Medicine and Health Sciences Commons^™

Understanding The Needs Of A Mobile Phone-Based Telemonitoring Program For Pregnant Women At High Risk For Pre-Eclampsia: Interpretive Qualitative Description Study, Anam Shahil Feroz, Kristina De Vera, Nadia D. Bragagnolo, Sarah Saleem, Zulfiqar Ahmed Bhutta, Emily Seto

Community Health Sciences

Background: Lack of early risk detection, diagnosis, and treatment of pregnant women at high risk for pre-eclampsia can result in high maternal mortality and morbidity not only in Pakistan but also in other low- to middle-income countries (LMICs). A potential tool for supporting pregnant women at high risk for pre-eclampsia for early detection is telemonitoring (TM). However, there is a limited body of evidence on end-user needs and preferences to inform the design of the TM programs for pregnant women at high risk for pre-eclampsia, specifically in LMICs such as Pakistan.
Objective: This study aims to explore the needs of …

Unintended Pregnancy And The Associated Factors Among Pregnant Females: Sukh Survey-Karachi, Pakistan, Shama Razzaq, Saleem Jessani, Narjis Rizvi, Sarah Saleem

Community Health Sciences

Objective: Unintended pregnancy has become a substantially growing reproductive health concern in South Asian region. The objective of this survey was to assess the magnitude of unintended pregnancy along with its correlates among currently pregnant women residing in Karachi.
Methods: A community based, multistage, cross-sectional study was carried out among 612 currently pregnant women from squatter settlements of Karachi, inquiring for "unintended pregnancy", outcome of interest. Multivariable logistic analysis was done using SPSS v.19 to determine associated factors.
Results: Of 612 pregnant women interviewed, 168(27.4%) reported their pregnancies as unintended. The multivariable regression identified a high likelihood of unintended pregnancy …

Cervical Cancer-Associated Suffering: Estimating The Palliative Care Needs Of A Highly Vulnerable Population, Eric L. Krakauer, Xiaoxiao Kwete, Khadidjatou Kane, Gauhar Afshan, Lisa Bazzett-Matabele, Danta Dona Ruthnie Bien-Aimé, Sarah Byrne-Martelli, Stephen Connor, Raimundo Correa, C R Beena Devi C R Beena Devi

Department of Anaesthesia

Purpose: To enable design of optimum palliative care for women with cervical cancer, we studied the most common types of suffering and their severity, prevalence, and duration.
Methods: We first reviewed the literature on the major types, severity, prevalence, and duration of suffering associated with cervical cancer. We then conducted a modified Delphi process with experts in cervical cancer care to supplement the literature. For each type of suffering, we distinguished between decedents (those who die from cervical cancer in a given year) and nondecedents (those who have cervical cancer in a given year but do not die). By applying …

A Risk Prediction Model For The Assessment And Triage Of Women With Hypertensive Disorders Of Pregnancy In Low-Resourced Settings: The Minipiers (Pre-Eclampsia Integrated Estimate Of Risk) Multi-Country Prospective Cohort Study, Beth A. Payne, Jennifer A. Hutcheon, J. Mark Ansermino, David R. Hall, Zulfiqar Ahmed Bhutta, Shereen Z. Bhutta, Christine Biryabarema, William A. Grobman, Henk Groen, Farizah Haniff, Jing Li, Laura A. Magee, Mario Merialdi, Annettee Nakimuli, Ziguang Qu, Rozina Sikandar Sultanali, Nelson Sass, Diane Sawchuck, D. Wilhelm Steyn, Mariana Widmer, Jian Zhou, Peter Von Dadelszen

Department of Obstetrics & Gynaecology

Background: Pre-eclampsia/eclampsia are leading causes of maternal mortality and morbidity, particularly in low- and middle- income countries (LMICs). We developed the miniPIERS risk prediction model to provide a simple, evidence-based tool to identify pregnant women in LMICs at increased risk of death or major hypertensive-related complications.

Methods and Findings: From 1 July 2008 to 31 March 2012, in five LMICs, data were collected prospectively on 2,081 women with any hypertensive disorder of pregnancy admitted to a participating centre. Candidate predictors collected within 24 hours of admission were entered into a step-wise backward elimination logistic regression model to predict a composite …