2030). Geneva: WHO; 2015.\u003c/li\u003e\n \u003cli\u003eStarfield B, Shi L, Macinko J. Contribution of primary care to health systems and health. Milbank Q. 2005;83(3):457-502. doi:10.1111/j.1468-0009.2005.00409.x\u003c/li\u003e\n \u003cli\u003eMcIntyre D, Thiede M, Dahlgren G, Whitehead M. What are the economic consequences for households of illness and of paying for health care in low- and middle-income country contexts? Soc Sci Med. 2006;62(4):858-865. doi:10.1016/j.socscimed.2005.07.012\u003c/li\u003e\n \u003cli\u003eWHO. Quality of care for maternal and newborn health: a monitoring framework for network countries. Geneva: WHO; 2023.\u003c/li\u003e\n\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-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gestational diabetes mellitus, Occupational status, Pre-pregnancy BMI, Perinatal outcomes, Propensity score matching, Primiparity, Health equity, Standardized antenatal care","lastPublishedDoi":"10.21203/rs.3.rs-8462398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8462398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to determine whether employment status influences pregnancy outcomes in nulliparous women with gestational diabetes mellitus (GDM) who received standardized care.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective analysis of deliveries (2020\u0026ndash;2024) at a tertiary hospital. To minimize parity-related confounding, the primary analysis was restricted to nulliparous women with GDM (n\u0026thinsp;=\u0026thinsp;240). Propensity-score matching (1:1, caliper\u0026thinsp;=\u0026thinsp;0.15) based on age, height, and pre-pregnancy BMI generated 62 matched employed\u0026ndash;unemployed pairs. Continuous and categorical outcomes were compared using Student's t-test/Mann\u0026ndash;Whitney U test and χ\u0026sup2;/Fisher's exact test, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the full cohort (n\u0026thinsp;=\u0026thinsp;595), employed women had a lower pre-pregnancy BMI than unemployed women (21.80 vs. 22.21 kg/m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.039). After matching, no significant differences were observed between groups in gestational weight gain (12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 kg vs. 11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5 kg, p\u0026thinsp;=\u0026thinsp;0.331), cesarean delivery rate (27.4% vs. 37.1%, p\u0026thinsp;=\u0026thinsp;0.252), gestational age at delivery (39.3 vs. 39.2 weeks, p\u0026thinsp;=\u0026thinsp;0.695), or fetal birth weight (3101\u0026thinsp;\u0026plusmn;\u0026thinsp;365 vs. 3225\u0026thinsp;\u0026plusmn;\u0026thinsp;353 g, p\u0026thinsp;=\u0026thinsp;0.073).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eStandardized, protocol-driven GDM care eliminated perinatal disparities associated with employment status, although a pre-pregnancy BMI gap persisted. Investment in equitable, high-quality antenatal programs may serve as an actionable strategy to advance health equity in GDM management by 2030.\u003c/p\u003e","manuscriptTitle":"Standardized Care Pathways Mitigate Occupational Disparities in Pregnancy Outcomes Among Nulliparous Women with Gestational Diabetes: A Propensity-Score-Matched Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 15:27:59","doi":"10.21203/rs.3.rs-8462398/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"322614599205313610735723133372387907915","date":"2026-01-26T07:39:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T11:20:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-01T05:28:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-30T03:56:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-30T03:55:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-12-27T16:51:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27236d26-a994-4542-9646-a4f8abc85953","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-23T15:27:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 15:27:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8462398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8462398","identity":"rs-8462398","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}