Linked Lives: Does Disability and Marital Quality Influence Risk of Marital Dissolution among Older Couples?

Using fourteen waves of data from the Health and Retirement Study (HRS), a longitudinal panel survey with respondents in the United States, this research explores whether marital quality—as measured by reports of enjoyment of time together—influences risk of divorce or separation when either spouse acquires basic care disability. Discrete-time event history models with multiple competing events were estimated using multinomial logistic regression. Respondents were followed until they experienced the focal event (i.e., divorce or separation) or right-hand censoring (i.e., a competing event or were still married at the end of observation). Disability among wives was predictive of divorce/separation in the main effects model. Low levels of marital quality (i.e., enjoy time together) were associated with marital dissolution. An interaction between marital quality and disability yielded a significant association among couples where at least one spouse acquired basic care disability. For couples who acquired disability, those who reported low enjoyment were more likely to divorce/separate than those with high enjoyment; however, the group with the highest predicted probability were couples with low enjoyment, but no acquired disability.

Hospital outcomes of community-acquired COVID-19 versus influenza: Insights from the Swiss hospital-based surveillance of influenza and COVID-19

Background Since the onset of the COVID-19 pandemic, the disease has frequently been compared with seasonal influenza, but this comparison is based on little empirical data. Aim This study compares in-hospital outcomes for patients with community-acquired COVID-19 and patients with community-acquired influenza in Switzerland. Methods This retrospective multi-centre cohort study includes patients > 18 years admitted for COVID-19 or influenza A/B infection determined by RT-PCR. Primary and secondary outcomes were in-hospital mortality and intensive care unit (ICU) admission for patients with COVID-19 or influenza. We used Cox regression (cause-specific and Fine-Gray subdistribution hazard models) to account for time-dependency and competing events with inverse probability weighting to adjust for confounders. Results In 2020, 2,843 patients with COVID-19 from 14 centres were included. Between 2018 and 2020, 1,381 patients with influenza from seven centres were included; 1,722 (61%) of the patients with COVID-19 and 666 (48%) of the patients with influenza were male (p < 0.001). The patients with COVID-19 were younger (median 67 years; interquartile range (IQR): 54–78) than the patients with influenza (median 74 years; IQR: 61–84) (p < 0.001). A larger percentage of patients with COVID-19 (12.8%) than patients with influenza (4.4%) died in hospital (p < 0.001). The final adjusted subdistribution hazard ratio for mortality was 3.01 (95% CI: 2.22–4.09; p < 0.001) for COVID-19 compared with influenza and 2.44 (95% CI: 2.00–3.00, p < 0.001) for ICU admission. Conclusion Community-acquired COVID-19 was associated with worse outcomes compared with community-acquired influenza, as the hazards of ICU admission and in-hospital death were about two-fold to three-fold higher.

School-based HPV Vaccination: The Challenges in a Brazilian Initiative

Objective The present study assesses the implementation and the impact after 2 years of a school-based human papillomavirus (HPV) vaccination program in a Brazilian city. Methods A prospective study assessing the implementation of the program, offering quadrivalent HPV vaccine in two annual doses to girls and boys aged from 9 to 10 years old. The program was started in the city of Indaiatuba, state of São Paulo, Brazil, in 2018, and had authorization from the National Immunization Program. The number of HPV vaccine first doses applied and the coverage in 2018 was calculated and compared to the year 2017. There were described events that have influenced the results. Results The program invited 4,878 children through schools (87.1% of the target population), and 7.5% refused vaccination. Several concurrent events required or competed for health professionals of the vaccination teams. The coverage of the first dose (between 9 and 10 years old) was 16.1% in 2017 and increased to 50.5% in 2018 (p < 0.0001). The first dose in all ages increased 78% in 2018 compared with 2017 (6,636/3,733). Competing demands over the program continued in 2019, and the first dose coverage dropped (26.9%). For 2020, a municipal law instituted school-based vaccination and the creation of dedicated teams for vaccination, and these strategies are waiting to be tested. Conclusion School-based annual HPV vaccination in children between 9 and 10 years old was feasible and increased vaccination coverage, regardless of gender, although the program was vulnerable to competing events.

Survival Curves Projection and Benefit Time Points Estimation using a New Statistical Method

Survival analysis concerns the analysis of time-to-event data and it is essential to study in fields such as oncology, the survival function, S(t), calculation is usually used, but in the presence of competing risks (presence of competing events), is necessary introduce other statistical concepts and methods, as is the Cumulative incidence function CI(t). This is defined as the proportion of subjects with an event time less than or equal to. The present study describe a methodology that enables to obtain numerically a shape of CI(t) curves and estimate the benefit time points (BTP) as the time (t) when a 90, 95 or 99% is reached for the maximum value of CI(t). Once you get the numerical function of CI(t), it can be projected for an infinite time, with all the limitations that it entails. To do this task the R function Weibull.cumulative.incidence() is proposed. In a first step these function transforms the survival function (S(t)) obtained using the Kaplan–Meier method to CI(t). In a second step the best fit function of CI(t) is calculated in order to estimate BTP using two procedures, 1) Parametric function: estimates a Weibull growth curve of 4 parameters by means a non-linear regression (nls) procedure or 2) Non parametric method: using Local Polynomial Regression (LPR) or LOESS fitting. Two examples are presented and developed using Weibull.cumulative.incidence() function in order to present the method. The methodology presented will be useful for performing better tracking of the evolution of the diseases (especially in the case of the presence of competitive risks), project time to infinity and it is possible that this methodology can help identify the causes of current trends in diseases like cancer. We think that BTP points can be important in large diseases like cardiac illness or cancer to seek the inflection point of the disease, treatment associate or speculate how is the course of the disease and change the treatments at those points. These points can be important to take medical decisions furthermore.

Non-Infectious Morbidity during Pediatric Acute Myeloid Leukemia Chemotherapy in the Netherlands: A 15-Year Overview

Introduction Together with considerable improvement in diagnostics and supportive care measures, treatment intensification has contributed to the improvement of pediatric acute myeloid leukemia (AML) survival outcomes. However, treatment-related toxicity is a consequence of intensified chemotherapy regimens, which has profound implications for both morbidity and mortality. Despite the known devastating impact of all types of toxicity on quality of life (QoL), studies presenting the different forms of non-infectious toxicity prevalent during the most recent Dutch pediatric AML protocols are lacking given that only prior protocols have been evaluated in this manner as of yet. Furthermore, the literature on non-infectious toxicity incidence in pediatric AML patients is relatively scarce. Therefore, the objectives of this study were to: 1) evaluate the cumulative incidence (C.I.) of severe, but non-lethal, non-infectious toxicity during AML treatment according to the last three consecutive protocols of the Dutch Childhood Oncology Group (DCOG), and 2) compare treatment-related toxicity frequencies between protocols. Methods A retrospective chart review was performed on 245 Dutch patients diagnosed with de novo AML (acute promyelocytic leukemia, myeloid leukemia of Down Syndrome, and secondary AML were excluded) and treated according to ANLL-97/AML-12 (1998-2005) n=118, AML-15 (2005-2010) n=60, or DB-AML-01 (2010-2013) n=67. Table 1 details protocol specifics, including drug dosages per course. Grade 3-4 toxicities, including hematological toxicity, cardiotoxicity, respiratory toxicity, mucositis, typhlitis, nephrotoxicity, hepatotoxicity, neurotoxicity, pain, allergic anaphylactic reaction, and elevation of alanineaminotransferase or bilirubin were defined according to Common Terminology Criteria for Adverse Event version 4.0, excluding infectious toxicity. Grade 5 toxicity (treatment-related mortality) was beyond the scope of this study. Intensive care unit (ICU) admission data was additionally assessed. Toxicity C.I.s were determined via competing events analyses. Relapse and death were considered competing events. Patients were censored at time of stem cell transplantation. Per-protocol comparisons were conducted via Chi-square test, due to lack of sufficient power required to calculate C.I.s. Results Median age at diagnosis was 6.0 years [interquartile range (IQR) 1.0-12.0], 58% were male. Mucositis was the most frequent form of non-hematological toxicity with a C.I. of 86.1% (standard error (S.E.) 3.5%), followed by hepatotoxicity (C.I. 27.4%, S.E. 2.9%), and respiratory toxicity (C.I. 24.6%, S.E. 3.0%) (Table 2). Eighty-three patients (C.I. 33.9%, S.E. 3.0%) were admitted to the ICU at least once, for a median of 6.0 days [IQR 2.0-15.0] (Table 2). Relatively more blood transfusions (both erythrocyte and thrombocyte) were administered to patients treated according to AML-15 (98.3%) and DB-AML-01 (97.0%) compared to AML-12 (86.4%) (p=0.01 and p=0.02, respectively). Relatively more patients suffered from severe mucositis during DB-AML-01 compared to AML-12 (43.4% vs. 25.4%, p=0.01). More patients treated according to AML-15 had bilirubin levels &gt;3x upper limit of normal (ULN) compared to those treated according to AML-12 (13.3% vs. 3.4%, p=0.01). There were no differences in non-infectious toxicity frequency between AML-15 and DB-AML-01. Other forms of toxicity did not differ significantly between protocols. The percentage of patients admitted to the ICU at least once during treatment was 33.9% in AML-12, 26.7% in AML-15, and 40.3% in DB-AML-01. Conclusions The high incidence of severe short- and long-term toxicities during pediatric AML therapy poses substantial challenges for patients, families, and care providers. Mucositis was the most common form of non-hematological, non-infectious toxicity in the whole cohort and across all Dutch protocols. Toxicities were more prevalent during the more recent protocols (AML-15 and DB-AML-01) compared to AML-12. Therefore, our findings are important in that they substantiate the need to optimize pediatric AML care in a manner which decreases treatment-related toxicity and QoL impairment. High treatment-related morbidity rates stress the urge to improve supportive care and develop less toxic treatment, while maintaining efficacy. Figure 1 Figure 1. Disclosures Zwaan: Sanofi: Consultancy.

Subdistribution-Based Imputation for Deep Survival Analysis with Competing Events

With the popularity of deep neural networks (DNNs) in recent years, many researchers have proposed DNNs for the analysis of survival data (time-to-event data). These networks learn the distribution of survival times directly from the predictor variables without making strong assumptions on the underlying stochastic process. In survival analysis, it is common to observe several types of events, also called competing events. The occurrences of these competing events are usually not independent of one another and have to be incorporated in the modeling process in addition to censoring. In classical survival analysis, a popular method to incorporate competing events is the subdistribution hazard model, which is usually fitted using weighted Cox regression. In the DNN framework, only few architectures have been proposed to model the distribution of time to a specific event in a competing events situation. These architectures are characterized by a separate subnetwork/pathway per event, leading to large networks with huge amounts of parameters that may become difficult to train. In this work, we propose a novel imputation strategy for data preprocessing that incorporates the subdistribution weights derived from the classical model. With this, it is no longer necessary to add multiple subnetworks to the DNN to handle competing events. Our experiments on synthetic and real-world datasets show that DNNs with multiple subnetworks per event can simply be replaced by a DNN designed for a single-event analysis without loss in accuracy.

Impact of assistance on peritonitis due to breach in aseptic procedure in diabetic patients: A cohort study with the RDPLF data

Background: Diabetic patients often have physical impairment that could lead to manipulation errors in peritoneal dialysis (PD) and touch contamination. Nurse assistance in diabetic PD patients is known to help prevent peritonitis. We made the hypothesis that this lower risk of peritonitis was observed thanks to prevention of breach in aseptic procedure. We evaluated the impact of nurse-assisted PD on specific causes of peritonitis, especially on peritonitis due to a breach in aseptic procedure. Methods: This was a retrospective observational study of the data from the French Language Peritoneal Dialysis Registry. All diabetic patients older than age 18 years starting PD in France between 1 January 2012 and 31 December 2015 were included in the study. The event of interest was the first peritonitis event due to a breach in aseptic procedure. Death, kidney transplantation and peritonitis due to another mechanism were considered as competing events. We examined the association of the covariates with all the possible outcomes using a subdistribution hazard model developed for survival analysis in the presence of competing risks. Results: Four thousand one hundred one diabetic patients incident in PD were included in the study. At least one peritonitis event occurred in 1611 patients over the study period. A breach in aseptic procedure was reported in 441/1611 cases (27.3%): 209/575 (36.3%) in the self-care PD group, 56/217 (25.8%) in the family-assisted PD group and 176/819 (21.5%) in the nurse-assisted PD group. Both nurse and family assistance were associated with a lower risk of peritonitis due to breach in aseptic procedure in bivariate analysis. After adjustment on age, modified Charlson index, sex and diabetic nephropathy, patients treated by nurse-assisted PD (subdistribution hazard ratio (sd-HR) 0.52, 95% confidence interval (CI) 0.40–0.67) and those treated by family-assisted PD (sd-HR 0.70, 95% CI 0.51–0.95) had a lower likelihood of peritonitis due to a connection error compared to self-care PD in multivariate analysis. The modality of assistance was not associated with other causes of peritonitis in the multivariate analysis. Conclusion: While both nurse-assisted PD and family-assisted PD were associated with lower risk of peritonitis due to a breach in aseptic procedure compared to self-care PD in our study, the protective effect was greater with nurse assistance.

Insights into the association of ACEIs/ARBs use and COVID-19 prognosis: a multistate modelling study of nationwide hospital surveillance data from Belgium

ObjectivesThe widespread use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) by patients with chronic conditions raised early concerns on the potential exacerbation of COVID-19 severity and fatality. Previous studies addressing this question have used standard methods that may lead to biased estimates when analysing hospital data because of the presence of competing events and event-related dependency. We investigated the association of ACEIs/ARBs’ use with COVID-19 disease outcomes using time-to-event data in a multistate setting to account for competing events and minimise bias.SettingNationwide surveillance data from 119 Belgian hospitals.ParticipantsMedical records of 10 866 patients hospitalised from 14 March 2020to 14 June 2020 with a confirmed SARS-CoV-19 infection and information about ACEIs/ARBs’ use.Primary outcome measureMultistate, multivariate Cox-Markov models were used to estimate the hazards of patients transitioning through health states from admission to discharge or death, along with transition probabilities calculated by combining the baseline cumulative hazard and regression coefficients.ResultsAfter accounting for potential confounders, there was no discernable association between ACEIs/ARBs’ use and transfer to intensive care unit (ICU). Contrastingly, for patients without ICU transfer, ACEIs/ARBs’ use was associated with a modest increase in recovery (HR 1.07, 95% CI 1.01 to 1.13, p=0.027) and reduction in fatality (HR 0.83, 95% CI 0.75 to 0.93, p=0.001) transitions. For patients transferred to ICU admission, no evidence of an association between ACEIs/ARBs’ use and recovery (HR 1.16, 95% CI 0.97 to 1.38, p=0.098) or in-hospital death (HR 0.91, 95% CI 0.73 to 1.12, p=0.381) was observed. Male gender and older age were significantly associated with higher risk of ICU admission or death. Chronic cardiometabolic comorbidities were also associated with less recovery.ConclusionsFor the first time, a multistate model was used to address magnitude and direction of the association of ACEIs/ARBs’ use on COVID-19 progression. By minimising bias, this study provided a robust indication of a protective, although modest, association with recovery and survival.

A generalized theory of separable effects in competing event settings

In competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However, effects of treatment on the competing event may indirectly contribute to this total effect, complicating its interpretation. We previously proposed the separable effects to define direct and indirect effects of the treatment on the event of interest. This definition was given in a simple setting, where the treatment was decomposed into two components acting along two separate causal pathways. Here we generalize the notion of separable effects, allowing for interpretation, identification and estimation in a wide variety of settings. We propose and discuss a definition of separable effects that is applicable to general time-varying structures, where the separable effects can still be meaningfully interpreted as effects of modified treatments, even when they cannot be regarded as direct and indirect effects. For these settings we derive weaker conditions for identification of separable effects in studies where decomposed, or otherwise modified, treatments are not yet available; in particular, these conditions allow for time-varying common causes of the event of interest, the competing events and loss to follow-up. We also propose semi-parametric weighted estimators that are straightforward to implement. We stress that unlike previous definitions of direct and indirect effects, the separable effects can be subject to empirical scrutiny in future studies.