TRACO 2017: Epidemiology and Health disparities

TRACO 2017: Epidemiology and Health disparities

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Our, first speaker today is neil capper oh so he. Got his MD. From. The University of, Medicine and Dentistry of, New, Jersey, in 1980. He did a residency in, internal medicine and, then he joined NCI, as an oncology, fellow in the medicine branch, he. Became. A biotechnology. Fellow, in the, environmental. Epidemiology. Branch at NCI and then. He became chief, of the. Genetic, epidemiology branch. In. 2011. He's. Now in the Occupational, and environmental. Studies branch and he's going to talk to us about epidemiology. Neil, thank, you. Welcome. Everybody and. I'm. Gonna, give you a rapid. Introduction. To epidemiology. Which. Is a population. Perspective on. Cancer. So. Many of you are clinicians, but. Epidemiologists. Work about worry about the population, as a whole so. I'm going to cover what. Is epidemiology. What, are the accomplishments. What. Can go wrong or what other challenges, a, case, study of what can go really wrong and then. I'll take, a peek at the future, so. What is epidemiology. And, I. Looked, for one slide, that could kind of convey. What epidemiology. Tries to do and I, think this slide captures. It if you. Allow market. Forces, to. Tell us what's. Good for us and what's bad for us you, just have to go back a few decades and. You can see that, advertisers. Used, images. Of doctors, to, sell us cigarettes. And. Probably, the best established. Association, in all of epidemiology. Is that smoking. Causes lung cancer so. We really, need a scientific. Discipline, that, steps, in and examines. The causes, of cancer and. If you say to yourself well that. Was then what, about now. There, are. Potential. Causes of cancer and human disease that. Maybe today, we. Don't really suspect, and I think Halloween is a good time to raise. The issue of sugar so, I will come, back to this a little bit later in the talk, okay. So what is epidemiology. It's concerned. With you in population, and it's. An observational. Science, like, astronomy, for example, and, contrast. That with experimental. Science, in, epidemiology. We're. Not allowed, to administer. Potentially. Harmful treatments to people we can't assign, 50. People to the smoking, group and 50, to the non-smoking, group but, we have to observe their. Current status so that's why it's. Called. An observational, science. The. Work of Epidemiology, takes place in NCI. In the division of Cancer Epidemiology and. Genetics and. I'm. Currently working, in the occupation, environmental. Epidemiology. Branch where we're focused. On environmental. And, occupational causes. Of cancer. So. Epidemiology. In. Dceg. Has. Contributed to regulatory, changes. Clinical. Practice. And. Preventive. Interventions. And I'll give a few specific examples as, we go forward just. To orient you, to where we are. NCI. Is part, of NIH. And our. Division. The division of Cancer Epidemiology and genetics is, intramural. Now. 85%. Of the dollars go extramural. E2 grants but. The intramural. Part. Is a very. Important. Component. And. In. The division of Cancer Epidemiology and, genetics there. Are a number of branches in, addition to ours which, is of course the most important, there. Are branches concerned, with metabolic. Factors like. Nutrition and warm. Infection. Genetics. Statistics. Radiation. In. Our work focuses. On the environment, on genetic, causes of cancer in the population. High. Quality, high impact. Evaluated. Research there. Are studies that are both national and international in. Scope and. We. Have a lot of scientific partnerships. With laboratories. Investigating. The molecular, epidemiology. Of cancer the, next speaker, Britt Ryan will be focusing, on some of the. Health. Disparities that. Are key. In international. Studies, so. Just. To mention our, studies. Are worldwide, so. We. Have, investigations. In China and this has been for decades. Pretty, much all over the world including places. You might not suspect, like Iran, through. South America Africa. And of course all, across the United States.

So. There's a nice website. That, you can visit and you can learn about our, specific studies, and the investigators, and it's, a lot of great information there, and also, tools so. Breast. Melanoma, colorectal cancer, assessment. Tools. So. Back. To. Observational. Versus, experimental. As. I. Mentioned, epidemiologists. Are ethically prohibited from, performing experiments, on people so. We, observe, large, populations. And then relate, their outcomes, to, what people do two key exposures. The. Weakness, of. Observational. Studies was something, that was exploited. By the tobacco companies, when, the first. Studies. Emerged, relating. Tobacco, to cancer they said well this. Isn't really not have a firm, basis. And. How. Do you know that it's not smoking, but it's the personality. Smokers. Have a more adventurous, outgoing, personality. And that, may be what really causes the lung cancer. Association. And so, epidemiologist. From the very beginning have, been, concerned. With establishing. Causality. So. I'll give some examples, of that in a second. Generally. There's a hierarchy of, studies, in terms of the stream, of. Establishing. Evidence, of causality, and the. Weakest studies are, anecdotes. In, other words I heard somebody, ate. Wheat germ and live. To be 90 okay. That's nice. But. The. Second level would be small. Unrepresentative. Samples. Samples. Of convenience. So to speak, then. Across. That sectional, studies also known as prevalence, studies the. Problem, with these kinds, of studies is that they're biased by, differential, survival. Case. Control studies, and. Of course there's a range of quality, in all these kinds of studies. Generally. The next level cohort. Studies have, the advantage, of identifying. A large group. Prospectively. And following, them over, time for different outcomes, and so, they tend to avoid, selection. Bias to, some degree that plagues, case, control studies and finally. Randomized. Clinical trials. Are thought to be the highest level of evidence. So. The goals of Epidemiology. We. Want to identify the causes of cancer we, want to quantify risks. Epidemiologists. Are very concerned, with public health and, public. Health services. How much how. Much is needed how many cases, of breast. Cancer ago we going to have next year and what kinds of Health, Service is doing e to. To. Support them we. Want to identify syndromes. Trends. And epidemic, and we. Want to understand, mechanisms. Epidemiologists. Said. To emphasize. Prevention. So. I think, about, vaccines. Think. About clean, water. Smoking. Cessation. This. Approach tends. To be much cheaper than. Treatment. And so. It's really. In that, way a very effective and good. Approach we. Want to eliminate disease, at the source it's, generally, requires, education. On. Communication. There's. A big downside. To. Prevention, and that's. A, political. Issue. The. Issues this you can't, go. To Congress, and. Point. To all the cases of a condition, that you've prevented when. You can show, the evidence but. It's. Not like you have grateful, patients, that, are saying, yes because you gave. Me the latest treatment, I'm now alive.

You. Have just have a lot of people without disease. So. You. Have to rely, on statistics. On they're just inherently, less. Germanic. Than treatment. So sometimes, it's harder to get funding, for, public health even though it is more effective and also it takes time so, we eliminate, an exposure, now. It. May, take decades for, this, to show up in health statistics and finally. Epidemiologists. Don't, tend to get Nobel, Prizes, so. This. Is kind of a downside for, the individuals, and it. You. Know it's not a big deal but. Really. For the scientists. That identified, tobacco as the carcinogen, and did those first studies I think, that you, could argue that they've saved more lives than, virtually. Any other. Intervention. You can imagine and, they certainly are deserving. Ok. Epidemiologists. Are concerned, with bias, systematic. Deviation from, the truth and this. Could be an entire. One-hour. Talk easily. But. I'm just going to give you one example and. That. Is that um you. Can have a bias it's extremely. Common in. Virtually, any study, based. On the participation, rate and. This. Is because, if you only have, a small, proportion. Of people. Who are eligible for study, participating. You. Don't know if they're, really. Representative. Of the, target population. And. They may not be generalizable, to, the general, population. So. That's a concern and if. I was to ask you. An. Example. Of a gigantic. Study, like UK, biobank in the in England. What. Percentage, of eligible people. Actually. Participated. In that study. The, percentage, turns out to be extremely, small about, five, percent or less so. That. Population, is probably biased. And, although. It is somewhat, generalizable. And we use that study all the time it's. Very very, difficult to get a highly. Generalizable. Population. So. As one example. My. Group did a large, case. Control study at the time we did this it was the largest case control study in the world of lung cancer 2,000, cases in 2000, controls and. When. We initially did a phone survey, this, was in Milan Italy to. Determine, whether, people, would agree to the study only, 30%. Of the population, of. Folks, that we called said. Yeah we'd participate, in that study and, our. Site. Visit at that time said no no no you can't do that well you know we can't have that you, either have to increase, the participation. Rate. Or we're, not going to give you the 12 million dollars you need for the study so, we spent a year and we. Did, invitation. Letters follow, up by phone. Advertisements. Offer. Them cash got, a letter from their physician, and offered, to study them at the hospital, or at home that. Increased to Perdition, rate to. 49%. Finally. The. Two key things we. Got extremely charming, interviewers. Of the opposite sex. And we. Gave them gas coupons which at the time in Italy was extremely. Desirable. And, we, had some other we had to letter from the mayor they actually like the mayor that was a good thing and, we got the participation, rate in, controls.

Up To 70% it, was a little easier to get cases because they were in the hospital so. That's. An example of what you have to do to. Improve. Your, participation, rate and that effort took a year and cost a million dollars. Okay. Epidemiologists. Worry about controls, and as I said it can be expensive, to get really. Good controls. You, want them to be representative. Having. Population. Controls, let. You do, some things, computationally. That normal, you couldn't do an example. Is you could calculate. Absolute. Rates as opposed to relative, risks. Now. Relative risks are very useful, so, for, example I can tell you that using hairspray, increases. The relative risk of lymphoma, by. 1.4. To 1.6, that's. A forty to sixty percent increased, rate but, is that relat, relat n't, to an individual, patient no, because, your absolute. Risk is still training. So. It's. Very important, to understand differences, between absolute, and relative, risk. And. I mentioned to you already that convenience. Controls, are the least desirable. So. If you, seek. An epidemiologist. As a consultant. Because you want to do a study the, kinds of questions are going to ask you what's. Your study design where'd. You get your controls, did, you collect, key, covariants. So you can take into account, confounding. And determine. If your case and control populations. Are similar, did. You consider bias. What. Was your original hypothesis. And now that you have your data are you. Looking at other hypotheses, if so you, really should be considering, multiple comparisons. In your, statistical, approach, have. You done power calculations. So you know. What kind of effect your, study size permits, you to detect, and what. Kind of validation, did you you do if you did biomarkers. The. Most common, question. Epidemiologists. Get is. Can. You explain to me why my. Grandmother who. Smoked, all her life ate bacon. Outlived. Their doctors, how. Can you explain that she, had all these unhealthy lifestyle, factors, and. One. Answer to this there's a lot of different answers is that. Epidemiology. Is concerned, with probabilistic, factors. Not, deterministic, and that, we. Know a lot of things that are associated with cancer but we don't know if, a given individual. Will, have a specific. Outcome of interest so. Risk. Is normally, distributed and, you're always going to get people at the end of that distribution. Okay. I'm going to touch on a few tools that epidemiologists. Use that. Are very useful. One. Of them is cancer, maps and. Here. For example is a map of.

Melanoma. Distribution. And what is this map tell you that would be really, hard to figure out in any other way as, soon as we looked at this map what's, obvious, is that it. Has something to do with Sun because, as you go south the, rates of melanoma increase. Today. More. Sophisticated, approaches. Geographic. Information systems. Based, on satellite data is used, to study a wide variety, of, exposures. And this is increasingly. Common. And used to for. All sorts of, studies. In epidemiology. Seer. Surveillance. Epidemiology and, End results covers. About a quarter, of the, US. Population 11. Different states and generates. Extremely. Detailed health. Statistics, on cancer. And so. This is a tool that. Anyone. Can use you can go on to the seer website, and. Download. Tables, cancer. Fact sheets and it's, very easy to actually, do your own statistics, and ask. Different questions. If. You want. And. What kinds of things do, you learn from seer data. First. I'll show you a little source. Of bias so. Here our rates of cancer. Across, different, races from 1975. To 2000. And oh look, in. Men, there's, a big jump up in the cancer rates here in the early 90s, we. Don't see it in women what. Happened, there and. Here's. The answer you, can see the rates of a. Variety. Of cancers are relatively, flat but. Prostate, jumped up and what, happened right around this time, PSA. Screening. Was. Introduced, and so. What, happened was you had early, detection of, a. Variety of cancers during this period. And. Here's, the. Distribution by. Race. And, it's. Well known that African. American men have higher rates than white. Men and you. Can see the. Distribution there. Here's. Another thing you can look at in seer the, difference between incidence. And mortality what. Does it mean if a specific, cancer has a big, difference, between, incidence. And mortality well. One thing it means is that, that. Particular, cancer may be amenable to successful, treatment, so, it refers to childhood, cancer where. A lot, of cancers have a great, success. Rate for. Example a childhood. AOL, better. Than 90% Kure. Okay. I said I'd touch on causation. And. Epidemiologists. Have been concerned, with. How do you prove a cause and. Bradford. Hill way. Back in the 60s, identified. Some criteria. That, are, associated. With. True. Causal. Factors one, is you want a high. Relative. Risk or odds ratio so, if. You have a risk of 1.2, uh. That. May well be biased, that's causing, that but, if you have a risk of 12. That, tends to be something, that you, really want to think about as to cause you'd. Like whatever. You identify. As causal, to be consistent. You'd. Like to see a dose-response if you, have more of the cause you'd, like to have more of the cancer, the. Cause should. Precede. The, outcome, temporarily. Or else. It's, not really, a good idea that it's a cause and finally. You, would like biologic. Plausibility. Today. At least mention, that there are newer approaches. To identifying, causality.

One, Of them is mediation. Analysis. And this, refers, to. When. You have a. Independent. Variable and a, dependent variable, and, you, have an intermediary, variable. In. Between them and so. Mediation. Analysis, involves. Mathematical. Techniques to relate each, to the other and try, to infer, something about cause. Mendelian. Randomization. Refers. To using, genetic, markers, to. Infer. Causality so the way this works is, suppose. We want, to understand, if tobacco, really. Is a causal, factor in lung cancer well. You. Could if you have genes, that predispose. People. To smoking, which, their Jia studies, in fact we do have what. You can do is identify, a, group of those genes and give. People with, lung cancer a score, based. On those genes and determine. If in fact they. Have more of those genes than, you'd. Expect and in, fact when that kind of analysis, is done you. Do find, evidence for causality with tobacco and lung cancer but. For, other. Interesting. Associations. You don't find it in a classical. Example of that is a tl cholesterol. And heart, disease so. Drug. Companies have tried to develop. Drugs. That actually raise. HDL, cholesterol. So, it's thought to be protective. But in fact when, you're doing Mendelian, randomization and. Analysis. You, find no, evidence for causality so there's likely some other, factor. Associated. With both, HDL. Cholesterol that's. Has. The. Real. Effect that might be exercised, or it might be insulin resistance, Oh. Molecular. Epidemiology, this, refers, to in, getting, using, biomarkers, in, epidemiology. Studies to. Help. Define. Mechanisms. And I'll come back to molecular, epidemiology, in a minute so, here's just an example of one of the early cohort, studies that, showed a beautiful. Dose response, between. Cigarette. Smoke per day and rates. Of lung cancer and. I'm. Looking at the clock because I will end this talk, with. At least 10 minutes before the end to. Allow folks chance, to ask questions. Here's, temporal. Temporal. Issue so you can see that male smoking, increased, in the population. Really. Decades, before the, rates of lung. Cancer increased the. Same thing with women. And. Here. Are animal, studies and beetles, that, showed the, Premio, plastic, changes, in the bronchial, epithelium, that.

Were Similar to those seen. In humans, in dogs. That were put on smoking, machines so, this was a early. Study, which. Increased. The biological, plausibility that. Tobacco, actually. Was, the causal factor you couldn't explain, this by, saying that the beagles, had, different, personalities. Here's another great. Epidemiologic. Study these are cohorts, where, you had people that quit smoking, and you. Could see that as. They. Quit smoking, over. Time the, rates of lung cancer, in a very consistent, way across. Three different cohorts. Declined. Okay. So. Let. Me touch on some of Epidemiology. X' accomplishments. So. In general. Epidemiologists. Have been successful, in identifying some. Of the general, and specific. Causes of cancer, they. Have been advocates, of public health I. Will touch. On tobacco again. The. Role of secondary, tobacco smoke I think is really important, and I'm going to say a word about molecular, epidemiology. So. In. Dceg. In, addition, to. Various. Studies in area in particular. Areas, often. Congress. Or a. Paper. Will come out and NCI. Will call on experts, in dceg to. Make, a comment and to. Discuss. Different. Cancer. Causes, so that's happened with silicone, breast implants, Chernobyl. Oral. Cancer and. Mouthwash. Abortion. As a possible. Causal. Factor in breast cancer which it is not cellphones. And for. Fukushima, at. Folks. In the radiation. Academia. Ology branch actually went over there to help. Construct. Studies, to follow the population, in terms of cancer risk. So. What are the general risk, factors for cancer so. It's of course, age environmental. Factors genetics. And the combination, these are the general. Risks, then if the, specific causes, tobacco. Is. Responsible. For about a third of cancer. Diet. Certainly. Contributes, although it's highly, problematic. What the dietary, causes of cancer are and all, other causes contribute. To about a third. It's kind, of a rule of thumb in epidemiology that most, cancer, is due. To the environment, and. That's. Because. Variation. By geography and overtime, only. Are compatible, with extrinsic. Environmental. Carcinogens. So. There's a vast body of descriptive. Ecological. An, analytical. Data that establishes, this and by the way when I say the word analytic. Epidemiology, it, refers to defined. Case, control and cohort studies where, confounders, are taken into account. So. Here's, a study that could. Be in the next talk on health disparities but. What this points. Out is that the, highest, the. Ratio of the highest, the lowest, relative. Risks, across. Countries. Is, dramatic. So. Cancer, risk factors really, do, vary. Greatly and. So. This, tends to point to, environmental. Factors now you. Might raise the question well how do you know it's. Not genetic, I mean, Asians, have a different. Gene. Pool. To some degree than Africans, Americans. Africans, or. Caucasians. And in. Fact we, know that from migration. Studies so. When folks migrate, from. One study to the other one. Country to another with. The exception, of a few cancers, that I'll mention, they, tend to rapidly. Assume. The cancer, rates of the new country. Our. Cancer, maps help, us implicate, exposure, and a nice.

Example. Was. From one, of the first cancer maps of lung cancer, which, found this high risk, area in Montana, and what. Was in this area but. A, copper. Smelting, plant that. Was contaminated, with arsenic, and, this plant was actually, removed and when, it was removed the cancer rates, improved. And. That red area went away. Another, example is. Jen. Way in China, where we actually have had studies for a few decades and this. Is the highest risk area of. Lung. Cancer in China and when, you go to Jen wait what. Do you find you. Find indoor. Air pollution you. Find the beds that, burns smoky, coal that. And, are. Poorly, vented, in the houses so. Folks, breathe in the smoke and, it's. As, good, as smoking a lot of cigarettes and it, results in higher cancer, rates and studies. When, they remove, these. Indoor. Sources of air pollution show. That the rates of cancer dramatically. Declined. Okay. So, cigarettes. Are the big the, big hitter and I. Will say a word about tobacco that. You. Know this is something you can look at later but. It is still the major cause of preventable. Morbidity. And mortality and. It's. Hard to believe but, in, this. Century it's. Still expected. To cause hundreds. Of millions, of deaths so. That's. Because, in the developing, world, cigarettes. Are still heavily, marketed, and sweater, the declines, in, the United States and Europe in China and, India, in. Other areas. Smoking. Is still going strong. Smoking, strongly, associated, with about seven, cancers. And we, still do studies it's, just a nice study by Neal Freedman showing the association, between smoking and bladder cancer risk. It. Takes decades, to, change, behavior. So this is the Surgeon General's report. In. The. Late 60s, first, studies. Of tobacco and cancer in the 50s and the rate of smoking in the United States and adults has, slowly, declined. So. It's, about. Twelve, thirteen percent among, adults, today but in certain area the areas, of the country it's much higher. One of the effects of. Decline. In the rate of smoking that maybe. You're not aware of is that it. Was shown a, decade, later that. Environmental. Tobacco smoke was, a risk factor for the spouses. Of smokers. And, this. In turn led. To clean air laws, that. Eliminated. Smoking, on airplanes. Public. Buildings. Movies. Hospitals. All, those places where you would go in and have. To breathe other people, smoke so. This was a terrific. Public. Health boon &. Incalculable. Numbers, of lives have been saved and. Based. On this clean air legislation. So. This is really something you can thank, immunologists. For I. Just. Wanted to mention that there's a lot of research. Today, on, smoking and this is a paper we did, just in the last year when. We looked at the rates of light and intermittent. Smoking, and. This. Is the fastest, growing segment. In smokers. Over the last 15 years and this, refers, to smokers. That, smoke, not. Every, day and less. Than five. Cigarettes. Per, day so, what these smokers do is that. They. May get together with their friends or go to a bar and have a cigarette or they may sneak. Out of the house because their, spouse doesn't, want them to smoke but, for whatever reason they, smoke intermittently. And these smokers have unique. Characteristics. So, there are differences by ethnic group by. Education, by age. Probably, the biggest difference is that they're less, dependent. Smokers they have less nicotine, dependency. So. We. Followed, this with a study again. Led by other investigators. That. Showed that even. If you smoke a little bit, you're. All cause, mortality. Was. Sharply. Increased. So. Even. Smoking that small amount and you'd expect, this based. On the data. On. Espousal. Smoking, or smoke. Me in the workplace, or smoke. Me in the family that. The. Rate of lung cancer was increased and so it's. The rate of mortality so. Smoking. Is still an ongoing concern, even, today, ok, I'm not going to spend too much time on the other major risk factors for cancer this is something you can look up on your own but alcohol is number two after. Smoking associated, with a variety of cancers. Sometimes. Epidemiologist. Do some good by. Proving that a, particular. Factor. Is. Not. Associated, with cancer so, it turns out that coffee drinking, is.

Actually. Good for you in many ways you or you're good. For your liver. Good. For cardiovascular endpoints. And. Slightly. Less all cause mortality. Ionizing. Radiation, is responsible. For a variety of cancers I'm, not going to go into that a. Lot of studies from our group I'm, not going to go into non ionizing radiation. Tanning. Beds. A. Lot. Of research has gone into infections. In cancer it's a major. Etiologic. Factor. In, cancers, including. Newer infectious. Hypotheses. And. I'm not going to go into microbiome. And how, that has now, implicated. Some, causes of cancer probably the best. Studied. Is fusobacterium and, a. Lot, of studies are trying to nail down the. Association, of this particular bug, with. Colorectal cancer. And. In. Cervical cancer. The. Associate. The differing, Association, of different, strains of HPV, with. Cancers one of the factors, that. Helped. Really, nail that down and the, earliest associations. Were. With number of sexual partners but, as you got more specific and could. Identify HPV. The odds ratios. Became, higher with. Cervical cancer and. Finally. Occupation, there's a lot of. Occupational. Factors, that are human carcinogens. And, this. Is a politically. Difficult area. Because. When you implicate, something. Like Diesel and lung. Cancer. There. Are. Supporters. And. Lobbying. Groups that. Complain. To Congress, and then Congress, calls you in and wants you to testify and they, want your data and so they file Freedom, of Information Act. Requests, and they. Request. Your data and immediately, reanalyze. It to try to prove that you did it wrong, and in, fact looking at the data in some other way, shows. That diesel exhaust is, really good for you so, it's, really important, that you have well-funded, groups, that. Investigate. Particularly. These, environmental. And occupational, carcinogens. Okay. What. Can go wrong and first. I want to just mention some of the ordinary, challenges. That epidemiologists. Face. And one of them is that. There, are substantial, gaps. In what we understand, about. Environmental. Exposures, in cancer, so. Some. Of those big international, differences I showed you. We. Don't understand, and so more, work is. Needed. And. Some exposures, that are thought to be important, or difficult or, impossible to, assess and so. I will talk in the last, segment. Of this talk. About, how, we get at some of those difficult, exposures. So. Here's a favorite. Disease, of mine chronic lymphocytic leukemia. The. Most common adult leukemia in. The Western, world but. We, don't understand. Any extrinsic. Environmental. Cause. For that leukemia, the major best. Understood, the, illogic factors age and family history and. About. 30. Different snips, have been associated, with Co, l but we. Really don't have a great understanding, what the extrinsic, sources. Are. Dietary. Risk factors remain, highly, controversial. There. Are a few that have been implicated, but.

In General, for. A lot of cancers that were most interested, in we. Really, have. Not nailed, down the. Specific. Dietary. Causes, I'm going to brush, by diet again, in, a minute and going to this one. Study we did in. Our large, case control study was. On meat consumption, and, so, we did show that increasing. Rates of meat consumption, fresh, meat or processed, meat was associated, with. Increasing. Rates of lung cancer but I would. Caution you that those, studies are, fraught. With difficulty. And one, reason, is that in. Our questionnaire. We imagine, that we're asking people precisely. About their sources of meat consumption, but, in fact what, they're really eating, are a. Collection. Of other, items that, may bias. The. Assessment. Of meat and so, for. Example. Food. Questionnaires. Is at the meat or the nitrates, in the meat is. That the high fructose corn syrup in the big bolus of sugar that they're taking in. What. About the. Load of carbohydrates. What. About the, the processed. Food. That's. That's. In the bun or in the french fries the. The, fat. The. Glyphosate. Or, maybe. That person, just that, ate all this is not. The most health-conscious. Person, and has other unmeasured. Confounders. That were not covered in our study so, diet is a difficult. Area, it's one of the more challenging areas. And we have a lot left. To do in that area. Okay. Of, course. Epidemiologists. Have, delved, into genetics. Increasingly. And on. The genetics side we have challenges, as well one. Is that most. Of the genes in jiya studies. Since about 2007. Associated. With, common cancers, confirm, minimal. Incremental. Risk. They. Explained only a small portion of the variation, when. You put them in risk models, they help a little bit but. They don't really, give. You that much how. Gene and environment, work together is, really. Poorly, understood so, you'll see a lot about gene environment and the, dream, with gene environment is, that you have a high, that. A certain, exposure. Is only working in the presence of certain gene and if you knew, that if you could define that, gene environment interaction. Really well all you'd, have to do is interrupt. The gene or interrupt, the exposure and you knock out a big portion of risk well. Nice. Idea but there are very few, if any, practical. Applications. Of that again. There could be a whole talk, on gene environment interaction. Which won't be this talk and, finally. For families we've. Studied cancer, families, for a number, of decades and, use. Them to try to identify genes. By classical, methods like linkage analysis. And. More, recently exome, sequencing, and. We. Found a few but. Again, it's. Difficult. So. All cancer on some level, is due to genetic changes but you have to specify what you mean by genetic, do you mean germline or somatic, mutations. In the tumor do. You mean those population. Kinds. Of genes that we discover, in G wasps or do, you mean major genes, that we discover in families, and you. Mean identifying. Our favorite candidate.

Gene And looking for that specifically, or do, you mean the agnostic. Search across. The whole genome. So. Here's examples, of our cancer families, where, we, have a group. Of lymphoproliferative, malignancies. In one kindred, and we, have for example 50. Of these families, for CLL. And. These, kinds of families, have classically. Left led to the cloning, of different at or, espresso genes. Whereas. In population, studies you. Had only. About, five, years ago 240. Disease. Low so I think that figure, now in, 2017. Is close. To 7000. Still. Those genes explain a small component, of risk so. I'll, mention our study Eagle again quickly we did a study a case control study of lung cancer. Driven. By the idea that, in. Spite, of the fact that we understand, a lot about the causes of lung cancer which is smoking it's still the leading cause of cancer, mortality in, the United States and treatment. And screening, both. Posed very, very significant, challenges. So, was 10 years ago that we fielded Eagle. This. Case, control study of. 2000. Cases in 2000, controls one, of the first things we showed was that, family. History was a risk factor so. We wanted to identify. The genes after, you adjust for pretty much every, other known, lung. Cancer risk factor. One, of the features, of this, study is that, we like to say it was a, molecular. Or. Integrative, epidemiology. Study and to. Explain this in a nutshell. Originally. What Epidemiology, did was it, would identify a risk factor by, questionnaire, and relate. It to an outcome like a cancer, and use. Statistical. Techniques, to relate, the, exposure and disease and on, the basis, of showing. A correlation, or. Some. Measure of statistical Association. Infer. That, oh yes tobacco, is a risk factor well. The idea of molecular, epidemiology. Is that. You would also measure. Biomarkers. And so. By, measuring, biomarkers. You, could gain an additional. Amount. Of evidence so maybe we would measure a tobacco. Marker, in the blood, internal. Dose and and, know the internal, dose of tobacco or, maybe we would measure a DNA, addict, of tobacco. Or, polycyclic. Aromatic, hydrocarbon. Addict, and, in, that way in. Inform. And gather further evidence, of on that on the. Sequence. Of causation, so adding biomarkers. To investigate, genes and mechanisms, and then. More recently. Integrative. Epidemiology. The idea, that we, can learn even more by. Looking at the behaviors. That, are related to exposure and. By. Looking at outcomes, we, can understand, something about the factors, that makes somebody. Have a good or bad prognosis, or a good or bad outcome. So. In our case, control, study we, actually got liquid, nitrogen canisters. Into a number of our hospitals, and would, sit there and wait, for the surgeon. To resect, the tumor and, then, the pathologist, would take the tumor and, cut. Pieces and we'd have a piece, of paper and mark where they came from and we'd get pieces of tumor to, conduct, these kinds of studies we, also had. A refined, questionnaire. So in addition, to asking people, what, they ate we. Would assess, things like doneness so. Certain carcinogens. Like heterocyclic, amines, are related, to the time and temperature. Of cooking so understand, if people would eat their steaks. Well-done. Like. This or, more. Rare, could. Be related to the levels of those carcinogens. So. Here's some of the instruments, we included on the behavior, side the, Fager strum test for nicotine dependency. I'll show you how we use that and. Then. Test some questionnaires. Related, to anxiety depression. Personality. And. Other factors. So. What has a molecular, epidemiology. Contributed. For. Example we understand, in, part based on biomarker, studies that HPV, is the cause of a hundred percent of cervical cancer, and that. Prevention is therefore possible with the vaccine, we. Understand, that cutting down on smoking is, ineffective. In terms of reducing the rate of cancer because. As you, could see by. The manner in which you inhale, and so. A, biomarker. Studies show, that levels. Of carcinogens, don't decline. And. Jia studies have been performed, for. Hundreds of conditions, based on biomarker. Studies there's, a lot more this, again could be a whole lecture. Consortium. Are commonly. Used in epidemiologic studies, and groups, have sprung up to. Expand. Data collection, and reduce Mis classification. By using, specialized. Instruments. In those studies. Okay. So, I'm going to cover the last two sections rapidly. What, can go wrong. I'm. Reminded. Of Coons. Classical. Work on the structure of scientific revolutions, and. This work points. Out that our. Understanding. Of scientific, paradigms, doesn't, change in a continuous.

Manner But. Tends, to be discontinuous. The. Wake you described, it basically. Models. Have, to be overturned. So. Really. What happens is the advocates. For certain ways of thinking have, to die before. Anything. Changes, now, a lot of us are skeptical. About that we think no I'm, not that attached to my hypothesis, in the moment, something. New comes out I'm gonna look. At the evidence objectively. And adopt it in fact that's, not what happens in practice so. Let me give you the example of obesity. Obesity. Is. Strikingly. Increasing. In the United States. It's. Also strikingly. Increasing. Worldwide. If. You look at different, countries the rates are rising this, is overweight, this obesity. 53%. Of the adult population in, the United States, 21%. Is, obese, all. You have to do is walk into a Walmart and see the carts and you get an idea why. Are we concerned and in, cancer. Well. Being overweight is associated, with 13, kinds. Of cancer. What. Caused this, one. Way, to trace this back to the cause is Ancel, keys I don't want to put all the blame on this poor guy he's, the guy that invented k-rations. He did some of the very earliest, dietary. Intervention studies. Which, are fascinating. To read about today. But. One thing he did was a. Multi-country. Study, where. He, found, an association between, saturated, fat and cardiovascular, disease. And this. Led to recommendations. To. Eat less fat which, if. You, have three, macronutrients, fat. Carbohydrates. And protein, you're invariably, going to increase, the amount of, carbohydrate. In the diet. So. This led to USDA. Recommendations. To, eat. Less. Fat and eat, more carbohydrates and. This. Led to institutions. Adopting. These recommendations, which, are still some. Degree adopted, today by, the American Diabetes Association, the American Heart Foundation etc. In. The, American public did indeed follow, the recommendations. Carb. Intake has gone, up by over 30% and, fat has gone down, saturated. Fat went down and. What were the results of these interventions. Well. Here's. An. Obesogenic, diet. That's given to fatten up rats and in, fact our, diet of 40%. Refined. Carbohydrates. And 40% oil. Which, is a doughnut. Is. Exactly, what's. Given. To rats, and, it's. What's. Currently. The standard American, diet and this. Has resulted in, a. Stark. Increase, in obesity and overweight. However. When you do randomized. Clinical trials. To look at the effect of, reducing. Saturated. Fat, generally. These. Multi-million. Dollar studies, have, come up empty so. There's been no difference. Pretty. Much with a few exceptions. In. Cardiovascular. Events especially in mortality. So, this intervention. Has not been particularly, helpful. And there's a vast literature. On this now. However. What has happened, is that diabetes in, the United States has steadily increased and. If. You look at diabetes prevalence this is 2012. Up. To 20% even, more of the adult population across. The obesity Belt particularly. Has. Diabetes, and a much higher proportion have, pre-diabetes. So. What's. The cause well. Dietary. Changes have to be number one. We. Give a whole talk on the topic of light at night but, the idea of increasing. Light, exposure. May. Have something to do with obesity, and then there's a variety of other causes I. Don't. Have time to go into these but. Processed. Foods. Increasing. Intake. Of nutritionally, empty foods, a. Less, active population obesogenic. Toxins. Economic. Pressure poorer. People can only afford fast. Food and, nutritionally. Poor food and less, home cooking, are some, examples. Light. At night. Hypothesis. Is a fascinating, one I don't have time to go into it but there, is some evidence that, light. Pollution, at night and less exposure, to light during the day disrupts. Our circadian rhythm, and disrupted, circadian rhythm, first, effect of that is weight. Gain. So. Before. We develop diabetes we, develop, insulin, resistance, for, many decades, and. Insulin. Resistance is associated, with a variety of bad endpoints, like Alzheimer's. Overall. Mortality and. Cancer. Mortality so. This is an area of current research right. Now. Okay. That's all I can say about that I will take two. Minutes to talk about what's next.

Technology. So, we use technology, to capture some exposures, that have previously been. Accessible. We. Can improve miss classification. It's, very critical to validate, these what. Can we get at that we couldn't before, examples. Are sleep physical. Activity, vital. Signs circadian. Variation, social. Factors location. Where you are and pulse, oximetry, these are some examples, relative, relevant. To lung cancer so. Sleep, many, of you have Fitbit. Or some ones are kind of watch. Using. Your Fitbit you can. Precisely. Quantitate. The stages, of sleep REM sleep and non REM sleep, extremely. Important. Sleep. Is related, to obesity, in, data for men Haynes if you sleep this, is our published work less than six hours per night your. Rate of addiction. Goes. Up your rate of diabetes goes, up. Physical. Activity, likewise. Can. Be tracked. By actigraphy. Vital. Signs very. Important, I don't. Have time to go into it, we're, focusing. A lot of our effort on circadian, variation. Social. Data who, you hang out with is very very, important, in terms of a lot, of traits and. Finally. Pulse ox as most. Of you probably are, aware you can just put your finger on your phone and get your pulse ox and pulse, ox is. Related. To all, cause mortality. So. It. Can be tracked easily, in cohorts. This. All of this data the, epidemiologic. Data genes. Biomarkers. And technology. Is what, we think will eventually create, future, improved, risk, models so. I. Think. This is probably a good place to stop, leaning. A little less time than I hope they would but I'm happy if there are a few questions thank you. Yes. So. E cigarettes. Are, being, tracked in the path, study. And. Newer. Newer. Studies will try to follow, them but most of the classic, cohorts. One, of the problems, is having to wait two, decades for people to get different diseases, there, are no questions in, PLCO. Or AARP. Or any of those cohorts on e-cigarettes. And. The problem with doing case control, studies, where. You can look at rapidly. Emerging, risk, factors, is that it. Takes a while for them, to have health endpoints so it's gonna be, some. Time before we have good human epidemiology. Data what. We will have though is molecular. Epidemiology. Data we will have biomarker. Data from. Path, and, some other studies.

Like Path that. Give. Questionnaires, to young people and the, ones that are using these cigarettes we will have biomarker, studies on them in the next year, or two. Yes. So. So. I'm sorry it wasn't polycyclic. Aromatic, hydrocarbons. It was heterocyclic, amines, and those. Are. Specific. What's. Best studied is meat whether, other, proteins, form them I don't think they do it's really an issue with. Various. Kinds of meat and the. Latest studies. Tend. Even though these are established, carcinogens, not, show big risks, associated. With doneness, or the heterocyclic amines. It. Was a big thing, 5-10 years ago and it's kind of not. Emerged, as a dramatic. Risk factor lately. So. I put my email. On the, front page you have any questions, that we didn't get going I realized I covered, a wide variety, not. Very deep so if you have questions please do email me, and I try to get. Back at you for announcements. We're. Doing our pathology. Tour. Today at 6 o'clock so. For those of you who want to, go. See the pathology, facility, in building 10 will, be meeting out in the hallway, at, the end of the lecture. So. Our next speaker is Britta. He, got her. Undergraduate. Training in Ireland, she. Got her PhD at, the University, College of Dublin, and then in 2006. Joined. The NCI cancer prevention, fellowship, program, and. She. Then worked as a postdoctoral. Fellow, in. Kirk. Harris's, lab and, she's. Now an NCI Earl Stephan in, your track investigator and. She. Injured her legs so she's going to have to sit down when she lectures, so. Be, kind to her. Yeah. Ok. All, right, okay. So as Terry said my name is Brian and I'm an investigator, here at at. NCI and, I did my postdoc here so I've been here for 10 years now. My. Lab studies, lung. Cancer and specifically, in lung cancer health disparities so, a lot. Of the talk today you'll, see some examples from lung cancer but this is a talk really about in a broader context, similar to what Neal was talking about in terms of cancer. Health disparities so, again. We'll touch on different themes it's not all going to be lung cancer specific, but, again if I touch on something that you want more information, on or if. You feel you just want to follow those questions my email address is here on the front and as, I said I work, here on in building 37 here in the main campus. All. Right so. The. Three main parts, today of the talk I'm going to talk a little bit about, key. Just, just, or give you an overview of what cancer health disparities in, the u.s. look like at the moment in. The second part we're going to talk a little bit about some. Of the key factors that contribute, to disparities, and then, the third part very briefly we're just going to talk about where, we are right now and, what, the future, focuses, are in this area. All. Right. And. We'll start with a couple of definition so there's a couple of slides like they're so bare so bear with us. Throughout. The talk I often mention things like racial, disparities, or ethnic disparities, but I think it's very important, at the beginning to define them and to, know that both are important. In this type of research when. We use the term race, what, we're generally referring, to our biological differences.

Between Groups assume to have different, biogeographical. Ancestry their genetic makeup, so, often when we talk about race it's referring to ancestral, or genetic factors. Ethnicity. While related, is distinct, and here, what we're talking about it's a multi-dimensional, construct reflecting. Biological, factors geographical. Origins, historical. Influences, shared. Beliefs, and customs. That. May or may not have a common genetic origin, and that is important and as I mentioned both of these are important. In studying, disparities. Research. So. As I'd mentioned one of the first things that I want to do is just give you a sense of what despair you look like in the u.s. so shown here on the graph. On the left you. Can see differences. In life expectancy, in. The United States, specifically. Just based on race and the, first thing that you should hopefully appreciate is, that, for both men and women, in general. Black men are expected, to live six and a half years shorter, and black women about five years and that's a really significant. Difference in life expectancy. Studies. Of course have been done to try and figure out one of the contributing factors to this and while yes cardiovascular. Disease. And. Issues contribute, quite a big chunk to this differential. Life. Expectancy, cancer, also plays, a very important, role in this and of course cancer health disparities are, the topic of the presentation, today. All. Right, again. Addicted. To definitions. Here today but just that you know how, the NCI. Defines, a cancer, health disparity, its, differences, in the incidence prevalence. Mortality. And burden. Of cancer and/or related adverse health outcomes that, exist among specific populations. Within the United States and, specifically. The NCI and indeed many other organizations. Have noted, that, african-americans, have the highest death, rates from all lung, cancers combined, including. Malignancies. Of the lung colon, rectum, breast, prostate, and service unless of all racial groups in the United States so, that gives you a sense in some, degree of their to burden, and scope of the problem. Okay. So these are incidence rates this is just looking by men this is data from the CDC and. What it's showing is, the incidence of lung cancer by, racial ethnic group over. A period of time so stage this graph actually goes back to 1999, and, what you can see is that of all groups, the United States black. Men and in this case men but also women have, the highest, mortality. Sorry. The highest incidence excuse me in addition. It's not just that the incidence, is higher what a lot of studies have now shown is, that in general, the. Age, at which becomes, just diagnosed is also earlier so, what's graphed on this. What's. Shown here actually is from colleagues. Of eras in. Dceg. From where neil just presented, and what it shown is on the right hand side african-americans. Or blacks are, expected, to be diagnosed, at a later. Age but. You can see the majority of cancers including, lung are more, likely to be diagnosed at an earlier age in african-americans. Compared, with European Americans so, it's not just a higher burden is that the actual incidence, is also diagnosed, as an earlier time in their life. So. What. Are the reasons for this so there are several reasons that were proposed in in. This paper and one, of the most of course etiologic, Western, Asia so we. Know for example that the cause of a cancer can different across can vary I should say across various groups and so. In that case the cancer can occur at a different age in, addition. It could be related to the timing of the or the intensity of the exposure so, one example here could be an, HPV, exposure, so if you're exposed earlier than the disease might be more likely to occur earlier are the same thing with tobacco for example if you start smoking earlier one might imagine that you'd be diagnosed with the cancer earlier, and, of, course there's other.

Possibilities. That, we'll touch on a little bit later looking. At the timing and presence and frequency, of already detection and we'll talk a little bit about that. Because. Of this very. Strong observation, that many cancers, are in fact diagnose, at an earlier age amongst, African Americans the, NCI, has now organized, the early. Onset malignancy. Initiative, and they're working with various centers throughout the United States to, try and study this in more detail to see can we understand, why. These disparities, occur, and understanding. It what can we actually do to intervene. Something. That's also been studied. Quite. An. Area that's been, showing you somewhat, more and again, by some of our colleagues in dceg. Including. Lindsay. Morton is this idea of second cancers so. A lot of studies to date that have described cancer, health disparities they're. Looking at primary, cancers through the first incidence, of the cancer but, of late people have been asking the question will. Pod about second primaries so, as we have more and more cancer, survivors, which of course is a good thing it's, also the field of cancer survivorship, is very important, because we need to follow those populations. And try and understand, what. Is their survivorship, like are they a risk for second cancers as you. May know a lot of treatments, for, cancer. Include radiation, and radiation itself, is of course a risk factor for cancer and some, of the studies that have been conducted this, is just one example. By. Her. Name is got out of my head at the moment but, what. They found is studying endometrial, cancer and following, those survivors is they looked to see what, was the incidence of second cancers and in, that example there again, they also found disparities. So overall, if they looked at every cancer site you, can see that the standard incidence, rate was 20%, there's. A 20%, increase in our incidence rate of all cancer, types in. In. The black participants, but, it was in. Fact even less in the white so. More. Work of other things needs to be done in this area but what this study points to is the importance, of studying cancer health disparities not. Just in the context of a primary cancer but, also within the context, of secondary, tumors as well. So. We talked a little bit about incidence, rates but what about mortality, so. Here again shown, for. Both, men and women our data from the CDC showing. That the mortality. From cancer for both men and women, is also much higher compared. To all other racial. Ethnic groups in the United States. So. Looking at this little bit closer this, is data from, German. College a couple years ago and it's focusing specifically, on the four main types of cancers so breast prostate. Lung and colorectal and, what, it shows here is you can kind of get a sense of what the degree of the disparity, looks like so it's not just higher but how much higher is it and you, can see that for prostate and breast cancer this the the, difference in mortality between, European, Americans and African Americans is quite, high, now. It's important to overall to emphasize, that oftentimes. When we do these studies we look at a snapshot it's one measure at a particular time but, what's equally important, is to look to see how trends change over, time and I see, in some of the work that Neal described he took you through some of this. This. Is what's done in this. Type of analysis, is what's done here so, this is looking at the. Mortality. Of patients with prostate cancer broken. Down by whether the participants, were white black, or Asian and as, you can see here it shows that it looks at it over time and look to see are we doing any better what. You can see is that compared, to people who were diagnosed between 1990, 1994. - those who are diagnosed, in 2005, 2009. The. Likelihood, that these people will survive is much better you, can see it's improving over time but. You see for black patients is actually improving even more and this is a good trend this is what we want to see however.

There Is data from breast cancer suggesting. That we have to see the opposite trend so. In addition the the point, of this slide is to say while yes it's important, to look at what's happening right now was, perhaps also, equally as important, is to look at how these trends vary over time because. It can also pretend, a new, or, an emerging. Disparity. That you might want to look at and that you might want to address. Okay. So, just, like neo had gone through in his talk and it's. A very many, these topics are very very broad and it certainly isn't possible to go I could just give you a whole talk just on you, know one of these factors or you know one, of five factors within each of these it's very very complex so, the goal really of the presentation, today is to give you a sense for the multifactorial, nature. Of the problem and it, being multifactorial. Of course there are many many disciplines that. Are required to research it to try and get a bit a better picture of what's going on. I've. Broken it down into four it could be, it. Could be many more groups but at. One level. The. Full area that I mainly focus on of course is on genetic susceptibility, in biology that's my background and they're the parts that we study we'll talk a little bit about that later on but, you cannot give a talk about cancer health disparities without. Acknowledging, the huge importance, of access to care which, we're going to discuss the. Importance, of exposures behaviors, and lifestyle factors again just like Neal took you through in the previous hour and also, social determinants, and the, idea is of course that each of these factors don't, operate alone they all interact, together and so, while, I myself not, may not study each of these individual, factors all, the research that we do is acutely aware that, all of these things are important and where possible we try talk to our colleagues to, share that to share our findings and. For a possible look, for synergy within them. So. In terms of factors that could contribute to disparities so at a very sort. Of a high. Level picture the, first thing to note is that geography, matters. So, why does your with you matter this is just a picture of the United States broken, down by state and, what, it shows is for all cancers, combined just. The fact that if depending on where you live as you can see the, the, the pale colors are low is a, low incidence of cancer whereas the more intense, blue colors reflect a higher incidence and just, based on geography, within the United States what, you see is that there's a very large difference in the incidence rates of all cancers, there. Are many different factors that can contribute to this but, the point is that your graffia matters. So. Why does it matter and how could it matter well. A few of the things of course to mention is that. Again. You could give an entire talk, just on how social economic status contributes. To disparities, but it's a hugely important. Factor. So. We know for example that, a low socioeconomic status, neighborhood, in addition. To itself being a risk, factor for many. Types of cancer including lung, it, also confers an additional incidence, of mortality, risk beyond an individual's, socio-economic, status so what does that mean it means that the neighborhood that one lives in is, an. Important determinant of, cancer, incidents rather than just your individual, m so. Second omics dashes and that's important, so how does it do that well of course pollution. Again as Neil talked about in his seminar, is a, key, cause of cancer disparities and, it's known that in certain communities that, have a higher representation. Of african-americans, within them those, areas, also experience. A higher burden of pollution these, are all important, factors. Depending. On where you live you, may also have decreased, access to, preventive, services so. These are things even as simple, as. Smoking cessation services. Smoking. The, decreases, that we've seen in cancer incidence over the past number of decades are, hugely. Due to the decline in smoking in the United States so smoking cessation.

Has Been and continues to be the number one thing that we can do to decrease the prevalence of cancer so that's hands-down but. Depending on where you live you may have more or less access to the types of services so if you're more, likely to live in an area where, you. Have access to many, health. Clinics, or have access to a G what, we call a primary care physician for, example, at, those visits they ask you do you smoke and if you said I do smoke they'll say well you know you should stop smoking, and here are some services where I can go and help you do that, the. Access, to those types of resources is, not uniform, across the United States we talked a little bit in a moment about rural and urban disparities. But they're very real factors, that can contribute to an, individual's, health, behaviors, but also their, overall health themselves. In. In. Recent, years, and especially I think in the last 12, to 18 months this, idea of studying. And continuing, to study in more detail rural and urban health disparities, has, really. Really. Taken hold of. Course it's been an area that's been studied for a long time but I think there's an increasing recognition that. There are very significant, disparities, in terms of both, cancer incidence, and mortality. Determined. By whether the county that you live in is rural, or the CLO population, versus, urban and. The. Studies now that have been shown that rural populations, are more likely to have an increased cancer incidence not, of every cancer so it's important state output of certain cancers but, certainly overall, as well in terms of mortality that are certain a higher burden and often. This can be because of an equal, burden of pollution again we touched on that earlier but, also, in. General. The socioeconomic status, tends to be lower, in, rural, areas and for, that. And. Again the egg will get to it again but it also, goes to access to health care so both the financial asks, access to health care but also the geographical. Access to health care becomes exceptionally important. This. Is just again some collective. Studies showing that rule cancer disparities have included higher rates of tobacco associated. Cancers this, is primarily because the. Prevalence. Of smoking in. Areas is higher than it is in urban areas and again, that gets back to you can see how it's intertwined because in, general there tends to be reduced access, to, preventative Services. What's. Been seen recently in fact is while temporal. Trends, of HPV associated cancers has, been, somewhat stable, in urban, areas we've, actually start to see an increase of, that in rural, areas and that is something that needs to be keenly monitored, and the reason that these things need to be monitored, is based on this it's all about impact, and that, is once we start to see trends emerge if like, and just using this as an example the tobacco associated, cancer is we know that access to tobacco cessation. Interventions. Is a proven effective method but. Also in terms of HPV, vaccinations. If we, see there's an increasing, trend with from a policy perspective or from a public health perspective that's. An opportunity for intervention in a way to try and introduce. Ways. To, to. Prevent those cancers. So. This is also just some recent work that was put out again by the CDC, and just to sort of take you through this this. Is just a part of a bigger picture but the reference here at the bottom shows you where the rest the data are but, essentially, the. More, Brown the color the more urban the, county, or the area where the person lives and again, just if you look at lung cancer overall the, incidence of lung cancer is much higher in rural, areas, and we think that's largely because of the higher exposure, to tobacco but. You see the a different, thing for breast cancer so, again. What you actually start to see is an, increased. Or sorry a higher incidence of breast cancer in the, more urban areas and, I. Put up this graph to make the point that I don't want to leave you with the impression that every cancer is higher, and has a higher incidence and mortality in, a rural area compared, to an urban area that's not the case in, general yes we see that there's a there, is a disparity in cancer in rural areas but it is complex so for anyone who is an interest in the area this.

It's, Actually a good primer to start to get you interested in us and. It's very recent data so some of the most recent. Data that they have so, in the box here on the left it shows incidence, and over here on the right it shows the. The death or the mortality race. So. Some of the studies as well that we have done actually also in collaboration with the CDC was. So, as. I mentioned my lab is interested in lung cancer were interested in disparities, and one of the questions we had is so, we. Know that if you look at the entire population, the. Incidence, of lung cancer varies about 30, to 40 percent higher in african-americans, compared to European Americans but, that very first picture that I showed you about geography shows, a very mixed. Landscape, right so depending on where you live disparities, could be higher or lower so we want to try and do within a more granular way, ask the question whether. Or not you, were living in a rural area or an urban area by county, level determine. Whether this was their sort of hotspots of disparities so to speak and. One of the things that we learned from the study is that, whether, it's, for, an adenocarcinoma and squamous cell carcinoma, are just two specific, subtypes. Of lung cancers this is a lung cancer study but. No. Matter what, county we studied in the United States the, incidence. Of lung cancer was higher in african-americans, compared to European, Americans that was the first observation. But, the second thing that we learned is that the more rural your. Location. Was in terms, of where you lived the, higher the higher that is barri

2017-11-25 12:00

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