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1.  Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling 
There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 μm in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial–temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003–2005. Via cross-validation experiments, our model had an out-of-sample prediction R2 of 0.78 and a root mean-squared error (RMSE) of 3.61 μg/m3 between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial–temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined.
PMCID: PMC4065210  PMID: 24368510
empirical/statistical models; exposure modeling; particulate matter
2.  A spatial time-to-event approach for estimating associations between air pollution and preterm birth 
Journal of the Royal Statistical Society. Series C, Applied statistics  2012;62(2):10.1111/j.1467-9876.2012.01056.x.
The paper describes a Bayesian spatial discrete time survival model to estimate the effect of air pollution on the risk of preterm birth. The standard approach treats prematurity as a binary outcome and cannot effectively examine time varying exposures during pregnancy. Time varying exposures can arise either in short-term lagged exposures due to seasonality in air pollution or long-term cumulative exposures due to changes in length of exposure. Our model addresses this challenge by viewing gestational age as time-to-event data where each pregnancy becomes at risk at a prespecified time (e.g. the 28th week). The pregnancy is then followed until either a birth occurs before the 37th week (preterm), or it reaches the 37th week, and a full-term birth is expected. The model also includes a flexible spatially varying baseline hazard function to control for unmeasured spatial confounders and to borrow information across areal units. The approach proposed is applied to geocoded birth records in Mecklenburg County, North Carolina, for the period 2001–2005.We examine the risk of preterm birth that is associated with total cumulative and 4-week lagged exposure to ambient fine particulate matter.
PMCID: PMC3864864  PMID: 24353351
Air pollution; Fine particulate matter; Preterm birth; Reproductive epidemiology; Spatial survival data
3.  Time series analysis of personal exposure to ambient air pollution and mortality using an exposure simulator 
This paper describes a modeling framework for estimating the acute effects of personal exposure to ambient air pollution in a time series design. First, a spatial hierarchical model is used to relate Census tract-level daily ambient concentrations and simulated exposures for a subset of the study period. The complete exposure time series is then imputed for risk estimation. Modeling exposure via a statistical model reduces the computational burden associated with simulating personal exposures considerably. This allows us to consider personal exposures at a finer spatial resolution to improve exposure assessment and for a longer study period. The proposed approach is applied to an analysis of fine particulate matter of <2.5 μm in aerodynamic diameter (PM2.5) and daily mortality in the New York City metropolitan area during the period 2001–2005. Personal PM2.5 exposures were simulated from the Stochastic Human Exposure and Dose Simulation. Accounting for exposure uncertainty, the authors estimated a 2.32% (95% posterior interval: 0.68, 3.94) increase in mortality per a 10 μg/m3 increase in personal exposure to PM2.5 from outdoor sources on the previous day. The corresponding estimates per a 10 μg/m3 increase in PM2.5 ambient concentration was 1.13% (95% confidence interval: 0.27, 2.00). The risks of mortality associated with PM2.5 were also higher during the summer months.
PMCID: PMC3657344  PMID: 22669499
exposure modeling; particulate matter; time series analysis
4.  A Spectral Method for Spatial Downscaling 
Biometrics  2014;70(4):932-942.
Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrate model output with monitoring data to adjust for model biases and improve spatial prediction. In this article, we propose a new spectral method to study and exploit complex relationships between model output and monitoring data. Spectral methods allow us to estimate the relationship between model output and monitoring data separately at different spatial scales, and to use model output for prediction only at the appropriate scales. The proposed method is computationally efficient and can be implemented using standard software. We apply the method to compare Community Multiscale Air Quality (CMAQ) model output with ozone measurements in the United States in July 2005. We find that CMAQ captures large-scale spatial trends, but has low correlation with the monitoring data at small spatial scales.
PMCID: PMC4276454  PMID: 24965037
Computer model output; Data fusion; Kriging; Multiscale analysis
5.  An Empirical Assessment of Exposure Measurement Error and Effect Attenuation in Bipollutant Epidemiologic Models 
Environmental Health Perspectives  2014;122(11):1216-1224.
Background: Using multipollutant models to understand combined health effects of exposure to multiple pollutants is becoming more common. However, complex relationships between pollutants and differing degrees of exposure error across pollutants can make health effect estimates from multipollutant models difficult to interpret.
Objectives: We aimed to quantify relationships between multiple pollutants and their associated exposure errors across metrics of exposure and to use empirical values to evaluate potential attenuation of coefficients in epidemiologic models.
Methods: We used three daily exposure metrics (central-site measurements, air quality model estimates, and population exposure model estimates) for 193 ZIP codes in the Atlanta, Georgia, metropolitan area from 1999 through 2002 for PM2.5 and its components (EC and SO4), as well as O3, CO, and NOx, to construct three types of exposure error: δspatial (comparing air quality model estimates to central-site measurements), δpopulation (comparing population exposure model estimates to air quality model estimates), and δtotal (comparing population exposure model estimates to central-site measurements). We compared exposure metrics and exposure errors within and across pollutants and derived attenuation factors (ratio of observed to true coefficient for pollutant of interest) for single- and bipollutant model coefficients.
Results: Pollutant concentrations and their exposure errors were moderately to highly correlated (typically, > 0.5), especially for CO, NOx, and EC (i.e., “local” pollutants); correlations differed across exposure metrics and types of exposure error. Spatial variability was evident, with variance of exposure error for local pollutants ranging from 0.25 to 0.83 for δspatial and δtotal. The attenuation of model coefficients in single- and bipollutant epidemiologic models relative to the true value differed across types of exposure error, pollutants, and space.
Conclusions: Under a classical exposure-error framework, attenuation may be substantial for local pollutants as a result of δspatial and δtotal with true coefficients reduced by a factor typically < 0.6 (results varied for δpopulation and regional pollutants).
Citation: Dionisio KL, Baxter LK, Chang HH. 2014. An empirical assessment of exposure measurement error and effect attenuation in bipollutant epidemiologic models. Environ Health Perspect 122:1216–1224;
PMCID: PMC4216163  PMID: 25003573
6.  Inter-Model Comparison of the Landscape Determinants of Vector-Borne Disease: Implications for Epidemiological and Entomological Risk Modeling 
PLoS ONE  2014;9(7):e103163.
Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.
PMCID: PMC4114569  PMID: 25072884
7.  Using self-organizing maps to develop ambient air quality classifications: a time series example 
Environmental Health  2014;13:56.
Development of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies.
Present a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles.
Eight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques.
Our analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (<2 day persistence), and long-term and seasonal trends. Meteorological summaries revealed strong day type weather dependencies and pollutant concentration summaries provided interesting scenarios for further investigation. Comparison with traditional methods found SOM produced similar classifications with added insight regarding between-class relationships.
We find SOM to be an attractive framework for developing ambient air quality classification because the approach eases interpretation of results by allowing users to visualize classifications on an organized map. The presented approach provides an appealing tool for developing multipollutant metrics of air quality that can be used to support multipollutant health studies.
PMCID: PMC4098670  PMID: 24990361
Air pollution; Classification; Cluster analysis; Kohonen Map
8.  Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors 
Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis, the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001–2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057–2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses—including altered phenology—of disease vectors to altered climate.
PMCID: PMC3997168  PMID: 24772388
vector-borne disease; spatially-explicit; dynamic; population model; Ixodes scapularis; climate change; temperature; population response; deer ticks
9.  Multi-TGDR, a multi-class regularization method, identifies the metabolic profiles of hepatocellular carcinoma and cirrhosis infected with hepatitis B or hepatitis C virus 
BMC Bioinformatics  2014;15:97.
Over the last decade, metabolomics has evolved into a mainstream enterprise utilized by many laboratories globally. Like other “omics” data, metabolomics data has the characteristics of a smaller sample size compared to the number of features evaluated. Thus the selection of an optimal subset of features with a supervised classifier is imperative. We extended an existing feature selection algorithm, threshold gradient descent regularization (TGDR), to handle multi-class classification of “omics” data, and proposed two such extensions referred to as multi-TGDR. Both multi-TGDR frameworks were used to analyze a metabolomics dataset that compares the metabolic profiles of hepatocellular carcinoma (HCC) infected with hepatitis B (HBV) or C virus (HCV) with that of cirrhosis induced by HBV/HCV infection; the goal was to improve early-stage diagnosis of HCC.
We applied two multi-TGDR frameworks to the HCC metabolomics data that determined TGDR thresholds either globally across classes, or locally for each class. Multi-TGDR global model selected 45 metabolites with a 0% misclassification rate (the error rate on the training data) and had a 3.82% 5-fold cross-validation (CV-5) predictive error rate. Multi-TGDR local selected 48 metabolites with a 0% misclassification rate and a 5.34% CV-5 error rate.
One important advantage of multi-TGDR local is that it allows inference for determining which feature is related specifically to the class/classes. Thus, we recommend multi-TGDR local be used because it has similar predictive performance and requires the same computing time as multi-TGDR global, but may provide class-specific inference.
PMCID: PMC4234477  PMID: 24707821
Threshold gradient descent regularization (TGDR); Multi-class classification; Metabolic profile; Hepatocellular carcinoma (HCC); Feature selection; Metabolomics; Omics data
10.  Classification and regression trees for epidemiologic research: an air pollution example 
Environmental Health  2014;13:17.
Identifying and characterizing how mixtures of exposures are associated with health endpoints is challenging. We demonstrate how classification and regression trees can be used to generate hypotheses regarding joint effects from exposure mixtures.
We illustrate the approach by investigating the joint effects of CO, NO2, O3, and PM2.5 on emergency department visits for pediatric asthma in Atlanta, Georgia. Pollutant concentrations were categorized as quartiles. Days when all pollutants were in the lowest quartile were held out as the referent group (n = 131) and the remaining 3,879 days were used to estimate the regression tree. Pollutants were parameterized as dichotomous variables representing each ordinal split of the quartiles (e.g. comparing CO quartile 1 vs. CO quartiles 2–4) and considered one at a time in a Poisson case-crossover model with control for confounding. The pollutant-split resulting in the smallest P-value was selected as the first split and the dataset was partitioned accordingly. This process repeated for each subset of the data until the P-values for the remaining splits were not below a given alpha, resulting in the formation of a “terminal node”. We used the case-crossover model to estimate the adjusted risk ratio for each terminal node compared to the referent group, as well as the likelihood ratio test for the inclusion of the terminal nodes in the final model.
The largest risk ratio corresponded to days when PM2.5 was in the highest quartile and NO2 was in the lowest two quartiles (RR: 1.10, 95% CI: 1.05, 1.16). A simultaneous Wald test for the inclusion of all terminal nodes in the model was significant, with a chi-square statistic of 34.3 (p = 0.001, with 13 degrees of freedom).
Regression trees can be used to hypothesize about joint effects of exposure mixtures and may be particularly useful in the field of air pollution epidemiology for gaining a better understanding of complex multipollutant exposures.
PMCID: PMC3977944  PMID: 24625053
Air pollution; CART; Classification and regression trees; Multipollutant; Mixtures; Pediatric asthma
11.  Estimating the Health Impact of Climate Change with Calibrated Climate Model Output 
Studies on the health impacts of climate change routinely use climate model output as future exposure projection. Uncertainty quantification, usually in the form of sensitivity analysis, has focused predominantly on the variability arise from different emission scenarios or multi-model ensembles. This paper describes a Bayesian spatial quantile regression approach to calibrate climate model output for examining to the risks of future temperature on adverse health outcomes. Specifically, we first estimate the spatial quantile process for climate model output using nonlinear monotonic regression during a historical period. The quantile process is then calibrated using the quantile functions estimated from the observed monitoring data. Our model also down-scales the gridded climate model output to the point-level for projecting future exposure over a specific geographical region. The quantile regression approach is motivated by the need to better characterize the tails of future temperature distribution where the greatest health impacts are likely to occur. We applied the methodology to calibrate temperature projections from a regional climate model for the period 2041 to 2050. Accounting for calibration uncertainty, we calculated the number of of excess deaths attributed to future temperature for three cities in the US state of Alabama.
PMCID: PMC3772546  PMID: 24039385
Bayesian spatial quantile regression; climate change; model calibration; health impacts
12.  Estimating the acute health effects of coarse particulate matter accounting for exposure measurement error 
Biostatistics (Oxford, England)  2011;12(4):637-652.
In air pollution epidemiology, there is a growing interest in estimating the health effects of coarse particulate matter (PM) with aerodynamic diameter between 2.5 and 10 μm. Coarse PM concentrations can exhibit considerable spatial heterogeneity because the particles travel shorter distances and do not remain suspended in the atmosphere for an extended period of time. In this paper, we develop a modeling approach for estimating the short-term effects of air pollution in time series analysis when the ambient concentrations vary spatially within the study region. Specifically, our approach quantifies the error in the exposure variable by characterizing, on any given day, the disagreement in ambient concentrations measured across monitoring stations. This is accomplished by viewing monitor-level measurements as error-prone repeated measurements of the unobserved population average exposure. Inference is carried out in a Bayesian framework to fully account for uncertainty in the estimation of model parameters. Finally, by using different exposure indicators, we investigate the sensitivity of the association between coarse PM and daily hospital admissions based on a recent national multisite time series analysis. Among Medicare enrollees from 59 US counties between the period 1999 and 2005, we find a consistent positive association between coarse PM and same-day admission for cardiovascular diseases.
PMCID: PMC3202305  PMID: 21297159
Air pollution; Coarse particulate matter; Exposure measurement error; Multisite time series analysis
13.  Coarse Particulate Matter Air Pollution and Hospital Admissions for Cardiovascular and Respiratory Diseases Among Medicare Patients 
Health risks of fine particulate matter of 2.5 µm or less in aerodynamic diameter (PM2.5) have been studied extensively over the last decade. Evidence concerning the health risks of the coarse fraction of greater than 2.5 µm and 10 µm or less in aerodynamic diameter (PM10-2.5) is limited.
To estimate risk of hospital admissions for cardiovascular and respiratory diseases associated with PM10-2.5 exposure, controlling for PM2.5.
Design, Setting, and Participants
Using a database assembled for 108 US counties with daily cardiovascular and respiratory disease admission rates, temperature and dew-point temperature, and PM10-2.5 and PM2.5 concentrations were calculated with monitoring data as an exposure surrogate from January 1, 1999, through December 31, 2005. Admission rates were constructed from the Medicare National Claims History Files, for a study population of approximately 12 million Medicare enrollees living on average 9 miles (14.4 km) from collocated pairs of PM10 and PM2.5 monitors.
Main Outcome Measures
Daily counts of county-wide emergency hospital admissions for primary diagnoses of cardiovascular or respiratory disease.
There were 3.7 million cardiovascular disease and 1.4 million respiratory disease admissions. A 10-µg/m3 increase in PM10-2.5 was associated with a 0.36% (95% posterior interval [PI], 0.05% to 0.68%) increase in cardiovascular disease admissions on the same day. However, when adjusted for PM2.5, the association was no longer statistically significant (0.25%; 95% PI, −0.11% to 0.60%). A 10-µg/m3 increase in PM10-2.5 was associated with a nonstatistically significant unadjusted 0.33% (95% PI, −0.21% to 0.86%) increase in respiratory disease admissions and with a 0.26% (95% PI, −0.32% to 0.84%) increase in respiratory disease admissions when adjusted for PM2.5. The unadjusted associations of PM2.5 with cardiovascular and respiratory disease admissions were 0.71% (95% PI, 0.45%–0.96%) for same-day exposure and 0.44% (95% PI, 0.06% to 0.82%) for exposure 2 days before hospital admission.
After adjustment for PM2.5, there were no statistically significant associations between coarse particulates and hospital admissions for cardiovascular and respiratory diseases.
PMCID: PMC3169813  PMID: 18477784
14.  Impact of Climate Change on Ambient Ozone Level and Mortality in Southeastern United States 
There is a growing interest in quantifying the health impacts of climate change. This paper examines the risks of future ozone levels on non-accidental mortality across 19 urban communities in Southeastern United States. We present a modeling framework that integrates data from climate model outputs, historical meteorology and ozone observations, and a health surveillance database. We first modeled present-day relationships between observed maximum daily 8-hour average ozone concentrations and meteorology measured during the year 2000. Future ozone concentrations for the period 2041 to 2050 were then projected using calibrated climate model output data from the North American Regional Climate Change Assessment Program. Daily community-level mortality counts for the period 1987 to 2000 were obtained from the National Mortality, Morbidity and Air Pollution Study. Controlling for temperature, dew-point temperature, and seasonality, relative risks associated with short-term exposure to ambient ozone during the summer months were estimated using a multi-site time series design. We estimated an increase of 0.43 ppb (95% PI: 0.14–0.75) in average ozone concentration during the 2040’s compared to 2000 due to climate change alone. This corresponds to a 0.01% increase in mortality rate and 45.2 (95% PI: 3.26–87.1) premature deaths in the study communities attributable to the increase in future ozone level.
PMCID: PMC2922733  PMID: 20717546
climate change; health impact; ground-level ozone
15.  Single nucleotide polymorphisms in obesity-related genes and all-cause and cause-specific mortality: a prospective cohort study 
BMC Medical Genetics  2009;10:103.
The aim of this study was to examine the associations between 16 specific single nucleotide polymorphisms (SNPs) in 8 obesity-related genes and overall and cause-specific mortality. We also examined the associations between the SNPs and body mass index (BMI) and change in BMI over time.
Data were analyzed from 9,919 individuals who participated in two large community-based cohort studies conducted in Washington County, Maryland in 1974 (CLUE I) and 1989 (CLUE II). DNA from blood collected in 1989 was genotyped for 16 SNPs in 8 obesity-related genes: monoamine oxidase A (MAOA), lipoprotein lipase (LPL), paraoxonase 1 and 2 (PON1 and PON2), leptin receptor (LEPR), tumor necrosis factor-α (TNFα), and peroxisome proliferative activated receptor-γ and -δ (PPARG and PPARD). Data on height and weight in 1989 (CLUE II baseline) and at age 21 were collected from participants at the time of blood collection. All participants were followed from 1989 to the date of death or the end of follow-up in 2005. Cox proportional hazards regression was used to obtain the relative risk (RR) estimates and 95% confidence intervals (CI) for each SNP and mortality outcomes.
The results showed no patterns of association for the selected SNPs and the all-cause and cause-specific mortality outcomes, although statistically significant associations (p < 0.05) were observed between PPARG rs4684847 and all-cause mortality (CC: reference; CT: RR 0.99, 95% CI 0.89, 1.11; TT: RR 0.60, 95% CI 0.39, 0.93) and cancer-related mortality (CC: reference; CT: RR 1.01, 95% CI 0.82, 1.25; TT: RR 0.22, 95% CI 0.06, 0.90) and TNFα rs1799964 and cancer-related mortality (TT: reference; CT: RR 1.23, 95% CI 1.03, 1.47; CC: RR 0.83, 95% CI 0.54, 1.28). Additional analyses showed significant associations between SNPs in LEPR with BMI (rs1137101) and change in BMI over time (rs1045895 and rs1137101).
Findings from this cohort study suggest that the selected SNPs are not associated with overall or cause-specific death, although several LEPR SNPs may be related to BMI and BMI change over time.
PMCID: PMC2763854  PMID: 19818126
16.  Female sexual function and pelvic floor disorders 
Obstetrics and gynecology  2008;111(5):1045-1052.
Sexual function is an important dimension of adult life and yet very little is known about the relationships between female sexuality and chronic health conditions, including pelvic floor disorders. Our goal was to investigate the hypothesis that pelvic floor disorders are associated with female sexual problems, independent of other related factors.
The study population included 301 adult women seeking outpatient gynecologic and urogynecologic care. Pelvic floor disorders were assessed with the Pelvic Floor Disorders Inventory-20 (PFDI) and the Pelvic Organ Prolapse Quantification examination. Sexual function was assessed with the Personal Experiences Questionnaire. Using ordinal regression analysis, we identified characteristics and conditions associated with decreased libido, infrequent orgasm, decreased arousal, and dyspareunia.
Sexual function was poorer among 78 women (26%) without a current sexual partner than among 223 with a partner (p<0.01). Among the 223 with a current partner, women with a high PFDI score were significantly more likely to report decreased arousal (p<0.01), infrequent orgasm (p<0.01) and increased dyspareunia (p<0.01). A similar pattern was observed for the urinary, colorectal-anal, and prolapse scales of the PFDI, although some associations were marginally significant. Stage III–IV prolapse was significantly associated with infrequent orgasm (p=0.02), but other sexual complaints were not more common with increasing prolapse stage.
Pelvic floor symptoms are significantly associated with reduced sexual arousal, infrequent orgasm, and dyspareunia. Clinicians who care for women with pelvic floor disorders should be aware of this association and should specifically address sexual concerns with women seeking treatment of incontinence and prolapse.
PMCID: PMC2746737  PMID: 18448734

Results 1-16 (16)