The implications of using maternity care deserts to measure progress in access to obstetric care: a mixed-integer optimization analysis.

BACKGROUND: Lack of access to risk-appropriate maternity services, particularly for rural residents, is thought to be a leading contributor to disparities in maternal morbidity and mortality. There are several existing measures of access to obstetric care in the literature and popular media. In this study, we explored how current measures of obstetric access inform the number and location of additional obstetric care facilities required to improve access. METHODS: We formulated two facility location optimization models to determine the number of new facilities required to minimize the number of reproductive-aged women who lack access to obstetric care. We define regions with a lack of access as either maternity care deserts, designated by the March of Dimes to be counties with no obstetric care facility or obstetric providers, or regions further than 50 miles from critical care obstetric (CCO) services. We gathered information on hospitals with obstetric services from Georgia Department of Public Health public reports and estimated the female reproductive-age population by census block group using the American Community Survey. RESULTS: Out of the 1,910,308 reproductive-aged women who live in Georgia, 104,158 (5.5%) live in maternity care deserts, 150,563 (7.9%) reproductive-aged women live further than 50 miles from CCO services, and 38,202 (2.0%) live in both maternity care desert and further than 50 miles from CCO services. Our optimization analysis suggests that at least 56 new obstetric care facilities (a 67% increase) would be required to eliminate maternity care deserts in Georgia. However, the expansion of 8 facilities would ensure all women in Georgia live within 50 miles of CCO services. CONCLUSIONS: Current measures of access to obstetric care may not be sufficient for evaluating access and planning action toward improvements. In a state like Georgia with a large number of small counties, eliminating maternity care deserts would require a prohibitively large number of new obstetric care facilities. This work suggests that additional measures and tools are needed to estimate the number and type of obstetric care facilities that best match practical resources to meet obstetric care needs.

Students' preferences for returning to colleges and universities during the COVID-19 pandemic: A discrete choice experiment.

Importance: When an emerging infectious disease outbreak occurs, such as COVID-19, institutions of higher education (IHEs) must weigh decisions about how to operate their campuses. These decisions entail whether campuses should remain open, how courses should be delivered (in-person, online, or a mixture of the two), and what safety plans should be enacted for those on campus. These issues have weighed heavily on campus administrators during the on-going COVID-19 pandemic. However, there is still limited knowledge about how such decisions affect students' enrollment decisions and campus safety in practice when considering compliance. Objectives: To assess 1) students' willingness to comply with health protocols and contrast their perception of their classmates' compliance, 2) whether students prefer in-person or online learning during a pandemic, and 3) the importance weights of different aspects of campus operations (i.e., modes of course delivery and safety plans) for students when they decide to enroll or defer. Design setting and participants: An internet-based survey of college students took place from June 25, 2020 to July 10, 2020. Participants included 398 industrial engineering students at the Georgia Institute of Technology, a medium-size public university in Atlanta, Georgia. The survey included a discrete choice experiment with questions that asked students to choose whether to enroll or defer when presented with hypothetical scenarios related to Fall 2020 modes of course delivery and aspects of campus safety. The survey also asked students about expected compliance with health protocols, whether they preferred in-person or online courses, and sociodemographic information. Main outcomes and measures: We examine students' willingness to comply with potential health protocols. We estimated logistic regression models to infer significant factors that lead to a student's choice between in-person and online learning. Additionally, we estimated discrete choice models to infer the importance of different modes of course delivery and safety measures to students when deciding to enroll or defer. Results: The survey response rate was 20.8%. A latent class model showed three classes of students: those who were "low-concern" (comprising a 29% expected share of the sample), those who were "moderate-concern" (54%) and those who were "high-concern" (17%). We found that scenarios that offered an on-campus experience with large classes delivered online and small classes delivered in-person, strict safety protocols in terms of mask-wearing, testing, and residence halls, and lenient safety protocols in terms of social gatherings were broadly the scenarios with the highest expected enrollment probabilities. The decision to enroll or defer for all students was largely determined by the mode of delivery for courses and the safety measures on campus around COVID-19 testing and mask-wearing. A logistic regression model showed that a higher perceived risk of infection of COVID-19, a more suitable home environment, being older, and being less risk-seeking were significant factors for a person to choose online learning. Students stated for themselves and their classmates that they would comply with some but not all health protocols against COVID-19, especially those limiting social gatherings. Conclusions and relevance: The majority of students indicated a preference to enroll during the COVID-19 pandemic so long as sufficient safety measures were put in place and all classes were not entirely in-person. As IHEs consider different options for campus operations during pandemics, they should consider the heterogeneous preferences among their students. Offering flexibility in course modes may be a way to appeal to many students who vary in terms of their concern about the pandemic. At the same time, since students overall preferred some safety measures placed around mask-wearing and COVID-19 testing on campus, IHEs may want to recommend or require wearing masks and doing some surveillance tests for all students, faculty, and staff. Students were expecting themselves and their fellow classmates to comply with some but not all health protocols, which may help IHEs identify protocols that need more education and awareness, like limits on social gatherings and the practice of social distancing at social gatherings.

Toward Expanded Access to Cancer Care With Cost Awareness: An Optimization Modeling Analysis of Rwanda.

PURPOSE: Cancers are a growing cause of mortality especially in low- and middle-income countries in Africa. Rwanda is no exception. Two cancer centers currently provide care to the public, but there are both political and human interest in expanding access to tertiary cancer care. Improved geographic access could lead to both better patient outcomes and a better understanding of the existing cancer burden across Rwanda. METHODS: To identify cost-aware ways of expanding geographic access, we adopt an optimization approach and identify expansion plans that minimize the average travel time to a cancer center across the country while remaining under a given monetary budget. RESULTS: Three additional hospitals could reduce average travel times by 40%, with the largest decrease in travel times observed in populations with long travel times. However, such an expansion would require a 50% increase in the number of in-country oncologists. We find that oncologist scarcity, as opposed to monetary constraints, is likely to be a limiting factor for improved access to cancer care. CONCLUSION: We present an array of expansion plans and suggest that further modeling approaches that incorporate oncologist scarcity can help deliver better policy recommendations.

Racial/ethnic differences in pre-pregnancy conditions and adverse maternal outcomes in the nuMoM2b cohort: A population-based cohort study.

OBJECTIVES: To determine how pre-existing conditions contribute to racial disparities in adverse maternal outcomes and incorporate these conditions into models to improve risk prediction for racial minority subgroups. STUDY DESIGN: We used data from the "Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b)" observational cohort study. We defined multimorbidity as the co-occurrence of two or more pre-pregnancy conditions. The primary outcomes of interest were severe preeclampsia, postpartum readmission, and blood transfusion during pregnancy or up to 14 days postpartum. We used weighted Poisson regression with robust variance to estimate adjusted risk ratios and 95% confidence intervals, and we used mediation analysis to evaluate the contribution of the combined effects of pre-pregnancy conditions to racial/ethnic disparities. We also evaluated the predictive performance of our regression models by racial subgroup using the area under the receiver operating characteristic curve (AUC) metric. RESULTS: In the nuMoM2b cohort (n = 8729), accounting for pre-existing conditions attenuated the association between non-Hispanic Black race/ethnicity and risk of severe preeclampsia. Cardiovascular and kidney conditions were associated with risk for severe preeclampsia among all women (aRR, 1.77; CI, 1.61-1.96, and aRR, 1.27; CI, 1.03-1.56 respectively). The mediation analysis results were not statistically significant; however, cardiovascular conditions explained 36.6% of the association between non-Hispanic Black race/ethnicity and severe preeclampsia (p = 0.07). The addition of pre-pregnancy conditions increased model performance for the prediction of severe preeclampsia. CONCLUSIONS: Pre-existing conditions may explain some of the association between non-Hispanic Black race/ethnicity and severe preeclampsia. Specific pre-pregnancy conditions were associated with adverse maternal outcomes and the incorporation of comorbidities improved the performance of most risk prediction models.

Modeling the spread of circulating vaccine-derived poliovirus type 2 outbreaks and interventions: A case study of Nigeria.

Background: Despite the successes of the Global Polio Eradication Initiative, substantial challenges remain in eradicating the poliovirus. The Sabin-strain (live-attenuated) virus in oral poliovirus vaccine (OPV) can revert to circulating vaccine-derived poliovirus (cVDPV) in under-vaccinated communities, regain neurovirulence and transmissibility, and cause paralysis outbreaks. Since the cessation of type 2-containing OPV (OPV2) in 2016, there have been cVDPV type 2 (cVDPV2) outbreaks in four out of six geographical World Health Organization regions, making these outbreaks a significant public health threat. Preparing for and responding to cVDPV2 outbreaks requires an updated understanding of how different factors, such as outbreak responses with the novel type of OPV2 (nOPV2) and the existence of under-vaccinated areas, affect the disease spread. Methods: We built a differential-equation-based model to simulate the transmission of cVDPV2 following reversion of the Sabin-strain virus in prolonged circulation. The model incorporates vaccinations by essential (routine) immunization and supplementary immunization activities (SIAs), the immunity induced by different poliovirus vaccines, and the reversion process from Sabin-strain virus to cVDPV. The model's outcomes include weekly cVDPV2 paralytic case counts and the die-out date when cVDPV2 transmission stops. In a case study of Northwest and Northeast Nigeria, we fit the model to data on the weekly cVDPV2 case counts with onset in 2018-2021. We then used the model to test the impact of different outbreak response scenarios during a prediction period of 2022-2023. The response scenarios included no response, the planned response (based on Nigeria's SIA calendar), and a set of hypothetical responses that vary in the dates at which SIAs started. The planned response scenario included two rounds of SIAs that covered almost all areas of Northwest and Northeast Nigeria except some under-vaccinated areas (e.g., Sokoto). The hypothetical response scenarios involved two, three, and four rounds of SIAs that covered the whole Northwest and Northeast Nigeria. All SIAs in tested outbreak response scenarios used nOPV2. We compared the outcomes of tested outbreak response scenarios in the prediction period. Results: Modeled cVDPV2 weekly case counts aligned spatiotemporally with the data. The prediction results indicated that implementing the planned response reduced total case counts by 79% compared to no response, but did not stop the transmission, especially in under-vaccinated areas. Implementing the hypothetical response scenarios involving two rounds of nOPV2 SIAs that covered all areas further reduced cVDPV2 case counts in under-vaccinated areas by 91-95% compared to the planned response, with greater impact from completing the two rounds at an earlier time, but it did not stop the transmission. When the first two rounds were completed in early April 2022, implementing two additional rounds stopped the transmission in late January 2023. When the first two rounds were completed six weeks earlier (i.e., in late February 2022), implementing one (two) additional round stopped the transmission in early February 2023 (late November 2022). The die out was always achieved last in the under-vaccinated areas of Northwest and Northeast Nigeria. Conclusions: A differential-equation-based model of poliovirus transmission was developed and validated in a case study of Northwest and Northeast Nigeria. The results highlighted (i) the effectiveness of nOPV2 in reducing outbreak case counts; (ii) the need for more rounds of outbreak response SIAs that covered all of Northwest and Northeast Nigeria in 2022 to stop the cVDPV2 outbreaks; (iii) that persistent transmission in under-vaccinated areas delayed the progress towards stopping outbreaks; and (iv) that a quicker outbreak response would avert more paralytic cases and require fewer SIA rounds to stop the outbreaks.

The implications of using maternity care deserts to measure progress in access to obstetric care: A mixed-integer optimization analysis.

Background: Among the factors contributing to the maternal mortality crisis in the United States is a lack of risk-appropriate access to obstetric care. There are several existing measures of access to obstetric care in the literature and popular media. In this study, we explored how current measures of obstetric access inform the number and location of additional obstetric care facilities required to improve access. Methods: We formulated two facility location optimization models to determine the number of new facilities required to minimize the number of reproductive-aged women living in obstetric care deserts. We define deserts as either "maternity care deserts", designated by the March of Dimes to be counties with no obstetric care hospital or obstetric providers, or regions further than 50 miles from critical care obstetric (CCO) services. We gathered information on hospitals with obstetric services from Georgia Department of Public Health public reports and estimated the female reproductive-age population by census block group using the American Community Survey. Results: Out of the 1,910,308 reproductive-aged women who live in Georgia, 104,158 (5.5%) live in maternity care deserts, 150,563 (7.9%) reproductive-aged women live further than 50 miles from CCO services, and 38,202 (2.0%) live in both "maternity care desert" and further than 50 miles from CCO services. Our optimization analysis suggests that 16 new obstetric facilities (a 19% increase from the current 83 facilities) are required to reduce the number of reproductive-aged women living in "maternity care deserts" by 50% (from 104,158 to 51,477). At least 56 new obstetric care facilities (a 67% increase) would be required to eliminate maternity care deserts in Georgia. Meanwhile, expansion of 2 obstetric care facilities to offer CCO services would reduce the number of reproductive-aged women living further than 50 miles from CCO services by 50% (from 150,563 to 57,338), and 8 facilities would ensure all women in Georgia live within 50 miles of CCO services. Conclusions: Current measures of access to obstetric care may not be sufficient for evaluating access and tracking progress toward improvements. In a state like Georgia with a large number of small counties, eliminating maternity care deserts would require a prohibitively large number of new obstetric care facilities. This work suggests that additional measures and tools are needed to estimate the number and type of obstetric care facilities that best match practical resources to obstetric care needs.

Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses.

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students' learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility-a methodology we refer to as WiFi mobility models (WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC). WiMob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WiMob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WiMob, we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. WiMob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.

The Impact of Testing Capacity and Compliance With Isolation on COVID-19: A Mathematical Modeling Study.

Introduction: Diagnostic tests can play an important role in reducing the transmission of infectious respiratory diseases, particularly during a pandemic. The potential benefit of diagnostic testing depends on at least 4 factors: (1) how soon testing becomes available after the beginning of the pandemic and (2) at what capacity; (3) compliance with isolation after testing positive; and (4) compliance with isolation when experiencing symptoms, even in the absence of testing. Methods: To understand the interplay between these factors and provide further insight into policy decisions for future pandemics, we developed a compartmental model and simulated numerous scenarios using the dynamics of COVID-19 as a case study. Results: Our results quantified the significant benefits of early start of testing and high compliance with isolation. Early start of testing, even with low testing capacity over time, could significantly slow down the disease spread if compliance with isolation is high. By contrast, when the start of testing was delayed, the benefit of testing on reducing infection spread was limited, even when testing capacity was high; the additional testing capacity required increased superlinearly for each day of delay to achieve a similar infection attack rate as in starting testing earlier. Conclusions: Our study highlighted the importance of the early start of testing and public health messaging to promote isolation compliance when needed for an ongoing effective response to COVID-19 and future pandemics.

Frequency and correlates of suicidal ideation and behaviors in treatment-seeking Post-9/11 Veterans.

OBJECTIVE: Post-9/11 U.S. veterans and servicemembers are at increased risk for suicide, indicating an important need to identify and mitigate suicidal ideation and behaviors in this population. METHOD: Using data modeling techniques, we examined correlates of suicidal ideation and behavior at intake in 261 Post-9/11 veterans and servicemembers seeking mental health treatment. RESULTS: Our sample endorsed high rates of suicidal ideation and behavior. Approximately 40% of our sample scored in a range on the Suicide Behaviors Questionnaire-Revised (SBQ-R), indicating high clinical risk for suicide. Results from multivariate analyses indicate that greater state and/or trait depression severity, greater anger and anger expression, less impulse control, and lower rank were consistently associated with suicidal ideation and behavior across our models. Negative posttraumatic thoughts about the self, gender, and military branch of service were also significantly associated with suicidal ideation and behavior. CONCLUSIONS: Suicidal ideation and behaviors are common in veterans seeking mental health treatment. State and/or trait depression, anger and impulse control were predictors of increased risk for suicidal ideation and behavior across models. Consistencies and differences across models as well as limitations and practical implications for the findings are discussed.

Cost-effectiveness of pediatric norovirus vaccination in daycare settings.

OBJECTIVE: Noroviruses are the leading cause of acute gastroenteritis in the United States and outbreaks frequently occur in daycare settings. Results of norovirus vaccine trials have been promising, however there are open questions as to whether vaccination of daycare children would be cost-effective. We investigated the incremental cost-effectiveness of a hypothetical norovirus vaccination for children in daycare settings compared to no vaccination. METHODS: We conducted a model-based cost-effectiveness analysis using a disease transmission model of children attending daycare. Vaccination with a 90% coverage rate in addition to the observed standard of care (exclusion of symptomatic children from daycare) was compared to the observed standard of care. The main outcomes measures were infections and deaths averted, quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratio (ICER). Cost-effectiveness was analyzed from a societal perspective, including medical costs to children as well as productivity losses of parents, over a two-year time horizon. Data sources included outbreak surveillance data and published literature. RESULTS: A 50% efficacious norovirus vaccine averts 571.83 norovirus cases and 0.003 norovirus-related deaths per 10,000 children compared to the observed standard of care. A $200 norovirus vaccine that is 50% efficacious has a net cost increase of $178.10 per child and 0.025 more QALYs, resulting in an ICER of $7,028/QALY. Based on the probabilistic sensitivity analysis, we estimated that a $200 vaccination with 50% efficacy was 94.0% likely to be cost-effective at a willingness-to-pay of $100,000/QALY threshold and 95.3% likely at a $150,000/QALY threshold. CONCLUSION: Due to the large disease burden associated with norovirus, it is likely that vaccinating children in daycares could be cost-effective, even with modest vaccine efficacy and a high per-child cost of vaccination. Norovirus vaccination of children in daycare has a cost-effectiveness ratio similar to other commonly recommended childhood vaccines.