Monir Hossain

Type 2-diabetes is associated with elevated levels of TNF-alpha, IL-6 and adiponectin and low levels of leptin in a population of Mexican Americans: A cross-sectional study

The goal of the study was to determine the association between diabetes and inflammation in clinically diagnosed diabetes patients. We hypothesized that low-grade inflammation in diabetes is associated with the level of glucose control. Using a cross-sectional design we compared pro- and anti-inflammatory cytokines in a community-recruited cohort of 367 Mexican Americans with type 2-diabetes having a wide range of blood glucose levels. Cytokines (IL-6, TNF-α, IL-1β, IL-8) and adipokines (adiponectin, resistin and leptin) were measured using multiplex ELISA. Our data indicated that diabetes as whole was strongly associated with elevated levels of IL-6, leptin, CRP and TNF-α, whereas worsening of glucose control was positively and linearly associated with high levels of IL-6, and leptin. The associations remained statistically significant even after controlling for BMI and age (p=0.01). The association between TNF-α, however, was attenuated when comparisons were performed based on glucose control. Strong interaction effects between age and diabetes and BMI and diabetes were observed for IL-8, resistin and CRP. The cytokine/adipokine profiles of Mexican Americans with diabetes suggest an association between low-grade inflammation and quality of glucose control. Unique to in our population is that the chronic inflammation is accompanied by lower levels of leptin.

Using hospitalization for ambulatory care sensitive conditions to measure access to primary health care: an application of spatial structural equation modeling

BackgroundIn data commonly used for health services research, a number of relevant variables are unobservable. These include population lifestyle and socio-economic status, physician practice behaviors, population tendency to use health care resources, and disease prevalence. These variables may be considered latent constructs of many observed variables. Using health care data from South Carolina, we show an application of spatial structural equation modeling to identify how these latent constructs are associated with access to primary health care, as measured by hospitalizations for ambulatory care sensitive conditions. We applied the confirmatory factor analysis approach, using the Bayesian paradigm, to identify the spatial distribution of these latent factors. We then applied cluster detection tools to identify counties that have a higher probability of hospitalization for each of the twelve adult ambulatory care sensitive conditions, using a multivariate approach that incorporated the correlation structure among the ambulatory care sensitive conditions into the model.ResultsFor the South Carolina population ages 18 and over, we found that counties with high rates of emergency department visits also had less access to primary health care. We also observed that in those counties there are no community health centers.ConclusionLocating such clusters will be useful to health services researchers and health policy makers; doing so enables targeted policy interventions to efficiently improve access to primary care.

Design of a prospective, dose-escalation study evaluating the Safety of Pioglitazone for Hematoma Resolution in Intracerebral Hemorrhage (SHRINC).

Rationale Preclinical work demonstrates that the transcription factor peroxisome proliferator-activated receptor gamma plays an important role in augmenting phagocytosis while modulating oxidative stress and inflammation. We propose that targeted stimulation of phagocytosis to promote efficient removal of the hematoma without harming surrounding brain cells may be a therapeutic option for intracerebral hemorrhage. Aims The primary objective is to assess the safety of the peroxisome proliferator-activated receptor gamma agonist, pioglitazone, in increasing doses for three-days followed by a maintenance dose, when administered to patients with spontaneous intracerebral hemorrhage within 24 h of symptom onset compared with standard care. We will determine the maximum tolerated dose of pioglitazone. Study Design This is a prospective, randomized, blinded, placebo-controlled, dose-escalation safety trial in which patients with spontaneous intracerebral hemorrhage are randomly allocated to placebo or treatment. The Continual Reassessment Method for dose finding is used to determine the maximum tolerated dose of pioglitazone. Hematoma and edema resolution is evaluated with serial magnetic resonance imaging (MRI) at specified time points. Functional outcome will be evaluated at three- and six-months. Outcomes The primary safety outcome is mortality at discharge. Secondary safety outcomes include mortality at three-months and six-months, symptomatic cerebral edema, clinically significant congestive heart failure, edema, hypoglycemia, anemia, and hepatotoxicity. Radiographic outcomes will explore the time frame for resolution of 25%, 50%, and 75% of the hematoma. Clinical outcomes are measured by the National Institutes of Health Stroke Scale (NIHSS), the Barthel Index, modified Rankin Scale, Stroke Impact Scale-16, and EuroQol at three- and six-months.

Missed opportunities for diagnosis and treatment of diabetes, hypertension, and hypercholesterolemia in a Mexican American population, Cameron County Hispanic Cohort, 2003-2008.

Introduction Diabetes, hypertension, and hypercholesterolemia are common chronic diseases among Hispanics, a group projected to comprise 30% of the US population by 2050. Mexican Americans are the largest ethnically distinct subgroup among Hispanics. We assessed the prevalence of and risk factors for undiagnosed and untreated diabetes, hypertension, and hypercholesterolemia among Mexican Americans in Cameron County, Texas. Methods We analyzed cross-sectional baseline data collected from 2003 to 2008 in the Cameron County Hispanic Cohort, a randomly selected, community-recruited cohort of 2,000 Mexican American adults aged 18 or older, to assess prevalence of diabetes, hypertension, and hypercholesterolemia; to assess the extent to which these diseases had been previously diagnosed based on self-report; and to determine whether participants who self-reported having these diseases were receiving treatment. We also assessed social and economic factors associated with prevalence, diagnosis, and treatment. Results Approximately 70% of participants had 1 or more of the 3 chronic diseases studied. Of these, at least half had had 1 of these 3 diagnosed, and at least half of those who had had a disease diagnosed were not being treated. Having insurance coverage was positively associated with having the 3 diseases diagnosed and treated, as were higher income and education level. Conclusions Although having insurance coverage is associated with receiving treatment, important social and cultural barriers remain. Failure to provide widespread preventive medicine at the primary care level will have costly consequences.

Toscana meningoencephalitis: A comparison to other viral central nervous system infections

BACKGROUND Toscana virus (TOSV) is an emerging pathogen causing central nervous system (CNS) infection in Mediterranean countries, mostly during summer season. OBJECTIVES To compare the clinical and laboratory characteristics of Toscana CNS infections to the most common viral pathogens seen in the United States. STUDY DESIGN We performed a case series of patients with 41 TOSV infection and compared the clinical characteristics, laboratory findings, imaging results and clinical outcomes to the most commonly recognized viral causes of meningoencephalitis in the US [enterovirus (n=60), herpes simplex virus (n=48), and West Nile virus (n=30)] from our multi-center study of patients with aseptic meningoencephalitis syndromes in the Greater Houston area. RESULTS TOSV infection occurs in different age groups compared to enterovirus, HSV, and WNV. All infections most frequently occur during summer-fall except HSV which distributes throughout the year. All patients with TOSV had history of travel to endemic areas. There are differences in clinical presentation and CSF findings comparing TOSV and enterovirus, HSV, and WNV infection. There are no significant differences in outcomes of each infection except WNV meningoencephalitis which had a poorer outcome compared to TOSV infection. CONCLUSIONS TOSV is an emerging pathogen that should be considered in the differential diagnosis of patients with CNS infections and a recent travel history to endemic areas.

Air pollution and stillbirth risk: exposure to airborne particulate matter during pregnancy is associated with fetal death.

Objective To test the hypothesis that exposure to fine particulate air pollution (PM2.5) is associated with stillbirth. Study Design Geo-spatial population-based cohort study using Ohio birth records (2006-2010) and local measures of PM2.5, recorded by the EPA (2005-2010) via 57 monitoring stations across Ohio. Geographic coordinates of the mother’s residence for each birth were linked to the nearest PM2.5 monitoring station and monthly exposure averages calculated. The association between stillbirth and increased PM2.5 levels was estimated, with adjustment for maternal age, race, education level, quantity of prenatal care, smoking, and season of conception. Results There were 349,188 live births and 1,848 stillbirths of non-anomalous singletons (20-42 weeks) with residence ≤10 km of a monitor station in Ohio during the study period. The mean PM2.5 level in Ohio was 13.3 μg/m3 [±1.8 SD, IQR(Q1: 12.1, Q3: 14.4, IQR: 2.3)], higher than the current EPA standard of 12 μg/m3. High average PM2.5 exposure through pregnancy was not associated with a significant increase in stillbirth risk, adjOR 1.21(95% CI 0.96,1.53), nor was it increased with high exposure in the 1st or 2nd trimester. However, exposure to high levels of PM2.5 in the third trimester of pregnancy was associated with 42% increased stillbirth risk, adjOR 1.42(1.06,1.91). Conclusions Exposure to high levels of fine particulate air pollution in the third trimester of pregnancy is associated with increased stillbirth risk. Although the risk increase associated with high PM2.5 levels is modest, the potential impact on overall stillbirth rates could be robust as all pregnant women are potentially at risk.

Space-time latent component modeling of geo-referenced health data.

Latent structure models have been proposed in many applications. For space-time health data it is often important to be able to find the underlying trends in time, which are supported by subsets of small areas. Latent structure modeling is one such approach to this analysis. This paper presents a mixture-based approach that can be applied to component selection. The analysis of a Georgia ambulatory asthma county-level data set is presented and a simulation-based evaluation is made.

Revised estimates of influenza-associated excess mortality, United States, 1995 through 2005.

BackgroundExcess mortality due to seasonal influenza is thought to be substantial. However, influenza may often not be recognized as cause of death. Imputation methods are therefore required to assess the public health impact of influenza. The purpose of this study was to obtain estimates of monthly excess mortality due to influenza that are based on an epidemiologically meaningful model.Methods and ResultsU.S. monthly all-cause mortality, 1995 through 2005, was hierarchically modeled as Poisson variable with a mean that linearly depends both on seasonal covariates and on influenza-certified mortality. It also allowed for overdispersion to account for extra variation that is not captured by the Poisson error. The coefficient associated with influenza-certified mortality was interpreted as ratio of total influenza mortality to influenza-certified mortality. Separate models were fitted for four age categories (<18, 18–49, 50–64, 65+). Bayesian parameter estimation was performed using Markov Chain Monte Carlo methods. For the eleven year study period, a total of 260,814 (95% CI: 201,011–290,556) deaths was attributed to influenza, corresponding to an annual average of 23,710, or 0.91% of all deaths.ConclusionAnnual estimates for influenza mortality were highly variable from year to year, but they were systematically lower than previously published estimates. The excellent fit of our model with the data suggest validity of our estimates.

Risk score for identifying adults with CSF pleocytosis and negative CSF Gram stain at low risk for an urgent treatable cause

BACKGROUND We aimed to derive and validate a risk score that identifies adults with cerebrospinal fluid (CSF) pleocytosis and a negative CSF Gram stain at low risk for an urgent treatable cause. METHODS Patients with CSF pleocytosis and a negative CSF Gram stain were stratified into a prospective derivation (n = 193) and a retrospective validation (n = 567) cohort. Clinically related baseline characteristics were grouped into three composite variables, each independently associated with a set of predefined urgent treatable causes. We subsequently derived a risk score classifying patients into low (0 composite variables present) or high (≥ 1 composite variables present) risk for an urgent treatable cause. The sensitivity of the risk score was determined in the validation cohort and in a prospective case series of 214 adults with CSF-culture proven bacterial meningitis, CSF pleocytosis and a negative Gram stain. FINDINGS A total of 41 of 193 patients (21%) in the derivation cohort and 71 of 567 (13%) in the validation cohort had an urgent treatable cause. Sensitivity of the dichotomized risk score to detect an urgent treatable cause was 100.0% (95% CI 93.9-100.0%) in the validation cohort and 100.0% (95% CI 97.8-100.0%) in bacterial meningitis patients. INTERPRETATION The risk score can be used to identify adults with CSF pleocytosis and a negative CSF Gram stain at low risk for an urgent treatable cause.

Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Data

We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999–2008.