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Under your leadership you say about the theme Blakey mix over then how about 10 min? Okay. And.

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The topic is. Sharply heightened the mortality among African-american women. And MPII per on bearings genetic variants and Obitanic environment.

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My co-ser's, at the University of Cincinnati.

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2.

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So the and the overall objective and The outcome is mortality but the we're very interested in the and social class and lifestyle.

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And the genetic obesity. So what the everybody have the genes but some people have the certain alleles, certain form of the gene that's prone.

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To a obesity.

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Longevity may be the single most important indicators of rural health. This is one of the main health concerns currently in the US.

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1,000 pieces based upon. The 97. And BMI, a sorted Geneva variant variables.

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That were identified in a large Do you know why the Association studies so essentially And today the United States would assemble you know, 300,000 individuals to get their they would measure which individuals 10 million genes tonight variable.

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And they would try to associate with predictor BMI. So those are top pays that's their significant that the p-value is as small as 5 times to 10 to 98.

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Active 8 is 0 0 8 0. So that's the type of results. So we're using.

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And. The relationship between a piece and motel is not very clear. The pub if you want to predict mortality by BMI and the result is mixed.

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And Another thing for BMI is very complicated. It changes over dramatically over lifetime. This is naturally increasing BMI.

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But also if you use BMI that's very close at older ages, that's indigenous.

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That's already the outcome rather than and you know the The predictor.

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So we, we, we argued that genetic capacity, so that was formed at first. You know at the, in the sperm and egg.

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United. So that's the time that genes work were formed. So it happened very early. The relatively is oddness.

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And so this is the.

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You can see this is a BMI average BMI that changes over age. You can see the changes dramatically.

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And. So for the 1,979 variants we use. And we used the composite scores.

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We use 97 scores. And we. Derive us one single number for each individual. So each individual have a score.

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And so that's like a variable. Like, you know, like a BMI, but this is genetic.

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Overall score genetically as related to obesity.

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We also, we do, and for the Gene, this is a continuous variable, but we used it in different ways, but we used it in different ways.

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We tried, we tested as a continuous variable as like 5 categories, 4 categories and the results are the same eventually.

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And we use them to 4 categories we cut this continued score into 4 categories and the weight normal overweight in the piece.

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So 4 categories. The proportional to the observed. Individually in this category is consistent, the same as observed a proportion in the US national data.

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Like with national health and nutrition. Exam examination survey.

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So we use data from health and retirement survey. And we use it survival monitors. And.

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Mortal is age of death. Okay. Yes.

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So we and we analyzed a different ethnic. And corps, gender groups and the results for African-american women outstanding.

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And the other one's not very obvious. But if you look at the proportion of a beast, BMI old 30.

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And the average in the US is 40% here, 39.8. White women, 22.6%.

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African-american women, 28%. African name are American. Women is 56, only 56%, BMI over 30.

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And so this is so that the The genetic source and correlate with PMI predicts BMI.

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So that's. And so there's some some concerns why but most of the you are genetic studies based upon European populations But we focus on, we use it for the African-american.

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Women populations but we showed that here essentially the same genetic variance as also predict they write from European. General population, white population, but also predictive of Yeah, my and BMI among African American women.

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American African environment, African American women. I think that's the to grow up here.

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You can see here we are. We use the, this, the own ethnicities and gender as this white male, white female.

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Blackmail black female. You see only the black, among the black females. So this is genetic variable here in full categories.

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You can see only here. Significantly so that mortality rate and increase by 100%. I'm African American women.

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If you If you have that. And more tonight. For obesity.

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You know, controlling everything else we we have here. Education, smoking, physical activities. And the sample size not huge because we need ethnic minority also genetic information.

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A 1,000.

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And so this is so the The genetic variable. Can be used as continuous as 5 category 4 category we use different ways basically we get the same results and sort of robust.

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And with respect how we use this variable.

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And here we did interactions. This is so-called team interaction. So among African-american females and So if you are and You did not have good education, very poor education.

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Plus you have this team. You know, related to a obesity. And so the They won't have it rate, it's it's skyrocketed even higher.

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But if you have a good education that's protected even if you have the gene. I, you know, even above high school education would be protecting.

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And I think there's what we calculate the gene environment actions. I think if you are, so here it means that if you have a high off you have high school graduation even if you present that a piece tip from Jean.

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And they they change 1990 in fact on motel is barely there you can see here 1.1 point 0 3 is like it's 3% But if you, yeah, but if you have both.

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Which in the, And very applied education led from a high school education. And the mortality rate is raised by 147%.

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147%

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And another thing we did that we We use the age categories, 50 to 64. 65, 79, among African-american women.

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You can see the 50 to 64. And this is traumatic. I mean, it has to be replicated.

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We don't have very large samples. But it does like in this age group, 50, middle-aged.

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For African-american women, if the possessed the F, the BISTY, prone genes and you mortality is raised by 400%.

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This traumatic results. But again, we have to, it's not huge. No, not huge sample, but we

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And so the bars basically say the same thing here.

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And we tried the course of death and Alice, but we just don't have enough sonboard to do it.

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You know, to look at the PC, cardiovascular. Diseases. In the summary.

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The genetic history raised the mortality rate among African-american women by more than 100%. On the average.

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And among African-american females, age 50 to 65 that that age group Do you not do pieces to risk the mortality rate by more than 350%?

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And they chin by education. Interaction and Alice indicates that higher level of education attainment even just above high school reduce the negative effects of genetic activity.

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And be announced, again, course of that did not yield any significant findings possibly because for much reduced sums.

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And I think that, thank you very much for your attention.

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We have a admitted normal presenter to maybe have time for Yeah, sure. So anyone has

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Pulled by the second student. Just know to see the city in Japan.

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In the paper we try to, we try to have a review of this. In the in the US and lots of especially You pick cities, be an environment for African Americans generally, also for females.

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It's not very, you know, not very pleasant, even the grocery. Fresh coaster is not available.

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And Walmart and not even opening stores. In the in those areas. I mean not to mention the exercise the facilities.

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Yeah, also, we, I think it's maybe controversial that. My African-american. And community sometimes.

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And they have You know, they have a. Certain cultural and you know traditions that's different you know that's Not so freed of the, holiday way.

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This if you can we we look at it later to the you can and some of the music, the songs, also So the praising the big qualities.

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But again, this could be. It's complicated, right? I mean, this is not, you should not blame the victims.

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How'd you like? African-american women have a much higher rate of reducing.

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Because there's like less climbing, I mean more driving in the cities. If you look at New York City for example.

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Way after explains a bit. Maybe not, not quite as much percentage but it doesn't like. So what we find sometimes sometimes somebody at least to be said the place where they've lived.

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These films get very serious. These aren't Oh, the way, and we see. I'm thinking of that that perhaps would be neutral for regions or Very much urban areas versus less.

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There are there. I totally agree. We should look at the sub-groups. And but it's very difficult to get a genetic data, the nice ethnic minorities now.

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And so I mean there's 1 million I think veteran data now and the US government are trying to collect the data in lots of I think a good proportion of them.

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African-americans and but for the general US service Very few, very few. You know, this is a large national lunch student in Surrey but still we have like a thousand 4,000 individuals.

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Could not, you know, into the subgroups. I think even for for European women.

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And in rural areas, they could also have similar results. But we just don't have enough.

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You're very precious. 2 we want to be arguments. Zenter who is here, but our one in the program is in Oh, that's me at the same of the center.

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People less cycles to live with, right? No, that would have been our 70%. So that's what I realized that you may have been our third presenter.

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So like, yes, with our second presenter, it is not here. So, so, so, our second presenter was so much, from, from Wednesday.

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University in the United States. And so we are our representatives. So.

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Hooked up with the University of New York City.

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Good afternoon. Thank you. So I think in many ways our research Share some commonalities with the previous speakers in that we're looking at small populations that are characterized sociologically as disadvantaged looking at some of the health issues that are tricky to measure, hardly related to small sample sizes in many of the

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available data sets. We looked at specifically cardiomatabolic health disparities among sexual minority populations.

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To give a bit of context to the research question. Sexual identity as much as ethno-cultural and racial identity is increasingly being recognized as a social determinant of health.

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So unlike the previous research where there are identified biogenetic factors, there's really little biomedical evidence ascertaining why persons who belong to a sexual minority group should have different health outcomes compared to the heterosexual majority.

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And yet there's a large variety of research that is showing these disparities, particularly when it comes to sexual health indicators, as well as mental health and behavioral indicators.

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There's an emerging body of literature as of yet quite small but emerging that is suggesting that some of these same minority stressors that may impact mental health are also associated with chronic cardio metabolic conditions, including risk of type 2 diabetes and some other related conditions.

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And yet this is an emerging body of literature to date. Much of the studies either are based on propulsive samples with unclear generalizability, focus on the risk factors.

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Rather than the outcomes rely exclusively on self-reported data or and this is something that I myself was entirely guilty of doing ever since I graduated from studies and never looked back at the issue but oh yeah automatically assume missing attributes in our sample they occur at random.

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Right, we're all I think. Those of us in social statistics guilty of making that assumption. So we wanted to go back and challenge some of our own common assumptions.

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An aspect which the International Journal of Population Data Science recently has come out in suggesting that very, very poorly documented in the literature.

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So our objective was to look at the associations of sexual identity based on self-identified status from a large-scale population health survey and how it associated with cardio metabolic risk factors and healthcare outcomes in particular based on objectively collective health care administrative data.

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And we did this linking multiple types of population based data and multiple years to obtain some of those sample sizes that tend to hinder other analyses looking at small subgroups in the population.

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This is largely grounded in the notion that social stressors among sexual minority populations may be syndically damaging to physical health as well as their mental health as originally postulated by mayor in 2,003.

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In an international conference, give a little bit of a context to Canada where I'm from.

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We've just hit the 40 million mark in terms of our population, one that is rapidly aging, one where we have a population maybe not quite as hefty as the United States but We're getting there right now the latest estimates about 20% of our adult population are at high cardiometabolic risk.

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Differing from the United States context, but similar to our Australian hosts, we are a single pair, but similar to our Australian hosts, but similar to our Australian hosts.

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We are a single pair universal healthcare coverage, complete funding for physician and hospital services for the resident population like in Australia.

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A byproduct of this which facilitates health research tremendously is that means we have complete administrative recording of hospital admissions.

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We used for this. A piece of research linked administrative hospital records with the large-scale population based survey data to be able to merge some of those social identity variables that are often lacking in the administrative health data.

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So we pulled together 10 years of the Canadian Community Health Survey, which is an annual survey of approximately 65,000 Canadians aged 12 and older.

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We teased out those in the working age population. We linked them to the hospital discharge abstract database which is a complete recording of all acute care admissions in 9 of Canada's 13 jurisdictions.

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And we linked these 2 to come to some of those issues around place or vanity, rurality. We link those to the geocoded index of remoteness which measures on a continuous scale from the most urban highly accessible parts of the country to the most rural communities, particularly northern communities that may lack year-round access to a main road

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network in the cold, cold, deep and dark winter months. Our target population was working age, 18 to 59 and in particular the Canadian Community Health Survey was the first in the country to routinely ask questions on sexual identity.

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So the question. As a large-scale survey was categorical do you identify as heterosexual homosexual lesbian or gay or bisexual.

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We looked at not just Rex factors but also a number of outcomes including access to a primary care provider.

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We may have universal health care not without its own shortcomings. Including access. To primary care providers as well as the objectively measured hospitalized for a cardio metabolic condition as mapped to the international classification of diseases.

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So similar, I think, again, approaches as as in the previous piece of research. As well. And what is often overlooked in the available literature We categorize not just those who declared being a sexual minority versus majority, but also those who did not disclose.

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Their sexual identity in the survey. Often assumed to be occurring at random those pesky masking values what we wanted to test that exclusively most research today we found only one study out of the UK that approached this most of the day we found only one study out of the UK that approached this most of the times the very small sample sizes.

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Most of the times the very small sample sizes don't allow this. Most of the times the very small sample sizes don't allow this type of in-depth analysis but by pooling together the multiple years we were able to get at least some interesting results out of that.

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So what did that mean? What does pulling all of this data together look like I go over it in a few sentences, but it took my ever so trusty research assistant from the mathematics department several weeks to actually do the data behind do the data linkages and coding and all.

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Very strong thank you to to my research assistant Jihal Sheng and Samuel Cookson for their work without.

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Whom this would not have been possible. So in the Canadian Community Health Survey, so there's often speculation.

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That old people won't disclose this in a survey. But we found that's that's not necessarily the case.

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If people, respondents understand the purpose of the data. They're willing to disclose this. Over 99% of survey respondents did disclose their sexual identity and the survey.

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2.7% of that pooled sample self identified just belonging to a sexual minority group in particular.

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So part of our question was how do those non respondents look the same or differently as those who did respond?

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So another sort of common assumption that people make is, well, people in rural areas, they're not going to respond and they're certainly not going to declare that they belong to a sexual minority group.

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So let's see if that was what we found. So firstly, we found that the non-disclosure, so non-response rate, was higher among Women compared to man.

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Among those who are older, Compared to those younger, that's probably what many of us would have speculated.

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Among those less highly educated. Again, probably. What many of us might have been speculating, but resided more often in the most urban and accessible parts of the country.

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The opposite of what intuitively Again, very little research on the actual issue so we couldn't find any other studies to compare but The opposite direction of what we were.

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Expecting intuitively.

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Secondly, we found some important health differences in the population that did not disclose in that those who did not disclose were more likely to report living with diabetes, heart disease.

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Hypertension or effects of a stroke. Based on the objectively measured hospitalization data from the linked administrative records, they were more likely to be hospitalized for a cardio metabolic condition.

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Not explained by the theory, not explained. By the literature and we have found no other studies. To compare or contrast these results.

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With.

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We also controlled for a number of different variables, the usual suspects, education, etc. We found that among those who did declare their sexual identity.

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Person who identified as a minority group plus being gay or bisexual. Reported significantly less likely to have a regular health care provider than their heterosexual.

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Peers. Not perhaps entirely unintended, given the literature on accessibility and social disadvantage.

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Though of course not entirely comfortable as a Canadian where we pride ourselves in our universal health care system as well.

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That's certain social groups controlling for all other factors, including geography, still have less access. Simply because of their social identity.

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We also found, and this is where at least The theory helped guide our interpretation is that among those within controlling for having a pre-existing cardio metabolic condition.

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Residing in the most rural areas, those who did identify as a sexual minority were more likely to be hospitalised for a cardio metabolic condition than those who belong to the heterosexual majority.

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So Bringing to the 4 front the policy and practice implications of the need to better address issues of continuity of care.

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Particularly for those residing in more rural remote areas of the country. Remember the hospitalization data are national.

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We didn't necessarily control for whether they were hospitalised within their community. An advantage to the approach though is that the sample size then wasn't limited to then being hospitalized within or near their community but anywhere in the country.

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So what are some of our conclusions coming out of this research? So one, the literature on sexual identity, which to date has.

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Largely been focused on issues of mental health, sexual health, behavioral health and substance abuse problems.

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We can do better as a research community. There are a whole gamut of research. Questions, health issues, the data from population health surveys should not be problematic in and of itself for us to investigate.

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Some of these questions help advance understanding of sexual minority health across otherwise unexplored under explored variables. Second of all, let's go back to our social statistics 101 and revisit that ingrained behavior amongst many of us that non-responses? Oh, they're pesky.

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Let's exclude them from the analysis under the assumption that they are randomly distributed in the population.

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Many of us And the literature certainly suggests this are guilty of Making that assumption without transparently reporting on whether that assumption was tested to be valid or not.

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We did find significant differences by many demographic and place-based indicators.

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Some of the limitations like any of any study chances are we've underestimated the population who identifies as a sexual minority based on the population who identifies as a sexual minority based on the categories used in the Canadian Community Health Survey. The population who identifies as a sexual minority based on the categories used in the Canadian Community Health Survey.

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One question, limited number of choices. Even though we pulled together multiple years of data, both from the surveys and the administrative and had national complete coverage.

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The number of cardio metabolic admissions was still quite small among our already small subproopulations.

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So we only had about 300 hospitalizations among unique patients after pulling together those. Those multiple years so that limited the Further disaggregation, for example, type one hyperglasmia versus type 2.

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Admissions. 3 going back to the social statistics dimension. Survey waits. Again, we largely apply them.

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As you know goals that okay well the survey implementing agency created them they must be valid very very few in fact we didn't actually find any validation studies of whether they remain applicable when looking at heart to sample sub-populations of whether they remain applicable when looking at hard-to-sample sub-population studies of whether they remain applicable when looking at hard-to-sample

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subpopulations, with looking at hard-to-sample subpopulations, those with perhaps higher than the general rate of non-disclosure So what are our next steps?

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So, our next step, first of all, the 2021 Canadian census. First of the world.

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Austria, I think also had a question, but their survey, their census was a little bit after us, so we're, they're survey, their census was a little bit after us, so we're still going to say first in the world to, so we're still going to say first in the world to collect distinct questions on

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gender identity allowing for non-binary response options. And a separate question on biological sex at birth.

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So this will allow the world of research on sexual and gender minority populations to really grow in areas that just weren't.

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Possible for when relying on sample surveys. As we even look further from that, so the 2,021 census data just become available for research use last week and I was here so I couldn't be available for research use last week and I was here so I couldn't run for research use last week and I was here so I couldn't run into the secure computing environment

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to start playing with those data. But as soon as I get back, hopefully by the next World Congress of sociology in Korea we'll have some fascinating results on that.

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And then by that point the next Canadian census has already started mapping new standards to allow for greater categorization as well, including doing some.

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They're currently doing some psychometric testing and evaluation of other identities such as pansexual or 2 spirit among First Nations.

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In North America. So again, thank you to My trustee research assistant, Samuel Cookson, a number of funding agencies who have helped support me through this broad piece of work.

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Part of the result since the time of submission of this abstract have now been published in the International Journal of Environmental Research and Public Health.

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The OI is there on the screen if anybody is interested in reading further. And thank you very much.

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Thank you.

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Time I think we're getting some questions from the political banal was still breaking our minds and then

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As actually minorities. There are literature events to describe. And

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Big size and make life. So. Be any

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So. Oh, I'll be those by alongside the relationship. Also, you know, because they still have their opinions.

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Different right the way the Oh

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So I think much like the research on black women in the United States, so part of our investigation was opened by the availability of data and part of it.

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Was closed based on the smaller sample sizes. So on the one hand we wanted to use the objectively measured administrative health care data which opened up research doors, particularly in terms of the rigor of the study, but on the other hand, because the number of person who experienced our key outcome the most costly of the health care service utilizations and

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inpatient hospitalization was small. We couldn't then tease out the subgroups of lesbian versus gay versus bisexual male versus bisexual female.

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Short, absolute limitation to this work is treating this group. As homogeneous when we know it is not.

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Our hope is that this work is exploratory, gets the research community to challenge perhaps some of our assumptions and just as a stepping stone now some of our project work also includes outreach with provincial governments in Canada towards feasibility and acceptability of including social identity variables more systematically within the routine administrative health care program data.

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I mean that's that's a ways off logistically as well as financially that's a huge investment, but there's certainly a.

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Greater push to have more social identity variables within routine administrative data. I think that's the next step in the evolution of of this.

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Field. Neighbors is a in terms of ethnicity.

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The sample of Yeah. Yeah, so the Canadian Community Health Survey in and of itself is representative of itself as representative of the Canadian Community Health Survey in and of itself is representative of the Canadian population health survey in and of itself is representative of the Canadian population.

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So we didn't exclude any particular ethnic group. Indigenous First Nations communities run separate health surveys.

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So that's the main exclusion. From this but off reserve largely urbanized for First Nations. And METI populations are included.

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But again, I go into discussion. What do you think is going on with the role? One.

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Yeah, so that's one of the issues. There's probably one of the issues. One of the outstanding questions, one of the outstanding questions it was a surprise and again we found no studies.

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It was a surprise and again we found no studies from anywhere else against which we found no studies from anywhere else against which to compare and again we found no studies from anywhere else against which to compare and contrast these results from anywhere else against which to compare and contrast these results.

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So probably a certain amount to compare and contrast these results. So probably a certain amount might be related to more limited access to a regular primary care provider and therefore impacts on these results.

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So probably a certain amount might be related to more limited access to a regular primary care provider and therefore impacts on continuity of care and emphasizing with its strengths and limitations, particularly in terms of sample size, but emphasizing that there are impacts in terms of the most expensive drivers to health care system is precisely the type of Analysis then that

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gets. Health administrators attentions. And that was a big reason why we wanted to look at this variable.

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You know, not to be Controversial, but hey, why not? I think in sociology we look at the variables that matter to us.

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As individual researchers and of course That's what drives us. That's what gets us out of bed in the morning and and motivates us.

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But if we want our work to be impactful, I think we also need to give. More consideration of what are the types of variables that drive policy decisions.

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Thank you so much for your presentation. So I'm really glad to get back to the slide because I was really shocked by the non-response.

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Category and so earlier presentation you mentioned the stress theory or stress exposure as none of the kind of mechanisms that would explain in these health mysteries for section priorities, but what kinds of factors are mechanisms?

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Elevated whisper diving as well ever since, but nonetheless.

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So none, and that's, yeah, that's the tricky part. So, yeah.

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So.

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And that's where, yeah, because they weren't necessarily. You know using tobacco or alcohol or displaying those other kinds of risk behaviors more often.

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So part of it might be that techno geek explanation, you know, the survey weights particularly when you're taking a small very very small population, linking it to an administrative data and we're just assuming that the survey weights will flow naturally.

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Part of it I think is not. Our assumptions as to what we can. Do with missing non-response.

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So survey weights are themselves. Based on based on assumptions of how the non-response patterns are holding.

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By age, by sex. By census dissemination area. But not by social identity variables. And I think that in and of itself.

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Is probably one of the caveats. It might be an artifact of the research. Other is our assumptions of what do non-disclosers mean?

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Is it for this question specifically, either being afraid to disclose or being offended by the question. Or just.

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A risk of error. So as of yet and again, you know, part of this is hampered by no point of comparison in the literature.

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Against which we can say yes this is expected or not expected.

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Any further questions?

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Thanks.

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Director of the Ventures of the E of the Australian National University. Do anything to make it. Alright.

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Okay. Bit of a shift. So As I disclosed earlier to some of you, I'm a demographer, not a sociologist.

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So apologies there's not going to be a huge amount of delving into theory here. But hopefully you'll find it interesting.

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So the presentation I'm giving today is on inequalities and healthy life expectancy in the Asia Pacific.

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Looking at across national analysis. Kind of early stage work on a project that I've got running right now.

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And so this is kind of the The big idea is to try to figure out how inequality is in life expectancy in the age of specific have arisen.

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This is step one of the process, the next step, which I think I'd love to present probably as the next ISA is focusing specifically on social inequalities within countries and their comparability.

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But for now, we're just getting sort of a nation-to-nation level. Comparisons of healthy life expectancy in the Asia Pacific.

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And I'll note that this is joint work with Kim Shu, Jessica Aw and myself.

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So a little bit on. Aging in I guess our region here in Australia. So this is just looking at projected population growth by different age groups in the Asia Pacific and a couple of different regions.

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And the big thing to note here is just that in every sort of region of Asia, population growth of older adult populations is massively outpacing total population growth.

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So in Eastern Asia, you know, Basically the population age 65 plus is expected to grow by you know about 250% while the population the total population stays about the same.

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So just a huge. Shift in terms of population age structure towards a much older age structure than we've seen frankly almost ever before in human history.

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And this is true in Eastern Asia. It's history in Southeast Asia as well and also true here in Australia and New Zealand.

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Just substantially the growth of aging population is substantially outpacing total population growth. And you know the the goal here is really to kind of understand you know what this sort of rapid trajectory is of population aging, how these interplay with the overall health of aging population.

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So are we, is this going to be a good story where we're all ageing healthier and living longer and better into the future or is this?

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As dire as some of these craft show that were, you know, going to anticipate a huge burden of chronic diseases and disability among aging populations as these populations grow.

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So I love this fact and want to put it in all my presentations. The pace of aging that we're seeing in the Asia Pacific is just faster than we've seen anywhere else in the world.

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Current UN projections suggest that China is going to transition from having 5% of its population age 65 and over to having 15% of its population age 65 and over.

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That transition is happening sometime in the next 2 or 3 years, I believe. And this transition is happening in the span of 20 years.

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So that shift from 5% age 65 plus to 15% age 65 plus took I've got it noted here 180 years in France.

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It took 90 years here in Australia and it took 75 years in the U.S.A. and it's taking 20 years in China.

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So just rapid, rapid aging. And this is also happening in a context where there have been rapid improvements in living standards.

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So this kind of growing disconnect between. You know, sort of wealth and economic conditions in early life versus later life.

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And you know, we don't really have a great understanding of how living through rapid improvements in living standards, impacts health and healthy longevity.

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But we do know that this wealth comes along with a whole number of changes to health behaviors and risk factors for chronic diseases.

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So all of these, you know, might kind of act to work against the fact that, you know, with wealth, hopefully greater health follows, but also if that wealth is being used on instant noodles and alcohol.

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That's generally not going to do great things for population level health. And you know linked to these changes in health behaviors and changes in risk factors for chronic diseases come changing disease profiles.

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So we've seen increases in prevalence of hypertension, diabetes, high cholesterol and other dietary and behavioral.

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Related diseases in the Asia Pacific. I especially in middle income and recently transitioned high income countries. So a quick summary of existing knowledge on health inequalities and inequalities and healthy life expectancy in the region.

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Frankly, there's not a ton of research out there. So I guess it's not that recent systematic review on health expectancies in the Asia Pacific finds not a whole lot of cross-nationally comparative analyses have been done.

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A lot of the studies that exist have been combined to single countries and these mostly are in Japan, China, Australia, little bit in South Korea, and very little is out there that's tried to sort of compare and contrast across the region in systematic and comparable ways.

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With the exception of one great paper that I found from data from the late ninetys and I have tried every way possible to find these data and have failed.

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So if anyone knows. So the, basic basic aims of this research again this is sort of step one of a larger project is just kind of getting the big overview of.

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Trends and patterns and healthy longevity in a set of countries in the Asia Pacific. And what we're trying to do here is just examine the current lay of the land, what current conditions are looking like, which populations are succeeding in terms of healthy aging, which populations are potentially lagging behind.

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And within that we'd like to delve a little bit into some of the age patterns and gender differences in healthy longevity within the region as well.

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I've said a lot about health expectancies. There's a lot to say about health expectancies.

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There's a lot to say about health expectancies. I'm a demographer focused on multi-state modeling.

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This is my bread and butter. But health isn't just health. There's a lot of different ways we can measure it and all of the ways that we can measure health can lead to very different sort of results and outcomes.

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So I think a lot of the studies that do exist in the region usually focus on a single country and a single outcome.

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I don't know that that's the best way to understand you know broad regional changes because there's just there's so many different types of health we can measure, why not measure them all?

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Make a giant really hard to write paper out of this. So what we're focusing on in this paper, sort of 3 broad categories of measuring health.

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We've got healthy life expectancy in terms of self-rated health. I'm so just asking individuals, how healthy do you think you are?

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Which Well, we can talk about this later. It does a really good job of ranking people's health within a country, but it's a little tough to compare across countries because people have very different norms in terms of how they report their health.

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Disability free life expectancy, so this is based on usually ADL limitation so ability to self-care.

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So and sort of physical abilities to self-care so whether you can dress and bathe and walk and toilet, that sort of thing.

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And then morbidity-free life expectancy. So looking at chronic morbidities, here we're using the sort of big 5 chronic diseases so it's whether or not you've received a previous diagnosis of heart disease, cancer, stroke, lung disease, or diabetes.

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And I'll note that In our models, you can move back and forth between healthy and unhealthy life between active and disabled life, but once you've been diagnosed with a chronic morbidity, you don't get to go back to disease free state even if it's well controlled just because controls a really hard thing to

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measure in big national level surveys. We've done a bunch of sensitivity analyses about this.

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It doesn't really wind up mattering that much. So getting into the data, we started off and I wrote a grant on.

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That had a about 9 countries in it and we've whittled down to 5 that we could actually support analysis with the data.

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A broader issue that we have lots of really great longitudinal aging studies in high-income countries.

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We've got There's like 3 or 4 really good ones in the US and then a lot of the globe where there's just not much of anything or someone will start a pilot study that looks really promising and it won't get funding for a second wave and so for someone like me who needs longitudinal data and

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really wants to understand this stuff. Very frustrating. Anyways. So data from Australia comes from the Hilda study.

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So I'll note that these are all harmonized HRSister studies except for Australia, which doesn't have an HRS or study, but you can kind of fake it with Hill the data.

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It asks a lot of similar questions. It's pretty easy to harmonize. China, the China health and retirement survey, Indonesia, the IFLS, Japan using data from JSTAR and South Korea using data from Closa.

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The analysis model, I'm happy to talk about this in depth with anyone because I love this stuff, but briefly it's a multi-state life table model, so a way of estimating how long people expect to live in certain levels or certain with certain health conditions or without certain health conditions and their total life expectancy.

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I'm estimating the multi-state life table using micro simulation. Essentially it's just built off of the transition probabilities.

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So we've got a set of individuals with certain age and gender characteristics. We observe how they transition between healthy and unhealthy life.

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And their mortality rates from healthy and unhealthy life over age, create a bunch of big transition matrices and use these as input to generate synthetic populations using micro simulation.

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It's sort of a more flexible approach than the kind of classic multi-state approach. And we generate confidence intervals via bootstrapping.

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So just sort of repeating the whole process resampling from the original surveys and all of the analyses are done using survey weights.

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So the baseline characteristics, mostly I just wanted to show this table that hopefully everyone can see.

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Just to get a handle on, The potential problems with just using one indicator as your measure of health.

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So if you look at, there's just large differences in the prevalence of disability of self-rated health and chronic diseases across countries.

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So you've got really low reporting of disability of ADL visibility in Indonesia, Japan and South Korea much higher reporting of disability in China and Australia.

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And how much of this is actual differences in ability to self-care versus reporting differences is a little tough to tease out.

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Same thing with self-rated health. You know, you've got in Japan for governing as females about 19% of individuals reported that they were in poor self-rated health.

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You compare this with 74% of women in China. And suffering it up again, it's a great measure.

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It's super predictive of five-year mortality risk. But comparing it across countries is really tough.

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And this even comes into play with things like chronic diseases where you think it's a fairly objective measure but in a context like Indonesia where we know from previous research that both individuals linkages to sort of primary care is not great.

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And quality of that primary care especially for chronic disease care is also not great. There's some huge burdens of diabetes that are being unreported in Indonesia similarly with a lot of other cardiovascular conditions.

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So even something like a you know a morbidity free life expectancy can be a little suspect in a place where there's not great primary care.

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All of that being said, here's some results. I like to be very open and honest with the problems and then try to tease out what we can actually get because cross-national analyses are just tough.

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They're hard and they're miserable to do, but I keep doing them because I think there's some really key important kernels of truth you can get out of them.

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The big takeaway here, this is life and health expectancy is at age 50 in Australia, Japan's South Korea, China and Indonesia.

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This is focused on women. So we can see just huge heterogeneity in terms of healthy life expectancy by which measure of health we use.

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Both you know by which measure and within country by by measure so there's a lot of contrast within countries.

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So for instance, you know, 36% of remaining life is spent in good self-rated health in South Korea versus 93% of life in South Korea pressing females being free of ADL limitations.

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So it's a population that's reporting really low levels of functional disability and inability to do their sort of daily tasks at the same time reporting really high levels of poor health.

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Which of these we believe, I don't know, but I think you kind of. Start to triangulate between measures.

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We also see really low morbidity-free life expectancy among Chinese women. You know, keep coming back to China as a place that's aging incredibly fast and seems to be aging incredibly fast with a really troubling burden of chronic morbidities.

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So Chinese women are kind of an outlier here with only about 37% of remaining life spent without any chronic morbidities.

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For men, patterns are relatively similar to women, but sort of lower overall reporting of ADL disability and of physical limitation as well.

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And just to know, we didn't have a physical limitation measure in South Korea, so that's why it's blacked out there.

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So here we wanted to kind of look at how these how these expectancies of this is The proportion of remaining life spent healthy across the 4 measures of health by country.

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So looking at the age patterns, how health seems to decline over age. And we find that the slopes, there's variation in both the slopes and the levels in terms of how functional health and how self-rated health decline over age.

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There's also just very different ordering by measure of health. You know, generally it's not just that, you know, Japan's doing the best and you know, some other places doing the worst.

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There's a lot of heterogeneity in terms of the patterns by outcome. Although I will say that across morebidity, disability, and physical limitation, we're seeing this pattern where Chinese women are experiencing trends that are pretty troubling have the worst overall reported life expectancy free of morbidity disability and

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physical limitation

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Looking at this for men, again, health of older adults in China looks a little bit worse than other countries in terms of both sort of objective morbidity measures and disability and physical limitation.

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And there's really pretty big gaps in self-rated health across countries. Again, just sort of highlighting the fact that suffering and health is think a problematic measure to compare across countries.

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So, you know, as I said, these sort of across-country comparisons are a little difficult.

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There are differences in health. And reporting norms and frankly translation could drive disparities. So we tried to look at the sex gap here.

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So it's potentially a more interesting outcome in that it's sort of normed within country. And it's reported in proportionate terms.

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Here any number less than one indicates a male advantage in terms of health expectancy any number greater than one indicates a male advantage in terms of health expectancy.

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Any number greater than one indicates a female advantage in terms of health expectancy. So generally we find in Australia, Japan and South Korea, there's a bit of a female advantage in morbidity.

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But in China, kind of alone in China, there's actually substantial male advantage in terms of morbidity free life expectancy.

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So males are living. Proportionately longer without chronic morbidities than than older women in China.

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And for disability and physical limitation, this kind of lines up with what we've seen in previous literature that kind of lines up with what we've seen in previous literature that we've seen in previous literature that the sort of male advantage in terms of male advantage in terms of disability and the sort of male advantage in terms of disability and functional limitations is pretty much

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universal. So men tend to at least report having greater function later into their older years.

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And further than this, we kind of wanted to dig into the chronic conditions measure. We've got the 5 chronic conditions that we work with and we added arthritis here just because we knew that there was probably some substantial gender differences in ourthritis so we find you know male advantage in terms of arthritis free

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life expectancy. And a lot of just heterogeneity in terms of other chronic diseases.

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So, you know, for cancer, for diabetes, for heart diseases, lung diseases. And even a little bit for stroke.

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Some countries there's a female advantage in terms of life expectancy without this condition. Some countries there's a male advantage.

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This is sort of one of these figures that we we put together and then we're like this is I don't know that it's gonna make it into the paper because it asks so many more questions than it answers.

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Every pain of this is a big story. But I think it's kind of interesting to present at least just to give you guys a little sneak preview on just the.

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How much heterogeneity there is working here. And the big thing that kind of The seems to be driving at least the sort of higher rates about the greater or less morbidity-free life expectancy among Chinese women seems to be heart diseases.

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So specifically the most of that difference in morbidity-free life expectancy between Chinese men and Chinese women as being driven by Chinese women having greater likelihood of transitioning into having heart diseases than men, which is interesting and surprising and I don't have a great explanation for it right now, but I figured I'd bring it

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up. Okay. So overall, I know I'm probably short on time. We find that there's just There's a lot of heterogeneity.

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There's really different regimes of health and later life being experienced across populations in the Asia Pacific.

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And Well, you know, I think what our argument for why this is important is that right now there's just not a lot for why this is important is that right now there's just not a lot of cross-national evidence that right now there's just not a lot of cross-national evidence out there.

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And I think it's just not a lot of cross-national evidence out there. And I think it's really useful to understand which populations and in which sort of domains and health populations are succeeding in supporting healthy aging or kind of falling behind and experiencing some of these troubling trends.

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I'll leave it there. I'm happy to answer any questions. Thank you.

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Other questions? Anything. It's sometimes, you know, so often complimenting. The Chinese member actually Generally, it's small, Yeah, they don't really, don't create drinks over generations.

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I think Yeah. I'm wondering if you talk about, I think the comparison for health is getting very complicated.

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In comparison. Grasshopper comparing or had it. They just get much better for their cut.

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I'm wondering if the results are 10 to the start.

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Costly difference would be consistently. Retaliate differences. That would be a chat, right? If Chinese women suffer a lot more about how to their habitat actually works.

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But it's not worse than something this is vicious. Yeah So we have a separate paper. The under, just got revised and resubmitted.

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Kind of looking at that and looking at the sort of sex gaps in life expectancy across this set of countries and how this expo.

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Yeah, yeah, I mean, females have advantage in this text gap, but the sex gap Trying to remember results from a paper that I haven't looked at in a little while.

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My recollection was that the sex gap in China, my recollection was that the sex gap in China based on this sort of breakdown of chronic conditions was it was sort of smaller than you would guess it should be.

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Like it was it was smaller in cross national comparison than in some of these other countries. So and that seems to not be male advantage, it seems to be female disadvantage.

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But again, I'd have to go back and dig through that paper because I don't want that in the next 6 months.

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That's a really interesting question. Thank you. Yeah. You don't have to remember.

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Yes. Yeah, yeah. And yeah, the sort of Chinese women didn't have as much of a mortality advantage over Chinese men as we thought they would based on life expectancy gap comparisons from other countries in the region.

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If that makes sense. That's the thing. Mean people basically don't have basic Yeah, it's very, very different.

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But this Charles is basically waiting. Right, their criteria is about the country. Yeah, whatever it is.

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Anytime you've got a sample survey of 14,000 individuals representing a billion point something, yeah, you're never going to be perfect.

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Software is much more homogeneous. Yeah. Yes, yeah, yeah. So, yes.

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Once in terms of the self-braining of help and how How difficult it is in terms of the differences that you're going to find based on culture.

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I'm listening. So I'm wondering if you could be with you. Like, for example, with my own analysis, I have found that first that people get Nordings in the US say say that they have excellent When I have done that interview myself for example a 75 year old man who's never been hospitalized in his

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life. Prototyping model, who we would say has excellent output. So when we get to that I would say what is your So he was in fear.

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And so at that point I thought I could do the purpose of research and maths. It's a big layer.

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You know, you're like 10 or 5 years old, you've never been hospitalized, you're facing it just now, we need to take more pressure education.

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What might I expect? Response I would get over and over none at my age. I'm 75 years old and got to have And that reflects why in so many surveys we find very few in the excellent categories.

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Slightly more or still newer. These are very good. And then we have most of our sampling that there, which seems to me to be more accurate.

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The structure of self-rated health. Yeah, I think one of the things that I would Love to see but doesn't seem to be asked in a super sort of comparable way across surveys.

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Some places ask. Just overall rate your health in some places asked compared to your peers. So those around you who are around your age, same gender.

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How would you rate your health in comparison with sort of your little comparison with it? That's not asking us super systematic way across surveys and I wish it was because I think it might be a more useful measure, something that was a little bit normal to yourself as opposed to like, well, yeah, I guess my help is.

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Fair compared to what it was 20 years ago or you know it's that who are you benchmarking against?

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I've been destined for the more major market. Yeah, yeah. I mean, I, in some ways, self-reated health is exists in this paper as a little bit of a punching.

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Because I, it is something that people have used. A fair bit in the past and cross national analyses and I think it's just, yeah, it's kind of a problematic measure for doing that with I have a question for our Turns out Do have a renewal service.

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It's the only one to get to that legal person now and get the

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Phone is muted.

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I'm on mute. Can you hear me now?

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Yes, yep. You can. You can.

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Hello. I will share screen and I will also start my presentation immediately. I am very happy to meet everybody and at the onset I want to Thank everybody for allowing me to present.

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In this remote kind of situation. So I'm presenting from India. My name is And I got the d car.

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I am a sociologist and I teach. Sociology at the UG that is undergraduate level.

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In Sofia College. Today's presentation and today's paper is all about terminization of aging as you all must have seen.

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And, it is a demography as well as sociology that I am going to be marrying together.

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To present a qualitative analysis of where we stand, where India's stats. So we all know like the presenters have said earlier as well.

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That the process of aging which has now led to population aging in several of the countries, several of the continents and Asia started feeling the tremors of it.

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Recently, we have heard about China already and India is Which China facing the problem of Ajax?

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So population aging, which is also called demographic aging as we know was first discussed in 1982.

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And it just implies that increase in the proportion of old people. To the other 8 segments. This is different than individual aging is something that we are completely aware of.

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At a global level we all also know as sociologists demographers And, as gerontologist.

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We know that the oldest region is Europe in terms of the fact that the population aging is felt in this region with the highest number of elderly.

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If we compare the size of the group with the other cohorts. The youngest region of course is Africa and I don't have to say this to the esteem audience that the most youthful nations are generally associated with.

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A relatively low standard of living. As far as India is concerned. You will see that 5.0 6% of population that was 60 and plus.

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Good. To 8.6% to 0 1 one and 2 0 2 one it is even higher than that.

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But in understanding demography in India, it is important also to keep in mind that our old people reside predominantly in not so urban spaces.

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They are in rural spaces as you can see there's a 71% this is a 2,011 not 2,021 the data we are still playing with of 2,021 though the data are available not to say that the data are not available, but, this is a 2,011 data that we go

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with. This is 71% are in rural and 29% in urban. So when we talk about, old people, there is this particular category that we got to categorization that we got to keep in mind.

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Besides that, you will find that the men and women in rural pockets, the literacy rate is different.

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And in. Again, the working population or the work related population, you will see the rural urban differentiation there.

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So it is important to keep in mind that as far as the old men and women are concerned. The variability sharply, sociologically speaking, is in terms of whether they are in rural or urban and whether they are, then with literacy and what is their work related status.

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I am now coming to the beginning of the preposition that I'm trying to make. Let us keep in mind.

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That the process of aging is not the same across the gender groups. So I'm not just talking about.

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Men and women. I'm also talking about the whole gender spectrum. What, need to, what my colleague, what my friend said in the second presentation, we got to keep in mind this big category of LGBTQ I plus the categories that are not exactly in a binary kind of system of

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men and women. So the process of aging we got to keep in mind is not the same. Across the gender groups.

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For women. The aging process translates into a phenomenon and experience and a narrative. That is different than.

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For aging men.

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So when I say that the aging process is not uniform and

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To the process also of experiencing age. In a particular way. So the pace. The nature of aging process, the quality of aging.

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All of these things vary. From group to group and subgroup to subgroup and cohort to cohort.

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So I would I am sure you all agree with the fact That is 60 plus. Man is different than a 60 plus woman.

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Certainly as we have managed to establish it, but a 60 plus man is definitely different than a 75 plus man as well.

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So many of these variables that have been operating. These variables I've tried to list out here So you know the access to the life.

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So sources, the situations, the opportunities that resources, the space. The special understanding of it. Explorations and experimentations that old people can get into and the risk that they can take.

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All of these. Very tremendously. As we talk about aging.

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My people is about how The number of aging women, the number of old women. Are outnumbering.

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Main. So one of the biggest challenges right now is increase in the number of Elderly women again in India you can see that The data till 2,01111.

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Is showing a constant increase in the number of women. No one wonders demographically this is so clear.

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What is it that is so challenging about it? And here I come in with a situation where

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What happens? When women are more in number. In the old population and the older population. And the parameters, there are 3 parameters and then I'll go to the fourth one that is on the screen.

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The health parameter is a very important parameter which I'm going to talk about in this presentation very quickly.

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It is also about in a country and society like India about economic power because most of these women have even have been working even though have been working.

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Have not really managed. To be financially powerful because they are not the people who have taken decisions about investment, about allocation of resources, about having property on their mean.

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So the economic or financial power is something that, becomes a very loose area and a very soft spot.

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In this whole demographic qualitative understanding. Caregiving is another parameter that I have always been worried about because no women are Wonderful care givers culturally in Indian scenario when they are needing care.

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There is nobody around who is going to be giving them unconditional selfless kind of care. So caregiving becomes yet another parameter when women become the care receivers.

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And not the care givers. Therefore, the fourth and the final variable of decision making who takes decisions when women fall ill.

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Who takes decisions when women need financial support? Who takes decisions when women need care and who gives them care?

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So the paper has a very strong qualitative paradigm here. I'm right now not presenting numbers but the numbers are in the bank drop of this particular study.

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I have borrowed these numbers, been part of this wonderful study that happened in India. Which is supported by the government of India and the Ministry of Health and Family Welfare, where the data collection project was conducted by International Institute for Population Sciences.

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And the national program for healthcare for elderly. And, what I am going to be looking at right now to arrive at the conclusions are these following things.

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These are of course, let's get this absolutely clear. This is self-rated health. Like we said earlier when we talk about self-rated health as far as women are concerned when they are asked about their own parameters and their own perceptions.

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The reporting is not completely full proof we are aware of this So I have taken up here only the 3 important aspects.

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I've not gotten to the others, but the cardiac and the related aspects where we talk about whether women and men.

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Have reported self reported have reported in their experience that chest pain or a discomfort which can occur because of lose oxygen supply to the heart muscles.

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The second one is pain which was assessed. The respondents reported pain that they were troubled by or they required some medication or treatment to go for.

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And the third one was sleep problems where the population where the sample had a problem in had a difficulty in falling asleep or sting.

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Asleep or whether they had disturbed sleep and problematic kind of situation there and there was therefore a daytime impairment because of lack of sleep or disturbances and sleep.

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So having seen these 3 parameters. It is interesting to see that the For all of these, the problematic situations that they are, the rate is higher among older women.

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Than in older men. So all older women in the sample of around 10,000. Said that. You know, it is Oh, there's just been a discomfort, there's pain, that sleep problem, yes, it is higher, there's sleep problem, yes, it is higher though it is still a celebrated

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kind of health system that we are looking at. It is further important to also see that all of these are higher among The men who are living alone.

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I mean, I am saying that sociologically when I use this term, they are not without they are without their life partner.

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And also those who are living alone because their children are not living with them. They are not just likely, but they have also projected that there is a higher SRH there.

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The, the Lassie also did. Direct health examination. On the biomarkers that I have given to you and several others.

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So hypertension, for example, lounge function, the visual impairment, nutritional status, the grip strength, whether they can hold on to things and whether they can lift things.

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And of course, their balance whether they can hold on to their post shop. Again, here you will find older women as very vulnerable.

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Older women. Or are not with great health and are not really showing a very wonderful kind of situation there.

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So older women than men seem to be facing all of these problems. And it's not to say that men are healthier than them, but yes, women are less healthy.

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There's something that I certainly can arrive at. A very quick understanding also into before I go to my final conclusions.

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Very quick understanding about mental health. And the biomarkers here in terms of recognition in terms of, memory.

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In terms of how lonely they feel, how marginalized they feel and what is the depression level there.

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Again, women seem to be in a situation which is, problematic. A situation which is not a happy situation.

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They are not in a happy zone at all. The study is arrived at. Working understanding that if you use a life cycle approach.

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That is a strong interplay between gender. And several other sub variables like class and education. And I'm sorry, I'll go back to this because I need to go back to this.

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So class and education and their status when they're married or they're single or they're unmarried, their cultural background, their family composition, etc.

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Whatever of these, variables that are factored in. If you put them with Men. The fact remains that women are facing exceedingly impoverished, impoverishing kind of situation as far as aging is concerned.

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So Women are scared of saying that they are 60 plus because they are scared they are insecure they are marginalized And I am going to blame it largely on this pressure of patriarchy, a societal pressure of cultural programming where women are, the feminization process is also burdened by a very strong

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patriarchal, cultural layer. So women are unhappy aging because of their health pressures. That is a whole marginalization that goes with the process of aging I call it a triple marginalization.

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So one is because they are women and the gender is not in gender. Dynamics is not in favor of them.

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The marginalization depends when their age progresses. From 60 to 65 to 70 to 75 to 80 and 80 plus.

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It further. Deepens because India is at present at a situation where there isn't Oh very The cover is not very enriching.

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We're not in a situation where we are happy to receive protections on either social, nor so much of constitutional, certainly not, legal.

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Certainly not a well fairest kind of piano proposition that we are happy with. So this paper tried to utilize the quantitative as well as a qualitative parameters and I hope to take the study further.

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To check out how best. We can assist. Aging women in India. How best we can hear their voices because there's a lot of invisibility, though you can see a lot of women around.

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So feminization of aging is a true phenomenon, but at the same time you don't You do not.

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The you cannot see them. You cannot talk to them because a lot of them in research, when you try and reach out to them for data collection.

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They say they are happy with their lives. But when you ask a qualitative interview scheduled when you are operating with the help of that, what comes out very clearly is that either they are pretending to be happy or they are in a situation where they want to deny their requirements.

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They do not come out as unhappy if you interview them. But you know that the situation is not in favor of them.

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So as a sociologist, as a social, gerontologist. I constantly feel that there is a lot more hidden there that.

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Researchers have not managed to capture as a woman, researcher. I feel very, challenged and I feel that there's a lot that we will have to do.

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Because demographic tells us something sociology is giving us yet another picture and what the data collectors tell you.

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Is different than what you're perceiving it to be. So on that note, I'm going to now be open to questions and queries.

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Thank you very much for loving me to present my pipa. Thank you.

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Thank you. Thanks very much, Lieutenant Carter. Field. Are there any questions?

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I'll be happy to take I can't hear questions so if person takes a mic.

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And, What? I think she will need to.

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Thank you very much for that presentation. I guess I have one question that sparks to some of us.

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Oh

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I'm using the Royal Us in the room, distinguishing between demographers versus sociologists. And I say this as somebody who trained in demography and is currently working in a department of sociology and I guess my question relates to how do we or how do you define and use the term feminization?

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If I put on my demographer hat I have a very explicit clear-cut definition of increasing proportions of women of biological female sex in the population over time.

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That's right.

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Put on a sociologist hat and it becomes perhaps a little bit new on so I'm From a demographers perspective, it is entirely expected that women form the Numerical dominance of the aging population because we are biologically the stronger sex.

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But what does that mean, feminization then sociologically?

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Oh, thank you, Nero. So, when we talk about Feminization, I spoke about Feminization of aging.

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That means that in the process of aging now when you look at the aged population. Everywhere the women are stronger in number.

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In their presence. And that is how the term is come as feminization of aging. So just like we talk about aging of the agent, here I'm talking about the fact that if we look at women.

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Who are 60 plus, per 1,000 men. But if you check that out, the number is higher and increasing number of women continue.

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To live for longer lives than, men. Now, in general, when we try and understand, of course, again, like you said, we're talking with, we are talking, as demographers as well as gerontologists and sociologist.

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My concern is that yeah, it's a demographic reality that there are more women. In the group of agent and a group of older adults.

471
01:35:15.000 --> 01:35:26.000
But, sociologically the concern is that they do not know what to do. How to take decisions about their later life because they have not been decision makers.

472
01:35:26.000 --> 01:35:36.000
Oh, throughout their life in a culturally strapped up country. So feminization, yes, has come in because there are, a bigger number of women.

473
01:35:36.000 --> 01:35:49.000
In the old age system and that's how the child's urbanization has come in which is technically and accepted an approved term, as well as gerontology is concerned.

474
01:35:49.000 --> 01:35:59.000
If I've answered that. Properly. Please step. Needle.

475
01:35:59.000 --> 01:36:15.000
Thank you. She can't hear it, right? I have to open my phone. Where's what's on there?

476
01:36:15.000 --> 01:36:16.000
Thank you.

477
01:36:16.000 --> 01:36:21.000
Thank you very much. We keep your comments and I not believe there's one more question. Is there one more question?

478
01:36:21.000 --> 01:36:33.000
So then that includes our session. Thank you very much. And.

479
01:36:33.000 --> 01:36:39.000
She's so not connected or considered. I don't think she's fishes to just.

480
01:36:39.000 --> 01:36:48.000
So thank you very much and our session is done. So thank you. Thank you.