r2mlm: An R package calculating R-squared measures for multilevel models.

Multilevel models are used ubiquitously in the social and behavioral sciences and effect sizes are critical for contextualizing results. A general framework of R-squared effect size measures for multilevel models has only recently been developed. Rights and Sterba (2019) distinguished each source of explained variance for each possible kind of outcome variance. Though researchers have long desired a comprehensive and coherent approach to computing R-squared measures for multilevel models, the use of this framework has a steep learning curve. The purpose of this tutorial is to introduce and demonstrate using a new R package - r2mlm - that automates the intensive computations involved in implementing the framework and provides accompanying graphics to visualize all multilevel R-squared measures together. We use accessible illustrations with open data and code to demonstrate how to use and interpret the R package output.

Addressing a crisis of generalizability with large-scale construct validation.

Because of the misspecification of models and specificity of operationalizations, many studies produce claims of limited utility. We suggest a path forward that requires taking a few steps back. Researchers can retool large-scale replications to conduct the descriptive research which assesses the generalizability of constructs. Large-scale construct validation is feasible and a necessary next step in addressing the generalizability crisis.

The epidemiological impact of the Canadian COVID Alert app.

OBJECTIVES: We analyzed the effectiveness of the Canadian COVID Alert app on reducing COVID-19 infections and deaths due to the COVID-19 virus. METHODS: Two separate but complementary approaches were taken. First, we undertook a comparative study to assess how the adoption and usage of the COVID Alert app compared to those of similar apps deployed in other regions. Next, we used data from the COVID Alert server and a range of plausible parameter values to estimate the numbers of infections and deaths averted in Canada using a model that combines information on number of notifications, secondary attack rate, expected fraction of transmissions that could be prevented, quarantine effectiveness, and expected size of the full transmission chain in the absence of exposure notification. RESULTS: The comparative analysis revealed that the COVID Alert app had among the lowest adoption levels among apps that reported usage. Our model indicates that use of the COVID Alert app averted between 6284 and 10,894 infections across the six Canadian provinces where app usage was highest during the March-July 2021 period. This range is equivalent to 1.6-2.9% of the total recorded infections across Canada in that time. Using province-specific case fatality rates, 57-101 deaths were averted during the same period. The number of cases and deaths averted was greatest in Ontario, whereas the proportion of cases and deaths averted was greatest in Newfoundland and Labrador. App impact measures were reported so rarely and so inconsistently by other regions that the relative assessment of impact is inconclusive. CONCLUSION: While the nationwide rates are low, provinces with widespread adoption of the app showed high ratios of averted cases and deaths (upper bound was greater than 60% of averted cases). This finding suggests that the COVID Alert app, when adopted at sufficient levels, can be an effective public health tool for combatting a pandemic such as COVID-19.

RéSUMé: OBJECTIFS: Nous avons analysé l'efficacité de l'application canadienne Alerte COVID sur la réduction des infections à la COVID-19 et des décès dus au virus COVID-19. MéTHODES: Deux approches distinctes mais complémentaires ont été adoptées. D'abord, nous avons entrepris une étude comparative pour évaluer comment l'adoption et l'utilisation de l'application Alerte COVID se comparent à celles d'applications similaires déployées dans d'autres régions. Ensuite, nous avons utilisé les données du serveur Alerte COVID et plusieurs valeurs de paramètres plausibles pour estimer le nombre d'infections et de décès évités au Canada à l'aide d'un modèle combinant des informations sur le nombre de notifications, le taux d'attaque secondaire, la fraction attendue des transmissions pouvant être prévenues, l'efficacité de la mise en quarantaine et la taille attendue de la chaîne de transmission complète en l'absence de notification d'exposition. RéSULTATS: L'analyse comparative a révélé que l'application Alerte COVID avait l'un des niveaux d'adoption les plus bas parmi les applications qui ont rapporté une utilisation. Notre modèle indique que l'utilisation de l'application Alerte COVID a évité entre 6 284 et 10 894 infections dans les six provinces canadiennes où l'utilisation de l'application était la plus élevée au cours de la période de mars à juillet 2021. Ces nombres correspondent à 1,6 % à 2,9 % du nombre total d'infections enregistrées au Canada pendant cette période. En utilisant les taux de létalité propres à chaque province, 57 à 101 décès ont été évités au cours de la même période. Le nombre de cas et de décès évités était le plus élevé en Ontario, tandis que la proportion de cas et de décès évités était la plus élevée à Terre-Neuve-et-Labrador. Les mesures d'impact des applications dans d'autres régions ont été rapportées si rarement et de manière si incohérente que l'évaluation relative de l'impact n'est pas concluante. CONCLUSION: Bien que les taux à l'échelle nationale soient faibles, les provinces où l'adoption de l'application était généralisée ont affiché des ratios élevés de cas et de décès évités (la limite supérieure était supérieure à 60 % des cas évités). Ces résultats suggèrent que l'application Alerte COVID, lorsqu'elle est adoptée à des niveaux suffisants, peut être un outil de santé publique efficace pour lutter contre une pandémie telle que la COVID-19.

Improving uptake of cervical cancer screening services for women living with HIV and attending chronic care services in rural Malawi.

Malawi has the second highest age-standardised incidence rate and the highest mortality rate of cervical cancer in the world. Though the prevalence of HIV is currently 11.7% for Malawian women of reproductive age, cervical cancer screening rates remain low. To address this issue, we integrated cervical cancer screening into a dual HIV and non-communicable disease clinic at a rural district hospital in Neno, Malawi. The project was implemented between January 2017 and March 2018 using the Plan-Do-Study-Act model of quality improvement (QI). At baseline (January to December 2016), only 13 women living with HIV were screened for cervical cancer. One year after implementation of the QI project, 73% (n=547) of women aged 25 to 49 years living with HIV enrolled in HIV care were screened for cervical cancer, with 85.3% of these receiving the screening test for the first time. The number of women living with HIV accessing cervical cancer services increased almost 10 times (from four per month to 39 per month, p<0.001). Key enablers in our QI process included: strong mentorship, regular provision of cervical cancer health talks throughout the hospital, nationally accredited cervical cancer prevention training for all providers, consistent community engagement, continuous monitoring and evaluation, and direct provision of resources to strengthen gaps in the public system. This practical experience integrating cervical cancer screening into routine HIV care may provide valuable lessons for scale-up in rural Malawi.