Lung cancer mortality and associated predictors: systematic review using 32 scientific research findings.

Background: Cancer is a chronic disease brought on by mutations to the genes that control our cells' functions and become the most common cause of mortality and comorbidities. Thus, this study aimed to assess the comprehensive and common mortality-related risk factors of lung cancer using more than thirty scientific research papers. Methods: Possible risk factors contributing to lung cancer mortality were assessed across 201 studies sourced from electronic databases, including Google Scholar, Cochrane Library, Web of Science (WOS), EMBASE, Medline/PubMed, the Lung Cancer Open Research Dataset Challenge, and Scopus. Out of these, 32 studies meeting the eligibility criteria for meta-analysis were included. Due to the heterogeneous nature of the studies, a random-effects model was applied to estimate the pooled effects of covariates. Results: The overall prevalence of mortality rate was 10% with a 95% confidence interval of 6 and 16%. Twenty studies (62.50%) studies included in this study considered the ages of lung cancer patients as the risk factors for mortality. Whereas, eighteen (56.25%) and thirteen (40.63%) studies incorporated the gender and smoking status of patients respectively. The comorbidities of lung cancer mortality such as cardiovascular disease, hypertension, diabetes, and pneumonia were also involved in 7 (21.90%), 6 (18.75%), 5 (15.63%), and 2 (6.25%) studies, respectively. Patients of older age are more likely to die as compared to patients of younger age. Similarly, lung patients who had smoking practice were more likely to die as compared to patients who hadn't practiced smoking. Conclusion: The mortality rate of lung cancer patients is considerably high. Older age, gender, stage, and comorbidities such as cardiovascular, hypertension, and diabetes have a significant positive effect on lung cancer mortality. The study results will contribute to future research, management, and prevention strategies for lung cancer.

Mapping the disparities in intimate partner violence prevalence and determinants across Sub-Saharan Africa.

Objective: This study aimed to map disparities in prevalence and associated factors across countries in Sub-Saharan Africa. Methods: We used National Demographic and Health Survey (DHS) data from 26 countries in the region with 114,340 participants. Women and girls in the reproductive age group of 15 to 49 years were included in the study. To map disparities across countries and their provinces, we employed the kriging interpolation technique. We used STATA for data management. Result: The prevalence of physical, emotional and sexual IPV in Sub-Saharan Africa was 30.58, 30.22, and 12.6% respectively, and at least one form of IPV was 42.62%. Disparities were observed across the countries and provinces in each country. Younger age, secondary-level education and above, moderate participation in decision-making, not working out of home, not afraid of the spouse, rich (wealth index), not having a child, high maternal literacy, and rural residence relatively decreased the odds of IPV. The husbands' lower education, alcohol consumption, and high controlling behavior increased the probability of IPV. Conclusion: The prevalence of Intimate Partner Violence in Sub-Saharan Africa is the highest in the world, a signal that the global agenda to end all forms of violence against women will be difficult to achieve. There is a large gap across countries and provinces in each country. Area-specific intervention packages that focus on modifiable factors should be strengthened.

Statistical models for longitudinal zero-inflated count data: application to seizure attacks.

BACKGROUND: Chronic non-communicable diseases:- such as epilepsy, are increasingly recognized as public health problems in developing and African countries. This study aimed at finding determinants of the number of epileptic seizure attacks using different count data modeling techniques. METHODS: Four common fixed-effects Poisson family models were reviewed to analyze the count data with a high proportion of zeros in longitudinal outcome, i.e., the number of seizure attacks in epilepsy patients. This is because, in addition to the problem of extra zeros, the correlation between measurements upon the same patient at different occasions needs to be taken into consideration. RESULTS: The investigation remarkably identified some important factors associated with epileptic seizure attacks. As people grow old, the number of seizure attacks increased and male patients had more seizures than their female counterparts. In general, a patient's age, sex, monthly income, family history of epilepsy andservice satisfaction were some of the significant factors responsible for the frequency of seizure attacks (P value<0.05). CONCLUSION: This study suggests that zero-inflated negative binomial is the best model for predicting and describing the number of seizure attacks as well as identifying the potential risk factors. Addressing these risk factors will definitely contain the progression of seizure attack.