Assessing and applying evidence-based interventions at the community level in India as a model policy to reduce neonatal mortality rates in Nigeria

Background. The United Nations' Millennium Development Goal (MDG) 4 aims for a two-thirds reduction in death rates for children under the age of five by 2015. The greatest risk of death is in the first week of life, yet most of these deaths can be prevented by such simple interventions as improved hygiene, exclusive breastfeeding, and thermal care. The percentage of deaths in Nigeria that occur in the first month of life make up 28% of all deaths under five years, a statistic that has remained unchanged despite various child health policies. This paper will address the challenges of reducing the neonatal mortality rate in Nigeria by examining the literature regarding efficacy of home-based, newborn care interventions and policies that have been implemented successfully in India. ^ Methods. I compared similarities and differences between India and Nigeria using qualitative descriptions and available quantitative data of various health indicators. The analysis included identifying policy-related factors and community approaches contributing to India's newborn survival rates. Databases and reference lists of articles were searched for randomized controlled trials of community health worker interventions shown to reduce neonatal mortality rates. ^ Results. While it appears that Nigeria spends more money than India on health per capita ($136 vs. $132, respectively) and as percent GDP (5.8% vs. 4.2%, respectively), it still lags behind India in its neonatal, infant, and under five mortality rates (40 vs. 32 deaths/1000 live births, 88 vs. 48 deaths/1000 live births, 143 vs. 63 deaths/1000 live births, respectively). Both countries have comparably low numbers of healthcare providers. Unlike their counterparts in Nigeria, Indian community health workers receive training on how to deliver postnatal care in the home setting and are monetarily compensated. Gender-related power differences still play a role in the societal structure of both countries. A search of randomized controlled trials of home-based newborn care strategies yielded three relevant articles. Community health workers trained to educate mothers and provide a preventive package of interventions involving clean cord care, thermal care, breastfeeding promotion, and danger sign recognition during multiple postnatal visits in rural India, Bangladesh, and Pakistan reduced neonatal mortality rates by 54%, 34%, and 15–20%, respectively. ^ Conclusion. Access to advanced technology is not necessary to reduce neonatal mortality rates in resource-limited countries. To address the urgency of neonatal mortality, countries with weak health systems need to start at the community level and invest in cost-effective, evidence-based newborn care interventions that utilize available human resources. While more randomized controlled studies are urgently needed, the current available evidence of models of postnatal care provision demonstrates that home-based care and health education provided by community health workers can reduce neonatal mortality rates in the immediate future.^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Investigation of I -compounds (newly discovered indigenous DNA modifications) by different approaches

I-compounds are newly discovered covalent DNA modifications detected by the $\sp{32}$P-postlabeling assay. They are age-dependent, tissue-specific and sex-different. The origin(s), chemistry and function(s) of I-compounds are unknown. The total level of I-compounds in 8-10 month old rat liver is 1 adduct in 10$\sp7$ nucleotides, which is not neglectable. It is proposed that I-compounds may play a role in spontaneous tumorigenesis and aging.^ In the present project, I-compounds were investigated by several different approaches. (1) Dietary modulation of I-compounds. (2) Comparison of I-compounds with persistent carcinogen DNA adducts and 5-methylcytosine. (3) Strain differences of I-compounds in relation to organ site spontaneous tumorigenesis. (4) Effects of nongenotoxic hepatocarcinogenes on I-compounds.^ It was demonstrated that the formation of I-compounds is diet-related. Rats fed natural ingredient diet exhibited more complex I-spot patterns and much higher levels than rats fed purified diet. Variation of major nutrients (carbohydrate, protein and fat) in the diet, produced quantitative differences in I-compounds of rat liver and kidney DNAs. Physiological level of vitamin E in the diet reduced intensity of one I-spot compared with vitamin E deficient diet. However, extremely high level of vitamin E in the diet gave extra spot and enhanced the intensities of some I-spots.^ In regenerating rat liver, I-compounds levels were reduced, as carcinogen DNA adducts, but not 5-methylcytosine, i.e. a normal DNA modification.^ Animals with higher incidences of spontaneous tumor or degenerative diseases tended to have a lower level of I-compounds.^ Choline devoid diet induced a drastic reduction of I-compound level in rat liver compared with choline supplemented diet. I-compound levels were reduced after multi-doses of carbon tetrachloride (CCl$\sb4$) exposure in rats and single dose exposure in mice. An inverse relationship was observed between I-compound level and DNA replication rate. CCl$\sb4$-related DNA adduct was detected in mice liver and intensities of some I-spots were enhanced 24 h after a single dose exposure.^ The mechanisms and explanations of these observations will be discussed. I-compounds are potentially useful indicators in carcinogenesis studies. ^