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The annals of applied statistics  2014;8(1):352-376.
Genetic association studies have been a popular approach for assessing the association between common Single Nucleotide Polymorphisms (SNPs) and complex diseases. However, other genomic data involved in the mechanism from SNPs to disease, e.g., gene expressions, are usually neglected in these association studies. In this paper, we propose to exploit gene expression information to more powerfully test the association between SNPs and diseases by jointly modeling the relations among SNPs, gene expressions and diseases. We propose a variance component test for the total effect of SNPs and a gene expression on disease risk. We cast the test within the causal mediation analysis framework with the gene expression as a potential mediator. For eQTL SNPs, the use of gene expression information can enhance power to test for the total effect of a SNP-set, which are the combined direct and indirect effects of the SNPs mediated through the gene expression, on disease risk. We show that the test statistic under the null hypothesis follows a mixture of χ2 distributions, which can be evaluated analytically or empirically using the resampling-based perturbation method. We construct tests for each of three disease models that is determined by SNPs only, SNPs and gene expression, or includes also their interactions. As the true disease model is unknown in practice, we further propose an omnibus test to accommodate different underlying disease models. We evaluate the finite sample performance of the proposed methods using simulation studies, and show that our proposed test performs well and the omnibus test can almost reach the optimal power where the disease model is known and correctly specified. We apply our method to re-analyze the overall effect of the SNP-set and expression of the ORMDL3 gene on the risk of asthma.
PMCID: PMC3981558
Causal Inference; Data Integration; Mediation Analysis; Mixed Models; Score Test; SNP Set Analysis; Variance Component Test
2.  A Three-way Decomposition of a Total Effect into Direct, Indirect, and Interactive Effects 
Epidemiology (Cambridge, Mass.)  2013;24(2):224-232.
Recent theory in causal inference has provided concepts for mediation analysis and effect decomposition that allow one to decompose a total effect into a direct and an indirect effect. Here, it is shown that what is often taken as an indirect effect can in fact be further decomposed into a “pure” indirect effect and a mediated interactive effect, thus yielding a three-way decomposition of a total effect (direct, indirect, and interactive). This three-way decomposition applies to difference scales and also to additive ratio scales and additive hazard scales. Assumptions needed for the identification of each of these three effects are discussed and simple formulae are given for each when regression models allowing for interaction are used. The three-way decomposition is illustrated by examples from genetic and perinatal epidemiology, and discussion is given to what is gained over the traditional two-way decomposition into simply a direct and an indirect effect.
PMCID: PMC3563853  PMID: 23354283
3.  Policy-relevant proportions for direct effects 
Epidemiology (Cambridge, Mass.)  2013;24(1):175-176.
PMCID: PMC3523303  PMID: 23232624
4.  On the interpretation of subgroup analyses in randomized trials: heterogeneity versus secondary interventions 
Annals of internal medicine  2011;154(10):10.7326/0003-4819-154-10-201105170-00008.
In randomized trials with subgroup analyses, the primary treatment or intervention of interest is randomized but the secondary factors defining subgroups are not. The commentary clarifies when confounding is or is not an issue in subgroup analyses. If investigators are simply interested in targeting subpopulations for intervention, control for confounding does not need to be made. If investigators are interested in intervening on the secondary factor defining the subgroup in order to increase the treatment effect or in attributing the subgroup differences to the secondary factor itself then confounding is relevant and must be controlled for. The point is illustrated using randomized trials published in the literature.
PMCID: PMC3825512  PMID: 21576536
5.  On the Reciprocal Association Between Loneliness and Subjective Well-being 
American Journal of Epidemiology  2012;176(9):777-784.
Loneliness has been shown to longitudinally predict subjective well-being. The authors used data from a longitudinal population-based study (2002–2006) of non-Hispanic white, African-American, and nonblack Latino-American persons born between 1935 and 1952 and living in Cook County, Illinois. They applied marginal structural models for time-varying exposures to examine the magnitude and persistence of the effects of loneliness on subjective well-being and of subjective well-being on loneliness. Their results indicate that, if interventions on loneliness were made 1 and 2 years prior to assessing final subjective well-being, then only the intervention 1 year prior would have an effect (standardized effect = −0.29). In contrast, increases in subjective well-being 1 year prior (standardized effect = −0.26) and 2 years prior (standardized effect = −0.13) to assessing final loneliness would both have an effect on an individual's final loneliness. These effects persist even after control is made for depressive symptoms, social support, and psychiatric conditions and medications as time-varying confounders. Results from this study indicate an asymmetrical and persistent feedback of fairly substantial magnitude between loneliness and subjective well-being. Mechanisms responsible for the asymmetry are discussed. Developing interventions for loneliness and subjective well-being could have substantial psychological and health benefits.
PMCID: PMC3571255  PMID: 23077285
causal models; loneliness; marginal structural models; subjective well-being
6.  Bounding the Infectiousness Effect in Vaccine Trials 
Epidemiology (Cambridge, Mass.)  2011;22(5):686-693.
In vaccine trials, the vaccination of one person might prevent the infection of another; a distinction can be drawn between the ways such a protective effect might arise. Consider a setting with 2 persons per household in which one of the 2 is vaccinated. Vaccinating the first person may protect the second person by preventing the first from being infected and passing the infection on to the second. Alternatively, vaccinating the first person may protect the second by rendering the infection less contagious even if the first is infected. This latter mechanism is sometimes referred to as an “infectiousness effect” of the vaccine. Crude estimators for the infectiousness effect will be subject to selection bias due to stratification on a postvaccination event, namely the infection status of the first person. We use theory concerning causal inference under interference along with a principal-stratification framework to show that, although the crude estimator is biased, it is, under plausible assumptions, conservative for what one might define as a causal infectiousness effect. This applies to bias from selection due to the persons in the comparison, and also to selection due to pathogen virulence. We illustrate our results with an example from the literature.
PMCID: PMC3792580  PMID: 21753730
7.  Mediation analysis with multiple versions of the mediator 
Epidemiology (Cambridge, Mass.)  2012;23(3):454-463.
The causal inference literature has provided definitions of direct and indirect effects based on counterfactuals that generalize the approach found in the social science literature. However, these definitions presuppose well defined hypothetical interventions on the mediator. In many settings there may be multiple ways to fix the mediator to a particular value and these different hypothetical interventions may have very different implications for the outcome of interest. In this paper we consider mediation analysis when multiple versions of the mediator are present. Specifically, we consider the problem of attempting to decompose a total effect of an exposure on an outcome into the portion through the intermediate and the portion through other pathways. We consider the setting in which there are multiple versions of the mediator but the investigator only has access to data on the particular measurement, not which version of the mediator may have brought that value about. We show that the quantity that is estimated as a natural indirect effect using only the available data does indeed have an interpretation as a particular type of mediated effect; however, the quantity estimated as a natural direct effect in fact captures both a true direct effect and an effect of the exposure on the outcome mediated through the effect of the version of the mediator that is not captured by the mediator measurement. The results are illustrated using two examples from the literature, one in which the versions of the mediator are unknown and another in which the mediator itself has been dichotomized.
PMCID: PMC3771529  PMID: 22475830
8.  Components of the indirect effect in vaccine trials: identification of contagion and infectiousness effects 
Epidemiology (Cambridge, Mass.)  2012;23(5):751-761.
Vaccination of one person may prevent the infection of another either because the vaccine prevents the first from being infected and from infecting the second, or because, even if the first person is infected, the vaccine may render the infection less infectious. We might refer to the first of these mechanisms as a contagion effect and the second as an infectiousness effect. In the simple setting of a randomized vaccine trial with households of size two, we use counterfactual theory under interference to provide formal definitions of a contagion effect and an unconditional infectiousness effect. Using ideas analogous to mediation analysis, we show that the indirect effect (the effect of one person’s vaccine on another’s outcome) can be decomposed into a contagion effect and an unconditional infectiousness effect on the risk-difference, risk-ratio, odds-ratio and vaccine-efficacy scales. We provide identification assumptions for such contagion and unconditional infectiousness effects, and describe a simple statistical technique to estimate these effects when they are identified. We also give a sensitivity-analysis technique to assess how inferences would change under violations of the identification assumptions. The concepts and results of this paper are illustrated with hypothetical vaccine-trial data.
PMCID: PMC3415570  PMID: 22828661
9.  Invited Commentary: Assessing Mechanistic Interaction Between Coinfecting Pathogens for Diarrheal Disease 
American Journal of Epidemiology  2012;176(5):396-399.
The interaction estimates from Bhavnani et al. (Am J Epidemiol. 2012;176(5):387–395) are used to evaluate evidence for mechanistic interaction between coinfecting pathogens for diarrheal disease. Mechanistic interaction is said to be present if there are individuals for whom the outcome would occur if both of 2 exposures are present but would not occur if 1 or the other of the exposures is absent. In the epidemiologic literature, mechanistic interaction is often conceived of as synergism within Rothman's sufficient-cause framework. Tests for additive interaction are sometimes used to assess such synergism or mechanistic interaction, but testing for positive additive interaction only allows for the conclusion of mechanistic interaction under fairly strong “monotonicity” assumptions. Alternative tests for mechanistic interaction, which do not require monotonicity assumptions, have been developed more recently but require more substantial additive interaction to draw the conclusion of the presence of mechanistic interaction. The additive interaction reported by Bhavnani et al. is of sufficient magnitude to provide strong evidence of mechanistic interaction between rotavirus and Giardia and between rotavirus and Escherichia. coli/Shigellae, even without any assumptions about monotonicity.
PMCID: PMC3499113  PMID: 22842718
coinfecting pathogens; diarrhea; interaction; mechanism; synergism
11.  Results on Differential and Dependent Measurement Error of the Exposure and the Outcome Using Signed Directed Acyclic Graphs 
American Journal of Epidemiology  2012;175(12):1303-1310.
Measurement error in both the exposure and the outcome is a common problem in epidemiologic studies. Measurement errors in the exposure and the outcome are said to be independent of each other if the measured exposure and the measured outcome are statistically independent conditional on the true exposure and true outcome (and dependent otherwise). Measurement error is said to be nondifferential if measurement of the exposure does not depend on the true outcome conditional on the true exposure and vice versa; otherwise it is said to be differential. Few results on differential and dependent measurement error are available in the literature. Here the authors use formal rules governing associations on signed directed acyclic graphs (DAGs) to draw conclusions about the presence and sign of causal effects under differential and dependent measurement error. The authors apply these rules to 4 forms of measurement error: independent nondifferential, dependent nondifferential, independent differential, and dependent differential. For a binary exposure and outcome, the authors generalize Weinberg et al.’s (Am J Epidemiol. 1994;140(6):565–571) result for nondifferential measurement error on preserving the direction of a trend to settings which also allow measurement error in the outcome and to cases involving dependent and/or differential error.
PMCID: PMC3491975  PMID: 22569106
bias (epidemiology); causality; directed acyclic graphs; measurement error; misclassification
12.  Genetic Variants on 15q25.1, Smoking, and Lung Cancer: An Assessment of Mediation and Interaction 
American Journal of Epidemiology  2012;175(10):1013-1020.
Genome-wide association studies have identified variants on chromosome 15q25.1 that increase the risks of both lung cancer and nicotine dependence and associated smoking behavior. However, there remains debate as to whether the association with lung cancer is direct or is mediated by pathways related to smoking behavior. Here, the authors apply a novel method for mediation analysis, allowing for gene-environment interaction, to a lung cancer case-control study (1992–2004) conducted at Massachusetts General Hospital using 2 single nucleotide polymorphisms, rs8034191 and rs1051730, on 15q25.1. The results are validated using data from 3 other lung cancer studies. Tests for additive interaction (P = 2 × 10−10 and P = 1 × 10−9) and multiplicative interaction (P = 0.01 and P = 0.01) were significant. Pooled analyses yielded a direct-effect odds ratio of 1.26 (95% confidence interval (CI): 1.19, 1.33; P = 2 × 10−15) for rs8034191 and an indirect-effect odds ratio of 1.01 (95% CI: 1.00, 1.01; P = 0.09); the proportion of increased risk mediated by smoking was 3.2%. For rs1051730, direct- and indirect-effect odds ratios were 1.26 (95% CI: 1.19, 1.33; P = 1 × 10−15) and 1.00 (95% CI: 0.99, 1.01; P = 0.22), respectively, with a proportion mediated of 2.3%. Adjustment for measurement error in smoking behavior allowing up to 75% measurement error increased the proportions mediated to 12.5% and 9.2%, respectively. These analyses indicate that the association of the variants with lung cancer operates primarily through other pathways.
PMCID: PMC3353137  PMID: 22306564
gene-environment interaction; lung neoplasms; mediation; pathway analysis; smoking
14.  Conditioning on intermediates in perinatal epidemiology 
It is common practice in perinatal epidemiology to calculate gestational-age-specific or birth-weight-specific associations between an exposure and a perinatal outcome. Gestational age or birth weight, for example, might lie on a pathway from the exposure to the outcome. This practice of conditioning on a potential intermediate has come under critique for various reasons. First, if one is interested in assessing the overall effect of an exposure on an outcome, it is not necessary to stratify, and indeed it is important not to stratify, on an intermediate. Second, if one does condition on an intermediate, to try to obtain what might conceived of as a “direct effect” of the exposure on the outcome, then various biases and paradoxical results can arise. It is now well documented theoretically and empirically, that when there is an unmeasured common cause of the intermediate and the outcome, associations adjusted for the intermediate are subject to bias. In this paper we propose three approaches to facilitate valid inference when effects conditional on an intermediate are in view. These three approaches correspond to (i) conditioning on the predicted risk of the intermediate, (ii) conditioning on the intermediate itself in conjunction with sensitivity analysis, and (iii) conditioning on the subgroup of individuals for whom the intermediate would occur irrespective of the exposure received. The second and third approaches both require sensitivity analysis, and they result in a range of estimates. Each of the three approaches can be used to resolve the “birth-weight paradox” that exposures such as maternal smoking appear to have a protective effect among low-birth-weight infants. The various methodologic approaches described in this paper are applicable to a number of similar settings in perinatal epidemiology.
PMCID: PMC3240847  PMID: 22157298
15.  Rising Preterm Birth Rates, 1989-2004: Changing Demographics or Changing Obstetric Practice? 
Social science & medicine (1982)  2011;74(2):196-201.
Preterm birth rates are higher in the United States than in most industrialized countries, and have been rising steadily. Some attribute these trends to changing demographics, with more older mothers, more infertility, and more multiple births. Others suggest that changes in obstetrics are behind the trends. We sought to determine what the preterm birth rate in 2004 would have been if demographic factors had not changed since 1989. We examined complete US birth certificate files from 1989 and 2004 and used logistic regression models to estimate what the 2004 preterm birth rates (overall, spontaneous, and medically induced) would have been if maternal age, race, nativity, gravidity, marital status, and education among childbearing women had not changed since 1989. While the overall preterm births increased from 11.2% to 12.8% from 1989-2004, medically induced rates increased 94%, from 3.4% to 6.6%, and spontaneous rates declined by 21%, from 7.8% to 6.2%. Had demographic factors in 2004 been what they were in 1989, the 2004 rates would have been almost identical. Changes in multiple births accounted for only 16% of the increase in medically induced rates. Our analysis suggests that the increase in preterm births is more likely to be due primarily to changes in obstetric practice, rather than to changes in the demographics of childbearing. Further research should examine the degree to which these changes in obstetric practice affect infant morbidity and mortality.
PMCID: PMC3259145  PMID: 22177849
C-section; Demographic Trends; Induction; Prenatal care; Preterm birth; USA
16.  A Weighting Approach to Causal Effects and Additive Interaction in Case-Control Studies: Marginal Structural Linear Odds Models 
American Journal of Epidemiology  2011;174(10):1197-1203.
Estimates of additive interaction from case-control data are often obtained by logistic regression; such models can also be used to adjust for covariates. This approach to estimating additive interaction has come under some criticism because of possible misspecification of the logistic model: If the underlying model is linear, the logistic model will be misspecified. The authors propose an inverse probability of treatment weighting approach to causal effects and additive interaction in case-control studies. Under the assumption of no unmeasured confounding, the approach amounts to fitting a marginal structural linear odds model. The approach allows for the estimation of measures of additive interaction between dichotomous exposures, such as the relative excess risk due to interaction, using case-control data without having to rely on modeling assumptions for the outcome conditional on the exposures and covariates. Rather than using conditional models for the outcome, models are instead specified for the exposures conditional on the covariates. The approach is illustrated by assessing additive interaction between genetic and environmental factors using data from a case-control study.
PMCID: PMC3246690  PMID: 22058231
case-control studies; interaction; linear model; structural model; synergism; weighting
17.  Causal interactions in the proportional hazards model 
Epidemiology (Cambridge, Mass.)  2011;22(5):713-717.
The paper relates estimation and testing for additive interaction in proportional hazards models to causal interactions within the counterfactual framework. A definition of a causal interaction for time-to-event outcomes is given that generalizes existing definitions for dichotomous outcomes. Conditions are given concerning the relative excess risk due to interaction in proportional hazards models that imply the presence of a causal interaction at some point in time. Further results are given that allow for assessing the range of times and baseline survival probabilities for which parameter estimates indicate that a causal interaction is present, and for deriving lower bounds on the prevalence of such causal interactions. An interesting feature of the time-to-event setting is that causal interactions can disappear as time progresses i.e. whether a causal interaction is present depends on the follow-up time. The results are illustrated by hypothetical and data analysis examples.
PMCID: PMC3150431  PMID: 21558856
18.  Causal mediation analysis with survival data 
Epidemiology (Cambridge, Mass.)  2011;22(4):582-585.
Causal mediation analysis is considered for time-to-event outcomes and survival analysis models. Different possible effect decompositions are discussed for the survival function, hazard, mean survival time and median survival scales. Approaches to mediation analysis in the social sciences are related to counterfactual approaches using additive hazard, proportional hazard and accelerated failure time models. The product-coefficient method from the social sciences gives mediated effects on the hazard difference scale for additive hazard models, on the log mean survival time difference scale for accelerated failure time models, and on the log hazard scale for the proportional hazards model but only if the outcome is rare. With the proportional hazards model and a common outcome, the product-coefficient method can provide a valid test for the presence of a mediator effect but does not provide a measure. When additive hazard, accelerated failure time, or the rare-outcome proportional hazards models are employed and combined with the counterfactual approach, exposure-mediator interactions can be accommodated in a relatively straightforward manner.
PMCID: PMC3109321  PMID: 21642779
19.  Remarks on Antagonism 
American Journal of Epidemiology  2011;173(10):1140-1147.
Different forms of antagonism are classified in terms of response types and are related to the sufficient-cause framework. These forms of antagonism include “synergy under recoding of an exposure,” “synergism under recoding of the outcome,” and so-called “competing response types,” with synergism itself conceived of as causal co-action within the sufficient-cause framework. In this paper, the authors show that subadditivity necessarily implies at least one of these 3 forms of antagonism. Empirical conditions for specific forms of antagonism are given for settings in which monotonicity assumptions are and are not considered plausible. The implications of subadditivity and superadditivity for causal co-action for either an outcome or its absence are characterized under various assumptions about monotonicity. A simple computational procedure is described for assessing whether any specific form of causal co-action can be detected for either an outcome or its absence for both cohort and case-control data. The results in this paper are illustrated by application to examples drawn from the existing literature on gene-gene and gene-environment interactions.
PMCID: PMC3121324  PMID: 21490044
causality; dichotomous response; interaction; minimal sufficiency; synergism
20.  Pancreatic beta-cell function and type 2 diabetes risk: quantify the causal effect using a Mendelian randomization approach based on meta-analyses 
Human Molecular Genetics  2012;21(22):5010-5018.
The objective of the study is to quantify the causal effect of β-cell function on type 2 diabetes by minimizing residual confounding and reverse causation. We employed a Mendelian randomization (MR) approach using TCF7L2 variant rs7903146 as an instrument for lifelong levels of β-cell function. We first conducted two sets of meta-analyses to quantify the association of the TCF7L2 variant with the risk of type 2 diabetes among 55 436 cases and 106 020 controls from 66 studies by calculating pooled odds ratio (OR) and to quantify the associations with multiple direct or indirect measures of β-cell function among 35 052 non-diabetic individuals from 31 studies by calculating pooled mean difference. We further applied the method of MR to obtain the causal estimates for the effect of β-cell function on type 2 diabetes risk based on findings from the meta-analyses. The OR [95% confidence interval (CI)] was 0.87 (0.81–0.93) for each five unit increment in homeostasis model assessment of insulin secretion (HOMA-%B) (P = 3.0 × 10−5). In addition, for measures based on intravenous glucose tolerance test, ORs (95% CI) associated with type 2 diabetes risk were 0.24 (0.08–0.74) (P = 0.01) and 0.14 (0.04–0.48) (P = 0.002) for per 1 standard deviation increment in insulin sensitivity index and disposition index, respectively. Findings from the present study lend support to a causal role of pancreatic β-cell function itself in the etiology of type 2 diabetes.
PMCID: PMC3607483  PMID: 22936689
21.  Unmeasured Confounding for General Outcomes, Treatments, and Confounders 
Uncontrolled confounding in observational studies gives rise to biased effect estimates. Sensitivity analysis techniques can be useful in assessing the magnitude of these biases. In this paper, we use the potential outcomes framework to derive a general class of sensitivity-analysis formulas for outcomes, treatments, and measured and unmeasured confounding variables that may be categorical or continuous. We give results for additive, risk-ratio and odds-ratio scales. We show that these results encompass a number of more specific sensitivity-analysis methods in the statistics and epidemiology literature. The applicability, usefulness, and limits of the bias-adjustment formulas are discussed. We illustrate the sensitivity-analysis techniques that follow from our results by applying them to 3 different studies. The result bias formulas are particularly simple and easy to use in settings in which the unmeasured confounding variable is binary with constant effect on the outcome across treatment and covariate levels, and with a constant prevalence difference across covariate levels when comparing 2 treatment levels.
PMCID: PMC3073860  PMID: 21052008
22.  Odds Ratios for Mediation Analysis for a Dichotomous Outcome 
American Journal of Epidemiology  2010;172(12):1339-1348.
For dichotomous outcomes, the authors discuss when the standard approaches to mediation analysis used in epidemiology and the social sciences are valid, and they provide alternative mediation analysis techniques when the standard approaches will not work. They extend definitions of controlled direct effects and natural direct and indirect effects from the risk difference scale to the odds ratio scale. A simple technique to estimate direct and indirect effect odds ratios by combining logistic and linear regressions is described that applies when the outcome is rare and the mediator continuous. Further discussion is given as to how this mediation analysis technique can be extended to settings in which data come from a case-control study design. For the standard mediation analysis techniques used in the epidemiologic and social science literatures to be valid, an assumption of no interaction between the effects of the exposure and the mediator on the outcome is needed. The approach presented here, however, will apply even when there are interactions between the effect of the exposure and the mediator on the outcome.
PMCID: PMC2998205  PMID: 21036955
case-control studies; causal inference; decomposition; dichotomous response; epidemiologic methods; interaction; logistic regression; odds ratio
23.  Compound Treatments and Transportability of Causal Inference 
Epidemiology (Cambridge, Mass.)  2011;22(3):368-377.
Ill-defined causal questions present serious problems for observational studies—problems that are largely unappreciated. This paper extends the usual counterfactual framework to consider causal questions about compound treatments for which there are many possible implementations (for example, “prevention of obesity”). We describe the causal effect of compound treatments and their identifiability conditions, with a special emphasis on the consistency condition. We then discuss the challenges of using the estimated effect of a compound treatment in one study population to inform decisions in the same population and in other populations. These challenges arise because the causal effect of compound treatments depends on the distribution of the versions of treatment in the population. Such causal effects can be unpredictable when the versions of treatment are unknown. We discuss how such issues of “transportability” are related to the consistency condition in causal inference. With more carefully framed questions, the results of epidemiologic studies can be of greater value to decision-makers.
PMCID: PMC3805254  PMID: 21399502
24.  Sufficient cause interactions for categorical and ordinal exposures with three levels 
Biometrika  2010;97(3):647-659.
Definitions are given for weak and strong sufficient cause interactions in settings in which the outcome is binary and in which there are two exposures of interest that are categorical or ordinal. Weak sufficient cause interactions concern cases in which a mechanism will operate under certain values of the two exposures but not when one or the other of the exposures takes some other value. Strong sufficient cause interactions concern cases in which a mechanism will operate under certain values of the two exposures but not when one or the other of the exposures takes any other value. Empirical conditions are derived for such interactions when exposures have two or three levels and are related to regression coefficients in linear and log-linear models. When the exposures are binary, the notions of a weak and a strong sufficient cause interaction coincide, but not when the exposures are categorical or ordinal. The results are applied to examples concerning gene-gene and gene-environment interactions.
PMCID: PMC3372241  PMID: 22822251
Categorical data; Gene-environment; Interaction; Sufficient cause; Synergism
25.  Provision of Spiritual Support to Patients With Advanced Cancer by Religious Communities and Associations With Medical Care at the End of Life 
JAMA internal medicine  2013;173(12):1109-1117.
revious studies report associations between medical utilization at the end-of-life (EoL) and religious coping and spiritual support from the medical team. However, the influence of clergy and religious communities on EoL outcomes is unclear.
To determine whether spiritual support from religious communities influences terminally ill patients’ medical care and quality of life (QoL) near death.
Design, Setting, and Participants
A US-based, multisite cohort study of 343 patients with advanced cancer enrolled from September 2002 through August 2008 and followed up (median duration, 116 days) until death. Base-line interviews assessed support of patients’ spiritual needs by religious communities. End-of-life medical care in the final week included the following: hospice, aggressive EoL measures (care in an intensive care unit [ICU], resuscitation, or ventilation), and ICU death.
Main Outcomes and Measures
End-of-life QoL was assessed by caregiver ratings of patient QoL in the last week of life. Multivariable regression analyses were performed on EoL care outcomes in relation to religious community spiritual support, controlling for confounding variables, and were repeated among high religious coping and racial/ethnic minority patients.
Patients reporting high spiritual support from religious communities (43%) were less likely to receive hospice (adjusted odds ratio [AOR], 0.37; 95% CI, 0.20-0.70 [P=.002]), more likely to receive aggressive EoL measures (AOR, 2.62; 95% CI, 1.14-6.06 [P=.02]), and more likely to die in an ICU (AOR, 5.22; 95% CI, 1.71-15.60 [P=.004]). Risks of receiving aggressive EoL interventions and ICU deaths were greater among high religious coping (AOR, 11.02; 95% CI, 2.83-42.89 [P<.001]; and AOR, 22.02; 95% CI, 3.24-149.58 [P=.002]; respectively) and racial/ethnic minority patients (AOR, 8.03; 95% CI, 2.04-31.55 [P=.003]; and AOR, 11.21; 95% CI, 2.29-54.88 [P=.003]; respectively). Among patients well-supported by religious communities, receiving spiritual support from the medical team was associated with higher rates of hospice use (AOR, 2.37; 95% CI, 1.03-5.44 [P=.04]), fewer aggressive interventions (AOR, 0.23; 95% CI, 0.06-0.79 [P=.02]) and fewer ICU deaths (AOR, 0.19; 95% CI, 0.05-0.80 [P=.02]); and EoL discussions were associated with fewer aggressive interventions (AOR, 0.12; 95% CI, 0.02-0.63 [P=.01]).
Conclusions and Relevance
Terminally ill patients who are well supported by religious communities access hospice care less and aggressive medical interventions more near death. Spiritual care and EoL discussions by the medical team may reduce aggressive treatment, highlighting spiritual care as a key component of EoL medical care guidelines.
PMCID: PMC3791610  PMID: 23649656

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