A prospective study of the effect of delivery type on neonatal weight gain pattern in exclusively breastfed neonates born in Shiraz, Iran

<p>Abstract</p> <p>Background</p> <p>In this exploratory study, the contribution of delivery type to the weight gain pattern for full-term infants with exclusive breastfeeding in the first month of infancy was determined. In addition, breastfeeding success among cesarean section (C-section) delivery mothers based on their neonate's weight gain at the end of the first month of infancy was evaluated.</p> <p>Methods</p> <p>A cohort of 92 neonates born in Shiraz, from July 10 to August 10, 2007 was followed longitudinally. The data were collected during the first month postpartum at three occasions: 3 to 7 days postpartum, 10-21 days postpartum and 24-31 days postpartum.</p> <p>Results</p> <p>Among 92 mothers in this study, 35 (38%) were delivered by C-section. Generalized estimating equation (GEE) showed that delivery type (p < 0.01), receipt of advice about breastfeeding (p = 0.03) and neonate's age (p < 0.01) significantly affected weight gain. GEE estimated the values of the parameters under study and the testing contribution of each factor to weight gain, leading to the conclusion that gender, parities and maternal education did not contribute to weight gain. The neonate's weight gain pattern for C-section deliveries lies below that of normal vaginal deliveries until 25 days postpartum, when weight gain for C-section deliveries became higher than that for normal vaginal deliveries.</p> <p>Conclusions</p> <p>Type of delivery contributes strongly to the weight gain pattern in the first month of infancy. In spite of greater weight loss among C-section birth neonates in the first days of life, at the end of the first month neonates showed a similar weight gain. Consequently, mothers with C-section delivery can successfully exclusively breastfeed.</p

Explaining the Challenges of the Iranian Health System in Fighting the Covid-19 Pandemic: a qualitative study

Introduction: The covid-19 pandemic has become a global threat for the general public and health care workers and it has created major challenges for all healthcare sectors. The challenges created by this disease can vary in different countries depending on cultural, social, and economic factors. To explain the challenges of the Iranian health system in fighting the covid-19 pandemic from the managers’ and executive authorities’ viewpoints. Methods The present study is a basic-applied research performed using a qualitative approach. It has studied 30 managers of the hospitals and medical centers’ managers, and deputies of the Ministry of Health, and the universities of medical sciences which were selected by purposive and snowball sampling with the maximum variety in March-September 2020. Data collection was done through semi-structured interviews and content analysis was used to explain the challenges of the Iranian health system in fighting the covid-19 pandemic (2020). Results Most of the interviewees (87%) had a Ph.D. degree (34%), and 40% of the participants were graduated in management and health economy and policymaking fields. Analysis and synthesis of the data collected from the interviews led to the creation of 19 sub-themes and 12 main themes classified into four general scopes including the organizational factors, resources, management factors, and other factors. Conclusion Identifying the mentioned challenges can provide useful information for the managers and policymakers to develop appropriate plans and take the necessary measures for resolving the challenges and using the available resources to provide the most effective services

Studying the Status of Job Burnout and its Relationship with Demographic Characteristics of Nurses in Shiraz Nemazee Hospital

Background: Today, in order to provide desirable health care services, too much emphasis is placed on the physical and mental health of nurses, and job burnout among nurses is introduced as harmful elements to the health of nurses. Objective: This study was performed to evaluate job burnout in Shiraz Nemazee Hospital in relation to demographic characteristics. Methods: This research is a cross-sectional and descriptive-analytic study. The research community included all nurses in the whole sections of Nemazee Hospital out of which 245 were selected by classified random sampling as the study sample. A questionnaire was used to collect the data. After collection, data were entered in statistical package for social sciences SPSS software (version 18) and T-test, and analysis of variance (ANOVA) and Kruskal-Wallis tests were used to analyze the variables. Results: The mean score of emotional exhaustion, lack of personal accomplishment and job burnout were at an average level, and depersonalization was at a low level. As regards the intensity of burnout, most nurses were moderate. Between components of depersonalization of job burnout with marital status and age, there was a significant relationship (PConclusion: According to the findings of this study and in terms of the stressful nature of nursing profession, it is necessary that hospital managers and healthcare authorities pay attention to job burnout in nurses, its level, as well as provide and implement strategies for its prevention, thereby decreasing its effects and risks

A comparative study of model-centric and data-centric approaches in the development of cardiovascular disease risk prediction models in the UK Biobank

Aims A diverse set of factors influence cardiovascular diseases (CVDs), but a systematic investigation of the interplay between these determinants and the contribution of each to CVD incidence prediction is largely missing from the literature. In this study, we leverage one of the most comprehensive biobanks worldwide, the UK Biobank, to investigate the contribution of different risk factor categories to more accurate incidence predictions in the overall population, by sex, different age groups, and ethnicity. Methods and results The investigated categories include the history of medical events, behavioural factors, socioeconomic factors, environmental factors, and measurements. We included data from a cohort of 405 257 participants aged 37–73 years and trained various machine learning and deep learning models on different subsets of risk factors to predict CVD incidence. Each of the models was trained on the complete set of predictors and subsets where each category was excluded. The results were benchmarked against QRISK3. The findings highlight that (i) leveraging a more comprehensive medical history substantially improves model performance. Relative to QRISK3, the best performing models improved the discrimination by 3.78% and improved precision by 1.80%. (ii) Both model- and data-centric approaches are necessary to improve predictive performance. The benefits of using a comprehensive history of diseases were far more pronounced when a neural sequence model, BEHRT, was used. This highlights the importance of the temporality of medical events that existing clinical risk models fail to capture. (iii) Besides the history of diseases, socioeconomic factors and measurements had small but significant independent contributions to the predictive performance. Conclusion These findings emphasize the need for considering broad determinants and novel modelling approaches to enhance CVD incidence prediction

An interpretable machine learning framework for measuring urban perceptions from panoramic street view images

The proliferation of street view images (SVIs) and the constant advancements in deep learning techniques have enabled urban analysts to extract and evaluate urban perceptions from large-scale urban streetscapes. However, many existing analytical frameworks have been found to lack interpretability due to their end-to-end structure and “black-box” nature, thereby limiting their value as a planning support tool. In this context, we propose a five-step machine learning framework for extracting neighborhood-level urban perceptions from panoramic SVIs, specifically emphasizing feature and result interpretability. By utilizing the MIT Place Pulse data, the developed framework can systematically extract six dimensions of urban perceptions from the given panoramas, including perceptions of wealth, boredom, depression, beauty, safety, and liveliness. The practical utility of this framework is demonstrated through its deployment in Inner London, where it was used to visualize urban perceptions at the Output Area (OA) level and to verify against real-world crime rate

Prognostic factors of survival time after hematopoietic stem cell transplant in acute lymphoblastic leukemia patients: Cox proportional hazard versus accelerated failure time models

<p>Abstract</p> <p>Background</p> <p>The aim of this study is to evaluate the prognostic factors of overall survival (OS) after haematopoietic stem cell transplant (HSCT) in acute lymphoblastic leukaemia (ALL) patients using accelerated failure time (AFT), Cox proportional hazard (PH), and Cox time-varying coefficient models.</p> <p>Methods</p> <p>206 patients were enrolled after HSCH in Shariati Hospital between 1993 and 2007. There was evidence of marked departures from the proportional hazards assumption with two prognostic factors, relapse and chronic graft-versus-host disease (cGVHD) (P < .001). Performance among AFT and Cox's models was assessed using explained variation and goodness of fit methods. Discrimination among the exponential, Weibull, generalized gamma (GG), log-logistic, and lognormal distributions was done using maximum likelihood and Akaike information criteria.</p> <p>Results</p> <p>The 5-year OS was 52% (95%CI: 47.3–56.7). Peak mortality hazard occurred at months 6–7 after HSCT followed by a decreasing trend. In univariate analysis, the data was better fitted by GG distribution than by other distributions. Univariate analysis using GG distribution showed a positive association between OS with acute graft-versus-host disease (aGVHD) (P = .021), no relapse (P < .001), cGVHD (P < .001), neutrophil recovery (P < .001) and platelet recovery (P < .001). Based on Cox PH models; however cGVHD and relapse were the predictive factors of OS (P < .001). Multivariate analysis indicated that, OS is related to relapse (P < .001) and platelet recovery (P = .037), where predictive power of Weibull AFT models was superior to Cox PH model and Cox with time-varying coefficient (R²= 0.46 for AFT, R²= .21 for Cox PH and R²= .34 for Cox time-varying coefficient). Cox-Snell residual shows Weibull AFT fitted to data better than other distributions in multivariate analysis.</p> <p>Conclusion</p> <p>We concluded that AFT distributions can be a useful tool for recognizing prognostic factors of OS in acute lymphoblastic leukemia patients.</p

Systolic blood pressure, chronic obstructive pulmonary disease and cardiovascular risk

Objective In individuals with complex underlying health problems, the association between systolic blood pressure (SBP) and cardiovascular disease is less well recognised. The association between SBP and risk of cardiovascular events in patients with chronic obstructive pulmonary disease (COPD) was investigated. Methods and analysis In this cohort study, 39 602 individuals with a diagnosis of COPD aged 55–90 years between 1990 and 2009 were identified from validated electronic health records (EHR) in the UK. The association between SBP and risk of cardiovascular end points (composite of ischaemic heart disease, heart failure, stroke and cardiovascular death) was analysed using a deep learning approach. Results In the selected cohort (46.5% women, median age 69 years), 10 987 cardiovascular events were observed over a median follow-up period of 3.9 years. The association between SBP and risk of cardiovascular end points was found to be monotonic; the lowest SBP exposure group of <120 mm Hg presented nadir of risk. With respect to reference SBP (between 120 and 129 mm Hg), adjusted risk ratios for the primary outcome were 0.99 (95% CI 0.93 to 1.05) for SBP of <120 mm Hg, 1.02 (0.97 to 1.07) for SBP between 130 and 139 mm Hg, 1.07 (1.01 to 1.12) for SBP between 140 and 149 mm Hg, 1.11 (1.05 to 1.17) for SBP between 150 and 159 mm Hg and 1.16 (1.10 to 1.22) for SBP ≥160 mm Hg. Conclusion Using deep learning for modelling EHR, we identified a monotonic association between SBP and risk of cardiovascular events in patients with COPD. Data availability statement Data may be obtained from a third party and are not publicly available. More details of the data and data sharing is found on the CPRD website (https://www.cprd.com). Targeted-BEHRT source code can be found on the Deep Medicine GitHub site (https://github.com/deepmedicine/Targeted-BEHRT). Example code for conducting an observational study on mock data and estimating risk ratio can also be found in this code repository

Using Ergometer and Timing Activities in Determining the Manpower Required in Chosen Units of Laboratory in the Shiraz Faghihi Hospital

Background: The quantity and quality of manpower, especially in health systems, are major factors affecting speed of service delivery, cost, and accuracy, or, in other words, the quality of service. Objective: This study purposed to investigate the manpower required in various units of the laboratory at Shiraz Faghihi Hospital. Methods: This is a cross-sectional and descriptive-analytical research conducted on all testing processes in the fields of parasitology, hormone biology, microbiology, and urinalysis performed at the studied hospital. The Westinghouse ergometer and timing method was used to estimate manpower requirements, and SPSS18 software was used to analyze data. Results: The average standard time of every duty cycle in parasitology units, hormone biology, microbiology, and urinalysis are 12, 5, 9, and 5 minutes, respectively. The numbers of human resources required in said units were estimated to be 2.6, 3, 4, and 3.7 respectively. In parasitology unit there is lack of 1 manpower. In hormone unit there are two manpower surpluses. In biology unit there is one manpower surplus and urinalysis unit is estimated to be proportional to the number of troops. Conclusion: It is suggested that new, scientific tools be used to evaluate the status of department staff and make improvements to avoid the high costs and difficulties that manpower shortages and surpluses cause for the organization

Systolic blood pressure and cardiovascular risk in patients with diabetes: a prospective cohort study

Background: Whether the association between systolic blood pressure (SBP) and risk of cardiovascular disease is monotonic or whether there is a nadir of optimal blood pressure remains controversial. We investigated the association between SBP and cardiovascular events in patients with diabetes across the full spectrum of SBP. Methods: A cohort of 49 000 individuals with diabetes aged 50 to 90 years between 1990 and 2005 was identified from linked electronic health records in the United Kingdom. Associations between SBP and cardiovascular outcomes (ischemic heart disease, heart failure, stroke, and cardiovascular death) were analyzed using a deep learning approach. Results: Over a median follow-up of 7.3 years, 16 378 cardiovascular events were observed. The relationship between SBP and cardiovascular events followed a monotonic pattern, with the group with the lowest baseline SBP of <120 mm Hg exhibiting the lowest risk of cardiovascular events. In comparison to the reference group with the lowest SBP (<120 mm Hg), the adjusted risk ratio for cardiovascular disease was 1.03 (95% CI, 0.97–1.10) for SBP between 120 and 129 mm Hg, 1.05 (0.99–1.11) for SBP between 130 and 139 mm Hg, 1.08 (1.01–1.15) for SBP between 140 and 149 mm Hg, 1.12 (1.03–1.20) for SBP between 150 and 159 mm Hg, and 1.19 (1.09–1.28) for SBP ≥160 mm Hg. Conclusions: Using deep learning modeling, we found a monotonic relationship between SBP and risk of cardiovascular outcomes in patients with diabetes, without evidence of a J-shaped relationship