Progress toward reducing the malaria burden in Africa has been measured, or modeled, using datasets with relatively short time-windows. These restricted temporal analyses may miss the wider context ...of longer-term cycles of malaria risk and hence may lead to incorrect inferences regarding the impact of intervention.
1147 age-corrected Plasmodium falciparum parasite prevalence (PfPR2-10) surveys among rural communities along the Kenyan coast were assembled from 1974 to 2014. A Bayesian conditional autoregressive generalized linear mixed model was used to interpolate to 279 small areas for each of the 41 years since 1974. Best-fit polynomial splined curves of changing PfPR2-10 were compared to a sequence of plausible explanatory variables related to rainfall, drug resistance and insecticide-treated bed net (ITN) use.
P. falciparum parasite prevalence initially rose from 1974 to 1987, dipped in 1991-92 but remained high until 1998. From 1998 onwards prevalence began to decline until 2011, then began to rise through to 2014. This major decline occurred before ITNs were widely distributed and variation in rainfall coincided with some, but not all, short-term transmission cycles. Emerging resistance to chloroquine and introduction of sulfadoxine/pyrimethamine provided plausible explanations for the rise and fall of malaria transmission along the Kenyan coast.
Progress towards elimination might not be as predictable as we would like, where natural and extrinsic cycles of transmission confound evaluations of the effect of interventions. Deciding where a country lies on an elimination pathway requires careful empiric observation of the long-term epidemiology of malaria transmission.
To conduct analyses exploring trial-level and patient-level associations between overall response rate (ORR), progression-free survival (PFS), and overall survival (OS) in advanced non-small-cell ...lung cancer (NSCLC) trials.
We identified 14 trials (N = 12,567) submitted to US Food and Drug Administration since 2003 of treatments for advanced NSCLC. Only randomized, active-controlled trials with more than 150 patients were included. Associations between trial-level PFS hazard ratio (HR), OS HR, and ORR odds ratio were analyzed using a weighted linear regression model. Patient-level responder analyses comparing PFS and OS between patients with and without an objective response were performed using pooled data from all studies.
In the trial-level analysis, the association between PFS and ORR was strong (R(2) = 0.89; 95% CI, 0.80 to 0.98). There was no association between OS and ORR (R(2) = 0.09; 95% CI, 0 to 0.33) and OS and PFS (R(2) = 0.08; 95% CI, 0 to 0.31). In the patient-level responder analyses, patients who achieved a response had better PFS and OS compared with nonresponders (PFS: HR, 0.40; 95% CI, 0.38 to 0.42; OS: HR, 0.40; 95% CI, 0.38 to 0.43).
On a trial level, there is a strong association between ORR and PFS. An association between ORR and OS and between PFS and OS was not established, possibly because of cross-over and longer survival after progression in the targeted therapy and first-line trials. The patient-level analysis showed that responders have a better PFS and OS compared with nonresponders. A therapy in advanced NSCLC with a large magnitude of effect on ORR may have a large PFS effect.
In treatment of physician choice (TPC) trials, patients randomized to the control arm are treated with one of several drugs which are predetermined prior to the initiation of the trial. Statistical ...analysis in TPC trials typically hinge on a comparison of the experimental arm to the control arm, with the data from the control arm pooled across treatments. The robustness of the treatment effect for time-to-event endpoints estimated using the Cox proportional hazard model depends on the proportionality assumption. This assumption is violated if the magnitudes of the effects of the control treatments relative to the experimental treatment vary. The resulting effect is an averaged effect and is sensitive to censoring. We express the resulting hazard ratio from the Cox model where data from the control arm are pooled, as a weighted average of the head-to-head effects comparing controls treatments to the experimental treatments. These weights are function of the physicians preferences of the control treatments. Through simulation studies, we propose a method of identifying trial scenarios where the heterogeneity of the control treatments might lead to misleading results. We also propose an alternative estimator for the averaged effect which is unaffected by censoring.
We report a robust strategy for conjugating mixtures of two or more protein domains to nonfouling polyurethane surfaces. In our strategy, the carbamate groups of polyurethane are reacted with ...zirconium alkoxide from the vapor phase to give a surface-bound oxide that serves as a chemical layer that can be used to bond organics to the polymer substrate. A hydroxyalkylphosphonate monolayer was synthesized on this layer, which was then used to covalently bind primary amine groups in protein domains using chloroformate-derived cross-linking. The effectiveness of this synthesis strategy was gauged by using an ELISA to measure competitive, covalent bonding of cell-binding (III9–10) and fibronectin-binding (III1–2) domains of the cell adhesion protein fibronectin. Cell adhesion, spreading, and fibronectin matrix assembly were examined on surfaces conjugated with single domains, a 1:1 surface mixture of III1–2 and III9–10, and a recombinant protein “duplex” containing both domains in one fusion protein. The mixture performed as well as or better than the other surfaces in these assays. Our surface activation strategy is amenable to a wide range of polymer substrates and free amino group-containing protein fragments. As such, this technique may be used to create biologically specific materials through the immobilization of specific protein groups or mixtures thereof on a substrate surface.
Nutritional rickets is a public health concern in developing countries despite tropical climates and a re-emerging issue in developed countries. In this study, we reviewed pediatric admission data ...from the Clinical Information Network (CIN) to help determine hospital and region based prevalence of rickets in three regions of Kenya (Central Kenya, Western Kenya and Nairobi County). We also examine the association of rickets with other diagnosis, such as malnutrition and pneumonia, and study the effect of rickets on regional hospital stays.
We analyzed discharge records for children aged 1 month to 5 years from county (formerly district) hospitals in the CIN, with admissions from February 1
2014 to February 28
2015. The strength of the association between rickets and key demographic factors, as well as with malnutrition and pneumonia, was assessed using odds ratios. The Fisher exact test was used to test the significance of the estimated odd ratios. Kaplan-Meier curves were used to analyze length of hospital stays.
There was a marked difference in prevalence across the three regions, with Nairobi having the highest number of cases of rickets at a proportion of 4.01%, followed by Central Region at 0.92%. Out of 9756 admissions in the Western Region, there was only one diagnosis of rickets. Malnutrition was associated with rickets; this association varied regionally. Pneumonia was found to be associated with rickets in Central Kenya. Children diagnosed with rickets had longer hospital stays, even when cases of malnutrition and pneumonia were excluded in the analysis.
There was marked regional variation in hospital based prevalence of rickets, but in some regions it is a common clinical diagnosis suggesting the need for targeted public health interventions. Factors such as maternal and child nutrition, urbanization and cultural practices might explain these differences.
Background:
Nutritional rickets is a public health concern in developing countries despite tropical climates and a re-emerging issue in developed countries. In this study, we reviewed pediatric ...admission data from the Clinical Information Network (CIN) to help determine hospital and region based prevalence of rickets in three regions of Kenya (Central Kenya, Western Kenya and Nairobi County). We also examine the association of rickets with other diagnosis, such as malnutrition and pneumonia, and study the effect of rickets on regional hospital stays.
Methods:
We analyzed discharge records for children aged 1 month to 5 years from county (formerly district) hospitals in the CIN, with admissions from February 1
st
2014 to February 28
th
2015. The strength of the association between rickets and key demographic factors, as well as with malnutrition and pneumonia, was assessed using odds ratios. The Fisher exact test was used to test the significance of the estimated odd ratios. Kaplan-Meier curves were used to analyze length of hospital stays.
Results:
There was a marked difference in prevalence across the three regions, with Nairobi having the highest number of cases of rickets at a proportion of 4.01%, followed by Central Region at 0.92%. Out of 9756 admissions in the Western Region, there was only one diagnosis of rickets. Malnutrition was associated with rickets; this association varied regionally. Pneumonia was found to be associated with rickets in Central Kenya. Children diagnosed with rickets had longer hospital stays, even when cases of malnutrition and pneumonia were excluded in the analysis.
Conclusion:
There was marked regional variation in hospital based prevalence of rickets, but in some regions it is a common clinical diagnosis suggesting the need for targeted public health interventions. Factors such as maternal and child nutrition, urbanization and cultural practices might explain these differences.
Background Progress toward reducing the malaria burden in Africa has been measured, or modeled, using datasets with relatively short time-windows. These restricted temporal analyses may miss the ...wider context of longer-term cycles of malaria risk and hence may lead to incorrect inferences regarding the impact of intervention. Methods 1147 age-corrected Plasmodium falciparum parasite prevalence (PfPR2-10) surveys among rural communities along the Kenyan coast were assembled from 1974 to 2014. A Bayesian conditional autoregressive generalized linear mixed model was used to interpolate to 279 small areas for each of the 41 years since 1974. Best-fit polynomial splined curves of changing PfPR2-10 were compared to a sequence of plausible explanatory variables related to rainfall, drug resistance and insecticide-treated bed net (ITN) use. Results P. falciparum parasite prevalence initially rose from 1974 to 1987, dipped in 1991-92 but remained high until 1998. From 1998 onwards prevalence began to decline until 2011, then began to rise through to 2014. This major decline occurred before ITNs were widely distributed and variation in rainfall coincided with some, but not all, short-term transmission cycles. Emerging resistance to chloroquine and introduction of sulfadoxine/pyrimethamine provided plausible explanations for the rise and fall of malaria transmission along the Kenyan coast. Conclusions Progress towards elimination might not be as predictable as we would like, where natural and extrinsic cycles of transmission confound evaluations of the effect of interventions. Deciding where a country lies on an elimination pathway requires careful empiric observation of the long-term epidemiology of malaria transmission.
Malaria is a vector-borne disease which, despite recent scaled-up efforts to achieve control in Africa, continues to pose a major threat to child survival. The disease is caused by the protozoan ...parasite Plasmodium and requires mosquitoes and humans for transmission. Rainfall is a major factor in seasonal and secular patterns of malaria transmission along the East African coast.
The goal of the study was to develop a model to reliably forecast incidences of paediatric malaria admissions to Kilifi District Hospital (KDH).
In this article, we apply several statistical models to look at the temporal association between monthly paediatric malaria hospital admissions, rainfall, and Indian Ocean sea surface temperatures. Trend and seasonally adjusted, marginal and multivariate, time-series models for hospital admissions were applied to a unique data set to examine the role of climate, seasonality, and long-term anomalies in predicting malaria hospital admission rates and whether these might become more or less predictable with increasing vector control.
The proportion of paediatric admissions to KDH that have malaria as a cause of admission can be forecast by a model which depends on the proportion of malaria admissions in the previous 2 months. This model is improved by incorporating either the previous month's Indian Ocean Dipole information or the previous 2 months' rainfall.
Surveillance data can help build time-series prediction models which can be used to anticipate seasonal variations in clinical burdens of malaria in stable transmission areas and aid the timing of malaria vector control.
Background Malaria is a vector-borne disease which, despite recent scaled-up efforts to achieve control in Africa, continues to pose a major threat to child survival. The disease is caused by the ...protozoan parasite Plasmodium and requires mosquitoes and humans for transmission. Rainfall is a major factor in seasonal and secular patterns of malaria transmission along the East African coast. Objective The goal of the study was to develop a model to reliably forecast incidences of paediatric malaria admissions to Kilifi District Hospital (KDH). Design In this article, we apply several statistical models to look at the temporal association between monthly paediatric malaria hospital admissions, rainfall, and Indian Ocean sea surface temperatures. Trend and seasonally adjusted, marginal and multivariate, time-series models for hospital admissions were applied to a unique data set to examine the role of climate, seasonality, and long-term anomalies in predicting malaria hospital admission rates and whether these might become more or less predictable with increasing vector control. Results The proportion of paediatric admissions to KDH that have malaria as a cause of admission can be forecast by a model which depends on the proportion of malaria admissions in the previous 2 months. This model is improved by incorporating either the previous month's Indian Ocean Dipole information or the previous 2 months' rainfall. Conclusions Surveillance data can help build time-series prediction models which can be used to anticipate seasonal variations in clinical burdens of malaria in stable transmission areas and aid the timing of malaria vector control.
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