A tentative step towards healthy public policy

More consistent attention to implementing healthy public policy, and amassing the evidence for it, are urgently required

Mathematical modelling of health impacts

Mathematical modelling is seldom applied to research of global measures of health or health inequalities mainly because of the lack of studies of interventions necessary to underpin modelling research

Causal inference for continuous-time processes when covariates are observed only at discrete times

Most of the work on the structural nested model and g-estimation for causal inference in longitudinal data assumes a discrete-time underlying data generating process. However, in some observational studies, it is more reasonable to assume that the data are generated from a continuous-time process and are only observable at discrete time points. When these circumstances arise, the sequential randomization assumption in the observed discrete-time data, which is essential in justifying discrete-time g-estimation, may not be reasonable. Under a deterministic model, we discuss other useful assumptions that guarantee the consistency of discrete-time g-estimation. In more general cases, when those assumptions are violated, we propose a controlling-the-future method that performs at least as well as g-estimation in most scenarios and which provides consistent estimation in some cases where g-estimation is severely inconsistent. We apply the methods discussed in this paper to simulated data, as well as to a data set collected following a massive flood in Bangladesh, estimating the effect of diarrhea on children's height. Results from different methods are compared in both simulation and the real application.Comment: Published in at http://dx.doi.org/10.1214/10-AOS830 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Defining and Estimating Intervention Effects for Groups that will Develop an Auxiliary Outcome

It has recently become popular to define treatment effects for subsets of the target population characterized by variables not observable at the time a treatment decision is made. Characterizing and estimating such treatment effects is tricky; the most popular but naive approach inappropriately adjusts for variables affected by treatment and so is biased. We consider several appropriate ways to formalize the effects: principal stratification, stratification on a single potential auxiliary variable, stratification on an observed auxiliary variable and stratification on expected levels of auxiliary variables. We then outline identifying assumptions for each type of estimand. We evaluate the utility of these estimands and estimation procedures for decision making and understanding causal processes, contrasting them with the concepts of direct and indirect effects. We motivate our development with examples from nephrology and cancer screening, and use simulated data and real data on cancer screening to illustrate the estimation methods.Comment: Published at http://dx.doi.org/10.1214/088342306000000655 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

What do we need for robust and quantitative health impact assessment?

Health impact assessment (HIA) aims to make the health consequences of decisions explicit. Decision-makers need to know that the conclusions of HIA are robust. Quantified estimates of potential health impacts may be more influential but there are a number of concerns. First, not everything that can be quantified is important. Second, not everything that is being quantified at present should be, if this cannot be done robustly. Finally, not everything that is important can be quantified; rigorous qualitative HIA will still be needed for a thorough assessment. This paper presents the first published attempt to provide practical guidance on what is required to perform robust, quantitative HIA. Initial steps include profiling the affected populations, obtaining evidence from for postulated impacts, and determining how differences in subgoups' exposures and suscepibilities affect impacts. Using epidemiological evidence for HIA is different from carrying out a new study. Key steps in quantifying impacts are mapping the causal pathway, selecting appropriate outcome measures and selecting or developing a statistical model. Evidence from different sources is needed. For many health impacts, evidence of an effect may be scarce and estimates of the size and nature of the relationship may be inadequate. Assumptions and uncertainties must therefore be explicit. Modelled data can sometimes be tested against empirical data but sensitivity analyses are crucial. When scientific problems occur, discontinuing the study is not an option, as HIA is usually intended to inform real decisions. Both qualitative and quantitative elements of HIA must be performed robustly to be of value

Hepatitis C Virus in Pregnancy: Case Reports and Literature Review

Background: Hepatitis C virus (HCV) is now recognized as the cause of 90% of non-A, non-B (NANB) hepatitis. This virus is responsible for a large percentage of chronic persistent and chronic active hepatitis in the United States. Parenteral and sexual transmission are well described, so a significant population of pregnant patients is at risk. Vertical transmission of the virus to the fetus is dependent upon the level of maternal viremia

Solid fuel use and cooking practices as a major risk factor for ALRI mortality among African children

Background: Almost half of global child deaths due to acute lower respiratory infections (ALRIs) occur in sub-Saharan Africa, where three-quarters of the population cook with solid fuels. This study aims to quantify the impact of fuel type and cooking practices on childhood ALRI mortality in Africa, and to explore implications for public health interventions. Methods: Early-release World Health Survey data for the year 2003 were pooled for 16 African countries. Among 32 620 children born during the last 10 years, 1455 (4.46%) were reported to have died prior to their fifth birthday. Survival analysis was used to examine the impact of different cooking-related parameters on ALRI mortality, defined as cough accompanied by rapid breathing or chest indrawing based on maternal recall of symptoms prior to death. Results: Solid fuel use increases the risk of ALRI mortality with an adjusted hazard ratio of 2.35 (95% CI 1.22 to 4.52); this association grows stronger with increasing outcome specificity. Differences between households burning solid fuels on a well-ventilated stove and households relying on cleaner fuels are limited. In contrast, cooking with solid fuels in the absence of a chimney or hood is associated with an adjusted hazard ratio of 2.68 (1.38 to 5.23). Outdoor cooking is less harmful than indoor cooking but, overall, stove ventilation emerges as a more significant determinant of ALRI mortality. Conclusions: This study shows substantial differences in ALRI mortality risk among African children in relation to cooking practices, and suggests that stove ventilation may be an important means of reducing indoor air pollution

1/Nc1/N_c Rotational Corrections to gAg_A and Isovector Magnetic Moment of the Nucleon

The $1/N_c$ rotational corrections to the axial vector constant and the isovector magnetic moment of the nucleon are studied in the Nambu -- Jona-Lasinio model. We follow a semiclassical quantization procedure in terms of path integrals in which we can include perturbatively corrections in powers of angular velocity $\Omega \sim \frac 1{N_c}$. We find non-zero $1/N_c$ order corrections from both the valence and the Dirac sea quarks. These corrections are large enough to resolve the long-standing problem of a strong underestimation of both $g_A$ and $\mu^{IV}$ in the leading order. The axial constant $g_A$ is well reproduced, whereas the isovector magnetic moment $\mu^{IV}$ is still underestimated by 25 \%.Comment: (Revtex), 10 pages (3 figures available on request), report RUB-TPII-53/9