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Psychosocial

Primary Care Corner with Geoffrey Modest MD: texas abortion law changes and its effects

3 Mar, 17 | by EBM

By Dr. Geoffrey Modest

This blog will not shock you: when Texas closed abortion facilities, decreasing access and making women travel further distances to get an abortion, there was a substantial decrease in the number of abortions done with a small increase in out-of-state abortions (see doi:10.1001/jama.2016.17026).

Details:

  • In 2013 Texas enacted a restrictive abortion law which was partially reversed by the Supreme Court in 2016 as being unconstitutional
  • After passage of the law, the number of abortion facilities declined, with the following changes:
    • 2012 (prior to the law): 66,098 abortions done in Texas, 97 done out-of-state. 41 abortion facilities in 17 counties in Texas
    • 2014: 53,882 abortions done in Texas, 254 done out-of-state. 21 abortion facilities in 6 counties in Texas
  • The median distance to a facility increased by 51 miles. There was a clear, consistent trend between the decrease in abortions and the distance to the nearest facility (p<0.001 for trend):
    • 0 distance assoc with 1.3% decline
    • 25-49 miles assoc with 25.3% decline
    • >100 miles assoc with 50.3% decline
  • Even counties with open facilities and minimal distance (within 5 miles)  had a 15.9% decline in abortions [perhaps from less local access, with the number of women seeking abortions being greater than the new capacity]

Commentary:

  • The above does not include abortions done in Mexico, or self-induced abortions
  • Adding together the in-state and out-of-state abortions confirms a 18.2% decline, despite the shift to more being out-of-state
  • None of this is terribly surprising. The concern now is that the new US president and vice-president, their Cabinet appointees, their likely Supreme Court appointees, and the Republican House and Senate are pretty likely to make matters much more difficult for women in the near future. And perhaps decrease funding for/access to birth control, making the situation even more untenable (g. closing Planned Parenthood or not funding abortion/contraceptive services). Especially for poorer women with fewer resources/alternatives.
  • I would just add that in Illinois in the late 1960s/early 1970s, when abortion was illegal, I was told that there were 2 full wards at Cook County Hospital (about 50 people) filled with women having septic abortions (serious uterine infections, in this case after “back-alley” abortions done by coat-hangers, ). And there was a 10% mortality rate….

Primary Care Corner with Geoffrey Modest MD: Life expectancy decreases in the US in 2015

9 Dec, 16 | by EBM

By Dr. Geoffrey Modest

The CDC just released the life expectancy statistics for the US from 2015, finding a decrease of 0.1 years from the 2014 numbers (see http://www.cdc.gov/nchs/products/databriefs/db267.htm  ).

Details:

  • In 2015, there were 2,712,630 deaths, an increase in 86,212
  • In 2015, life expectancy was 78.8 years, with 0.1 year decrease from 2014
    • For males, the decrease was 0.2 years from 76.5 to 76.3 in 2015
    • For females, the decrease was 0.1 years from 81.3 to 81.2 in 2015
    • For both, the change was only in life expectancy from birth, with no change in the group who made it to 65 yo [life expectancy for males aged 65 was 18.0 years, females 20.6 years: no change from 2014 to 2015. But no indication in their data as to why.  ??from more opiate deaths??]
  • The age-adjusted death rate increased 1.2% from 724.6 deaths/100,000 to 733.1 in 2015
    • This increase was highest for non-Hispanic white females (1.6%), then non-Hispanic white males (1%), then non-Hispanic black males (0.9%)
  • No change in 10 leading causes of death, but death rate increased for 8 of them and decreased for 1. (the 10 leading causes of death account for 74.2% of all deaths)
    • Increased for heart disease with age-adjusted death rate increase from 167 to 168.5, chronic lower respiratory disease (40.5 to 41.6), unintentional injuries (40.5 to 43.2), stroke (36.5 to 37.6), Alzheimer’s (25.4 to 29.4), diabetes (20.9 to 21.3), kidney disease (13.2 to 13.4), and suicide (13.0 to 13.2), but decreased for cancer (161.2 to 158.5)),
  • Infant mortality rate was no different, at 589.5/100K live births (was 582.1 in 2014, reflecting 240 more infant deaths, but the only significant change was an 11.3% increase in unintentional injuries
  • 10 leading causes of infant death were the same, though 2 changed rankings (maternal complications moved from #3 to #4 and SIDS from #4 to #3) in 2015

So, though life expectancy in the US has increased pretty dramatically since around 1970, and has had a largely linear increase in the past couple of decades, this new data is concerning (and more concerning if the trend continues). And there may be several impending changes in US policies in the next several years which could exacerbate this trend, potentially with more uninsured people without access to health care, a decrease in the social safety net which provides some basic needs to poor and disabled, changes in abortion laws (which in the past has led to maternal mortality associated with getting illegal back-alley abortions), perhaps increasing trends in interpersonal violence (which we are starting to see now), even perhaps too-early/more accelerated FDA approval of new meds and medical devices with potentially more adverse outcomes, increased pollution and health effects from possibly gutting the EPA and empowering fossil fuel producers/accelerating climate change, increased worker deaths from relaxed regulations, the adverse social and health effects of increasing income inequality, etc etc etc etc…… [not a pretty picture]

See http://blogs.bmj.com/ebm/2016/02/24/primary-care-corner-with-geoffrey-modest-md-increasing-disparities-in-life-expectancy/ and http://blogs.bmj.com/ebm/2016/04/26/primary-care-corner-with-geoffrey-modest-md-life-expectancy-and-income/  which review studies showing that increasing longevity in the US tracks with income inequality

Primary Care Corner with Geoffrey Modest MD: Lifestyle Changes and Genetic Risk for CAD

17 Nov, 16 | by EBM

By Dr. Geoffrey Modest

A recent study looked at the relative effects of genetic risk and healthy lifestyle in the development of coronary artery disease (see DOI: 10.1056/NEJMoa1605086).

Details:

  • 3 prospective cohorts were followed: the Atherosclerosis Risk In Communities (ARIC, with 7814 white people between the ages of 45 and 64), the Woman’s Genome Health Study (WGHS, with 21222 white female health professionals), and the Malmo Diet and Cancer Study (MDCS, with 22389 Swedish people aged 44 to 73 and free from prevalent cardiac disease). Also included were 4260 people the cross-sectional BioImage Study who had genetic/risk factor data and had coronary artery calcium (CAC) scores
  • They evaluated these people for up to 50 single–nucleotide polymorphisms (SNPs) known to be associated with coronary artery disease, and then derived a polygenic risk score based on the number of risk alleles at each SNP, multiplied by the sum of the literature-based clinical effect size.
  • They also assessed 4 lifestyle behaviors: no current smoking, BMI <30, physical activity at least once a week, and a healthy diet pattern (consisting of increased amounts of fruit, nuts, vegetables, whole grains, fish, dairy, as well as reduced amounts of refined grains, processed meats, red meats, sugar sweetened beverages, trans fats, and sodium)
  • A favorable lifestyle was defined as at least 3 of the 4 healthy lifestyle factors, with an intermediate lifestyle being 2 of these factors.

Results:

  • 1230 cardiac events were observed in the ARIC cohort over 18.8 years, 971 in the WGHS cohort over 20.5 years, and 2902 in the MDCS cohort over 19.4 years
  • A risk gradient was noted across quintiles of genetic risk, pretty consistently among the studies, with a hazards ratio of 1.91 comparing the top vs. bottom quintile of risk scores, reflecting a 91% higher attributable genetic risk, controlling for age, sex, self-reported education level, and analysis of ancestry when available
  • Levels of LDL cholesterol were only modestly increased across categories of genetic risk, and genetic risk scores were independent of other cardiometabolic risk factors as well as the 10- year predicted cardiovascular risk
  • A family history of coronary disease was an imperfect surrogate for genotype-defined risk [perhaps part of the issue: several older studies i have seen have found pretty dramatic discordance between elicited family history of heart disease and actual known heart disease events in the family members, up to 35-40%; the Framingham study found a 17% discordance.]
  • Each of the lifestyle factors were associated individually with decreased coronary risk: non-smoking with a 44% decreased risk, BMI <30 with a 34% decrease risk, regular physical exercise with a 12% decreased risk, and a healthy diet with a 9% decreased risk
  • People with unfavorable lifestyles (<2 of the 4) had higher rates of hypertension and diabetes, higher BMI, and less favorable lipids; overall they had an adjusted hazard ratio of cardiac disease of approximately 2 in each of the three prospective cohorts (i.e., twice the risk).
  • Within each genetic risk category, lifestyle factors were strong predictors of coronary events.
    • Adherence to a favorable lifestyle (at least 3 of the 4 factors) vs. an unfavorable lifestyle (<2) was associated with a 45% lower relative risk in the group at low genetic risk, a 47% lower relative risk among those when intermediate genetic risk and 46% among those at a higher genetic risk. [i.e., the relative risk was equivalently lower in each genetic subgroup with more favorable lifestyle]
    • Among people at high genetic risk, in the 3 prospective cohorts the 10-year coronary event rates were also approximately twice as high in those with unfavorable vs. favorable lifestyles.
  • Further analysis showed that those with high genetic risk but healthy lifestyles had about the same number of cardiac events as those with low genetic risk but unhealthy lifestyles in each of the 3 prospective cohorts, though this was partially explained by differences in traditional risk factors
  • There were limited data for the black population and generally less well-validated genetic loci for coronary disease, although evaluating the black cohort in the ARIC study yielded similar findings
  • In the BioImage study, both genetic and lifestyle factors were associated with higher CAC scores, and within each genetic group there was a significant trend toward decreased CAC scores in those with a healthier lifestyle

Commentary:

  • This study validates that both genetic risk as well as lifestyle are continuously related to cardiac events, and that the genetic risk is largely independent of traditional cardiac risk factors
  • But, this study really undercuts genetic determinism. In fact, there was the same relative decrease in cardiac risk in each of the genetic subgroups (high vs. low genetic risk) with improved lifestyle. And, in terms of absolute risk, those at the highest genetic risk had much more benefit from a healthy lifestyle change than those of lower genetic risk.
  • One general concern I have, tangentially related to the above, is with the availability of genetic analyses for the general population (e.g., 23andme, which advertises “discover what your 23 pairs of chromosomes say about you”, and costs only $199). My concerns are in part that we don’t really know how to interpret many of these genetic findings prospectively, but also that this analysis may reinforce a sense of genetic determinism in the population.
  • It was also a little surprising that the benefits for the individual lifestyle markers were what they were. We do know from prior studies that smoking cessation is the single most beneficial individual intervention in decreasing cardiac risk, as also suggested in this study which found that current non-smoking was associated with the lowest cardiac risk . What was most surprising to me was that the benefit of exercise and healthy diet were not so profound: I suspect that the quantification of diet and exercise was likely not that accurately represented as a bimodal, all-or-none issue (e.g., unclear what the cutpoint was for defining a healthy vs. unhealthy diet, and using one day of exercise a week without even quantifying the amount/type of exercise makes these lifestyle markers hard to interpret, especially as compared to smoking or BMI which have clear bimodal cut-points)

So, I think this study provides some important information in dealing with patients. I have certainly seen many patients who feel that they are destined to die from heart disease because of their family history. This study provides a tool to speak even more convincingly with patients, in that those at higher genetic risk actually achieve the most benefit by improved lifestyle.

Primary Care Corner with Geoffrey Modest MD: Neighborhood Deprivation and Diabetes Risk

24 Aug, 16 | by EBM

By Dr. Geoffrey Modest

There have been many studies finding that poverty or living in poorer neighborhoods is associated with increased morbidity or mortality. However, it is hard to dissociate the array of potential risk factors associated with poverty to validate a true association (for example, do those with more morbidities overall tend to move to poorer neighborhoods since their income tends to be lower, etc. (“social drift”) – so that the association is really with the burden of increased morbidities?). In this light, a quasi-experimental situation existed in Sweden finding that those refugees assigned to poorer neighborhoods had more diabetes (See White JS. Lancet Diabetes Endocrinol 2016; 4: 517).

Details:

  • 61,386 refugees aged 25-50, who arrived in Sweden from 1987-91, were assigned to one of 4833 different neighborhoods in a quasi-random fashion (90% of all refugees were randomly assigned. Those reuniting with family members or those with financial resources to support themselves were not randomly assigned). The goal of Sweden’s policy was to distribute the refugee workforce more evenly throughout the country. All refugees received Swedish language and training courses and social welfare support for about 18 months. There was no restriction on the refugees’ subsequent mobility within Sweden.
  • 85% were 25-34 yo, 74% married/cohabitating, 30% with 2 children, 45% from the Middle East/northern Africa or Iran/19% Eastern Europe/14% Latin America
  • The neighborhoods were classified as high deprivation, moderate deprivation, or low deprivation based on the different levels of poverty and unemployment, schooling, and social welfare participation.
  • 45% of refugees were assigned to a moderate-deprivation and 47% to high-deprivation neighborhoods, though only 8% to a low-deprivation one.
  • They excluded any with diagnoses of diabetes within the first 5 years after arrival in Sweden, as a means to filter out those with incipient diabetes.
  • Primary outcome was the diagnosis of type 2 diabetes between 2002-2011

Results:

  • Cumulative diabetes incidence was 5.8% in low-deprivation, 7.2% in moderate-deprivation, and 7.9% in high-deprivation neighborhoods, with background diabetes prevalence in Sweden being 4-6%
    • In adjusted models, being assigned to high- vs low-deprivation neighborhoods was associated with a 22% increased risk of diabetes [OR 1.22 (1.07-1.38), p=0.001], and moderate- vs low-deprivation neighborhoods having a 15% increased incidence.
    • Diabetes risk accumulated over time: 5 years of additional exposure to high-deprivation vs low-deprivation neighborhoods was associated with a 9% increased diabetes risk

Commentary

  • So, this study does account for some of the expected different circumstances which could account for some of the preselection bias of differences in morbidity/mortality in people living in communities of differing deprivation levels (e.g., social drift).
  • The resulting diabetes incidence differences are therefore likely related to neighborhood-specific differences, such as that those living in poorer neighborhoods tend to eat cheaper and less healthy foods that predominate there, have fewer psychosocial supports, and have less access to safe exercise venues.
  • However, this was not a true randomized trial, especially because those refugees with higher incomes may have opted out of this process and there was a significantly lower % assigned to the low-deprivation areas. So, that does limit the generalizability of the conclusions somewhat (though the numbers of people involved and the differences they found in diabetes incidence were quite impressive)
  • There is an important social context here: though there were significant differences in the low vs high deprivation neighborhoods in Sweden, overall these differences are much more profound in the US, where both the basic differences between neighborhoods is more striking (higher income inequality) and the available social resources in the poorer communities are considerably less (Sweden is known for its strong public safety net).

For more blogs on the relationship between socio-economic status (SES) and morbidity/mortality, see:

http://blogs.bmj.com/ebm/2016/07/13/primary-care-corner-with-geoffrey-modest-md-ses-and-mortality/ reviewed a different Swedish study finding that those with diabetes who had lower socio-economic status had higher rates of all-cause, cardiovascular, diabetes-related mortality.

And an array of blogs in the grouping http://blogs.bmj.com/ebm/category/psychosocial/ which look at BMI, height and the attendant SES; life expectancy and income; income disparities and life expectancy; etc.

Primary Care Corner with Geoffrey Modest MD: SES and Mortality

13 Jul, 16 | by EBM

By Dr. Geoffrey Modest

A recent Swedish study found that socioeconomic status (SES) was independently associated with mortality, cardiovascular disease, and cancer in patients with type 2 diabetes (see doi:10.1001/jamainternmed.2016.2940).

Details:

  • 217,364 people <70 yo, with type 2 diabetes in the Sweden National Diabetes Register (from 2001-2011), assessing all-cause, cardiovascular, diabetes-related and cancer mortality.
  • Median age 58, 60% male
  • 10% from non-Western countries: of these, 10% Latin America/Caribbean, 17% East or South Asia, 60% Middle East/North Africa, 14% sub-Saharan Africa
  • Results (all adjusted for age, sex, duration of diabetes, marital status, income level, educational level, country of birth. further adjustment for smoking, HbA1c, eGFR, BMI, diabetes treatment, albuminuria, heart failure, MI, stroke, stage 5 CKD, and baseline cancer did not affect the associations much):
    • 19,105 all-cause deaths: 60% cardiovascular, 37% diabetes-related, 34% cancer-related
    • Marital status: comparing married vs single, overall 13.0 deaths /1000 vs 18.82 deaths/1000
      • 27% decreased all-cause mortality [HR 0.73 (0.70-0.77)]
      • 33% decreased cardiovasc mortality [HR 0.67 (0.63-0.71)]
      • 38% decreased diabetes-related mortality [HR 0.62 (0.57-0.67)]
      • No difference in cancer-related mortality, other than 33% decreased risk for prostate cancer [HR 0.67 (0.50-0.90)]
    • Income: comparing lowest to highest income quintiles, overall 8.92 deaths /1000 vs 18.33 deaths/1000. This risk varied continuously as income level changes
      • 71% increased all-cause mortality [HR1.71 (1.60-1.83)]
      • 87% increased cardiovasc mortality [HR 1.87 (1.72-2.05)]
      • 80% increased diabetes-related mortality [HR 1.80 (1.61-2.01)]
      • 28% increased cancer-related mortality [HR 1.28 (1.14-1.44)]
    • Income: comparing non-Western immigrants to native Swedes (with covariate adjustment)
      • 45% decreased all-cause mortality [HR 0.55 (0.48-0.63)]
      • 54% decreased cardiovasc mortality [HR 0.46 (0.38-0.56)]
      • 62% decreased diabetes-related mortality [HR 0.38 (0.29-0.49)]
      • 28% decreased cancer-related mortality [HR 0.72 (58-88)]
    • Education: comparing college/university degree vs 9 yrs or less education. this risk varied continuously as educational level increased
      • 15% decreased all-cause mortality [HR 0.85 (0.80-0.90)]
      • 16% decreased cardiovasc mortality [HR 0.84 (0.78-0.91)]
      • 16% decreased cancer-related mortality [HR 0.84 (0.76-0.93)]

Commentary:

  • This study complements many prior studies from many countries over the past many decades showing that SES is a powerful predictor of morbidity and mortality, including both the development of and mortality from diabetes
  • Sweden provides not just a large and rigorous database for analysis (clinical as well as individual-level data on risk factors and socioeconomic variables), but a country where SES has minimal effect on access to and use of health care services. For example, being hospitalized in Sweden costs approximately $10/day independent of the level of care or the number/type of interventions done. Immigrants in general receive evidence-based treatments earlier than native Swedes!!!
  • Clearly there are limits to drawing definitive conclusions from an observational study. For example, some important covariates were not measured (e.g., alcohol intake, amount of smoking). Perhaps others as well.
  • So, an important aspect of this study in Sweden was that there appeared to be relatively equal access to medical care in all groups. This again reinforces the concept that health care is much more than medical care, and health outcomes really depend a lot on broader social issues (and, this is in a country with a much larger social safety net and more extensive social programs/support networks than the United States, for example). In terms of the potential mechanism leading to increased mortality in those with lower SES, one might posit that their attendant stress is associated with many potentially adverse hormonal changes (especially as mediated by the known stress-related increase in cortisol), coupled with perhaps less healthy eating habits, lack of social supports/community cohesion: all possibly leading to increased morbidity and mortality.

Some other SES blogs:

http://blogs.bmj.com/ebm/2016/04/28/primary-care-corner-with-geoffrey-modest-md-bmi-height-and-socioeconomic-status/ which found (through Mendelian randomization) that SES was related both to the individual’s height and BMI, and that part of the association was mediated through genetics, but mostly through social factors.

http://blogs.bmj.com/ebm/2016/04/26/primary-care-corner-with-geoffrey-modest-md-life-expectancy-and-income/ found a striking relationship in the US between income and life expectancy.

And, for those readers who are microbiome-oriented, http://blogs.bmj.com/ebm/2015/08/11/primary-care-corner-with-geoffrey-modest-md-early-life-stress-in-mice-changes-in-microbiome-and-later-anxiety/ which showed that early life stresses in mice leads to long-lasting adverse changes in microbiota

Primary Care Corner with Geoffrey Modest MD: Dropping Teen Birthrates

18 May, 16 | by EBM

By Dr. Geoffrey Modest

MMWR found a decrease in teen birth rates and prior disparities for those aged 15-19, comparing 2013-14 with 2006-7  (see http://www.cdc.gov/mmwr/volumes/65/wr/mm6516a1.htm?s_cid=mm6516a1_w ).

Findings:

  • From 1991-2014, the overall birth rate among those 15-19 yo declined 61% !!!, from 61.8 to 24.2 births/1000 women, and is currently the lowest ever recorded
  • Nationally, from 2006 – 2014 the teen birthrate decreased 41% overall, comparing 2006-7 and 2013-14:
    • 35% among whites (from 26.7 to 17.3/1000 teens)
    • 51% decrease among Hispanics (from 77.4 to 38.0/1000 teens), with the birth rate ratio vs whites declining from 2.9 to 2.2
    • 44% decrease among blacks (from 61.9 to 34.9/1000 teens), with the birth rate ratio vs whites declining from 2.3 to 2.0
  • By states, all had pretty dramatic decreases, the least in N Dakota at 13.4% and West Virginia at 14.9%, the most in Arizona at 47.8%, Connecticut at 47.6% and Colorado at 47.6%; almost all the other states are in the 30-40% range
  • There was huge variation of teen birth rates by county: from 3.1 to 119.0/1000 females aged 15-19. The highest birth rates being in Texas and much of the south (New Mexico, Oklahoma, Arkansas, Louisiana, Mississippi, Kentucky, Georgia)
  • For the states in the highest quintile of teen birth rates, the mean % of the population >15yo who are unemployed, mean % of population >24yo with an associates degree or higher, and mean family income were 10.5%, 19.9% and $46,005; in the lowest quintile, those numbers were 7.6%, 40.4%, and $73,967 and p<0.001 for all comparisons

So, a few observations:

  • It is certainly welcoming that the racial disparity gap is improving, but it has a long way to go
  • In some states this reflected a cross-ethnic consistency – e.g. in New Jersey: the teen birth rate in 2013-4 among whites was 4.8/1000 (below the national average of 18.0), for blacks was 27.4/1000 and Hispanics 31.3/1000 (also below the national averages of 37.0 and 39.8), though still a pretty staggering 6-7 fold higher than for whites
  • But in others, the disparities diverged: e.g. Nebraska, birth rate for whites was 16.2 (approx the national average) whereas the rates for black and Hispanic (42.6 and 53.9) were far above the national average for these groups.
  • The county-by-county map basically shows the highest teen birth rates are in the South, and largely coincides with those states there that refused to expand Medicaid through Obamacare
  • And, not so surprisingly, high teen birth rates also coincide with those states with the highest poverty rates and (somewhat less impressively, though pretty clearly) with highest numbers of African-Americans and Hispanics (see: map for poverty:  http://www.povertyusa.org/the-state-of-poverty/poverty-map-state/# ; map for racial disparities: https://www.google.com/search?safe=active&espv=2&biw=1280&bih=899&site=imghp&tbm=isch&source=hp&biw=&bih=&q=map+of+ethnicities&oq=ethnicities+map&gs_l=img.1.0.0i8i30.15110.19529.0.22183.19.13.3.3.3.0.151.960.10j2.12.0….0…1ac.1.64.img..1.18.983…0j0i24.p9s7jYX4pj8#safe=active&tbm=isch&q=map+of+ethnicities&chips=q:map+of+ethnicities,g_1:usa&imgrc=WU9o2tjooIVI-M%3A )
  • The US Dept of Health and Human Services has been funding community-wide initiatives in 9 communities with some of the highest teen birth rates, focusing on black and Hispanic teens, with a goal to address the social determinants of health at the community level. Mostly these have focused on access to health care services and pregnancy prevention programs [but, not really looking at the fundamental issues of poverty, unemployment, inadequate education, etc.]
  • As a reference point, the adolescent birth rate is 34.2/1000 females in the US, 25.1 in the UK, 15.5 in Australia, and pretty much under 10 in the rest of Europe (see http://internationalcomparisons.org/intl_comp_files/sheet010.htm )
  • So, to put this together: teen pregnancy is clearly decreasing in the US, with decreasing racial/ethnic disparities, but the US overall has the highest rate as compared to other resource-rich countries, and there are large discrepancies in different regions of the US, largely tracking those areas of poverty, racial disparities, and fewer federal resources (which in some cases are those states have chosen to reject). Again, this really speaks to the need for a national, coherent approach to the inequities in our society, as a basis to improve public health outcomes overall.

Primary Care Corner with Geoffrey Modest MD: BMI, Height and Socioeconomic Status

28 Apr, 16 | by EBM

By Dr. Geoffrey Modest

One clear limitation of observational epidemiologic studies is that they are only capable of showing an association, not causality. One way to assess possible causality is through a technique called “mendelian randomization”.  There was a recent study looking at the association between BMI or height and socioeconomic status (see BMJ 2016;352:i582​). In this study, one might suspect that the well-known association between BMI and socioeconomic status (SES) could be bidirectional (high BMI might predispose a person, through conscious or unconscious bias, to be less likely to be hired or make much money, hence the lower SES; and, low SES might predispose people to a higher BMI since they are less likely to be able to afford or even have access to more expensive vegetables and fruit, have less ability to exercise because of unsafe neighborhoods, etc.). Mendelian randomization is a process where one looks at genetic differences (which are randomly allocated at conception) as an unconfounded proxy for the risk factor (in this case, BMI and height). So, since the outcome of SES cannot influence the underlying genetic variation which is set at birth, by looking at the genetic code itself instead of the complexities of BMI or height (which may reflect the genes but also be confounded by the social environment), one might see that, for example, the genes associated with increased BMI or height really are the potential drivers of low SES. The ability to do this genetic testing is now greatly enhanced because of the widespread availability of cheap genome-wide studies, currently identifying 10’s of genetic variants associated with BMI and 100’s associated with height.

Details of this study:

  • 119,669 British men and women, aged 37 to 73 in a UK Biobank that had recruited 500,000 people from across the UK from 2006-2010, and included demographics, health status, lifestyle, blood pressure and stored samples of urine, sputum and blood. This analysis was limited to white people with “British ancestry” because those from other ethnic groups did not have enough representation to be adequately powered for a statistical association.
  • Main outcome: the relationship between BMI or height and the following 5 markers of SES: age completed full-time education, level of education, job class, annual household income, and Townsend deprivation index (a composite of unemployment, non-car ownership, non-home ownership, and household overcrowding).

Results:

  • Overall analyses:
    • Pretty much all of the five SES markers were very highly correlated with coronary artery disease, hypertension, type 2 diabetes and “long illness”
    • Tall stature was related inversely to all of the above SES indicators individually, equivalently for men and women; by genetic analysis, these associations were only significant for men
    • Higher BMI was related directly to all of the above SES indicators individually, equivalently for men and women​; by genetic analysis, was only significant for women, and only for the annual household income and the Townsend deprivation index
  • Genetic analysis found that these relationships were partly causal:
    • A 1-SD (6.3cm) taller stature “caused” :
      • 06 year older age of completing full-time education (0.02-0.09, p=0.01)
      • 12 times higher odds of working in a skilled profession (1.07-1.18, p<0.001)
      • $1615 higher annual income ($970-2260, p<0.001), with stronger association in men
    • A 1-SD higher BMI (4.6 kg/m2) “caused”:
      • $4200 lower annual income ($2400-6000, p<0.001) in women
      • 10 higher level of deprivation (0.04-0.16, p=0.001), but only in women

So, what are the implications of this study:

  • We know from many studies that lower SES is association with poorer health and shorter life expectancy
  • And, we know that adult height and BMI are associated with SES, with many studies in richer countries finding that taller people and those with lower BMI have higher SES and better health
  • I cited a previous mendelian randomization trial for alcohol and cardiovascular events, assessing the often-found association between drinking more and less CAD. Mendelian randomization found the opposite: those drinkers with a genetic variant for alcohol dehydrogenase had lower alcohol consumption and lower levels of CAD risk factors (blood pressure, inflammatory markers, etc.) compared to those without this allele, and that the lower risk of CAD events was present in those with this allele who actually were lower alcohol consumers (see BMJ 2014; 349:g4164 4 doi: 10.1136/bmj.g4164,as well as the editorial in Addiction: doi:10.1111/add.12828​). So this suggests that alcohol consumption by itself is not cardioprotective, but there was likely some confounding or bias inherent in the observational studies. And this bias was the cause of the prior held association of increased alcohol consumption and cardioprotection.
  • The current study did find that the observational associations between BMI and height with SES were in fact significantly stronger than the genetic analyses, suggesting that genetics played some role, but actually only a small part in the association (i.e., a significant part of the association between BMI/height and SES may well be social and possibly bidirectional)
  • The authors do acknowledge several limitations: even if there is a genetic predisposition to a higher BMI, do people with higher BMI move to areas where they are more comfortable? (Areas with others having higher BMI, and perhaps these are areas of lower SES, leading to their children being born in areas of lower SES, getting less good education, etc. And the higher BMI itself does have pretty clear adverse health effects, leading to more illness and less ability to be employed and decreasing SES. So, the genetic association may well be dwarfed by the social issues. A little harder to make equivalent arguments about height.
  • Although this is a huge dataset, it seems to me that there are a couple of limitations to the interpretation (I should add that these are my assessments based on what I glean of the mandelian randomization methodology, and would defer to anyone more statistically or biologically sophisticated than I — would welcome feedback and would like to send around contrary interpretations). One is: do we really know all of the important genetic “determinants”, and which really are the important ones? And is there any significant interaction between the many identified genetic markers (i.e., does it make sense to look at individual genetic markers singly as in this study or in combination?).  Second: while it is undoubtedly true that social circumstances cannot change one’s genes fundamentally, it is also true that the function of genes can change at pretty much anytime through epigenetics by DNA methylation and histone modification, leading to changes in the expression of these genes. For example, rats exposed to marijuana have changes in DNA methylation, which can affect function (see http://blogs.bmj.com/ebm/2015/07/29/primary-care-corner-with-geoffrey-modest-md-marijuana-passing-through-the-generations/ ). This blog goes through some of the basic principles of epigenetics, raising the concern that this post-conception phenomenon may have very profound clinical effects. In the case of the rats, where DNA methylation was actually passed to a subsequent generation, those infant mice were more prone to opioid addiction. And there are a variety of epigenetic changes associated with the environment, lifestyle, and chronic diseases themselves. For example, there are animal studies showing that there is a pretty strong association between nutrition and DNA methylation (e.g. J Nutr Biochem 2012; 23: 853); in fact there is a new journal “Environmental Epigenetics” with articles on such things as epigenetics as a mediator between air pollution and preterm birth, chemical exposures and autism, etc.
  • So, bottom line: there may well be genetic variants leading to obesity and short stature, and these may predict a small part in their determining decreased SES. But overall, I think this study reinforces that the relationship between BMI or height with SES is predominantly a social one: there are social biases, conscious or not, which directly affect the overall ability of overweight women and short men to achieve their full human potential, and this is reflected in their lower SES. And that lower SES may well contribute to higher BMI. I bring up this article because there are more and more studies using mendelian randomization, including a recent one on HDL-cholesterol, and that it seemed reasonable to think about the potential value and shortcomings of this technique.

Primary Care Corner with Geoffrey Modest MD: Life Expectancy and Income

26 Apr, 16 | by EBM

By Dr. Geoffrey Modest

A massive data analysis was done looking at the relationship between income and life expectancy in the US from 2001-2014 (see doi:10.1001/jama.2016.4226).

Details:

  • Income data was from 1.4 billion de-identified tax records from 1999-2014, using pretax household earnings as measure of income. For those not filing a tax return, they looked at the sum of all wage earnings (W-2 forms) plus unemployment benefits. Income was adjusted to 2012 dollars
  • Mortality data from Social Security Administration tax records.
  • Assessed life expectancy at 40 years of age by household income percentile, sex, geographical area.
  • Evaluated 1,408,287,218 person-year observations for people aged 40-76. Mean age 53. mean household earnings $61,175/yr
  • For people under 63 yo, mortality rates were calculated based on income percentile 2 years earlier (they chose 63 yo, since income rates after age 61 correlate less well with earlier earnings). They used models to estimate mortality rates after age 76, and were adjusted for racial/ethnic composition of income groups. local variations of life expectancy were assessed based on zip code of where their income tax returns were mailed

Results:

  • ​Total of 4,114,380 deaths among men ​(mortality rate of 596.3 per 100K); 2,694,808 deaths among women (mortality rate of 375.1 per 100K)
  • Higher income was associated with greater longevity throughout the income distribution, based on the income distribution at age 40
    • Gap in life expectancy between the richest 1% and the poorest 1% was 14.6 years (14.4-14.8) for men and 10.1 years (9.9-10.3) for women
    • Gap between men and women was more pronounced in the bottom 1% [6.0 years (5.9-6.2)] vs in the top 1% [1.5 years (1.3-1.8)]
    • The overall gap was non-linear by $$: going from the 95th to 100th% ($224K to $1.95M annual income) had much smaller gains in life expectancy than going from the 10th to 15th% ($14K to $20K)
  • Inequality in life expectancy increased over time
    • Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 for women in the top 5% of the income distribution, but only 0.32 years for men and 0.04 years for women in the bottom 5% (p<0.001 for both sexes)
  • ​Life expectancy in low-income people varied substantially across local areas. In the bottom income quartile, life expectancy varied by approx 4.5 years between areas with the highest and lowest longevity
    • Changes in life expectancy between 2001 to 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas
    • The variations formed “belts” in their maps of the US: the states with the lowest life expectancy in the bottom income quartile were from Michigan to Kansas (including Ohio, Indiana, Kentucky, Tennessee Arkansas, Oklahoma); the states with the highest were California, New York and Vermont. In terms of the highest income quartile, the lowest life expectancy (<85.3 yrs) were Nevada, Hawaii,and Oklahoma; and the highest life expectancy (>87.6 yrs) was in Utah, Washington DC, and Vermont
    • Also, trends in life expectancy over time varied: greatest increase was in Massachusetts (>0.19 years annually) for those in the bottom income quartile;  and life expectancy decreased (losing > 0.09 years annually) in Alaska, Iowa and Wyoming
  • Local geographical differences in life expectancy in the lowest income quartile were significantly correlated with health behaviors (e.g., negatively with smoking, r=-0.69, p<0.001 and obesity, r=-0.47, p<0.001; but positively with exercise rates, r=0.32, p=0.004), but not with access to medical care for the lowest income group (assessing health insurance spending, quality of primary care), physical environmental factors (air pollution, lack of access to healthy food; though of note, there was almost statistically significant differences between rich and poor life expectancy in those areas with the most residential segregation), income inequality/social cohesion (income inequality seems to be within income groupings, but they also looked at % religious, % Black), or labor market conditions (unemployment rates, changes in population, changes in size of labor force).
    • Life expectancy for low-income people was positively correlated with the local fraction of immigrants (r=0.72, p<0.001), median home values (r=0.66, p<0.001), fraction of college graduates (r=0.42, p<0.001), population density (r=0.48, p<0.001) and local per-capita government expenditures (r=0.57, p<0.001)
    • Life expectancy for high-income people was less variable, but notable for exercise rates (r=0.46, p<0.001); negatively correlated with Medicare expenditures (r=-0.55, p<0.001) but positively with index of preventive care (r=0.55, p<0.001)
    • Data from the National Center for Health Statistics (NCHS) show that the majority of the variations in those with low SES are related to medical causes (heart disease, cancer) vs external causes (accidents, suicide, homicide)

A few perspective issues:

  • ​As a point of international reference, the life expectancy for men in the bottom 1% income in the US is similar to the mean life expectancy for men in Sudan and Pakistan; men in the top 1% in the US have higher life expectancy than the mean for any other country
  • As an internal point of reference, the 10-year gap in life expectancy between the top and bottom 1% in the US is equivalent to the decrement in life expectancy attributable to lifetime smoking
  • ​From NCHS statistics, the difference in the improved life expectancy for the rich over the poor (around 3 years, in comparing top and bottom 5%) is equivalent to the increase in life expectancy at birth if all cancer deaths were eliminated (3.2 years)

So, some points:

  • This is a quick and dirty, but large and powerful study which gets into some of the details of health inequalities in the US.  The measurements of income, based on tax returns mostly, is undoubtedly missing much, especially from the low income group (typically still very low incomes but from under-the-table jobs, unreported income by undocumented workers, etc.) and from very high income group (offshore shell companies, legal and illegal shenanigans to hide income). Also, the mortality rates in those >76 yo is based on mathematical modeling/estimates. And some of the health outcome differences between the income groups is no doubt affected by local circumstances (even the rich in some areas are exposed to the same environmental exposures as the poor, perhaps some of the same food quality, cultural issues such as types of food eaten or priority to do exercise, or access to the highest tech medical care). Some support for this last argument is that the poor do better if living in areas of higher education and more affluence (higher median home values, etc.), which may represent certain local cultural values, such as less smoking (and, in our poorer communities in Boston, there is clearly less smoking, for example, than in many other areas of the country). Also, the finding that the trend in communities with more geographical separation to have more longevity differences also supports that the more isolation of the different income communities from each other tends to make the longevity lines clearer.
  • Perhaps some of the most poignant and socially-relevant findings are:
    • The gaps are increasing, with some of the poorer communities experiencing a decrease in life expectancy
    • ​One other inequity (noted in other blogs) is the regressive nature of our Social Security system: the rich live longer, so get social security payments for a longer time
    • Although just reading numbers sometimes makes it hard to really understand the impact of the differences, the comments about equivalency of these differences with lifetime smoking or zero deaths from cancer really put this in perspective
    • Also, the international perspective I think is quite useful. again, the US does quite poorly overall in life expectancy, except for the very wealthy, and, per several other blogs (see below), this really does correlate with our remarkably poor social systems (or even “safety nets”) as compared to essentially every other westernized, resource-rich country (as noted in the blog below, the book The American Health Care Paradoxshows quite well that we spend huge amounts on “health care”, but unlike other countries, the vast majority of this is for medical care instead of social or public health programs that promote public health). It is telling in the above JAMA study that longevity was associated with local governmental expenditures, which may reflect more social or public health programs
  • I am pretty surprised that access to care was not so related to life expectancy. Not sure how to explain that, since so much literature has come out, for example, about cancer or heart disease outcome differences by income or by race/ethnicity. One interesting finding was that Medicare coverage at age 65 did not affect longevity, and one might think that there would be improved health care access when getting Medicare; though I certainly see many patients who do not qualify for Medicare (did not work the 40 quarters, or were undocumented workers who may have worked 2 jobs for way more than 40 quarters…). Perhaps some of the issue is that they really only looked at income and did not do a detailed analysis by race/ethnicity (many of their analyses did include race- and ethnicity-adjusted life expectancy, but I’m not really sure what that means, and they did not breakdown any of the more specific data above by these important demographics).

See http://blogs.bmj.com/ebm/2016/02/24/primary-care-corner-with-geoffrey-modest-md-increasing-disparities-in-life-expectancy/​ which goes into detail about the increasing life expectancy disparity paralleling income disparity, with a pretty long list of comments, which I will not repeat here, which also includes an article on mortality in younger people, finding a much higher mortality in the US than in other countries.

Primary Care Corner with Geoffrey Modest MD: Low Back Pain Improves with Stress Reduction — Mindfulness and Cognitive Behavioral Therapy

7 Apr, 16 | by EBM

Dr. Geoffrey Modest

I recently sent out the review from AHRQ on low back pain (LBP) management, noting that psychological therapies (especially restoration or cognitive-behavioral therapies) have small-to-moderate effect for improving pain or function (for full review of pharmacologic and nonpharmacologic therapies, see http://blogs.bmj.com/ebm/2016/03/17/primary-care-corner-with-geoffrey-modest-md-low-back-pain-treatment-per-ahrq-review/ . A new study was just published in JAMA on the efficacy of mindfulness-based stress reduction (MBSR) in reducing pain and improving function in those with chronic low back pain (see JAMA. 2016;315(12):1240). This study was sponsored by the National Center for Complementary and Alternative Medicine of the NIH.

Details:

  • 342 adults aged 20-70 with chronic low back pain of at least 3 months, recruited from 2012-2014 and randomly assigned to MBSR or cognitive behavioral therapy (CBT) vs usual care
  • Mean age 49.3, 65.7% women, 82.5% white/6.8% Hispanic, mean duration of LBP of 7.3 years
  • Interventions:
    • 8 weekly 2-hour MBSR sessions, including education about mindfulness, increasing awareness of body sensations, techniques to promote mindful practice (yoga, meditation), learning how to understand and change how we react to stress, understand the relationship between stress and pain, etc. People were asked to practice this daily for up to 45 minutes during the intervention and afterwards. Most instructors were trained in the Center for Mindfulness at U Mass Medical School. [MBSR focuses on “increasing awareness and acceptance of moment-to-moment experiences including physical discomfort and difficult emotions”, with the hypothesis that “practicing mindfulness skills improves one’s ability to experience pain without excessive emotional reactivity, leads to cognitive changes, and promotes relaxation”.]
    • 8 weekly 2-hour CBT sessions, focusing on education about maladaptive thoughts (e.g. catastrophizing) and beliefs, education about chronic pain and the relation between thoughts and emotional and physical reactions, challenging negative thoughts, using positive coping strategies…
    • Usual care typically includes using meds, seeing primary care providers, physical therapists, etc.
  • Assessed: clinically meaningful (≥ 30%) improvement in functional limitations by the Roland Disability Questionnaire (RDQ) on a scale from 0-23 (baseline mean was 11.4), and in self-reported LBP bothersomeness on a scale from 0-10 (baseline mean was 6.0)

Results:

  • 53.7% of individuals (n=123) attended 6 or more of the 8 sessions
  • In intention-to-treat analyses at 26 weeks
    • 60.5% assigned to MBSR and 57.7% with CBT had clinically meaningful improvements in RDQ, vs 44.1% in usual care (p=0.04)
      • RR for MBSR vs usual care was 1.37 (1.06-1.77); RR for CBT vs usual care are 1.31 (1.01-1.69)
    • 43.6% in the MBSR and 44.9% in the CBT groups had clinically meaningful improvements in pain bothersomeness at 26 weeks vs 26.6% of usual care (p=0.01)
      • RR for MBSR vs usual care was 1.64 (1.15-2.34); RR for CBT vs usual care are 1.69 (1.18-2.41)
    • At 52 weeks
      • Little change in the MBSR group for either RDQ questionnaire or pain bothersomeness symptoms; there was some deterioration of CBT for both of these outcomes, leading to their not being statistically significant
    • These differences in RDQ and bothersomeness of pain were considered to be of moderate degrees.

So, several issues:

  • As we know in primary care, chronic LBP is one of the hardest and most frustrating diagnoses (for both the patients and us), since we do not have great treatments. As noted in the AHRQ review, there are remarkable limitations to both the pharmacologic and nonpharmacologic approaches. (See the blog for more details and comments)
  • It is certainly impressive that both of these psychological interventions seem to work, and better than our “usual treatment”. But perhaps the most interesting point is that they work long-term, that in some ways these therapies, after only an 8-week intervention, seems to enable and empower people to take care of themselves better (in both the MBSR and CBT groups, people were given DVDs and other aids to help them continue the therapy after the intervention)
  • These results were likely dwarfed by the fact that only about 1/2 of the patients actually participated fully in the psych interventions. The article did not give specific statistics about benefit in those who fully participated, but one might presume that they did especially well. And that if we were even more successful in encouraging patients in participating, we might have even better results (in fact, we as primary care clinicians with strong relationships with patients might well do better in encouraging patient participation than people hired in this study did)
  • So, it seems that both MBSR and CBT are effective therapies for chronic LBP, with potentially long-lasting moderate efficacy. And, though there are other studies showing the effectiveness of CBT in patients with chronic LBP, access to CBT is not universal, so MBSR seems to provide a reasonable alternative with perhaps even better long-term benefit.

Primary Care Corner with Geoffrey Modest MD: Increasing Disparities in Life Expectancy

24 Feb, 16 | by EBM

By Dr. Geoffrey Modest

The NY Times just featured an article on the growing longevity disparity associated with income disparity (see http://www.nytimes.com/2016/02/13/health/disparity-in-life-spans-of-the-rich-and-the-poor-is-growing.html?emc=edit_th_20160213&nl=todaysheadlines&nlid=67866768 ), based on a report released by the Brookings Institute. See http://www.brookings.edu/research/reports2/2016/02/life-expectancy-gaps-promise-social-security#recent/ for a brief review of the report and http://www.brookings.edu/~/media/Research/Files/Reports/2016/01/life-expectancy-gaps-promise-social-security/BosworthBurtlessZhang_retirementinequalitylongevity_012815.pdf?la=en for the full 174 page report.

Main points:

  • In the early 1970s, a 60-year old man in the top half of the earnings’ ladder had life expectancy 1.2 years longer than one in the bottom half. In 2001, the gap was 5.8 years.
  • The Brookings report found that, comparing life expectancy between those in the top vs bottom 10% of earners (data are based on life expectancy at age 50 yo):
    • For men born in 1920, there was a 6-year difference; for men born in 1950, there was a 14-year difference.
    • For women born in 1920, there was a 4.7-year difference; for women born in 1950, there was a 13-year difference.
    • In a separate analysis, the Brookings report noted that life expectancies in those born in 1920 vs 1940, comparing the bottom to the top 10% of mid-career income distribution were:
      • Those in the bottom 10%: 80.4 years for women (no change); 74.3 increasing to 76.0 in men
      • Those in the top 10%: 84.1 years for women increasing to 90.5!!!; 79.3 increasing to 88.0!!! In men
    • Why are the differences so great and getting dramatically greater? Hard to pinpoint exactly (and studies looked at different endpoints), but some differences:
      • Cigarette smoking: decreased more in wealthy, could explain 1/5 to 1/3 in the gap between men with college degrees vs those with high school degrees; 1/4 of the gap in women
      • Obesity: rates of obesity between rich and poor narrowed from 1990-2010, when 37% of poorer and 31% of richer adults were obese
      • ​Prescription drug abuse has disproportionately increased mortality in poor communities
      • Of note, limited access to care was not found to play much of a role (they reference an article by Steven Schroeder: N Engl J Med 2007; 357:1221), stating that only 10% of the disparity has to do with medical care [note that this statement was not footnoted, so I cannot check on the reliability of it].
    • One side note is that wealthier people live longer and therefore collect more years of social security payments as well as longer utilization of Medicare services, disproportionate financial benefits for the wealthy.
    • These longevity disparities are not necessarily reflected in other countries: in Canada, men in the poorest urban areas had the largest declines in heart disease mortality from 1971-1996, and the overall gaps in longevity decreased over this time period. Cancer survival rates in low-income residents in Toronto were significantly better than in Detroit, yet there was no difference for middle- and high-income residents (see Am J Public Health. 1997; 87(7): 1156).
    • The Brookings report also commented on the fact that higher wage earners are retiring later (they attribute this to the fact that their jobs are higher-paying which is especially important since most jobs now do not come with a pension or guaranteed income after retirement, the jobs usually are more rewarding, and social security benefits were pushed up a year to age 66). Lower age workers tend to retire earlier with only 13.8% getting social security at age 66. They do not comment explicitly (so, I will): the increase in age for social security from 65 to 66 is much less significant for an office worker than someone doing hard manual labor, where they likely have chronic musculoskeletal pains/problems, and the possibility of extending the work-life another year may be painful and undoable. But getting social security early adds to income inequality, since the payout is much less/yr.

—————————————————————————————-

In a (somewhat) related recent article (see JAMA. 2016;315(6):609), researchers looked at life expectancy from birth (vs from age 50 in above), as a means to evaluate mortality in younger people, where both the major causes of death are different from those >50yo (more from injury/trauma/drugs), and the impact is greater (more years of expected life are lost). They focused on motor vehicle traffic crashes (MVT), firearm injuries, and drug poisonings (e.g. overdoses). The table below shows the contribution of these injuries/traumas to the life expectancy of men and women, also comparing the US rates to those of a variety of other countries. From this data, overall death from injury accounted for 48% of the longevity gap in men (1.02 years of the 2.15 years of the all-cause difference), with firearm-related injuries accounting for 21% of the overall gap, drug poisonings 14% and MVT crashes 13%. For women injuries/traumas accounted for 19% of the gap, with 4% from firearms, 9% from drug poisonings, and 6% from MVT crashes. Overall, the impact of these injuries in the US is far greater than in a combination of other countries. The other table (not shown) details the specifics per country, showing for example that although Portugal is the only country in the list with an overall death rate higher than the US, their death rate from injuries is much lower than the US, so that Portugal still has a life-expectancy 0.5 years longer than the US (i.e., because there are fewer injuries/overdoses which disproportionately affect younger people). A caveat here is that they are relying on death coding across different countries.

A few comments:

  • There are very real reasons why lower wage earners have lower life expectancy, as noted in many prior blogs. Obesity is a major problem but is exacerbated by lack of access to good, affordable foods. Doing exercise can be an obstacle when people live in unsafe neighborhoods. Manual laborers tend to have more disabilities (I’m not sure I have met any construction workers, masons, plumbers, who do not have significant musculoskeletal problems by the age of 40). Air quality tends to be worse in poor neighborhoods. General stress tends to be higher.
  • I do have concerns about writing off access to medical care as not much of a factor in the longevity discrepancy. It is clear that inadequate access to care is an issue for the poor only. And there are huge discrepancies within that group. If you happen to live in Massachusetts, access is generally quite good. If you live in rural Mississippi or Louisiana, access is terrible/can be effectively nonexistent.
  • Though I do think that, overall, the predominant issue is that, though we spend lots of $$ in the US on health care, unlike other countries (including many with far fewer resources than in the US), we spend the vast majority on “medical care” (where in other countries a higher % of health care money goes to making sure people have good food, housing, jobs, and an array of social services –see The American Health Care Paradox, by E Bradley and L Taylor, published in 2013, noting that:
    • We spend almost twice as much money as the next most expensive health care system; yet we have really terrible comparable health outcomes, e.g. ranking 26th in life expectancy.
    • ​Countries with far better health outcomes spend much more money on social services to enhance well-being, such as “investments in housing, nutrition, education, the environment and unemployment support” (which dovetails with the way the World Health Organization defines health as “a state of complete physical, mental and social well-being”); we spend dramatically less than other countries on these social services.
    • ​And, if you add up the strictly medical as well as the social costs invested by different countries for health care, the US is somewhere in the middle of the pack in terms of per capita spending.
  • So,  I think this is why longevity of wealthier people in the US (who need fewer social services) is pretty much as good at those living in the highest ranking countries (Japan, Iceland), but poorer people have the life expectancy of those in Poland and the Czech republic.
  • There are several reports finding a temporal relationship between divergences in income inequality and longevity inequality over the past 40 years.
  • And the JAMA study reinforces the overall importance of traumatic or drug-related deaths overall (which is largely missed in the Brookings analysis), and especially in the young​.

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