Modelling alternately recurring events using subject specific hazard estimation approach.

The motivation for this paper is to account for subject specific variations in a Cox proportional hazard model for alternating recurrent events. This is done through two sets of frailty components, whose marginal distributions are bound together by a copula function. The likelihood function involves unobservable variables, which requires the use of the EM algorithm. This leads to intractable integrals, which after some approximations, are solved using computationally intensive techniques. The results are applied to a real-life data. A simulation study is also carried out to check for consistency.

Arsenic exposure from drinking water and staple food (rice): A field scale study in rural Bengal for assessment of human health risk.

Arsenic is a well-known carcinogen with emerging reports showing a range of health outcomes even for low to moderate levels of exposure. This study deals with arsenic exposure and associated increased lifetime cancer risk for populations in arsenic-endemic regions of rural Bengal, where arsenic-safe drinking water is being supplied at present. We found a median total exposure of inorganic arsenic to be 2. 9 µg/Kg BW/day (5th and 95th percentiles were 1.1 µg/Kg BW/day and 7.9 µg/Kg BW/day); with major contribution from cooked rice intake (2.4 µg/Kg BW/day). A significant number of households drank arsenic safe water but used arsenic-rich water for rice cooking. As a result, 67% participants had inorganic arsenic intake above the JEFCA threshold value of 3 µg/Kg BW/day for cancer risk from only rice consumption when arsenic contaminated water was used for cooking (median: 3.5 µg/Kg BW/day) compared to 29% participants that relied on arsenic-free cooking water (median: 1.0 µg/kg BW/day). Arsenic in urine samples of study participants ranged from 31.7 to 520 µg/L and was significantly associated with the arsenic intake (r = 0.76); confirming the preponderance of arsenic exposure from cooked rice. The median arsenic attributable cancer risks from drinking water and cooked rice were estimated to be 2.4 × 10-5 and 2.7 × 10-4 respectively, which further emphasized the importance of arsenic exposure from staple diet. Our results show that any mitigation strategy should include both drinking water and local staple foods in order to minimize the potential health risks of arsenic exposure.

An analysis of COVID-19 clusters in India : Two case studies on Nizamuddin and Dharavi.

BACKGROUND: In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. METHODS: A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. RESULTS: The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. CONCLUSIONS: The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

Endemic infections, vaccinations, and variability of SARS-COV2 worldwide epidemiology: A cross-sectional study.

The present article aims to analyze epidemiologic aspects of the novel coronavirus pandemic (COVID-19) over different countries across the globe. While analyzing the overall spread of the disease, clusters of countries could be identified where the population-adjusted number of cases and mortality rates (MRs) were significantly different from the others. To draw a comparison over the countries at the same stage of infection, the nature and spread of the infection was evaluated at the 90th day of the pandemic for each country. It was observed that the countries with prevalent malarial transmission tended to have lesser population-adjusted COVID-19 caseloads. It was further observed that high population coverage of the Bacillus Calmette-Guérin vaccination was negatively associated with population-adjusted caseloads and MRs due to COVID-19. The present cross-sectional study is an attempt to bring in several social, economic, and structural confounders into understanding of the nature and spread of this novel pandemic globally.

Probing ADP Induced Aggregation Kinetics During Platelet-Nanoparticle Interactions: Functional Dynamics Analysis to Rationalize Safety and Benefits.

Platelets, one of the most sensitive blood cells, can be activated by a range of external and internal stimuli including physical, chemical, physiological, and/or non-physiological agents. Platelets need to respond promptly during injury to maintain blood hemostasis. The time profile of platelet aggregation is very complex, especially in the presence of the agonist adenosine 5'-diphosphate (ADP), and it is difficult to probe such complexity using traditional linear dose response models. In the present study, we explored functional analysis techniques to characterize the pattern of platelet aggregation over time in response to nanoparticle induced perturbations. This has obviated the need to represent the pattern of aggregation by a single summary measure and allowed us to treat the entire aggregation profile over time, as the response. The modeling was performed in a flexible manner, without any imposition of shape restrictions on the curve, allowing smooth platelet aggregation over time. The use of a probabilistic framework not only allowed statistical prediction and inference of the aggregation signatures, but also provided a novel method for the estimation of higher order derivatives of the curve, thereby allowing plausible estimation of the extent and rate of platelet aggregation kinetics over time. In the present study, we focused on the estimated first derivative of the curve, obtained from the platelet optical aggregometric profile over time and used it to discern the underlying kinetics as well as to study the effects of ADP dosage and perturbation with gold nanoparticles. In addition, our method allowed the quantification of the extent of inter-individual signature variations. Our findings indicated several hidden features and showed a mixture of zero and first order kinetics interrupted by a metastable zero order ADP dose dependent process. In addition, we showed that the two first order kinetic constants were ADP dependent. However, we were able to perturb the overall kinetic pattern using gold nanoparticles, which resulted in autocatalytic aggregation with a higher aggregate mass and which facilitated the aggregation rate.

An update on the prevalence of low back pain in Africa: a systematic review and meta-analyses.

BACKGROUND: Low back pain (LBP) remains a common health problem and one of the most prevalent musculoskeletal conditions found among developed and developing nations. The following paper reports on an updated search of the current literature into the prevalence of LBP among African nations and highlights the specific challenges faced in retrieving epidemiological information in Africa. METHODS: A comprehensive search of all accessible bibliographic databases was conducted. Population-based studies into the prevalence of LBP among children/adolescents and adults living in Africa were included. Methodological quality of included studies was appraised using an adapted tool. Meta-analyses, subgroup analyses, sensitivity analyses and publication bias were also conducted. RESULTS: Sixty-five studies were included in this review. The majority of the studies were conducted in Nigeria (n = 31;47%) and South Africa (n = 16;25%). Forty-three included studies (66.2%) were found to be of higher methodological quality. The pooled lifetime, annual and point prevalence of LBP in Africa was 47% (95% CI 37;58); 57% (95% CI 51;63) and 39% (95% CI 30;47), respectively. CONCLUSION: This review found that the lifetime, annual and point prevalence of LBP among African nations was considerably higher than or comparable to global LBP prevalence estimates reported. Due to the poor methodological quality found among many of the included studies, the over-representation of affluent countries and the difficulty in sourcing and retrieving potential African studies, it is recommended that future African LBP researchers conduct methodologically robust studies and report their findings in accessible resources. TRIAL REGISTRATION: The original protocol of this systematic review was initially registered on PROSPERO with registration number CRD42014010417 on 09 July 2014.

State and national household concentrations of PM2.5 from solid cookfuel use: results from measurements and modeling in India for estimation of the global burden of disease.

BACKGROUND: Previous global burden of disease (GBD) estimates for household air pollution (HAP) from solid cookfuel use were based on categorical indicators of exposure. Recent progress in GBD methodologies that use integrated-exposure-response (IER) curves for combustion particles required the development of models to quantitatively estimate average HAP levels experienced by large populations. Such models can also serve to inform public health intervention efforts. Thus, we developed a model to estimate national household concentrations of PM2.5 from solid cookfuel use in India, together with estimates for 29 states. METHODS: We monitored 24-hr household concentrations of PM2.5, in 617 rural households from 4 states in India on a cross-sectional basis between November 2004 and March 2005. We then, developed log-linear regression models that predict household concentrations as a function of multiple, independent household level variables available in national household surveys and generated national / state estimates using The Indian National Family and Health Survey (NFHS 2005). RESULTS: The measured mean 24-hr concentration of PM2.5 in solid cookfuel using households ranged from 163 µg/m3 (95% CI: 143,183; median 106; IQR: 191) in the living area to 609 µg/m3 (95% CI: 547,671; median: 472; IQR: 734) in the kitchen area. Fuel type, kitchen type, ventilation, geographical location and cooking duration were found to be significant predictors of PM2.5 concentrations in the household model. k-fold cross validation showed a fair degree of correlation (r = 0.56) between modeled and measured values. Extrapolation of the household results by state to all solid cookfuel-using households in India, covered by NFHS 2005, resulted in a modeled estimate of 450 µg/m3 (95% CI: 318,640) and 113 µg/m3 (95% CI: 102,127) , for national average 24-hr PM2.5 concentrations in the kitchen and living areas respectively. CONCLUSIONS: The model affords substantial improvement over commonly used exposure indicators such as "percent solid cookfuel use" in HAP disease burden assessments, by providing some of the first estimates of national average HAP levels experienced in India. Model estimates also add considerable strength of evidence for framing and implementation of intervention efforts at the state and national levels.

Part 1. Short-term effects of air pollution on mortality: results from a time-series analysis in Chennai, India.

This report describes the results of a time-series analysis of the effect of short-term exposure to particulate matter with an aerodynamic diameter < or = 10 pm (PM10) on mortality in metropolitan Chennai, India (formerly Madras). This was one of three sites in India chosen by HEI as part of its Public Health and Air Pollution in Asia (PAPA) initiative. The study involved integration and analysis of retrospective data for the years 2002 through 2004. The data were obtained from relevant government agencies in charge of routine data collection. Data on meteorologic confounders (including temperature, relative humidity, and dew point) were available on all days of the study period. Data on mortality were also available on all days, but information on cause-of-death (including accidental deaths) could not be reliably ascertained. Hence, only all-cause daily mortality was used as the major outcome for the time-series analyses. Data on PM10, nitrogen dioxide (NO2), and sulfur dioxide (SO2) were limited to a much smaller number of days, but spanned the full study period. Data limitations resulting from low sensitivity of gaseous pollutant measurements led to using only PM10 in the main analysis. Of the eight operational ambient air quality monitor (AQM) stations in the city, seven met the selection criteria set forth in the common protocol developed for the three PAPA studies in India. In addition, all raw data used in the analysis were subjected to additional quality assurance (QA) and quality control (QC) criteria to ensure the validity of the measurements. Two salient features of the PM10 data set in Chennai were a high percentage of missing readings and a low correlation among daily data recorded by the AQMs. The latter resulted partly because each AQM had a small footprint (approximate area over which the air pollutant measurements recorded in the AQM are considered valid), and partly because of differences in source profiles among the 10 zones within the city. The zones were defined by the Chennai Corporation based on population density. Alternative exposure series were developed to control for these data features. We first developed exposure series based on data from single AQMs and multiple AQMs. Because neither was found to satisfactorily represent population exposures, we subsequently developed an exposure series that disaggregated pollutant data to individual zones within the city boundary. The zonal series, despite some uncertainties, was found to best represent population exposures among other available choices. The core model was thus a zonal model developed using disaggregated mortality and pollutant data from individual zones. We used quasi-Poisson generalized additive models (GAMs) with smooth functions of time, temperature, and relative humidity modeled using penalized splines. The degrees of freedom (df) for these confounders were selected to maximize the precision with which the relative risk for PM10 was estimated. This is a deviation from the traditional approaches to degrees of freedom selection, which usually aim to optimize overall model fit. Our approach led to the use of 8 df/year for time, 6 df/year for temperature, and 5 df/year for relative humidity. The core model estimated a 0.44% (95% confidence interval [CI] = 0.17 to 0.71) increase in daily all-cause mortality per 10-pg/m3 increase in daily average PM10 concentrations. Extensive sensitivity analyses compared models constructed using alternative exposure series and contributions of model parameters to the core model with regard to confounder degrees of freedom, alternative lags for exposure and meteorologic confounders, inclusion of outliers, seasonality, inclusion of multiple pollutants, and stratification by sex and age. The sensitivity analyses showed that our estimates were robust to a range of specifications and were also comparable to estimates reported in previous time-series studies: PAPA, the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), Air Pollution and Health: A European Approach (APHEA), and Air Pollution and Health: A European and North American Approach (APHENA). While the approaches developed in previous studies served as the basis for our model development, the present study has new refinements that have allowed us to address specific data limitations (such as missing measurements and small footprints of air pollution monitors). The methods developed in the study may allow better use of routine data for time-series analysis in a broad range of settings where similar exposure and data-related issues prevail. We hope that the estimates derived in this study, although somewhat tentative, will facilitate local environmental management initiatives and spur future studies.

Comparison of drinking water, raw rice and cooking of rice as arsenic exposure routes in three contrasting areas of West Bengal, India.

Remediation aimed at reducing human exposure to groundwater arsenic in West Bengal, one of the regions most impacted by this environmental hazard, are currently largely focussed on reducing arsenic in drinking water. Rice and cooking of rice, however, have also been identified as important or potentially important exposure routes. Quantifying the relative importance of these exposure routes is critically required to inform the prioritisation and selection of remediation strategies. The aim of our study, therefore, was to determine the relative contributions of drinking water, rice and cooking of rice to human exposure in three contrasting areas of West Bengal with different overall levels of exposure to arsenic, viz. high (Bhawangola-I Block, Murshidibad District), moderate (Chakdha Block, Nadia District) and low (Khejuri-I Block, Midnapur District). Arsenic exposure from water was highly variable, median exposures being 0.02 µg/kg/d (Midnapur), 0.77 µg/kg/d (Nadia) and 2.03 µg/kg/d (Murshidabad). In contrast arsenic exposure from cooked rice was relatively uniform, with median exposures being 0.30 µg/kg/d (Midnapur), 0.50 µg/kg/d (Nadia) and 0.84 µg/kg/d (Murshidabad). Cooking rice typically resulted in arsenic exposures of lower magnitude, indeed in Midnapur, median exposure from cooking was slightly negative. Water was the dominant route of exposure in Murshidabad, both water and rice were major exposure routes in Nadia, whereas rice was the dominant exposure route in Midnapur. Notwithstanding the differences in balance of exposure routes, median excess lifetime cancer risk for all the blocks were found to exceed the USEPA regulatory threshold target cancer risk level of 10(-4)-10(-6). The difference in balance of exposure routes indicate a difference in balance of remediation approaches in the three districts.

Arsenic exposure induces genomic hypermethylation.

Gene-specific hypermethylation has previously been detected in Arsenic exposed persons. To monitor the level of whole genome methylation in persons exposed to different levels of Arsenic via drinking water, DNA was extracted from peripheral blood mononuclear cells of 64 persons. Uptake of methyl group from (3)H labeled S-Adenosyl Methionine after incubation of DNA with SssI methylase was measured. Results showed statistically significant (P = 0.0004) decrease in uptake of (3)H methyl group in the persons exposed to 250-500 microg/L arsenic, indicating genomic hypermethylation.

The comparison of alternative smoothing methods for fitting non-linear exposure-response relationships with Cox models in a simulation study.

We examined the behavior of alternative smoothing methods for modeling environmental epidemiology data. Model fit can only be examined when the true exposure-response curve is known and so we used simulation studies to examine the performance of penalized splines (P-splines), restricted cubic splines (RCS), natural splines (NS), and fractional polynomials (FP). Survival data were generated under six plausible exposure-response scenarios with a right skewed exposure distribution, typical of environmental exposures. Cox models with each spline or FP were fit to simulated datasets. The best models, e.g. degrees of freedom, were selected using default criteria for each method. The root mean-square error (rMSE) and area difference were computed to assess model fit and bias (difference between the observed and true curves). The test for linearity was a measure of sensitivity and the test of the null was an assessment of statistical power. No one method performed best according to all four measures of performance, however, all methods performed reasonably well. The model fit was best for P-splines for almost all true positive scenarios, although fractional polynomials and RCS were least biased, on average.

Comparing smoothing techniques in Cox models for exposure-response relationships.

To allow for non-linear exposure-response relationships, we applied flexible non-parametric smoothing techniques to models of time to lung cancer mortality in two occupational cohorts with skewed exposure distributions. We focused on three different smoothing techniques in Cox models: penalized splines, restricted cubic splines, and fractional polynomials. We compared standard software implementations of these three methods based on their visual representation and criterion for model selection. We propose a measure of the difference between a pair of curves based on the area between them, standardized by the average of the areas under the pair of curves. To capture the variation in the difference over the range of exposure, the area between curves was also calculated at percentiles of exposure and expressed as a percentage of the total difference. The dose-response curves from the three methods were similar in both studies over the denser portion of the exposure range, with the difference between curves up to the 50th percentile less than 1 per cent of the total difference. A comparison of inverse variance weighted areas applied to the data set with a more skewed exposure distribution allowed us to estimate area differences with more precision by reducing the proportion attributed to the upper 1 per cent tail region. Overall, the penalized spline and the restricted cubic spline were closer to each other than either was to the fractional polynomial.