Browsing by Author "Grimby-Ekman, Anna"

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Epidemiological aspects of musculoskeletal pain in the upper body. Analyzing common and recurrent binary outcomes
(2010-11-08) Grimby-Ekman, Anna
The overall aim of this thesis is to gain epidemiological knowledge about musculoskeletal pain in the upper body in light physical work, in relation to gender, psychosocial factors, and computer use; and to compare different methods for analyzing common and recurrent binary outcomes. Two study groups were investigated using questionnaire data: (a) computer users in the Swedish workforce and (b) a cohort of university students. Regression models used were ordinary logistic model, Cox model (for calculating prevalence ratios), marginal logistic model (GEE), random intercept logistic model (GLMM), Markov logistic model and Poisson model. Effect measures used were odds ratio, risk ratio and risk difference. Musculoskeletal pain in the upper body was more prevalent among women than among men, even among young adults. Risk factors among computer users in the workforce were high work demands, and using the computer most of the work-day (women). Protective factors were work control and to learn and develop at work, and for women support from superiors. In the university cohort stress, high work/study demands and computer use break pattern were identified as risk factors for neck pain. Stress was a risk factor associated both with developing and ongoing neck pain, and had an impact on both the group-average risk and the subject-specific risk of neck pain. Computer use break pattern had an impact on the group-average risk for neck pain, but on the subject-specific risk only for women. Among women stress and computer use break pattern interacted. The effect of presence of both factors exceeded the additive effect of each. Simple questions, about present neck pain and neck pain period past year, captured features of pain, such as general health, sleep disturbance, stress, and general performance. Neck pain period past year did not reflect more serious pain compared to present neck pain. The choice of statistical model should be based on whether a group-average risk or a subject-specific risk is of clinical relevance. Women and men differed more in the absolute effect measures than in the relative, regarding neck pain. The causality between risk factors and neck pain may differ between women and men.
Experiences of handling daily life with chronic pain and preferences towards primary care - A qualitative report from a workshop
(2022) Ranås, Ebba; Dahlrot, Amanda; Grimby-Ekman, Anna; Ahlström, Linda
Prediction models for planning health care resources. During the first wave of the Covid-19 pandemic 2020
(University of Gothenburg, 2022) Grimby-Ekman, Anna; Cronie, Ottmar; Bock, David; Palaszewski, Bo; Norman Kjellström, Anna; School of Public Health and Community Medicine, Institute of Medicine
Due to the Covid-19 pandemic, has emphasized a need for planning health care resources based on only a few aggregated data points and little knowledge of the data-generating process. In the first part of the report, we present the process of our work in spring 2020 during the first wave and especially during the first part with the high demands on health care resources. In the second part of the report, we discuss the logistic growth model (LGM), one of our models used to predict the peak height and the peak timing. We present some different approaches to use the LGM, and compare these to a different data set, Belgium data. For the Swedish regional data, the LGM on raw observations gave a good estimate on the peak height. The adjusted LGM, using cumulative new inpatient beds, fitted the Swedish regional data to a satisfying degree. For the Belgium data, the LGM on raw observations gave a good estimate on peak height and timing. The adjusted LGM, using cumulative new inpatient beds, did not work for the Belgium data as it gave a too early peak time and a too low peak height. The experience from our work, in combination with now existing literature, the process in a similar future situation would include better knowledge on how to find and combine data to get as reliable forecasts as possible and to use creativity in combination with theoretical competence.
Self-reported symptoms and their relation to COVID-19 infection and its severity: A Swedish pilot study
(School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, 2023) Grimby-Ekman, Anna; Grönkvist, Rode; Gomez, Maria F.; Sudre, Carole
On March 11th, 2020, the World Health Organization officially recognized COVID-19 as a global pandemic. Health systems worldwide were overwhelmed, in part due to the variety of new variants that began to emerge, presenting diverse symptom profiles and levels of infectiousness. This study aimed to develop a predictive scale based on Rasch analysis, using symptoms reported by individuals with positive PCR tests. Additionally, the project sought to evaluate the scale's effectiveness in screening for positive PCR tests among those tested and in predicting hospitalization among those who tested positive. Data were obtained from the COVID Symptom Study smartphone application in the United Kingdom, United States of America, and Sweden, with a focus on Swedish data from the COVID Symptom Study Sweden. Early symptoms, within the first 5 days, were of particular interest for early prediction of COVID-19 infection severity. A Rasch analysis was used to investigate whether the symptoms could form a measurement scale related to COVID-19 infection. This could indicate the importance of the symptoms in regard to severity of the infection and regarding predictability. A low location of the symptoms on the the scale indicates that they are common and could indicate low severity. A high scale location would then indicate more rare symptoms that could be connected to more severe infection. Logistic regression was used to predict positive PCR tests among individuals who underwent PCR testing, as well as hospitalization among those with positive PCR tests. The scale's fit to the Rasch model was moderate, its predictive ability for hospitalization among individuals with positive PCR tests was acceptable, as indicated by an area under the curve (AUC) of 0.7 (Mandrekar, 2010), but maybe not clinically useful (Fan et al., 2006). However, the representation of COVID-19 cases in the Swedish data during the first half of 2020 was limited to more severe cases, primarily reflecting individuals with severe symptoms. 5 (38) In conclusion, symptom clustering holds promise in understanding patterns in COVID-19 symptoms and could serve as a valuable screening tool for identifying severe cases. Further research, particularly focusing on predictive models and comparative analyses, is necessary to fully understand these symptom patterns and their practical applications. Our findings indicate that predicting COVID-19 severity is feasible, making continued research in this field imperative.