Population Health Metrics最新文献

Background: The multiple imputation by chained equations (MICE) is a widely used approach for handling missing data. However, its robustness, especially for high missing proportions in health indicators, is under-researched. The study aimed to provide a preliminary guideline for the choice of the extent of missing proportion to impute longitudinal health-related data using the MICE method.

Methods: The study obtained complete data on five mortality-related health indicators of 100 countries (2015-2019) from the Global Health Observatory. Nine incomplete datasets with missing rates from 10 to 90% were generated and imputed using MICE. The robustness of MICE was assessed through three approaches: comparison of means using the Repeated Measures- Analysis of variance, estimation of evaluation metrics (Root mean square error, mean absolute deviation, Bias, and proportionate variance), and visual inspection of box plots of imputed and non-imputed data.

Results: The Repeated Measures- Analysis of variance revealed significant differences between complete and imputed data, primarily in imputed data with over 50% missing proportions. Evaluation metrics exhibited 'high performance' for the dataset with a 50% missing proportion for various health indicators However, with missing proportions exceeding 70%, the majority of indicators demonstrated a 'low' performance level in terms of most evaluation metrics. The visual inspection of the box plot revealed severe variance shrinkage in imputed datasets with missing proportions beyond 70%, corroborating the findings from the evaluation metrics.

Conclusion: It demonstrates high robustness up to 50% missing values, with marginal deviations from complete datasets. Caution is warranted for missing proportions between 50 and 70%, as moderate alterations are observed. Proportions beyond 70% lead to significant variance shrinkage and compromised data reliability, emphasizing the importance of acknowledging imputation limitations for practical decision-making.

Background: The population figures in Germany are obtained by updating the results of the latest census with information from the statistics on birth, deaths and migration statistics. The Census 2011 in Germany corrected population figures, which have only been updated over a long period of time. The aim of this work is to show the effect of the census-based correction of the population figures on the magnitude of mortality rates in Germany 2011-2013.

Methods: We compared mortality rates (total, cancer, and cardiovascular disease) for the period 2011-2013 based on the uncorrected and Census 2011 corrected population figures. We also compared the effect of the choice of different standard populations in the age standardization of rates on the difference in uncorrected and corrected mortality rates.

Results: There is a clear decline in age-specific cancer mortality among men aged 90 and over when using the uncorrected population figures, which is reversed as soon as the corrected population figures are used. Among women, there is hardly any difference between the uncorrected and corrected mortality rates. The correction of the population figures does not lead to a qualitatively different pattern in the mortality rates for cardiovascular diseases and myocardial infarction, but it increases the magnitude of the rates, particularly for elderly men. Standard populations with higher weights at older ages produced larger corrections in mortality rates.

Conclusions: Even though the Census 2011 corrected nationwide mortality rates without age stratification differed only slightly from the uncorrected rates, there were noticeable increases in mortality, particularly in the city states of Hamburg and Berlin and in old age. Due to the particularly large error in the population figures in the older age range, an age standard that assigns lower weights at older ages should be used for age standardization of rates wherever possible.

Seasonal variations in the environment induce observable changes in the human physiological system and manifest as various clinical symptoms in a specific human population. Our earlier studies predicted four global severe seasonal sensitive comorbid lifestyle diseases (SCLDs), namely, asthma, obesity, hypertension, and fibrosis. Our studies further indicated that the SCLD category of the human population may be maladapted or unacclimatized to seasonal changes. The current study aimed to explore the major seasonal symptoms associated with SCLD and evaluate their seasonal linkages via Google Trends (GT). We used the Human Disease Symptom Network (HSDN) to dissect common symptoms of SCLD. We then exploited medical databases and medical literature resources in consultation with medical practitioners to narrow down the clinical symptoms associated with four SCLDs, namely, pulmonary hypertension, pulmonary fibrosis, asthma, and obesity. Our study revealed a strong association of 12 clinical symptoms with SCLD. Each clinical symptom was further subjected to GT analysis to address its seasonal linkage. The GT search was carried out in the Indian population for the period from January 2015-December 2019. In the GT analysis, 11 clinical symptoms were strongly associated with Indian seasonal changes, with the exception of hypergammaglobulinemia, due to the lack of GT data in the Indian population. These 11 symptoms also presented sudden increases or decreases in search volume during the two major Indian seasonal transition months, namely, March and November. Moreover, in addition to SCLD, several seasonally associated clinical disorders share most of these 12 symptoms. In this regard, we named these 12 symptoms the "seasonal sensitive comorbid symptoms (SSC)" of the human population. Further clinical studies are needed to verify the utility of these symptoms in screening seasonally maladapted human populations. We also warrant that clinicians and researcher be well aware of the limitations and pitfalls of GT before correlating the clinical outcome of SSC symptoms with GT.

Background: We have previously developed and reported on a procedure for estimating the purported benefits of immunity mandates using a novel variant of the number needed to treat (NNT) which we called the number needed to isolate (NNI). Here we demonstrate its broader properties as a useful population health metric.

Main body: The NNI is analogous to the number needed to treat (NNT = 1/ARR), except the absolute risk reduction (ARR) is the absolute transmission risk in a specific population. The NNI is the number of susceptible hosts in a population who need to be isolated to prevent one transmission event from them. The properties and utility of the NNI were modeled using simulated data and its model predictions were validated using real world data. The properties of the NNI are described for three categories of data from a previous study on transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): (1) in different settings, (2) after a specific exposure and (3) depending on symptomaticity status of susceptible hosts.

Conclusions: We provide a demonstration of the utility of the NNI as a valuable population health metric to quantify the transmission reductions from isolation interventions.

Background: The disability weight (DW) reflects the severity of non-fatal outcomes and is an important parameter in calculating the burden of disease. However, the universality of the global, national, or subnational DWs remains controversial. This study aims to measure DWs specific to Hubei Province of China using non-parametric regression to anchor the DWs.

Methods: Paired comparison (PC) data collected from a web-based survey in Hubei Province targeting the general population were used to estimate the DWs of 196 health states. Specifically, PC data from 33,925 respondents were analyzed by probit regression analysis, and the results were then anchored to 0-1 scale using non-parametric regression based on the DWs from Global Burden of Disease (GBD) 2013. The absolute DW values and rankings were compared to those in the Chinese disability weight measurement study, GBD 2013, and Japan.

Results: The DWs for 196 health states ranged from 0.003 for mild distance vision impairment to 0.663 for severe heroin and opioid dependence in Hubei Province, China. Quite a lot mental disorders, such as moderate/severe episode of major depressive disorder, were considered more severe than the terminal phase with/without medication among Hubei residents. DW rankings of the health states are relatively stable in Hubei Province irrespective of the anchoring method used. A very small proportion (4 of 196, 2%) of DW rankings changed by 10 or more positions in China when compared with our results, but approximately 61% in GBD 2013 and 59% in Japan. Among the top 25 health states in this study, 9 of 11 health states categorized as mental, behavioral, and substance use disorders resulted in a lower ranking in GBD 2013, and all 6 states in Japan also showed a lower ranking, whereas China shared a similar ranking.

Conclusions: The burden of mental disorders among Hubei residents, especially moderate or severe major depressive disorder, deserves further attention. When using different anchoring methods, DW rankings were maintained relatively stable rather than the absolute values in Hubei. Substantial differences of DW rankings between our results and that in China, GBD 2013, and Japan draw attention to the need for deriving local disability for disease burden calculation.

Background: The most recent and 11th revision of the International Classification of Disease (ICD-11) is in use as of January 2022, and countries around the globe are now preparing for the implementation of ICD-11 and transition from the 10th revision (ICD-10). Translation of current coding is required for historical comparisons.

Methods: We applied the World Health Organization (WHO) mapping tables to current Centers for Disease Control and Prevention (CDC) Lists of ICD-10 coding of underlying causes of death to assess what ICD-11 codes look like in an Alberta sample of causes of death (COD). We prepared frequency tables for a single year of COD in Alberta based on the CDC grouping of COD.

Results: The mapping success rate at the ICD-10 code level for the adult population (> 18 years) was 96.6% and 100% for children (1-17 years) and infants (< 1 year). The mapping success rate by patient was 99.5% for the adult population patient deaths and 100% for children and infants. We mapped ICD-11 codes to identify the ten most frequently reported underlying COD in Alberta for 24,645 deaths in adults, children, and infants in 2017.

Conclusions: Apart from two codes, all ICD-10 codes could be mapped to ICD-11 for underlying COD. These findings suggest that the ability to translate from the two iterations of coding will be feasible for future applications of health services data.

Background: In countries with high life expectancy, a growing share of the population is living with several diseases, a situation referred to as multi-morbidity. In addition to health data, cause-of-death data, based on the information reported on death certificates, can help monitor and characterize this situation. This requires going beyond the underlying cause of death and accounting for all causes on the death certificates which may have played various roles in the morbid process, depending on how they relate to each other.

Methods: Apart from the underlying cause, the cause-of death data available in vital registration systems do not differentiate all other causes. We developed an algorithm based on the WHO rules that assigns a "role" to each entry on the death certificate. We distinguish between the following roles: originating (o), when the condition has initiated a sequence of events leading directly to death; precipitating (p), when it was caused by an originating condition or one of its consequences; associated (a), when it contributed to death but was not part of the direct sequence leading to death; ill-defined (i), i.e., conditions such as symptoms or signs or poorly informative causes. We applied this algorithm to all death records in four countries (Italy, France, Spain and the US) in 2017.

Results: The average number of originating causes is similar in the four countries. The proportion of death certificates with more than one originating cause-a situation typical of multi-morbidity-ranges from 10% in the US to 18% in Spain. All ages combined, the proportion of deaths with at least one associated cause is higher in Italy (41%) and in the US (42%) than in France (29%) and in Spain (27%). It is especially high in the US at all adult ages. Variations in the average number of causes between the four countries are mainly due to precipitating and ill-defined causes.

Conclusions: The output of our algorithm sheds light on cross-country differences in the average number of causes on death certificates. It also opens the door for improvements in the methods used for multiple cause-of-death analysis.

This year marked the launch of the Global Burden of Disease (GBD) 2021 study, the first presentation of the study to incorporate the devastating direct, and indirect, worldwide impacts from the COVID-19 pandemic on population health. Understanding how the study differs from its predecessors is important to inform the innumerable secondary research opportunities from its use. Population Health Metrics prioritise the appraisal of innovative GBD research that moves the dial beyond reporting population health trends already available from the variety of publicly available GBD data visualisations and tools.Burden of disease studies remain a prominent area of research that contribute towards Population Health Metrics achieving its aim of publishing research that informs advances in the science of population health assessment internationally, nationally, and locally. It also remains important that we appraise the gaps in the GBD study, particularly those which are potentially of high impact in policy-influencing discussions. Innovative local and national research has an important role to play in influencing the development of the future GBD study, as well as research which utilises GBD estimates in innovative ways to achieve positive policy impact.

Background: In the winter of 2022/2023, excess death estimates for Germany indicated a 10% elevation, which has led to questions about the significance of this increase in mortality. Given the inherent errors in demographic forecasting, the reliability of estimating a 10% deviation is questionable. This research addresses this issue by analyzing the error distribution in forecasts of weekly deaths. By deriving empirical prediction intervals, we provide a more accurate probabilistic study of weekly expected and excess deaths compared to the use of conventional parametric intervals.

Methods: Using weekly death data from the Short-term Mortality Database (STMF) for 23 countries, we propose empirical prediction intervals based on the distribution of past out-of-sample forecasting errors for the study of weekly expected and excess deaths. Instead of relying on the suitability of parametric assumptions or the magnitude of errors over the fitting period, empirical prediction intervals reflect the intuitive notion that a forecast is only as precise as similar forecasts in the past turned out to be. We compare the probabilistic calibration of empirical skew-normal prediction intervals with conventional parametric prediction intervals from a negative-binomial GAM in an out-of-sample setting. Further, we use the empirical prediction intervals to quantify the probability of detecting 10% excess deaths in a given week, given pre-pandemic mortality trends.

Results: The cross-country analysis shows that the empirical skew-normal prediction intervals are overall better calibrated than the conventional parametric prediction intervals. Further, the choice of prediction interval significantly affects the severity of an excess death estimate. The empirical prediction intervals reveal that the likelihood of exceeding a 10% threshold of excess deaths varies by season. Across the 23 countries studied, finding at least 10% weekly excess deaths in a single week during summer or winter is not very unusual under non-pandemic conditions. These results contrast sharply with those derived using a standard negative-binomial GAM.

Conclusion: Our results highlight the importance of well-calibrated prediction intervals that account for the naturally occurring seasonal uncertainty in mortality forecasting. Empirical prediction intervals provide a better performing solution for estimating forecast uncertainty in the analyses of excess deaths compared to conventional parametric intervals.

Background: Incidence-based multistate models of population health are commonly applied to calculate state expectancies, such as a healthy life expectancy (HLE), or unhealthy life expectancy (UHE). These models also allow the computation of other summary indices, such as the distributions of healthy or unhealthy lifespans.

Objective: We aim to show how a multistate health model implies a multistate death distribution, giving joint information on years lived in good and poor health. We also propose three aggregate indices of joint health and mortality inequality.

Methods: We propose a double-accounting approach to increment-decrement life table methods to intuitively derive a multistate health distribution over age and cumulative duration spent in each state. We then define a variety of summary lifespan inequality indices based on different distance metrics, namely Euclidean, Chebyshev, and Manhattan distances.

Results: We apply the method to multistate transition probabilities between health states based on the activities of daily living index for Italian women from the Survey of Health, Ageing and Retirement in Europe in 2015-2017. We demonstrate the added value of accounting for joint years lived in health states in multistate models for our understanding of the period health and mortality conditions from the perspective of health-specific lifespans of individuals.

Conclusions: Multivariate state distributions and summary indices derived from them give a holistic representation of population health inequality. We offer selected summary indices of the multivariate distribution with different demographic interpretations from the measures derived from univariate distributions. Although more theoretical and methodological work is required to motivate a single comprehensive population health inequality index, this direction is a promising path for a better understanding of population health dynamics and relationships between univariate statistics.