BACKGROUNDGenetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking. METHODSWe used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity. RESULTSProtein-truncating variants in 5 genes (ATM, BRCA1, BRCA2, CHEK2, and PALB2) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (BARD1, RAD51C, RAD51D, and TP53) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in ATM and CHEK2, odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in BARD1, BRCA1, BRCA2, PALB2, RAD51C, and RAD51D, odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in ATM, CHEK2, and TP53 were associated with a risk of breast cancer overall with a P value of less than 0.001. For BRCA1, BRCA2, and TP53, missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants. CONCLUSIONSThe results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.

The MoCA-S is a valid screening tool and is useful for identifying MCI and MD in Colombia. The MoCA-S is valid and adequate for application in Colombia with good internal consistency, inter-observer reliability, and content validity. However, the average educational level was high in this study; thus, caution should be exercised when extrapolating these results to individuals with lower educational levels.

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RESUMENLos estudios sobre polisomnografía en lactantes entre los 2200 m y los 2800 m de altura son escasos. Esta investigación tuvo como objetivo describir la saturación de oxígeno (SpO 2 ) durante el sueño en niños de 1-4 meses a 2560 m y, como objetivos secundarios, la descripción de los índices de apnea y respiración periódica (RP). Se realizaron polisomnografías en 35 lactantes sanos de 1-4 meses en Cuenca (Ecuador) a 2560 m. La mediana para la SpO 2 fue 92% y 4,9% para la RP. El índice de apnea central tuvo una mediana de 23,7/hora, que disminuyó a 4,5/hora al descontar las apneas asociadas a RP. No hubo correlación entre la RP y la SpO 2 . Conclusión: la SpO 2 fue inferior a los valores del nivel del mar, y la RP y el índice de apnea central, mayores. Cuando las apneas asociadas a RP se descontaron, el índice de apnea central fue similar al del nivel del mar. Palabras clave : sueño, lactante, altitud, oxígeno, polisomnografía. http://dx

Objectives. To quantify socioeconomic inequalities in COVID-19 mortality in Colombia and to assess the extent to which type of health insurance, comorbidity burden, area of residence, and ethnicity account for such inequalities. Methods. We analyzed data from a retrospective cohort of COVID-19 cases. We estimated the relative and slope indices of inequality (RII and SII) using survival models for all participants and stratified them by age and gender. We calculated the percentage reduction in RII and SII after adjustment for potentially relevant factors. Results. We identified significant inequalities for the whole cohort and by subgroups (age and gender). Inequalities were higher among younger adults and gradually decreased with age, going from RII of 5.65 (95% confidence interval [CI] = 3.25, 9.82) in participants younger than 25 years to RII of 1.49 (95% CI = 1.41, 1.58) in those aged 65 years and older. Type of health insurance was the most important factor, accounting for 20% and 59% of the relative and absolute inequalities, respectively. Conclusions. Significant socioeconomic inequalities exist in COVID-19 mortality in Colombia. Health insurance appears to be the main contributor to those inequalities, posing challenges for the design of public health strategies. (Am J Public Health. 2022;112(S6):S586–S590. https://doi.org/10.2105/AJPH.2021.306637 )

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Objetivo. Cuantificar las desigualdades socioeconómicas en la mortalidad por COVID-19 en Colombia y evaluar en qué medida el tipo de seguro de salud, la carga de enfermedades concomitantes, la zona de residencia y el origen étnico explican estas desigualdades. Métodos. Se analizaron los datos de una cohorte retrospectiva de casos de COVID-19. Se estimó el índice relativo de desigualdad (IRD) y el índice de desigualdad basado en la pendiente (IDP) utilizando modelos de supervivencia con todos los participantes, y estratificándolos por edad y sexo. El porcentaje de reducción del IRD y el IDP se calculó después de ajustar con respecto a factores que podrían ser relevantes. Resultados. Se pusieron en evidencia desigualdades notables en toda la cohorte y en los subgrupos (edad y sexo). Las desigualdades fueron mayores en los adultos más jóvenes y disminuyeron de manera gradual con la edad, pasando de un IRD de 5,65 (intervalo de confianza de 95% [IC 95%] = 3,25-9,82) en los participantes menores de 25 años a un IRD de 1,49 (IC 95% = 1,41-1,58) en los mayores de 65 años. El tipo de seguro de salud fue el factor más importante, al cual se atribuyó 20% de las desigualdades relativas y 59% de las absolutas. Conclusiones. La mortalidad por COVID-19 en Colombia presenta importantes desigualdades socioeconómicas. El seguro de salud aparece como el factor que más contribuye a estas desigualdades, lo cual plantea retos al diseño de las estrategias de salud pública.