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What happens to population health when the doctors leave? Evidence from the exit of Cuban doctors in Brazil
No full textThis paper studies the effects of a large-scale exit of doctors on population health outcomes, health production inputs, outputs, and health system adaptation in Brazil. Identification exploits the exogenous timing of the Cuban exit from municipalities that relied more versus less on Cuban doctors within the More Doctors Program. We find persistent reductions in the care of chronic diseases, while service utilization for conditions requiring immediate care, such as maternal-related services and infections, quickly recovered. Reductions in utilization did not translate into changes in health outcomes. Supply-side response and demand diversion helped mitigate major adverse repercussions for population health at the market level
Practical SVBRDF and shape acquisition using deep dual Imaging
No full textThe appearance of objects can be described by their geometric shapes and spatially varying optical reflectance maps. Traditional methods typically require specialized and complex hardware to recover geometric shapes and underlying physical models while controlling lighting conditions. Although the results are usually accurate, the costly equipment and complex capture process no longer meet the rapidly growing demands of digital content creation. With the advancement of deep learning, many rapid and straightforward methods have emerged, reducing the required number of images to a minimum—sometimes only one. However, these outstanding studies still lag in result quality, especially for structurally complex 3D objects. In this thesis, we focus on using the simplest possible capture devices, such as Off-the-shelf smartphones, and eliminate restrictions on lighting conditions as much as possible. Based on deep neural networks, our methods use two images to predict high-quality shapes and spatially varying reflectance of 3D objects and human faces, finding a sweet spot between quality and capture costs, significantly reducing the user’s capture expenses.
We utilize a smartphone’s multi-lens imaging technology to acquire high-quality shapes and spatially varying reflectance of 3D objects. Our method captures two images simultaneously using the zoom and the wide-angle lens of a smartphone, under both natural illumination and phone flash, working as efficiently as single-shot methods and achieving superior results. Furthermore, specifically for the most popular and intricate 3D object—the human face—we propose a deep learning-based imaging method that predicts the shape and SVBRDF of a subject’s face directly from two images taken from each side of the face, avoiding the need for a pre-learned morphable model and facilitating ease of use. Compared to single-image facial reconstruction methods, our approach excels in rendering faces at extreme angles and provides texture maps that are directly usable in most rendering systems.Open Acces
Quantifying the relationship between airport capacity and delay using causal inference methods
No full textGlobal air traffic is growing due to economic expansion, increased connectivity, rising middle-class incomes, and post-pandemic recovery. Consequently, most airports are operating at or near capacity, causing congestion-related delays. Addressing these challenges requires effective operational policies and strategic planning for airport capacity. To inform such efforts, this thesis aims to analyze the relationship between airport capacity and delay at both operational and strategic levels. Existing literature examines these relationships from an associational perspective, failing to capture true impacts of capacity on delay. This thesis is the first to analyze causal relationships, providing empirical insights reproducible across diverse operational scenarios, leveraging high-granularity operational data and advanced causal inference models. The analysis is structured around three themes: the first two addressing operational aspects and the third on strategic considerations.
First, this thesis proposes an empirical model to estimate runway system capacity across varying operational scenarios. The estimation employs confounding-adjusted Stochastic Frontier Analysis controlling for unobserved operational factors such as air traffic control that could confound the results.
Secondly, the technology driving congestion in airport surface operations is explored by modeling surface delay versus runway and ground capacity utilization, using runway capacity estimates from the first theme. This congestion technology (CT) is estimated using Bayesian non-parametric instrumental variables to address confounding from unobserved operational factors. The CT delivers key insights into surface-use efficiency such as optimum operating capacity utilization.
Finally, this thesis evaluates how strategic capacity interventions alleviate delays. A causal statistical framework built on sharp Regression Discontinuity in Time adjusts for biases from interaction of capacity intervention, demand, and delay. This framework is applied to yield unbiased empirical effects of two infrastructure expansions on delay, passengers, airlines, the environment, and safety.
These methodological contributions and findings carry significant implications for both daily operation management and strategic investment planning.Open Acces
Immediate and delayed immune and inflammatory responses to COVID-19
No full textThe COVID-19 pandemic has had widespread effects. New SARS-CoV-2 variants continue to cause disease outbreaks and many infections result in debilitating sequelae termed ’long COVID’. A better understanding of local, mucosal IgA responses to SARS-CoV-2 infection may support development of new vaccines that can prevent infection and transmission. Furthermore, understanding why sequelae develop may expedite treatment discovery for long COVID.
Local and systemic immune and inflammatory responses were measured sequentially over 1 year in a cohort of patients, previously hospitalised with COVID-19. Within the multicentre UK studies, PHOSP-COVID and ISARIC4C, a total of 446 individuals had longitudinal nasal and plasma antibody responses measured. Strong nasal anti-N and anti-S IgA responses were demonstrated after COVID-19, which persisted for nine months, but were independent of plasma IgG responses. Unlike plasma IgG anti-S responses, nasal IgA anti-S responses did not rise after vaccination.
In a subgroup of PHOSP-COVID participants, clinical data collected from 719 individuals 6 months after hospitalisation was used to group patients according to their long COVID status. Inflammatory proteins were measured via Olink to determine immunobiological signatures associated with each long COVID subgroup, when compared to recovered individuals. Elevated markers of myeloid inflammation and complement activation were associated with each long COVID subgroup. However, subtle differences were observed between groups. Distinct signatures suggestive of tissue-specific responses were observed in association with cardio-respiratory, GI and cognitive symptoms. SARSCoV-2-specific IgG was persistently elevated in plasma from all individuals with long COVID, but virus was not detected in sputum. Taken together, this work highlights the need for mucosal vaccines that can induce and boost mucosal immunity, limiting SARS-CoV-2 transmission, COVID-19 sequelae and evolution of new variants. Furthermore, it highlights potential inflammatory processes underlying different long COVID symptoms, highlighting the possibility of different endotypes that need to be accounted for in clinical trials.Open Acces
Novel genetic variants in tuberculosis-associated immune reconstitution inflammatory syndrome
No full textConstraining nonequilibrium isotopic effects in carbonates: through clumped isotopes, trace metal analysis, and numerical modelling
No full textCalcium carbonates are important sedimentary rocks that are not only volumetrically important reservoir rocks but also pivotal paleoenvironment archives. The clumped isotope thermometer (△47) is the most advanced paleo-thermometer for carbonates and a powerful tool in related research. However, the kinetic effects on clumped isotopes are ambiguous and controversial. The pairing of clumped isotope and trace metal concentration found in natural calcite cement was recently suggested as a promising tool to identify kinetic effects in clumped isotopes but needs to be further confirmed via experiments. This PhD work breaks down the knowledge gap into three linked research questions: [1] Key controls on polymorphism, [2] Mathematical description of trace metal incorporation, and [3] Potential causes of kinetic effects on clumped isotopes.
To address the gap, I synthesised CaCO3 under controlled laboratory conditions. The obtained precipitates were analysed for various properties. Based on the results, I concluded: [1] Temperature and the combination of temperature > 25℃ with rapid precipitation rates are primary controls on polymorphism, with ionic impurities and precipitation rate as secondary and tertiary controls. [2] The partitioning of Fe3+ and Mn2+ in CaCO3 is independent in the polymorph and exhibited strong correlations with the precipitation rates and temperatures. [3] The examined parameters didn’t solely cause kinetic effects on the clumped isotope. However, the elevated dehydration of cation impurities can lead to kinetic effects. I interpret this as the shift in the crystal growth mechanism. I extended the △47-T calibration to cryogenic temperatures with a range of -15 to 250℃.
This PhD work provides a laboratory framework for the research of carbonate clumped isotope kinetic effects and suggests that the pairing of clumped isotope and trace metal concentration may not be a widely applicable tool.Open Acces
Information, learning, and drug diffusion
No full textAdvancements in technology and pharmaceutical innovations enhance medical care access, improve patient outcomes, and reduce healthcare costs. However, the diffusion of these innovations is often slow and uneven, leading to disparities that can adversely affect public health. This thesis examines the determinants of individual adoption decisions in the context of new pharmaceutical developments.
Chapter 2 examines the impact of a patient-sharing physician network on the prescription behaviours of generic versus branded drugs, using the universe of statin prescriptions in Finland from 2000 to 2008. The results show that the patient-sharing network positively affects the adoption of generic drugs. Higher connection intensity, measured by the number of patients exchanged between physicians, further increases physicians’ likelihood of prescribing generic drugs.
In Chapter 3, I develop and estimate a structural Bayesian learning model where physicians update their beliefs about the quality of generic drugs based on patient information signals. The estimation results show that physicians initially hold negative perceptions of the quality of generic drugs. Both the volume and composition of information signals influence generic adoptions. New patient signals from other physicians, despite bringing noises, eventually increase long-term drug diffusion by boosting physicians’ expectations about generic drug quality.
Chapter 4 evaluates the informational effect of generic substitution (GS) policy on the adoption of generic drugs. The analysis suggests that the effective communication of GS outcomes from pharmacies to physicians could more than double the generic adoption rates compared to relying solely on learning from prescription histories. The results highlight that successful policy implementation should focus on proactive early-stage promotion through the information channel.
Overall, this thesis highlights the role of information and learning in shaping adoption decisions, providing insights for policymakers to promote the diffusion of pharmaceutical innovations.Open Acces
The population genetics of rare variants in the Anopheles gambiae genome, and their use for demographic inference
No full textMosquitoes of the Anopheles gambiae species complex are the primary vector for malaria in sub-Saharan Africa, causing over 600,000 deaths annually due to the disease. However, there is still much uncertainty regarding their demography, despite its importance for strategies to reduce transmission. In this work I use population genomic data and simulations to investigate the demography of these species. I focus on analyses involving rare variants in the genome, specifically doubletons, to enable fine scale demographic inference. I first examine doubleton mutations in the Anopheles gambiae genome, using data from the Anopheles gambiae 1000 Genomes project. I estimate that up to 16% of these are recurrent mutations, and develop a probabilistic approach to identifying those most likely to be non-recurrent. Applying this I observe that shared ancestral DNA (haplotypes) around these doubletons convey information on several biological processes including, crucially, demography. I then examine the effects that different aspects of demography have on doubleton haplotypes, by developing a coalescent genomic simulation framework utilising a 2-D stepping stone model. I show both population size, N, and the number of migrants, Nm, have clear, independent impacts on haplotypes, and that these discrete models can be used to approximate continuous space. Finally, I extend this simulation framework to approximate the habitat and sampling of An. gambiae in West Africa to attempt demographic parameter inference using approximate Bayesian computation. I show that doubleton haplotypes in this region potentially reveal patterns of relatedness missed by common variants, and that with these simulations both population density and dispersal rates can be inferred, although the estimates obtained differ from previous estimates in the literature. Overall, my work demonstrates that rare variant haplotypes are informative on the demography of An. gambiae and warrants further development of this parameter inference approach.Open Acces
Exploring the fermentation potential of Kluyveromyces marxianus NS127 for single-cell protein production
No full textKluyveromyces marxianus is a food-grade yeast known for its diverse beneficial traits, making it an attractive candidate for both food and biotechnology applications. This study explores the potential of Kluyveromyces marxianus as a promising alternative protein source for single-cell protein (SCP) production. Various Kluyveromyces strains were isolated and screened from traditional fermented dairy products, with Kluyveromyces marxianus NS127 identified as the most promising strain due to its superior growth characteristics, high SCP yield, and environmental tolerance. Notably, Kluyveromyces marxianus NS127 demonstrated significant substrate conversion capacity with a biomass yield of 0.63 g biomass/g molasses, achieving a dry biomass concentration of 66.64 g/L and a protein yield of 28.37 g/L. The protein extracted from the dry biomass exhibited excellent solubility (62.55%) and emulsification properties (13.15 m2/g) under neutral conditions, alongside high foaming stability (93.70–99.20%) across a broad pH range (3–11). These results underscore the potential of Kluyveromyces marxianus NS127 as a viable alternative protein source and provide a solid theoretical foundation for its industrial application