Methodological guidelines for the estimation of attributable mortality using a prevalence-based method: the STREAMS-P tool

Pérez-Ríos, Mónica; Rey-Brandariz, Julia; Galán, Isolina Riaño; Fernández, Esteve; Martínez, A.; Santiago-Pérez, María Isolina; Giraldo-Osorio, Alexandra; Ruano-Raviña, Alberto

doi:10.1016/j.jclinepi.2022.03.016

Cited by 20 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, sensitivity analysis provided similar results, even when using another approach (propensity scoring) to estimate attributable mortality. Second, we strictly followed established methodological guidelines for estimation and interpretation of PAF ARDS [ 28 ]. Third, it included two large, diverse prospective cohorts from distinct centers which provide a generalizable population with external validity to estimate PAF ARDS .…”

Section: Discussionmentioning

confidence: 99%

Causes and attributable fraction of death from ARDS in inflammatory phenotypes of sepsis

Evrard,

Sinha,

Delucchi

et al. 2024

Crit Care

View full text Add to dashboard Cite

Background Hypoinflammatory and hyperinflammatory phenotypes have been identified in both Acute Respiratory Distress Syndrome (ARDS) and sepsis. Attributable mortality of ARDS in each phenotype of sepsis is yet to be determined. We aimed to estimate the population attributable fraction of death from ARDS (PAFARDS) in hypoinflammatory and hyperinflammatory sepsis, and to determine the primary cause of death within each phenotype. Methods We studied 1737 patients with sepsis from two prospective cohorts. Patients were previously assigned to the hyperinflammatory or hypoinflammatory phenotype using latent class analysis. The PAFARDS in patients with sepsis was estimated separately in the hypo and hyperinflammatory phenotypes. Organ dysfunction, severe comorbidities, and withdrawal of life support were abstracted from the medical record in a subset of patients from the EARLI cohort who died (n = 130/179). Primary cause of death was defined as the organ system that most directly contributed to death or withdrawal of life support. Results The PAFARDS was 19% (95%CI 10,28%) in hypoinflammatory sepsis and, 14% (95%CI 6,20%) in hyperinflammatory sepsis. Cause of death differed between the two phenotypes (p < 0.001). Respiratory failure was the most common cause of death in hypoinflammatory sepsis, whereas circulatory shock was the most common cause in hyperinflammatory sepsis. Death with severe underlying comorbidities was more frequent in hypoinflammatory sepsis (81% vs. 67%, p = 0.004). Conclusions The PAFARDS is modest in both phenotypes whereas primary cause of death among patients with sepsis differed substantially by phenotype. This study identifies challenges in powering future clinical trials to detect changes in mortality outcomes among patients with sepsis and ARDS.

show abstract

Section: Discussionmentioning

confidence: 99%

Causes and attributable fraction of death from ARDS in inflammatory phenotypes of sepsis

Evrard,

Sinha,

Delucchi

et al. 2024

Crit Care

View full text Add to dashboard Cite

show abstract

“…Five studies used National Civil Registration Data (Safavi-Naini et al, 33 ; Tadbiri et al 43 ; Ghafari et al, 44 ; Ghafari et al, 46 ; Lewnard et al, 22 ). Two studies each used the Health and Demographic Surveillance System (Otiende et al 35 ; Hani et al, 40 ), death registers (Acosta et al 32 ; Rasambainarivo et al 47 ) and imaging of burial sites/grounds (Besson et al 25 ; Warsame et al 26 ). One study used only primary data (census/survey) data (Barnwal et al, 27 ) and another study used Bureau of Vital Statistics data (Ahmadi et al 41 ; Wijaya et al 34 ) to estimate excess mortality.…”

Section: Methods and Data Used To Estimate Excess Mortality In Llmicsmentioning

confidence: 99%

“…In 7 countries, the observed deaths were higher than expected [India (Le er et al 31 and Lewnard et al 22 ), Iran (Safavi-Naini et al 33 ), Kyrgyzstan (Sanmarchi et al 30 ), Uzbekistan (Sanmarchi et al 30 ), Tunisia, (Sanmarchi et al 30 ), Bolivia (Sanmarchi et al 30 )]. In three countries, negative excess mortality was recorded, thus the observed deaths were lower than the number expected in the absence of the pandemic [Indonesia (Wijaya et al 34 ), Kenya (Otiende et al 35 ) and Mongolia (Sanmarchi et al 30 )].…”

Section: Study Characteristicsmentioning

confidence: 98%

Excess mortality in low-and lower-middle-income countries: A systematic review and meta-analysis

Gmanyami,

Quentin,

Lambert

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: The COVID-19 pandemic caused a massive death toll, but its effect on mortality remains uncertain in low- and lower-middle-income countries (LLMICs). This review summarized the available literature on excess mortality in LLMICs, including methods, data sources, and factors that might have influenced excess mortality. Methods: The protocol was registered in PROSPERO (ID: CRD42022378267). We searched PubMed, Embase, Web of Science, Cochrane Library, Google Scholar, and Scopus for studies conducted in LLMICs on excess mortality. These included studies with at least a one-year non-COVID-19 period as the comparator in estimating excess mortality and with publication dates from 2019 to date. The meta-analysis included studies with extractable data on excess mortality, methods, population size, and observed and expected deaths. We used the Mantel-Haenszel method to estimate the pooled risk ratio of excess mortality with 95% confidence intervals. Results: The review included studies from 29 countries, of which 10 were included in the meta-analysis. Of 1,405,128,717 individuals, 2,152,474 deaths were expected, and 3,555,880 deaths were reported. The pooled excess mortality was 100.3 deaths per 100,000 population per pandemic period. The excess risk of death was 1.65 (95% CI: 1.649, 1.655 p<0.001). Data sources included civil registration systems, obituary notifications, surveys, public cemeteries, funeral counts, burial site imaging, and demographic surveillance systems. Techniques used to estimate excess mortality were mainly statistical forecast modelling and geospatial analysis. Of the 24 studies, only one found higher excess mortality in urban settings. Conclusion: Our results show that excess mortality in LLMICs during the pandemic was substantial. There is uncertainty around excess mortality estimates given comparatively weak data. Further studies are needed to identify the drivers of excess mortality by exploring different methods and data sources.

show abstract

“…Our study employed a prevalence-based analysis model, as recommended by Pérez-Ríos and Montes 12 and Pérez-Ríos et al 13 , for estimating attributable mortality related to tobacco smoking. This model relies on the population attributable fraction (PAF), quantifying the proportion of deaths in the population attributed to a specific risk factor, such as tobacco smoking.…”

Section: Methodsmentioning

confidence: 99%

“…Firstly, it allows the computation of SAM rates even when comprehensive cancer mortality data for non-smokers are lacking. Secondly, it adheres to international standards for estimating tobacco-related mortality 13 . Thirdly, it facilitates meaningful comparisons with other studies utilizing similar prevalence-based models, thereby enhancing the generalizability of our findings.…”

Section: Methodsmentioning

confidence: 99%

Tobacco smoking-attributable mortality in Kenya, 2012–2021

Odeny,

Gathecha,

Mwenda

et al. 2024

Tob. Induc. Dis.

View full text Add to dashboard Cite

Methodological guidelines for the estimation of attributable mortality using a prevalence-based method: the STREAMS-P tool

Cited by 20 publications

References 48 publications

Causes and attributable fraction of death from ARDS in inflammatory phenotypes of sepsis

Causes and attributable fraction of death from ARDS in inflammatory phenotypes of sepsis

Excess mortality in low-and lower-middle-income countries: A systematic review and meta-analysis

Tobacco smoking-attributable mortality in Kenya, 2012–2021

Contact Info

Product

Resources

About