Applications and limitations of Centers for Disease Control and Prevention miniature light traps for measuring biting densities of African malaria vector populations: a pooled-analysis of 13 comparisons with human landing catches.

BACKGROUND: Measurement of densities of host-seeking malaria vectors is important for estimating levels of disease transmission, for appropriately allocating interventions, and for quantifying their impact. The gold standard for estimating mosquito-human contact rates is the human landing catch (HLC), where human volunteers catch mosquitoes that land on their exposed body parts. This approach necessitates exposure to potentially infectious mosquitoes, and is very labour intensive. There are several safer and less labour-intensive methods, with Centers for Disease Control light traps (LT) placed indoors near occupied bed nets being the most widely used. METHODS: This paper presents analyses of 13 studies with paired mosquito collections of LT and HLC to evaluate these methods for their consistency in sampling indoor-feeding mosquitoes belonging to the two major taxa of malaria vectors across Africa, the Anopheles gambiae sensu lato complex and the Anopheles funestus s.l. group. Both overall and study-specific sampling efficiencies of LT compared with HLC were computed, and regression methods that allow for the substantial variations in mosquito counts made by either method were used to test whether the sampling efficacy varies with mosquito density. RESULTS: Generally, LT were able to collect similar numbers of mosquitoes to the HLC indoors, although the relative sampling efficacy, measured by the ratio of LT:HLC varied considerably between studies. The overall best estimate for An. gambiae s.l. was 1.06 (95% credible interval: 0.68-1.64) and for An. funestus s.l. was 1.37 (0.70-2.68). Local calibration exercises are not reproducible, since only in a few studies did LT sample proportionally to HLC, and there was no geographical pattern or consistent trend with average density in the tendency for LT to either under- or over-sample. CONCLUSIONS: LT are a crude tool at best, but are relatively easy to deploy on a large scale. Spatial and temporal variation in mosquito densities and human malaria transmission exposure span several orders of magnitude, compared to which the inconsistencies of LT are relatively small. LT, therefore, remain an invaluable and safe alternative to HLC for measuring indoor malaria transmission exposure in Africa.

Patients' and Nurses' Perceptions of Diabetes Self-Management in Oman: A Qualitative Study.

Patient-centered care enhances diabetes self-management; however, the primary care nurse's role in promoting diabetes self-management within a patient-centered care model is unexplored. This study investigated the perceptions of Omani patients with type-2 diabetes and their clinic nurses on the nurses' role in promoting diabetes self-management within a patient-centered care approach. The thematic analysis of the data from individual interviews with patients (n = 24) revealed two themes: patients experienced "missteps on an unclear path" and "nurses doing their best." Patients struggled to identify treatment goals and faltered in their attempts to adopt diabetes self-management behaviors. The nurses' role was perceived as task-oriented. Nurse narratives (n = 21) revealed that very few nurses were aware of the patient-centered care philosophy. A theme emerged of nurses "needing a new perspective" to transition their care delivery to align with the patient-centered care model. Nurses expected patients to comply with their instructions and missed opportunities for assessment, engagement, and collaborative problem-solving during patient encounters. The shift from a physician-based medical model to a patient-centered primary care delivery system may necessitate that nurses engage more effectively with patients, collaborate on an individual treatment plan, and motivate them to adopt self-management behaviors.

Health inequalities among older men and women in Africa and Asia: evidence from eight Health and Demographic Surveillance System sites in the INDEPTH WHO-SAGE Study.

BACKGROUND: Declining rates of fertility and mortality are driving demographic transition in all regions of the world, leading to global population ageing and consequently changing patterns of global morbidity and mortality. Understanding sex-related health differences, recognising groups at risk of poor health and identifying determinants of poor health are therefore very important for both improving health trajectories and planning for the health needs of ageing populations. OBJECTIVES: To determine the extent to which demographic and socio-economic factors impact upon measures of health in older populations in Africa and Asia; to examine sex differences in health and further explain how these differences can be attributed to demographic and socio-economic determinants. METHODS: A total of 46,269 individuals aged 50 years and over in eight Health and Demographic Surveillance System (HDSS) sites within the INDEPTH Network were studied during 2006-2007 using an abbreviated version of the WHO Study on global AGEing and adult health (SAGE) Wave I instrument. The survey data were then linked to longitudinal HDSS background information. A health score was calculated based on self-reported health derived from eight health domains. Multivariable regression and post-regression decomposition provide ways of measuring and explaining the health score gap between men and women. RESULTS: Older men have better self-reported health than older women. Differences in household socio-economic levels, age, education levels, marital status and living arrangements explained from about 82% and 71% of the gaps in health score observed between men and women in South Africa and Kenya, respectively, to almost nothing in Bangladesh. Different health domains contributed differently to the overall health scores for men and women in each country. CONCLUSION: This study confirmed the existence of sex differences in self-reported health in low- and middle-income countries even after adjustments for differences in demographic and socio-economic factors. A decomposition analysis suggested that sex differences in health differed across the HDSS sites, with the greatest level of inequality found in Bangladesh. The analysis showed considerable variation in how differences in socio-demographic and economic characteristics explained the gaps in self-reported health observed between older men and women in African and Asian settings. The overall health score was a robust indicator of health, with two domains, pain and sleep/energy, contributing consistently across the HDSS sites. Further studies are warranted to understand other significant individual and contextual determinants to which these sex differences in health can be attributed. This will lay a foundation for a more evidence-based approach to resource allocation, and to developing health promotion programmes for older men and women in these settings.