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Background: In Northwestern Switzerland, recent legislation tackles the needs of community-dwelling older adults by creating Information and Advice Centers (IACs). IACs are a new service in the community that aims to assess the needs and provide information on age-related issues to community-dwelling older adults and their families. Previous studies reported difficulties in reaching community-dwelling older adults for community-based programs. We aimed to: 1) systematically identify implementation strategies to promote the IAC among community care providers, older adults and informal caregivers; 2) monitor the delivery of these strategies by the IAC management; and 3) describe the impact of those strategies on reach of community-dwelling older adults. This study was conducted as part of the TRANS-SENIOR project. Methods: As part of the INSPIRE feasibility assessment, we conducted a pre-test post-test study between March and September 2022. The sample included 8,840 older adults aged 65 + visiting/calling or being referred to the IAC for the first time. Implementation strategies were selected using implementation mapping and organized in bundles for each group of community care providers and older adults/caregivers. Our evaluation included: estimation of fidelity to the delivery of implementation strategies and bundles by the IAC management and their coverage; referral source of older adults to the IAC; and impact of the strategies on reach of the IAC on the 65 + population living in the care region. Adaptations to the strategies were documented using the FRAME-IS. Descriptive statistics were calculated and reported. Results: Seven implementation strategies were selected and organized in bundles for each community care provider and older adults and their caregivers. The lowest fidelity score was found in implementation strategies selected for nursing homes whereas the highest score corresponded to strategies targeting older adults and caregivers. "Informational visits" was the strategy with the lowest coverage (2.5% for nursing homes and 10.5% for hospitals and specialized clinics). The main referral sources were self-referrals and referrals by caregivers, followed by nursing homes. The IAC reach among the 65 + population was 5.4%. Conclusion: We demonstrated the use of implementation mapping to select implementation strategies to reach community-dwelling older adults. The reach was low suggesting that higher fidelity to the delivery of the strategies, and reflection on the causal pathway of the implementation strategies might be needed. [ABSTRACT FROM AUTHOR]

Background: Malaria remains one of the deadliest diseases worldwide, especially among young children in sub-Saharan Africa. Predictive models are necessary for effective planning and resource allocation; however, statistical models suffer from association pitfalls. In this study, we used empirical dynamic modelling (EDM) to investigate causal links between climatic factors and intervention coverage with malaria for short-term forecasting., Methods: Based on data spanning the period from 2008 to 2022, we used convergent cross-mapping (CCM) to identify suitable lags for climatic drivers and investigate their effects, interaction strength, and suitability ranges on malaria incidence. Monthly malaria cases were collected at St. Elizabeth Lwak Mission Hospital. Intervention coverage and population movement data were obtained from household surveys in Asembo, western Kenya. Daytime land surface temperature (LSTD), rainfall, relative humidity (RH), wind speed, solar radiation, crop cover, and surface water coverage were extracted from remote sensing sources. Short-term forecasting of malaria incidence was performed using state-space reconstruction., Results: We observed causal links between climatic drivers, bed net use, and malaria incidence. LSTD lagged over the previous month; rainfall and RH lagged over the previous two months; and wind speed in the current month had the highest predictive skills. Increases in LSTD, wind speed, and bed net use negatively affected incidence, while increases in rainfall and humidity had positive effects. Interaction strengths were more pronounced at temperature, rainfall, RH, wind speed, and bed net coverage ranges of 30-35°C, 30-120 mm, 67-80%, 0.5-0.7 m/s, and above 90%, respectively. Temperature and rainfall exceeding 35°C and 180 mm, respectively, had a greater negative effect. We also observed good short-term predictive performance using the multivariable forecasting model (Pearson correlation coefficient = 0.85, root mean square error = 0.15)., Conclusions: Our findings demonstrate the utility of CCM in establishing causal linkages between malaria incidence and both climatic and non-climatic drivers. By identifying these causal links and suitability ranges, we provide valuable information for modelling the impact of future climate scenarios., Competing Interests: Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests., (Copyright © 2024 by the Journal of Global Health. All rights reserved.)

Background: Malaria mortality is influenced by several factors including climatic and environmental factors, interventions, socioeconomic status (SES) and access to health systems. Here, we investigated the joint effects of climatic and non-climatic factors on under-five malaria mortality at different spatial scales using data from a Health and Demographic Surveillance System (HDSS) in western Kenya., Methods: We fitted Bayesian spatiotemporal (zero-inflated) negative binomial models to monthly mortality data aggregated at the village scale and over the catchment areas of the health facilities within the HDSS, between 2008 and 2019. First order autoregressive temporal and conditional autoregressive spatial processes were included as random effects to account for temporal and spatial variation. Remotely sensed climatic and environmental variables, bed net use, SES, travel time to health facilities, proximity from water bodies/streams and altitude were included in the models to assess their association with malaria mortality., Results: Increase in rainfall (mortality rate ratio (MRR)=1.12, 95% Bayesian credible interval (BCI): 1.04-1.20), Normalized Difference Vegetation Index (MRR=1.16, 95% BCI: 1.06-1.28), crop cover (MRR=1.17, 95% BCI: 1.11-1.24) and travel time to the hospital (MRR=1.09, 95% BCI: 1.04-1.13) were associated with increased mortality, whereas increase in bed net use (MRR=0.84, 95% BCI: 0.70-1.00), distance to the nearest streams (MRR=0.89, 95% BCI: 0.83-0.96), SES (MRR=0.95, 95% BCI: 0.91-1.00) and altitude (MRR=0.86, 95% BCI: 0.81-0.90) were associated with lower mortality. The effects of travel time and SES were no longer significant when data was aggregated at the health facility catchment level., Conclusion: Despite the relatively small size of the HDSS, there was spatial variation in malaria mortality that peaked every May-June. The rapid decline in malaria mortality was associated with bed nets, and finer spatial scale analysis identified additional important variables. Time and spatially targeted control interventions may be helpful, and fine spatial scales should be considered when data are available., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)