Search

HIV estimation using data from the demographic and health surveys (DHS) is limited by the presence of non‐response and test refusals. Conventional adjustments such as imputation require the data to be missing at random. Methods that use instrumental variables allow the possibility that prevalence is different between the respondents and non‐respondents, but their performance depends critically on the validity of the instrument. Using Manski's partial identification approach, we form instrumental variable bounds for HIV prevalence from a pool of candidate instruments. Our method does not require all candidate instruments to be valid. We use a simulation study to evaluate and compare our method against its competitors. We illustrate the proposed method using DHS data from Zambia, Malawi and Kenya. Our simulations show that imputation leads to seriously biased results even under mild violations of non‐random missingness. Using worst case identification bounds that do not make assumptions about the non‐response mechanism is robust but not informative. By taking the union of instrumental variable bounds balances informativeness of the bounds and robustness to inclusion of some invalid instruments. Non‐response and refusals are ubiquitous in population based HIV data such as those collected under the DHS. Partial identification bounds provide a robust solution to HIV prevalence estimation without strong assumptions. Union bounds are significantly more informative than the worst case bounds without sacrificing credibility. [ABSTRACT FROM AUTHOR]

Background: It is unclear whether thoracic aortic volume (TAV) is useful for cardiovascular (CV) disease prognosis and risk assessment. Purpose: This study evaluated cross‐sectional associations of TAV with CV risk factors, and longitudinal association with incident CV events in the multiethnic study of atherosclerosis. Study Type: Retrospective cohort analysis of prospective data. Population: 1182 participants (69 ± 9 years, 54% female, 37% Caucasian, 18% Chinese, 31% African American, 14% Hispanic, 60% hypertensive, and 20% diabetic) without prior CV disease. Field Strength and sequences: Axial black‐blood turbo spin echo or bright blood steady‐state free precession images on 1.5T scanners. Assessment: TAV was calculated using Simpson's method from axial images, and included the ascending arch and descending segments. Traditional CV risk factors were assessed at the time of MRI. CV outcomes over a 9‐year follow‐up period were recorded and represented a composite of stroke, stroke death, coronary heart disease (CHD), CHD death, atherosclerotic death, and CVD death. Statistical Tests: Multivariable linear regression models adjusted for height and weight were used to determine the relationship (β coefficient) between TAV and CV risk factors. Cox regression models assessed the association of TAV and incident CV events. A P‐value of <0.05 was deemed statistically significant. Results: Mean TAV was = 139 ± 41 mL. In multivariable regression, TAV was directly associated with age (β = 1.6), male gender (β = 23.9), systolic blood pressure (β = 0.1), and hypertension medication use (β = 7.9); and inversely associated with lipid medication use (β = −5.3) and treated diabetes (β = −8.9). Compared to Caucasians, Chinese Americans had higher TAV (β = 11.4), while African Americans had lower TAV (β = −7.0). Higher TAV was independently associated with incident CV events (HR: 1.057 per 10 mL). Conclusion: Greater TAV is associated with incident CV events, increased age, and hypertension in a large multiethnic population while treated diabetes and lipid medication use were associated with lower TAV. Level of Evidence: 2 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

The possibility of non-constant discounting is important in environmental and resource management problems where current decisions affect welfare in the far-distant future, as with climate change. The difficulty of analyzing models with non-constant discounting limits their application. We describe and provide software to implement an algorithm to numerically obtain a Markov Perfect Equilibrium for an optimal control problem with non-constant discounting. The software is available online. We illustrate the approach by studying welfare and observational equivalence for a particular renewable resource management problem.

In this paper, we employ recently completed 'poverty maps' for three countries as tools for an ex ante evaluation of the distributional incidence of geographic targeting of public resources. We simulate the impact on poverty of transferring an exogenously given budget to geographically defined sub-groups of the population according to their relative poverty status. We find large gains from targeting smaller administrative units, such as districts or villages. However, these gains are still far from the poverty reduction that would be possible had the planners had access to information on household level income or consumption. Our results indicate that a useful way forward might be to combine fine geographic targeting using a poverty map with within-community targeting mechanisms. JEL classification: C15; I32; H53 Keywords: Targeting; Poverty; Poverty maps