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.

BackgroundPrevious 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.MethodsWe 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).ResultsThe 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.ConclusionsThe 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.

BackgroundLow 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.MethodsA 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.ResultsSixty-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.ConclusionThis 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 registrationThe original protocol of this systematic review was initially registered on PROSPERO with registration number CRD42014010417 on 09 July 2014.

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.

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 (Psplines), 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.

Repeated observation of multiple outcomes is common in biomedical and public health research. Such experiments result in multivariate longitudinal data, which are unique in the sense that they allow the researcher to study the joint evolution of these outcomes over time. Special methods are required to analyse such data because repeated observations on any given response are likely to be correlated over time while multiple responses measured at a given time point will also be correlated. We review three approaches for analysing such data in the light of the associated theory, applications and software. The first method consists of the application of univariate longitudinal tools to a single summary outcome. The second method aims at estimating regression coefficients without explicitly modelling the underlying covariance structure of the data. The third method combines all the outcomes into a single joint multivariate model. We also introduce a multivariate longitudinal dataset and use it to illustrate some of the techniques discussed in the article.

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.

This paper develops a likelihood-based method for fitting additive models in the presence of measurement error. It formulates the additive model using the linear mixed model representation of penalized splines. In the presence of a structural measurement error model, the resulting likelihood involves intractable integrals, and a Monte Carlo expectation maximization strategy is developed for obtaining estimates. The method's performance is illustrated with a simulation study.