Perceived Impact of COVID-19 Among Callers to the National Suicide Prevention Lifeline.

Background: Research indicates that the COVID-19 pandemic caused increases in psychological distress and suicidal ideation. Aims: To describe the ways suicidal callers to the National Suicide Prevention Lifeline (Lifeline) perceived COVID-19 to have impacted them and assess whether these callers perceived COVID-19-related stress as contributing to their suicidal thoughts. Method: Telephone interviews were conducted with 412 suicidal callers to 12 Lifeline centers. Logistic regression analyses were used to examine the associations between demographic factors and individual COVID-19 stressors and to determine whether callers who endorsed COVID-19-related stress as contributing to their suicidal thoughts differed from those who did not regarding demographics, current suicide risk, history of suicidality, Lifeline use, or individual COVID-19 stressors. Results: Over half of callers reported that COVID-19-related stress contributed to their suicidal ideation (CRSSI). Callers who endorsed CRSSI had higher odds than those who did not of mentioning financial difficulties when asked how COVID-19 impacted them. The two groups of callers did not differ on the other factors examined. Limitations: Interviewed callers may not be representative of all Lifeline callers. Conclusion: Despite the subjective burden of COVID-19-related stress on suicidal Lifeline callers, this was not associated with new suicidality or heightened suicide risk.

Latino Political Attitudes: Myths and Misconceptions.

The Latino population explosion is changing the electoral calculus in American politics. But Latino voters are often misunderstood, their heterogeneity underappreciated, the importance of their religious identities overstated, and their political clout ignored because of geographic concentration. In this article, we address these widely held misconceptions.

Quantifying and reducing statistical uncertainty in sample-based health program costing studies in low- and middle-income countries.

OBJECTIVES: In many low- and middle-income countries, the costs of delivering public health programs such as for HIV/AIDS, nutrition, and immunization are not routinely tracked. A number of recent studies have sought to estimate program costs on the basis of detailed information collected on a subsample of facilities. While unbiased estimates can be obtained via accurate measurement and appropriate analyses, they are subject to statistical uncertainty. Quantification of this uncertainty, for example, via standard errors and/or 95% confidence intervals, provides important contextual information for decision-makers and for the design of future costing studies. While other forms of uncertainty, such as that due to model misspecification, are considered and can be investigated through sensitivity analyses, statistical uncertainty is often not reported in studies estimating the total program costs. This may be due to a lack of awareness/understanding of (1) the technical details regarding uncertainty estimation and (2) the availability of software with which to calculate uncertainty for estimators resulting from complex surveys. We provide an overview of statistical uncertainty in the context of complex costing surveys, emphasizing the various potential specific sources that contribute to overall uncertainty. METHODS: We describe how analysts can compute measures of uncertainty, either via appropriately derived formulae or through resampling techniques such as the bootstrap. We also provide an overview of calibration as a means of using additional auxiliary information that is readily available for the entire program, such as the total number of doses administered, to decrease uncertainty and thereby improve decision-making and the planning of future studies. RESULTS: A recent study of the national program for routine immunization in Honduras shows that uncertainty can be reduced by using information available prior to the study. This method can not only be used when estimating the total cost of delivering established health programs but also to decrease uncertainty when the interest lies in assessing the incremental effect of an intervention. CONCLUSION: Measures of statistical uncertainty associated with survey-based estimates of program costs, such as standard errors and 95% confidence intervals, provide important contextual information for health policy decision-making and key inputs for the design of future costing studies. Such measures are often not reported, possibly because of technical challenges associated with their calculation and a lack of awareness of appropriate software. Modern statistical analysis methods for survey data, such as calibration, provide a means to exploit additional information that is readily available but was not used in the design of the study to significantly improve the estimation of total cost through the reduction of statistical uncertainty.

Evaluating Public Health Interventions: 3. The Two-Stage Design for Confounding Bias Reduction-Having Your Cake and Eating It Two.

In public health evaluations, confounding bias in the estimate of the intervention effect will typically threaten the validity of the findings. It is a common misperception that the only way to avoid this bias is to measure detailed, high-quality data on potential confounders for every intervention participant, but this strategy for adjusting for confounding bias is often infeasible. Rather than ignoring confounding altogether, the two-phase design and analysis-in which detailed high-quality confounding data are obtained among a small subsample-can be considered. We describe the two-stage design and analysis approach, and illustrate its use in the evaluation of an intervention conducted in Dar es Salaam, Tanzania, of an enhanced community health worker program to improve antenatal care uptake.