Trends in abortion care in the United States are changing quickly, affected by many epidemiological factors as well as a varying political climate. Surgical abortions are the more common method of conducting abortion care. Recent CDC National Surveillance Data has shown an increase in second-trimester abortion, correlating to an increased need for providers experienced in surgical abortions and cervical preparation agents, such as misoprostol, mifepristone, and laminaria. Furthermore, recent studies have shown an increase in long-acting reversible contraceptive options including post-abortion contraceptive use. We hoped to compare the trends in abortion of pregnancy in our low-resource urban environment against the national trends to better understand what demographic factors might influence decision-making. We identified a need for studies on trends in abortions of pregnancy in a low-resource urban setting which can become applicable across similar neighborhoods, some of which might not participate in CDC abortion surveillance reports. Our study shows an increase in dilation and evacuation procedures, correlating with an increase in the use of misoprostol and laminaria for cervical preparation as well as digoxin for induction of fetal demise, both of which would occur at higher frequency in the second trimester. We also found a preference towards no contraception after abortion, which slightly differs from national trends in recent years. Our study aims to evaluate these trends and identify the need for further quality assurance and improvement in this care.Full article

An unhealthy dietary habit leads to excess calorie consumption (overnutrition) or inadequate supply of one or more essential micronutrients (undernutrition).This nutritional imbalance is assessed by Anthropometric measurements, Biochemical estimations, Clinical examination and assessment of Dietary intakes. Anthropometry is an inexpensive, rapid and non-invasive method that provides details on different components of body structure and is highly sensitive to the broad spectrum of nutritional status. Hence, it has always been an important tool for screening and early diagnosis of malnutrition. Undernutrition in below 5 year children is life-threatening epidemic contributing to about 45% of under 5 child deaths. Children with Severe Acute Malnutrition (SAM) are nine times more likely to die, compared to their healthy counterparts. Therefore, early and accurate diagnosis of children with SAM is crucial for its management and prevention of morbidity and mortality from the same. SAM is defined as weight-for-height Z scores (WHZ) below -3SD of the median or a mid upper arm circumference (MUAC) of <115mm in children of 6-59 months age. The cut-offs for MUAC and WHZ are scientifically approximated to each other and both are used to diagnose children with SAM (Severe Wasting). However, the research findings from various countries revealed that the agreement between WHZ and MUAC is poor as both indices classify the children with SAM differently, with a small overlap, which varies greatly among countries. These discrepancies have an implication when using either one alone for measuring the prevalence of acute malnutrition. Therefore, it is pertinent to adopt both WHZ and MUAC indices to assess the burden of severe acute malnutrition (SAM) in the community.Full article

Background: Family planning is one of the key cornerstones of safe parenthood and a reproductive rights issue. In underdeveloped nations, women experiencing unmet FP needs formed a considerable proportion of all women of reproductive age and are ongoing public health concerns in The Gambia. The study was set out to explore the influence of socio-demographic and cultural factors on contraceptive uptake among rural women in The Gambia. Methods: The study used a community-based cross-sectional analytical design. A multistage sampling strategy, comprising simple random and cluster sampling, was utilized to obtain a sample of 634 childbearing women (15-49 years) from rural Gambia's sampled clusters. Data collection was conducted using pre-tested structured interview questionnaires. The association was examined using chi-square/fisher's exact test with a significance level set at p<0.05. Binary logistic regression analysis was performed to examine the effect of socio-demographic and cultural determinants on uptake of contraceptives, with corresponding computed adjusted odds ratios (aOR). IBM SPSS version 25 was used for data entry and analysis. Results: The uptake of contraceptives among the study participants was 30.4%. The total demand for FP was 59.4% while the satisfaction of demand for FP was 57.6%. The significant predictors of FP uptake were the age of women (aOR=1.097, p=0.014), reason for using FP (aOR=1.139, p=0.011), use of contraceptives before (aOR=24.416, p<0.001) and reason for not discussing FP with a partner (aOR=1.787, p=0.029). Conclusion: The study showed low contraceptive uptake among women in rural communities. Thus, spousal communications on FP concerns are key intermediate steps towards eventual acceptance and sustained usage of FP services. The program should focus on improving access to and availability of FP services in rural areas. The program should prioritize addressing women's needs through consistent community-based interventions including targeted home visits.Full article

Air pollution is currently considered a global problem in both developing and developed countries. Substances that invade our spaces are components of air pollution that cause a strong negative impact on health for those who are exposed, not only in the cardiovascular and respiratory systems but are being related to the etiology of pathologies throughout the body, with a decrease in life expectancy and even an increase in mortality and alterations of the genetic material. This literature review aims to collect employing a search the implications that the components of air pollution have on the health of those exposed, from a clinical and molecular point of view. For the search, the DeCS descriptors created by BIREME were used: air pollution, cardiovascular system, respiratory diseases. The following databases were consulted: PubMed, ScienceDirect, and Scopus. The search criteria considered the year of publication and whether the original language was English or Spanish. It was concluded that the study of the different particles and the consequences that exposure to them entails is of vital importance for the development of control, prevention, and treatment mechanisms; since they can generate pathologies that range from something as tangible as lung diseases and occlusive heart disease to epigenetic changes that affect health.Full article

The popularity of e-cigarette is growing worldwide. Its health hazards and role in smoking cessation is controversial. There is no doubt that health care professionals can play a vital role in assisting patients who wish to use e-cigarettes to quit smoking but there is a gap in the evidence. The present study aimed to reveal the perception of e-cigarettes as a quit smoking tool and its health hazards among physicians in Bangladesh. A cross-sectional study was conducted by means of a survey via self-administered structured questionnaires in Bangladesh. Data was collected from September 2019 to February 2020 and analysed by descriptive frequency and chi-square test using SPSS. A total of 145 physicians have participated in this study, 88.9% provided professional advice on quit smoking to their patients. Total 51.7%, 51.9%, 41.3%, 52.4%, 42.8%, and 47.6% physicians mentioned that e-cigarettes may cause throat irritations, cough, headache, dryness of mouth, cardiovascular disease, and cancer respectively. Chi-square test revealed that there is no association between a physician’s professional advice for quit smoking and perception of e-cigarettes as a smoking cessation tool. The physician’s perception of e-cigarettes is crucial for reducing any type of tobacco consumption. Evidence based e-cigarette related public health intervention for physicians are required to mitigate the use of e-cigarette to quit smoking.Full article

Background: Cancer surveillance data provide information on the incidence and trends of cancer in the population level. Analyzing cancer trends according to these characteristics plays an important role in cancer surveillance. Knowledge of the causes of cancer allow better prevent the appearance of it. A large number of epidemiological evidence supporting the effect of smoking on the causes of cancer there is strong evidence supporting a role for smoking in the etiology of cancers. Alcohol appears to interact with the tobacco significantly and can be considered a risk factor in the development of cancers. Obesity which is now well recognized as a public health problem increases the risk of developing cancers. All these factors are characterized by uncertainty, complexity and imprecision. Methods: In this study, we propose an analysis of these factors based on the principles of fuzzy logic inference system. The data were collected from WHO data. As this technique addresses the uncertain, its application in this area is perfectly adequate. Results: A database is established, after the analysis system is done, it will be possible to read the prevalence of cancer by introducing randomly the values in inputs variables. Conclusion: like cancer has become a national scourge, this application allows predicting the impact of it just from the introduction inputs variables such as BMI, degree of physical activity, tobacco and sex.Full article

Predictive modeling, supported by machine learning technology, aims to analyze data in order to guide decision-making towards actions generating desired values in the future. It encompasses the set of techniques used to build models that estimate the value of a certain variable predicting a forthcoming event from the past or current values of relevant attributes. In predictive healthcare modeling, the built models represent the relationship among the data concerning customer, provider, production, and other aspects of the healthcare environment in order to assist the decision processes in the prevention of diseases and in the planning of preventive actions by detection of high-risk patients. Contrary to trend analysis, whose goal is to describe past events, predictive models aim to provide useful indications regarding future events and changes. Predictive healthcare modeling supports actions that try to prevent the manifestation of diseases in healthy individuals or try to diagnose as early as possible the incidence of a disease in patients at risk. A sound predictive analysis encompasses not only the model-training task, but also the aspects of data quality, preprocessing, and fusion during its entire implementation lifecycle to ensure appropriate input data preparation. The robustness of the predictive model and its results depends highly on data quality. Due to the variety of data sources in healthcare environments, it becomes essential to use preprocessing in order to remove noise and inconsistencies. The increasing number of endorsable data exchange standards makes each data exchange achievable, but it demands the implementation of a data-governance program. In addition, the influence of the hospital-database architect on the architecture of an early-diagnosis model is important to guarantee appropriate input-formatting modularity.Full article

Architectural frameworks for large-scale Electronic Health Record (EHR) data platforms are described. Existing EHR data platform architectures often leverage multiple cloud-based solutions blended with institutional infrastructures to manage and analyze clinical data at scale. Key design principles governing the scale of existing EHR data architecture include model design, governance structure, data access management, data security/policy/protection, data-information-language-based standardization, and analytics tool alignment, among others. The rapidly evolving technology landscape and the unprecedented volume of incident and retrospective clinical data being collected and generated within healthcare organizations have led to the emergent need for a dedicated architectural framework to support large-scale computing in the health informatics domain. The application areas of large-scale computing in health informatics include real-time predictive analytics, risk stratification, patient cohort analytics, development of predictive models for specific institutions or population groups, and many more. The use of EHR data for a multitude of decision-making processes in both clinical and non-clinical settings has prompted the establishment of policies prescribing the conditions of access and use of EHR data for non-employed individuals in the organization. Consequently, the demand for accessing, using, and managing EHR data at scale has impacted the over.Full article