Preconception sleep duration, non-daytime work schedules, and incidence of spontaneous abortion: a prospective cohort study.

STUDY QUESTION: To what extent do self-reported sleep duration and non-daytime work schedules in either partner affect the rate of spontaneous abortion (SAB)? SUMMARY ANSWER: Incidence of SAB had little association with female sleep duration and a modest positive association with male short sleep duration, female work at night, and discrepant work schedules among partners. WHAT IS KNOWN ALREADY: Several studies have reported an association between short sleep duration in either partner and reproductive health outcomes, including fecundability. Moreover, certain types of female occupational exposures during pregnancy have been associated with an increased risk of SAB. No studies have evaluated SAB risk in relation to male sleep and work schedules, or joint exposures within a couple. STUDY DESIGN, SIZE, DURATION: This prospective cohort study included 9357 female participants and 2602 of their male partners residing in North America (June 2013 to April 2023). PARTICIPANTS/MATERIALS, SETTING, METHODS: Participants enrolled when they were attempting pregnancy and completed self-administered baseline questionnaires about their average sleep duration and work schedules. Among those who conceived, we ascertained SAB and gestational age at loss via follow-up questionnaires. We used multivariable Cox proportional hazards models with gestational weeks as the time scale to estimate hazard ratios (HRs) and 95% CIs relating SAB with sleep duration and non-daytime work schedules for female and male participants, and the couple. We used inverse probability weighting to account for potential selection bias due to the possibility of differential participation of male partners with respect to the exposures. MAIN RESULTS AND THE ROLE OF CHANCE: Compared to female participants with recommended sleep (7-8.9 h), those reporting short sleep duration (<6 h) did not have a higher rate of SAB (HR 0.88, 95% CI 0.69, 1.13). Short self-reported sleep duration among male participants was modestly associated with a higher rate of SAB (adjusted and weighted HR 1.30, 95% CI 0.96, 1.75). Female night work at night (adjusted HR 1.19, 95% CI 1.02, 1.38) and male non-daytime work (adjusted and weighted HR 1.26, 95% CI 1.00, 1.59) were associated with modestly higher rates of SAB, whereas female rotating shift work was not (adjusted HR 0.91, 0.78, 1.05) compared with daytime workers. Couples in which work schedules were discrepant had an elevated rate of SAB if the male partner worked a non-daytime shift (adjusted and weighted HR 1.46, 95% CI 1.13, 1.88) compared with couples in which both members worked during the day. The corresponding HR if only the female partner worked a non-daytime shift was 1.21 (95% CI 0.92, 1.58). LIMITATIONS, REASONS FOR CAUTION: Data on sleep duration and work schedules were based on self-report, which is vulnerable to misclassification, particularly since participants were asked to report their average sleep duration during the past month. Work exposures were heterogeneous, as many different types of employment may require night and shift work and may have different associations with SAB. WIDER IMPLICATIONS OF THE FINDINGS: Our findings are consistent with previous research indicating that some types of female employment schedules may be associated with SAB incidence. This is the first study to indicate a relationship between SAB and male employment schedules, indicating that discrepant work schedules within a couple might be relevant. STUDY FUNDING/COMPETING INTEREST(S): This work was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development grants R01HD105863 (PIs: L.A.W. and M.L.E.), R01HD086742 (PIs: L.A.W. and E.E.H.), and R21HD072326 (PI: L.A.W.). PRESTO has received in-kind donations from Swiss Precision Diagnostics and Kindara.com for primary data collection. L.A.W. is a consultant for AbbVie, Inc. and the Gates Foundation. M.L.E. is an advisor for and holds stock in Ro, Hannah, Dadi, Underdog, Vseat, & Doveras. The other authors have no competing interests to declare. TRIAL REGISTRATION NUMBER: N/A.

Modeling Social Network Influences on Oral Health Outcomes among Women Living in Public Housing.

OBJECTIVE: Research into the influence and role of social networks on oral health outcomes has been limited. This study aims to demonstrate via explanatory modeling the influence of social networks on oral health outcomes among women in who live in public housing in Boston, Massachusetts. METHODS: Individual- and network-level data were obtained from a cross-sectional survey of adult female residents of 2 public housing developments in Boston, Massachusetts. Participants responded to close-ended questions about sociodemographic characteristics, oral and general health status, and health-related behaviors for themselves as well as their named social contacts. Based on this information, network-level variables were calculated for each participant, including the proportion of the social network with certain characteristics or attributes. To assess the salience of network measures in explaining the variability in self-reported oral health status, overall health status, use of dental services within the last year, and current dental treatment needs, logistic regression models with individual-level covariates were compared with corresponding models that additionally included network-level variables with McFadden R2 for comparison purposes. RESULTS: Model comparisons for each outcome of interest demonstrated that adding network-level covariates significantly improved model fit. Additionally, network-level covariates displayed strong independent associations with the outcomes of interest. The network proportion needing dental treatment was positively associated with participants' odds of reporting current dental treatment needs (odds ratio, 4.71; 95% CI, 1.47 to 15.05). An individual's odds of reporting no dental visit within the past year decreased with increasing exposure to social contacts with reported dental visits within the past year (odds ratio, 0.26; 95% CI, 0.07 to 0.89). CONCLUSION: The salience of specific network attributes depended on the outcome under study. Interventions aimed at reducing the poor oral health burden in this and similar population groups may benefit from integrating information on social networks, including tailoring intervention delivery and/or messaging to account for the potential influence of social networks. KNOWLEDGE TRANSFER STATEMENT: This work may be beneficial to those developing policy solutions and interventions aimed at reducing the burden of poor oral health outcomes in socioeconomically disadvantaged populations, such as women who reside in public housing developments. The potential for leveraging social networks to seed messaging about oral health, for example, may enhance efforts to reduce oral health disparities.

Quantitative Assessment of Systematic Bias: A Guide for Researchers.

Observational research provides valuable opportunities to advance oral health science but is limited by vulnerabilities to systematic bias, including unmeasured confounding, errors in variable measurement, or bias in the creation of study populations and/or analytic samples. The potential influence of systematic biases on observed results is often only briefly mentioned among the discussion of limitations of a given study, despite existing methods that support detailed assessments of their potential effects. Quantitative bias analysis is a set of methodological techniques that, when applied to observational data, can provide important context to aid in the interpretation and integration of observational research findings into the broader body of oral health research. Specifically, these methods were developed to provide quantitative estimates of the potential magnitude and direction of the influence of systematic biases on observed results. We aim to encourage and facilitate the broad adoption of quantitative bias analyses into observational oral health research. To this end, we provide an overview of quantitative bias analysis techniques, including a step-by-step implementation guide. We also provide a detailed appendix that guides readers through an applied example using real data obtained from a prospective observational cohort study of preconception periodontitis in relation to time to pregnancy. Quantitative bias analysis methods are available to all investigators. When appropriately applied to observational studies, findings from such studies can have a greater impact in the broader research context.

Self-reported periodontitis and fecundability in a population of pregnancy planners.

STUDY QUESTION: Is a history of periodontitis among women associated with reduced fecundability? SUMMARY ANSWER: A history of periodontitis, as assessed by three different self-reported measures, may be associated with reduced fecundability. WHAT IS KNOWN ALREADY: Periodontitis is a chronic inflammatory condition affecting the hard and soft tissues surrounding the teeth. Few studies have evaluated the association between periodontitis and time to pregnancy, and findings are mixed. It is hypothesized that periodontitis may adversely affect time to pregnancy. STUDY DESIGN, SIZE, DURATION: We conducted a prospective cohort study of 2764 female pregnancy planners residing in North America (March 2015-June 2020). PARTICIPANTS/MATERIALS, SETTING, METHODS: Eligible participants had been attempting pregnancy for six or fewer menstrual cycles at enrollment and were not using fertility treatment. Women answered questions about their oral health. Pregnancy was ascertained via bi-monthly follow-up questionnaires. We used proportional probabilities regression models to estimate fecundability ratios (FRs) and 95% confidence intervals (CIs) for three different measures indicative of a history of periodontitis: ever diagnosed with periodontitis (N = 265), ever received treatment for periodontitis (N = 299), and ever had an adult tooth become loose on its own (N = 83). We adjusted for potential confounders and precision variables. Women at risk of misclassification of periodontitis diagnosis due to pregnancy-related gingivitis were reclassified in a sensitivity analysis. MAIN RESULTS AND THE ROLE OF CHANCE: All three indices of periodontitis may be associated with reduced fecundability. FRs were 0.89 (95% CI 0.75-1.06) comparing women with and without a previous periodontitis diagnosis, 0.79 (95% CI 0.67-0.94) comparing women with and without previous periodontitis treatment, and 0.71 (95% CI 0.44-1.16) comparing women with and without a tooth that became loose. After reclassification of pregnancy-related gingivitis in the sensitivity analysis, the FR for periodontitis diagnosis was 0.83 (95% CI 0.68-1.00). Weaker FRs were observed among parous women as compared with nulliparous women for periodontitis diagnosis and tooth becoming loose, but not for periodontitis treatment. LIMITATIONS, REASONS FOR CAUTION: Though we used validated self-report measures of periodontitis, clinical confirmation is the gold standard. These questions may be functioning as markers of different levels of periodontitis severity, but we were unable to measure disease severity in this population. Finally, we cannot eliminate the possibility of unmeasured confounding. WIDER IMPLICATIONS OF THE FINDINGS: This is the first preconception prospective cohort study to evaluate the association between self-reported periodontitis and fecundability. Our results indicate that periodontitis may be associated with lower fecundability. STUDY FUNDING/COMPETING INTEREST(S): This work was partially funded by R01HD086742/Eunice Kennedy Shriver National Institute of Child Health and Human Development and R21HD072326/Eunice Kennedy Shriver National Institute of Child Health and Human Development. PRESTO has received in-kind donations from Swiss Precision Diagnostics, Sandstone Diagnostics, FertilityFriend.com, and Kindara.com for primary data collection. L.A.W. is a fibroid consultant for AbbVie, Inc. J.C.B., S.W., J.Y., K.J.R., E.E.H., and B.H. have no conflicts of interest to disclose. TRIAL REGISTRATION NUMBER: N/A.

Computer model for simulation of emergency medical systems

This study reports the design and development of a computer model that can be utilized to evaluate the requirements for any emergency medical system. The concepts employed represent the logical relationships that exist in the care of the most serious trauma patient. For any given military or civilian emergency, this model encompasses the individual parts required to make the EMS work. The only assumptions are that the problem exists and treatment is required. Patients are categorized according to anatomical location and severity of injury. The user of the model can decide the patient mix, the modes of transportation, the number of facilities, and the number of medical personnel, as well as other variables. It is clear from the results that this model can be a valuable tool in determining the resource requirements for optimal function of any emergency medical system(AU)