Health disparities among older adults following tropical cyclone exposure in Florida

Tropical cyclones (TCs) pose a significant threat to human health, and research is needed to identify high-risk subpopulations. We investigated whether hospitalization risks from TCs in Florida (FL), United States, varied across individuals and communities. We modeled the associations between all storms in FL from 1999 to 2016 and over 3.5 million Medicare hospitalizations for respiratory (RD) and cardiovascular disease (CVD). We estimated the relative risk (RR), comparing hospitalizations during TC-periods (2 days before to 7 days after) to matched non-TC-periods. We then separately modeled the associations in relation to individual and community characteristics. TCs were associated with elevated risk of RD hospitalizations (RR: 4.37, 95% CI: 3.08, 6.19), but not CVD (RR: 1.04, 95% CI: 0.87, 1.24). There was limited evidence of modification by individual characteristics (age, sex, or Medicaid eligibility); however, risks were elevated in communities with higher poverty or lower homeownership (for CVD hospitalizations) and in denser or more urban communities (for RD hospitalizations). More research is needed to understand the potential mechanisms and causal pathways that might account for the observed differences in the association between tropical cyclones and hospitalizations across communities.

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Implementation of Causal Inference Methods

This report provides a final summary of the principal findings and key conclusions of a study supported by an HEI grant aimed at "Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution." It is the second and final report on this topic. The study was designed to advance four critical areas of inquiry and methods development. First, it focused on predicting short- and long-term exposures to ambient fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) at high spatial resolution (1 km × 1 km) for the continental United States over the period 2000-2016 and linking these predictions to health data. Second, it developed new causal inference methods for estimating exposure-response (ER) curves (ERCs) and adjusting for measured confounders. Third, it applied these methods to claims data from Medicare and Medicaid beneficiaries to estimate health effects associated with short- and long-term exposure to low levels of ambient air pollution. Finally, it developed pipelines for reproducible research, including approaches for data sharing, record linkage, and statistical software. Our HEI-funded work has supported an extensive portfolio of analyses and the development of statistical methods that can be used to robustly understand the health effects of short- and long-term exposure to low levels of ambient air pollution. Our Phase 1 report (Dominici et al. 2019) provided a high-level overview of our statistical methods, data analysis, and key findings, grouped into the following five areas: (1) exposure prediction, (2) epidemiological studies of ambient exposures to air pollution at low levels, (3) sensitivity analysis, (4) methodological contributions in causal inference, and (5) an open access research data platform. The current, final report includes a comprehensive overview of the entire research project.

Considering our (1) massive study population, (2) numerous sensitivity analyses, and (3) transparent assessment of covariate balance indicating the quality of causal inference for simulating randomized experiments, we conclude that conditionally on the required assumptions for causal inference, our results collectively indicate that long-term PM2.5 exposure is likely to be causally related to mortality. This conclusion assumes that the causal inference assumptions hold and, more specifically, that we accounted adequately for confounding bias. We explored various modeling approaches, conducted extensive sensitivity analyses, and found that our results were robust across approaches and models. This work relied on publicly available data, and we have provided code that allows for reproducibility of our analyses.

Our work provides comprehensive evidence of associations between exposures to PM2.5, NO2, and O3 and various health outcomes. In the current report, we report more specific results on the causal link between long-term exposure to PM2.5 and mortality, even at PM2.5 levels below or equal to 12 μg/m3, and mortality among Medicare beneficiaries (ages 65 and older). This work relies on newly developed causal inference methods for continuous exposure.

For the period 2000-2016, we found that all statistical approaches led to consistent results: a 10-μg/m3 decrease in PM2.5 led to a statistically significant decrease in mortality rate ranging between 6% and 7% (= 1 - 1/hazard ratio [HR]) (HR estimates 1.06 [95% CI, 1.05 to 1.08] to 1.08 [95% CI, 1.07 to 1.09]). The estimated HRs were larger when studying the cohort of Medicare beneficiaries that were always exposed to PM2.5 levels lower than 12 μg/m3 (1.23 [95% CI, 1.18 to 1.28] to 1.37 [95% CI, 1.34 to 1.40]).

Comparing the results from multiple and single pollutant models, we found that adjusting for the other two pollutants slightly attenuated the causal effects of PM2.5 and slightly elevated the causal effects of NO2 exposure on all-cause mortality. The results for O3 remained almost unchanged.

We found evidence of a harmful causal relationship between mortality and long-term PM2.5 exposures adjusted for NO2 and O3 across the range of annual averages between 2.77 and 17.16 μg/m3 (included >98% of observations) in the entire cohort of Medicare beneficiaries across the continental United States from 2000 to 2016. Our results are consistent with recent epidemiological studies reporting a strong association between long-term exposure to PM2.5 and adverse health outcomes at low exposure levels. Importantly, the curve was almost linear at exposure levels lower than the current national standards, indicating aggravated harmful effects at exposure levels even below these standards.

There is, in general, a harmful causal impact of long-term NO2 exposures to mortality adjusted for PM2.5 and O3 across the range of annual averages between 3.4 and 80 ppb (included >98% of observations). Yet within low levels (annual mean ≤53 ppb) below the current national standards, the causal impacts of NO2 exposures on all-cause mortality are nonlinear with statistical uncertainty.

The ERCs of long-term O3 exposures on all-cause mortality adjusted for PM2.5 and NO2 are almost flat below 45 ppb, which shows no statistically significant effect. Yet we observed an increased hazard when the O3 exposures were higher than 45 ppb, and the HR was approximately 1.10 when comparing Medicare beneficiaries with annual mean O3 exposures of 50 ppb versus those with 30 ppb.

institutions, including those that support the Health Effects Institute; therefore, it may not reflect the views or policies of these parties, and no endorsement by them should be inferred.

A list of abbreviations and other terms appears at the end of this volume.

Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis

Assessing whether long-term exposure to air pollution increases the severity of COVID-19 health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcome data for representative populations are available only as area-level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United States, where we found that higher historical PM_2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the challenges, and outline promising directions and opportunities.

Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis

Assessing whether long-term exposure to air pollution increases the severity of COVID-19 health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcome data for representative populations are available only as area-level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United States, where we found that higher historical PM_2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the challenges, and outline promising directions and opportunities.

Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis

Assessing whether long-term exposure to air pollution increases the severity of COVID-19 health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcome data for representative populations are available only as area-level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United States, where we found that higher historical PM_2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the challenges, and outline promising directions and opportunities.

Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis

Assessing whether long-term exposure to air pollution increases the severity of COVID-19 health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcome data for representative populations are available only as area-level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United States, where we found that higher historical PM2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the challenges, and outline promising directions and opportunities.

Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly

Many studies link long-term fine particle (PM2.5) exposure to mortality, even at levels below current U.S. air quality standards (12 micrograms per cubic meter). These findings have been disputed with claims that the use of traditional statistical approaches does not guarantee causality. Leveraging 16 years of data-68.5 million Medicare enrollees-we provide strong evidence of the causal link between long-term PM2.5 exposure and mortality under a set of causal inference assumptions. Using five distinct approaches, we found that a decrease in PM2.5 (by 10 micrograms per cubic meter) leads to a statistically significant 6 to 7% decrease in mortality risk. Based on these models, lowering the air quality standard to 10 micrograms per cubic meter would save 143,257 lives (95% confidence interval, 115,581 to 170,645) in one decade. Our study provides the most comprehensive evidence to date of the link between long-term PM2.5 exposure and mortality, even at levels below current standards.

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Phase 1

INTRODUCTION

This report provides a summary of major findings and key conclusions supported by a Health Effects Institute grant aimed at "Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Pollution." Our study was designed to advance four critical areas of inquiry and methods development.

METHODS

First, our work focused on predicting short- and long-term exposures to ambient PM2.5 mass (particulate matter ≤ 2.5μm in aerodynamic diameter) and ozone (O3) at high spatial resolution (1 km × 1 km) for the continental United States during the period 2000-2012 and linking these predictions to health data. Second, we developed new causal inference methods for exposure-response (ER) that account for exposure error and adjust for measured confounders. We applied these methods to data from the New England region. Third, we applied standard regression methods using Medicare claims data to estimate health effects that are associated with short- and long-term exposure to low levels of ambient air pollution. We conducted sensitivity analyses to assess potential confounding bias due to lack of extensive information on behavioral risk factors in the Medicare population using the Medicare Current Beneficiary Survey (MCBS) (nationally representative sample of approximately 15,000 Medicare enrollees per year), which includes abundant data on individual-level risk factors including smoking. Finally, we have begun developing tools for reproducible research - including approaches for data sharing, record linkage, and statistical software.

RESULTS

Our HEI-funded work has supported an extensive portfolio of analysis and the development of statistical methods that can be used to robustly understand the health effects of long- and short-term exposure to low levels of ambient air pollution. This report provides a high-level overview of statistical methods, data analysis, and key findings, as grouped into the following four areas: (1) Exposure assessment and data access; (2) Epidemiological studies of ambient exposures to air pollution at low levels; (3) Methodological contributions in causal inference; and (4) Open science research data platform.

CONCLUSION

Our body of work, advanced by HEI, lends extensive evidence that short- and long-term exposure to PM2.5 and O3 is harmful to human health, increasing the risks of hospitalization and death, even at levels that are well below the National Ambient Air Quality Standards (NAAQS).

Multifunctional PLA-PHB/cellulose nanocrystal films: processing, structural and thermal properties

Cellulose nanocrystals (CNCs) synthesized from microcrystalline cellulose by acid hydrolysis were added into poly(lactic acid)-poly(hydroxybutyrate) (PLA-PHB) blends to improve the final properties of the multifunctional systems. CNC were also modified with a surfactant (CNCs) to increase the interfacial adhesion in the systems maintaining the thermal stability. Firstly, masterbatch pellets were obtained for each formulation to improve the dispersion of the cellulose structures in the PLA-PHB and then nanocomposite films were processed. The thermal stability as well as the morphological and structural properties of nanocomposites was investigated. While PHB increased the PLA crystallinity due to its nucleation effect, well dispersed CNC and CNCs not only increased the crystallinity but also improved the processability, the thermal stability and the interaction between both polymers especially in the case of the modified CNCs based PLA-PHB formulation. Likewise, CNCs were better dispersed in PLA-CNCs and PLA-PHB-CNCs, than CNC.

A preliminary comparison of the effect of 0.3% versus 0.2% chlorhexidine mouth rinse on de novo plaque formation: a monocentre randomized double-blind crossover trial

OBJECTIVE

Chlorhexidine (CHX) is considered the gold standard against gram-negative microorganisms. Little has been written about the simultaneous influence that both time and concentration could have on antiplaque formation effectiveness of CHX. The aim of this study is to compare the clinical and microbiological effectiveness of two different CHX concentrations and time applications in a 4-day plaque regrowth study model.

MATERIAL AND METHODS

Twenty volunteers were enrolled in a randomized double-blind crossover study comparing the effectiveness of CHX 0.3% and CHX 0.2% mouth rinses applied for 15 and 30 s, respectively. Plaque index (PII), total bacterial counts and the detection of specific periopathogens were measured at the 5th day of each mouth rinse application. Taste acceptance was evaluated using a questionnaire.

RESULTS

Chlorhexidine 0.3% resulted in a statistically greater reductions (10%) in PIl and periopathogens compared to CHX 0.2%. Furthermore, patients reported comparable taste acceptance in both groups.

CONCLUSION

Chlorhexidine is an effective oral antiseptic. The CHX 0.3% mouth rinse formulation used for 15 s resulted in superior clinical and microbiological outcomes compared to the CHX 0.2% formulation, used for 30 s.

Morphological observations and Fractological considerations on orthodontics miniscrews

AIM

The purpose of this study was to evaluate morphological and biomechanical characteristics of a commercial mini-implant.

METHODS

Orthodontic low head mini implants, in the two available sizes of 1.5 and 2 mm, have been used. The electronic scanning microscope evaluation has been tested on 3 groups each one composed of 4 samples, observed in numerous enlargements and in the three dimensions of the space. The groups were composed of new mini implants, undamaged mini implants observed after their clinical use and mini-implants broken at the moment of their insertion or removal after their therapeutical use.

RESULTS

The microscope analysis of unused mini implants has shown how the diameter reduction of the neck represents the area of greater weakness; such parameter is very important in determining screw resistance to fracture. In fact the fracture observed in vivo always happened to this degree. Some micro carvings and cracks were shown on the surface, these irregularities could facilitate or promote the enlargement of the cracking itself.

CONCLUSION

From the results obtained it is clear that the steel is adequate for permanence in the oral cavity, and moreover the material itself is adapted to bear the pressure put on it during therapy. However, it remains advisable to make certain changes to better the surface and geometry of the miniscrews.

Multiple arm lipomatosis and posterior interosseus nerve palsy

BACKGROUND

Lipomas are common benign soft tissue tumours which tend to be indolent and risk free. Lipomas rarely spread in the deep soft tissue causing posterior interosseous nerve (PIN) neuropathy.

METHODS (CASE DESCRIPTION)

We present two patients with multiple lipomatosis of the arms and PIN paralysis, with a brief review of the cases reported in literature.

RESULTS AND CONCLUSION

We emphasize the role of electromyographic study as unique methodical capable to reveal an early radial nerve damage, permitting an optimal post-surgical nerve function recovering.

Outcomes of kidneys from donors after cardiac death: implications for allocation and preservation

Although donation after cardiac death (DCD) kidneys have a high incidence of delayed graft function (DGF) and have been considered marginal, no tool for stratifying risk of graft loss nor a specific policy governing their allocation exist. We compared outcomes of 2562 DCD, 62,800 standard criteria donor (SCD) and 12,812 expanded criteria donor (ECD) transplants reported between 1993 and 2005, and evaluated factors associated with risk of graft loss and DGF in DCD kidneys. Donor age was the only criterion used in the definition of ECD kidneys that independently predicted graft loss among DCD kidneys. Kidneys from DCD donors <50 had similar long-term graft survival to those from SCD (RR 1.1, p = NS). While DGF was higher among DCD compared to SCD and ECD, limiting cold ischemia (CIT) to <12 h decreased the rate of DGF 15% among DCD <50 kidneys. These findings suggest that DCD <50 kidneys function like SCD kidneys and should not be viewed as marginal or ECD, and further, limiting CIT <12 h markedly reduces DGF.

Evidence of altered motor axon properties of the ulnar nerve in carpal tunnel syndrome

OBJECTIVE

To analyse recruitment properties of ulnar nerve motor axons in 60 CTS patients with negative ulnar nerve electrodiagnostic tests.

METHODS

Recruitment properties of the ulnar nerve were studied by analysing the relationship between the intensity of electrical stimulation and the size of motor response, i.e. the stimulus-response curve. Parameters of the curve (threshold, slope and plateau) were compared with those of the corresponding curve of the median nerve and both with parameters of 30 control curves.

RESULTS

The ulnar nerve stimulus-response curve was strikingly abnormal and, except for severity, closely resembled that of the median nerve. The slope of the curve was significantly less than that of controls and decreased with increasing abnormalities of the median nerve. This suggested that the pathological process involving the ulnar nerve was contingent with the severity of median nerve involvement.

CONCLUSIONS

We propose that the ulnar nerve may be subject to compression in Guyon's canal as a consequence of high pressure in the carpal tunnel of CTS patients.

SIGNIFICANCE

Ectopic activity from ulnar axons may contribute to clinical spread of symptoms outside the median nerve territory in CTS. This does not exclude possible involvement of central plasticity mechanisms in producing extra-median symptoms in CTS patients.

[Smoke habit in a population of apprentices in middle of Italy]

This study assessed the smoking habit in 7,159 apprentices (females: 53.3%) in central Italy. In the period 1996-2006, each apprentice underwent a compulsory health examination to assess fitness for work carried out by specialists in Occupational Medicine. Demographic data and information on schooling, smoking habit, alcohol and coffee intake were collected by a questionnaire. Overall, 51.9% of apprentices were smokers. Smokers were fewer among female apprentices than among the males (48.3% vs 55.9%; chi-square test, p < 0.001). No decrease in the prevalence of smokers was evident during the study period. Low educational level, and daily intake of alcohol and coffee significantly increased the risk of being a smoker. Finding a high percentage of smokers among Italian apprentices with a low educational level who are employed in jobs for which few qualifications are required indicates the need for work-place activities against tobacco smoking habit. Specialists in Occupational Medicine who perform regular health surveillance of workers could play a role in the anti-smoking campaign by integrating their routine work with brief counselling sessions aimed at preventing smoking in young workers and helping smokers to stop and by collaborating in work-place health promotion programmes.

Changes in the recruitment curve of the soleus H-reflex associated with chronic low back pain

OBJECTIVE

We investigated whether patients with chronic low back pain (CLBP) manifest changes in the excitability of the soleus H-reflex.

METHODS

H-reflex stimulus-response curve was studied in 14 CLBP patients and 14 age-matched healthy subjects. H-threshold, H-maximum size, H-steepness and H-latency were determined for both legs. Homosynaptic depression (HD), following a train of H-reflexes, and presynaptic inhibition (PI) from flexor afferents onto soleus Ia afferents were also evaluated.

RESULTS

H-threshold was significantly increased, H-size as a function of stimulus intensity was significantly different, and H-recruitment curve steepness was significantly lower in CLBP patients compared to healthy subjects. No significant difference in the amount of HD and PI of the H-reflex was found between the two groups. H-latency and Hmax/Mmax ratio was comparable between the subjects groups.

CONCLUSIONS

In CLBP there is a reduced excitability of group Ia afferent fibres from the soleus muscle to which presynaptic factors do not seem to contribute and that presumably depend on changes in the peripheral sensory input.

SIGNIFICANCE

Changes in H-reflex excitability may underlie a decrease in the gain of a peripheral sensor in CLBP. Estimation of soleus H-threshold and H-recruitment curve may contribute to the diagnostic evaluation of CLBP and may be used to monitor the efficacy of treatment.

Changes in motor axon recruitment in the median nerve in mild carpal tunnel syndrome

OBJECTIVE

To determine whether patients with mild carpal tunnel syndrome (CTS) and conventional electrodiagnostic evidence of selective involvement of sensory conduction show changes in motor axon recruitment in the median nerve.

METHODS

Wrist-to-abductor pollicis brevis (APB) motor axon conduction was studied by analysing the relationship between the intensity of electrical stimulation and the size of motor response (input-output curve) in 30 CTS patients with conventional electrodiagnostic evidence of selective involvement of sensory conduction. Parameters (threshold, slope and plateau) of input-output curves were compared with those obtained in 30 controls.

RESULTS

The slope of the input-output curve of CTS patients was less steep than that of controls. For stimulus intensity above M-wave threshold (MTh), fewer motor axons were recruited in patients than controls.

CONCLUSIONS

Motor fibres are affected in CTS when conventional electrodiagnostic tests show normal motor conduction. Altered recruitment of motor axons could mainly be due to impairment of energy-dependent processes which affect temporal dispersion of the compound volley or axonal conduction block.

SIGNIFICANCE

In mild CTS, motor fibres are more often affected than was originally thought. The sensitivity of wrist-to-APB motor conduction studies may be increased by using submaximal stimulus intensities.

A case-crossover study of fine particulate matter air pollution and onset of congestive heart failure symptom exacerbation leading to hospitalization

Persons with congestive heart failure may be susceptible to ambient air pollution. The authors evaluated the association between exposure to particulate matter with an aerodynamic diameter of <2.5 microm (PM2.5) and onset of symptom exacerbation leading to hospital admission in Baltimore, Maryland. They used a case-crossover design for 135 case events occurring among 125 persons with prevalent congestive heart failure who were admitted to a single hospital through the emergency department during 2002. The case period was assigned using three index times: 8-hour and 24-hour periods of symptom onset and date of hospital admission. Controlling for weather, the authors detected a modest relative increase in risk for cases defined by 8-hour symptom onset for an interquartile-range increase in PM2.5 at a 2-day lag (odds ratio=1.09, 95% confidence interval: 0.91, 1.30). A corresponding increase in risk was not observed when admission date was used to define the case period. A series of simulations based on study data indicated that the study had adequate statistical power to detect odds ratios of 1.2 or higher. Although overall findings were not statistically significant, the identification of case events defined by an 8-hour onset period may be more relevant than either a 24-hour onset period or the admission date for estimating harmful effects of air pollutant exposure on cardiovascular health.

Fetal growth in Peru: comparisons with international fetal size charts and implications for fetal growth assessment

OBJECTIVES

To compare fetal biometry measurements obtained in a Peruvian population with reference fetal size charts obtained in Peruvian and non-Peruvian populations.

METHODS

Fetal biometry measurements collected prospectively in 195 uncomplicated pregnancies were included in the presented analysis. At 20, 24, 28, 32, 36 and 38 weeks' gestation, fetal head circumference, abdominal circumference and femur diaphysis length were measured. Fetal biometry measurements were compared with fetal size charts obtained from another Peruvian and two non-Peruvian populations from North America and Europe.

RESULTS

When compared with ultrasound-based reference fetal size charts obtained from North American and European populations, fetuses from the studied population appeared to grow more slowly with advancing gestational age. This trend was not observed when a Peruvian population, similar to the one studied here, was used as a reference.

CONCLUSIONS

The results suggest that fetal growth in this Peruvian population may not be adequately assessed by using reference charts obtained from other populations and have implications for the use of growth standards in antenatal management.

Cortico-motoneuronal output to intrinsic hand muscles is differentially influenced by static changes in shoulder positions

We investigated whether shoulder position influenced the recruitment properties of the abductor digiti minimi muscle (ADM) and first dorsal interosseous muscle (FDI). ADM and FDI motor evoked potentials (MEPs) in response to transcranial magnetic stimulation (TMS) were obtained in seven healthy volunteers at two different static positions of the shoulder joint (30 degrees adduction vs 30 degrees abduction) while the arm was passively supported at shoulder level (90 degrees in the horizontal plane) and the elbow joint was fixed at 90 degrees . ADM and FDI voluntary activity was also examined during (1) externally paced finger abductions at 2 Hz in the two different shoulder positions (EMG(ADM) and EMG(FDI) was back-averaged time-locked to the end of finger abduction) and (2) maximal voluntary abduction of the little finger and the index finger. Maximal EMG power and force were analysed in the two shoulder positions. H-reflexes from ADM and FDI were also obtained in two subjects. The ADM stimulus-response curve to TMS showed that the slope and plateau level were significantly reduced with the shoulder at 30 degrees abduction. In contrast, the FDI stimulus-response curve to TMS was not influenced by shoulder position. The back-averaged EMG(ADM) showed a significant decrease in peak amplitude and area with the shoulder at 30 degrees abduction, while no change in EMG(FDI) was observed under the same condition. Similarly, maximal EMG(ADM) and force exertion by the little finger were significantly reduced with the shoulder at 30 degrees abduction, while no such effect was observed for FDI. ADM H-reflex, but not FDI, was also decreased with shoulder abduction. These results indicate that the corticospinal pathway to ADM is less accessible to TMS and to voluntary command when the shoulder is placed at 30 degrees abduction. In contrast, activation of FDI, whether by TMS or by volition, is not influenced by shoulder position. This finding suggests that there are differences in the corticospinal innervation to ADM and FDI, possibly due to the different role of these muscles in hand function.

Changes in corticomotor excitability of hand muscles in relation to static shoulder positions

We examined whether the recruitment properties of the corticospinal pathway to intrinsic hand muscles are influenced by variations of the shoulder joint angle. Abductor digiti minimi (ADM) motor evoked potentials (MEPs) in response to transcranial magnetic stimulation were examined during different static positions of the shoulder joint in the horizontal plane from 30 degrees adduction to 30 degrees abduction with respect to the neutral position at 0 degrees, while elbow and wrist joints were constrained statically at 90 degrees and 180 degrees respectively. We found that 30 degrees abduction of the shoulder significantly depressed MEP size and prolonged MEP latency in comparison with 30 degrees shoulder adduction. The neutral shoulder angle position (at 0 degrees ) significantly reduced MEP size but had no effect on MEP latency in comparison with 30 degrees shoulder abduction. The input-output relationship between MEP size and stimulus intensity was sigmoidal. The plateau value and maximum slope were significantly lower at 30 degrees abduction than at 30 degrees adduction of the shoulder. However, the threshold value did not differ significantly between the two positions. To differentiate excitability changes at cortical versus subcortical sites, intracortical inhibition (ICI) and intracortical facilitation (ICF) were assessed using a paired-magnetic pulse paradigm. A significant decrease in ICF was observed after changing shoulder position from 30 degrees adduction to 30 degrees abduction. In contrast, no variation in the amount of ICI occurred in relation to the same changes in shoulder position. ADM F-waves elicited by electrical stimulation of the ulnar nerve at the wrist were significantly decreased at 30 degrees shoulder abduction in comparison with 30 degrees adduction. A similar pattern was observed in one subject in whom the H-reflex could be exceptionally elicited in ADM. We conclude that shoulder position influences the recruitment efficiency (gain) of the corticospinal volleys to motoneurons of intrinsic hand muscles. It is proposed that activity of peripheral receptors signalling static shoulder position influences corticomotor excitability of hand muscles both at the cortical and at the spinal level. This modulation may be functionally relevant when reaching to grasp objects.

Insulin-like growth factor 1 (IGF-1) and aging: controversies and new insights

Insulin/insulin-like growth factor (IGF) signaling plays a major role in the control of aging and life span in invertebrates. Major extension of life span in growth hormone receptor knock out (GHR-KO) mice that are GH resistant, and subsequently, IGF-I-deficient indicates that similar mechanisms may operate in mammals. This conclusion is supported by association of reduced IGF-I levels and delayed aging in three different GH-deficient mutant mice and in animals subjected to caloric restriction, but is difficult to reconcile with neuroprotective effects of IGF-I and with the suspected role of declining GH levels during aging. We suggest that the role of IGF in the regulation of growth and adult body size is important in mediating the effects of longevity genes on aging and life span. Suspected mechanisms of IGF-I action in aging also include reduced insulin signaling, enhanced sensitivity to insulin, and reduced thermogenesis with diminished oxidative damage of macromolecules being the likely final common pathway of these effects. We suspect that IGF-I is important in evolutionarily conserved mechanisms that link life history, including development, reproduction, and aging with availability of energy resources.

Bayesian semiparametric analysis of developmental toxicology data

Modeling of developmental toxicity studies often requires simple parametric analyses of the dose-response relationship between exposure and probability of a birth defect but poses challenges because of nonstandard distributions of birth defects for a fixed level of exposure. This article is motivated by two such experiments in which the distribution of the outcome variable is challenging to both the standard logistic model with binomial response and its parametric multistage elaborations. We approach our analysis using a Bayesian semiparametric model that we tailored specifically to developmental toxicology studies. It combines parametric dose-response relationships with a flexible nonparametric specification of the distribution of the response, obtained via a product of Dirichlet process mixtures approach (PDPM). Our formulation achieves three goals: (1) the distribution of the response is modeled in a general way, (2) the degree to which the distribution of the response adapts nonparametrically to the observations is driven by the data, and (3) the marginal posterior distribution of the parameters of interest is available in closed form. The logistic regression model, as well as many of its extensions such as the beta-binomial model and finite mixture models, are special cases. In the context of the two motivating examples and a simulated example, we provide model comparisons, illustrate overdispersion diagnostics that can assist model specification, show how to derive posterior distributions of the effective dose parameters and predictive distributions of response, and discuss the sensitivity of the results to the choice of the prior distribution.

Fine particulate air pollution and mortality in 20 U.S. cities, 1987-1994

BACKGROUND

Air pollution in cities has been linked to increased rates of mortality and morbidity in developed and developing countries. Although these findings have helped lead to a tightening of air-quality standards, their validity with respect to public health has been questioned.

METHODS

We assessed the effects of five major outdoor-air pollutants on daily mortality rates in 20 of the largest cities and metropolitan areas in the United States from 1987 to 1994. The pollutants were particulate matter that is less than 10 microm in aerodynamic diameter (PM10), ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide. We used a two-stage analytic approach that pooled data from multiple locations.

RESULTS

After taking into account potential confounding by other pollutants, we found consistent evidence that the level of PM10 is associated with the rate of death from all causes and from cardiovascular and respiratory illnesses. The estimated increase in the relative rate of death from all causes was 0.51 percent (95 percent posterior interval, 0.07 to 0.93 percent) for each increase in the PM10 level of 10 microg per cubic meter. The estimated increase in the relative rate of death from cardiovascular and respiratory causes was 0.68 percent (95 percent posterior interval, 0.20 to 1.16 percent) for each increase in the PM10 level of 10 microg per cubic meter. There was weaker evidence that increases in ozone levels increased the relative rates of death during the summer, when ozone levels are highest, but not during the winter. Levels of the other pollutants were not significantly related to the mortality rate.

CONCLUSIONS

There is consistent evidence that the levels of fine particulate matter in the air are associated with the risk of death from all causes and from cardiovascular and respiratory illnesses. These findings strengthen the rationale for controlling the levels of respirable particles in outdoor air.

Estimating particulate matter-mortality dose-response curves and threshold levels: an analysis of daily time-series for the 20 largest US cities

Numerous studies have shown a positive association between daily mortality and particulate air pollution, even at concentrations below regulatory limits. These findings have motivated interest in the shape of the exposure-response relation. The authors have developed flexible modeling strategies for time-series data that include spline and threshold exposure-response models; they apply these models to daily time-series data for the 20 largest US cities for 1987-1994, using the concentration of particulate matter <10 microm in aerodynamic diameter (PM10) as the exposure measure. The spline model showed a linear relation without indication of threshold for PM10 and relative risk of death for all causes and cardiorespiratory causes; by contrast, for other causes, the risk did not increase until approximately 50 microg/m3 PM10. For all-cause mortality, a linear model without threshold was preferred to the threshold model and to the spline model, using the Akaike information criterion (AIC). The findings were similar for cardiovascular and respiratory deaths combined. By contrast, for causes other than cardiovascular and respiratory, a threshold model was more competitive with a threshold value estimated at 65 microg/m3. These findings indicate that linear models without a threshold are appropriate for assessing the effect of particulate air pollution on daily mortality even at current levels.

The National Morbidity, Mortality, and Air Pollution Study. Part II: Morbidity and mortality from air pollution in the United States

BACKGROUND

Epidemiologic time-series studies conducted in a number of cities have identified, in general, an association between daily changes in concentration of ambient particulate matter (PM) and daily number of deaths (mortality). Increased hospitalization (a measure of morbidity) among the elderly for specific causes has also been associated with PM. These studies have raised concerns about public health effects of particulate air pollution and have contributed to regulatory decisions in the United States. However, scientists have pointed out uncertainties that raise questions about the interpretation of these studies. One limitation to previous time-series studies of PM and adverse health effects is that the evidence for an association is derived from studies conducted in single locations using diverse analytic methods. Statistical procedures have been used to combine the results of these single location studies in order to produce a summary estimate of the health effects of PM. Difficulties with this approach include the process by which cities were selected to be studied, the different analytic methods applied to each single study, and the variety of methods used to measure or account for variables included in the analysis. These individual studies were also not able to account for the effects of gaseous air pollutants in a systematic manner.

The National Morbidity, Mortality, and Air Pollution Study. Part I: Methods and methodologic issues

The Health Effects Institute, established in 1980, is an independent and unbiased source of information on the health effects of motor vehicle emissions. HEI supports research on all major pollutants, including regulated pollutants (such as carbon monoxide, ozone, nitrogen dioxide, and particulate matter) and unregulated pollutants (such as diesel engine exhaust, methanol, and aldehydes). To date, HEI has supported more than 200 projects at institutions in North America and Europe and has published over 100 research reports. Typically, HEI receives half its funds from the US Environmental Protection Agency and half from 28 manufacturers and marketers of motor vehicles and engines in the US. Occasionally, funds from other public and private organizations either support special projects or provide resources for a portion of an HEI study. Regardless of funding sources, HEI exercises complete autonomy in setting its research priorities and in reaching its conclusions. An independent Board of Directors governs HEI. The Institute's Research and Review Committees serve complementary scientific purposes and draw distinguished scientists as members. The results of HEI-funded studies are made available as Research Reports, which contain both the Investigators' Report and the Review Committee's evaluation of the work's scientific quality and regulatory relevance.

Combining contingency tables with missing dimensions

We propose a methodology for estimating the cell probabilities in a multiway contingency table by combining partial information from a number of studies when not all of the variables are recorded in all studies. We jointly model the full set of categorical variables recorded in at least one of the studies, and we treat the variables that are not reported as missing dimensions of the study-specific contingency table. For example, we might be interested in combining several cohort studies in which the incidence in the exposed and nonexposed groups is not reported for all risk factors in all studies while the overall numbers of cases and cohort size is always available. To account for study-to-study variability, we adopt a Bayesian hierarchical model. At the first stage of the model, the observation stage, data are modeled by a multinomial distribution with fixed total number of observations. At the second stage, we use the logistic normal (LN) distribution to model variability in the study-specific cells' probabilities. Using this model and data augmentation techniques, we reconstruct the contingency table for each study regardless of which dimensions are missing, and we estimate population parameters of interest. Our hierarchical procedure borrows strength from all the studies and accounts for correlations among the cells' probabilities. The main difficulty in combining studies recording different variables is in maintaining a consistent interpretation of parameters across studies. The approach proposed here overcomes this difficulty and at the same time addresses the uncertainty arising from the missing dimensions. We apply our modeling strategy to analyze data on air pollution and mortality from 1987 to 1994 for six U.S. cities by combining six cross-classifications of low, medium, and high levels of mortality counts, particulate matter, ozone, and carbon monoxide with the complication that four of the six cities do not report all the air pollution variables. Our goals are to investigate the association between air pollution and mortality by reconstructing the tables with missing dimensions, to determine the most harmful pollutant combinations, and to make predictions about these key issues for a city other than the six sampled. We find that, for high levels of ozone and carbon monoxide, the number of cases with a high number of deaths increases as the levels of particulate matter, PM10, increases and that the most harmful combinations corresponds to high levels of PM10, confirming prior findings that levels of PM10 higher than the NAAQS standard are harmful.

A measurement error model for time-series studies of air pollution and mortality

One barrier to interpreting the observational evidence concerning the adverse health effects of air pollution for public policy purposes is the measurement error inherent in estimates of exposure based on ambient pollutant monitors. Exposure assessment studies have shown that data from monitors at central sites may not adequately represent personal exposure. Thus, the exposure error resulting from using centrally measured data as a surrogate for personal exposure can potentially lead to a bias in estimates of the health effects of air pollution. This paper develops a multi-stage Poisson regression model for evaluating the effects of exposure measurement error on estimates of effects of particulate air pollution on mortality in time-series studies. To implement the model, we have used five validation data sets on personal exposure to PM10. Our goal is to combine data on the associations between ambient concentrations of particulate matter and mortality for a specific location, with the validation data on the association between ambient and personal concentrations of particulate matter at the locations where data have been collected. We use these data in a model to estimate the relative risk of mortality associated with estimated personal-exposure concentrations and make a comparison with the risk of mortality estimated with measurements of ambient concentration alone. We apply this method to data comprising daily mortality counts, ambient concentrations of PM10measured at a central site, and temperature for Baltimore, Maryland from 1987 to 1994. We have selected our home city of Baltimore to illustrate the method; the measurement error correction model is general and can be applied to other appropriate locations.Our approach uses a combination of: (1) a generalized additive model with log link and Poisson error for the mortality-personal-exposure association; (2) a multi-stage linear model to estimate the variability across the five validation data sets in the personal-ambient-exposure association; (3) data augmentation methods to address the uncertainty resulting from the missing personal exposure time series in Baltimore. In the Poisson regression model, we account for smooth seasonal and annual trends in mortality using smoothing splines. Taking into account the heterogeneity across locations in the personal-ambient-exposure relationship, we quantify the degree to which the exposure measurement error biases the results toward the null hypothesis of no effect, and estimate the loss of precision in the estimated health effects due to indirectly estimating personal exposures from ambient measurements.