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Western memorabilia, vintage advertising, antique buttons, glass and lighting, and estate treasures and classic collectibles were among the subjects featured in auction/shop/show advertisements that recently appeared in Antique Trader.
Antique Shows
Button collectors gather Aug. 4-6
More than 500,000 buttons will be on display and available for sale during the National Button Show Aug. 4-6 in Denver, Colorado.
Political item collectors to open convention show to public
The American Political Items Collectors (APIC) will host its annual convention July 6-10, and for the first time, will open the show portion of the convention to the public.
Listing your antique shows and auctions in Antique Trader is free and easy
Following the rules is the only cost of getting your free listings for antique shows and auctions in Antique Trader magazine and on antiquetrader.co
Perfume group promises to 'Keep Portland Weird'
Perfume bottle collectors from around the globe are preparing to converge on Portland, Ore. April 27-May 1, for the International Perfume Bottle Association (IPBA)'s 28th annual convention.
Rare Marriage Contract signed by Napoleon & Josephine tells the story of history's most celebrated couple
Centuries before the fascination with celebrity relationships of today, Napoleon and Josephine were one of the 'it' couples of the late 18th century. Lion Heart Autographs is bringing an seldom-seen artifact bearing both of their signatures to The Palm Beach Jewelry, Art & Antique Show Feb. 10-16.
Des Moines hosting Antiques Spectacular Feb. 5-8
The Knapp Animal Learning Center on the Iowa State Fairgrounds will be transformed into the Antiques Spectacular Feb. 5-8.
Junk Bonanza launches 2016 season in Calif. Feb. 19-20
The first Junk Bonanza event of 2016 will take place in Del Mar, Calif., featuring careful curated vendors offering vintage furniture, decor, jewelry, textiles and apparel, as well as some handcrafted items.
Thousands of maps, charts, atlases coming to Miami Feb. 5-7
Feb. 5-7 promises to be a cartography fans dream weekend in Miami, as the Miami International Map Fair brings in dealers with centuries of maps, from around the world.
Jewelry History Series returns Jan. 26-27, 2016
A viewing of the acclaimed documentary "Sharing the Rough" will be part of the Third Annual Jewelry History Series (Jan. 26-27), a lead in conference to The Original Miami Beach Antique Show, Jan. 28-Feb.1.


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WSU Hosting Girls In Science Conference
Associated Press - October 9, 2013 6:24 am
An affidavit says a slain Wichita State University student contacted a woman charged in his death about sexual services she advertised online shortly before he was killed.
Wichita State University is scheduled to host a conference aimed at encouraging middle-school girls to consider careers in science disciplines.
The Expanding Your Horizons conference is set for Nov. 2 at WSU. The university says in a release the conference is part of a national program that gives girls a chance to meet role models in science, technology, engineering and math.
Registration is limited to the first 100 participants and runs through Oct. 15. The registration fee is $12.
Workshops will include "Robot Basketball," and "Pet Rescue 911." Parents can attend sessions on financing college and ACT test prep.
Associated Press information from: The Wichita Eagle

subset: commoncrawl


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- Overview
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Pearl Izumi Race RD III Road ShoesThe Pearl Izumi Race RD III Road Shoes are a great choice for road cyclists who want benchmark performance with the secure fit of the BOA closure system for optimum power transfer. Select 1:1 Power Plate is lightweight, rigid and its built-in Longitudinal Arch support increases pedaling efficiency. Bi-directional closure distributes pressure evenly across the foot and the secure BOA reel lets you customize the fit, plus it's easy to adjust while riding.
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Pearl Izumi Race RD III Road Shoes
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Anniversary Sale: Event available 7/22/16 - 7/31/16 at all Performance Bicycle Retail locations, online and by phone. Advertised category and/or brand savings taken off lowest marked price on all applicable items during the event period. Some brand and/or item exclusions may apply to this promotion – visit Terms and Conditions for details. Bikes and items labeled Doorbusters are priced as marked and are excluded from advertised category and/or brand discounts. Doorbuster availability limited to stock on hand. Online, discount taken at checkout. Taxes, gift cards, Team Performance memberships/renewals, Spin Doctor Services/Service Plans or shipping and handling on web/phone orders are not eligible to receive discounts. Discounts cannot be applied to previous purchases. Sorry, no rainchecks. In retail stores, colors, styles and select brand availability varies by location. Photographic, typographic and pricing errors are unintentional and subject to change.


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tag:blogger.com,1999:blog-9215652504344172254.post6019068702069310953..comments2015-08-13T19:43:13.349-04:00Comments on Black Aces: Gong Show: The SequelJeremy Milkshttp://email@example.com:blogger.com,1999:blog-9215652504344172254.post-5463155155545712932011-01-07T00:50:34.343-05:002011-01-07T00:50:34.343-05:00It seems that Murray's press conference has ki...It seems that Murray's press conference has killed the story. That's a good thing, because things could have gotten pretty crazy.

That said, if the story that he called Melnyk to fire Clouston, is true, then I gotta think that Murray is toast now.

Imagine you're Melnyk. You had a private conversation with your GM. And, you just found out that he leaked it to the media.

Not very smart. But, we just found out that it didn't happen, so all is well ...

As for Clouston, I think that he`s a good coach, but it ain`t working anymore. I think that he's carried himself with a lot of dignity and class, at least that's how it appears on TV, while the media debates his survival.

Will things improve if he`s fired? I'd bet no. I think the problems run deeper.

But, you gotta think that Murray takes over if Clouston is cut loose. It's Murray's show, maybe he can pull a rabbitt out of his hat. He did a fine job coaching in 05-06 and 06-07. Although, he had a better team back then.

As for Lehner, I disagree with you, but not strongly. I think they should let him develop slowly in the AHL. When was the last time a 19-year old goalie had an impact in the NHL? He may be the goalie of the future, but I bet that he's a few years away from primetime. Fleury, Lehtonen, Dipietro, Price, and even Leclaire were all higher draft picks than Lehner and they took years to develop. I wouldn't rush the guy.

That said I don't think "ruining him" is a concern. The team is so bad that there's no pressure from the fan base right now. But, the blueprint should be the way Cowen has been handled. He's clearly made strides.

As for Chris Phillips, he's got to be signed. He's still got many years left. He's still good. He'll be even better when we have a better team. He knows how to win. He has tons of playoff experience.

I can go on and on. He has to be re-signed.

These guys like Phillips, Fisher, Spezza, Kelly, Neil ... they're not performing their best, but they don't look old out there. We need their playoff experience, their leadership, and their skills when we turn it around next year.

Who knows, maybe we even turn it around this year. But, for sure, with a new management team, I'd bet this team bounces back nicely next email@example.com:blogger.com,1999:blog-9215652504344172254.post-79611689008623511002011-01-06T09:15:14.686-05:002011-01-06T09:15:14.686-05:00I don't think anyone can say Murray is an idio...I don't think anyone can say Murray is an idiot. ANyone can stick in this league for an indefinite period, however. It's an old boys club and Murray's propensity to throw others under the bus and milk his Shawville roots (As if that matters? Hey Tampa Bay Lightning, please don't beat us, I'm from Shawville! Hey, Ducks, could you trade us Emerson Etem? I'm from Shawville!) while failing to take responsibility for his own actions. As Garry Galley said, leadership starts at the top and there is ZERO leadership from Bryan Murray.

Disagree again on Phillips. He is EXACTLY the guy, along with Fisher, that I want out. Any guy that WANTS to stay in a losing, underachieving environment is a guy I want far away from a team that is going to be moulding young players. I want guys that want to win a CUp, not just punch in for three hours then hit the BBQ/bar circuit with their pals. Fisher's statements in the Sun yesterday that he doesn't want to go anywhere paint him as a loser. Same with Phillips. A winner would want to get out of dodge and win while he still had the chance, instead of staying in this God-forsaken franchise.

If Phillips is signed to a multi-year deal I will be calling for the general manager's head. Let him go, replace him with a younger player. The veteran experience can come from Alfredsson (for whom gained a TON of respect when he said he wouldn't object to a trade if the team was terrible) or Neil or those guys, or the young guys just grow together.

Enough of the Kanata Boys Club already; clear them out and replace them with young, fast, cheap, speedy, hungry players that don't act as if they have a sense of entittlement a mile wide.

Walk away from Phillips, trade Fisher.

Let the new Roman Era begin!

PierreSenshttp://firstname.lastname@example.com


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Spitfire x Skeleton Key Coaches Windbreaker Jacket - Black#001035944£69.99Skeleton Key are all about the skateboarding! It's a good place to be when you have no idea what's "cool" in skateboarding, because you are just too focused about going out and skating and not what is ...more
Spitfire was created in 1987 in San Francisco under Deluxe Distribution by Jim Thiebaud and Fausto Vitello. They manufacture some of the best skate wheels designing them for function and speed. Using the Spitfire team rider's specification they are continually improving their wheels using top quality urethane to ensure the perfect ride. Spitfire has a great team of riders including Peter Ramondetta, Dylan Rieder, Dennis Busenitz and Sean Malto.


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... I got tagged. By to be exact.
The rules:
Each person has to share 10 facts about themselves.
Choose 10 people and put their icons in your journal.
If you're chosen person doesn't watch you, go to their page and inform them that they have been tagged. It's just courtesy.
Not something like "you're tagged if you read this"
You have to legitimately tag ten people, or less if you don't have many people to tag.
No tag backs.
Finally, all of this is completely optional. You can choose to keep this going, or you can just know that I tagged you and leave it at that.
With that all set, here are my answers to the tagger's questions:
1: Favorite game or game series?
Ooh, that's a tough one, I'd probably have to go with Fire Emblem, I can always count on it to keep me entertained.
2: If you could fix one mistake in your past, what would it be?
Okay, story time, when five years old, I was a ring bearer for my cousin's wedding. It was a merry occasion, everyone was having fun. The problem was, we were in the middle of a heat wave, so none of us had slept at all the previous night. Towards the end of the reception, I was tired and didn't want to be there anymore, so I decided to sit out when the photographer was taking the group photo. After the photo was taken, I don't know what it was, but there was this one thing that I REALLY didn't want to do, so I threw a tantrum right there, my father hoisting me over his shoulder to get me to the car so we could make a hasty escape. To this day we still have that group photo of twenty three smiling faces and one scowling little boy.
3: You're going alone on a trip through a desert, and you can bring only 15 things with you in your backpack. What would you bring? (size matters, so the base size of an item would be a standard-size canteen)
I honestly have no clue. I hate heat, so I probably wouldn't be taking a trip through the desert anyway.
4: If you could be any creature, real or fictional, what would it be?
Agh! Decisions! Maybe a gryphon? Some kind of dragon? Or perhaps some form of elemental? I dunno.
5: What is your worst fear?
That one's kinda hard to describe. I'm not really scared of the dark per se, but what I don't know could be hiding in it, kinda like how I'm also scared of the future because I don't know what it will hold.
6: Favorite anime (if not anime then TV show)?
Right now, I'd probably have to go with Fullmetal Alchemist or Hunter X Hunter.
7: If you were a character in Smash Bros. (or any fighting game you preference), what would be your special attacks, and if you want an ability, what would it be?
Why are all of these questions so hard to answer? *sigh* Hmmm... I think I would have some kind of energy powers or something. Maybe a few different attacks based on that, shooting beams or waves of blue energy at the very least.
8: If you could have any one superpower, what would it be?
Invulnerability, hands down. But kinda like, voluntary invulnerability? So that I can control what parts of me become indestructible. That way I wouldn't have to worry about getting hurt without realizing it, but still get an IV if I need to. Is that cheating?
9: Which do you prefer: Cinnamon or Butterscotch?
*Clears throat* Yes
10: What's your best talent?
Probably my artistic ability and creativity? I think that would be it. I can't really think of anything else.
Aight, so... I don't really know what kind of questions I'd ask, but I guess I'll give it a try anyway:
1: Favorite game or game series?
2: Worst subject in school?
3: Most hated holiday and why?
4: What RPG class would be most suited to you?
5: Favorite classical element? (e.i. Fire, water, earth and air)
6: Favorite way to eat potatoes?
7: How would you deal with this moral dilemma? [link]
8: If you could bring an endless supply of one particular item to a deserted island, what would it be?
9: Ketchup or Mustard?
10: What's your best talent?
I guess 7 will have to do!


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Learn more
Best of Bridge Slow Cooker Cookbook: 200 Delicious Recipes Spiral-bound – Jul 26 2012
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About the Author
Sally Vaughan-Johnston is a professional chef, food writer and food stylist. This is her third collaboration with the ladies of Best of Bridge -- a stellar collection of recipes that will be treasured for years to come.
What is the Best of Bridge?
In 1975, at a weekend getaway, eight women friends had an idea: Since a consistent highlight of their decade-old bridge group was the food they prepared and enjoyed together, perhaps they should think about writing a cookbook. This spur-of- the-moment notion was the impetus for The Best of Bridge, which went on to become one of the most successful brands in Canadian publishing, with more than three million books sold.
Written in a welcoming style, the recipes are easy to execute, yet produce "gourmet" results. Their unique approach, which includes jokes to keep cooks smiling, has ensured a faithful following. The Best of Bridge recipes are staples at potlucks across the country, and the generation of children raised on them is now cooking for themselves. "We get lots of mail from people looking for specific books to give to their adult kids," the Ladies report. "They want to make dishes that were always served at family celebrations. Our recipes have become part of their domestic traditions."
As the Ladies would say, "You should have these books. We've tested, tasted and revised. We promise you'll love every recipe."
What Other Items Do Customers Buy After Viewing This Item?
The Complete Best of Bridge Cookbooks Volume One Spiral-boundThe Editors of Best of BridgeCDN$ 22.44
The Complete Best of Bridge Cookbooks Volume Two Spiral-boundThe Editors of Best of BridgeCDN$ 15.95
Customer Reviews
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The recipes vary from great takes on old standbys (like the chicken stew and dumplings, which is excellent) to newer twists like Turkey Korma Meatballs, which were delicious! I have made at least two dozen of the recipes and have had only one flop - and when I posted about my disappointment on their Facebook page, they did some troubleshooting and figured out for me where I went wrong! (My red wine vinegar had gone rancid - not the cookbook's fault at all!)
There are occasionally some more unusual ingredients (like sambal oolek, Asian chili paste) but they are used in multiple recipes. Most of the recipes require usual staples.
The authors have come up with some great techniques for enhancing flavour and minimizing mushiness - many veggies are added in the last 20 minutes of cooking.
Prep time varies anywhere from 5-10 up to 40 minutes, depending on the recipe. Often there is a little chopping to do when you get home, to throw in the last ingredient.
Highly, highly recommended if you'd like to use your slow cooker more and are seeking healthy recipes that won't turn to mush.
Most recent customer reviews
There's a reason that there is "best" in the title, need I say more?Published 2 months ago by Brenda Cremer
I live in a cold climate and slow cooker recipes are the way to go. The aroma, after you walk in the door after work, cold and hungry, is like a big, warm hug of deliciousness.Published 5 months ago by Bey King


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The Apple Store is located in the Mall at Wellington Green, at 10300 West Forest Hill Boulevard in Wellington, FL. Traveling on I-95, exit Forest Hill Boulevard, head west, and cross State Road 7. Wellington Green is on the left side of Forest Hill Boulevard. The Apple Store is near Gap, on the second floor. Closest parking for the Apple Store is between JCPenney and Dillard's.
Apple Store, Wellington Green
Apple Camp Free sessions where kids can make their own movies, create interactive stories, or program robots. Learn more
iPhone Upgrade Program. Get a new iPhone every year and the coverage of AppleCare+. You can also buy iPhone 6s with carrier financing. See how it works
Join us for free iPhone workshops. Explore all the amazing things you can do in our hands-on workshops. Learn the basics or go further with your new iPhone. Learn more
The new MacBook Available to order online. Visit an Apple Retail Store for a hands-on demo. Learn more
Upcoming Store Events and Workshops
There are no upcoming events or workshops at this time. Please check back...
Meet the Business Team.
Whether you own a business or just use Apple products at your job, our Business Team can work with you to choose the right solutions.Learn more
Enjoy past Apple Store events.
Past Apple Store events from around the world are available as free podcasts from the iTunes Store.View in iTunes
Personal Setup.
With Personal Setup, we'll get your new Apple product up and running, get you started with iCloud, and more — before you leave the store.Learn more


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The role a mother plays in the development of a child cannot be underestimated. The bond between a mom and her son or daughter may be tested (particularly during adolescence...) but not broken. If you're planning on becoming a mom, or if you're a mom already and you're just looking for a few pointers along the way, you've come to the right place.
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65% of Americans Spend More Time With Their Computers Than Their Spouses
It's hard to find time for family time. But a new study, conducted by independent research firm Kelton Research for...Source: Education.com
Stages of Parenthood
Parenting is a process by which parents and children grow and develop, each influencing the other throughout their...Source: Pearson Allyn Bacon Prentice Hall
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Free personalized radio that
plays the music you love
Make Me Believe
Features of This Trackpop rock qualities
r & b influences
reggae influences
a subtle use of vocal counterpoint
mild rhythmic syncopation
acoustic rhythm piano
repetitive melodic phrasing
extensive vamping
a clear focus on recording studio production
string section beds
mixed minor & major key tonality
a vocal-centric aesthetic
a breathy female lead vocalist
acoustic rhythm guitars
romantic lyrics
These are just a few of the hundreds of attributes cataloged for this track by the Music Genome Project.show more
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text:
Most Popular "Defense Plant" Titles
- Movies or TV
- Genres
- Keywords
- IMDb Rating
- Instant Watch Options
- In Theaters
- On TV
- Release Year
1. Falling Down (1993)
R | 113 min | Crime, Drama, Thriller
A borderline personality disordered defense worker frustrated with the various flaws he sees in society, begins to psychotically and violently lash out against them.
Writer: Ebbe Roe Smith | Director: Joel Schumacher | Stars: Michael Douglas, Robert Duvall, Barbara Hershey, Rachel Ticotin
Votes: 134,982 | Gross: $40.90M
2. Best Defense (1984)
R | 94 min | Comedy, War
A military weapons engineer struggles to do his job responsibly, while a hapless tank commander has to live with the consequences in combat years later.
Writers: Robert Grossbach, Gloria Katz, Willard Huyck | Director: Willard Huyck | Stars: Dudley Moore, Eddie Murphy, Kate Capshaw, George Dzundza
Votes: 4,661
3. Since You Went Away (1944)
Approved | 177 min | Drama, Romance, War
While husband Tim is away during World War II, Anne Hilton copes with problems on the homefront. Taking in a lodger, Colonel Smollett, to help make ends meet and dealing with shortages and ... See full summary »
Writers: Margaret Buell Wilder, David O. Selznick | Directors: John Cromwell, Edward F. Cline, Tay Garnett, David O. Selznick | Stars: Claudette Colbert, Jennifer Jones, Joseph Cotten, Shirley Temple
Votes: 3,326
4. War Dogs (1942)
Approved | 64 min | Action, Adventure, Drama
A young boy donates his pet, a police dog, to the army to be trained as a war dog.
Writers: John Vlahos, Ande Lamb | Director: S. Roy Luby | Stars: Billy Lee, Addison Richards, Bradley Page, Kay Linaker
Votes: 42
5. Carolina Blues (1944)
Approved | 81 min | Musical
Band Leader Kay Kyser wants to take a holiday, but his publicist Charlotte has promised that he'll give a concert for defense plant workers. Due to the fact that his vocalist has quit to ... See full summary »
Writers: M.M. Musselman, Kenneth Earl, Joseph Hoffman, Al Martin, Jack Henley | Director: Leigh Jason | Stars: Kay Kyser, Ann Miller, Victor Moore, Jeff Donnell
Votes: 106
6. And a Nightingale Sang (1989 TV Movie)
95 min
Writers: Jack Rosenthal, C.P. Taylor | Director: Robert Knights | Stars: Phyllis Logan, Tom Watt, Joan Plowright, John Woodvine
Votes: 57
7. The Masked Marvel (1943)
Passed | 197 min | Action, Adventure, Drama
A superhero called The Masked Marvel battles Japanese saboteurs intent on blowing up America.
Writers: Royal K. Cole, Ronald Davidson, Basil Dickey, Jesse Duffy, Grant Nelson, George H. Plympton, Joseph F. Poland | Director: Spencer Gordon Bennet | Stars: William Forrest, Louise Currie, Johnny Arthur, Rod Bacon
Votes: 115
8. Swooner Crooner (1944)
Approved | 7 min | Animation, Family, Short
Porky Pig's egg faces production problems when a crooning rooster distracts the hens from their jobs.
Writer: Warren Foster | Director: Frank Tashlin | Stars: Mel Blanc, Sara Berner, Richard Bickenbach
Votes: 380
9. He's My Guy (1943)
Approved | 92 min | Comedy, Music
The former members of a vaudeville team meet up again in a defense plant during WW II.
Writers: M. Coates Webster, Grant Garett, Kenneth Higgins | Director: Edward F. Cline | Stars: Dick Foran, Irene Hervey, Joan Davis, Fuzzy Knight
10. Rosie the Riveter (1944)
Passed | 75 min | Comedy, Musical, Romance
In wartime 1944 in California,defense plant workers Rosalind "Rosie" Warren and her friend Vera Watson must share, on a rotating schedule, the town's last available rental-room with Charlie... See full summary »
Writers: Dorothy Curnow Handley, Jack Townley, Aleen Leslie | Director: Joseph Santley | Stars: Jane Frazee, Frank Albertson, Barbara Jo Allen, Frank Jenks
Votes: 26
11. The Life and Times of Rosie the Riveter (1980)
65 min | Documentary, History, War
Documentary about women working in factories during WWII and resultant loss of jobs after the war. Five women tell their stories. Fascinating clips of racist, sexist propaganda of the era.
Director: Connie Field | Stars: Wanita Allen, Gladys Belcher, Lyn Childs, Lola Weixel
Votes: 220
12. Seeing Hands (1943)
Approved | 11 min | Short, War, Drama
This serious entry in Pete Smith Specialty series encourages industry to hire people with disabilities to help with the war effort. As a boy, Ben Helwig was blinded in an accident while ... See full summary »
Writer: Joe Ansen | Director: Gunther von Fritsch | Stars: Barbara Bedford, William Bishop, Robert Frazer, Russell Gleason
Votes: 91
13. Beautiful But Broke (1944)
Approved | 74 min | Comedy, Music
Theatrical agent Waldo Main (John Eldredge) is inducted into the army, and turns his (now clientless) agency over to his secretary Dottie Duncan (Joan Davis). Dottie decides to organize an ... See full summary »
Writers: Arthur Housman, Monte Brice, Manuel Seff | Director: Charles Barton | Stars: Joan Davis, John Hubbard, Jane Frazee, Judy Clark
Votes: 33
14. Rubber Racketeers (1942)
67 min | Crime, Drama
Gilin, is just released from prison and now that the government has clamped down on the sale of tires, decides to enter the tire-stealing-for-resell racket. Driving home, he has a car wreck... See full summary »
Writer: Henry Blankfort | Director: Harold Young | Stars: Ricardo Cortez, Rochelle Hudson, William Henry, Barbara Read
Votes: 29
15. Underground Agent (1942)
Approved | 70 min | Action, Drama, Romance
Writers: J. Robert Bren, Gladys Atwater | Director: Michael Gordon | Stars: Bruce Bennett, Leslie Brooks, Frank Albertson, Julian Rivero
Votes: 11
16. The Old Homestead (1942)
Approved | Comedy, Drama, Music
Another in the series of films Republic made from 1939 to 1944 headlining the rustic family musical group known as "The Weaver Brothers and Elviry." In real life, June Weaver (Elviry) was ... See full summary »
Writers: Dorrell McGowan, Stuart E. McGowan | Director: Frank McDonald | Stars: Leon Weaver, Frank Weaver, June Weaver, Dick Purcell
17. High Explosive (1943)
Approved | 60 min | Action, Drama
Mike Douglas (Barry Sullivan), owner of a nitroglycerin concern hires his old friend "Buzz" Mitchell (Chester Morris), a race-driver of midget-auto cars who has been banned from racing, to ... See full summary »
Writers: Maxwell Shane, Howard J. Green, Joseph Hoffman | Director: Frank McDonald | Stars: Chester Morris, Jean Parker, Barry Sullivan, Ralph Sanford
Votes: 5
18. Men of the Sky (1942)
Approved | 20 min | Short, Drama, War
A propaganda film, made in the early months of World War II, dramatizing a new group of U.S. Army Air Force pilots receiving their wings from Lt. General H.H. Arnold: on off-screen narrator... See full summary »
Writer: Owen Crump | Director: B. Reeves Eason | Stars: Tod Andrews, Eleanor Parker, Don DeFore, Ray Montgomery
Votes: 54
19. Sundown Valley (1944)
Approved | 55 min | War, Western
Gunsight Hawkins has developed a new gunsight for the Army. His daughter is in charge of production and Steve is the personnel manager trying to keep the men on the job. Baxter is out to ... See full summary »
Writer: Luci Ward | Director: Benjamin H. Kline | Stars: Charles Starrett, Dub Taylor, Jeanne Bates, The Tennessee Ramblers
20. Main Street Today (1944)
Approved | 20 min | Short
This patriotic MGM short film, set in World War II has a factory owner who produces the breech block for a key piece of army artillery. The military wants to increase production by 50% but ... See full summary »
Writers: Karl Kamb, Julian Harmon | Director: Edward L. Cahn | Stars: Ray Collins, Henry O'Neill, Addison Richards, John Nesbitt
Votes: 58


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Arizona Cardinals
NFL analyst: Bruce Arians, Tom Moore good hires for Arizona Cardinals
Bruce Arians was brought on as the new Arizona Cardinals head coach because of his leadership, quarterback prowess and ability to get the most out of the talent on his roster.
One FOX NFL analyst believes the Cardinals' decision to bring Arians into the fold was a smart move, and one that should pay dividends going forward.
"I think it's good. I really do," Daryl Johnston told Arizona Sports 620's Doug & Wolf Monday. "You're talking about a guy who had a big impact on what Indianapolis did in his time there. I know that Peyton Manning has the utmost respect for him. I think it's a good move."
With the addition of Arians came a bounty of changes to the Cardinals' coaching staff. The team supported Arians' decision to choose his own staff, which has begun to take shape with the official hirings of three assistant coaches.
Arians hired Todd Bowles as defensive coordinator, Harold Goodwin as offensive coordinator and Tom Moore as assistant head coach/offensive coordinator.
Johnston trusts Arians brought the right personnel in, especially on the offensive side, to help the Cardinals rectify some troublesome areas from a disappointing 2012 campaign.
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The Parenting at Mealtime and Playtime (PMP) Learning Collaborative offers resources to pediatric practices to help counsel families of infants and young children (ages birth to 5 years) about good nutrition and positive parent-child interactions during mealtime and playtime. This Quality Improvement program provides tools for physicians to enhance prevention counseling strategies, become adept at assessing "risk," and intervene at the earliest possible stage before a child is overweight.
Parenting at Mealtime and Playtime will be recruiting soon for Wave 4!
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We are excited to unveil the new Parenting at Mealtime and Playtime app to help parents get quick, trusted, age-appropriate advice outside of their doctor's office.
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Click here to check out the app. Click here to see a media interview outlining how to use the app.
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Project News:
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This article was published at www.annals.org on 12 January 2016.
From Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC; Harvard Pilgrim Health Care Institute, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, Massachusetts; Albert Einstein College of Medicine, Bronx, New York; Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; UC Davis School of Medicine, Davis, Stanford University, Stanford, and University of California, San Francisco, San Francisco, California; Group Health Research Institute, Seattle, Washington; Information Management Services, Calverton, and National Cancer Institute, Bethesda, Maryland; Carbone Cancer Center, University of Wisconsin, Madison, Wisconsin; University of Texas MD Anderson Cancer Center, Houston, Texas; Norris Cotton Cancer Center and Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire; and College of Medicine, and University of Vermont, Burlington, Vermont.
Portions of earlier versions of this research were presented on the USPSTF Web site as part of a Technical Report (AHRQ Publication No. 14-05201-EF-4) (33).
Note: This work was done by 6 independent modeling teams, for the Breast Cancer Working Group of the Cancer Intervention and Surveillance Modeling Network (CISNET) and the Breast Cancer Surveillance Consortium (BCSC). The teams were from Dana-Farber Cancer Institute (principal investigator [PI], Dr. Lee); Erasmus MC, University Medical Center Rotterdam (PI, Dr. de Koning); Georgetown University Medical Center, Lombardi Comprehensive Cancer Center (PI, Dr. Mandelblatt) and Albert Einstein College of Medicine (PI, Dr. Schechter); Harvard Medical School and Harvard Pilgrim Health Care (PI, Dr. Stout) and University of Wisconsin (PIs, Drs. Trentham-Dietz and Alagoz); University of Texas MD Anderson Cancer Center (PI, Dr. Berry); and Stanford University (PI, Dr. Plevritis). Drs. Mandelblatt, Cronin, de Koning, Miglioretti, Schechter, and Stout were the coordinating committee for the project; Drs. Mandelblatt, Stout, Schechter, and Cronin were the writing committee; Drs. de Koning and Cronin served as dual senior authors; and Dr. Feuer was responsible for overall CISNET project direction. The BCSC collaborated and also provided key data inputs to the modeling teams. Members of the BCSC are listed at http://breast screening.cancer.gov.
Disclaimer: The investigators worked with members of the USPSTF and AHRQ staff to develop the scope and key questions for this research. The USPSTF, AHRQ, and the funding sources had no role in study conduct. AHRQ staff distributed earlier versions of the draft for peer review. The investigators are solely responsible for the content and the decision to submit the manuscript for publication.
Acknowledgment: The authors thank CISNET consultants Elizabeth Burnside, Allison Kurian, Donald Weaver, and Diana Buist for review of earlier versions of the models for structure, parameters, and assumptions for clinical face validity; Jennifer Croswell from AHRQ for assistance with analysis and review of results; members of the USPSTF and the Oregon Evidence-based Practice Center for comments on earlier versions of this research; and Jessica Garshell for data processing. They also thank Adrienne Ryans for manuscript preparation and John Wong, Stuart Baker, William Lawrence, and Tom Trikalinos for helpful suggestions on earlier versions of this paper.
Grant Support: By the National Institutes of Health (National Cancer Institute grant U01 CA152958 and National Cancer Institute–funded BCSC grant P01 CA154292, contract HSN261201100031C, and grant U54CA163303), and in part by American Cancer Society grant MRSG 14-027-01 CPHPS (Dr. Chang). The collection of BCSC cancer and vital status data used in this study was supported in part by several state public health departments and cancer registries throughout the United States. A full list of BCSC investigators and a description of these sources is available at http://breastscreen ing.cancer.gov/work/acknowledgement.html.
Disclosures: Dr. Stout reports grants from the National Cancer Institute during the conduct of the study. Dr. Schechter reports grants from the National Cancer Institute during the conduct of the study. Dr. Miglioretti reports grants from AHRQ and the National Cancer Institute during the conduct of the study. Dr. Trentham-Dietz reports grants from the National Cancer Institute during the conduct of the study. Dr. van Ravesteyn reports grants from the National Cancer Institute during the conduct of the study. Dr. Alagoz reports grants from the National Cancer Institute during the conduct of the study. Dr. Tosteson reports grants from the National Cancer Institute during the conduct of the study. Dr. Heijnsdijk reports grants from SCOR and personal fees from the American Society of Breast Surgeons outside the submitted work. Dr. Gangnon reports grants from the National Cancer Institute during the conduct of the study. Dr. Sprague reports grants from the National Institutes of Health during the conduct of the study. Dr. de Koning reports grants from the National Cancer Institute during the conduct of the study. Authors not named here have disclosed no conflicts of interest. Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterest Forms.do?msNum=M15-1536.
Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that she has no financial relationships or interests to disclose. Darren B. Taichman, MD, PhD, Executive Deputy Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Deborah Cotton, MD, MPH, Deputy Editor, reports that she has no financial relationships or interest to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Catharine B. Stack, PhD, MS, Deputy Editor for Statistics, reports that she has stock holdings in Pfizer.
Reproducible Research Statement:Study protocol, statistical code, and data set: Not available.
Requests for Single Reprints: Jeanne S. Mandelblatt, MD, MPH, Lombardi Comprehensive Cancer Center, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007; e-mail, firstname.lastname@example.com.
Current Author Addresses: Dr. Mandelblatt: Lombardi Comprehensive Cancer Center, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007.
Dr. Stout: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 6th Floor, Boston, MA 02215.
Dr. Schechter: Department of Family and Social Medicine, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Black Building 406, Bronx, NY 10461.
Mr. van den Broek and Drs. van Ravesteyn, Heijnsdijk, and de Koning: Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, the Netherlands.
Dr. Miglioretti: Department of Public Health Sciences, UC Davis School of Medicine, One Shields Avenue, Med Sci 1C, Room 145, Davis, CA 95616.
Mr. Krapcho: Information Management Services, Inc., 3901 Calverton Boulevard, Suite 200, Calverton, MD 20705.
Dr. Trentham-Dietz: University of Wisconsin Carbone Cancer Center, 610 Walnut Street, WARF Room 307, Madison, WI 53726.
Dr. Munoz: Stanford University, 318 Campus Drive, Room S255, Stanford, CA 94305
Dr. Lee: Dana-Farber Cancer Institute, Harvard Medical School, Mailstop CLS11007, 450 Brookline Avenue, Boston, MA 02215.
Dr. Berry: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, 4-5062 Pickens Academic Tower, Houston, TX 77030-1402.
Dr. Alagoz: University of Wisconsin–Madison, 1513 University Avenue, Madison, WI 53706.
Dr. Kerlikowske: Veterans Affairs Medical Center (111A1), 4150 Clement Street, San Francisco, CA 94121.
Dr. Tosteson: Geisel School of Medicine at Dartmouth, One Medical Center Drive (HB7505), Lebanon, NH 03756.
Ms. Near, Ms. Hoeffken, and Dr. Chang: Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street NW, Suite 4100, Washington, DC 20007.
Mr. Chisholm: Department of Gynecologic Oncology and Reproductive Medicine, Unit 1362, The University of Texas MD Anderson Cancer Center, P.O. Box 301439, Houston, TX 77230-1439.
Dr. Huang: The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1411, P.O. Box 301402, Houston, TX 77230-1402.
Ms. Huang: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215.
Mr. Ergun: University of Wisconsin–Madison, 3233 Mechanical Engineering Building, 1513 University Avenue, Madison, WI, 53706.
Dr. Gangnon: University of Wisconsin–Madison, Department of Population Health Sciences, 610 Walnut Street, Madison, WI 53726.
Dr. Sprague: University of Vermont, Office of Health Promotion Research, 1 South Prospect Street, Burlington, VT 05401.
Dr. Plevritis: Department of Radiology, Stanford University, James H. Clark Center, Room S255, MC 5442, 318 Campus Drive, Stanford, CA 94305.
Drs. Feuer and Cronin: Division of Cancer Control and Population Sciences, National Cancer Institute, 9609 Medical Center Drive, MSC 9765, Room 4E534, Bethesda, MD 20892-9765.
Author Contributions: Conception and design: O. Alagoz, D.A. Berry, Y. Chang, K.A. Cronin, H.J. de Koning, J.S. Mandelblatt, C.B. Schechter, N.K. Stout.
Analysis and interpretation of the data: O. Alagoz, D.A. Berry, Y. Chang, Gary B.B. Chisholm, K.A. Cronin, H.J. de Koning, M.A. Ergun, E.J. Feuer, R.E. Gangnon, A.S. Hoeffken, X. Huang, H. Huang, K. Kerlikowske, M. Krapcho, S.J. Lee, J.S. Mandelblatt, D.L. Miglioretti, D.F. Munoz, A.M. Near, C.B. Schechter, B. Sprague, N.K. Stout, A.N. Tosteson, J.J. van den Broek, N.T. van Ravesteyn.
Drafting of the article: O. Alagoz, Gary B.B. Chisholm, K.A. Cronin, H.J. de Koning, A.S. Hoeffken, J.S. Mandelblatt, C.B. Schechter, N.K. Stout.
Critical revision of the article for important intellectual content: O. Alagoz, D.A. Berry, H.J. de Koning, R.E. Gangnon, A.S. Hoeffken, K. Kerlikowske, J.S. Mandelblatt, D.L. Miglioretti, A.M. Near, C.B. Schechter, B. Sprague, N.K. Stout, A.N. Tosteson, A. Trentham-Dietz, J.J. van den Broek, N.T. van Ravesteyn.
Final approval of the article: O. Alagoz, D.A. Berry, Y. Chang, Gary B.B. Chisholm, K.A. Cronin, H.J. de Koning, M.A. Ergun, E.J. Feuer, R.E. Gangnon, E.A.M. Heijnsdijk, A.S. Hoeffken, X. Huang, H. Huang, K. Kerlikowske, M. Krapcho, S.J. Lee, J.S. Mandelblatt, D.L. Miglioretti, D.F. Munoz, A.M. Near, S.K. Plevritis, C.B. Schechter, B. Sprague, N.K. Stout, A.N. Tosteson, A. Trentham-Dietz, J.J. van den Broek, N.T. van Ravesteyn.
Provision of study materials or patients: K. Kerlikowske, A. Trentham-Dietz.
Statistical expertise: D.A. Berry, Gary B.B. Chisholm, K.A. Cronin, H.J. de Koning, E.J. Feuer, R.E. Gangnon, A.S. Hoeffken, X. Huang, J.S. Mandelblatt, D.L. Miglioretti, D.F. Munoz, A.M. Near, C.B. Schechter, J.J. van den Broek.
Obtaining of funding: D.A. Berry, E.J. Feuer, J.S. Mandelblatt, D.L. Miglioretti, C.B. Schechter, B. Sprague, A. Trentham-Dietz.
Administrative, technical, or logistic support: M.A. Ergun, E.J. Feuer, A.S. Hoeffken, J.S. Mandelblatt.
Collection and assembly of data: D.A. Berry, H.J. de Koning, K. Kerlikowske, M. Krapcho, S.J. Lee, J.S. Mandelblatt, D.L. Miglioretti, C.B. Schechter, B. Sprague, N.K. Stout, A. Trentham-Dietz, J.J. van den Broek.
Background: Controversy persists about optimal mammography screening strategies.
Objective: To evaluate screening outcomes, taking into account advances in mammography and treatment of breast cancer.
Design: Collaboration of 6 simulation models using national data on incidence, digital mammography performance, treatment effects, and other-cause mortality.
Setting: United States.
Patients: Average-risk U.S. female population and subgroups with varying risk, breast density, or comorbidity.
Intervention: Eight strategies differing by age at which screening starts (40, 45, or 50 years) and screening interval (annual, biennial, and hybrid [annual for women in their 40s and biennial thereafter]). All strategies assumed 100% adherence and stopped at age 74 years.
Measurements: Benefits (breast cancer–specific mortality reduction, breast cancer deaths averted, life-years, and quality-adjusted life-years); number of mammograms used; harms (false-positive results, benign biopsies, and overdiagnosis); and ratios of harms (or use) and benefits (efficiency) per 1000 screens.
Results: Biennial strategies were consistently the most efficient for average-risk women. Biennial screening from age 50 to 74 years avoided a median of 7 breast cancer deaths versus no screening; annual screening from age 40 to 74 years avoided an additional 3 deaths, but yielded 1988 more false-positive results and 11 more overdiagnoses per 1000 women screened. Annual screening from age 50 to 74 years was inefficient (similar benefits, but more harms than other strategies). For groups with a 2- to 4-fold increased risk, annual screening from age 40 years had similar harms and benefits as screening average-risk women biennially from 50 to 74 years. For groups with moderate or severe comorbidity, screening could stop at age 66 to 68 years.
Limitation: Other imaging technologies, polygenic risk, and nonadherence were not considered.
Conclusion: Biennial screening for breast cancer is efficient for average-risk populations. Decisions about starting ages and intervals will depend on population characteristics and the decision makers' weight given to the harms and benefits of screening.
Primary Funding Source: National Institutes of Health.
12 12016.
Multiple alternative mammography screening strategies exist.
This modeling study estimated outcomes of 8 strategies that differed by starting age and interval. Biennial screening from age 50 to 74 years avoided a median of 7 breast cancer deaths; in contrast, annual screening from age 40 to 74 years avoided an additional 3 deaths but yielded 1988 more false-positive results and 11 more overdiagnoses per 1000 women screened. Annual screening from age 40 years for high-risk women had similar outcomes as screening average-risk women biennially from 50 to 74 years of age.
Imaging technologies other than mammography and nonadherence were not modeled.
Biennial mammography screening for breast cancer is efficient for average-risk women.
Despite decades of mammography screening for early detection of breast cancer, there is no consensus on optimal strategies, target populations, or the magnitude of harms and benefits (1–11). The 2009 US Preventive Services Task Force (USPSTF) recommended biennial film mammography from age 50 to 74 years and suggested shared decision making about screening for women in their 40s (12). Since that recommendation was formulated, new data on the benefits of screening have emerged (2, 6, 8, 9, 11, 13, 14), digital mammography has essentially replaced plain film (15), and increasingly effective systemic treatment regimens for breast cancer have become standard (16). There has also been growing interest in consumer preferences and personalized screening approaches (17–20). These factors could each affect the outcomes of breast cancer screening programs or alter policy decisions about population screening strategies (17).
Modeling can inform screening policy decisions because it uses the best available evidence to evaluate a wide range of strategies while holding selected conditions (such as treatment effects) constant, facilitating strategy comparisons (21, 22). Modeling also provides a quantitative summary of outcomes in different groups and assesses how preferences affect results. Collaboration of several models provides a range of plausible effects and illustrates the effects of differences in model assumptions on results (1, 7, 23).
We used 6 well-established simulation models to synthesize current data and examine the outcomes of digital mammography screening at various starting ages and intervals among average-risk women. We also examined how breast density, risk, or comorbidity levels affect results and whether preferences for health states related to screening and its downstream consequences affected conclusions.
We evaluated 8 strategies that varied by starting age (40, 45, or 50 years) and interval (annual, biennial, and hybrid [annual for women in their 40s and biennial thereafter]); all strategies stop screening at age 74 years. We included "no screening" as a baseline.
The models used to evaluate the screening strategies were developed within the Cancer Intervention and Surveillance Modeling Network (CISNET) (24–30), and the research was institutional review board–approved. They were named model D (Dana-Farber Cancer Institute, Boston, Massachusetts), model E (Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands), model GE (Georgetown University Medical Center, Washington, DC, and Albert Einstein College of Medicine, Bronx, New York), model M (MD Anderson Cancer Center, Houston, Texas), model S (Stanford University, Stanford, California), and model W (University of Wisconsin, Madison, Wisconsin, and Harvard Medical School, Boston, Massachusetts). The Appendix provides information on model validation.
Since earlier analyses (1), the models have undergone substantial revision to reflect advances in breast cancer control, including portrayal of molecular subtypes based on estrogen receptor (ER) and human epidermal growth factor-2 receptor (HER2) status (23); current population incidence (31) and competing non–breast cancer mortality; digital screening; and the most current therapies (32). All models except model S include ductal carcinoma in situ (DCIS).
The general modeling approach is summarized in this article; full details, including approach, construction, data sources, assumptions, and implementation, are available at https://resources.cisnet.cancer.gov/registry and reference (33). Additional information is available on request, and the models are available for use via collaboration.
The models begin with estimates of breast cancer incidence (31) and ER/HER2-specific survival trends without screening or adjuvant treatment, and then overlay data on screening and molecular subtype–specific adjuvant treatment to generate observed incidence and breast cancer–specific mortality trends in the U.S. population (1, 7, 17, 23, 33, 34). Breast cancer has a distribution of preclinical screen-detectable periods (sojourn time) and clinical detection points. Performance characteristics of digital mammography depend on age, first versus subsequent screen, time since last mammogram, and breast density. ER/HER2 status is assigned at diagnosis on the basis of stage and age. Molecular subtype– and stage-specific treatment reduces the hazard of breast cancer death (models D, GE, M, and S) or results in a cure for some cases (models E and W). Women can die of breast cancer or other causes. Screen detection of cancer during the preclinical screen-detectable period can result in the identification and treatment of earlier-stage or smaller tumors than might occur via clinical detection, with a corresponding reduction in breast cancer mortality.
We used a cohort of women born in 1970 with average-risk and average breast density and followed them from age 25 years (because breast cancer is rare before this age [0.08% of cases]) until death or age 100 years.
The models used a common set of age-specific variables for breast cancer incidence, performance of digital mammography, treatment effects, and average and comorbidity-specific non–breast cancer causes of death (20, 35). The parameter values are available at www.uspreventiveservicestaskforce.org/Page/Document/modeling-report-collaborative-modeling-of-us-breast-cancer-1/breast-cancer-screening1 (33). In addition, each group included model-specific inputs (or intermediate outputs) to represent preclinical detectable times, lead time, and age- and ER/HER2-specific stage distribution in screen- versus non–screen-detected women on the basis of their specific model structure (1, 7, 23–30). These model-specific parameters were based on assumptions about combinations of values that reproduced U.S. trends in incidence and breast cancer–specific mortality, including proportions of DCIS that were nonprogressive and would not be detected without screening. Models M and W also assumed some small nonprogressive invasive cancers. The models adopted an age–period–cohort modeling approach to project incidence rates of breast cancer in the absence of screening (31, 36); model M used 1975–1979 rates from the Surveillance, Epidemiology, and End Results program. The models assumed 100% adherence to screening and receipt of the most effective treatment to isolate the effect of varying screening strategies.
Four models used age-specific sensitivity values for digital mammography that were observed in the Breast Cancer Surveillance Consortium (BCSC) for detection of invasive and DCIS cancers combined (model S uses data for invasive cancers only). Separate values were used for initial and subsequent mammography by screening interval, using standard BCSC definitions: "Annual" includes data from screens occurring within 9 to 18 months of the prior screen, and "biennial" includes data on screens within 19 to 30 months (37, 38). Model D used these data as input variables (28), and models GE, S, and W used the data for calibration (24, 25, 27). Models E and M fit estimates from the BCSC and other data (26, 29).
Women with ER-positive tumors received 5 years of hormone therapy and an anthracycline-based regimen accompanied by a taxane. Women with ER-negative invasive tumors received anthracycline-based regimens with a taxane. Those with HER2-positive tumors also received trastuzumab. Women with ER-positive DCIS received hormonal therapy (16). Treatment effectiveness was based on clinical trials and was modeled as a reduction in breast cancer–specific mortality risk or increase in the proportion cured compared with ER/HER2-specific survival in the absence of adjuvant treatment (32).
Screening benefits (vs. no screening or incremental to other strategies) included percentage of reduction in breast cancer mortality, breast cancer deaths averted, and life-years and quality-adjusted life-years (QALYs) gained because of averted or delayed breast cancer death. Benefits (and harms) were accumulated from age 40 to 100 years to capture the lifetime effect of screening.
We considered preferences, or utilities, to account for morbidity from screening and treatment. A disutility for age- and sex-specific general population health was first applied to quality-adjust the life-years (39). These were further adjusted to account for additional decrements in life-years related to undergoing screening (–0.006 for 1 week, or –1 hour), evaluating a positive screen (–0.105 for 5 weeks, or –3.7 days), undergoing initial treatment by stage (for the first 2 years after diagnosis), and having distant disease (for the last year of life for all women who die of breast cancer) (Appendix Table 1) (33, 40, 41).
Use of services focused on the number of mammograms required for the screening strategy. Harms included false-positive mammograms, benign biopsies, and overdiagnosis. Rates of false-positive mammograms were calculated as mammograms read as abnormal or needing further work-up in women without cancer divided by the total number of screening mammograms. Benign biopsies were defined as biopsies among women with false-positive screening results; we assumed 100% compliance with biopsy recommendations (42). Overdiagnosis was defined as all cases that would not have been clinically detected in the absence of screening because of lack of progressive potential or death from competing non–breast cancer mortality. The effect of overdiagnosis on QALYs was captured by the disutility of being treated for cancer but dying of other causes.
For each model, strategies were ranked by the number of mammograms performed. We report the median use, benefits, and harms and range across models. We also obtained an efficiency frontier by plotting the sequence of points that represent the largest incremental percentage of reduction in breast cancer mortality (or life-years or QALYs) per mammogram performed or harm entailed. Screening strategies that fell on this frontier were considered the most efficient (that is, they have the steepest slope such that no alternative exists that provides more benefit with less use or fewer harms).
Three models (E, GE, and W) also evaluated results based on combinations of breast cancer risk and density. Risk levels included 1.3 times higher than average (for example, nulliparity or age at first live birth >30 years) (18, 43); 2.0 times higher than average (for example, family history of 1 first-degree relative) (18); or 4.0 times higher than average (for example, family history of 2 or more first-degree relatives) (18, 44). Greater risk levels, such as those seen with BRCA1 and BRCA2 mutations, were not considered because such groups have specific screening guidelines. We made the simplifying assumption that risk affected incidence, but not other aspects of disease.
Breast density was modeled as entirely fatty ("a"), scattered density ("b"), heterogeneously dense ("c") and extremely dense ("d"). On the basis of observed age-specific prevalence rates, density was assigned at age 40 years and remained the same or decreased by 1 level at age 50 years and again at age 65 years (45). Density modified the sensitivity and specificity of mammography on the basis of age, interval, and first versus subsequent screening (33). Density also modified the age group–specific (40 to 49 years, 50 to 64, and ≥65 years) risk for breast cancer, using age-group-specific risk among those with average population density as the referent category (BCSC. Unpublished data) (44, 46). Density was assumed not to affect molecular subtype or disease natural history. Results for density were grouped into low ("a and b") or high ("c and d") for presentation. The risk- and density-specific results were also compared with those for screening average-risk and average-density groups biennially from 50 to 74 years, because many guideline groups accept the latter.
In other analyses, 2 models (model E and GE) examined the effect of comorbidity on screening cessation by using comorbidity-specific life expectancy groups. Examples of conditions that placed women in severe and moderate comorbidity groups included congestive heart failure and diabetes, respectively; the specific conditions and their associated life expectancies are reported elsewhere (20, 35, 47). We compared results of continuing to screen biennially past age 74 years among women with no or low comorbidity or stopping earlier than 74 years for those with moderate or high comorbidity. These analyses included women who survived and were free of breast cancer up until the point where screening was to be extended or stopped.
Four models evaluated whether high disutility values would eliminate screening benefits. Finally, we evaluated the ability of the models to independently predict external trends and results (Appendix Figure 1 and Appendix Table 2).
Modeled versus observed incidence of breast cancer and breast cancer–specific mortality in women aged 40 to 100 years.
The models closely estimate observed U.S. trends in incidence of invasive disease (top), incidence of invasive disease and DCIS (middle)*, and breast cancer–specific mortality (bottom). Using inputs for actual dissemination of screening and treatment in the United States, the models all captured the major trends in incidence over time. Early increases with the advent of mammography in the mid-1980s are seen, followed by a downturn in the 2000s and then a leveling off. The models also captured the general shape of decreases in breast cancer–specific mortality over time. All models show an increase in incidence with the introduction of mammography screening. Model GE has a steep peak in incidence in 2005 owing to the specific method for capturing the transition from plain film to digital mammography, because digital mammography has higher sensitivity and detection of ductal carcinoma in situ than plain film mammography; other models include a more gradual transition surrounding this period. D = Dana-Farber Cancer Institute; DCIS = ductal carcinoma in situ; E = Erasmus Medical Center; GE = Georgetown University Medical Center and Albert Einstein College of Medicine; M = MD Anderson Cancer Center; QALY = quality-adjusted life-year; S = Stanford University; SEER = Surveillance, Epidemiology, and End Results; W = University of Wisconsin and Harvard Medical School.
* Model S does not include DCIS.
We worked with the USPSTF and Agency for Healthcare Research and Quality (AHRQ) to develop the scope and key questions for this research. National Cancer Institute investigators (K.C., E.F.) collaborated in their role as scientific project officers. AHRQ staff distributed earlier versions of the draft for peer review. The agencies had no role in the study conduct or decision to submit the manuscript for publication. The investigators are solely responsible for the content and the decision to submit the manuscript for publication.
The models produced consistent rankings of the screening strategies (Table 1). For instance, biennial screening from age 50 to 74 years yielded a median 25.8% reduction in breast cancer mortality compared with no screening (range, 24.1% to 31.8%). Annual screening led to slightly greater reductions in mortality than biennial strategies. However, biennial strategies maintained a median of 79.8% to 81.3% of the breast cancer–specific mortality reduction of annual screening (range, 68.3% to 98.9%) (Appendix Table 3).
Biennial screening also maintained the most annual benefits for life-years and QALYs, and quality adjustment did not change the ranking of strategies. Across all strategies, the largest decrement from quality adjustment to life-years was related to declines in general health as women aged; smaller decrements occurred owing to the disutility of undergoing diagnostic evaluation of an abnormal screening examination and for having cancer. The disutility associated with screening itself had a minimal effect on QALYs (33).
The incremental benefits of initiating screening at age 40 years were slightly greater than those of starting at age 50 years in terms of breast cancer deaths averted with both annual and biennial screening (median, 1.3 [range, 1.1 to 1.7] and 1.0 [0.8 to 1.7] per 1000 women screened, respectively) (Table 2). Initiating screening at age 45 years yielded benefits intermediate between beginning at 40 and 50 years, although there were slightly greater incremental benefits when starting at age 45 years (vs. 50 years) than starting at age 40 years (vs. 45 years) (for example, 10.6 vs. 8.0 and 15.4 vs. 7.9 QALYs for biennial and annual strategies, respectively) (Table 1).
All models projected more harms (false-positive results, benign biopsies, and overdiagnosed cases) under annual versus biennial schedules and by starting earlier than age 50 years versus at age 50 years (Table 3). For instance, if biennial screening began at age 40 years instead of age 50 years, for every 1000 women screened there would be a median of 1 more death averted, but 576 more false-positive results, 67 more benign biopsies, and 2 additional overdiagnosed cases. Compared with screening initiation at age 45 years, starting screening at age 40 years had 1 or fewer added deaths averted depending on interval, but more incremental harms.
Efficiency frontier plots were used to graphically depict the balance between the number of mammograms and benefits (life-years gained) of screening strategies. Biennial strategies starting at age 40, 45, or 50 years were all efficient (Figure and Appendix Figure 2). Points that were close to but fell below the frontier were less efficient than those on the frontier line. For example, compared with the point on the efficient frontier for biennial screening at age 45 years, the hybrid strategy of annual screening at 45 years was less efficient than biennial screening starting at 40 years. This is because the hybrid strategy at 45 years would require 405.8 more mammograms to gain an additional life-year for every 1000 women screened compared with biennial screening at 45 years, whereas biennial screening starting at 40 years would require only 189.5 extra mammograms to gain an additional life-year.
Efficiency frontier for life-years gained versus mammograms performed per 1000 women in model D (Dana-Farber Cancer Institute).
Efficiency frontier graphs for all models are shown in Appendix Figure 2. This graph plots the average gain in life-years per additional mammogram performed per 1000 women for each screening strategy (vs. no screening) in model D. Biennial strategies are indicated with a square; hybrid strategies (annual in the 40s followed by biennial from 50 to 74 years of age) with a triangle; and annual strategies with a circle. Efficient strategies were plotted (i.e., those in which increases in mammography use resulted in greater life-years gained than the next less intensive strategy). The line represents the "efficiency frontier" by joining efficient strategies in which increases in mammography use resulted in greater life-years gained than the next less intensive efficient strategy. Strategies on this line would be considered efficient because they achieve the greatest gain in benefit (life-years gained) per harm or use of mammograms. Strategies that use more mammograms but still have small benefits (i.e., a shallower slope than the next best strategy) are considered to be less efficient (i.e., weakly dominated). When and if the slope in the efficiency frontier plot levels off, it means that the additional life-years gained per increase in mammography are small relative to the previous strategies and could indicate a point at which additional screening might be considered as having a low return (or additional benefit). There is no definitive inflection point across the models for the strategies or metrics evaluated. Black strategies are efficient; gray strategies close to the efficiency frontier are less efficient; and open gray strategies are inefficient (inferior, or dominated). Reference 33 provides efficiency frontiers for other harm and benefit metrics.
Efficiency frontier for life-years gained versus mammograms performed for each screening strategy, by model.
The average gain in life-years per additional mammogram performed per 1000 women for each screening strategy (vs. no screening). Biennial strategies are indicated with a square; hybrid strategies (annual in the 40s followed by biennial from 50 to 74 years of age) with a triangle; and annual strategies with a circle. Efficient strategies were plotted (those in which increases in mammography use resulted in greater life-years gained than the next least-intensive strategy). The line represents the "efficiency frontier" by joining efficient strategies in which increases in mammography use resulted in greater life-years gained than the next less intensive efficient strategy. Strategies on this line would be considered efficient because they achieve the greatest gain in benefit (life years gained) per harm or use of mammograms. Strategies that use more mammograms but still have small benefits (i.e., a shallower slope than the next best strategy) are considered to be less efficient (i.e., weakly dominated). When and if the slope in the efficiency frontier plot levels off, it means that the additional life-years gained per increase in mammography are small relative to the previous strategies and could indicate a point at which additional screening might be considered as having a low return (or additional benefit). There is no definitive inflection point across the models for the strategies or metrics evaluated. Black strategies are efficient; gray strategies close to the efficiency frontier are less efficient; and open gray strategies are inefficient (inferior, or dominated). Reference 33 provides efficiency frontiers for other harm and benefit metrics. D = Dana-Farber Cancer Institute; E = Erasmus Medical Center; GE = Georgetown University Medical Center and Albert Einstein College of Medicine; M = MD Anderson Cancer Center; QALY = quality-adjusted life-year; S = Stanford University; W = University of Wisconsin and Harvard Medical School.
Finally, annual screening from age 50 to 74 years was consistently inferior to other strategies (that is, it was inefficient, or dominated) because it yielded the same or fewer benefits than the next less resource intensive efficient strategy but required more mammograms or entailed more harms (for example, A50 vs. B40 in Figure 1). These patterns were generally seen with other harm and benefit metrics (Appendix Figure 2).
Varying the disutilities for usual health, screening, diagnosis, and treatment did not affect strategy rankings for average-risk populations, and QALY gains persisted under all screening strategies, although their magnitude decreased with increasing disutility (33).
The balance of harms and benefits differed by risk group, with women at higher risk having fewer false-positive results per 1000 women screened and higher gains from screening than lower-risk groups. Screening higher-risk women also yielded a lower ratio of overdiagnosed cases per breast cancer death averted than screening average-risk women. However, annual screening from age 50 to 74 years had the same or less benefit and more harms than other strategies at all risk levels (33).
For women with a 2- to 4-fold increase in risk, annual screening starting at age 40 or 45 years had similar or more favorable harm–benefit ratios (on the basis of false-positive results) as biennial screening of average-risk women from 50 to 74 years of age. If the harm–benefit ratio of 185.8 (range, 169.5 to 268.0) false-positive results per death averted for average-risk women screened biennially from 50 to 74 years of age is considered to be acceptable, then for women with a 2- to 4-fold increase in risk, annual screening starting at age 40 or 45 years had similar or more favorable harm–benefit ratios. For instance, women with a 2-fold increase in risk undergoing annual screening at age 40 years have a slightly more favorable corresponding ratio, at 182.5 (range, 177.4 to 231.9) false-positive results per death averted. For women with a 1.3-fold increase in risk, biennial screening starting at age 40 years had similar harm–benefit ratios as biennial screening of average-risk women from age 50 to 74 years.
Breast density (low vs. high) changed absolute benefits, but annual screening from 50 to 74 years remained inefficient across breast density groups. Women in the low-density group had a greater proportion of their cancers detected owing to greater sensitivity of digital mammography, and therefore a greater reduction in breast cancer–specific mortality than the high-density group. However, women in the high-density group had a greater absolute number of cancer cases detected because their risk for cancer was higher, leading to more life-years saved among these women than in the low-density group (33).
For women with no comorbidity, biennial screening could continue to age 78 or 80 years and still have similar harm–benefit ratios as screening women with average non–breast cancer mortality biennially from 50 to 74 years. However, for women with moderate to severe comorbidity, the comparable ratios were equivalent at about age 66 to 68 years (33).
We used 6 established models to estimate the potential efficacy of different breast cancer screening strategies in the United States. All 6 models demonstrated that screening initiation at age 40 years has some benefits for average-risk populations, but also higher levels of harms, than strategies starting at age 50 years. The findings also suggest that comorbidity levels could be used to tailor the age of screening cessation. Biennial screening strategies were the most efficient, but annual screening could be considered from age 40 to 74 years in groups with a 2- to 4-fold higher-than-average risk.
Results from all models indicated that digital mammography screening of average-risk women in their 40s modestly lowers breast cancer–specific mortality and extends the length and quality of life, even after disutilities related to the screening process are considered. The absolute benefits of starting screening in the 40s varied somewhat on the basis of model structure and assumptions, but were consistent with observations from randomized trials (6). However, starting at age 40 versus 45 years was associated with increasing incremental harms relative to the increase in benefits. Thus, decisions about initiating screening before age 50 years may depend on the weight attached to screening benefits and harms.
Consistent with other analyses of upper age limits for screening (20, 48–50) and other recommendations (12, 51), our results suggested that the balance of harms and benefits of screening was affected by competing non–breast cancer mortality, so that age at screening cessation could be tailored by comorbidity levels.
Similar to our 2009 analysis (1), biennial strategies are most consistently efficient. Screening annually from age 50 to 74 years had the same or fewer benefits for any given harm for all population groups in virtually all models and would be considered inefficient. However, annual screening in the 40s followed by biennial screening at age 50 years, and the most intensive schedule evaluated (annual screening from 40 to 74 years), were also efficient or close to being efficient. In addition, annual screening of women with a 2- to 4-fold increased risk (for example, due to non–BRCA-related family history) from age 40 to 74 years had harm–benefit ratios similar to those of biennial screening from age 50 to 74 years in average-risk populations.
The results also suggest that benefits of screening vary by breast density, at least when grouped into low and high categories. Women with dense breasts have a higher risk for cancer and a higher absolute detection rate, but a lower relative detection rate (19, 52). This is because the sensitivity of digital mammography, although optimized for density, is still lower in women with dense breasts than those with nondense breasts (53–56). Improving outcomes for women with dense breasts (55) may require new innovations in imaging (57–60) or identification of risk biomarkers (61, 62).
This analysis extends our prior work by explicitly considering overdiagnosis as a screening harm. Depending on screening strategy, the models estimated that 2% to 12% of invasive disease cases and 30% to 50% of DCIS cases might represent overdiagnosis. Although the models differed in absolute estimates, they agreed on how overdiagnosis affected the ranking of strategies and the finding that most overdiagnosed cases were DCIS. The model results for overdiagnosis are not directly comparable with other published estimates (8, 63) because the models followed women for their entire lives. The models also made assumptions about unobservable input parameters related to natural history. Although there is no agreement on methods to estimate overdiagnosis (64) or on its true rate (65, 66), there is agreement that it is an important harm. Active surveillance for DCIS with a low risk for progression is one potential future approach to reduce harms from DCIS overdiagnosis. More information is also needed on consumer knowledge of and willingness to risk overdiagnosis (67).
Overall, this study has several important strengths, including collaboration of 6 long-established, independent modeling groups; use of well-calibrated models that reproduce temporal epidemiologic trends and a screening trial result; inclusion of digital technology; incorporation of increasingly effective treatments; and consideration of quality of life, risk factors, breast density, and comorbidity (68). The conclusions about the ranking of screening strategies are robust and should provide greater credibility than inferences based on 1 model alone.
Our study also had limitations. First, to evaluate program efficacy, we assumed 100% adherence to screening, prompt evaluation of abnormal results, and full use of optimal treatment. Actual benefits will fall short of our projected results because adherence is not perfect.
Second, we focused only on hybrid strategies for women in their 40s. Alternative hybrid strategies may be important to examine in future research.
Third, the analysis did not consider other imaging technologies for average-risk populations or for groups with high breast density; such technologies include ultrasonography (69), computer-aided detection (70), tomosynthesis, or magnetic resonance imaging. Data on tomosynthesis performance and needs for radiologist retraining are still emerging (58).
Fourth, we did not model any radiation-induced breast cancers, owing to more intensive mammography schedules (71). Fifth, we assumed that risk factors influenced the incidence of disease, but not its natural history.
Sixth, certain risk factors, such as family history, are age-dependent in their effects (18, 72). Because we held relative risk levels constant over age, our benefit estimates could be over- or underestimated for specific risk factors (17).
Seventh, we did not consider polygenic risk (73, 74) or explicitly model menopausal status; we used age 50 years as a proxy for the average age of menopause. Eighth, the analysis did not include screening program costs or utility estimates specific to some of the newest treatments.
Finally, compared with our earlier research (1), the models all estimated similar, but somewhat greater, reductions in breast cancer–specific mortality (for example, a median 22% vs. 25.8% reduction with biennial screening from 50 to 74 years in 2009 vs. current models, respectively). The primary reasons for this modeled improvement relate to the increased sensitivity of digital compared with film mammography, advances in molecular-targeted therapies, and changes in underlying breast cancer trends.
Overall, the 6 models conclude that biennial screening strategies are the most efficient. Choices about the optimal age at initiation (and cessation) and screening intervals will ultimately depend on program goals, the weight attached by the decision maker to screening harms and benefits (75), and considerations of efficiency.
Each model has a different structure and assumptions and some varying input variables, so no single method can be used to validate results against an external standard. Therefore, we used several approaches.
First, considering actual screening and treatment patterns instead of the efficacy strategies simulated in the base case, we compared model projections of incidence, breast cancer–specific mortality, and stage distribution with those reported by the Surveillance, Epidemiology, and End Results program for 1975 to 2010. In our previous work, results of each model accurately projected trends for incidence and breast cancer–specific mortality by ER status for 1975 to 2000 (23). Next, we approximated the Age screening trial (6), assuming perfect adherence to invitations for annual screening with 13-year follow-up of women aged 40 to 49 years (6). Finally, we examined the consistency of results across models.
Using inputs for actual dissemination of screening and treatment in the United States, the models captured the major trends in incidence and the general shape of breast cancer–specific mortality decreases over time (Appendix Figure 1). They also closely matched current stage distribution (not shown) and the Age trial results (Appendix Table 2) (6, 33).
Thus, the models replicated patterns of observed US incidence and breast cancer–specific mortality over time. The models also estimated similar breast cancer–specific mortality reduction as that observed among women aged 40 to 49 years who actually attended screening in the Age trial, although the model results are slightly more optimistic than the trial because the models assume 100% screening and use of the most effective systemic regimens (6). Overall, use of 6 models to project a range of plausible screening outcomes provides implicit cross-validation, with the range of results from the models as a measure of uncertainty.
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