Reproductive factors with respect to breast cancer risk and breast cancer survival

Aims: The primary aim of this thesis was to examine the potential relationship between indirect markers of exposure to hormones during pregnancy and the risk of and survival from breast cancer, with special emphasis on young patients. Our specific objectives were as follows: to determine whether the association between placental weight and offspring size, on the one hand, and maternal mortality from breast cancer, on the other, are influenced by tumor characteristics; to examine the association between birth weight and risk of breast cancer in the female member of opposite-sexed twins; and to investigate whether familial factors influence previously reported association between reproductive factors and risk of breast cancer. Methods: Based on the Swedish Quality Register of Breast Cancer, two different cohort studies were designed in the Stockholm-Gotland and Uppsala-Örebro regions, where records on characteristics of breast cancer have been collected since 1992. The first cohort was restricted to women who had a pregnancy between 1982 and 1989, and subsequently developed breast cancer. The cohort included 1,067 subjects and 180 deaths, and was conducted to investigate if placental weight is associated with maternal risk of dying from breast cancer, taking tumor characteristics into account. In the second study, we studied the possible association between birth weight and maternal risk of death from breast cancer, also taking tumor characteristics into account. We included 6,019 women who had a pregnancy between 1973 and 2008 and subsequently developed breast cancer, of whom 1017 died from the disease. Two case-control studies were also performed. In a nested case-control study, involving the female members of opposite sexed twin pairs, 543 cases and 2715 controls were included to investigate the potential association between offspring birth weight and risk of breast cancer, as well as a possible modifying effect of birth weight of the male twin sibling. Information on the twins (including birth weight, birth height, head circumference and gestational age of the females, and birth weight of the male co-twin) was extracted from the Swedish Twins Register and data on women diagnosed with breast cancer from the Swedish Cancer Register. A second case-control study examined the potential modifying effect of familial factors on the association between reproductive factors and the risk of breast cancer. All women who delivered between 1973 and 2010 and had a full sister were selected as the study population, using the Swedish Medical Birth Register. Information on breast cancer was obtained from the Swedish Cancer Register and sisters were identified using the Swedish Multi-Generation Register. The cases examined included all parous women diagnosed with breast cancer between 1973 and 2010 who were 50 years old or younger and had at least one sister who also gave birth during this same period. The two control groups were sister controls (including the sister without breast cancer and closest in age to the case) and population controls (all parous women without breast cancer with at least a full sister except those in the sisters control group). In total, 8,349 cases, 8,349 sister controls, and 1,053,688 population controls were used. Results: Our findings indicate that the association between higher placental weight in connection with the most recent pregnancy and maternal risk of mortality from premenopausal breast cancer is dependent on the receptor status of the tumor. A positive association was more pronounced in the case of ER-/PR- tumors, but we did not find a dose– response association. Birth weight demonstrated no association with maternal mortality from premenopausal breast cancer, even in analyses stratified by the time that elapsed between pregnancy and cancer diagnosis, tumor stage, and receptor status. There was an inverse association between birth-weight-for-gestational age and mortality from premenopausal breast cancer among uniparous women. The nested case-control study of opposite-sexed twins did not reveal any statistically significant association between birth weight and risk of breast cancer. Furthermore, we observed no associations between other birth characteristics, including co-twin birth weight, and the risk of developing pre- or postmenopausal breast cancer. Our last study provided some evidence that the association between reproductive factors and maternal risk of breast cancer or between maternal factors and maternal risk of breast cancer may differ when using population or sister controls. We found that parity exhibited an inverse association to premenopausal breast cancer using population controls and was a risk factor using sister controls, suggesting a gene-environment interaction. Very preterm delivery (<31 weeks) was associated with a higher breast cancer risk using sister controls than when population controls were used, also suggesting a gene-environment interaction. environment interaction, as was the association between gestational age and the risk of breast cancer. Conclusions: We found some, but no strong evidence in support of the hypothesis that higher hormone levels during pregnancy are associated with mortality from premenopausal breast cancer. The hypothesis was supported when placental weight was employed as indirect indicator of estrogen levels during pregnancy, although birth weight showed no such association. The more pronounced effect of placental weight among ER-/PR- tumors suggests that premenopausal hormonal exposure might exert a greater impact on such tumors. The association between parity and risk of premenopausal breast cancer was modified by a gene-environment interaction, as was the association between gestational age and the risk of breast cancer

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review

AimTo perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources.Study eligibility criteriaCohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.Data sourcesArticles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened.Data extractionWe extracted information on data sources, AI models, and epidemiological aspects of retrieved studies.Bias assessmentA bias assessment of AI models was done using PROBAST.ParticipantsPatients tested positive for COVID-19.ResultsWe included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size &lt;5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values &gt;0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability.ConclusionsA broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected

Comparison of drug prescribing before and during the COVID‐19 pandemic : a cross‐national European study

Purpose: The COVID-19 pandemic had an impact on health care, with disruption to routine clinical care. Our aim was to describe changes in prescription drugs dispensing in the primary and outpatient sectors during the first year of the pandemic across Europe. Methods: We used routine administrative data on dispensed medicines in eight European countries (five whole countries, three represented by one region each) from January 2017 to March 2021 to compare the first year of the COVID-19 pandemic with the preceding 3 years. Results: In the 10 therapeutic subgroups with the highest dispensed volumes across all countries/regions the relative changes between the COVID-19 period and the year before were mostly of a magnitude similar to changes between previous periods. However, for drugs for obstructive airway diseases the changes in the COVID-19 period were stronger in several countries/regions. In all countries/regions a decrease in dispensed DDDs of antibiotics for systemic use (from −39.4% in Romagna to −14.2% in Scotland) and nasal preparations (from −34.4% in Lithuania to −5.7% in Sweden) was observed. We observed a stockpiling effect in the total market in March 2020 in six countries/regions. In Czechia the observed increase was not significant and in Slovenia volumes increased only after the end of the first lockdown. We found an increase in average therapeutic quantity per pack dispensed, which, however, exceeded 5% only in Slovenia, Germany, and Czechia. Conclusions: The findings from this first European cross-national comparison show a substantial decrease in dispensed volumes of antibiotics for systemic use in all countries/regions. The results also indicate that the provision of medicines for common chronic conditions was mostly resilient to challenges faced during the pandemic. However, there were notable differences between the countries/regions for some therapeutic areas

Risk of Premenopausal and Postmenopausal Breast Cancer among Multiple Sclerosis Patients.

To investigate risk of premenopausal and postmenopausal breast cancer among Multiple Sclerosis (MS) patients, considering tumor stage.The Swedish Patient Register identified 19,330 women with MS between 1968 and 2012, matched individually with a cohort of 193,458 without MS. Matching variables were year of birth, sex, region of residence and vital status at the time of diagnosis. The cancer register identified 471 and 5,753 breast cancer cases among the MS and non-MS cohorts, respectively. Cox proportional hazard models estimated hazard ratios (HR) and 95% confidence intervals (CI) for premenopausal and postmenopausal breast cancer.Overall risk of postmenopausal breast cancer was 13% higher among MS patients compared with women without MS (HR = 1.13, 95% CI 1.02-1.26). Stratified analyses showed that the risk was statistically significantly increased in women diagnosed between 1968 and 1980 and those who were diagnosed at age 65 or older age. We observed a non-statistically significant risk only for stage 0-1 postmenopausal breast cancer (HR = 1.17, 95% CI 0.93-1.48). MS was not associated with premenopausal breast cancer.The modest increased risk of postmenopausal breast cancer in women with MS may be due to surveillance bias, where contact with health services for one disease increases the risk of a second diagnosis being recorded

A Comparison Study on Socio-Economic Variables and Life Satisfaction Among the Elders people, Gorgan, in 2004 and 2009

Objectives: The elder population and their proportion of the total population are increasing in our country. Their population has reached to 7.3 percent of total population in 2006 compared with their population at 1996 and it shows a 1.5 times increase during ten years.The aim of this study was to compare the socio-economic situation of elder people who were living in two areas in Gorgan city, Golestan province in north of Iran, which is covered by 4th and 5th urban health center between in 2004 and 2009. Methods & Materials: A descriptive-analytical cross-sectional study has been conducted among 884 elder people who were resident in the study area at 2009. Data collection has been done through a questionnaire, filled out by trained persons. The results are compared with the outcomes of pervious unpublished study at 2004 which has been carried out among 315 elder people in the same study area. Qui-Square and independent T-test statistical methods used to analysis the data. We use SAS version 9.2 to analyze the data. Results: Mean age of elder people was 67.2&plusmn;6.7 at 2009 and 67.6&plusmn;6.7 years at 2004. Educational level had a significant change in 2009 compared with 2004 (P=0.0002). Compared with 2004, marital status (P=0.0021) and economical level (P<0.0001) had statistically significant changes in 2009. Moreover, visiting friends, going to park, going for movies and visiting the family showed statistical significant change in 2009 compared to 2004 by P<0.001, P=0.0173, P=0.0001, P=0.0435 and P=0.0001 respectively. In addition, being high energetic showed a statistically significant change (P<0.01) in 2009 compared to 2004, when we considered the satisfaction of life among elders. Conclusion: It is necessary to pay more attention to social, economic and life satisfaction problems of elder people which are dramatically growing by increasing the elder population and their higher proportion in entire population of the world particularly in Iran. We need a comprehensive plan to cover and solve their problems in these fields

Characteristics of MS patients and non-MS matched comparators.

<p>Characteristics of MS patients and non-MS matched comparators.</p

Incidence Rate, Hazard ratios (HR) and 95% confidence intervals (CI) for association between MS and breast cancer, stratified by stage of cancer and menopausal status.

<p>Incidence Rate, Hazard ratios (HR) and 95% confidence intervals (CI) for association between MS and breast cancer, stratified by stage of cancer and menopausal status.</p

Incidence rate, Hazard ratios (HR) and 95% confidence intervals (CI) for association between MS and breast cancer, stratified by menopausal status.

<p>Incidence rate, Hazard ratios (HR) and 95% confidence intervals (CI) for association between MS and breast cancer, stratified by menopausal status.</p

Initiation of antihypertensive drugs to patients with confirmed COVID-19-A population-based cohort study in Sweden

Purpose Hypertension is an important risk factor for severe outcomes in patients with COVID-19, and antihypertensive drugs may have a protective effect. However, the pandemic may have negatively impacted health care services for chronic diseases. The aim of this study was to assess initiations of antihypertensive medicines in patients infected by COVID-19. Methods A cohort study including all Swedish residents 20-80 years old with a COVID-19 positive test compared with an unexposed group without COVID-19 matched for age, sex, and index date (date of confirmed COVID-19). Data were collected within SCIFI-PEARL, a study including linked data on COVID tests, hospital diagnoses, dispensed prescriptions, and socioeconomic data from Swedish national registers. Initiations of different antihypertensive drugs were studied from March 2020 until October 2020. Associations between COVID-19 and initiation of antihypertensives were assessed by a multivariable Cox proportional hazards model. Results A total of 224 582 patients (exposed and unexposed) were included. After adjusting for cardiovascular comorbidities and education level, ACEi was the most commonly initiated antihypertensive agent to patients with COVID-19. Hazard ratio and 95% confidence interval for initiation of drug therapy was 1.83 [1.53-2.19] for ACEi, followed by beta-blockers 1.74 [1.55-1.95], calcium channel blockers 1.61 [1.41-1.83], angiotensin receptor blockers 1.61 [1.40-1.86], and diuretics 1.53 [1.32-1.77]. Conclusion All antihypertensive medicines were initiated more frequently in COVID-19 patients. This can either be associated with hypertension caused by the COVID-19 infection, more frequent diagnosis of hypertension among people with COVID-19 since they consult health care, or residual confounding factors not adjusted for in the study

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death : a systematic review

Aim: To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources. Study eligibility criteria: Cohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Data sources: Articles recorded in OvidMEDLINE from 01/01/2019 to 22/08/2022 were screened. Data extraction: We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies. Bias assessment: A bias assessment of AI models was done using PROBAST. Participants: Patients tested positive for COVID-19. Results: We included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size &lt;5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values &gt;0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability. Conclusions: A broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected