Background: Globally, there are significant inequalities in risk for chronic respiratory disease patients with COVID-19 (CRD-COVID), and a comprehensive understanding of its determinants and their interactions is needed. This study quantified individual, environmental, and viral risks that impact hospital admission severity and survival outcomes in CRD-COVID patients utilizing multinational hospital records.
Methods: We analysed data on CRD-COVID from the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) dataset, covering January 2020 to July 2022 across 30 countries. The cohort included COVID-19 patients with asthma (Asthma, n = 36,365), chronic pulmonary disease (CPD, n = 36,332), and asthma-CPD overlap (ACO, n = 16,061). We matched these patients with their prehospital environmental and viral risk factors. The primary outcome was admission severity, which we assessed using generalised linear mixed models (GLMM), and GPBoost with Shapley Additive Explanations (SHAP) algorithm. The secondary outcome was 28-day mortality, evaluated using Cox regression and K-medoids clustering.
Results: The rates of severe admissions and 28-day mortality were 33.7% and 16.4% for the asthma cohort, 30.1% and 31.6% for the CPD cohort, and 15.9% and 25.8% for the ACO cohort, respectively. Common key risk factors impacting admission severity in CRD-COVID patients include age, sex, comorbidities, humidity, precipitation, and O3 concentration, while vaccination status, temperature, and SO2 concentration were only significant in asthma patients. The interactions analysis showed low Humidity had a greater impact on patients over 60 years of age and those with comorbid hypertension. Individual, environmental, and viral factors accurately predicted admission severity, and their contribution was different for asthma (58% individual, 28% environmental, and 14% viral variants), CPD (57%, 33%, and 10%) and ACO (63%, 31%, and 6%) patients. Four clusters stratified by these risk factors within each disease group showed significant differences in 28-day mortality rates, particularly in the asthma and CPD patients. The cluster with the highest 28-day mortality rates featured low humidity (mean 55.5% for asthma, 54.4% for CPD) and older age (60.1 and 74.2 years).
Conclusion: The impact of prehospital individual, environmental, and viral risk on the severity of CRD-COVID patients was heterogeneous. Older people exposed to low humidity were at greatest risk.
Background: Household overcrowding is a public health concern linked to increased morbidity and mortality. There is limited data available on the effects of COVID-19 on age-specific mortality in the context of household crowding in rural and peri-urban settings in Africa. Here we assess age-specific excess mortality in densely inhabited households before and during COVID-19.
Methods: We used data collected three times annually between 2019 and 2021 in the health and demographic surveillance project in DIMAMO, South Africa. Data inaccuracies or inconsistencies were identified and corrected using data validation rules or algorithms implemented at both application and database levels. The number of persons-per-room was used to determine the degree of crowding or household crowding index (HCI). HCI tertiles were categorized as low, medium, and high density.
Results: Throughout the study, people aged 70 years and above had the highest mortality rates compared to other age groups (40-54 and 55-69), with the highest mortality rates observed in overcrowded households (highest crowding index). MGH was observed as a risk factor for mortality during COVID-19. Individuals aged 70 years and older had the highest hazard ratios before and during COVID-19, where the risk increased during COVID-19 for densely populated households.
Conclusion: Overcrowding at the household level was associated with increased mortality during COVID-19 for individuals aged 70 years and older. Public health interventions in the case of future pandemics should consider how to address this risk factor.
Background: Despite contemporaneous declines in neonatal mortality, recent studies show the existence of left-behind populations that continue to have higher mortality rates than the national averages. Additionally, many of these deaths are from preventable causes. This reality creates the need for more precise methods to identify high-risk births, allowing policymakers to target them more effectively. This study fills this gap by developing unbiased machine-learning approaches to more accurately identify births with a high risk of neonatal deaths from preventable causes.
Methods: We link administrative databases from the Brazilian health ministry to obtain birth and death records in the country from 2015 to 2017. The final dataset comprises 8,797,968 births, of which 59,615 newborns died before reaching 28 days alive (neonatal deaths). These neonatal deaths are categorized into preventable deaths (42,290) and non-preventable deaths (17,325). Our analysis identifies the death risk of the former group, as they are amenable to policy interventions. We train six machine-learning algorithms, test their performance on unseen data, and evaluate them using a new policy-oriented metric. To avoid biased policy recommendations, we also investigate how our approach impacts disadvantaged populations.
Results: XGBoost was the best-performing algorithm for our task, with the 5% of births identified as highest risk by the model accounting for over 85% of the observed deaths. Furthermore, the risk predictions exhibit no statistical differences in the proportion of actual preventable deaths from disadvantaged populations, defined by race, education, marital status, and maternal age. These results are similar for other threshold levels.
Conclusions: We show that, by using publicly available administrative data sets and ML methods, it is possible to identify the births with the highest risk of preventable deaths with a high degree of accuracy. This is useful for policymakers as they can target health interventions to those who need them the most and where they can be effective without producing bias against disadvantaged populations. Overall, our approach can guide policymakers in reducing neonatal mortality rates and their health inequalities. Finally, it can be adapted for use in other developing countries.
Background: Multimorbidity, i.e., the co-presence of multiple diseases in an individual, is an increasing concern, particularly as the population ages. Addressing it is critical to improving health status and optimizing healthcare resources. Particularly relevant in this scenario is the concept of multimorbidity compression, i.e., the onset of chronic diseases is delayed more rapidly than the increase in life expectancy. According to this theory, the duration individuals spend in poor health should be shortened. Existing studies have started examining multimorbidity trends, yet often overlook the cumulative burden of multiple diseases.
Methods: We define the multimorbidity concept as a latent variable estimated with the disease burden described by the disability weights from the Global Burden of Diseases (GBD) project. Using a mixed-mixture model, we analyze the nonlinear relationship between multimorbidity and socioeconomic traits, accounting for zero inflation and spatial variability in Italy. We use twelve years of the surveillance system PASSI data to investigate the multimorbidity compression concept.
Results: Our findings suggest multimorbidity compression is acting in Italy: severe multimorbidities are increasingly concentrated later in life, indicating a positive impact of healthcare improvements on the quality of life. The phenomenon is observed in both socially advantaged and disadvantaged subpopulations.
Background: Non-Hodgkin lymphoma (NHL) is the most common hematologic cancer in the US. Validated projections of NHL cases are important for various stakeholders. The study aimed to identify and characterize methods forecasting NHL incidence, prevalence, and number of treatment eligible patients with NHL by line of therapy (LoT). In addition, methods evaluating the performance of cancer forecasting methods were also identified and utilized in selecting the most robust projection method applicable to NHL disease setting.
Methods: A comprehensive search was conducted in MEDLINE and EMBASE databases, covering January 2002 to April 2024 for English-language studies reporting methods evaluating cancer count estimation and NHL projection methods. Study characteristics were extracted and described. Criteria was developed to identify the most appropriate methods for evaluating projection methods. The identified methods of evaluation were then adopted to measure the accuracy of NHL projection methods.
Results: Twenty-nine articles met the inclusion criteria for methods of evaluation, with 58.6% evaluating projection methods through calculating relative difference between observed and predicted case numbers. The most appropriate methods found for evaluating cancer incidence and prevalence projection were the average absolute relative deviation (AARD) and percent variation (VAR%), respectively. These methods were applied to projection methods identified through literature review to determine the robust method to project incidence and prevalence. Among twenty-six articles met the inclusion criteria for NHL projection methods, the joinpoint regression model was determined as the most robust method for projecting NHL incidence in the US, with the lowest AARD (1.6%). The projection method with assumptions of a 52.8% cure rate, a cure beginning ten years post-diagnosis, and all surviving patients cured after 20 years was identified as the most robust method for projecting NHL prevalence, with the lowest VAR% (8.3%). Unfortunately, due to the limited number and quality of studies, no robust method was identified for projecting the number of treatment-eligible NHL patients by LoT.
Conclusion: This review identified the most appropriate method of evaluating projection methods, and identified methods for projecting NHL incidence and prevalence in the US. Nevertheless, further research is needed to validate and project the number of treatment-eligible NHL patients by LoT.
Objective: To develop new DALY-based indices for measuring productivity loss, health system resilience, and resource allocation efficiency for liver cancer across eight MENA countries. These will be combined into the Integrated Health-Adjusted Productivity Index (IHAPI) to aid health policy development.
Setting: The final analysis utilized 289,067 data entries from a total of 394,944, including information from Egypt, Iran, Jordan, Kuwait, Turkey, Saudi Arabia, Oman, and the UAE from 2000 to 2021.
Design: This study adopted a cross-country approach, employing secondary data to develop six composite measures: the Health-Adjusted Productivity Loss Index (HAPLI), the Economic Vulnerability to Health Impact Index (EVHI), the Relative Resilience to Liver Cancer Loss Index (RLCL), the Resource Allocation Efficiency Index (RAEI), the Health System Response Index (HSRI), and the Sustainable Development Health Equity Index (SD-HEI). These measures were aggregated into the IHAPI score.
Results: The analysis revealed that the most significant factor influencing the IHAPI score was the EVHI (feature importance = 0.73). Egypt exhibited the highest growth in Disability-Adjusted Life Years (DALY), leading to substantial productivity loss (HAPLI), while Saudi Arabia and Jordan demonstrated greater resilience (as indicated by higher RLCL scores and less variability in the IHAPI). The UAE and Turkey reported strong HSRI and Productivity Performance Index (PPI) rates, suggesting better-coordinated preventive investments. Conversely, the highest variability in the indices was observed in Iran and Oman, particularly in the SD-HEI and Total Productivity Loss and Inequality Index (TPLTI), indicating unstable equity and trends. Kuwait exhibited moderate performance in burden and resource allocation indices.
Conclusion: This paper presents an integrative model for evaluating both economic and health system impacts of liver cancer in MENA countries. The IHAPI and its related indices provide valuable insights that can be implemented to enhance equity, efficiency, and resilience in health policy.
The ongoing Gaza War has resulted in significant loss of life and intensified an existing humanitarian crisis. Despite increasing demand for accurate data, mortality estimates remain challenging due to the inherent 'statistical fog of war'. Accurate quantification is hindered by incomplete reporting and uncertain age-sex distributions of casualties. Official death tolls are likely influenced by damaged infrastructure, security disruptions, and political motivations, complicating detailed demographic verification. Our study introduces a novel methodological approach-a Bayesian model incorporating novel priors-to explicitly account for measurement errors in mortality estimation by addressing reporting completeness and uncertainty in demographic distributions. We use these methods to estimate sex- and age-specific mortality patterns and associated life expectancy (LE) and LE losses due to direct conflict deaths from the Gaza War. We find that LE in Gaza was 42.3 (39.4-45.0) in 2023 and 40.4 (37.5-43.0) in 2024, corresponding to LE losses of 34.4 (31.7-37.3) and 36.4 (33.8-39.3) years, respectively, compared to a counterfactual scenario with no conflict-related deaths. This corresponds to 78,318 (70,614-87,504) conflict deaths by the end of 2024, reflecting a 14-fold increase in all-cause mortality during the conflict's first year. The age-sex pattern of Gaza's conflict deaths aligns with UN-IGME profiles from past genocides. To contextualize these estimates, we compare them with LE losses observed in the Gaza Strip, the West Bank, and all of Palestine between 2012 and 2019. Our estimates align with previously published work, after adjusting the reporting priors to ignore underreporting. Our versatile and robust framework for mortality estimation under conditions of data scarcity can inform future conflict research.
Background: Data inequity occurs when racial and ethnic groups are aggregated during data collection or reporting despite their differences. To demonstrate racial data equity importance, we re-analyzed South Carolina's (SC) census data and COVID-19 case-rate and death-rate distributions according to age, sex, and new combined single and multiracial categories.
Methods: The new combined single and multiracial categories included individuals who identified as a single race alone (such as American Indian or Alaska Native, AI-AN) with those who identified as more than one race (such as AI-AN and White) regardless of Hispanic or Latino heritage. We compared those distributions to the single race categories using the American Community Survey 2018-2022 and COVID-19 case and death surveillance data, 2020-2023, for SC. We used principal components analysis to test for differences in age-sex distributions between single race alone and new combined single and multiracial categories for each race.
Results: Compared to the combined single and multiracial categories, single race alone categories lose information, underestimate the population of younger-aged people of AI-AN, Asian, and Native Hawaiian or Other Pacific Islander (NH-OPI) races, and result in COVID-19 case and death rates with extreme values across age groups, particularly for AI-AN and NH-OPI populations. Among AI-AN, certain age groups had different COVID-19 case rate patterns between females and males, but this was explained by race categorization (single race alone vs. combined single and multiracial, P < 0.0001).
Conclusions: Combined single and multiracial categories achieve data equity by avoiding data suppression or aggregation of small diverse populations. Differences in COVID-19 case rates across some age groups between females and males may be biased depending on how race is defined. Younger generations are increasingly multiracial and will be underrepresented if only single race categories are used in public health reporting practices.
Background: NCD expenditure estimates are necessary to estimate future health system expenditure trajectories for different prevention and treatment policies. However, no dataset of comparable estimates exists across OECD countries. This study generates disease expenditure estimates for all 38 OECD member countries in 2019, for 80 major NCDs by disease phase, sex, and age group.
Methods: Australian health expenditure (per person) by sex and age group was disaggregated by disease phase (first year of diagnosis, last year of life if dying of disease, otherwise prevalent) using Global Burden of Disease (GBD) data and New Zealand estimates of relative expenditure ratios by phase. These estimates were applied to GBD estimated case numbers in each OECD country and scaled to each country's total health system expenditure to estimate expenditure by NCDs in 2019. OECD purchasing power parities were used to adjust estimates to United States (US) dollars for cross-country comparability. Comparisons were made to pre-existing disease expenditure estimates for Norway, Switzerland, and the US.
Results: Average NCD expenditure across OECD countries was US$207 million per 100,000 population. Pooled across countries, musculoskeletal disorders had the highest proportion of total health expenditure (17.4%), followed by cancer (9.4%), and cardiovascular diseases (CVD) (9.1%). Within diseases, the percentage of expenditure was higher for females for musculoskeletal disorders (56.1%), mental and substance use disorders (55.8%), and neurological conditions (54.8%). For males, it was kidney and urinary diseases (63.8%), cancer (58.3%), and CVD (50.7%). First year of diagnosis represented on average 36.8% of total NCD expenditure, while last year of life expenditure accounted for 2.6%. While there were similarities between our expenditure estimates and pre-existing country-specific estimates for Norway, Switzerland and the US, notable differences were observed for musculoskeletal disorders, cancer, and mental and substance use disorders.
Conclusions: Our estimates represent a starting point for a cross-national dataset of disease-specific expenditure that can be used to forecast future expenditure and potential health system cost savings of preventive and treatment policies. We recommend evolving our paper's methods to include multiple country-level studies as inputs - augmented by covariates (e.g. GDP, public/private split) to better predict disease expenditure.
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