scholarly journals Quality of Life of Rural and Urban Population in Poland: Evaluation and Comparison

2020 ◽  
Vol XXIII (Issue 3) ◽  
pp. 645-656
Author(s):  
Agnieszka Sompolska-Rzechula ◽  
Agnieszka Kurdys-Kujawska
Keyword(s):  
Quality Of Life ◽  
Urban Population ◽  
Rural And Urban

scholarly journals The Quality of Life of Coronavirus Disease Survivors Living in Rural and Urban Area of Riau Province, Indonesia

2022 ◽  
Vol 14 (1) ◽  
pp. 33-42
Author(s):  
Suyanto Suyanto ◽  
Shashi Kandel ◽  
Rahmat Azhari Kemal ◽  
Arfianti Arfianti
Keyword(s):  
Quality Of Life ◽  
Rural Areas ◽  
Urban Areas ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Rural And Urban Areas ◽  
Rural And Urban ◽  
The Status ◽  
Rural And Urban Area

This study assesses the status of health-related quality of life (HRQOL) among coronavirus survivors living in rural and urban districts in Riau province, Indonesia. The cross-sectional study was conducted among 468 and 285 Coronavirus disease (COVID-19) survivors living in rural and urban areas, respectively in August 2021. The St. George Respiratory Questionnaire (SGRQ) was used to measure the HRQOL of COVID-19 survivors. A higher total score domain corresponds to worse quality of life status. Quantile regression with the respect to 50th percentile found a significant association for the factors living in rural areas, being female, having comorbidities, and being hospitalized during treatment, with total score of 4.77, 2.43, 7.22, and 21.27 higher than in their contra parts, respectively. Moreover, having received full vaccination had the score 3.96 in total score. The HRQOL of COVID-19 survivors living in rural areas was significantly lower than in urban areas. Factors such as living in rural areas, female sex, having comorbidities, and history of symptomatic COVID-19 infection were identified as significant predictors for lower quality of life. Meanwhile, having full vaccination is a significant predictor for a better quality of life. The results of this study can provide the targeted recommendations for improvement of HRQOL of COVID-19 survivors.

P002 IBD PARTNERSHIPS: UNDERSTANDING PATIENTS VS. CLINICIANS PERSPECTIVES OF IBD TREATMENT OPTIONS TO IMPROVE SHARED DECISION-MAKING

2020 ◽  
Vol 26 (Supplement_1) ◽  
pp. S51-S51
Author(s):  
Sandra Zelinsky ◽  
Catherine Finlayson
Keyword(s):  
Quality Of Life ◽  
Decision Making ◽  
Focus Groups ◽  
Shared Decision Making ◽  
Healthcare Providers ◽  
Treatment Decisions ◽  
Research Approach ◽  
Shared Decision ◽  
Rural And Urban

Abstract Background The patient is the only constant in the care journey, the person who experiences both processes and the outcomes of care. There is an international shift towards including patients as equal partners in research. Co-producing research with Inflammatory Bowel Disease (IBD) patients to understand their values, needs and priorities when making treatment decisions will potentially improve shared decision-making between IBD patients and their Healthcare Providers (HCPs). To facilitate this process patients and HCPs must have a common understanding of expected medication benefits, risks and the potential impact on quality of life. The information available to facilitate this conversation must be aligned and reflect the priorities that IBD Patients and Healthcare Providers consider when making treatment decisions. Both parties can then share information and work towards an agreement to what treatment plan should be implemented. Aims To understand what matters most to IBD patients when making treatment decisions by conducting a qualitative patient-led peer to peer study which will inform the development of an IBD patient and HCP survey. Methods IBD patients (≥ 18 years of age) were recruited through the IBD clinic at the University of Calgary and via social media. Focus groups were held in three separate provinces (British Columbia, Alberta and Ontario) in both rural and urban locations. The focus groups were facilitated by a Patient Engagement Researcher to alleviate any potential power dynamics and to create a safe space for IBD patients to share their perspectives. A participatory action research approach was used to encourage co-production with participants throughout the focus groups. The focus groups were audio recorded. Flip charts and sticky notes were used for brainstorming and prioritization exercises. All audio and written data were transcribed. Thematic analysis was used to identify emerging themes and patient priorities. Results A total of 21 participants attended the focus groups from both rural and urban locations. Participant diversity ranged in ethnicity and age. Most of the participants were female (18 females and 3 males) of which 4 were biologic naïve and 17 were biologic exposed. The Top 5 IBD Patient Priorities when making treatment decisions are 1) Risks(more serious/long term) 2) Education(Support/Evidence Based Information/Resources) 3) Side Effects(short term/less serious) 4) Efficacy 5) Impact(Quality of Life/ Lifestyle/Logistics). Conclusions Co-producing research ‘with’ and ‘by’ IBD patients helped to generate priorities that matter most to patients when making treatment decisions. The patient priorities will help in the development of an IBD Patient and HCP survey. The results from the two surveys will be compared to understand patient vs. HCP perspectives.

scholarly journals Using DEA and VEA to Evaluate Quality of Life in the Mid-Atlantic States

2005 ◽  
Vol 34 (2) ◽  
pp. 185-203 ◽  
Author(s):  
Elizabeth Marshall ◽  
James Shortle
Keyword(s):  
Quality Of Life ◽  
Data Envelopment Analysis ◽  
Single Unit ◽  
Efficiency Analysis ◽  
Performance Standard ◽  
Data Envelopment ◽  
Atlantic Region ◽  
Rural And Urban ◽  
Value Efficiency Analysis

In this study we use data envelopment analysis (DEA) and an extension of DEA called value efficiency analysis (VEA) to explore the “production” of quality of life within counties in the mid-Atlantic region and the extent to which production frontiers and efficiency differ between rural and urban counties. These methods allow us to identify counties that are inefficient in their quality of life production, and to rank (using DEA) those counties according to their distance from a performance standard established by other observed counties, or (using VEA) by a single unit designated as “most preferred.”

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