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Carter Walker
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Skachat Pdf Stratum Plus

The electrical properties of the epidermal stratum corneum were investigated by cellulose adhesive tape stripping. The distribution function of the relaxation times on the impedance locus of stratum corneum was deduced. It was found that this function of the thin layer does not satisfy the Cole-Cole circular arc, but the skin impedance remaining after some strippings compares favourably with the Cole-Cole circular arc. The various dispersions appearing in the skin impedance can be separated and evaluated. The average resistivity and dielectric constant of the stratum corneum and deeper tissues were determined. The direct current resitivity σ of the stratum corneum can be mathematically expressed by the exponential law ρ=ρo -αx where x is the distance from the skin surface. The maximum resistivity ρo on the outermost surface of the stratum corneum and the attenuation coefficient α are evaluated.

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Die elektrischen Eigenschaften der epidermen Stratum corneum wurden durch Abisolieren mit Zellulose-Klebeband untersucht. Die Verteilungsfunktion der Entspannungszeiten am Impedanzort der Stratum corneum wurde abgeleitet. Man stellte fest, daß diese Funktion der dünnen Schicht dem Cole-Cole-Rundbogen nicht entspricht. Die nach mehreren Abisolierungen verbleibende Hautimpedanz läßt sich jedoch günstig mit dem Cole-Cole-Rundbogen vergleichen. Die verschiedenen Dispersionen, die in der Hautimpedanz erscheinen, können getrennt und bewertet werden. Der durchschnittliche Widerstand und die dielektrischen Konstante der Stratum corneum und tiefer liegender Gewebe wurden bestimmt. Der direkte Stromwiderstand σ der stratum corneum läßt sich mathematisch durch den Exponentialsatz ρ=ρo -αx ausdrücken, wobeix der Abstand von der Hautoberfläche ist. Der maximale Widerstand ρo der äußersten Fläche der Stratum corneum und der Schwächungskoeffizient α werden bewertet.

Moreover, this study utilized a measure of inequality in the probability of U5Ds (the RD of mortality), to assess the risk differences of U5Ds among poor and non-poor households. Under-5 children who live in poor households in 34 LMICs faced higher risks of dying before age 5 compared with their counterparts who are from non-poor households in those countries. Although, this was not the case in some countries- Ethiopia, Tanzania, Zambia, Lesotho, Gambia, Sierra Leone and Maldives. The former scenario is expected since children born in poor families in LMICs are often deprived of basic resources like adequate nutritional intake, access to potable water, access to childhood vaccination/immunization coverage, conducive growing environment (or adequate housing facilities), access to quality healthcare etc. Consequently, the majority of them are predisposed to illnesses emanating from the interaction of those factors which may culminate into U5D. This assertion is corroborated by the conclusions made in the work of Houwelling and Kounst that the economic status of households in LMICs is strongly correlated with the risk of U5D [27]. They further submitted that, as a way pass-through, the relationship between childhood mortality and poverty can be as a result of the impact of economic deprivation on ill-health and can also be the other way round, suggesting a bi-directional relationship [27]. Houwelling et al assessed access to skilled maternal care among poor and non-poor households and found that there exist enormous inequalities in the use of professional maternal care services across different income groups in LMICs and that the services provided by nurses and mid-wives appear to favour households in the upper economic stratum relative at the expense of the poor ones [56]. Presumably, this finding may also partly explain the huge gap in child health outcomes among households in the different economic hierarchies. It is important to note that inequalities in U5D as shown in the RDs in U5Ds followed a similar pattern in all the countries apart from Sierra Leone where the burden of U5D is high both among poor and rich households. This suggests that there may be other fundamental issues affecting child health outcomes in the country.

Moreover, this study indicated that neighbourhood SES also contributed to the gap in U5Ds among poor and rich households. This finding is supported by the evidence revealed in a conducted to examine the pathways of the impacts of neighbourhood SES on the outcome of childbirth [44]. The study showed that neighbourhood SES had a direct effect on the occurrence of preterm birth and low birth weight among children. Another study has also reported that poor birth outcomes are higher among households in the poorest income stratum [45]. Apart from child sex and maternal age, other factors contributing to household wealth inequality in U5Ds like toilet type, birth order, maternal employment, multiple births, birth interval and drinking water are related to the wealth status of households in a way. For the majority of the countries included in this study, this finding further reiterates the need to develop and implement policies that will improve the living standards of households in those countries.

In situation 3 (Table 9), the outcome prevalence was highest, and there was a significant interaction between risk factor and confounder. Ignoring the interaction (i. e. using a misspecified model), the log-binomial model performed slightly worse than the Cox and Poisson models in relation to the point estimates. The latter presented a maximum difference of 2% compared to the MHPR, while the log-binomial estimates were up to 8.7% greater. In terms of interval estimates, only the Cox/Poisson models with robust variance presented differences less than 5% for both the original and modified data. An interaction term was included in the robust Poisson and log-binomial regressions. In the original situation, identical results (up to the third decimal place) were obtained from both models, matching the stratum-specific relative risks and confidence intervals. However, in the modified situation, the log-binomial model did not converge, while the robust Poisson model again reproduced the stratum-specific estimates. A common reason for non-convergence is inappropriate starting values for model parameters. Stata's option "search", which specifies that the command "glm" should search for good starting values, solved the problem and, with this option, the results obtained from the log-binomial model were again virtually identical to Poisson regression. 041b061a72