Browsing by Subject "Risk factors"

Background: Limpopo Province has the highest rates of intimate partner violence (IPV) in South Africa, with data suggesting that over half of women experience IPV in their lifetimes. However, data among young, university-attending women in this province is lacking. This study aimed to estimate the prevalence of IPV victimization among university women and examine factors associated with IPV history. The study also aimed to explore how university women recognize IPV, suggest ways victims seek help, and identify a victim’s coping strategies using qualitative methods.

Methods: This study utilized a mixed methods approach and was conducted at the University of Venda (UNIVEN), a rural-based university in Vhembe district. Convenience sampling was used to recruit female participants who were currently enrolled at the university, aged 18 to 31 years, and currently in a relationship or in a relationship within the past year. 113 females were enrolled in the study. After obtaining written informed consent, we conducted a self-administered cross-sectional survey. IPV was measured using the Revised Conflict Tactics Scale (CTS2), which assessed for both past year and lifetime IPV experiences. To explore the association between IPV and other factors, other measures included an alcohol use screening tool (AUDIT-C) and a measure assessing attitudes toward gender roles. Descriptive statistics and Fisher’s exact tests were performed to assess the relationship between potential risk factors and IPV. Logistic regression analyses examined the associations between exposure variables and lifetime IPV victimization. Short explanatory model interviews (SEMI) examining women’s perceptions of IPV-related issues using a custom vignette were administered directly following the survey. The interviews were recorded and later analyzed using thematic analysis.

Results: 92.23% of participants reported being victims of any form of IPV in their lifetime. Psychological aggression (82.52%) was the most prevalent type of lifetime violence, followed by sexual coercion (73.79%), physical assault (37.86%), and injury (15.53%). The joint frequency distribution of IPV victimization by subscale reveals that 9.71% of participants reported being victims of all four forms of IPV at least once in their lifetime, while most respondents reported experiencing two types of IPV (35.9%). Compared to having no sexual partners in the past year, having two or more sexual partners was significantly associated with higher odds of being a lifetime victim of sexual coercion (p = 0.031; OR: 4.41; 95% CI 1.14 - 17.02). Study findings support an increased odds of lifetime IPV (p = 0.030; OR: 7.04; 95% CI 1.21 – 40.97) and physical assault (p = 0.010; OR: 3.77; 95% CI 1.37 – 10.40) for participants who personally knew an IPV victim at UNIVEN compared to women who did not personally know a victim. Participants who disagreed or strongly disagreed that IPV should be viewed as a crime were 11.37 times more likely to be victims of lifetime sexual coercion than those who agreed (p = 0.027; OR: 11.37; 95% CI 1.32 - 97.82). The SEMI revealed most women recognized IPV in the vignette, and the recommended help-seeking behaviors included seeking informal and formal help, leaving the relationship, and changing behavior.

Conclusions: IPV prevalence among the study sample was reported nearly universally. Number of sexual partners, personally knowing a victim of IPV at the university, and attitudes toward gender roles were significantly associated with having a history of IPV. University commitment and multi-sectoral collaboration at all levels are critical for the provision of resources, services, and violence prevention efforts. Future research is needed to inform evidence-based interventions that will reduce victimization by addressing risk factors, under-reporting, and barriers to seeking help.

Background: Previous studies in Sri Lanka showed a high prevalence- 30% of postpartum depression (PPD). PPD screening using the Edinburgh Postnatal Depression Scales (EPDS) was included in postnatal care in 2012. This study aimed to estimate the prevalence of PPD in 2017 in two medical offices of health (MOH) areas, identify the association between risk factors and presence of postpartum depression, understand current practice, challenges, and suggestions of PPD screening in Sri Lanka.

Methods: The study consists of a population-based quantitative study and a qualitative study. PPD outcomes were assessed by mothers’ responses to the EPDS. Potential factors were extracted from routine paper-based medical records. The association was examined at unadjusted level first, and at adjusted level using multivariate linear regression and multivariate logistic regression models. Individual in-depth interviews were conducted among public health midwives. Framework approach was adopted to analyze the transcripts.

Results: The prevalence of PPD was 15.5% and 7.8% among mothers assessed 10 days postpartum (in Dankotuwa) and 4 weeks postpartum (in Bope Poddala), respectively. PPD was associated with earlier screening time, mothers’ delivery age > 35, >= 4 living children, and mothers’ illness. Mothers who attended prenatal sessions and whose partners were employed were less likely to report potential PPD. Other risk factors of PPD noted from interviews include socio-economic factors, interpersonal relationship, mother’s disease history, delivery method, and baby’s illness. The challenges of screening PPD included social stigma, mother’s difficulty of understanding EPDS and lack of privacy at home.

Conclusions: Mothers exposed to various socio-economic, interpersonal, and other risk factors deserve special attention. Family-based interventions, further cultural validation of EPDS, development of risk-assessing instrument could be introduced for future practice. Future research on other risk factors for PPD with larger sample size should be conducted, and qualitative research could engage other stakeholders in maternal mental health care to assess the accessibility, capacity, and quality of PPD care.

Abstract

Background: Traumatic brain injury (TBI) is a major cause of death and disability. About 10 million people annually are affected by TBI, with a prominent burden in low- and middle-income countries (LMICs). In Brazil, TBI is responsible for 125,500 admissions and 9700 hospital deaths annually. The poor prognosis could be caused by insufficient medical professionals and diagnostic machines. This study aims to find an optimum TBI prognostic model to serve as a diagnostic tool that can be adapted from prior work in Tanzania to Brazil. We aim to develop an effective TBI prognostic model that could be generalized in LMICs.

Methods: The study was a secondary data analysis on clinical and sociodemographic variables of 3209 TBI patients at Kilimanjaro Christian Medical Center (KCMC) and 725 TBI patients at six Brazilian traumatic care hospitals. We trained and tested eight machine learning models using three strategies: 1) using Tanzanian dataset trained models to test Brazilian dataset, 2) using Tanzanian-Brazilian combined dataset for training and testing and 3) using Brazilian dataset for training and testing. We compared the performance of models using confusion matrix statistics: area under the ROC curve(AUC), sensitivity, specificity, positive predictive value, negative predictive value and accuracy.

Findings: Models using Tanzanian-Brazilian combined dataset for training and testing outperformed models of other two strategies. The AUC of the models varied from 80.9% (K nearest neighbor) to 91.9% (Random Forest). The optimum model, Random Forest, had a strong predictive power of classification with sensitivity of 0.927, specificity of 0.756, positive predictive value of 0.960, negative predictive value of 0.620 and accuracy of 0.903.

Interpretations: Our study shows the successful adaptation of TBI prognostic model from Tanzania to Brazil. Additionally, it indicates the possibility of generalizing a TBI prognostic model to LMICs. With larger multi-national data, we hope to develop an effective model that could accurately predict the potential outcome of TBI patients. The model could serve as a powerful auxiliary tool for diagnosis and help reduce mortality of TBI patients in LMICs.

Source of Funding: The project is conducted with the funding from Duke Global Health Institute.