UNASSIGNED: The contribution of atrial fibrillation (AF) to the etiology and burden of stroke may vary by country income level.
UNASSIGNED: We examined differences in the prevalence of AF and described variations in the magnitude of the association between AF and ischemic stroke by country income level.
UNASSIGNED: In the INTERSTROKE case-control study, participants with acute first ischemic stroke were recruited across 32 countries. We included 10,363 ischemic stroke cases and 10,333 community or hospital controls who were matched for age, sex, and center. Participants were grouped into high-income (HIC), upper-middle-income (subdivided into two groups-UMIC-1 and UMIC-2), and lower-middle-income (LMIC) countries, based on gross national income. We evaluated the risk factors for AF overall and by country income level, and evaluated the association of AF with ischemic stroke.
UNASSIGNED: AF was documented in 11.9% (n = 1235) of cases and 3.2% (n = 328) of controls. Compared to HIC, the prevalence of AF was significantly lower in UMIC-2 (aOR 0.35, 95% CI 0.29-0.41) and LMIC (aOR 0.50, 95% CI 0.41-0.60) on multivariable analysis. Hypertension, female sex, valvular heart disease, and alcohol intake were stronger risk factors for AF in lower-income countries, and obesity a stronger risk factor in higher-income countries. The magnitude of association between AF and ischemic stroke was significantly higher in lower-income countries compared to higher-income countries. The population attributable fraction for AF and stroke varied by region and was 15.7% (95% CI 13.7-17.8) in HIC, 14.6% (95% CI 12.3-17.1) in UMIC-1, 5.7% (95% CI 4.9-6.7) in UMIC-2, and 6.3% (95% CI 5.3-7.3) in LMIC.
UNASSIGNED: Risk factors for AF vary by country income level. AF contributes to stroke burden to a greater extent in higher-income countries than in lower-income countries, due to a higher prevalence and despite a lower magnitude of odds ratio.

The study of the etiology of acute febrile illness (AFI) has historically been designed as a prevalence of pathogens detected from a case series. This strategy has an inherent unrealistic assumption that all pathogen detection allows for causal attribution, despite known asymptomatic carriage of the principal causes of acute febrile illness in most low- and middle-income countries (LMICs). We designed a semi-quantitative PCR in a modular format to detect bloodborne agents of acute febrile illness that encompassed common etiologies of AFI in the region, etiologies of recent epidemics, etiologies that require an immediate public health response and additional pathogens of unknown endemicity. We then designed a study that would delineate background levels of transmission in the community in the absence of symptoms to provide corrected estimates of attribution for the principal determinants of AFI.
A case-control study of acute febrile illness in patients ten years or older seeking health care in Iquitos, Loreto, Peru, was planned. Upon enrollment, we will obtain blood, saliva, and mid-turbinate nasal swabs at enrollment with a follow-up visit on day 21-28 following enrollment to attain vital status and convalescent saliva and blood samples, as well as a questionnaire including clinical, socio-demographic, occupational, travel, and animal contact information for each participant. Whole blood samples are to be simultaneously tested for 32 pathogens using TaqMan array cards. Mid-turbinate samples will be tested for SARS-CoV-2, Influenza A and Influenza B. Conditional logistic regression models will be fitted treating case/control status as the outcome and with pathogen-specific sample positivity as predictors to attain estimates of attributable pathogen fractions for AFI.
The modular PCR platforms will allow for reporting of all primary results of respiratory samples within 72 h and blood samples within one week, allowing for results to influence local medical practice and enable timely public health responses. The inclusion of controls will allow for a more accurate estimate of the importance of specific prevalent pathogens as a cause of acute illness.
Project 1791, Registro de Proyectos de Investigación en Salud Pública (PRISA), Instituto Nacional de Salud, Perú.

UNASSIGNED: The study of the etiology of acute febrile illness (AFI) has historically been designed as a prevalence of pathogens detected from a case series. This strategy has an inherent unrealistic assumption that all pathogen detection allows for causal attribution, despite known asymptomatic carriage of the principal causes of acute febrile illness in most low- and middle-income countries (LMICs). We designed a semi-quantitative PCR in a modular format to detect bloodborne agents of acute febrile illness that encompassed common etiologies of AFI in the region, etiologies of recent epidemics, etiologies that require an immediate public health response and additional pathogens of unknown endemicity. We then designed a study that would delineate background levels of transmission in the community in the absence of symptoms to provide corrected estimates of attribution for the principal determinants of AFI.
UNASSIGNED: A case-control study of acute febrile illness in patients ten years or older seeking health care in Iquitos, Loreto, Peru, was planned. Upon enrollment, we will obtain blood, saliva, and mid-turbinate nasal swabs at enrollment with a follow-up visit on day 21-28 following enrollment to attain vital status and convalescent saliva and blood samples, as well as a questionnaire including clinical, socio-demographic, occupational, travel, and animal contact information for each participant. Whole blood samples are to be simultaneously tested for 32 pathogens using TaqMan array cards. Mid-turbinate samples will be tested for SARS-CoV-2, Influenza A and Influenza B. Conditional logistic regression models will be fitted treating case/control status as the outcome and with pathogen-specific sample positivity as predictors to attain estimates of attributable pathogen fractions for AFI.
UNASSIGNED: The modular PCR platforms will allow for reporting of all primary results of respiratory samples within 72 hours and blood samples within one week, allowing for results to influence local medical practice and enable timely public health responses. The inclusion of controls will allow for a more accurate estimate of the importance of specific, prevalent pathogens as a cause of acute illness.
UNASSIGNED: Project 1791, Registro de Proyectos de Investigación en Salud Pública (PRISA), Instituto Nacional de Salud, Perú.

To provide the first estimates of the risk of suicide after bereavement by the suicide of any first-degree relative and the proportion of suicides in Denmark attributable to suicide bereavement.
We conducted a nationwide nested case-control study defining cases as all Danish-born individuals who died by suicide in Denmark between 01 January 1980 and 31 December 2016 (n = 32,248), age-matched to four living controls. Using three exposure categories (bereavement by the suicide of a relative [parent, offspring, sibling, and spouse/cohabitee]; non-suicide bereavement; no bereavement) and conditional logistic regression adjusted for pre-specified covariates we estimated the odds of exposure to suicide bereavement in cases versus controls. We tested whether associations differed for men and women, estimated the population attributable fraction (PAF) of suicides in our population at risk that could be attributed to a first-degree relative\'s suicide loss, and estimated the attributable fraction among the exposed (AFe).
Suicide bereavement was associated with an increased odds of suicide when compared with no bereavement (ORadj2  = 2.90, 95% CI: 2.46-3.40) or non-suicide bereavement (ORadj2  = 1.48, 95% CI: 1.25-1.74). There was no evidence to support any interaction with sex. PAF (0.69%; 95% CI: 0.62%-0.77%) and AFe (60.12%; 95% CI: 53.19%-66.03%) estimates suggested that in Denmark 0.69% of suicides, and 60% of suicides among suicide-bereaved relatives, could be prevented if it was possible to address all factors increasing suicide risk in suicide-bereaved relatives.
Suicide bereavement in relatives and partners contributes to at least one in 145 suicides in Denmark.

Our knowledge of suicide in low-income countries is limited. Understanding the importance of factors that contribute to suicide risk will allow for the appropriate allocation of limited resources. In order to prioritize suicide prevention activities in Bangladesh, we estimate the fractions of suicides attributable to key risk factors.
Using data from matched cases (100) and controls (100) as part of a psychological autopsy study in Dhaka, we estimate the population attributable fraction for key clinical (psychiatric disorders and physical disability), and social (life events, psychical and/or sexual abuse, unemployment, and social isolation) risk factors for suicide in Bangladesh.
Assuming a causal relationship, life events were responsible for the largest proportion of suicide deaths (85.9%; confidence interval [CI], 79.6-90.2), followed by mental disorder (49.5%; CI, 45.3-53.4). The population attributable fraction for the risk factors was 42.9% (CI, 40.6-45) for depression, 11% (CI, 8.9-13) for sexual abuse, and 34.9% (CI, 10.1-52.9) for social isolation.
The study determined the population attributable fraction of risk factors for suicide in Bangladesh. Prevention strategies should be prioritized on the management of the aftermaths of adverse life events, treatment of psychiatric disorders, sexual abuse, and social isolation in the country.

The relationship between reproductive factors and breast cancer (BC) risk has been investigated in previous studies. Considering the discrepancies in the results, the aim of this study was to estimate the causal effect of reproductive factors on BC risk in a case-control study using the double robust approach of targeted maximum likelihood estimation.
This is a causal reanalysis of a case-control study done between 2005 and 2008 in Shiraz, Iran, in which 787 confirmed BC cases and 928 controls were enrolled. Targeted maximum likelihood estimation along with super Learner were used to analyze the data, and risk ratio (RR), risk difference (RD), andpopulation attributable fraction (PAF) were reported.
Our findings did not support parity and age at the first pregnancy as risk factors for BC. The risk of BC was higher among postmenopausal women (RR = 3.3, 95% confidence interval (CI) = (2.3, 4.6)), women with the age at first marriage ≥20 years (RR = 1.6, 95% CI = (1.3, 2.1)), and the history of oral contraceptive (OC) use (RR = 1.6, 95% CI = (1.3, 2.1)) or breastfeeding duration ≤60 months (RR = 1.8, 95% CI = (1.3, 2.5)). The PAF for menopause status, breastfeeding duration, and OC use were 40.3% (95% CI = 39.5, 40.6), 27.3% (95% CI = 23.1, 30.8) and 24.4% (95% CI = 10.5, 35.5), respectively.
Postmenopausal women, and women with a higher age at first marriage, shorter duration of breastfeeding, and history of OC use are at the higher risk of BC.

UNASSIGNED: Congenital hypothyroidism (CH), as one of the most common endocrine disorders, is a preventable cause of mental retardation.
UNASSIGNED: This study aimed to identify familial-related risk factors for CH in Iranian newborns.
UNASSIGNED: A population-based case-control study was performed on the National Registry System of patients with CH in Iran. In this study, 906 controls and 454 cases were studied for one year. Familial related factors were investigated using logistic regression models. Population attributable fraction (PAF) was also calculated for each significant risk factor.
UNASSIGNED: Using multivariate analysis, an increased risk for CH was observed in patients with congenital anomalies (odds ratio (OR): 5.77, 95% confidence interval (CI): 2.37 - 14.01), history of mental retardation in family (OR:2.10, 95% CI: 1.15-3.83), mother\'s hypothyroidism during pregnancy (OR: 2.01, 95% CI: 1.33 - 3.03), intra-family marriage (OR:1.49, 95% CI: 1.18 - 1.89), gestational diabetes (OR: 1.69, 95% CI: 1.09 - 2.63), having a hypothyroid child in the family (OR: 2.48, 95% CI: 1.39 - 4.42), and twins or more (OR: 2.61, 95% CI: 1.31 - 5.21). The highest PAF among familial-related risk factors for CH is related to the intra-family marriage (14.9%).
UNASSIGNED: This study revealed that familial-related risk factors and consanguine marriages play an essential role in the high incidence of CH in Iran. About 15% of CH in Iran could be attributed to intra-family marriage alone.

Population attributable fraction is a widely used measure for quantifying the disease burden associated with a modifiable exposure of interest at the population level. It has been extended to a time-varying measure, population attributable hazard function, to provide additional information on when and how the exposure\'s impact varies over time. However, like the classic population attributable fraction, the population attributable hazard is generally biased if confounders are present. In this article, we provide a natural definition of adjusted population attributable hazard to take into account the effects of confounders, and its alternative that is identifiable from case-control studies under the rare disease assumption. We propose a novel estimator, which combines the odds ratio estimator from logistic regression model, and the conditional density function estimator of the exposure and confounding variables distribution given the failure times of cases or the current times of controls from a kernel smoother. We show that the proposed estimators are consistent and asymptotically normal with variance that can be estimated empirically from the data. Simulation studies demonstrate that the proposed estimators perform well in finite sample sizes. Finally, we illustrate the method by an analysis of a case-control study of colorectal cancer. Supplementary materials for this article are available online.

Modern medicine is overwhelmed by a plethora of both established risk factors and novel biomarkers for diseases. The majority of this information is expressed by probabilistic measures of association such as the odds ratio (OR) obtained by calculating differences in average \"risk\" between exposed and unexposed groups. However, recent research demonstrates that even ORs of considerable magnitude are insufficient for assessing the ability of risk factors or biomarkers to distinguish the individuals who will develop the disease from those who will not. In regards to coronary heart disease (CHD), we already know that novel biomarkers add very little to the discriminatory accuracy (DA) of traditional risk factors. However, the value added by traditional risk factors alongside simple demographic variables such as age and sex has been the subject of less discussion. Moreover, in public health, we use the OR to calculate the population attributable fraction (PAF), although this measure fails to consider the DA of the risk factor it represents. Therefore, focusing on CHD and applying measures of DA, we re-examine the role of individual demographic characteristics, risk factors, novel biomarkers and PAFs in public health and epidemiology. In so doing, we also raise a more general criticism of the traditional risk factors\' epidemiology. We investigated a cohort of 6103 men and women who participated in the baseline (1991-1996) of the Malmö Diet and Cancer study and were followed for 18 years. We found that neither traditional risk factors nor biomarkers substantially improved the DA obtained by models considering only age and sex. We concluded that the PAF measure provided insufficient information for the planning of preventive strategies in the population. We need a better understanding of the individual heterogeneity around the averages and, thereby, a fundamental change in the way we interpret risk factors in public health and epidemiology.

Population attributable fraction (PAF) is widely used to quantify the disease burden associated with a modifiable exposure in a population. It has been extended to a time-varying measure that provides additional information on when and how the exposure\'s impact varies over time for cohort studies. However, there is no estimation procedure for PAF using data that are collected from population-based case-control studies, which, because of time and cost efficiency, are commonly used for studying genetic and environmental risk factors of disease incidences. In this article, we show that time-varying PAF is identifiable from a case-control study and develop a novel estimator of PAF. Our estimator combines odds ratio estimates from logistic regression models and density estimates of the risk factor distribution conditional on failure times in cases from a kernel smoother. The proposed estimator is shown to be consistent and asymptotically normal with asymptotic variance that can be estimated empirically from the data. Simulation studies demonstrate that the proposed estimator performs well in finite sample sizes. Finally, the method is illustrated by a population-based case-control study of colorectal cancer.