Statistical methods in analysing health inequalities among the world citizens

While many international and national institutions world over, such as, World Health Organisation (WHO), the World Bank and national/ state health institutions, strive to promote health, prevent and control disease, formulate policies, programmes and evaluate interventions, and mobilise resources, all of these with an ultimate aim to prolong life of the world citizens as well as increase access to health care, health inequalities between advantaged and disadvantaged populations continue to increase at unprecedented rates and with greater complexity. In this digital age there is no dearth for data on public health, health disparities and related factors, such as socioeconomic, income and GDP. However, lately, transforming this digital data into useful knowledge to learn more about human wellbeing and public health issues linked with appropriate factors is becoming a huge challenge even though it is seen as an inevitable requisite for the international and national health institutions to overcome the health disparities that are reaching alarming proportions. Public as well as private health care research institutions, such as, WHO and Pan-American Health Organisation, academic studies use different sets of core data and approaches for generating composite indices that best present the situation, progress and areas of urgent need in spending. A literature review on the subject shows that our ability to understand the processes as becoming increasingly complicated. Empirical studies into theorising health inequalities, linking relevant factors with inequities and understanding the underlying cause, reveal that we are now investigating into gaining more insights in the mechanisms that link poorer health to poorer circumstance. This is being carried out after having established that all health inequities are inherently inequitable thereby needing political intervention which is understandably seen as a troubling presumption. In view of these factors, the paper presents an approach to group countries based on public health and related factors selected from an initial study by WHO using the World Bank and UN’s Millennium Ecosystem Assessment (MA) goals as well as tables with advanced (but simpler) statistical data analysis methodologies. The results portray the public health issues that dominate the national agenda, political intervention, evaluation measures and budget of these different country groups, especially the difference within countries are seen obvious and cannot be ignored even though they cannot be validated with standard statistical methods such as significance tests. For example, in developed countries the issues are focused on how to manage the escalating obesity and diabetes (type 2) by introducing more tax on fast food whereas, in African countries the focus is on reducing poverty to meet the basic needs of malnourished children, the younger generation, who are growing without their parents; victims of AIDS epidemic, starvation and natural disasters

Professional development perspectives across gender and age groups of under-qualified rural NEETs

Our study explored how self-efficacy perceptions are associated with Perceived Barriers (PB), meaning beliefs about constraints on current/future vocational development, and Professional Expectations (PE) among rural, under-qualified youths Not in Employment, nor in Education or Training (NEET). We also analyzed how the connections between these factors varied across gender and age groups. One-hundred and eighty eight NEETs participated in this study (58.20% female; (M = 23.05; SD = 1.96). Through linear regression analysis using PROCESS macros we found that higher self-efficacy was associated with stronger PB, after accounting for the effects of training offers and income. Moreover, men showing stronger self-efficacy also displayed stronger PB, while men depicting weaker self-efficacy presented weaker PB, compared to women in similar conditions. Being offered more training opportunities resulted in stronger perceptions of professional barriers for these NEETs. Thus, rural, under-qualified NEETs require person-centered approaches from employment services, to support their transition to employment/training.info:eu-repo/semantics/acceptedVersio

The risks and benefits of long-term use of hydroxyurea in sickle cell anemia: A 17.5 year follow-up.

A randomized, controlled clinical trial established the efficacy and safety of short-term use of hydroxyurea in adult sickle cell anemia. To examine the risks and benefits of long-term hydroxyurea usage, patients in this trial were followed for 17.5 years during which they could start or stop hydroxyurea. The purpose of this follow-up was to search for adverse outcomes and estimate mortality. For each outcome and for mortality, exact 95% confidence intervals were calculated, or tests were conducted at alpha = 0.05 level (P-value \u3c0.05 for statistical significance). Although the death rate in the overall study cohort was high (43.1%; 4.4 per 100 person-years), mortality was reduced in individuals with long-term exposure to hydroxyurea. Survival curves demonstrated a significant reduction in deaths with long-term exposure. Twenty-four percent of deaths were due to pulmonary complications; 87.1% occurred in patients who never took hydroxyurea or took it for \u3c5 years. Stroke, organ dysfunction, infection, and malignancy were similar in all groups. Our results, while no longer the product of a randomized study because of the ethical concerns of withholding an efficacious treatment, suggest that long-term use of hydroxyurea is safe and might decrease mortality

Text classification for medical informatics: a comparison of models for data mining radiological medical records

In this study we analyze 1024 free text digital records from pediatric patients who underwent CT scanning. The free text reports are from the digital records of patients who underwent CT scanning in a one-year period in 2004 at the Nagasaki University Medical Hospital in Japan. We use text mining algorithms to model the records. Each scan was evaluated by an expert in the field and classified as to whether the CT scan was necessary or not. A model was built that predicts this classification. The results show that models developed on raw text could contribute significantly to the physician’s decision to order a CT scan. Practically this is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children and thus the modeling of unnecessary scanning may lead to less exposure to radiation