An observational study on use of maternal risk factors, mean arterial pressure, mean uterine artery pulsatility index and serum placenta like growth factor for screening of preeclampsia in first trimester

Background: Preeclampsia (PE) affects 2-3% of all pregnancies and is a major cause of maternal and perinatal morbidity and mortality. In the last decade extensive research has been devoted to screening for PE with the aim of reducing the prevalence of the disease through pharmacological intervention in the high-risk group. In our study we used the combined screening method to evaluate the risk of developing preeclampsia in pregnant women. Our primary objective was to estimate the screen positivity rate for preeclampsia using the first trimester combined screening method (maternal risk factors and biophysical methods) in our population in a tertiary care hospital setting. Methods: Risk of preeclampsia was calculated using fetal medicine foundation algorithm accessed at https://fetalmedicine.org/research/assess/preeclampsia. Results: Using the combined screening method, 10 out of 75 women (13.33%) were found to be screen positive for risk of developing preterm preeclampsia (at <37 weeks) with a risk cut off of 1:100. Using the maternal risk factors approach only (as per NICE guidelines) again 10 out of 75 women (13.3%) were found to be screen positive. However, the subset of women who were screen positive by each method were not the same. There were only 4 out of 10 women who were screen positive by both methods. The screen positivity rate for preterm preeclampsia (<37 weeks) in our population using combined screening approach was 13%, which means aspirin would be advisable to 13/100 pregnant women to reduce the risk of preterm preeclampsia. Conclusions: Basis on our study we concluded that one cost effective method of screening could be, to offer aspirin to all women who are screen positive by the maternal risk factor approach (NICE guidelines approach). This approach does not require any extra blood test or skill to measure uterine artery pulsatility index

The stated preferences of community-based volunteers for roles in the prevention of violence against women and girls in Ghana: a discrete choice analysis

Violence against women and girls (VAWG) is a human rights violation with substantial health-related consequences. Interventions to prevent VAWG, often implemented at the community level by volunteers, have been proven effective and cost-effective. One such intervention is the Rural Response System in Ghana, a volunteer-run program which hires community based action teams (COMBATs) to sensitise the community about VAWG and to provide counselling services in rural areas. To increase programmatic impact and maximise the retention of these volunteers, it is important to understand their preferences for incentives. We conducted a discrete choice experiment (DCE) among 107 COMBAT volunteers, in two Ghanaian districts in 2018, to examine their stated preferences for financial and non-financial incentives that could be offered in their roles. Each respondent answered 12 choice tasks, and each task comprised four hypothetical volunteering positions. The first three positions included different levels of five role attributes. The fourth option was to cease volunteering as a COMBAT volunteer (opt-out). We found that, overall, COMBAT volunteers cared most for receiving training in volunteering skills and three-monthly supervisions. These results were consistent between multinomial logit, and mixed multinomial logit models. A three-class latent class model fitted our data best, identifying subgroups of COMBAT workers with distinct preferences for incentives: The younger ‘go getters’; older ‘veterans’, and the ‘balanced bunch’ encompassing the majority of the sample. The opt-out was chosen only 4 (0.3%) times. Only one other study quantitatively examined the preferences for incentives of VAWG-prevention volunteers using a DCE (Kasteng et al., 2016). Understanding preferences and how they vary between sub-groups can be leveraged by programme managers to improve volunteer motivation and retention. As effective VAWG-prevention programmes are scaled up from small pilots to the national level, data on volunteer preferences may be useful in improving volunteer retention

Discrete choice analysis of health worker job preferences in Ethiopia: Separating attribute non-attendance from taste heterogeneity.

When measuring preferences, discrete choice experiments (DCEs) typically assume that respondents consider all available information before making decisions. However, many respondents often only consider a subset of the choice characteristics, a heuristic called attribute non-attendance (ANA). Failure to account for ANA can bias DCE results, potentially leading to flawed policy recommendations. While conventional latent class logit models have most commonly been used to assess ANA in choices, these models are often not flexible enough to separate non-attendance from respondents' low valuation of certain attributes, resulting in inflated rates of ANA. In this paper, we show that semi-parametric mixtures of latent class models can be used to disentangle successfully inferred non-attendance from respondent's "weaker" taste sensitivities for certain attributes. In a DCE on the job preferences of health workers in Ethiopia, we demonstrate that such models provide more reliable estimates of inferred non-attendance than the alternative methods currently used. Moreover, since we find statistically significant variation in the rates of ANA exhibited by different health worker cadres, we highlight the need for well-defined attributes in a DCE, to ensure that ANA does not result from a weak experimental design

Discrete choice analysis of health worker job preferences in Ethiopia: Separating attribute non-attendance from taste heterogeneity.

When measuring preferences, discrete choice experiments (DCEs) typically assume that respondents consider all available information before making decisions. However, many respondents often only consider a subset of the choice characteristics, a heuristic called attribute non-attendance (ANA). Failure to account for ANA can bias DCE results, potentially leading to flawed policy recommendations. While conventional latent class logit models have most commonly been used to assess ANA in choices, these models are often not flexible enough to separate non-attendance from respondents' low valuation of certain attributes, resulting in inflated rates of ANA. In this paper, we show that semi-parametric mixtures of latent class models can be used to disentangle successfully inferred non-attendance from respondent's "weaker" taste sensitivities for certain attributes. In a DCE on the job preferences of health workers in Ethiopia, we demonstrate that such models provide more reliable estimates of inferred non-attendance than the alternative methods currently used. Moreover, since we find statistically significant variation in the rates of ANA exhibited by different health worker cadres, we highlight the need for well-defined attributes in a DCE, to ensure that ANA does not result from a weak experimental design

Development of the SAFE Checklist Tool for Assessing Site-Level Threats to Child Protection: Use of Delphi Methods and Application to Two Sites in India

Background: The child protection community is increasingly focused on developing tools to assess threats to child protection and the basic security needs and rights of children and families living in adverse circumstances. Although tremendous advances have been made to improve measurement of individual child health status or household functioning for use in low-resource settings, little attention has been paid to a more diverse array of settings in which many children in adversity spend time and how context contributes to threats to child protection. The SAFE model posits that insecurity in any of the following fundamental domains threatens security in the others: Safety/freedom from harm; Access to basic physiological needs and healthcare; Family and connection to others; Education and economic security. Site-level tools are needed in order to monitor the conditions that can dramatically undermine or support healthy child growth, development and emotional and behavioral health. From refugee camps and orphanages to schools and housing complexes, site-level threats exist that are not well captured by commonly used measures of child health and well-being or assessments of single households (e.g., SDQ, HOME). Methods: The present study presents a methodology and the development of a scale for assessing site-level child protection threats in various settings of adversity. A modified Delphi panel process was enhanced with two stages of expert review in core content areas as well as review by experts in instrument development, and field pilot testing. Results: Field testing in two diverse sites in India—a construction site and a railway station—revealed that the resulting SAFE instrument was sensitive to the differences between the sites from the standpoint of core child protection issues

Understanding the factors affecting attrition and intention to leave of health extension workers: a mixed methods study in Ethiopia.

BACKGROUND: The Health Extension Program (HEP) is Ethiopia's flagship community health program, launched in 2003. Health Extension Workers (HEWs) are key vehicles for the delivery of the HEP. While it is believed that there is high attrition among HEWs, the magnitude of or reasons for attrition is unknown. Their intention to leave their jobs in the next 5 years has also never been investigated on a national scale. This study aimed to assess the magnitude of, and factors affecting HEWs' attrition and intention to leave in Ethiopia. METHODS: The study used mixed methods to address the research objectives. Using stratified random sampling and regions as strata, 85 districts from nine regions were randomly selected in Ethiopia. Within each study district, six kebeles (village clusters) were randomly selected, and all HEWs working in these kebeles were interviewed to capture their 5-year intention to leave. The study team developed a data-extraction tool for a rapid review of district-level documents covering the period June 30, 2004 through June 30, 2019 to gather their attrition figures. We used survival analysis to model attrition data and checked model goodness-of-fit using the Cox-Snell residual test. We additionally collected qualitative data from HEWs who had left their positions. RESULTS: The attrition of HEWS over the lifespan of the HEP was 21.1% (95% CI 17.5-25.3%), and the median time to exit from HEWs workforce was 5.8 years. The incidence rate was 3.1% [95% CI 2.8-3.4]. The risk of attrition was lower amongst HEWs with level four certifications, with children, and among those working in urban settings. By contrast, HEWs who were not certified with a certificate of competency (COC), who were deployed after 2008, and those who were diploma/degree holders were more likely to exit the HEWs workforce. The magnitude of intention to leave was 39.5% (95% CI 32.5-47%) and the primary reasons to leave were low incentives, dearth of career development opportunities (50.8%), high workload (24.2%), and other psychosocial factors (25%). CONCLUSION: Although the magnitude of attrition is not worryingly high, we see high magnitude in HEWs' intention to leave, indicating a dissatisfied workforce. Multiple factors have contributed to attrition and intention to leave, the prevalence of many of which can be reduced to fit the needs of this workforce and to retain them for the sustained delivery of primary healthcare in the country. Ensuring HEWs' job satisfaction is important and linked with their career development and potentially higher rates of retention