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      Effectiveness of hygiene kit distribution to reduce cholera transmission in Kasaï-Oriental, Democratic Republic of Congo, 2018: a prospective cohort study

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          Abstract

          Introduction

          Household contacts of cholera cases are at a greater risk of Vibrio cholerae infection than the general population. There is currently no agreed standard of care for household contacts, despite their high risk of infection, in cholera response strategies. In 2018, hygiene kit distribution and health promotion was recommended by Médecins Sans Frontières for admitted patients and accompanying household members on admission to a cholera treatment unit in the Democratic Republic of Congo.

          Methods

          To investigate the effectiveness of the intervention and risk factors for cholera infection, we conducted a prospective cohort study and followed household contacts for 7 days after patient admission. Clinical surveillance among household contacts was based on self-reported symptoms of cholera and diarrhoea, and environmental surveillance through the collection and analysis of food and water samples.

          Results

          From 94 eligible households, 469 household contacts were enrolled and 444 completed follow-up. Multivariate analysis suggested evidence of a dose-response relationship with increased kit use associated with decreased relative risk of suspected cholera: household contacts in the high kit-use group had a 66% lower incidence of suspected cholera (adjusted risk ratio (aRR) 0.34, 95% CI 0.11 to 1.03, p=0.055), the mid-use group had a 53% lower incidence (aRR 0.47, 95% CI 0.17 to 1.29, p=1.44) and low-use group had 22% lower incidence (aRR 0.78, 95% CI 0.24 to 2.53, p=0.684), compared with household contacts without a kit. Drinking water contamination was significantly reduced among households in receipt of a kit. There was no significant effect on self-reported diarrhoea or food contamination.

          Conclusion

          The integration of a hygiene kit intervention to case-households may be effective in reducing cholera transmission among household contacts and environmental contamination within the household. Further work is required to evaluate whether other proactive localised distribution among patients and case-households or to households surrounding cholera cases can be used in future cholera response programmes in emergency contexts.

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          Most cited references93

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          Constructing socio-economic status indices: how to use principal components analysis.

          Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.
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            Updated Global Burden of Cholera in Endemic Countries

            Background The global burden of cholera is largely unknown because the majority of cases are not reported. The low reporting can be attributed to limited capacity of epidemiological surveillance and laboratories, as well as social, political, and economic disincentives for reporting. We previously estimated 2.8 million cases and 91,000 deaths annually due to cholera in 51 endemic countries. A major limitation in our previous estimate was that the endemic and non-endemic countries were defined based on the countries’ reported cholera cases. We overcame the limitation with the use of a spatial modelling technique in defining endemic countries, and accordingly updated the estimates of the global burden of cholera. Methods/Principal Findings Countries were classified as cholera endemic, cholera non-endemic, or cholera-free based on whether a spatial regression model predicted an incidence rate over a certain threshold in at least three of five years (2008-2012). The at-risk populations were calculated for each country based on the percent of the country without sustainable access to improved sanitation facilities. Incidence rates from population-based published studies were used to calculate the estimated annual number of cases in endemic countries. The number of annual cholera deaths was calculated using inverse variance-weighted average case-fatality rate (CFRs) from literature-based CFR estimates. We found that approximately 1.3 billion people are at risk for cholera in endemic countries. An estimated 2.86 million cholera cases (uncertainty range: 1.3m-4.0m) occur annually in endemic countries. Among these cases, there are an estimated 95,000 deaths (uncertainty range: 21,000-143,000). Conclusion/Significance The global burden of cholera remains high. Sub-Saharan Africa accounts for the majority of this burden. Our findings can inform programmatic decision-making for cholera control.
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              Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries

              Background Epidemiological studies often require measures of socio-economic position (SEP). The application of principal components analysis (PCA) to data on asset-ownership is one popular approach to household SEP measurement. Proponents suggest that the approach provides a rational method for weighting asset data in a single indicator, captures the most important aspect of SEP for health studies, and is based on data that are readily available and/or simple to collect. However, the use of PCA on asset data may not be the best approach to SEP measurement. There remains concern that this approach can obscure the meaning of the final index and is statistically inappropriate for use with discrete data. In addition, the choice of assets to include and the level of agreement between wealth indices and more conventional measures of SEP such as consumption expenditure remain unclear. We discuss these issues, illustrating our examples with data from the Malawi Integrated Household Survey 2004–5. Methods Wealth indices were constructed using the assets on which data are collected within Demographic and Health Surveys. Indices were constructed using five weighting methods: PCA, PCA using dichotomised versions of categorical variables, equal weights, weights equal to the inverse of the proportion of households owning the item, and Multiple Correspondence Analysis. Agreement between indices was assessed. Indices were compared with per capita consumption expenditure, and the difference in agreement assessed when different methods were used to adjust consumption expenditure for household size and composition. Results All indices demonstrated similarly modest agreement with consumption expenditure. The indices constructed using dichotomised data showed strong agreement with each other, as did the indices constructed using categorical data. Agreement was lower between indices using data coded in different ways. The level of agreement between wealth indices and consumption expenditure did not differ when different consumption equivalence scales were applied. Conclusion This study questions the appropriateness of wealth indices as proxies for consumption expenditure. The choice of data included had a greater influence on the wealth index than the method used to weight the data. Despite the limitations of PCA, alternative methods also all had disadvantages.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2021
                14 October 2021
                : 11
                : 10
                : e050943
                Affiliations
                [1 ]London School of Hygiene & Tropical Medicine , London, UK
                [2 ]Médecins Sans Frontières , Brussels, Belgium
                [3 ]Médecins Sans Frontières , Kinshasa, Congo
                [4 ]Ministry of Health , Kinshasa, Congo
                [5 ]UNICEF , Kinshasa, Congo
                Author notes
                [Correspondence to ] Ms Lauren D'Mello-Guyett; lauren.dmello-guyett@ 123456lshtm.ac.uk
                Author information
                http://orcid.org/0000-0002-8174-1737
                Article
                bmjopen-2021-050943
                10.1136/bmjopen-2021-050943
                8522665
                34649847
                b983a546-77e8-4fe3-b4fe-e54345f3f432
                © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 04 March 2021
                : 24 September 2021
                Funding
                Funded by: Medecins Sans Frontieres;
                Award ID: n/a
                Categories
                Public Health
                1506
                1724
                Original research
                Custom metadata
                unlocked

                Medicine
                epidemiology,public health,infectious diseases
                Medicine
                epidemiology, public health, infectious diseases

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