Multiple imputation of incomplete multilevel data using Heckman selection models.

Missing data is a common problem in medical research, and is commonly addressed using multiple imputation. Although traditional imputation methods allow for valid statistical inference when data are missing at random (MAR), their implementation is problematic when the presence of missingness depends on unobserved variables, that is, the data are missing not at random (MNAR). Unfortunately, this MNAR situation is rather common, in observational studies, registries and other sources of real-world data. While several imputation methods have been proposed for addressing individual studies when data are MNAR, their application and validity in large datasets with multilevel structure remains unclear. We therefore explored the consequence of MNAR data in hierarchical data in-depth, and proposed a novel multilevel imputation method for common missing patterns in clustered datasets. This method is based on the principles of Heckman selection models and adopts a two-stage meta-analysis approach to impute binary and continuous variables that may be outcomes or predictors and that are systematically or sporadically missing. After evaluating the proposed imputation model in simulated scenarios, we illustrate it use in a cross-sectional community survey to estimate the prevalence of malaria parasitemia in children aged 2-10 years in five regions in Uganda.

Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis.

BACKGROUND: Many children with pulmonary tuberculosis remain undiagnosed and untreated with related high morbidity and mortality. Recent advances in childhood tuberculosis algorithm development have incorporated prediction modelling, but studies so far have been small and localised, with limited generalisability. We aimed to evaluate the performance of currently used diagnostic algorithms and to use prediction modelling to develop evidence-based algorithms to assist in tuberculosis treatment decision making for children presenting to primary health-care centres. METHODS: For this meta-analysis, we identified individual participant data from a WHO public call for data on the management of tuberculosis in children and adolescents and referral from childhood tuberculosis experts. We included studies that prospectively recruited consecutive participants younger than 10 years attending health-care centres in countries with a high tuberculosis incidence for clinical evaluation of pulmonary tuberculosis. We collated individual participant data including clinical, bacteriological, and radiological information and a standardised reference classification of pulmonary tuberculosis. Using this dataset, we first retrospectively evaluated the performance of several existing treatment-decision algorithms. We then used the data to develop two multivariable prediction models that included features used in clinical evaluation of pulmonary tuberculosis-one with chest x-ray features and one without-and we investigated each model's generalisability using internal-external cross-validation. The parameter coefficient estimates of the two models were scaled into two scoring systems to classify tuberculosis with a prespecified sensitivity target. The two scoring systems were used to develop two pragmatic, treatment-decision algorithms for use in primary health-care settings. FINDINGS: Of 4718 children from 13 studies from 12 countries, 1811 (38·4%) were classified as having pulmonary tuberculosis: 541 (29·9%) bacteriologically confirmed and 1270 (70·1%) unconfirmed. Existing treatment-decision algorithms had highly variable diagnostic performance. The scoring system derived from the prediction model that included clinical features and features from chest x-ray had a combined sensitivity of 0·86 [95% CI 0·68-0·94] and specificity of 0·37 [0·15-0·66] against a composite reference standard. The scoring system derived from the model that included only clinical features had a combined sensitivity of 0·84 [95% CI 0·66-0·93] and specificity of 0·30 [0·13-0·56] against a composite reference standard. The scoring system from each model was placed after triage steps, including assessment of illness acuity and risk of poor tuberculosis-related outcomes, to develop treatment-decision algorithms. INTERPRETATION: We adopted an evidence-based approach to develop pragmatic algorithms to guide tuberculosis treatment decisions in children, irrespective of the resources locally available. This approach will empower health workers in primary health-care settings with high tuberculosis incidence and limited resources to initiate tuberculosis treatment in children to improve access to care and reduce tuberculosis-related mortality. These algorithms have been included in the operational handbook accompanying the latest WHO guidelines on the management of tuberculosis in children and adolescents. Future prospective evaluation of algorithms, including those developed in this work, is necessary to investigate clinical performance. FUNDING: WHO, US National Institutes of Health.

Recommendations for the use of propensity score methods in multiple sclerosis research.

BACKGROUND: With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies. OBJECTIVES: To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research. METHODS: We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice. RESULTS: Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses. CONCLUSION: This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods.

Antibody and Antigen Prevalence as Indicators of Ongoing Transmission or Elimination of Visceral Leishmaniasis: A Modeling Study.

BACKGROUND: Control of visceral leishmaniasis (VL) on the Indian subcontinent has been highly successful. Control efforts such as indoor residual spraying and active case detection will be scaled down or even halted over the coming years. We explored how after scale-down, potential recurrence of VL cases may be predicted based on population-based surveys of antibody or antigenemia prevalence. METHODS: Using a stochastic age-structured transmission model of VL, we predicted trends in case incidence and biomarker prevalence over time after scaling down control efforts when the target of 3 successive years without VL cases has been achieved. Next, we correlated biomarker prevalence with the occurrence of new VL cases within 10 years of scale-down. RESULTS: Occurrence of at least 1 new VL case in a population of 10 000 was highly correlated with the seroprevalence and antigenemia prevalence at the moment of scale-down, or 1 or 2 years afterward. Receiver operating characteristic curves indicated that biomarker prevalence in adults provided the most predictive information, and seroprevalence was a more informative predictor of new VL cases than antigenemia prevalence. Thresholds for biomarker prevalence to predict occurrence of new VL cases with high certainty were robust to variation in precontrol endemicity. CONCLUSIONS: The risk of recrudescence of VL after scaling down control efforts can be monitored and mitigated by means of population-based surveys. Our findings highlight that rapid point-of-care diagnostic tools to assess (preferably) seroprevalence or (otherwise) antigenemia in the general population could be a key ingredient of sustainable VL control.

Modelling the impact of COVID-19-related programme interruptions on visceral leishmaniasis in India.

BACKGROUND: In March 2020, India declared a nationwide lockdown to control the spread of coronavirus disease 2019. As a result, control efforts against visceral leishmaniasis (VL) were interrupted. METHODS: Using an established age-structured deterministic VL transmission model, we predicted the impact of a 6- to 24-month programme interruption on the timeline towards achieving the VL elimination target as well as on the increase of VL cases. We also explored the potential impact of a mitigation strategy after the interruption. RESULTS: Delays towards the elimination target are estimated to range between 0 and 9 y. Highly endemic settings where control efforts have been ongoing for 5-8 y are most affected by an interruption, for which we identified a mitigation strategy to be most relevant. However, more importantly, all settings can expect an increase in the number of VL cases. This increase is substantial even for settings with a limited expected delay in achieving the elimination target. CONCLUSIONS: Besides implementing mitigation strategies, it is of great importance to try and keep the duration of the interruption as short as possible to prevent new individuals from becoming infected with VL and continue the efforts towards VL elimination as a public health problem in India.

Delays in lymphatic filariasis elimination programmes due to COVID-19, and possible mitigation strategies.

BACKGROUND: In view of the current global coronavirus disease 2019 pandemic, mass drug administration interventions for neglected tropical diseases, including lymphatic filariasis (LF), have been halted. We used mathematical modelling to estimate the impact of delaying or cancelling treatment rounds and explore possible mitigation strategies. METHODS: We used three established LF transmission models to simulate infection trends in settings with annual treatment rounds and programme delays in 2020 of 6, 12, 18 or 24 months. We then evaluated the impact of various mitigation strategies upon resuming activities. RESULTS: The delay in achieving the elimination goals is on average similar to the number of years the treatment rounds are missed. Enhanced interventions implemented for as little as 1 y can allow catch-up on the progress lost and, if maintained throughout the programme, can lead to acceleration of up to 3 y. CONCLUSIONS: In general, a short delay in the programme does not cause a major delay in achieving the goals. Impact is strongest in high-endemicity areas. Mitigation strategies such as biannual treatment or increased coverage are key to minimizing the impact of the disruption once the programme resumes and lead to potential acceleration should these enhanced strategies be maintained.

Effect of an Er,Cr:YSGG Laser on the Debonding of Lithium Disilicate Veneers With Four Different Thicknesses.

Introduction: The objective of this study was to compare in vitro the resistance and type of failure in the debonding of lithium disilicate veneers with four different thicknesses using an erbium chromium yttrium-scandium gallium-garnet (Er,Cr:YSGG) laser. Methods: Sixty-eight bovine teeth were used to bond round lithium disilicate veneers with a 6-millimeter diameter and four different thicknesses: group 1 (0.4 mm), group 2 (0.8 mm), group 3 (1.2 mm) and group 4 (1.6 mm). Each sample was irradiated with an Er,Cr:YSGG laser with 4 W of power and a frequency of 50 Hz, during 60 seconds, scanning concentrically. The energy density per pulse or fluency applied was 5.33 J/cm2 for the four groups. The samples were subjected to a force in a universal testing machine and then observed under a microscope to determine the type of failure. Data were statistically analyzed with the non-parametric Kruskal-Wallis test. Results: The tendency in the results revealed that the thicker veneers showed more resistance to the debonding process. The debonding strength for group 3 was the highest (5.62 MPa), followed by group 4 (5.20 MPa), then group 2 (0.85 MPa) and finally group 1 (0.0 MPa). The most frequent type of failure was cohesive failure in cement (CC) for all groups, with 73.53% (P ≤ 0.083). Conclusion: Er,Cr:YSGG laser irradiation influences the debonding of lithium disilicate veneers with different thicknesses: the smaller thickness showed the greater debonding. The thickness of veneers was not associated with the type of failure.

Bayesian model selection in linear mixed models for longitudinal data.

Linear mixed models (LMMs) are popular to analyze repeated measurements with a Gaussian response. For longitudinal studies, the LMMs consist of a fixed part expressing the effect of covariates on the mean evolution in time and a random part expressing the variation of the individual curves around the mean curve. Selecting the appropriate fixed and random effect parts is an important modeling exercise. In a Bayesian framework, there is little agreement on the appropriate selection criteria. This paper compares the performance of the deviance information criterion (DIC), the pseudo-Bayes factor and the widely applicable information criterion (WAIC) in LMMs, with an extension to LMMs with skew-normal distributions. We focus on the comparison between the conditional criteria (given random effects) versus the marginal criteria (averaged over random effects). In spite of theoretical arguments, there is not much enthusiasm among applied statisticians to make use of the marginal criteria. We show in an extensive simulation study that the three marginal criteria are superior in choosing the appropriate longitudinal model. In addition, the marginal criteria selected most appropriate model for growth curves of Nigerian chicken. A self-written R function can be combined with standard Bayesian software packages to obtain the marginal selection criteria.

Impact of Changes in Detection Effort on Control of Visceral Leishmaniasis in the Indian Subcontinent.

BACKGROUND: Control of visceral leishmaniasis (VL) on the Indian subcontinent relies on prompt detection and treatment of symptomatic cases. Detection efforts influence the observed VL incidence and how well it reflects the underlying true incidence. As control targets are defined in terms of observed cases, there is an urgent need to understand how changes in detection delay and population coverage of improved detection affect VL control. METHODS: Using a mathematical model for transmission and control of VL, we predict the impact of reduced detection delays and/or increased population coverage of the detection programs on observed and true VL incidence and mortality. RESULTS: Improved case detection, either by higher coverage or reduced detection delay, causes an initial rise in observed VL incidence before a reduction. Relaxation of improved detection may lead to an apparent temporary (1 year) reduction in VL incidence, but comes with a high risk of resurging infection levels. Duration of symptoms in detected cases shows an unequivocal association with detection effort. CONCLUSIONS: VL incidence on its own is not a reliable indicator of the performance of case detection programs. Duration of symptoms in detected cases can be used as an additional marker of the performance of case detection programs.

Support infrastructure available to Canadian residents completing post-graduate global health electives: current state and future directions.

BACKGROUND: Global health electives offer medical trainees the opportunity to broaden their clinical horizons. Canadian universities have been encouraged by regulatory bodies to offer institutional support to medical students going abroad; however, the extent to which such support is available to residents has not been extensively studied. METHODS: We conducted a survey study of Canadian universities examining the institutional support available to post-graduate medical trainees before, during, and after global health electives. RESULTS: Responses were received from 8 of 17 (47%) Canadian institutions. Results show that trainees are being sent to diverse locations around the world with more support than recommended by post-graduate regulatory bodies. However, we found that the content of the support infrastructure varies amongst universities and that certain components-pre-departure training, best practices, risk management, and post-return debriefing-could be more thoroughly addressed. CONCLUSION: Canadian universities are encouraged to continue to send their trainees on global health electives. To address the gaps in infrastructure reported in this study, the authors suggest the development of comprehensive standardized guidelines by post-graduate regulatory/advocacy bodies to better ensure patient and participant safety. We also encourage the centralization of infrastructure management to the universities' global health departments to aid in resource management.

Factores de riesgo para algunas anomalías congénitas en población colombiana / Risk factors for some congenital anomalies in Colombian population

La frecuencia de las anomalías congénitas varía entre regiones, razas y a través del tiempo. En Colombia el conocimiento de este tema es fragmentario y escaso, así como las causas de estas patologías que pueden ser genéticas, ambientales o una combinación de ambas. Este estudio se realizó con datos recolectados en hospitales de Bogotá, Neiva y Cartagena, entre 1982 y 1993, bajo los lineamientos del ECLAMC-VERACC en recién nacidos, según un diseño de casos y controles. Los posibles factores de riesgo contemplados fueron: antecedentes de malformaciones familiares, edad de la madre, durante la gestación, metrorragia, enfermedades agudas, enfermedades crónicas, ingestión de medicamentos, consumo de alcohol, consumo de cigarrillos, exposición a factores físicos, exposición a inmunización y plaguicidas, únicamente durante el primer trimestre. El análisis de la información se hizo mediante Regresión Logística Multivariada "paso a paso". Se seleccionaron cuatro anomalías, por presentarse en un suficiente número de individuos, mostrar diferencias significativas entre las frecuencias de casos y controles y por ser clínicamente relevantes, estas son: labio leporino con o sin paladar hendido, polidactilia, sindrome de Down y deformidad en Varo. Se identificaron factores de riesgo para cada una de estas anomalías.