Discrete glucose profiles identified using continuous glucose monitoring data and their association with adverse pregnancy outcomes.

BACKGROUND: Continuous glucose monitoring has facilitated the evaluation of dynamic changes in glucose throughout the day and their effect on fetal growth abnormalities in pregnancy. However, studies of multiple continuous glucose monitoring metrics combined and their association with other adverse pregnancy outcomes are limited. OBJECTIVE: This study aimed to (1) use machine learning techniques to identify discrete glucose profiles based on weekly continuous glucose monitoring metrics in pregnant individuals with pregestational diabetes mellitus and (2) investigate their association with adverse pregnancy outcomes. STUDY DESIGN: This study analyzed data from a retrospective cohort study of pregnant patients with type 1 or 2 diabetes mellitus who used Dexcom G6 continuous glucose monitoring and delivered a nonanomalous, singleton pregnancy at a tertiary center between 2019 and 2023. Continuous glucose monitoring data were collapsed into 39 weekly glycemic measures related to centrality, spread, excursions, and circadian cycle patterns. Principal component analysis and k-means clustering were used to identify 4 discrete groups, and patients were assigned to the group that best represented their continuous glucose monitoring patterns during pregnancy. Finally, the association between glucose profile groups and outcomes (preterm birth, cesarean delivery, preeclampsia, large-for-gestational-age neonate, neonatal hypoglycemia, and neonatal intensive care unit admission) was estimated using multivariate logistic regression adjusted for diabetes mellitus type, maternal age, insurance, continuous glucose monitoring use before pregnancy, and parity. RESULTS: Of 177 included patients, 90 (50.8%) had type 1 diabetes mellitus, and 85 (48.3%) had type 2 diabetes mellitus. This study identified 4 glucose profiles: (1) well controlled; (2) suboptimally controlled with high variability, fasting hypoglycemia, and daytime hyperglycemia; (3) suboptimally controlled with minimal circadian variation; and (4) poorly controlled with peak hyperglycemia overnight. Compared with the well-controlled profile, the suboptimally controlled profile with high variability had higher odds of a large-for-gestational-age neonate (adjusted odds ratio, 3.34; 95% confidence interval, 1.15-9.89). The suboptimally controlled with minimal circadian variation profile had higher odds of preterm birth (adjusted odds ratio, 2.59; 95% confidence interval, 1.10-6.24), cesarean delivery (adjusted odds ratio, 2.76; 95% confidence interval, 1.09-7.46), and neonatal intensive care unit admission (adjusted odds ratio, 4.08; 95% confidence interval, 1.58-11.40). The poorly controlled profile with peak hyperglycemia overnight had higher odds of preeclampsia (adjusted odds ratio, 2.54; 95% confidence interval, 1.02-6.52), large-for-gestational-age neonate (adjusted odds ratio, 3.72; 95% confidence interval, 1.37-10.4), neonatal hypoglycemia (adjusted odds ratio, 3.53; 95% confidence interval, 1.37-9.71), and neonatal intensive care unit admission (adjusted odds ratio, 3.15; 95% confidence interval, 1.20-9.09). CONCLUSION: Discrete glucose profiles of pregnant individuals with pregestational diabetes mellitus were identified through joint consideration of multiple continuous glucose monitoring metrics. Prolonged exposure to maternal hyperglycemia may be associated with a higher risk of adverse pregnancy outcomes than suboptimal glycemic control characterized by high glucose variability and intermittent hyperglycemia.

Are populations of postpartum women differentially served by community health worker programs: an observational cohort study from Zanzibar, Tanzania.

BACKGROUND: Although community health worker (CHW) programs focus on improving access to healthcare, some individuals may not receive the intended quality or quantity of an intervention. The objective of this research was to examine if certain populations of pregnant women differentially experience the implementation of a community health worker-led maternal health intervention in Zanzibar. METHODS: We included pregnant women enrolled in the Safer Deliveries (Uzazi Salama) program, which operated in 10 of 11 districts in Zanzibar, Tanzania between January 1, 2017, and June 19, 2019 (N = 33,914). The outcomes of interest were receipt of the entire postpartum intervention (three CHW visits) and time to first postpartum CHW visit (days). Visits by CHWs were done at the women's home, however, a telehealth option existed for women who were unable to be reached in-person. We conducted statistical tests to investigate the bivariate associations between our outcomes and each demographic and health characteristic. We used multivariate logistic regression to estimate the relationships between covariates and the outcomes and multivariate linear regression to estimate the association between covariates and the average time until first postpartum visit. RESULTS: Higher parity (OR = 0.85; P = 0.014; 95%CI: 0.75-0.97), unknown or unreported HIV status (OR = 0.64; p < 0.001; 95%CI: 0.53-0.78), and receipt of phone consultations (OR = 0.77; p < 0.001; 95%CI: 0.69-0.87) were associated with a lower odds of receiving all postpartum visits. Similarly, women with an unknown or unreported HIV status (estimated mean difference of 1.81 days; p < 0.001; 95%CI: 1.03-2.59) and those who received a phone consultation (estimated mean difference of 0.83 days; p < 0.001; 95%CI: 0.43-1.23), on average, experienced delays to first visit. In addition, current delivery at a referral hospital was associated with lower odds of receiving a postpartum visit and longer time to first visit compared to delivery at home, cottage hospital, PHCU + , or district hospital. Women from all other districts received their first visit earlier than women from Kaskazini B. There were no differences in the odds of receiving the entire postpartum intervention by sociodemographic variables, including age, education, and poverty assessment indicators. CONCLUSION: The results indicate no differences in intervention contact across wealth and education levels, suggesting that the program is effectively reaching women regardless of SES. However, women with other characteristics (e.g., higher parity, unknown or unreported HIV status) had lower odds of receiving the complete intervention. Overall, this work generates knowledge on existing disparities in intervention coverage and enables future programs to develop approaches to achieve equity in health care utilization and outcomes.

Missing data and missed infections: investigating racial and ethnic disparities in SARS-CoV-2 testing and infection rates in Holyoke, Massachusetts.

Routinely collected testing data have been a vital resource for public health response during the COVID-19 pandemic and have revealed the extent to which Black and Hispanic persons have borne a disproportionate burden of SARS-CoV-2 infections and hospitalizations in the United States. However, missing race and ethnicity data and missed infections due to testing disparities limit the interpretation of testing data and obscure the true toll of the pandemic. We investigated potential bias arising from these 2 types of missing data through a case study carried out in Holyoke, Massachusetts, during the prevaccination phase of the pandemic. First, we estimated SARS-CoV-2 testing and case rates by race and ethnicity, imputing missing data using a joint modeling approach. We then investigated disparities in SARS-CoV-2 reported case rates and missed infections by comparing case rate estimates with estimates derived from a COVID-19 seroprevalence survey. Compared with the non-Hispanic White population, we found that the Hispanic population had similar testing rates (476 tested per 1000 vs 480 per 1000) but twice the case rate (8.1% vs 3.7%). We found evidence of inequitable testing, with a higher rate of missed infections in the Hispanic population than in the non-Hispanic White population (79 infections missed per 1000 vs 60 missed per 1000).

Evaluation of the implementation of a community health worker-led COVID-19 contact tracing intervention in Chiapas, Mexico, from March 2020 to December 2021.

BACKGROUND: Mexico is one of the countries with the greatest excess death due to COVID-19. Chiapas, the poorest state in the country, has been particularly affected. Faced with an exacerbated shortage of health professionals, medical supplies, and infrastructure to respond to the pandemic, the non-governmental organization Compañeros En Salud (CES) implemented a COVID-19 infection prevention and control program to limit the impact of the pandemic in the region. We evaluated CES's implementation of a community health worker (CHW)-led contact tracing intervention in eight rural communities in Chiapas. METHODS: Our retrospective observational study used operational data collected during the contract tracing intervention from March 2020 to December 2021. We evaluated three outcomes: contact tracing coverage, defined as the proportion of named contacts that were located by CHWs, successful completion of contact tracing, and incidence of suspected COVID-19 among contacts. We described how these outcomes changed over time as the intervention evolved. In addition, we assessed associations between these three main outcomes and demographic characteristics of contacts and intervention period (pre vs. post March 2021) using univariate and multivariate logistic regression. RESULTS: From a roster of 2,177 named contacts, 1,187 (54.5%) received at least one home visit by a CHW and 560 (25.7%) had successful completion of contact tracing according to intervention guidelines. Of 560 contacts with complete contact tracing, 93 (16.6%) became suspected COVID-19 cases. We observed significant associations between sex and coverage (p = 0.006), sex and complete contact tracing (p = 0.049), community of residence and both coverage and complete contact tracing (p < 0.001), and intervention period and both coverage and complete contact tracing (p < 0.001). CONCLUSIONS: Our analysis highlights the promises and the challenges of implementing CHW-led COVID-19 contact tracing programs. To optimize implementation, we recommend using digital tools for data collection with a human-centered design, conducting regular data quality assessments, providing CHWs with sufficient technical knowledge of the data collection system, supervising CHWs to ensure contact tracing guidelines are followed, involving communities in the design and implementation of the intervention, and addressing community member needs and concerns surrounding stigmatization arising from lack of privacy.

Disparities in SARS-CoV-2 Infection by Race, Ethnicity, Language, and Social Vulnerability: Evidence from a Citywide Seroprevalence Study in Massachusetts, USA.

OBJECTIVES: Uncovering and addressing disparities in infectious disease outbreaks require a rapid, methodical understanding of local epidemiology. We conducted a seroprevalence study of SARS-CoV-2 infection in Holyoke, Massachusetts, a majority Hispanic city with high levels of socio-economic disadvantage to estimate seroprevalence and identify disparities in SARS-CoV-2 infection. METHODS: We invited 2000 randomly sampled households between 11/5/2020 and 12/31/2020 to complete questionnaires and provide dried blood spots for SARS-CoV-2 antibody testing. We calculated seroprevalence based on the presence of IgG antibodies using a weighted Bayesian procedure that incorporated uncertainty in antibody test sensitivity and specificity and accounted for household clustering. RESULTS: Two hundred eighty households including 472 individuals were enrolled. Three hundred twenty-eight individuals underwent antibody testing. Citywide seroprevalence of SARS-CoV-2 IgG was 13.1% (95% CI 6.9-22.3) compared to 9.8% of the population infected based on publicly reported cases. Seroprevalence was 16.1% (95% CI 6.2-31.8) among Hispanic individuals compared to 9.4% (95% CI 4.6-16.4) among non-Hispanic white individuals. Seroprevalence was higher among Spanish-speaking households (21.9%; 95% CI 8.3-43.9) compared to English-speaking households (10.2%; 95% CI 5.2-18.0) and among individuals in high social vulnerability index (SVI) areas based on the CDC SVI (14.4%; 95% CI 7.1-25.5) compared to low SVI areas (8.2%; 95% CI 3.1-16.9). CONCLUSIONS: The SARS-CoV-2 IgG seroprevalence in a city with high levels of social vulnerability was 13.1% during the pre-vaccination period of the COVID-19 pandemic. Hispanic individuals and individuals in communities characterized by high SVI were at the highest risk of infection. Public health interventions should be designed to ensure that individuals in high social vulnerability communities have access to the tools to combat COVID-19.

hCG trends after mifepristone and misoprostol for undesired pregnancy of unknown location.

OBJECTIVES: To describe human chorionic gonadotropin (hCG) trends for patients with a pregnancy of unknown location (PUL) presenting for medication abortion by management strategy and outcome. STUDY DESIGN: This retrospective cohort study included patients presenting for medication abortion with a PUL at ≤42 days gestation managed with either (1) immediate mifepristone with serial hCG follow-up (same-day-start) or (2) hCG testing every 48 to 72 hours ± ultrasonography to confirm pregnancy location followed by treatment (delay-for-diagnosis). The primary outcome was percent hCG change over time between presentation and diagnosis, summarized using a multivariate regression model. RESULTS: Of the 55 same-day-start patients, none were treated for ectopic. The eight who eventually required suction curettage had median hCG percent changes (interquartile range) on days 3, 4, and 5 of +57% (-14 to 127; n = 2), +292% (226-353; n = 4), and +392% (n = 1), while the 41 successful medication abortions had declines of -64% (n = 1), -65% (-75 to -27; n = 17), and -77% (-85 to -68; n = 13). Of the 380 delay-for-diagnosis patients, the 30 ectopic pregnancies had day 3, 4, and 5 changes of +38% (-17 to 56; n = 14), +50% (17-71; n = 7), and +115% (87-177; n = 4). None of the ectopic pregnancies declined ≥50% by days 3 to 5. The hCG trend for ectopic pregnancies differed from successful medication abortions (p < 0.01), but not medication abortions with retained intrauterine pregnancies (p = 0.41). CONCLUSIONS: Serum hCG trends can help differentiate ectopic pregnancy from successful medication abortion, but cannot distinguish between ectopic and retained intrauterine pregnancy. IMPLICATIONS: Serial serum hCG testing is effective for confirming successful medication abortion and identifying patients requiring further follow-up among patients undergoing medication abortion for an undesired PUL.

The Journey of Zanzibar's Digitally Enabled Community Health Program to National Scale: Implementation Report.

BACKGROUND: While high-quality primary health care services can meet 80%-90% of health needs over a person's lifetime, this potential is severely hindered in many low-resource countries by a constrained health care system. There is a growing consensus that effectively designed, resourced, and managed community health worker programs are a critical component of a well-functioning primary health system, and digital technology is recognized as an important enabler of health systems transformation. OBJECTIVE: In this implementation report, we describe the design and rollout of Zanzibar's national, digitally enabled community health program-Jamii ni Afya. METHODS: Since 2010, D-tree International has partnered with the Ministry of Health Zanzibar to pilot and generate evidence for a digitally enabled community health program, which was formally adopted and scaled nationally by the government in 2018. Community health workers use a mobile app that guides service delivery and data collection for home-based health services, resulting in comprehensive service delivery, access to real-time data, efficient management of resources, and continuous quality improvement. RESULTS: The Zanzibar government has documented increases in the delivery of health facilities among pregnant women and reductions in stunting among children younger than 5 years since the community health program has scaled. Key success factors included starting with the health challenge and local context rather than the technology, usage of data for decision-making, and extensive collaboration with local and global partners and funders. Lessons learned include the significant time it takes to scale and institutionalize a digital health systems innovation due to the time to generate evidence, change opinions, and build capacity. CONCLUSIONS: Jamii ni Afya represents one of the world's first examples of a nationally scaled digitally enabled community health program. This implementation report outlines key successes and lessons learned, which may have applicability to other governments and partners working to sustainably strengthen primary health systems.

Use of Abortion Services in Massachusetts After the Dobbs Decision Among In-State vs Out-of-State Residents.

This cohort study compares observed vs expected abortion counts after Dobbs in Massachusetts among in-state vs out-of-state residents.

Predicting facility-based delivery in Zanzibar: The vulnerability of machine learning algorithms to adversarial attacks.

Background: Community health worker (CHW)-led maternal health programs have contributed to increased facility-based deliveries and decreased maternal mortality in sub-Saharan Africa. The recent adoption of mobile devices in these programs provides an opportunity for real-time implementation of machine learning predictive models to identify women most at risk for home-based delivery. However, it is possible that falsified data could be entered into the model to get a specific prediction result - known as an "adversarial attack". The goal of this paper is to evaluate the algorithm's vulnerability to adversarial attacks. Methods: The dataset used in this research is from the Uzazi Salama ("Safer Deliveries") program, which operated between 2016 and 2019 in Zanzibar. We used LASSO regularized logistic regression to develop the prediction model. We used "One-At-a-Time (OAT)" adversarial attacks across four different types of input variables: binary - access to electricity at home, categorical - previous delivery location, ordinal - educational level, and continuous - gestational age. We evaluated the percent of predicted classifications that change due to these adversarial attacks. Results: Manipulating input variables affected prediction results. The variable with the greatest vulnerability was previous delivery location, with 55.65% of predicted classifications changing when applying adversarial attacks from previously delivered at a facility to previously delivered at home, and 37.63% of predicted classifications changing when applying adversarial attacks from previously delivered at home to previously delivered at a facility. Conclusion: This paper investigates the vulnerability of an algorithm to predict facility-based delivery when facing adversarial attacks. By understanding the effect of adversarial attacks, programs can implement data monitoring strategies to assess for and deter these manipulations. Ensuring fidelity in algorithm deployment secures that CHWs target those women who are actually at high risk of delivering at home.

The impact of COVID-19 and national pandemic responses on health service utilisation in seven low- and middle-income countries.

BACKGROUND: The COVID-19 pandemic has disrupted health services worldwide, which may have led to increased mortality and secondary disease outbreaks. Disruptions vary by patient population, geographic area, and service. While many reasons have been put forward to explain disruptions, few studies have empirically investigated their causes. OBJECTIVE: We quantify disruptions to outpatient services, facility-based deliveries, and family planning in seven low- and middle-income countries during the COVID-19 pandemic and quantify relationships between disruptions and the intensity of national pandemic responses. METHODS: We leveraged routine data from 104 Partners In Health-supported facilities from January 2016 to December 2021. We first quantified COVID-19-related disruptions in each country by month using negative binomial time series models. We then modelled the relationship between disruptions and the intensity of national pandemic responses, as measured by the stringency index from the Oxford COVID-19 Government Response Tracker. RESULTS: For all the studied countries, we observed at least one month with a significant decline in outpatient visits during the COVID-19 pandemic. We also observed significant cumulative drops in outpatient visits across all months in Lesotho, Liberia, Malawi, Rwanda, and Sierra Leone. A significant cumulative decrease in facility-based deliveries was observed in Haiti, Lesotho, Mexico, and Sierra Leone. No country had significant cumulative drops in family planning visits. For a 10-unit increase in the average monthly stringency index, the proportion deviation in monthly facility outpatient visits compared to expected fell by 3.9% (95% CI: -5.1%, -1.6%). No relationship between stringency of pandemic responses and utilisation was observed for facility-based deliveries or family planning. CONCLUSIONS: Context-specific strategies show the ability of health systems to sustain essential health services during the pandemic. The link between pandemic responses and healthcare utilisation can inform purposeful strategies to ensure communities have access to care and provide lessons for promoting the utilisation of health services elsewhere.

Evaluation of the Access Bio CareStart rapid SARS-CoV-2 antigen test in asymptomatic individuals tested at a community mass-testing program in Western Massachusetts.

Point-of-care antigen-detecting rapid diagnostic tests (RDTs) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) represent a scalable tool for surveillance of active SARS-CoV-2 infections in the population. Data on the performance of these tests in real-world community settings are paramount to guide their implementation to combat the COVID-19 pandemic. We evaluated the performance characteristics of the CareStart COVID-19 Antigen test (CareStart) in a community testing site in Holyoke, Massachusetts. We compared CareStart to a SARS-CoV-2 reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) reference, both using anterior nasal swab samples. We calculated the sensitivity, specificity, and the expected positive and negative predictive values at different SARS-CoV-2 prevalence estimates. We performed 666 total tests on 591 unique individuals. 573 (86%) were asymptomatic. There were 52 positive tests by RT-qPCR. The sensitivity of CareStart was 49.0% (95% Confidence Interval (CI) 34.8-63.4) and specificity was 99.5% (95% CI 98.5-99.9). Among positive RT-qPCR tests, the median cycle threshold (Ct) was significantly lower in samples that tested positive on CareStart. Using a Ct ≤ 30 as a benchmark for positivity increased the sensitivity of the test to 64.9% (95% CI 47.5-79.8). Our study shows that CareStart has a high specificity and moderate sensitivity. The utility of RDTs, such as CareStart, in mass implementation should prioritize use cases in which a higher specificity is more important, such as triage tests to rule-in active infections in community surveillance programs.

Impact of COVID-19 on access to cancer care in Rwanda: a retrospective time-series study using electronic medical records data.

INTRODUCTION: The COVID-19 pandemic has caused disruptions in access to routine healthcare services worldwide, with a particularly high impact on chronic care patients and low and middle-income countries. In this study, we used routinely collected electronic medical records data to assess the impact of the COVID-19 pandemic on access to cancer care at the Butaro Cancer Center of Excellence (BCCOE) in rural Rwanda. METHODS: We conducted a retrospective time-series study among all Rwandan patients who received cancer care at the BCCOE between 1 January 2016 and 31 July 2021. The primary outcomes of interest included a comparison of the number of patients who were predicted based on time-series models of pre-COVID-19 trends versus the actual number of patients who presented during the COVID-19 period (between March 2020 and July 2021) across four key indicators: the number of new patients, number of scheduled appointments, number of clinical visits attended and the proportion of scheduled appointments completed on time. RESULTS: In total, 8970 patients (7140 patients enrolled before COVID-19 and 1830 patients enrolled during COVID-19) were included in this study. During the COVID-19 period, enrolment of new patients dropped by 21.7% (95% prediction interval (PI): -31.3%, -11.7%) compared with the pre-COVID-19 period. Similarly, the number of clinical visits was 25.0% (95% PI: -31.1%, -19.1%) lower than expected and the proportion of scheduled visits completed on time was 27.9% (95% PI: -39.8%, -14.1%) lower than expected. However, the number of scheduled visits did not deviate significantly from expected. CONCLUSION: Although scheduling procedures for visits continued as expected, our findings reveal that the COVID-19 pandemic interrupted patients' ability to access cancer care and attend scheduled appointments at the BCCOE. This interruption in care suggests delayed diagnosis and loss to follow-up, potentially resulting in a higher rate of negative health outcomes among cancer patients in Rwanda.

Adapting Lot Quality Assurance Sampling to accommodate imperfect diagnostic tests: application to COVID-19 serosurveillance in Haiti.

BACKGROUND: Lot Quality Assurance Sampling (LQAS), a tool used for monitoring health indicators in low resource settings resulting in "high" or "low" classifications, assumes that determination of the trait of interest is perfect. This is often not true for diagnostic tests, with imperfect sensitivity and specificity. Here, we develop Lot Quality Assurance Sampling for Imperfect Tests (LQAS-IMP) to address this issue and apply it to a COVID-19 serosurveillance study design in Haiti. METHODS: We first derive a modified procedure, LQAS-IMP, that accounts for the sensitivity and specificity of a diagnostic test to yield correct classification errors. We then apply the novel LQAS-IMP to design an LQAS system to classify prevalence of SARS-CoV-2 antibodies among healthcare workers at eleven Zanmia Lasante health facilities in Haiti. Finally, we show the performance of the LQAS-IMP procedure in a simulation study. RESULTS: We found that when an imperfect diagnostic test is used, the classification errors in the standard LQAS procedure are larger than specified. In the modified LQAS-IMP procedure, classification errors are consistent with the specified maximum classification error. We then utilized the LQAS-IMP procedure to define valid systems for sampling at eleven hospitals in Haiti. CONCLUSION: The LQAS-IMP procedure accounts for imperfect sensitivity and specificity in system design; if the accuracy of a test is known, the use of LQAS-IMP extends LQAS to applications for indicators that are based on laboratory tests, such as SARS-CoV-2 antibodies.

Service delivery reform for maternal and newborn health in Kakamega County, Kenya: study protocol for a prospective impact evaluation and implementation science study.

BACKGROUND: Maternal and neonatal mortality remain elevated in low and middle income countries, and progress is slower than needed to achieve the Sustainable Development Goals. Existing strategies appear to be insufficient. One proposed alternative strategy, Service Delivery Redesign for Maternal and Neonatal Health (SDR), centers on strengthening higher level health facilities to provide rapid, definitive care in case of delivery and post-natal complications, and then promoting delivery in these hospitals, rather than in primary care facilities. However to date, SDR has not been piloted or evaluated. METHODS: We will use a prospective, non-randomized stepped-wedge design to evaluate the effectiveness and implementation of Service Delivery Redesign for Maternal and Neonatal Health in Kakamega County, Kenya. DISCUSSION: This protocol describes a hybrid effectiveness/implementation evaluation study with an adaptive design. The impact evaluation ("effectiveness") study focuses on maternal and newborn health outcomes, and will be accompanied by an implementation evaluation focused on program reach, adoption, and fidelity.

Opioid Use After Laparoscopic Surgery for Endometriosis and Pelvic Pain.

STUDY OBJECTIVE: The primary objective was to quantify postoperative opioid use after laparoscopic surgery for endometriosis or pelvic pain. The secondary objective was to identify patient characteristics associated with greater postoperative opioid requirements. DESIGN: Prospective, survey-based study in which subjects completed 1 preoperative and 7 postoperative surveys within 28 days of surgery regarding medication usage and pain control. SETTING: Tertiary care, academic center. PATIENTS: A total of 100 women with endometriosis or pelvic pain. INTERVENTIONS: Laparoscopic same-day discharge surgery by fellowship-trained minimally invasive gynecologists. MEASUREMENTS AND MAIN RESULTS: A total of 100 patients were recruited and 8 excluded, for a final sample size of 92 patients. All patients completed the preoperative survey. Postoperative response rates ranged from 70.7% to 80%. The mean number of pills (5 mg oxycodone tablets) taken by day 28 was 6.8. The average number of pills prescribed was 10.2, with a minimum of 4 (n = 1) and maximum of 20 (n = 3). Previous laparoscopy for pelvic pain was associated with a significant increase in postoperative narcotic use (8.2 vs 5.6; p = .044). Hysterectomy was the only surgical procedure associated with a significant increase in postoperative narcotic use (9.7 vs 5.4; p = .013). There were no difference in number of pills taken by presence of deep endometriosis or pathology-confirmed endometriosis (all p >.36). There was a trend of greater opioid use in patients with diagnoses of self-reported chronic pelvic pain, anxiety, and depression (7.9 vs 5.7, p = .051; 7.7 vs 5.2, p = .155; 8.1 vs 5.6, p = .118). CONCLUSION: Most patients undergoing laparoscopic surgery for endometriosis and pelvic pain had a lower postoperative opioid requirement than prescribed, suggesting surgeons can prescribe fewer postoperative narcotics in this population. Patients with a previous surgery for pelvic pain, self-reported chronic pelvic pain syndrome, anxiety, and depression may represent a subset of patients with increased postoperative opioid requirements.

Machine learning for maternal health: Predicting delivery location in a community health worker program in Zanzibar.

Background: Maternal and neonatal health outcomes in low- and middle-income countries (LMICs) have improved over the last two decades. However, many pregnant women still deliver at home, which increases the health risks for both the mother and the child. Community health worker programs have been broadly employed in LMICs to connect women to antenatal care and delivery locations. More recently, employment of digital tools in maternal health programs have resulted in better care delivery and served as a routine mode of data collection. Despite the availability of rich, patient-level data within these digital tools, there has been limited utilization of this type of data to inform program delivery in LMICs. Methods: We use program data from 38,787 women enrolled in Safer Deliveries, a community health worker program in Zanzibar, to build a generalizable prediction model that accurately predicts whether a newly enrolled pregnant woman will deliver in a health facility. We use information collected during the enrollment visit, including demographic data, health characteristics and current pregnancy information. We apply four machine learning methods: logistic regression, LASSO regularized logistic regression, random forest and an artificial neural network; and three sampling techniques to address the imbalanced data: undersampling of facility deliveries, oversampling of home deliveries and addition of synthetic home deliveries using SMOTE. Results: Our models correctly predicted the delivery location for 68%-77% of the women in the test set, with slightly higher accuracy when predicting facility delivery versus home delivery. A random forest model with a balanced training set created using undersampling of existing facility deliveries accurately identified 74.4% of women delivering at home. Conclusions: This model can provide a "real-time" prediction of the delivery location for new maternal health program enrollees and may enable early provision of extra support for individuals at risk of not delivering in a health facility, which has potential to improve health outcomes for both mothers and their newborns. The framework presented here is applicable in other contexts and the selection of input features can easily be adapted to match data availability and other outcomes, both within and beyond maternal health.

Reliability and Discriminant Validity of a Checklist for Surgical Scrubbing, Gowning and Gloving.

Background: Surgical scrubbing, gowning, and gloving is challenging for medical trainees to learn in the operating room environment. Currently, there are few reliable or valid tools to evaluate a trainee's ability to scrub, gown and glove. The objective of this study is to test the reliability and validity of a checklist that evaluates the technique of surgical scrubbing, gowning and gloving (SGG). Methods: This Institutional Review Board-approved study recruited medical students, residents, and fellows from an academic, tertiary care institution. Trainees were stratified based upon prior surgical experience as novices, intermediates, or experts. Participants were instructed to scrub, gown and glove in a staged operating room while being video-recorded. Two blinded raters scored the videos according to the SGG checklist. Reliability was assessed using the intraclass correlation coefficient for total scores and Cohen's kappa for item completion. The internal consistency and discriminant validity of the SGG checklist were assessed using Cronbach alpha and the Wilcoxon rank sum test, respectively. Results: 56 participants were recruited (18 novices, 19 intermediates, 19 experts). The intraclass correlation coefficient demonstrated excellent inter-rater reliability for the overall checklist (0.990), and the Cohen's kappa ranged from 0.598 to 1.00. The checklist also had excellent internal consistency (Cronbach's alpha 0.950). A significant difference in scores was observed between all groups (p < 0.001). Conclusion: This checklist demonstrates a high inter-rater reliability, discriminant validity, and internal consistency. It has the potential to enhance medical education curricula.

Mifepristone and Misoprostol for Undesired Pregnancy of Unknown Location.

OBJECTIVE: To compare immediate initiation with delayed initiation of medication abortion among patients with an undesired pregnancy of unknown location. METHODS: This retrospective cohort study used electronic medical record data from the Planned Parenthood League of Massachusetts (2014-2019) for patients who requested medication abortion with a last menstrual period (LMP) of 42 days or less and pregnancy of unknown location (no gestational sac) on initial ultrasonogram. Clinicians could initiate medication abortion with mifepristone followed by misoprostol while simultaneously excluding ectopic pregnancy with serial serum human chorionic gonadotropin (hCG) testing (same-day-start group) or establish a diagnosis with serial hCG tests and repeat ultrasonogram before initiating treatment (delay-for-diagnosis group). We compared primary safety outcomes (time to diagnosis of pregnancy location [rule out ectopic], emergency department visits, adverse events, and nonadherence with follow-up) between groups. We also reported secondary efficacy outcomes: time to complete abortion, successful medication abortion (no uterine aspiration), and ongoing pregnancy. RESULTS: Of 5,619 medication abortion visits for patients with an LMP of 42 days or less, 452 patients had pregnancy of unknown location (8.0%). Three patients underwent immediate uterine aspiration, 55 had same-day start, and 394 had delay for diagnosis. Thirty-one patients (7.9%), all in the delay-for-diagnosis group, were treated for ectopic pregnancy, including four that were ruptured. Among patients with no major ectopic pregnancy risk factors (n=432), same-day start had shorter time to diagnosis (median 5.0 days vs 9.0 days; P=.005), with no significant difference in emergency department visits (adjusted odds ratio [aOR] 0.90, 95% CI 0.43-1.88) or nonadherence with follow-up (aOR 0.92, 95% CI 0.39-2.15). Among patients who proceeded with abortion (n=270), same-day start had shorter time to complete abortion (median 5.0 days vs 19.0 days; P<.001). Of those who had medication abortion with known outcome (n=170), the rate of successful medication abortion was lower (85.4% vs 96.7%; P=.013) and the rate of ongoing pregnancy was higher (10.4% vs 2.5%; P=.041) among patients in the same-day-start group. CONCLUSION: In patients with undesired pregnancy of unknown location, immediate initiation of medication abortion is associated with more rapid exclusion of ectopic pregnancy and pregnancy termination but lower abortion efficacy.