Coming theses from other universities
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Perspectives on concurrent jaw and neck pain : function, development and perceptions
Link: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-230935
Background: Jaw and neck pain are prevalent and are often concurrent. Despite this, jaw and neck pain are often assessed and managed separately. Jaw pain is mainly treated within dentistry, whereas pain elsewhere in the body is treated within healthcare. Patients with jaw pain have expressed a struggle with finding the right care. Moreover, early identification and treatment are important factors in pain management in terms of the longterm prognosis for the affected individual. This results in suffering over a prolonged time and increased costs for the health care system. The prevalence of jaw pain is twice as high in connection with a whiplash trauma. However, previous studies on the relationship between orofacial pain and whiplash trauma have mainly been cross-sectional. The main purpose of this thesis was to evaluate and explore function, development and perceptions of concurrent jaw and neck pain.
Methods: The thesis is based on four studies, all with different study designs. The studies were conducted at the Department of Odontology, Umeå University, Sweden, in collaboration with Umeå University Hospital, Sweden. In Study I, with an experimental design, the effect of resistance load on jaw and head movements were evaluated among 26 pain-free individuals. Studies II and III were based on a cohort consisting of 292 individuals that entailed 176 whiplash cases and 116 controls at baseline. All individuals answered questionnaires, and 200 of the 292 had a clinical examination at baseline (one month after the trauma). After two years, 223 individuals repeated the questionnaires and 120 of 223 individuals had a second clinical examination. In study II, clinical signs were evaluated, whereas in study III, predictive factors for jaw pain after two years were explored. In study IV, patients' perspectives on the development of concurrent jaw and neck pain in relation to navigating the health care system were explored. Sixteen individuals with concurrent jaw and neck were interviewed using individual semi-structured interviews.
Results: In the experimental study, the ratio between jaw and head movements was increased when resistance load was applied to the lower jaw, which indicates that the neck involvement increased. In the study regarding clinical signs, cases and women presented more pain on palpation at baseline and at the two-year follow-up. In the explorative study, whiplash trauma did not increase the odds for jaw pain over a two-year period. The development and maintenance of further jaw pain after whiplash trauma was not related to the trauma itself but more associated with non-specific physical symptoms or female gender. In the qualitative study, participants expressed that navigating the health care system was perceived as difficult, and they had a holistic approach regarding their pain and mental status.
Conclusions: Within this thesis we have demonstrated that function and pain in the jaw and neck regions are connected. In addition, navigating the health care system was perceived as difficult, and sufferers wanted to receive confirmation from their health care providers. Therefore, dentistry and healthcare should be aware of the connection between jaw and neck pain. Moreover, an increased collaboration is needed between dentistry and healthcare in terms of multidisciplinary management using a biopsychosocial perspective.
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Imprints on the gut microbiome : A study of sleep apnea, physical activity, and antibiotic use
Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-539738
Growing evidence has highlighted the importance of the gut microbiome for human health. However, detailed investigations on how specific host factors influence the gut microbiome are lacking. The research in this thesis examined the relationship of obstructive sleep apnea (OSA), physical activity, and antibiotic use with the gut microbiome. The studies in this thesis used gut microbiome data from deep fecal shotgun metagenomics in large population-based cohorts.
Study I used the baseline data of 3,570 individuals aged 50-65 from the Swedish CArdioPulmonary bioImage Study (SCAPIS). OSA was assessed with respiratory polygraphy. We identified 128 microbiome species associated with the number of oxygen desaturation events per hour of sleep or the percentage of sleep time in hypoxia. For instance, more severe hypoxia during sleep was associated with higher abundance of Collinsela aerofaciens and Blautia obeum. Additionally, C. aerofaciens was also associated with increased systolic blood pressure.
Study II used baseline data from 8,416 SCAPIS participants who had valid accelerometer-derived physical activity data. The distribution of awake time in sedentary behavior or physical activity of different intensities was associated with the abundance of 651 gut microbiome species. For example, longer time in moderate-intensity physical activity and shorter time in sedentary behavior were associated with higher abundance of Prevotella copri and Faecalibacterium prausnitzii and lower abundance of Escherichia coli and [Ruminococcus] torques.
Study III investigated the association between antibiotic use in the past eight years and the gut microbiome in 15,131 participants from SCAPIS, the Swedish Infrastructure for Medical Population-based Life-course and Environmental Research (SIMPLER), and the Malmö Offspring Study (MOS). Antibiotic use 4–8 years and 1–4 years earlier was associated with lower gut microbiome alpha diversity after adjustment for more recent use, sociodemographics, lifestyle, and comorbidities. Most of the species-level associations were found for the antibiotics clindamycin, fluoroquinolones, and flucloxacillin.
Study IV assessed the causal effect of physical activity on the gut microbiome using a two-sample Mendelian randomization (MR) analysis based on summary statistics from genome-wide association studies. We found evidence of a positive effect of moderate-to-vigorous intensity physical activity (MVPA) on gut microbiome alpha diversity. Using a multivariable MR analysis, we found that MVPA had a positive on alpha diversity independent of BMI, smoking, education, or liking of a low-calorie diet.
This thesis has applied diverse epidemiology and statistical methods to rigorously investigate host factor associations with the gut microbiome. Altogether, it underlines the tight host-microbiome connection through detailed analyses of OSA, physical activity, and antibiotic use with the gut microbiome.
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Mobile Phone Data Analytics to Support Disaster and Disease Outbreak Response
Link: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-355379
Natural disasters result in devastating losses in human life, environmental assets, and personal, regional, and national economies. The availability of different big data such as satellite images, Global Positioning System (GPS) traces, mobile Call Detail Records (CDR), social media posts, etc., in conjunction with advances in data analytic techniques (e.g., data mining and big data processing, machine learning and deep learning), can facilitate the extraction of geospatial information that is critical for rapid and effective disaster response. However, disaster response system development usually requires the integration of data from different sources (streaming data sources and data sources at rest) with different characteristics and types, which consequently have different processing needs. Deciding which processing framework to use for a specific big data to perform a given task is usually a challenge for researchers from the disaster management field. While many tasks can be accomplished with population and movement data, for disaster management, a key and arguably most important task is to analyze the displacement of the population during and after a disaster. Therefore, in this thesis, the knowledge and framework resulted from a literature review. These were used to select tools and processing strategies to perform population displacement (the forced movement or relocation of people from their original homes) analysis after a disaster. This is a use case of the framework as well as an illustration of the value and challenges (e.g., gaps in data due to power outages) of using CDR data analysis to support disaster management.
Displaced populations were inferred by analyzing the variation of home cell-tower for each anonymized mobile phone subscriber before and after a disaster using CDR data. The effectiveness of the proposed method is evaluated using remote sensing-based building damage assessment data and Displacement Tracking Matrix (DTM) from individuals’ survey responses at shelters after a severe cyclone in Beira city, central Mozambique, in March 2019. The results show an encouraging correlation coefficient (over 70\%) between the number of arrivals in each neighborhood estimated using CDR data and from DTM. In addition to this, CDR-based analysis derives the spatial distribution of displaced populations with high coverage of people, i.e., including not only people in shelters but everyone who used a mobile phone before and after disaster. Moreover, results suggest that if CDR data are available after a disaster, population displacement can be estimated. These details can be used for response activities and for example to contribute to reducing waterborne diseases (e.g., diarrheal disease) and diseases associated with crowding (e.g., acute respiratory infections) in shelters and host communities.
Although COVID-19 is not a post-disaster disease, it is an acute respiratory illness that can be severe. By assuming that its characteristics can be similar to an acute respiratory infection following a disaster, a deep learning approach was tested to predict the spread of COVID-19. The tested deep learning approach consists of multilayer BiLSTM. In order to train the model to predict daily COVID-19 cases in low-income countries, mobility trend data from Google, temperature, and relative humidity were used. The performance of the proposed multilayer BiLSTM is evaluated by comparing its RMSE with the one from multilayer LSTM (with the same settings as BiLSTM) in four developing countries namely Mozambique, Rwanda, Nepal, and Myanmar. The proposed multilayer BiLSTM outperformed the multilayer LSTM in all four countries. The proposed multilayer BiLSTM was also evaluated by comparing its root mean squared error (RMSE) with multilayer LSTM models, ARIMA- and stacked LSTM-based models in 8 countries, namely Italy, Turkey, Australia, Brazil, Canada, Egypt, Japan, and the UK. Finally, the proposed multilayer BiLSTM model was evaluated at the city level by comparing its average relative error (ARE) with the other four models, namely the LSTM-based model considering multilayer architecture, Google Cloud Forecasting, the LSTM-based model with mobility data only, and the LSTM-based model with mobility, temperature, and relative humidity data for 7 periods (of 28 days each) in six highly populated regions in Japan, namely Tokyo, Aichi, Osaka, Hyogo, Kyoto, and Fukuoka. The proposed multilayer BiLSTM model outperformed the multilayer LSTM model and other previous models by up to 1.6 and 0.6 times in terms of RMSE and ARE, respectively. Therefore, the proposed model enables more accurate forecasting of COVID-19 cases. This can support governments and health authorities in their decisions, mainly in developing countries with limited resources.
In addition to understanding the disease spread dynamics, rapid implementation of control measures is critical in the case of a post-disaster outbreak. This is crucial to stopping the spread of the disease. However, its implementation is based on informed decisions. Therefore, in order to support the decision-makers, a data-driven approach for estimating spatio-temporal exposure risk of locations using mobile phone data was tested. The approach used anonymized CDR from one of the biggest mobile network operators in Mozambique to estimate the daily origin-destination (OD) matrices. The daily OD matrices are estimated at province level since the available daily COVID-19 cases (validation data) are at that level. COVID-19 was used as a proxy of a post-disaster disease due to the unavailability of daily real-world data of a disease following a natural disaster in Mozambique. The estimated daily OD matrices are then used to construct the daily directed-weighted networks, in which the nodes represent provinces and the edges, the people flowing between each pair of provinces. Then, three centrality measures, namely weighted in-degree centrality, improved in-degree centrality, and weighted PageRank were used to estimate the daily exposure risk of each province. The results were evaluated by computing the Spearman’s rank correlation between risk score estimated using the daily COVID-19 reported cases and the exposure risk estimated using the three measures. The comparison results revealed that the overall weighted PageRank algorithm is the best measure at estimating exposure risk compared to the other two measures. Accordingly, three Poisson regression models were implemented to model the relationship between the COVID-19 cases in each province and the corresponding exposure risk estimated using the three centrality measures. The results showed that the coefficients of the models estimated using the maximum likelihood method are statistically significant (p-value <0.05). This means that the exposure risk does in fact influence the number of COVID-19 cases. Since the sign of the coefficients of the models is positive, we conclude that the number of COVID-19 cases in each province increases with an increase in the spatial exposure risk. The analysis was also conducted at district level, i.e., in Greater Maputo Area (GMA), which is located in the southern part of Mozambique and consists of all Maputo city districts (except Kanyaka), Matola city, Matola-Rio, Boane, and Marracuene districts. However, due to the unavailability of daily COVID-19 cases at district level, the evaluation was done by comparing the daily exposure risk estimated using the three centrality measures and the distribution of different types of points of interest, namely commercial, education, financial, government, healthcare, public, sport, and transport. The results revealed a good Spearman’s rank correlation between education, financial, and transport related points of interest and the three centrality measures. Government related points of interest presented the lowest correlation results compared to the three centrality measures. The remainder of points of interest showed medium-low to medium-high Spearman’s correlation coefficient compared to the three centrality measures. Therefore, anonymized CDR in conjunction with weighted PageRank algorithm can help decision-makers estimate the exposure risk in case of an outbreak and hence reduce the impact of a disease on human lives by imposing several informed interventions to contain and delay its spread.