Heterogeneous Treatment and Spillover Effects under Clustered Network Interference

The bulk of causal inference studies rules out the presence of interference between units. However, in many real-world settings units are interconnected by social, physical or virtual ties and the effect of a treatment can spill from one unit to other connected individuals in the network. In these settings, interference should be taken into account to avoid biased estimates of the treatment effect, but it can also be leveraged to save resources and provide the intervention to a lower percentage of the population where the treatment is more effective and where the effect can spill over to other susceptible individuals. In fact, different people might respond differently not only to the treatment received but also to the treatment received by their network contacts. Understanding the heterogeneity of treatment and spillover effects can help policy-makers in the scale-up phase of the intervention, it can guide the design of targeting strategies with the ultimate goal of making the interventions more cost-effective, and it might even allow generalizing the level of treatment spillover effects in other populations. In this paper, we develop a machine learning method that makes use of tree-based algorithms and an Horvitz-Thompson estimator to assess the heterogeneity of treatment and spillover effects with respect to individual, neighborhood and network characteristics in the context of clustered network interference. We illustrate how the proposed binary tree methodology performs in a Monte Carlo simulation study. Additionally, we provide an application on a randomized experiment aimed at assessing the heterogeneous effects of information sessions on the uptake of a new weather insurance policy in rural China

Physical and Stressful Psychological Impacts of Prolonged Personal Protective Equipment Use During the COVID-19 Pandemic: A Cross-Sectional Survey Study

BACKGROUND: Healthcare workers (HCWs) caring for COVID-19 infected patients are exposed to stressful and traumatic events with potential for severe and sustained adverse mental and physical health consequences. Our aim was to assess the magnitude of physical and mental health outcomes of HCWs due to the prolonged use of personal protective equipment (PPE) treating COVID-19 patients. METHODS: This cross-sectional study assessed the symptoms of stress, anxiety, insomnia, and psychological resilience using the Stress and Anxiety to Viral Epidemics (SAVE) scale, Insomnia Severity Index (ISI), and Resilience Scale (RS), respectively, in Italy between 1st February and 31st March 2022. The physical outcomes reported included vertigo, dyspnea, nausea, micturition desire, retroauricular pain, thirst, discomfort at work, physical fatigue, and thermal stress. The relationships between prolonged PPE use and psychological outcomes and physical discomforts were analyzed using Generalized Linear Models (GLMs). We calculated the factor mean scores and a binary outcome to measure study outcomes. FINDINGS: We found that 23% of the respondents reported stress related symptoms, 33% anxiety, 43% moderate to severe insomnia, and 67% reported moderate to very low resilience. The GLMs suggested that older people (\u3e55 years old) are less likely to suffer from stress compared to younger people (\u3c35 \u3ey.o); conversely, HCW aged more than 35 years are more inclined to suffer from insomnia than younger people (\u3c35 \u3ey.o). Female HCW reported a lower probability of resilience than males. University employed HCWs were less likely to report anxiety than those who worked in a community hospital. The odds of suffering from insomnia for social workers was significantly higher than for other HCWs. Female HCW\u3e3 years old, enrolled in training programs for nursing, social work, technical training and other healthcare professionals increased the probability of reported physical discomforts. HCW that worked on non COVID-19 wards and used PPE for low-medium exposure level, were at lower risks for lasting physical side effects as compared to the HCW who worked in high-risk PPE intense, COVID-19 environments. INTERPRETATION: The study suggests that frontline HCWs who had extensive PPE exposure while directly engaged in the diagnosis, treatment, and care for patients with COVID-19 are at significant risks for lasting physical and psychological harm and distress

Essays on causal inference and complex networks

This dissertation is a collection of articles that develop statistical methods for performing causal inference on network data. In bridging these two themes, causal inference and complex networks, the thesis develops four complementary methodological contributions in two main settings that often arise in network data: (i) both the treatment and the outcome are measured at the individual level but the treatment spills over through the network connections; (ii) both the treatment and outcomes are measured at dyadic level. In the first setting, it elaborates innovative techniques for assessing the direct and spillover effects of an intervention in a population of connected units, where the potential outcome of an agent is affected by the treatment status of other interfering agents. In particular, the articles featured in the dissertation expand the existing literature by developing methods that are useful for (i) estimating the effect of an observational multi-valued intervention in a sample of units connected through a weighted network; (ii) detecting and estimating heterogeneous treatment and spillover effects in presence of units who belong to exogenous clusters, and whose interactions are described by cluster-specific networks; (iii) accounting for hidden treatment diffusion processes in a partially unobserved network. In the second setting, the dissertation employs the potential outcomes framework to analyze causal relationships in network formation processes. Specifically, it develops an estimator for the causal effect that the existence of links in a “treatment network” has on the formation of links on an “outcome network,” with both networks being directed

Learning prevalent patterns of co-morbidities in multichronic patients using population-based healthcare data

Abstract The prevalence of longstanding chronic diseases has increased worldwide, along with the average age of the population. As a result, an increasing number of people is affected by two or more chronic conditions simultaneously, and healthcare systems are facing the challenge of treating multimorbid patients effectively. Current therapeutic strategies are suited to manage each chronic condition separately, without considering the whole clinical condition of the patient. This approach may lead to suboptimal clinical outcomes and system inefficiencies (e.g. redundant diagnostic tests and inadequate drug prescriptions). We develop a novel methodology based on the joint implementation of data reduction and clustering algorithms to identify patterns of chronic diseases that are likely to co-occur in multichronic patients. We analyse data from a large adult population of multichronic patients living in Tuscany (Italy) in 2019 which was stratified by sex and age classes. Results demonstrate that (i) cardio-metabolic, endocrine, and neuro-degenerative diseases represent a stable pattern of multimorbidity, and (ii) disease prevalence and clustering vary across ages and between women and men. Identifying the most common multichronic profiles can help tailor medical protocols to patients’ needs and reduce costs. Furthermore, analysing temporal patterns of disease can refine risk predictions for evolutive chronic conditions

Association of Early Norepinephrine Administration With 24-Hour Mortality Among Patients With Blunt Trauma and Hemorrhagic Shock

International audienceImportance Hemorrhagic shock is a common cause of preventable death after injury. Vasopressor administration for patients with blunt trauma and hemorrhagic shock is often discouraged. Objective To evaluate the association of early norepinephrine administration with 24-hour mortality among patients with blunt trauma and hemorrhagic shock. Design, Setting, and Participants This retrospective, multicenter, observational cohort study used data from 3 registries in the US and France on all consecutive patients with blunt trauma from January 1, 2013, to December 31, 2018. Patients were alive on admission with hemorrhagic shock, defined by prehospital or admission systolic blood pressure less than 100 mm Hg and evidence of hemorrhage (ie, prehospital or resuscitation room transfusion of packed red blood cells, receipt of emergency treatment for hemorrhage control, transfusion of >10 units of packed red blood cells in the first 24 hours, or death from hemorrhage). Blunt trauma was defined as any exposure to nonpenetrating kinetic energy, collision, or deceleration. Statistical analysis was performed from January 15, 2021, to February 22, 2022. Exposure Continuous administration of norepinephrine in the prehospital environment or resuscitation room prior to hemorrhage control, according to European guidelines. Main Outcomes and Measures The primary outcome was 24-hour mortality, and the secondary outcome was in-hospital mortality. The average treatment effect (ATE) of early norepinephrine administration on 24-hour mortality was estimated according to the Rubin causal model. Inverse propensity score weighting and the doubly robust approach with 5 distinct analytical strategies were used to determine the ATE. Results A total of 52 568 patients were screened for inclusion, and 2164 patients (1508 men [70%]; mean [SD] age, 46 [19] years; median Injury Severity Score, 29 [IQR, 17-36]) presented with acute hemorrhage and were included. A total of 1497 patients (69.1%) required emergency hemorrhage control, 128 (5.9%) received a prehospital transfusion of packed red blood cells, and 543 (25.0%) received a massive transfusion. Norepinephrine was administered to 1498 patients (69.2%). The 24-hour mortality rate was 17.8% (385 of 2164), and the in-hospital mortality rate was 35.6% (770 of 2164). None of the 5 analytical strategies suggested any statistically significant association between norepinephrine administration and 24-hour mortality, with ATEs ranging from –4.6 (95% CI, –11.9 to 2.7) to 2.1 (95% CI, –2.1 to 6.3), or between norepinephrine administration and in-hospital mortality, with ATEs ranging from –1.3 (95% CI, –9.5 to 6.9) to 5.3 (95% CI, –2.1 to 12.8). Conclusions and Relevance The findings of this study suggest that early norepinephrine infusion was not associated with 24-hour or in-hospital mortality among patients with blunt trauma and hemorrhagic shock. Randomized clinical trials that study the effect of early norepinephrine administration among patients with trauma and hypotension are warranted to further assess whether norepinephrine is safe for patients with hemorrhagic shock