Journal Article > ResearchFull Text
Glob Health Action. 25 April 2024; Volume 17 (Issue 1); 2331291.; DOI:10.1080/16549716.2024.2331291
Caleo G, Lokuge K, Kardamanidis K, Greig J, Belava J, et al.
Glob Health Action. 25 April 2024; Volume 17 (Issue 1); 2331291.; DOI:10.1080/16549716.2024.2331291
BACKGROUND
There is a lack of empirical data on design effects (DEFF) for mortality rate for highly clustered data such as with Ebola virus disease (EVD), along with a lack of documentation of methodological limitations and operational utility of mortality estimated from cluster-sampled studies when the DEFF is high.
OBJECTIVES
The objectives of this paper are to report EVD mortality rate and DEFF estimates, and discuss the methodological limitations of cluster surveys when data are highly clustered such as during an EVD outbreak.
METHODS
We analysed the outputs of two independent population-based surveys conducted at the end of the 2014-2016 EVD outbreak in Bo District, Sierra Leone, in urban and rural areas. In each area, 35 clusters of 14 households were selected with probability proportional to population size. We collected information on morbidity, mortality and changes in household composition during the recall period (May 2014 to April 2015). Rates were calculated for all-cause, all-age, under-5 and EVD-specific mortality, respectively, by areas and overall. Crude and adjusted mortality rates were estimated using Poisson regression, accounting for the surveys sample weights and the clustered design.
RESULTS
Overall 980 households and 6,522 individuals participated in both surveys. A total of 64 deaths were reported, of which 20 were attributed to EVD. The crude and EVD-specific mortality rates were 0.35/10,000 person-days (95%CI: 0.23-0.52) and 0.12/10,000 person-days (95%CI: 0.05-0.32), respectively. The DEFF for EVD mortality was 5.53, and for non-EVD mortality, it was 1.53. DEFF for EVD-specific mortality was 6.18 in the rural area and 0.58 in the urban area. DEFF for non-EVD-specific mortality was 1.87 in the rural area and 0.44 in the urban area.
CONCLUSION
Our findings demonstrate a high degree of clustering; this contributed to imprecise mortality estimates, which have limited utility when assessing the impact of disease. We provide DEFF estimates that can inform future cluster surveys and discuss design improvements to mitigate the limitations of surveys for highly clustered data.
There is a lack of empirical data on design effects (DEFF) for mortality rate for highly clustered data such as with Ebola virus disease (EVD), along with a lack of documentation of methodological limitations and operational utility of mortality estimated from cluster-sampled studies when the DEFF is high.
OBJECTIVES
The objectives of this paper are to report EVD mortality rate and DEFF estimates, and discuss the methodological limitations of cluster surveys when data are highly clustered such as during an EVD outbreak.
METHODS
We analysed the outputs of two independent population-based surveys conducted at the end of the 2014-2016 EVD outbreak in Bo District, Sierra Leone, in urban and rural areas. In each area, 35 clusters of 14 households were selected with probability proportional to population size. We collected information on morbidity, mortality and changes in household composition during the recall period (May 2014 to April 2015). Rates were calculated for all-cause, all-age, under-5 and EVD-specific mortality, respectively, by areas and overall. Crude and adjusted mortality rates were estimated using Poisson regression, accounting for the surveys sample weights and the clustered design.
RESULTS
Overall 980 households and 6,522 individuals participated in both surveys. A total of 64 deaths were reported, of which 20 were attributed to EVD. The crude and EVD-specific mortality rates were 0.35/10,000 person-days (95%CI: 0.23-0.52) and 0.12/10,000 person-days (95%CI: 0.05-0.32), respectively. The DEFF for EVD mortality was 5.53, and for non-EVD mortality, it was 1.53. DEFF for EVD-specific mortality was 6.18 in the rural area and 0.58 in the urban area. DEFF for non-EVD-specific mortality was 1.87 in the rural area and 0.44 in the urban area.
CONCLUSION
Our findings demonstrate a high degree of clustering; this contributed to imprecise mortality estimates, which have limited utility when assessing the impact of disease. We provide DEFF estimates that can inform future cluster surveys and discuss design improvements to mitigate the limitations of surveys for highly clustered data.
Journal Article > ResearchFull Text
BMC Public Health. 13 February 2018; Volume 18 (Issue 1); DOI:10.1186/s12889-018-5158-6
Caleo GNC, Duncombe J, Jephcott F, Lokuge K, Mills C, et al.
BMC Public Health. 13 February 2018; Volume 18 (Issue 1); DOI:10.1186/s12889-018-5158-6
Little is understood of Ebola virus disease (EVD) transmission dynamics and community compliance with control measures over time. Understanding these interactions is essential if interventions are to be effective in future outbreaks. We conducted a mixed-methods study to explore these factors in a rural village that experienced sustained EVD transmission in Kailahun District, Sierra Leone.
Journal Article > ResearchFull Text
Lancet Infect Dis. 25 June 2020; Volume 20 (Issue 11); DOI:10.1016/S1473-3099(20)30193-6
Caleo GNC, Theocharaki F, Lokuge K, Weiss HA, Inamdar L, et al.
Lancet Infect Dis. 25 June 2020; Volume 20 (Issue 11); DOI:10.1016/S1473-3099(20)30193-6
Background
Ebola virus disease case definition is a crucial surveillance tool to detect suspected cases for referral and as a screening tool for clinicians to support admission and laboratory testing decisions at Ebola health facilities. We aimed to assess the performance of the WHO Ebola virus disease case definitions and other screening scores.
Methods
In this systematic review and meta-analysis, we searched PubMed, Scopus, Embase, and Web of Science for studies published in English between June 13, 1978, and Jan 14, 2020. We included studies that estimated the sensitivity and specificity of WHO Ebola virus disease case definitions, clinical and epidemiological characteristics (symptoms at admission and contact history), and predictive risk scores against the reference standard (laboratory-confirmed Ebola virus disease). Summary estimates of sensitivity and specificity were calculated using bivariate and hierarchical summary receiver operating characteristic (when four or more studies provided data) or random-effects meta-analysis (fewer than four studies provided data).
Findings
We identified 2493 publications, of which 14 studies from four countries (Sierra Leone, Guinea, Liberia, and Angola) were included in the analysis. 12 021 people with suspected disease were included, of whom 4874 were confirmed as positive for Ebola virus infection. Six studies explored the performance of WHO case definitions in non-paediatric populations, and in all of these studies, suspected and probable cases were combined and could not be disaggregated for analysis. The pooled sensitivity of the WHO Ebola virus disease case definitions from these studies was 81·5% (95% CI 74·1–87·2) and pooled specificity was 35·7% (28·5–43·6). History of contact or epidemiological link was a key predictor for the WHO case definitions (seven studies) and for risk scores (six studies). The most sensitive symptom was intense fatigue (79·0% [95% CI 74·4–83·0]), assessed in seven studies, and the least sensitive symptom was pain behind the eyes (1·0% [0·0–7·0]), assessed in three studies. The performance of fever as a symptom varied depending on the cutoff used to define fever.
Interpretation
WHO Ebola virus disease case definitions perform suboptimally to identify cases at both community level and during triage at Ebola health facilities. Inclusion of intense fatigue as a key symptom and contact history could improve the performance of case definitions, but implementation of these changes will require effective collaboration with, and trust of, affected communities.
Ebola virus disease case definition is a crucial surveillance tool to detect suspected cases for referral and as a screening tool for clinicians to support admission and laboratory testing decisions at Ebola health facilities. We aimed to assess the performance of the WHO Ebola virus disease case definitions and other screening scores.
Methods
In this systematic review and meta-analysis, we searched PubMed, Scopus, Embase, and Web of Science for studies published in English between June 13, 1978, and Jan 14, 2020. We included studies that estimated the sensitivity and specificity of WHO Ebola virus disease case definitions, clinical and epidemiological characteristics (symptoms at admission and contact history), and predictive risk scores against the reference standard (laboratory-confirmed Ebola virus disease). Summary estimates of sensitivity and specificity were calculated using bivariate and hierarchical summary receiver operating characteristic (when four or more studies provided data) or random-effects meta-analysis (fewer than four studies provided data).
Findings
We identified 2493 publications, of which 14 studies from four countries (Sierra Leone, Guinea, Liberia, and Angola) were included in the analysis. 12 021 people with suspected disease were included, of whom 4874 were confirmed as positive for Ebola virus infection. Six studies explored the performance of WHO case definitions in non-paediatric populations, and in all of these studies, suspected and probable cases were combined and could not be disaggregated for analysis. The pooled sensitivity of the WHO Ebola virus disease case definitions from these studies was 81·5% (95% CI 74·1–87·2) and pooled specificity was 35·7% (28·5–43·6). History of contact or epidemiological link was a key predictor for the WHO case definitions (seven studies) and for risk scores (six studies). The most sensitive symptom was intense fatigue (79·0% [95% CI 74·4–83·0]), assessed in seven studies, and the least sensitive symptom was pain behind the eyes (1·0% [0·0–7·0]), assessed in three studies. The performance of fever as a symptom varied depending on the cutoff used to define fever.
Interpretation
WHO Ebola virus disease case definitions perform suboptimally to identify cases at both community level and during triage at Ebola health facilities. Inclusion of intense fatigue as a key symptom and contact history could improve the performance of case definitions, but implementation of these changes will require effective collaboration with, and trust of, affected communities.