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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.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.Tertiary care, academic center.A total of 100 women with endometriosis or pelvic pain.Laparoscopic same-day discharge surgery by fellowship-trained minimally invasive gynecologists.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).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.

Routinely collected testing data has been a vital resource for public health response during the COVID-19 pandemic and has 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 two types of missing data through a case study in Holyoke, Massachusetts during the pre-vaccination phase of the pandemic. First, we estimated SARS-CoV-2 testing and case rates by race/ethnicity, imputing missing data using a joint modelling approach. We then investigated disparities in SARS-CoV-2 reported case rates and missed infections by comparing case rate estimates to estimates derived from a COVID-19 seroprevalence survey. Compared to the non-Hispanic white population, we found that the Hispanic population had similar testing rates (476 vs. 480 tested per 1,000) 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 compared to the non-Hispanic white population (77 vs. 58 infections missed per 1,000).

Community health worker (CHW) interventions have been utilized to address barriers that prevent pregnant women from delivering in health facilities in low- and middle-income countries (LMICs). The objective of this research was to assess the programmatic factors that increase the likelihood of health facility delivery within a large digital health-supported CHW program in Zanzibar, Tanzania.This study included 36,693 women who were enrolled in the Safer Deliveries program with a live birth between January 1, 2017 and July 31, 2019. We assessed whether long-term enrollment, recency of CHW pregnancy visit prior to delivery, and number of routine home pregnancy visits were associated with an increased likelihood of health facility delivery compared to home delivery. We used Chi-squared tests to assess bivariate relationships and performed logistic regression analyses to assess the association between each programmatic variable and health facility delivery, adjusting for relevant confounders.We found that long-term enrollment was significantly associated with increased likelihood of health facility delivery, with the strongest relationship among women with a previous home delivery (OR = 1.4, 95%CI [1.0,1.7]). Among first-time mothers, two or more pregnancy visits by a CHW was positively associated with health facility delivery (OR = 1.8, 95%CI [1.2, 2.7]). Recent pregnancy visit by a CHW was positively associated with health facility delivery, but was not significant at the α = 0.05 level.In this program, we found evidence that at least two routine home pregnancy visits, longer length of enrollment in the program, and recency of home visit to the delivery date were strategies to increase health facility delivery rates among enrolled mothers. Maternal and child health programs should undertake similar evaluations to improve program delivery.

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.

Huang proposes a method for assessing the impact of a point treatment on mortality either directly or mediated by occurrence of a nonterminal health event, based on data from a prospective cohort study in which the occurrence of the nonterminal health event may be preemptied by death but not vice versa. The author uses a causal mediation framework to formally define causal quantities known as natural (in)direct effects. The novelty consists of adapting these concepts to a continuous-time modeling framework based on counting processes. In an effort to posit "scientifically interpretable estimands," statistical and causal assumptions are introduced for identification. In this commentary, we argue that these assumptions are not only difficult to interpret and justify, but are also likely violated in the hepatitis B motivating example and other survival/time to event settings as well.

The utilization of community health worker (CHW) programmes to improve maternal and neonatal health outcomes has become widely applied in low- and middle-income countries. While current research has focused on discerning the effect of these interventions, documenting the process of implementing, scaling and sustaining these programmes has been largely ignored. Here, we focused on the implementation of the Safer Deliveries CHW programme in Zanzibar, a programme designed to address high rates of maternal and neonatal mortality by increasing rates of health facility delivery and postnatal care visits. The programme was implemented and brought to scale in 10 of 11 districts in Zanzibar over the course of 3 years by D-tree International and the Zanzibar Ministry of Health. As the programme utilized a mobile app to support CHWs during their visits, a rich data resource comprised of 133 481 pregnancy and postpartum home visits from 41 653 women and 436 CHWs was collected, enabling the evaluation of numerous measures related to intervention fidelity and health outcomes. Utilizing the framework of Steckler et al., we completed a formal process evaluation of the primary intervention, CHW home visits to women during their pregnancy and postpartum period. Our in-depth analysis and discussion will serve as a model for process evaluations of similar CHW programmes and will hopefully encourage future implementers to report analogous measures of programme performance.

Objective To quantify the number of medically unnecessary clinical visits and in-clinic contacts monthly caused by US abortion regulations. Study Design We estimated the number of clinical visits and clinical contacts (any worker a patient may come into physical contact with during their visit) under the current policy landscape, compared to the number of visits and contacts if the following regulations were repealed: (1) State mandatory in-person counseling visit laws that necessitate two visits for abortion, (2) State mandatory-ultrasound laws, (3) State mandates requiring the prescribing clinician be present during mifepristone administration, (4) Federal Food and Drug Administration Risk Evaluation and Mitigation Strategy for mifepristone. If these laws were repealed, “no-test” telemedicine abortion would be possible for some patients. We modeled the number of visits averted if a minimum of 15 percent or a maximum of 70 percent of medication abortion patients had a “no-test” telemedicine abortion. Results We estimate that 12,742 in-person clinic visits (50,978 clinical contacts) would be averted each month if counseling visit laws alone were repealed, and 31,132 visits (142,910 clinical contacts) would be averted if all four policies were repealed and 70 percent of medication abortion patients received no-test telemedicine abortions. Over 2 million clinical contacts could be averted over the projected 18-month COVID-19 pandemic. Conclusion Medically unnecessary abortion regulations result in a large number of excess clinical visits and contacts. Policy Implications Repeal of medically unnecessary state and federal abortion restrictions in the United States would allow for evidence-based telemedicine abortion care, thereby lowering risk of SARS-CoV-2 transmission.

Background Most maternal health programs in low- and middle- income countries estimate gestational age to provide appropriate antenatal care at the correct times throughout the pregnancy. Although various gestational dating methods have been validated in research studies, the performance of these methods has not been evaluated on a larger scale, such as within health systems. The objective of this research was to investigate the magnitude and impact of errors in estimated delivery dates on health facility delivery among women enrolled in a maternal health program in Zanzibar. Methods This study included 4225 women who were enrolled in the Safer Deliveries program and delivered before May 31, 2017. The exposure of interest was error in estimated delivery date categorized as: severe overestimate, when estimated delivery date (EDD) was 36 days or more after the actual delivery date (ADD); moderate overestimate, when EDD was 15 to 35 days after ADD; accurate, when EDD was 6 days before to 14 days after ADD; and underestimate, when EDD was 7 days or more before ADD. We used Chi-squared tests to identify factors associated with errors in estimated delivery dates. We performed logistic regression to assess the impact of errors in estimated delivery dates on health facility delivery adjusting for age, district of residence, HIV status, and occurrence of past home delivery. Results In our data, 28% of the estimated delivery dates were a severe overestimate, 23% moderate overestimate, 41% accurate, and 8% underestimate. Compared to women with an accurate delivery date, women with a moderate or severe overestimate were significantly less likely to deliver in a health facility (OR = 0.71, 95% CI: [0.59, 0.86]; OR = 0.74, 95% CI: [0.61, 0.91]). When adjusting for multiple confounders, women with moderate overestimates were significantly less likely to deliver in a health facility (AOR = 0.76, 95% CI: [0.61, 0.93]); the result moved slightly towards null for women with severe overestimates (AOR = 0.84, 95% CI: [0.69, 1.03]). Conclusions The overestimation of women’s EDDs reduces the likelihood of health facility delivery. To address this, maternal health programs should improve estimation of EDD or attempt to curb the effect of these errors within their programs.

BackgroundSeroprevalence studies are important tools to estimate the prevalence of prior or recent SARS-CoV-2 infections, identifying hotspots and high-risk groups and informing public health responses to the COVID-19 pandemic. We conducted a city-level seroprevalence study in Holyoke, Massachusetts, USA to estimate the seroprevalence of SARS-CoV-2 antibodies and risk factors for seropositivity.MethodsWe invited inhabitants of 2,000 randomly sampled addresses between November 5 and December 31, 2020. Participants completed questionnaires measuring sociodemographic and health characteristics, and COVID-19 exposure history, and provided dried blood spots for measurement of SARS-CoV-2 IgG and IgM antibodies. We calculate total and subgroup seroprevalence estimates based on presence of IgG antibodies using a Bayesian procedure that incorporates uncertainty in antibody test sensitivity and specificity. We account for clustering by household and weighting based on demographic characteristics to ensure estimates represented the city’s population.FindingsWe enrolled 280 households including 472 individuals. 328 underwent antibody testing. The citywide seroprevalence estimate of SARS-CoV-2 IgG was 13.1% (95%CI 6.9-22.3) compared to 9.8% based on publicly reported case counts. Seroprevalence was 16.1% (95%CI 6.2-31.8) among individuals identifying as Hispanic compared to 9.4% (95%CI 4.6-16.4) among those identifying as non-Hispanic white. 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 living in high vulnerability areas (14.4%; 95% CI 7.1-25.5) compared to low vulnerability areas (8.2%; 95% CI 3.1-16.9).InterpretationThe measured SARS-CoV-2 seroprevalence of IgG antibodies in Holyoke was only 13.1% during the second surge of SARS-CoV-2 in this region, far from accepted thresholds for “herd immunity.” Already vulnerable communities were at highest risk of prior infection. Implementation of local serosurveys in tandem with proactive public health interventions that address disparities in SARS-CoV-2 exposure are crucial to ensure at-risk communities have appropriate educational materials and access to vaccines, testing, and timely treatment.FundingThe Sullivan Family Foundation, Harvard Data Science Institute Bias2program, the US Centers for Disease Control and Prevention.

The COVID-19 pandemic has heterogeneously affected use of basic health services worldwide, with disruptions in some countries beginning in the early stages of the emergency in March 2020. These disruptions have occurred on both the supply and demand sides of healthcare, and have often been related to resource shortages to provide care and lower patient turnout associated with mobility restrictions and fear of contracting COVID-19 at facilities. In this paper, we assess the impact of the COVID-19 pandemic on the use of maternal health services using a time series modelling approach developed to monitor health service use during the pandemic using routinely collected health information systems data. We focus on data from 37 non-governmental organisation-supported health facilities in Haiti, Lesotho, Liberia, Malawi, Mexico and Sierra Leone. Overall, our analyses indicate significant declines in first antenatal care visits in Haiti (18% drop) and Sierra Leone (32% drop) and facility-based deliveries in all countries except Malawi from March to December 2020. Different strategies were adopted to maintain continuity of maternal health services, including communication campaigns, continuity of community health worker services, human resource capacity building to ensure compliance with international and national guidelines for front-line health workers, adapting spaces for safe distancing and ensuring the availability of personal protective equipment. We employ a local lens, providing prepandemic context and reporting results and strategies by country, to highlight the importance of developing context-specific interventions to design effective mitigation strategies.

BackgroundPoint-of-care antigen-detecting rapid diagnostic tests (RDTs) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) represent a scalable tool for SARS-CoV-2 infections surveillance. Data on their performance in real-world community settings is paramount for their implementation.MethodWe evaluated the accuracy of CareStart™ COVID-19 Antigen test (CareStart) in a testing site in Holyoke, Massachusetts. We compared CareStart to a SARS-CoV-2 reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) reference, using anterior nasal swab samples. We calculated the sensitivity, specificity, and expected positive and negative predictive values at different SARS-CoV-2 prevalence estimates.ResultsWe performed 666 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 to 64.9% (95% CI: 47.5 – 79.8).ConclusionsCareStart 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.

Massachusetts' abortion parental consent law is associated with clinically significant delays, which may constrain options available for the clinical management of minors undergoing abortion., OBJECTIVE: To describe individual-level delay in obtaining abortion associated with use of the Massachusetts judicial bypass system, which legal minors (aged 17 years or younger) use to obtain abortion without consent of a parent or legal guardian in the setting of Massachusetts' parental consent law for abortion. METHODS: We conducted a retrospective cohort study of 2,026 abortions among minors at a large, statewide network of abortion clinics between 2010 and 2016. Delay was defined as the number of calendar days between the minor's first call to the clinic to schedule an abortion, and the day the abortion was received. RESULTS: In the study population, 1,559 (77%) abortions were obtained with parental consent and 467 (23%) using judicial bypass. Abortions after judicial bypass were more common among minors identifying as Hispanic, non-Hispanic black, or other race, those of low socioeconomic status (as indicated by having Medicaid insurance) and those with a prior birth or prior abortion (all P

Sickle cell disease (SCD) is the most common monogenic disorder in the world. Notably, there is extensive clinical heterogeneity in SCD that cannot be fully accounted for by known factors, and in particular, the extent to which the phenotypic diversity of SCD can be explained by genetic variation has not been reliably quantified. Here, in a family-based cohort of 449 patients with SCD and 755 relatives, we first show that 5 known modifiers affect 11 adverse outcomes in SCD to varying degrees. We then utilize a restricted maximum likelihood procedure to estimate the heritability of 20 hematologic traits, including fetal hemoglobin (HbF) and white blood cell count (WBC), in the clinically relevant context of inheritance from healthy carriers to SCD patients. We report novel estimations of heritability for HbF at 31.6% (±5.4%) and WBC at 41.2% (±6.8%) in our cohort. Finally, we demonstrate shared genetic bases between HbF, WBC, and other hematologic traits, but surprisingly little overlap between HbF and WBC themselves. In total, our analyses show that HbF and WBC have significant heritable components among individuals with SCD and their relatives, demonstrating the value of using family-based studies to better understand modifiers of SCD.

A variety of state-level restrictions were placed on abortion care in response to the COVID-19 pandemic, leading to drops in utilization and delays in time to abortion. Other pandemic-related factors also may have impacted receipt of abortion care, potentially exacerbating existing barriers to care. Massachusetts is an ideal setting to study the impact of these other pandemic-related factors on abortion care utilization because there was no wide-scale abortion policy change in response to the pandemic.This study aimed to evaluate the impact of the COVID-19 pandemic on abortion care utilization and disparities in utilization by patient age in Massachusetts.Using the electronic medical records from all abortions that occurred at the Planned Parenthood League of Massachusetts from May 1, 2017 through December 31, 2020 (N=35,411), we performed time series modeling to estimate monthly changes in the number of abortions from the expected counts during the COVID-19 pandemic. We also assessed if legal minors (18 years) experienced delays in time to abortion, based on gestational age at procedure, and whether minors were differentially impacted by the pandemic.There were 1725 less abortions than expected, corresponding to a 20% drop, from March 2020 to December 2020 (95% prediction interval, -2025 to -1394) with 888 less (20% reduction) abortions among adults, 792 (20% reduction) less among young adults, and 45 (27% reduction) among minors. Adults and young adults experienced significant reductions in the number of abortions beginning in March 2020, whereas decreases among minors did not begin until July 2020. The rate of abortions occurring ≥12 weeks gestational age was unchanged during the COVID-19 pandemic among minors (adjusted rate ratio, 0.92; 95% confidence interval, 0.55-1.51) and among adults (adjusted rate ratio, 0.92; 95% confidence interval, 0.78-1.09). Young adults had a lower rate of second trimester abortion during the pandemic (adjusted rate ratio, 0.79; 95% confidence interval, 0.66-0.95).Despite uninterrupted abortion service provision, abortion care utilization decreased markedly in Massachusetts during the pandemic. There was no evidence of an increase in second trimester abortions in any age group. Further research is needed to determine if a decline in the pregnancy rate or other factors, such as financial and travel barriers, fear of infection, or privacy concerns, may have contributed to this decline.

Background Early detection of SARS-CoV-2 circulation is imperative to inform local public health response. However, it has been hindered by limited access to SARS-CoV-2 diagnostic tests and testing infrastructure. In regions with limited testing capacity, routinely collected health data might be leveraged to identify geographical locales experiencing higher than expected rates of COVID-19-associated symptoms for more specific testing activities. Methods We developed syndromic surveillance tools to analyse aggregated health facility data on COVID-19-related indicators in seven low- and middle-income countries (LMICs), including Liberia. We used time series models to estimate the expected monthly counts and 95% prediction intervals based on 4 years of previous data. Here, we detail and provide resources for our data preparation procedures, modelling approach and data visualisation tools with application to Liberia. Results To demonstrate the utility of these methods, we present syndromic surveillance results for acute respiratory infections (ARI) at health facilities in Liberia during the initial months of the COVID-19 pandemic (January through August 2020). For each month, we estimated the deviation between the expected and observed number of ARI cases for 325 health facilities and 15 counties to identify potential areas of SARS-CoV-2 circulation. Conclusions Syndromic surveillance can be used to monitor health facility catchment areas for spikes in specific symptoms which may indicate SARS-CoV-2 circulation. The developed methods coupled with the existing infrastructure for routine health data systems can be leveraged to monitor a variety of indicators and other infectious diseases with epidemic potential.

Summary Standard methods for inference about direct and indirect effects require stringent no-unmeasured-confounding assumptions which often fail to hold in practice, particularly in observational studies. The goal of the paper is to introduce a new form of indirect effect, the population intervention indirect effect, that can be non-parametrically identified in the presence of an unmeasured common cause of exposure and outcome. This new type of indirect effect captures the extent to which the effect of exposure is mediated by an intermediate variable under an intervention that holds the component of exposure directly influencing the outcome at its observed value. The population intervention indirect effect is in fact the indirect component of the population intervention effect, introduced by Hubbard and Van der Laan. Interestingly, our identification criterion generalizes Judea Pearl’s front door criterion as it does not require no direct effect of exposure not mediated by the intermediate variable. For inference, we develop both parametric and semiparametric methods, including a novel doubly robust semiparametric locally efficient estimator, that perform very well in simulation studies. Finally, the methods proposed are used to measure the effectiveness of monetary saving recommendations among women enrolled in a maternal health programme in Tanzania.

Objective Describe the array of gender identities among procedural abortion patients. Study Design Cross-sectional survey of abortion patients in three clinics in Massachusetts. Following aspiration abortion procedures and prior to discharge, patients self-administered a survey on a tablet. Results From November 2017 through July 2018, 1,553 aspiration abortion patients completed the survey (participation rate: 82%). Patients reported several gender identities. Non-binary (0.4%) and agender (0.4%) were the most common identities after female (91.1%) and woman (6.0%). Overall, 2.7% of patients identified as a gender other than female or woman. Conclusion Aspiration abortion patients have a variety of gender identities. To promote quality of care for all patients, abortion providers can ensure their names, marketing materials, patient forms, and clinical environments are gender inclusive rather than focusing on women’s health.

BackgroundLot 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 in Haiti.DevelopmentAs part of the standard LQAS procedure, the user specifies allowable classification errors for the system, which is defined by a sample size and decision rule. We show that when an imperfect diagnostic test is used, the classification errors are larger than specified. We derive a modified procedure, LQAS-IMP, that accounts for the sensitivity and specificity of a diagnostic test to yield correct classification errors.ApplicationAt Zanmi Lasante health facilities in Haiti, the goal was to assess the prior circulation of COVID-19 among healthcare workers (HCWs) using a limited number of antibody tests. As the COVID-19 antibody tests were known to have imperfect diagnostic accuracy, we used the LQAS-IMP procedure to define valid systems for sampling at eleven hospitals in Haiti.ConclusionsThe 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 COVID-19 antibodies.Key MessagesIt is imperative to account for the sensitivity and specificity of the diagnostic test in serosurveillance studies.For prevalence estimation and inference, adjustments for imperfect testing properties are available to researchers. However, no adjustments currently exist for the design of Lot Quality Assurance Sampling systems, resulting in invalid classification systems when used with an imperfect diagnostic test.When sensitivity and specificity of a diagnostic test is known, Lot Quality Assurance Sampling for Imperfect Tests should be used to determine the sample size and decision rule for a system as it will result in valid classification errors.

Purpose The purpose of the study was to assess uptake of postabortion contraception across changes in insurance regulations and insurance type used on the day of abortion, accounting for demographic characteristics and consent type (parental vs. judicial) for abortion among Massachusetts adolescents. Methods We conducted a retrospective record review of 1,375 minors (≤17 years) presenting for their first lifetime surgical abortion at a statewide network of abortion clinics between 2010 and 2016. Postabortion contraceptive method was defined as long-acting reversible contraception (LARC) placed onsite, short-acting reversible contraception (SARC) provided onsite, or no method received. Results The proportion of minors leaving with no method dropped from 38% in 2010 to 21% in 2016, while LARC placement increased from 19% to 45%. No difference was observed by consent type. Both LARC and SARC were more prevalent among minors with Medicaid or private insurance compared to those not using insurance on the day of abortion. In a multinomial regression model accounting for consent type and demographic characteristics, minors who received care during the final epoch of the study (relative risk ratio [RRR] = 3.30; 95% confidence interval [CI]: 2.23–4.88) or used private insurance (RRR = 3.91; 95% CI: 2.24–6.84) or Medicaid (RRR = 5.54; 95% CI: 3.37–9.11) on the day of service had significantly higher relative risk of receiving LARC versus no method (p Conclusions Postabortion contraceptive uptake changed over time. Disparately low LARC uptake among minors not using insurance to pay for their abortions highlights a need to ensure equitable access to all methods, regardless of ability to pay.

Introduction To compare immediate versus delayed initiation of medication abortion (MAB) among patients with undesired pregnancy of unknown location (PUL). Method This retrospective cohort study used electronic medical record data from Planned Parenthood League of Massachusetts (2014–2019) to compare time to diagnosis of pregnancy location among patients requesting MAB with an LMP of Results Of 5619 eligible MAB visits, 452 patients had a PUL (8.0%). Three patients underwent uterine aspiration, 55 same-day-start and 394 delay-for-diagnosis. Among the 359 (80%) with a pregnancy location diagnosis, median days-to-diagnosis was 5.0 (IQR: [5.0, 13.0]) for same-day-start and 8.0 (IQR: [3.0, 9.0]) for delay-for-diagnosis (p = 0.020). Thirty patients (6.6%), all in the delay-for-diagnosis group, were treated for ectopic pregnancy and 4 ectopics ruptured. Twenty-four percent (13/55) of same-day-start and 33% (129/394) of delay-for-diagnosis patients visited an emergency department (p = 0.216). Of those who received mifepristone, the ongoing pregnancy rate was 9.1% (5/55) for same-day-start versus 1.9% (3/161) for delay-for-diagnosis (p = 0.027). Loss-to-follow-up without a diagnosis was 21.8% versus 16.2% among same-day versus delayed-for-diagnosis, respectively (p = 0.447). Conclusions In patients with undesired PUL, immediate initiation of medication abortion excludes ectopic pregnancy more rapidly than delay-for-diagnosis, with no impact on follow-up rates, but lower abortion efficacy.

STUDY OBJECTIVE To evaluate the long-term impact of laparoscopic excision of endometriosis on quality of life through pain reduction as measured by the Endometriosis Health Profile-30 (EHP-30) in uterine-sparing (preservation of the uterus and at least 1 ovary) and nonuterine-sparing (removal of the uterus) surgery. DESIGN Cohort study. SETTING Academic medical center. PATIENTS Sixty-one women who had undergone laparoscopic excision of endometriosis for pelvic pain were enrolled in a tissue-procurement study. INTERVENTIONS Patients who had previously completed an EHP-30 preoperatively and at 4 weeks postoperatively were mailed a copy of the EHP-30 2.6 to 6.8 years after their index surgery. MEASUREMENTS AND MAIN RESULTS The primary outcome was quality of life as measured by changes in the EHP-30 scores before their index surgery and those measured weeks and years later. The secondary outcome was a comparison of the EHP-30 scores between patients who underwent excision of endometriosis alone and those who underwent excision of endometriosis with hysterectomy +/- oophorectomy. From 2011 to 2015, 61 women underwent laparoscopic excision of endometriosis for pelvic pain. Forty-six of the 61 patients completed the EHP-30 for a response rate of 75%. The patients demonstrated significant improvement in all 5 scales of the EHP-30 (pain, control and powerlessness, emotional well-being, social support, and self-image) at 4 weeks postoperatively (p

The use of causal mediation analysis to evaluate the pathways by which an exposure affects an outcome is widespread in the social and biomedical sciences. Recent advances in this area have established formal conditions for identification and estimation of natural direct and indirect effects. However, these conditions typically involve stringent assumptions of no unmeasured confounding and that the mediator has been measured without error. These assumptions may fail to hold in many practical settings where mediation methods are applied. The goal of this article is two-fold. First, we formally establish that the natural indirect effect can in fact be identified in the presence of unmeasured exposure-outcome confounding provided there is no additive interaction between the mediator and unmeasured confounder(s). Second, we introduce a new estimator of the natural indirect effect that is robust to both classical measurement error of the mediator and unmeasured confounding of both exposure-outcome and mediator-outcome relations under certain no interaction assumptions. We provide formal proofs and a simulation study to illustrate our results. In addition, we apply the proposed methodology to data from the Harvard President's Emergency Plan for AIDS Relief (PEPFAR) program in Nigeria.

Recent advances in causal mediation analysis have formalized conditions for estimating direct and indirect effects in various contexts. These approaches have been extended to a number of models for survival outcomes including accelerated failure time models, which are widely used in a broad range of health applications given their intuitive interpretation. In this setting, it has been suggested that under standard assumptions, the "difference" and "product" methods produce equivalent estimates of the indirect effect of exposure on the survival outcome. We formally show that these two methods may produce substantially different estimates in the presence of censoring or truncation, due to a form of model misspecification. Specifically, we establish that while the product method remains valid under standard assumptions in the presence of independent censoring, the difference method can be biased in the presence of such censoring whenever the error distribution of the accelerated failure time model fails to be collapsible upon marginalizing over the mediator. This will invariably be the case for most choices of mediator and outcome error distributions. A notable exception arises in case of normal mediator-normal outcome where we show consistency of both difference and product estimators in the presence of independent censoring. These results are confirmed in simulation studies and two data applications.

Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into nonoverlapping groups such that outcomes of units in separate groups are independent. In this article, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i) nor (ii) holds. The proposed approach allows both for arbitrary forms of interference, whereby the outcome of a unit may depend on interventions received by other units with whom a network path through connected units exists; and long range dependence, whereby outcomes for any two units likewise connected by a path in the network may be dependent. Under network versions of consistency and no unobserved confounding, inference is made tractable by an assumption that the networks outcome, treatment and covariate vectors are a single realization of a certain chain graph model. This assumption allows inferences about various network causal effects via the auto-g-computation algorithm, a network generalization of Robins’ well-known g-computation algorithm previously described for causal inference under assumptions (i) and (ii). Supplementary materials for this article are available online.