Population Health Metrics最新文献

Background: Cause of death (CoD) diversity indices measure the extent to which some populations die from more similar or variegated causes than others. Higher CoD diversity implies higher unpredictability of the causes of individuals dying and greater challenges for health systems. In this paper, we propose a novel method to decompose overall CoD diversity as the sum of two interpretable parts: the within- and between-group components.

Methods: The novel approach is applied to Latin America and the Caribbean (LAC) region to illustrate its usefulness. We decompose overall CoD diversity, measured by the Simpson index of diversity, into between-country and within-country components. In addition, we provide further decompositions assessing how each cause of death and each country contributes to overall CoD diversity in the region.

Results: The CoD diversity in the region followed a nonmonotonic trend. From 2000 to 2018, the CoD diversity increased from 0.81 to 0.83 for women, reaching approximately 0.84 for men. El Salvador, Peru, and Uruguay are the countries that contribute the most to explaining the differences in the mortality profile between countries, but for very different and opposing reasons. While the high diversity in El Salvador and Peru can be explained by causes of deaths related to the early stages of the epidemiological transition, such as communicable causes, respiratory causes, and external causes, Uruguay presents a high diversity because the deaths are very dispersed between chronic conditions. Cardiovascular deaths are the main contributor to both CoD diversity levels and their changes over time. As cardiovascular deaths decline, they give way to other chronic causes, which become more prominent and contribute to diversifying the corresponding mortality profiles. However, external causes also significantly contribute to forming uneven epidemiological profiles.

Conclusions: The decomposition proposed in this paper makes possible to assess whether some groups contribute more or less to the uncertainty around the causes of individuals' deaths and identify the sources of CoD diversity. In this way, this approach can contribute to a better understanding of contemporary mortality dynamics, especially in a context with large health inequalities.

Background: Efforts to track the mortality and public health impact of the coronavirus disease (COVID-19) in Uganda have been hampered by weak Civil registration and vital statistics (CRVS) system and suboptimal health seeking behaviors or patterns. Evaluating unexplained increases in all-cause mortality provides a complete picture of the impact of COVID-19 pandemic and guide public health policies and resource allocation to protect the most vulnerable populations.

Methods: The longitudinal population cohort data on demographic events and socioeconomic status collected from 2015 to 2021 within the Iganga Mayuge Health and Demographic Surveillance System (IMHDSS) was used. Number of deaths and person years at risk were counted for each quarter of the year from January 2015 to December 2021 and classified as "pre-pandemic" (before January 2020), and "during pandemic" (January 2020 to December 2021). Crude mortality rates were computed comparing the two periods. Time series model was used to estimate excess mortality and to locate the exact time when excess deaths occurred. Cox Proportional Hazard model was used to estimate the Hazard ratio associated with death.

Results: A total of 132,367 individuals were followed up from 2015 to 2021 and 3,424 deaths were registered. Slightly more than a half of all deaths (53%, n = 1,827) were male, and 65.4% (n = 2,238) were rural residents. Children under five years had a significantly higher CMR during COVID-19 period of 18.9, (95% CI 17.2-20.8) per 1000 person compared to 12.5 (95% CI 11.6-13.4) per 1000 person years before COVID-19. The risk of dying among children under 5 years compared to those aged between 5 and 14 years was higher during the COVID-19 pandemic period (aHR = 18.0, 95% CI 13.6-24.0) than pre-pandemic (aHR = 10.4, 95% CI 8.8-12.3).

Conclusion: The COVID-19 pandemic increased all-cause mortality in the Iganga Mayuge HDSS population cohort in Eastern Uganda, particularly among children under five, likely due to restricted healthcare access and economic disruptions. Pandemic response measures should prioritize vulnerable populations at higher risk of malnutrition and preventable diseases to mitigate future negative impacts.

The COVID-19 pandemic has disproportionately impacted vulnerable populations, such as low-income households, exacerbating existing health and economic challenges. In Cuba, the crisis exposed the effects of long-standing economic difficulties, worsened by sanctions, but the country's robust public health system and independent vaccine development enabled an effective response. This study addresses the gap in understanding how socio-economic factors and individual behaviors interact to influence disease spread. It proposes a hybrid, efficient, and parsimonious model combining ABM (Agent-Based Modeling) and ARIMAX (AutoRegressive Integrated Moving Average with eXogenous variables) time series analysis to forecast COVID-19 cases, offering valuable insights for policymakers to tailor interventions and enhance crisis management.

Background: The psychometric properties of Seattle Angina Questionnaire (SAQ) have not been assessed longitudinally among people who underwent coronary artery bypass grafting (CABG). The purpose of this study was to examine psychometric characteristics of the SAQ in Serbian language in a cohort of patients who underwent CABG.

Methods: Study participants were recruited at the Department of Preoperative Patient Preparation, Clinic of Cardiac Surgery, Institute for Cardiovascular Diseases "Dedinje" (Belgrade, Serbia) from July 1, 2021 to December 31, 2023. The SAQ was administered at four time points: just before having CABG, 1, 6 and 12 months post-CABG. Also, the general health-related quality of life questionnaire Short Form-36 (SF-36) and Depression, Anxiety and Stress Scale-21 (DASS-21) were administered. Clinical parameters were retrieved from medical records.

Results: The study cohort included 192 participants (80.2% male, on average 64.5 ± 11.0 years old). Most α coefficients were good (> 0.70) or acceptable (> 0.60). Based on the correlations with clinical parameters, the criterion validity was good. Correlations with the SF-36 and depression and anxiety scores supported convergent and divergent validity, respectively. Based on the confirmatory factor analysis, the construct validity of the SAQ varied before and after CABG with regards to the ability to participate in high intensity activities and the angina occurrence, which was in line with the expected effect of CABG to free patients from angina, prolong survival and improve quality of life.

Conclusions: The SAQ in Serbian has good psychometric properties. However, the SAQ domains of Angina frequency and Quality of life were most applicable in the first month post-CABG, while Physical limitations domain was most applicable at 6 months and 12 months post-CABG.

Background: There is contradicting information regarding the effect of COVID-19 on mortality in African settings. Knowledge of the complete direct and indirect burden of COVID-19 on mortality is heavily reliant on the availability of a population-based surveillance system. Here we provide robust data on the effect of COVID-19 on mortality trends in a rural, coastal, Kenyan community.

Methods: A historical cohort study using data from the Kaloleni Rabai Health and Demographic Surveillance System was conducted with special focus on two discernible time periods representing the pre-COVID-19 (2018-2019) and COVID-19 (2020-2021) periods. Mortality rates were estimated as the total number of deaths divided by the person-time (years) at risk, accounting for attrition, and calculated separately for the two periods. A cox proportional hazards model was used to estimate the impact of COVID-19 on mortality.

Results: 1191 deaths occurred between 2018 and 2021. There was no significant change in overall mortality rates between pre-COVID-19 and COVID-19 periods (3.7 and 3.6 per 1000 person years at risk respectively, p = 0.74). Older age was significantly associated with mortality (a_HR: 1.05, 95% CI: 1.05-1.06; p < 0.001). However, an interaction term between age and time-period appeared to reverse this association (a_HR: 0.99, 95% CI: 0.99-1.00; p < 0.001).

Conclusions: Our findings suggest that although overall COVID-19 did not directly impact mortality rates within this rural population, the onset of the pandemic did appear to reverse and/or attenuate the impact of several risk factors on mortality. It is possible that COVID-19 brought health and wellness into sharp focus, making people more vigilant about their health, hygiene and associated preventive measures.

Background: Patients with non-communicable diseases (NCDs) face multiple risks of excess mortality during wars. The Gaza Strip's health services have been severely disrupted by Israel's campaign since October 2023. We developed a modelling approach to project NCD excess mortality under three defined scenarios.

Methods: We projected excess mortality from cancer (lung, colorectal, and breast), cardiovascular disease, diabetes mellitus type 1 patients, and chronic kidney disease requiring haemodialysis from February 2024 to August 2024. We defined three scenarios of treatment coverage: (i) ceasefire, (ii) status quo, and (iii) escalation. We used pre-war incidence and prevalence data to probabilistically simulate deaths among patient cohorts exposed to varying time-dependent mortality depending on their treatment status. We subtracted the expected non-crisis mortality based on pre-war data to compute excess deaths.

Results: We projected 1,680, 2,480 and 2,680 excess deaths under the ceasefire, status quo and escalation scenarios, respectively, from February till August 2024, plus 1489 in the war's earlier phase. Most deaths were projected among individuals aged >50 years old and from ischaemic heart disease.

Conclusion: To our knowledge this is the first attempt to project NCD mortality in a live war setting, demonstrating potential impacts on NCD burden, particularly due to cardiovascular causes. The model focusses only on a subset of NCDs and neglects the impact of the crisis on disease progression, thereby plausibly underestimating actual mortality. It could inform better humanitarian resource allocation and service planning but requires refinement and improved parameterisation.

Background: Disability weight (DW) quantifies the impact of disease impairments and symptoms on health statuses, and is a key parameter for estimating disease burden. In this study, we conducted an exploratory measurement of disability weights for common health states among children and adolescents in Hunan Province using a face-to-face survey methodology. This provides preliminary data to support the study of disease burden among this age group.

Methods: A household survey with face-to-face interviews was conducted to measure DWs for 148 children's health statuses. The questionnaire included 16 paired comparison (PC) questions and 3 population health equivalent (PHE) questions. Probit regression analysis was used to analyze PC responses, with probit regression results from the PC on the DW scale anchored using interval regression results from PHE responses.

Results: The DWs resulting from the study varied from 0.065 (95% uncertainty interval (UI): 0.047-0.087) for acute tonsillitis to 0.730 (UI: 0.709-0.750) for extremely severe intellectual developmental disorders. A comprehensive analysis shows that severe traumatic injuries, such as spinal cord injuries and amputations, as well as congenital defects like hearing loss, visual impairments, and intellectual disabilities, lead to higher DWs due to the long-lasting effects of permanent functional impairments. In contrast, minor bone fractures and transient infectious diseases result in lower DWs. The study demonstrates a high level of consistency in the comparative evaluations of various health outcomes across different sociodemographic characteristics.

Conclusions: The study strengthens the empirical basis for assessing DWs in children. The PC-PHE method for assessing the DWs of children and adolescents with various health conditions within the Hunan Province population demonstrated robust logic and severity discrimination, with stable results across different population characteristics.

All studies are inherently biased, but some are more biased than others. This variation on a key theme from George Orwell's Animal Farm underscores a significant issue in public health. Ultimately, optimizing public health begins with understanding population health-particularly when assessing the impact of specific health risks that are often intertwined with both benign and malign health determinants. The objective of this contribution is to provide an overview of sources of bias in epidemiological research, drawing inspiration from the work of Rudolph Agricola-Northern Europe's first humanist and a homo universalis. Agricola's methodological approach distinguished between different categories of informational sources, which he deliberately employed as instruments for structured argumentation. This article presents a contemporary variation of that approach in the form of a complementary taxonomy, outlining examples of material and procedural bias sources that, individually or in combination, can affect estimates of mental health problems. These include the nature of the outcome itself and the context of the sample-covering its vulnerability and exposure profile, as well as broader population characteristics-along with data collection methods and analytical techniques. The value of this structured approach to disentangling bias in modern population health research is illustrated with examples from recent studies on the impacts of disasters and the COVID-19 pandemic. Researchers are encouraged to be modest, to carefully consider "locations" or "origins" of bias, and to interpret study findings with caution-especially when using them to inform public health policy or to make arguments about the nature and severity of population health issues.

Objective: This study aimed to evaluate the influence of social facilitation of eating on dietary assimilation effects using a multi-method approach integrating whole-network autocorrelation analysis (WNA) and multilevel linear modeling (MLM).

Methods: We analyzed data from 26 participants (13 friend pairs) comprising 468 observations across multiple social and solitary eating contexts. The study combined WNA of 70 dietary similarity matrices with multilevel modeling to examine both systemic behavioral patterns and individual-level effects. MLM specifically assessed hierarchical data structure and fixed effects of social context and meal type.

Results: Our analyses revealed that 31.1% of variance in eating behaviors originated from individual and dyad levels (ICC = 0.311), confirming the necessity of multilevel approaches. Network analysis identified"Friendship_close," "Friendship_howWell," and"Menu_VigEx" as primary factors influencing dietary assimilation during social eating, with effects varying significantly across consecutive days and mealtimes. MLM results showed strong meal-type effects (P < 0.001) but non-significant main effects of social context alone (P = 0.266), suggesting that social influence operates through behavioral coordination mechanisms rather than mean-level consumption changes. Notably, dietary similarity networks exhibited instability in assimilation effects during solitary eating, while social contexts promoted consistent behavioral patterning.

Conclusions: Social facilitation of eating enhances dietary assimilation through behavioral coordination patterns that vary under specific temporal and contextual conditions. The complementary findings from network analysis and MLM indicate that social influence manifests primarily in synchronized eating patterns rather than overall consumption increases. Leveraging these coordinated behavioral patterns offers promising avenues for promoting healthy dietary norms through social contexts, potentially mitigating excess energy intake while fostering lifestyle norms conducive to physical and mental health. Future research should employ integrated methodological approaches to further elucidate these complex social dynamics.