Neighborhood Disadvantage and Cortisol

Kara Rudolph, PhD, MHS, MPH

krudolph@jhsph.edu

thank you, for that kind introduction and thanks to you all for coming today. my name is kara rudolph ; i'm currently a postdoc at Johns Hopkins and i'm going to be talking about some work that was just published on the relationship between neighborhood disadvantage and cortisol in adolescents. in particular, i'm going to be focusing on the challenges we encountered in trying to use cortisol data from from a large, nationally represtentative sample and how we sought to address those challenges.

Motivation

National Comorbidity Survey Replication Adolescent Supplment (NCS-A). Merikangas et al., 2010

My research so far has been motivated by the goal of working to improve the MH of teens in disadvantaged neighborhoods. This graph is from a US nationally representative ...dataset that I used for this work. IT shows the cumulative prevelance of different categories of disorders by age. We see that poor mental health is a major public health issue. For example, nearly 12% of adolescents had ever had major depressive disorder and nearly 32% ever had an anxiety disorder....

Motivation

Missed school and sociodevelopmental opportunities →

Education
Employment
Earnings
Family life

These disorders are not typically isolated, time-limited events, but can have consequence that extend over the lifecourse For example, they cause children and adolescents to Miss school and sociodevelopmental opportunities → which studies have shown negatively affects educational achievement (Breslau et al., 2008), future employment (Kessler et al., 2006), earnings (Kessler et al., 2008), family life (Kessler et al., 1998)

Motivation

Depression: 500 million disability days/year, cost of 36B
Anxiety: 700 million disability days/year
WHO disease burden rankings

MHdisorders in childhood also Increase risk of disorders in adulthood discorders in adulthood have been shown to have signiifcant → economic costs [read] in fact, depression and anxiety are the leading contributors to years lost to disability in the WHO disease burden rankings

Motivation: Importance of Place

So, it's clear that MH is imporatnat, but why neighborhood? well, intuitively, we understand that living in a neighborhood in Kensington may be a different experience than living in a neihgborhood in Rittenhouse Sq. For example, i taught creative writing to a class of public HS students in DC for a year. theyhad to deal wiht violence on a fairly regular basi-- one of their classmates was tragically even shot and killed. its hard to imagine a classroom of kids from one of the surrounding wealthy suburbs having a similar experience.

Motivation: Importance of Place

so living in a disadvantaged neighborhood is thought to entail exposure to more sources stress--e.g., violence, lack of opportunity for work and for education, low-collective efficacy--that act to dysregulate the stress response system within individuals. dysregulation of the stress response system has been linked to depression and anxiety disorders

Salivary Cortisol

Easy to sample
Free, unbound
Indicator of HPA axis

Cortisol is one component of the stress response system. It is frequently used in research studies as a proxy for the stress response system and for good reason. 1. easy to sample -- doesn't provoke stress -- good, because trying to figure out the effects of sources of stress external to the stress measurement itself! 2. salivary cortisol contains free, unbound cortisol, which is the only component of cortisol to reach the target tissue and elicit any effects. 3. indicator of Hypothalamic pituitary adrenal axis, which is a major player in the stress response system. and is particuarly relevant to thinking about neighborhood sources of stress, because it is sensitive to psychosocial sources of stress. e.g., low locus of control, maternal neglect and because it has been implicated in depressive and anxiety disorders

Research Question

To what extent does living in a disadvantaged neighborhood influence cortisol levels among adolescents?

Rudolph KE, Wand GS, Stuart EA, Glass TA, Marques AH, Duncko R, Merikangas KR. The association between cortisol characteristics and neighborhood disadvantage in a population-based sample of U.S. adolescents. Health Place. 2014; 25: 68-77.

So now to bring it back to the study at hand, our goal was to see to what extent does living in a disadvantaged neighborhood influence cortisol levels among adolescents?

Gaps

Few studies
Small sample size
Racial/ethnic homogeneity
Geographic homogeneity

In Examining the association between ND and cortisol, we were motivated by several gaps in the literature. 1. First, there has been surprisingly little research into this realtionship in adolescets. In fact, in my literature review, I only found 3 studies. 2. However, each of thes 3 studies were Limited by small sample size and racial/ethnic and geographic homogeneity. for example the biggest study only included 163 teens (163, 100, 79) 2/3 studiies were only of african americans and all 3 of the studies only sampled from single urban areas (flint, St Louis, Philadelphia)

NCS-A

Largest sample to date (2,490 have cortisol measurements).
Racially and ethnically diverse.
Participants drawn from different regions of the U.S., urbanicities.

the dataset that we had was well-suited to address these gaps. The dataset is the NCSA--or national comorbidity survey adolescent supplement. actually wellAgain, I thought the NCS-A was well-suited to address these gaps. [read] Nationally representative, cross-sectional sample of U.S. adolescents 13-17 years old, so racially and ethnically diverse and [read]

NCS-A

Sources of information relevant to this study:
- Face-to-face, computer-assisted interviews with the adolescent.
- Saliva collected before and after each interview.
- Self-administered questionnaire to parents or parent surrogate of the adolescent.
- Geocoded residence.

The NCS-A collects information from a variety of sources, but i'll just go through the sources that are relevant to this study. Now, although we felt we were well positioned to address some gaps, we still had some major challenges to overcome, so I'm going to spend some time talking about the challenges we faced and how we went about addressing them.

Broad Challenges

Confounding (neighborhood assignment not random)

First, there are a few broad challenges that arise with most neighborhood work. One is [read] This means that there could be many factors that could influence where people live and also influence their cortisol levels. We worry about this, because it may result it in appearing as though there is an association betwen neighborhood and cortisol when there actually isn't. One example may be race. Racial segregation of neighbrohood has roots in historical practices, like redlining, and persists today. So race may influence neighborhood and there is also some evidence that cortisol may differ by race.

Broad Challenges

Confounding (neighborhood assignment not random)
Positivity violations (economic and racial segregation of neighborhoods)

2. positivity violations represent another methodologic challenge. Pos viols means that for a particular set of covariate values, assignment of neighborhood is completely defined--there is no randonmess. I'll get into this more later, but one example of where this can come about in neihgborhood research is due to the economic and racial segregation of neihgborhoods in this country. For example...

Confounding

Propensity score methods

Propensity score = P(A=1|W)

One way that we addressed both confounding and positivity is through propensity socre methods. PS is the probability of treatment assignment con- ditional on a vector of observed variables. what it means in this case. how it is measured. Type of balancing score because theory says [assuming some things] that exact matching on propensity score (so for example, matching kids who live in nondisadvantaged neighborhoods and with kids who living in disadvantaged enighrborhoods and who have the same PS) ensures that the treatment groups being compared are balanced across a vector of covariates. That means, no more confounding. In practice, we can't exact match, and that's okay. matching on PS that are close to each other has been shown to greatly reduce bias.

Confounding

showing how well it worked in terms of balancing distribution of covariates across tx groups. describe figure

Confounding

Propensity score methods
Outcome regression

But our PS model could be wrong, right? To reduce dependence on this model, weThen, we ran the regular regression analyses we would have run had we not done matching on our matched datasets. This approach can be thought of as double robust, because xxx

Confounding

Propensity score methods
Outcome regression
Sensitivity analysis for an unobserved confounder

Now, maybe our tx or outcome model is correct for the covariates we observe, but what about for those covariates that we don't observe? BEcause neighborhood is not randomly assigned, there could still be imbalances in variables we don't observe that could act as confounders for the ND-cortisol relationship. To deal with this we did sensitivity analyses for unobserved confounding, which I'll show near the end if there is time.

Positivity Violations

Recall that another one of our broad challenges are positivity violations. That means that some folks in disadv neighborhoods have essentially a zero probability of living in a nondis neighborhood and vise versa. This is a problem, becasue we don't have anyone similar to compare these folks to. by excluding those without similar counterparts, we are essentially chopping off the tails. s such that for a given propensity score, there are participants in both categories of neihgborhood. meaning that for a given propensity socre, there is some randomness in neighborhood assingment.

Broad Challenges

Confounding (neighborhood assignment not random)
Positivity violations (economic and racial segregation of neighborhoods)
Lack of generalizability

A 3rd challenge is a Lack of generalizability. means that results estimated in some study samples may not be valid for other samples or target populations. I'm going to leave this alone for now, but come back to it later.

Cortisol Challenges

So, now I've talked about how we addressed some broad challenges related to our neighborhood, but we also had challenges related to cortisol. the stress response system is extremely complex, and so Examining the relationship between neighborhood sources of stress and cortisol involves requires stratgies to manage this complexity. given this complexity, cortisol is ideally measured under very strict laboratory protocols, but this isn't practical in a large, nationally representative sample like the NCSA we were left with the challenge: How to construct a controlled analysis?

Cortisol Challenges

Challenge Approach Not at-risk for a cortisol response Restrictive exclusion criteria some kids may not be at risk for cortisol being influenced by environmental sources of stress. for example, those kids whose cortisol levels are artificially influenced by things like steroid inhalers for asthma, birth control, smoking, drugs etc. so we applied very restrictive exclusion criteria

Cortisol Challenges

Challenge Approach Not at-risk for a cortisol response Restrictive exclusion criteria Blunting Sensitivity analysis Seoncd, the relationship between ND and cortisol also likely depense on the History of exposure to stress. With moderate amounts of stress, resting cortisol levels increase and reactivity also increases. But among those who have been exposed to really severe and persistant stress---children who are abused---the cortisol levels and reactivity actually are blunted---look more similar to unstressed children.This is called blunting, because the idea is that after repeated or prolonged activation of the stress response system, this response wears out. Including kids who experience severe, persistent stress could result in a possible washout effect. so we did a sensitivity analysis where we excluded these kids.

Cortisol Challenges

Challenge Approach Not at-risk for a cortisol response Restrictive exclusion criteria Blunting Sensitivity analysis Heterogeneity by CAR/ post-CAR Restricted to post-CAR

(Do et al., 2011.)

time of day. Graph: diurnal rhythm. explain CAR. and the relationship between enighborhood sources of stress may differ depending on time of day. explain graph

Cortisol Challenges

Challenge Approach Not at-risk for a cortisol response Restrictive exclusion criteria Blunting Sensitivity analysis Heterogeneity by CAR/ post-CAR Restricted to post-CAR Diurnal rhythm Propensity score matching with Mahalanobis matching Other strong predictors Propensity score matching with exact matching strong predictors like sample time, school day versus nonschool day, race/ethnciicy combined ps matching with matching on the subset of these especially important predictors. explain.

Results

Cortisol Outcomes

Pre-interview
Post-interview
Slope over the course of the interview

So now, at long last, I'm going to talk about our results. There were 3 cortisol outcomes that we examined in this analysis. [read the 3] These don't correspond to specific HPA axis dimensions, since cortisol wasn't measured under controlled conditions with standardized protocols, so I'm going to take a second to describe what these outcomes may reflect. Pre invterview levels likely reflect any activity the adolescent was engaged in prior to the interview, foods, as well as anticipation of the new situation of being interviewred in her home by a researcher for ta survey on mental health. Something out of the ordinary! And relevant, because the HPA access is particularly sensitive to novelty. The interview was specifically designed not to be stressful, so adoelscents probably became pretty bored over the course of the interview. The post-interview levels may relfect that as well as the fact that now the adolescent has been sitting down for 2 and a half hours. We also looked at slope over the course of the interview-- post - pre/ interview time. this can be thought of as a measure of recovery from this novel interview situation.