Population Health Metrics最新文献

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.

Introduction: Suicide is a significant global health issue, responsible for 759,028 deaths worldwide in 2019. In Iran, suicide rates have fluctuated significantly from 1980 to 2020, influenced by social, economic, and psychological factors. Adolescents and young adults, in particular, face high suicide rates related to financial problems and mental health disorders. This study aims to identify trends in suicide changes in Iran and the factors influencing them.

Methods: A mixed-methods approach was employed, with data collected from reputable sources such as the Ministry of Health and non-governmental organizations. Analytical methods included statistical software (SPSS and R) using ARIMA modeling and Joinpoint regression to assess trends, as well as the Augmented Dickey-Fuller test to ensure data stationarity.

Results: The analysis revealed that from 1980 to 2021, suicide rates in Iran declined, although women generally had higher rates than men. The highest suicide rate was observed in the age group of 20-24 years. Time series models predict that suicide rates will increase in the next five years, influenced by factors such as economic crises and the COVID-19 pandemic.

Conclusion: This study shows that despite an overall decline in suicide rates, certain groups remain at high risk. The predicted increase in suicide rates highlights the need for urgent interventions to address economic and psychological issues, as well as reduce the social stigma associated with mental health. A detailed analysis of data is crucial for developing effective preventive strategies to reduce suicide rates in Iran.

Background: High rates of child morbidity and developmental challenges among children under five remain critical challenges in sub-Saharan Africa. Despite Zambia's progress in reducing under-five morbidity, the rates remain high, with provincial-level disparities. These disparities are likely to be more pronounced at finer geographic levels, such as districts. However, demographic health surveys, designed for national and provincial estimates, lack sufficient data to produce reliable district-level morbidity statistics.

Objective: This study investigates the geospatial distribution of child morbidity prevalence across disaggregated administrative units using small area estimation (SAE) methods.

Data and methods: Data from the 2018 Zambia Demographic and Health Survey and the 2010 Zambian Census were used to derive direct estimates of child morbidity for small domains cross-classified by district and age group. A hierarchical Bayesian SAE model was developed to account for spatial and unobserved heterogeneity at provincial and district levels, including cross-classifications by age group.

Results:  Model-based estimates show lower standard errors compared to the direct estimates and significant differences in morbidity levels within and between districts and provinces. Under-five morbidity prevalence remains high at 25%, with the highest rates in Luapula (approximately 40%) and Western provinces (around 35%) and among children aged 11-23 months (nearly 40%). SAE estimates at the district and district-by-age levels were numerically consistent when aggregated to higher levels, such as province or child age group.

Conclusion: These data-driven detailed level estimates provide critical insights into the spatial distribution of child morbidity, supporting targeted interventions and informed policymaking at disaggregated levels.

Background: Health activation is an individual's knowledge, skills, and confidence in managing personal health and healthcare. The Consumer Health Activation Index (CHAI) is a freely available, 10-item measure originally developed in the United States. This study aimed to validate CHAI among community-dwelling adults in Singapore, examining its content validity, construct validity and test-retest reliability.

Methods: The study was conducted in two phases. In Phase 1, cognitive interviews with nine population health experts and eleven lay participants assessed face and content validity. In Phase 2, a cross-sectional survey of 572 adults, recruited via quota sampling aligned with national census distributions, was conducted. Participants completed the CHAI, EQ-5D-5L, EQ-VAS, and the Internal subscale of the Multidimensional Health Locus of Control (MHLC). Exploratory factor analysis (EFA) with principal axis factoring and varimax rotation, along with Cronbach's alpha, assessed structural validity and internal consistency respectively. Test-retest reliability was evaluated in a subsample of 32 participants, of whom 21 reported stable health status at follow-up.

Results: Content validity was acceptable, with a Scale-Level Content Validity Index (S-CVI) of 0.86, although minor wording issues were noted for CHAI items 5, 6, and 10. EFA supported a unidimensional structure, and the CHAI demonstrated strong internal consistency (α = 0.85). CHAI scores showed moderate positive correlations with the MHLC internal subscale (Pearson's r = 0.449) and weak to moderate positive correlations with EQ-5D-5 L and EQ-VAS, (r = 0.171-0.344). Known-group validity was supported by significantly higher CHAI scores among individuals with chronic diseases (p = 0.017). Test-retest reliability was good (ICC = 0.802, 95% CI = 0.544-0.911).

Conclusion: In summary, the CHAI is a reliable and valid measure of health activation for community-dwelling adults in Singapore. While overall psychometric performance was robust, minor refinements in phrasing may improve language clarity and cultural applicability. Longitudinal research is recommended to further establish CHAI's utility in both clinical and community local settings.

Introduction: Dyslipidemia as a modifiable risk factor for chronic non-communicable diseases has become a worldwide concern. We aim to explore different anthropometric measures as predictors of dyslipidemia using various machine learning methods.

Method: From the baseline of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study, a total of 9,640 participants were included in the analysis. Among them, 1,388 participants did not have dyslipidemia, while 8,252 participants had dyslipidemia. Various anthropometric indices were examined, including waist-to-height ratio (WHtR), body roundness index (BRI), abdominal volume index (AVI), weight-adjusted waist index (WWI), lipid accumulation product (LAP), visceral adiposity index (VAI), conicity index (C-index), body surface area (BSA), body adiposity index (BAI), and waist-to-hip ratio (WHR). The association between these indices and dyslipidemia was assessed using logistic regression (LR), decision tree (DT), random forest (RF), neural networks (NN), K-nearest neighbors (KNN), and eXtreme Gradient Boosting (XGBoost) models.

Results: Based on our LR model, we found that several factors included, BAI, BSA, age, and WHR were significant. For example, for each unit increase in WHR, the odds of dyslipidemia increase by 9 time (OR = 90.29, 95%CI (4.09,21.08)). Additionally, our DT model indicated that BMI was the most influential predictor, followed by age and WHR. The LR model outperforms other models with the highest accuracy (0.89) and AUC-ROC score (0.89), showing strong ability to classify dyslipidemia cases. Feature importance analysis reveals variables like "BSA" contribute differently across models, with XGBoost relying more on it than LR. LR's balanced performance makes it the best choice.

Conclusion: The findings from machine learning models were in agreement, highlighting the significance of BMI, WHR, BSA, and BAI as key anthropometric indices for predicting dyslipidemia. These indices consistently emerged as strong predictors underscoring their importance in assessing the risk of dyslipidemia.

In China, older migrant workers represent an especially vulnerable group, facing challenges to their quality of life as they grow older and move away from their hometowns. This study aimed to investigate the relationship between social integration, subjective well-being, and subjective fairness over a four-year period in a cohort of 1,394 older Chinese migrant workers aged 50 and older. Latent growth modeling showed a significant positive change over time in all three psychosocial constructs. Additionally, the parallel process latent growth modeling revealed that social integration had an indirect effect on subjective fairness by positively affecting subjective well-being, both at baseline and longitudinally. These findings highlight the crucial roles of social integration and subjective well-being in shaping subjective fairness over time in this marginalized population. Fostering social inclusion and emotional health of older migrants may have cascading benefits for social fairness. The complete longitudinal mediation suggests that improved subjective well-being serves as a mechanism translating increasing social integration into enhanced subjective fairness across the later stages of life. This study adds to our understanding of the psychological factors that can be modified to promote subjective fairness and perceived equality in migrant worker populations.

Background: In the United States, over half of all deaths are attributed to five leading underlying causes of death (at the ICD-3 digit level). However, these underlying causes represent only 25% of the total medical information documented on death certificates. While previous studies have investigated associations between causes of death, none have specifically examined the mechanisms of interaction among these causes. This study aims to explore the role of contributory causes of death recorded in Part 2 of the death certificate in the lethal process.

Methods: Working with U.S. Multiple Cause of Death Microdata in 2019, we use causal pie models to model the synergy between multiple causes of death.

Results: The findings show how contributory causes in Part 2 affect the sequence of morbid events leading to death. Three broad categories of roles can be distinguished: (i) some contributory causes act as mediators in the chain of morbid events, (ii) others do not exhibit any interaction with the conditions listed in Part 1, and (iii) some might play a role in the development of underlying causes.

Conclusion: Contributory causes listed in Part 2 play a crucial role in transitions to terminal morbid states. There is evidence that these are more than just conditions without a direct relationship to the underlying cause of death.

Background: The prevalence of mental illness, substance use disorders, and their dual diagnosis is disproportionately high among people in prisons compared to the community. Accurate prevalence estimates are required to inform resourcing of prison health services and reduce the risk of harm to people experiencing these conditions. Existing estimates, where available, often rely on only one data source.

Method: We used three data sources - self-reported history of diagnoses, in-prison medical records, and administrative data to estimate the prevalence of mental illness, substance use disorder, and dual diagnosis among two large cohorts of non-Indigenous and Aboriginal and Torres Strait Islander people in Australian prisons. We calculated population-weighted proportions of the samples with each condition. Inter-rater reliability metrics inform data source agreement.

Results: The prevalence of mental illness only, substance use disorder only, and dual diagnosis was 17.0% (95%CI 12.0-24.5), 14.8% (95%CI 9.6-18.1), and 44.2% (95%CI 33.2-54.7), respectively, for incarcerated, non-Indigenous adults. For incarcerated Aboriginal and Torres Strait Islander adults, our corresponding estimates were 7.0% (95%CI 4.3-11.5), 26.8% (95%CI 18.9-33.5), and 40.9% (95%CI 30.1-48.2). These estimates differed significantly from those derived from singular data sources. Individual data sources' agreement was weakest for substance use disorder diagnoses and strongest for dual diagnoses.

Conclusions: Individual data sources likely have high specificity and low sensitivity, thus under-ascertaining diagnoses. We recommend using multiple data sources to estimate prevalence to ensure adequate ascertainment of these conditions among people in prison and to ensure in-prison and transitional health services are appropriately resourced.