Dataset Information

Comparison of LiST measles mortality model and WHO/IVB measles model.

ABSTRACT: BACKGROUND: The Lives Saved Tool (LiST) has been developed to estimate the impact of health interventions and can consider multiple interventions simultaneously. Given its increasing usage by donor organizations and national program planner, we compare the LiST measles model to the widely used World Health Organization's Department of Immunization, Vaccines and Biologicals (WHO/IVB) measles model which is used to produce estimates serving as a major indicator of monitoring country measles epidemics and the progress of measles control. METHODS: We analyzed the WHO/IVB models and the LiST measles model and identified components and assumptions held in each model. We contrasted the important components, and compared results from the two models by applying historical measles containing vaccine (MCV) coverages and the default values of all parameters set in the models. We also conducted analyses following a hypothetical scenario to understand how both models performed when the proportion of population protected by MCV declined to zero percent in short time period. RESULTS: The WHO/IVB measles model and the LiST measles model structures differ: the former is a mixed model which applies surveillance data adjusted for reporting completeness for countries with good disease surveillance system and applies a natural history model for countries with poorer disease control program and surveillance system, and the latter is a cohort model incorporating country-specific cause-of-death (CoD) profiles among children under-five. The trends of estimates of the two models are similar, but the estimates of the first year are different in most of the countries included in the analysis. The two models are comparable if we adjust the measles CoD in the LiST to produce the same baseline estimates. In addition, we used the models to estimate the potential impact of stopping using measles vaccine over a 7-year period. The WHO/IVB model produced similar estimates to the LiST model with adjusted CoD. But the LiST model produced low estimates for countries with very low or eliminated measles infection that may be inappropriate. CONCLUSIONS: The study presents methodological and quantitative comparisons between the WHO/IVB and the LiST measles models that highlights differences in model structures and may help users to better interpret and contrast estimates of the measles death from the two models. The major differences are resulted from the usage of case-fatality rate (CFR) in the WHO/IVB model and the CoD profile in the LiST. Both models have their own advantages and limitations. Users should be aware of the issue and apply as update country parameters as possible. Advanced models are expected to validate the policy-planning tools in the future.

SUBMITTER: Chen WJ

PROVIDER: S-EPMC3231907 | biostudies-other | 2011

REPOSITORIES: biostudies-other

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Comparison of LiST measles mortality model and WHO/IVB measles model.

Chen Wei-Ju WJ

BMC public health 20110413

<h4>Background</h4>The Lives Saved Tool (LiST) has been developed to estimate the impact of health interventions and can consider multiple interventions simultaneously. Given its increasing usage by donor organizations and national program planner, we compare the LiST measles model to the widely used World Health Organization's Department of Immunization, Vaccines and Biologicals (WHO/IVB) measles model which is used to produce estimates serving as a major indicator of monitoring country measles ...[more]

PMID: 21501452

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Project description:BackgroundIn malaria-endemic countries, malaria prevention and treatment are critical for child health. In the context of intervention scale-up and rapid changes in endemicity, projections of intervention impact and optimized program scale-up strategies need to take into account the consequent dynamics of transmission and immunity.MethodsThe new Spectrum-Malaria program planning tool was used to project health impacts of Insecticide-Treated mosquito Nets (ITNs) and effective management of uncomplicated malaria cases (CMU), among other interventions, on malaria infection prevalence, case incidence and mortality in children 0-4 years, 5-14 years of age and adults. Spectrum-Malaria uses statistical models fitted to simulations of the dynamic effects of increasing intervention coverage on these burdens as a function of baseline malaria endemicity, seasonality in transmission and malaria intervention coverage levels (estimated for years 2000 to 2015 by the World Health Organization and Malaria Atlas Project). Spectrum-Malaria projections of proportional reductions in under-five malaria mortality were compared with those of the Lives Saved Tool (LiST) for the Democratic Republic of the Congo and Zambia, for given (standardized) scenarios of ITN and/or CMU scale-up over 2016-2030.ResultsProportional mortality reductions over the first two years following scale-up of ITNs from near-zero baselines to moderately higher coverages align well between LiST and Spectrum-Malaria -as expected since both models were fitted to cluster-randomized ITN trials in moderate-to-high-endemic settings with 2-year durations. For further scale-up from moderately high ITN coverage to near-universal coverage (as currently relevant for strategic planning for many countries), Spectrum-Malaria predicts smaller additional ITN impacts than LiST, reflecting progressive saturation. For CMU, especially in the longer term (over 2022-2030) and for lower-endemic settings (like Zambia), Spectrum-Malaria projects larger proportional impacts, reflecting onward dynamic effects not fully captured by LiST.ConclusionsSpectrum-Malaria complements LiST by extending the scope of malaria interventions, program packages and health outcomes that can be evaluated for policy making and strategic planning within and beyond the perspective of child survival.

Project description:BackgroundDiarrhea is a leading cause of morbidity and mortality among children under five years of age. The Lives Saved Tool (LiST) is a model used to calculate deaths averted or lives saved by past interventions and for the purposes of program planning when costly and time consuming impact studies are not possible.DiscussionLiST models the relationship between coverage of interventions and outputs, such as stunting, diarrhea incidence and diarrhea mortality. Each intervention directly prevents a proportion of diarrhea deaths such that the effect size of the intervention is multiplied by coverage to calculate lives saved. That is, the maximum effect size could be achieved at 100% coverage, but at 50% coverage only 50% of possible deaths are prevented. Diarrhea mortality is one of the most complex causes of death to be modeled. The complexity is driven by the combination of direct prevention and treatment interventions as well as interventions that operate indirectly via the reduction in risk factors, such as stunting and wasting. Published evidence is used to quantify the effect sizes for each direct and indirect relationship. Several studies have compared measured changes in mortality to LiST estimates of mortality change looking at different sets of interventions in different countries. While comparison work has generally found good agreement between the LiST estimates and measured mortality reduction, where data availability is weak, the model is less likely to produce accurate results. LiST can be used as a component of program evaluation, but should be coupled with more complete information on inputs, processes and outputs, not just outcomes and impact.SummaryLiST is an effective tool for modeling diarrhea mortality and can be a useful alternative to large and expensive mortality impact studies. Predicting the impact of interventions or comparing the impact of more than one intervention without having to wait for the results of large and expensive mortality studies is critical to keep programs focused and results oriented for continued reductions in diarrhea and all-cause mortality among children under five years of age.

Project description:Under the current liver-transplantation policy, donor organs are offered to patients with the highest risk of death.Using data derived from all adult candidates for primary liver transplantation who were registered with the Organ Procurement and Transplantation Network in 2005 and 2006, we developed and validated a multivariable survival model to predict mortality at 90 days after registration. The predictor variable was the Model for End-Stage Liver Disease (MELD) score with and without the addition of the serum sodium concentration. The MELD score (on a scale of 6 to 40, with higher values indicating more severe disease) is calculated on the basis of the serum bilirubin and creatinine concentrations and the international normalized ratio for the prothrombin time.In 2005, there were 6769 registrants, including 1781 who underwent liver transplantation and 422 who died within 90 days after registration on the waiting list. Both the MELD score and the serum sodium concentration were significantly associated with mortality (hazard ratio for death, 1.21 per MELD point and 1.05 per 1-unit decrease in the serum sodium concentration for values between 125 and 140 mmol per liter; P<0.001 for both variables). Furthermore, a significant interaction was found between the MELD score and the serum sodium concentration, indicating that the effect of the serum sodium concentration was greater in patients with a low MELD score. When applied to the data from 2006, when 477 patients died within 3 months after registration on the waiting list, the combination of the MELD score and the serum sodium concentration was considerably higher than the MELD score alone in 32 patients who died (7%). Thus, assignment of priority according to the MELD score combined with the serum sodium concentration might have resulted in transplantation and prevented death.This population-wide study shows that the MELD score and the serum sodium concentration are important predictors of survival among candidates for liver transplantation.

Project description:BACKGROUND:Measles is an extremely contagious, vaccine-preventable infection that was officially declared eradicated in the US in 2000. However, measles outbreaks are increasingly occurring in the US. Measles cases have considerable morbidity requiring hospitalization, yet little is known about hospitalization and complications from measles in recent years. OBJECTIVES:To analyze the frequency, predictors, costs and other outcomes of hospitalization for measles in the US. METHODS:The 2002-2016 Nationwide Inpatient Sample, containing a 20% sample of US hospitalizations (n = 96,568,625), was analyzed. Measles and comorbidities were defined by International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) or ICD-10-CM codes. Multivariable survey logistic regression and linear regression models controlling for sociodemographic demographic factors were constructed to understand associations with organ-specific complications, and cost of care and length of stay, respectively. RESULTS:Overall, 1,018 measles hospitalizations occurred in 2002-2016, and hospitalizations increased over time. In multivariable logistic regression models, measles was associated with higher odds of gastrointestinal, hematologic, infectious, neurologic, ophthalmologic, pulmonary, and renal complications, with the strongest association observed with encephalitis (39.84 [16.51-96.12], P<0.0001). Increased length of stay (LOS) and similar cost of care (mean [95% CI]; 4.8 [4.4-5.4]; $7,438 [$6,446-$8,582]) were observed versus (vs.) all other admissions (4.5 [4.4-4.5]; P<0.01; $7,854 [$7,774-$7,935], P>0.05). There were 34 deaths in hospitalized measles patients; inpatient mortality was numerically higher in those with vs. without measles (proportion ± SEM: 3.3±1.2% vs. 2.3±0.01%, P = 0.333). LIMITATIONS:Lack of outpatient or prescription data. CONCLUSIONS:Measles continues to pose a substantial and preventable health care burden, with serious complications, hospitalization and inpatient mortality. Further studies are needed to improve the prevention and management of measles.

Dataset Information

Comparison of LiST measles mortality model and WHO/IVB measles model.

Publications

Comparison of LiST measles mortality model and WHO/IVB measles model.

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