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A large number of taxonomies are used to rate the quality of an individual study and the strength of a recommendation based on a body of evidence. We have developed a new grading scale that will be used by several family medicine and primary care journals (required or optional), with the goal of allowing readers to learn one taxonomy that will apply to many sources of evidence. Our scale is called the Strength of Recommendation Taxonomy. It addresses the quality, quantity, and consistency of evidence and allows authors to rate individual studies or bodies of evidence. The taxonomy is built around the information mastery framework, which emphasizes the use of patient-oriented outcomes that measure changes in morbidity or mortality. An A-level recommendation is based on consistent and good quality patient-oriented evidence; a B-level recommendation is based on inconsistent or limited quality patientoriented evidence; and a C-level recommendation is based on consensus, usual practice, opinion, disease-oriented evidence, or case series for studies of diagnosis, treatment, prevention, or screening. Levels of evidence from 1 to 3 for individual studies also are defined. We hope that consistent use of this taxonomy will improve the ability of authors and readers to communicate about the translation of research into practice. Review articles (or overviews) are highly valued by physicians as a way to keep up to date with the medical literature. Sometimes, though, these articles are based more on the authors' personal experience, or anecdotes, or incomplete surveys of the literature than on a comprehensive collection of the best available evidence. As a result, there is an ongoing effort in the medical publishing field to improve the quality of review articles through the use of more explicit grading of the strength of evidence on which recommendations are based.

In the randomized, controlled trials, mammography reduced breast cancer mortality rates among women 40 to 74 years of age. Greater absolute risk reduction was seen among older women. Because these results incorporate several rounds of screening, the actual number of mammograms needed to prevent one death from breast cancer is higher. In addition, each screening has associated risks and costs.

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ife expectancy at birth, a common measure of a population's health, 1 has decreased in the United States for 3 consecutive years. 2 This has attracted recent public attention, 3 but the core problem is not new-it has been building since the 1980s. 4,5 Although life expectancy in developed countries has increased for much of the past century, US life expectancy began to lose pace with other countries in the 1980s 6,7 and, by 1998, had declined to a level below the average life expectancy among Organisation for Economic Cooperation and Development countries. 8 While life expectancy in these countries has continued to increase, 9,10 US life expectancy stopped increasing in 2010 and has been decreasing since 2014. 2,11 Despite excessive spending on health care, vastly exceeding that of other countries, 12 the United States has a long-standing health disadvantage relative to other high-income countries that extends beyond life expectancy to include higher rates of disease and cause-specific mortality rates. 6,7,10,13 This Special Communication has 2 aims: to examine vital statistics and review the history of changes in US life expectancy and increasing mortality rates; and to identify potential contributing factors, drawing insights from current literature and from a new analysis of state-level trends. Methods Data Analysis MeasuresThis report examines longitudinal trends in life expectancy at birth and mortality rates (deaths per 100 000) in the US population, IMPORTANCE US life expectancy has not kept pace with that of other wealthy countries and is now decreasing. OBJECTIVE To examine vital statistics and review the history of changes in US life expectancy and increasing mortality rates; and to identify potential contributing factors, drawing insights from current literature and an analysis of state-level trends.EVIDENCE Life expectancy data for 1959-2016 and cause-specific mortality rates for 1999-2017 were obtained from the US Mortality Database and CDC WONDER, respectively. The analysis focused on midlife deaths (ages 25-64 years), stratified by sex, race/ethnicity, socioeconomic status, and geography (including the 50 states). Published research from January 1990 through August 2019 that examined relevant mortality trends and potential contributory factors was examined.FINDINGS Between 1959 and, US life expectancy increased from 69.9 years to 78.9 years but declined for 3 consecutive years after 2014. The recent decrease in US life expectancy culminated a period of increasing cause-specific mortality among adults aged 25 to 64 years that began in the 1990s, ultimately producing an increase in all-cause mortality that began in 2010. During 2010-2017, midlife all-cause mortality rates increased from 328.5 deaths/100 000 to 348.2 deaths/100 000. By 2014, midlife mortality was increasing across all racial groups, caused by drug overdoses, alcohol abuse, suicides, and a diverse list of organ system diseases. The largest relative increases in midlife mortality rates occurred in

The number of publicly reported deaths from coronavirus disease 2019 (COVID-19) may underestimate the pandemic's death toll. Such estimates rely on provisional data that are often incomplete and may omit undocumented deaths from COVID-19. Moreover, restrictions imposed by the pandemic (eg, stay-at-home orders) could claim lives indirectly through delayed care for acute emergencies, exacerbations of chronic diseases, and psychological distress (eg, drug overdoses). This study estimated excess deaths in the early weeks of the pandemic and the relative contribution of COVID-19 and other causes. Methods | Weekly death data for the 50 US states and the District of Columbia were obtained from the National Center for Health Statistics for January through April 2020 and the preceding 6 years (2014-2019). 1,2 US totals excluded Connecticut and North Carolina because of missing data. The analysis included total deaths and deaths from COVID-19, influenza/pneumonia, heart disease, diabetes, and 10 other grouped causes (Supplement). Mortality rates for causes other than COVID-19 were available only for underlying causes. Death data with any mention of COVID-19 on the death certificate (as an underlying or contributing cause) were used to capture all deaths attributed to the virus. Population counts for calculating mortality rates were obtained from the US Census Bureau. 3,4 Observed deaths for the 8 weeks between March 1, 2020, and April 25, 2020, were taken from provisional data released on June 10, 2020. 2 Expected deaths (and 95% CIs) for these same weeks were estimated by fitting a hierarchical Poisson regression model to the weekly death counts for the period of