Air Pollution Exposure Is Associated With Cognitive Performance: Results From the Einstein Aging Study

Prior research has established that those exposed to higher levels of fine particulate matter (PM1, PM2.5) air pollution have higher levels of accumulated amyloid-beta (Aβ) and tau in frontal cortex at autopsy, higher error rates on cognitive function assessments, and lower scores on memory and both verbal and non-verbal intelligence assessments. We explored the relationship between regional air quality monitoring measures (EPA AirData) and baseline cognitive performance of 312 older adults, from the Einstein Aging Study (EAS, NIA P01AG003949). Participants completed neuropsychological assessments at baseline and each followup wave (i.e., delayed free recall and total recall; Trails A & B, Digit Symbol substitution task (DSST), MoCA). For each participant, based on their zipcode, we computed average PM2.5 exposure at various exposure windows (1-15, 30-60, 60-90, 90-120 days prior to baseline). Adjusting for age, education, and gender across all models, mean of daily particulate matter exposure at various exposure windows (30-60, 60-90 days) was significantly related to performance on the MoCA and Trails A & B, in expected directions (i.e., higher pollution, worse cognitive performance - more error, slower speed). Models with memory performance as the outcome indicated that only distant time horizons were related to memory performance (i.e., 60-90, 90-120 days prior). These findings suggest that particulate matter air pollution likely affects different cognitive domains at different timescales. This methodology cannot address contributions from indoor air quality and mobility - an exposure misclassification likely resulting in significant biases towards the null in the estimation of the effects of air pollution.

Food biodiversity and total and cause-specific mortality in 9 European countries: An analysis of a prospective cohort study

Background Food biodiversity, encompassing the variety of plants, animals, and other organisms consumed as food and drink, has intrinsic potential to underpin diverse, nutritious diets and improve Earth system resilience. Dietary species richness (DSR), which is recommended as a crosscutting measure of food biodiversity, has been positively associated with the micronutrient adequacy of diets in women and young children in low- and middle-income countries (LMICs). However, the relationships between DSR and major health outcomes have yet to be assessed in any population. Methods and findings We examined the associations between DSR and subsequent total and cause-specific mortality among 451,390 adults enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC) study (1992 to 2014, median follow-up: 17 years), free of cancer, diabetes, heart attack, or stroke at baseline. Usual dietary intakes were assessed at recruitment with country-specific dietary questionnaires (DQs). DSR of an individual’s yearly diet was calculated based on the absolute number of unique biological species in each (composite) food and drink. Associations were assessed by fitting multivariable-adjusted Cox proportional hazards regression models. In the EPIC cohort, 2 crops (common wheat and potato) and 2 animal species (cow and pig) accounted for approximately 45% of self-reported total dietary energy intake [median (P10–P90): 68 (40 to 83) species consumed per year]. Overall, higher DSR was inversely associated with all-cause mortality rate. Hazard ratios (HRs) and 95% confidence intervals (CIs) comparing total mortality in the second, third, fourth, and fifth (highest) quintiles (Qs) of DSR to the first (lowest) Q indicate significant inverse associations, after stratification by sex, age, and study center and adjustment for smoking status, educational level, marital status, physical activity, alcohol intake, and total energy intake, Mediterranean diet score, red and processed meat intake, and fiber intake [HR (95% CI): 0.91 (0.88 to 0.94), 0.80 (0.76 to 0.83), 0.69 (0.66 to 0.72), and 0.63 (0.59 to 0.66), respectively; PWald < 0.001 for trend]. Absolute death rates among participants in the highest and lowest fifth of DSR were 65.4 and 69.3 cases/10,000 person-years, respectively. Significant inverse associations were also observed between DSR and deaths due to cancer, heart disease, digestive disease, and respiratory disease. An important study limitation is that our findings were based on an observational cohort using self-reported dietary data obtained through single baseline food frequency questionnaires (FFQs); thus, exposure misclassification and residual confounding cannot be ruled out. Conclusions In this large Pan-European cohort, higher DSR was inversely associated with total and cause-specific mortality, independent of sociodemographic, lifestyle, and other known dietary risk factors. Our findings support the potential of food (species) biodiversity as a guiding principle of sustainable dietary recommendations and food-based dietary guidelines.

Misclassification and characterization of exposure to humidifier disinfectants using a questionnaire

Background Lung disease caused by exposure to chemical substances such as polyhexamethylene guanidine (PHMG) used in humidifier disinfectants (HDs) has been identified in Korea. Several researchers reported that exposure classification using a questionnaire might not correlate with the clinical severity classes determined through clinical diagnosis. It was asserted that the lack of correlation was due to misclassification in the exposure assessment due to recall bias. We identified the cause of uncertainty to recognize the limitations of differences between exposure assessment and clinical outcomes assumed to be true value. Therefore, it was intended to check the availability of survey using questionnaires and required to reduce misclassification error/bias in exposure assessment. Methods HDs exposure assessment was conducted as a face-to-face interview, using a questionnaire. A total of 5245 applicants participated in the exposure assessment survey. The questionnaire included information on sociodemographic and exposure characteristics such as the period, frequency, and daily usage amount of HDs. Based on clinical diagnosis, a 4 × 4 cross-tabulation of exposure and clinical classification was constructed. When the values of the exposure rating minus the clinical class were ≥ 2 and ≤ − 2, we assigned the cases to the overestimation and underestimation groups, respectively. Results The sex ratio was similar in the overestimation and underestimation groups. In terms of age, in the overestimation group, 90 subjects (24.7%) were under the age of 10, followed by 52 subjects (14.2%) in their 50s. In the underestimation group, 195 subjects (56.7%) were under the age of 10, followed by 80 subjects (23.3%) in their 30s. The overestimation group may have already recovered and responded excessively due to psychological anxiety or to receive compensation. However, relatively high mortality rates and surrogate responses observed among those under 10 years of age may have resulted in inaccurate exposure in the underestimation group. Conclusions HDs exposure assessment using a questionnaire might not correlate with adverse health effects due to recall bias and various other causes such as recovery of injury and psychological anxiety. This study revealed exposure misclassification and characteristics affected by HDs and proposed a questionnaire-based exposure assessment methodology to overcome the limitations of past exposure assessment.

Conflict violence reduction and pregnancy outcomes: A regression discontinuity design in Colombia

Background The relationship between exposure to conflict violence during pregnancy and the risks of miscarriage, stillbirth, and perinatal mortality has not been studied empirically using rigorous methods and appropriate data. We investigated the association between reduced exposure to conflict violence during pregnancy and the risks of adverse pregnancy outcomes in Colombia. Methods and findings We adopted a regression discontinuity (RD) design using the July 20, 2015 cease-fire declared during the Colombian peace process as an exogenous discontinuous change in exposure to conflict events during pregnancy, comparing women with conception dates before and after the cease-fire date. We constructed the cohorts of all pregnant women in Colombia for each day between January 1, 2013 and December 31, 2017 using birth and death certificates. A total of 3,254,696 women were followed until the end of pregnancy. We measured conflict exposure as the total number of conflict events that occurred in the municipality where a pregnant woman lived during her pregnancy. We first assessed whether the cease-fire did induce a discontinuous fall in conflict exposure for women with conception dates after the cease-fire to then estimate the association of this reduced exposure with the risks of miscarriage, stillbirth, and perinatal mortality. We found that the July 20, 2015 cease-fire was associated with a reduction of the average number of conflict events (from 2.64 to 2.40) to which women were exposed during pregnancy in their municipalities of residence (mean differences −0.24; 95% confidence interval [CI] −0.35 to −0.13; p < 0.001). This association was greater in municipalities where Fuerzas Armadas Revolucionarias de Colombia (FARC) had a greater presence historically. The reduction in average exposure to conflict violence was, in turn, associated with a decrease of 9.53 stillbirths per 1,000 pregnancies (95% CI −16.13 to −2.93; p = 0.005) for municipalities with total number of FARC-related violent events above the 90th percentile of the distribution of FARC-related conflict events and a decrease of 7.57 stillbirths per 1,000 pregnancies (95% CI −13.14 to −2.00; p = 0.01) for municipalities with total number of FARC-related violent events above the 75th percentile of FARC-related events. For perinatal mortality, we found associated reductions of 10.69 (95% CI −18.32 to −3.05; p = 0.01) and 6.86 (95% CI −13.24 to −0.48; p = 0.04) deaths per 1,000 pregnancies for the 2 types of municipalities, respectively. We found no association with miscarriages. Formal tests support the validity of the key RD assumptions in our data, while a battery of sensitivity analyses and falsification tests confirm the robustness of our empirical results. The main limitations of the study are the retrospective nature of the information sources and the potential for conflict exposure misclassification. Conclusions Our study offers evidence that reduced exposure to conflict violence during pregnancy is associated with important (previously unmeasured) benefits in terms of reducing the risk of stillbirth and perinatal deaths. The findings are consistent with such beneficial associations manifesting themselves mainly through reduced violence exposure during the early stages of pregnancy. Beyond the relevance of this evidence for other countries beset by chronic armed conflicts, our results suggest that the fledgling Colombian peace process may be already contributing to better population health.

P–776 Singleton pregnancies conceived with infertility treatments and the risk of neonatal and infant mortality

Study question Is maternal infertility treatment associated with an increased risk of neonatal and infant mortality when compared to natural conception? Summary answer Infertility treatment is associated with a 70% increased adjusted risk of neonatal mortality. This association is strongly mediated by preterm delivery. What is known already The number of assisted reproduction technology (ART) cycles performed in the United States (US) increased by 39% from 142,435 cycles in 2007 to 197,737 in 2016. Within this growing experience, several studies described an increased risk of preterm delivery, low birth weight, congenital malformations, neonatal intensive care unit admission, stillbirth, and perinatal mortality among singletons conceived through ART compared to those conceived naturally. Experts have called for ART patients to be advised of potential increased risk for adverse perinatal outcomes and for obstetricians to manage these pregnancies as high risk. Study design, size, duration This is a cross-sectional study of 11,289,466 pregnancies in the United States (US) from 2015–2017 that resulted in a non-malformed singleton live birth. The exposure group includes births resulting from any infertility treatment method, including ART and fertility-enhancing drugs. The control group includes births resulting from natural conceptions. The primary outcomes measured were neonatal (within 1 month), post-neonatal (1 month to a year), and infant (up to 1 year) mortality. Participants/materials, setting, methods Pregnancies (n = 11,289,466) resulting in a non-malformed singleton live birth in the US from 2015–2017. Associations were estimated from log-linear Poisson regression models with robust variance. Risk ratio (RR) and 95% confidence interval (CI) were derived as the effect measure with adjustments for confounders. The impact of exposure misclassification and unmeasured confounding biases were assessed. A causal mediation analysis of the infertility treatment-mortality association with preterm delivery (&lt;37 weeks) was performed. Main results and the role of chance Any infertility treatment was documented in 1.3% (n = 142,215) of singleton live births during the study period. Any infertility treatment was associated with a 70% increased adjusted risk of neonatal mortality (RR 1.70, 95% CI 1.54–1.88), with an even higher risk for early neonatal (RR 1.82, 95% CI 1.63–2.05) than late neonatal (RR 1.37, 95% CI 1.11–1.69) mortality. These risks were similar among pregnancies conceived through ART and treatment with fertility-enhancing drugs. The mediation analysis showed that 68% (95% CI 59–81) of the total effect of infertility treatment on neonatal mortality was mediated through preterm delivery. In a sensitivity analysis, following corrections for exposure misclassification and unmeasured confounding biases, these risks were higher for early neonatal (bias-corrected RR [RRbc] 2.94 95% CIbc 2.16–4.01), but not for late neonatal (RRbc 1.04, 95% CIbc 0.68–1.59) mortality. Limitations, reasons for caution Limitations of the study include the potential underreporting of infertility treatment on birth certificates and potential confounding from sociodemographic characteristics that were not accounted for in this study. Wider implications of the findings: Pregnancies conceived with infertility treatment are associated with increased neonatal mortality and this association is mediated by the increased risk of preterm delivery. Knowledge of this risk should be shared with prospective couples consulting for fertility care in order to best provide adequate informed consent. Trial registration number Not applicable

Exposure misclassification bias in the estimation of vaccine effectiveness

In epidemiology, a typical measure of interest is the risk of disease conditional upon exposure. A common source of bias in the estimation of risks and risk ratios is misclassification. Exposure misclassification affects the measurement of exposure, i.e. the variable one conditions on. This article explains how to assess biases under non-differential exposure misclassification when estimating vaccine effectiveness, i.e. the vaccine-induced relative reduction in the risk of disease. The problem can be described in terms of three binary variables: the unobserved true exposure status, the observed but potentially misclassified exposure status, and the observed true disease status. The bias due to exposure misclassification is quantified by the difference between the naïve estimand defined as one minus the risk ratio comparing individuals observed as vaccinated with individuals observed as unvaccinated, and the vaccine effectiveness defined as one minus the risk ratio comparing truly vaccinated with truly unvaccinated. The magnitude of the bias depends on five factors: the risks of disease in the truly vaccinated and the truly unvaccinated, the sensitivity and specificity of exposure measurement, and vaccination coverage. Non-differential exposure misclassification bias is always negative. In practice, if the sensitivity and specificity are known or estimable from external sources, the true risks and the vaccination coverage can be estimated from the observed data and, thus, the estimation of vaccine effectiveness based on the observed risks can be corrected for exposure misclassification. When analysing risks under misclassification, careful consideration of conditional probabilities is crucial.

An algorithm to identify residential mobility from electronic health-record data

Background Environmental, social and economic exposures can be inferred from address information recorded in an electronic health record. However, these data often contain administrative errors and misspellings. These issues make it challenging to determine whether a patient has moved, which is integral for accurate exposure assessment. We aim to develop an algorithm to identify residential mobility events and avoid exposure misclassification. Methods At Penn Medicine, we obtained a cohort of 12 147 pregnant patients who delivered between 2013 and 2017. From this cohort, we identified 9959 pregnant patients with address information at both time of delivery and one year prior. We developed an algorithm entitled REMAP (Relocation Event Moving Algorithm for Patients) to identify residential mobility during pregnancy and compared it to using ZIP code differences alone. We assigned an area-deprivation exposure score to each address and assessed how residential mobility changed the deprivation scores. Results To assess the accuracy of our REMAP algorithm, we manually reviewed 3362 addresses and found that REMAP was 95.7% accurate. In this large urban cohort, 41% of patients moved during pregnancy. REMAP outperformed the comparison of ZIP codes alone (82.9%). If residential mobility had not been taken into account, absolute area deprivation would have misclassified 39% of the patients. When setting a threshold of one quartile for misclassification, 24.4% of patients would have been misclassified. Conclusions Our study tackles an important characterization problem for exposures that are assigned based upon residential addresses. We demonstrate that methods using ZIP code alone are not adequate. REMAP allows address information from electronic health records to be used for accurate exposure assessment and the determination of residential mobility, giving researchers and policy makers more reliable information.

Exposure misclassification in propensity score-based time-to-event data analysis

In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situations involving exposure measured with moderate to substantial error, identifying the exposure effect using propensity score in Cox models remains a challenging yet unresolved problem. In this paper, we propose an estimating equation method to correct for the exposure misclassification-caused bias in the estimation of exposure-outcome associations. We also discuss the asymptotic properties and derive the asymptotic variances of the proposed estimators. We conduct a simulation study to evaluate the performance of the proposed estimators in various settings. As an illustration, we apply our method to correct for the misclassification-caused bias in estimating the association of PM2.5 level with lung cancer mortality using a nationwide prospective cohort, the Nurses’ Health Study. The proposed methodology can be applied using our user-friendly R program published online.

A data visualisation method for assessing exposure misclassification in case-crossover studies: the example of tricyclic antidepressants and the risk of hip fracture in older people

Background The case-crossover design is suited to medication safety studies but is vulnerable to exposure misclassification. Using the example of tricyclic antidepressants and the risk of hip fracture, we present a data visualisation tool for observing exposure misclassification in case-crossover studies. Methods A case-crossover study was conducted using Australian Government Department of Veterans’ Affairs claims data. Beneficiaries aged over 65 years who were hospitalised for hip fracture between 2009 and 2012 were included. The case window was defined as 1–50 days pre fracture. Control window one and control window two were defined as 101–150 and 151–200 days pre fracture, respectively. Patients were stratified by whether exposure status changed when control window two was specified instead of control window one. To visualise potential misclassification, each subject’s tricyclic antidepressant dispensings were plotted over the 200 days pre fracture. Results The study population comprised 8828 patients with a median age of 88 years. Of these subjects, 348 contributed data to the analyses with either control window. The data visualisation suggested that 14% of subjects were potentially misclassified with control window one while 45% were misclassified with control window two. The odds ratio for the association between tricyclic antidepressants and hip fracture was 1.18 (95% confidence interval = 0.91–1.52) using control window one, whereas risk was significantly increased (odds ratio = 1.43, 95% confidence interval = 1.11–1.83) using control window two. Conclusions Exposure misclassification was less likely to be present with control window one than with an earlier control window, control window two. When specifying different control windows in a case-crossover study, data visualisation can help to assess the extent to which exposure misclassification may contribute to variable results.