Modern Contraceptive and Dual Method Use among HIV-Infected Women in Lusaka, Zambia

HIV-infected women in sub-Saharan Africa are at substantial risk of unintended pregnancy and sexually transmitted infections (STIs). Linkages between HIV and reproductive health services are advocated. We describe implementation of a reproductive health counseling intervention in 16 HIV clinics in Lusaka, Zambia. Between November 2009 and November 2010, 18,407 women on antiretroviral treatment (ART) were counseled. The median age was 34.6 years (interquartile range (IQR): 29.9–39.7), and 60.1% of women were married. The median CD4+ cell count was 394 cells/uL (IQR: 256–558). Of the women counseled, 10,904 (59.2%) reported current modern contraceptive use. Among contraceptive users, only 17.7% reported dual method use. After counseling, 737 of 7,503 women not previously using modern contraception desired family planning referrals, and 61.6% of these women successfully accessed services within 90 days. Unmet contraceptive need remains high among HIV-infected women. Additional efforts are needed to promote reproductive health, particularly dual method use

Genetic diversity fuels gene discovery for tobacco and alcohol use

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury(1-4). These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries(5). Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.Peer reviewe

Participation in and Compliance with Public Voluntary Environmental Programs: An Evolutionary Approach

The joint evolution of participating and complying firms in a public VA, along with the evolution of the pollution stock is examined. Replicator dynamics modeling participation and compliance are combined with pollution stock dynamics. Fast-slow selection dynamics are used to capture the fact that decisions to participate in and further comply with the public VA evolve in different time scales. Evolutionary stable (ES) equilibria depend on the structure of the legislation and auditing probability. Partial participation and partial compliance can be ES equilibria, with possible multiplicities, in addition to the monomorphic equilibria of full (non) compliance. Convergence to these equilibria could be monotonic or oscillating. Full participation and compliance can be attained if the regulator is pre-committed to certain legislation and inspection probabilities, or by appropriate choices of the legislatively set emission level and the non-compliance fine

International Cooperation to Resolve International Pollution Problems

This article provides a non-technical overview of important results of the game theoretical literature on the formation and stability of international environmental agreements (IEAs) on transboundary pollution control. It starts out by sketching features of first and second best solutions to the problem of transboundary pollution. It then argues that most actual IEAs can be considered at best as third best solutions. Therefore, three questions are raised: 1) Why is there a difference between actual IEAs and first and second best solutions? 2) Which factors determine this difference? 3) Which measures can help to narrow this difference? This article attempts to answer these questions after giving an informal introduction to coalition models

Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions

A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2-degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P <1 x 10(-6)) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P <5 x 10(-8) using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models.Peer reviewe

Order Flow and the Formation of Dealer Bids: An Analysis of Information and Strategic Behavior in the Government of Canada Securities Auctions

Using data on Government of Canada securities auctions, this paper shows that in countries where direct access to primary issuance is restricted to government securities dealers, Order-flow" information is a key source of private information for these security dealers. Order-flow information is revealed to a security dealer through his interactions with customers, who can place bids in the auctions only through the security dealer. Since each dealer interacts with a different set of customers, they, in effect, see different portions of the market demand and supply curves, leading to differing private inferences of where the equilibrium price might

Individual demographic characteristics of individuals enrolled in the original study and missed population study, adults 15 years and older.

The original serosurvey was carried out in April—June 2022 in Ndola and Choma districts, Zambia, using stratified multi-stage clustering design. The follow-up missed population study was carried out in a subset of clusters of the original survey between July—August 2022. This study was carried out in a subsample of clusters from the original survey; in each selected cluster, a sample of households not available during listing of the original serosurvey, and hence excluded from its sampling frame, were randomly selected. (DOCX)</p

Healthcare-seeking and characteristics reported by caregivers of children 1–4 years old and 5–14 years old.

Results presented are for univariable analysis, by district and age group, and multivariable analysis, by age group only. “Original” refers to the serosurvey carried out in April—June 2022 in Ndola and Choma districts, Zambia, using stratified multi-stage clustering design. “Missed” refers to the study sample from the follow-up missed population study, carried out in a subset of clusters of the original survey between July—August 2022. This study was carried out in a subsample of clusters from the original survey; in each selected cluster, a sample of households not available during listing of the original serosurvey, and hence excluded from its sampling frame, were randomly selected.</p

Status of households enrolled in the original community-based measles serological survey and missed populations study, Ndola and Choma Districts, Zambia, 2022.

A. The distribution of household status from listing in the original serosurvey conducted in Choma and Ndola Districts, by cluster. Households classified as “Available” provided consent to participate in the study and reported that they would be available during the data collection; these households comprised the sampling frame for the original study. Households that refused (“Refused”) were excluded from the original study sampling frame and were ineligible for the missed populations study. Households classified as “Non-contact” were households that were locked at the time of listing (and during revisits), or if there was no adult respondent at home, and nobody was available to provide information about the household (e.g. neighbor). Finally, households that were listed but which reported not being available during data collection (“Contact, not available”) were excluded from the sampling frame in the original study. The households in the latter two categories were eligible for the missed populations study. Clusters are arranged in descending order by percentage of households eligible for the missed populations study (“Non-contact” and “Contact, not available” households). “X”‘s indicate clusters selected for the missed population study. B. Distribution of households that the data collection team attempted to reach by status, cluster, and district in the missed populations study. Households classified as “Completed” were successfully located and provided consent to participate in the study. “Household not found” indicates households identified for inclusion in the missed populations study that could not be located during this study. “Non-contact” refers to households which were physically located, but ones in which the data collection team could not contact its occupants. No household refused participation. Clusters are arranged in order of decreasing percent missed in the missed populations study, comprised of “Household Not Found” and “Non-contact” households.</p