The Neighbourhood Method for Measuring Differences in Maternal Mortality, Infant Mortality and Other Rare Demographic Events
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Mendeley | Further Information

{"title"=>"The neighbourhood method for measuring differences in maternal mortality, infant mortality and other rare demographic events", "type"=>"journal", "authors"=>[{"first_name"=>"Nurul", "last_name"=>"Alam", "scopus_author_id"=>"7007131448"}, {"first_name"=>"John", "last_name"=>"Townend", "scopus_author_id"=>"7006285512"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84894871466", "doi"=>"10.1371/journal.pone.0083590", "pui"=>"372500577", "pmid"=>"24392088", "scopus"=>"2-s2.0-84894871466", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"8aac598c-996e-3ce3-bf73-93e18fd1f33d", "abstract"=>"In the absence of reliable systems for registering rare types of vital events large surveys are required to measure changes in their rates. However some events such as maternal deaths are widely known about in the community. This study examined the utility of asking respondents about events in their neighbourhood as an efficient method for measuring relative rates of rare health events such as maternal and infant deaths. A survey was conducted in the health and demographic surveillance system (HDSS) in Matlab, Bangladesh, which includes two areas with different health care regimes. Adult women were asked about any maternal deaths; multiple births; infant deaths, live births and some other events they knew of in a small specified area around their home. Agreement between HDSS records and survey responses was moderate or better (kappa≥0.44) for all the events and greatest for maternal deaths (kappa = 0.77) with 84% being reported. Most events were more likely to be reported if they were recent (p<0.05). Infant mortality rate in one area was 0.56 times that in the other which was well reflected by the ratio of survey results (0.53). Simulations were used to study the ability of the method to detect differences in maternal mortality ratio. These suggested that a sample size around 5000 would give 80% power to detect a 50% decrease from a baseline of 183 which compared well with an estimated sample size around 10 times larger using the direct sisterhood method. The findings suggest that the Neighbourhood Method has potential for monitoring relative differences between areas or changes over time in the rates of rare demographic events, requiring considerably smaller sample sizes than traditional methods. This raises the possibility for interventions to demonstrate real effects on outcomes such as maternal deaths where previously this was only feasible by indirect methods.", "link"=>"http://www.mendeley.com/research/neighbourhood-method-measuring-differences-maternal-mortality-infant-mortality-other-rare-demographi", "reader_count"=>25, "reader_count_by_academic_status"=>{"Researcher"=>7, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>3, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Researcher"=>7, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>3, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>1, "Nursing and Health Professions"=>2, "Medicine and Dentistry"=>7, "Agricultural and Biological Sciences"=>2, "Business, Management and Accounting"=>2, "Social Sciences"=>6, "Immunology and Microbiology"=>1, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Social Sciences"=>{"Social Sciences"=>6}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1337432"], "description"=>"<p>(A) Ratios of observed neighbourhood indices for a comparison area and the reference area (<i>I</i><sub>comp</sub>/<i>I</i><sub>ref</sub>) compared with ratios of true MMRs (MMR<sub>comp</sub>/MMR<sub>ref</sub>) for a sample size of 20,000 per area. Each point represents the results of one simulation. In each case the MMR in the reference area was 183 per 100,000 live births. Ratios of neighbourhood indices which were significantly different (p<0.05) are shown in blue (filled circles); non-significant results are shown in orange (open circles). 1:1 line is also shown. (B) Detectable differences – the lines show ratios of MMR<sub>comp</sub>/MMR<sub>ref</sub> which could be detected with 80% power at the 5% significance level for a given sample size when MMR<sub>ref</sub> = 183. The equivalent curves using a direct sisterhood survey with reference period 3 years assuming 2 sisters per respondent, GFR = 83 and perfect information from survey responses are also shown.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Social epidemiology", "Spatial epidemiology", "Population metrics", "Death rate", "Clinical research design", "Survey research", "Epidemiological methods", "Survey methods", "Global health", "Non-clinical medicine", "Health care policy", "Child and adolescent health policy", "Health risk analysis", "Health statistics", "Obstetrics and gynecology", "Labor and delivery", "Public health", "Behavioral and social aspects of health", "Child health", "Health screening", "women's health", "Research assessment", "Sociology", "demography", "Social research"], "article_id"=>893141, "categories"=>["Biological Sciences", "Medicine", "Science Policy", "Sociology"], "users"=>["Nurul Alam", "John Townend"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0083590.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_simulations_/893141", "title"=>"Results of simulations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-02 03:50:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1337433"], "description"=>"<p>HDSS total  =  number of events that should have been reported (number of events in the HDSS x 2 respondents per cluster).</p><p>Un-reported  =  events missed by respondents (% of HDSS total).</p><p>Total reported  =  number of events reported (% of HDSS total) (includes correct reports and over-reports).</p><p>Correct reports  =  events reported that corresponded with actual events (% of total reported).</p><p>Over-reports  =  events reported over and above the actual number of events in the respondents' clusters (% of total reported).</p><p>All weighted κ scores were highly significant (p<0.001).</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Social epidemiology", "Spatial epidemiology", "Population metrics", "Death rate", "Clinical research design", "Survey research", "Epidemiological methods", "Survey methods", "Global health", "Non-clinical medicine", "Health care policy", "Child and adolescent health policy", "Health risk analysis", "Health statistics", "Obstetrics and gynecology", "Labor and delivery", "Public health", "Behavioral and social aspects of health", "Child health", "Health screening", "women's health", "Research assessment", "Sociology", "demography", "Social research", "clusters", "hdss", "records", "respondents"], "article_id"=>893142, "categories"=>["Biological Sciences", "Medicine", "Science Policy", "Sociology"], "users"=>["Nurul Alam", "John Townend"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0083590.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_of_reporting_of_events_in_the_respondents_own_clusters_and_agreement_with_HDSS_records_and_other_respondents_in_the_same_cluster_/893142", "title"=>"Accuracy of reporting of events in the respondents' own clusters and agreement with HDSS records and other respondents in the same cluster.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-02 03:50:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1337434"], "description"=>"<p>OR =  odds ratio (per year before the survey) for an event being reported (OR<1 means the event was less likely to be reported the greater the time it occurred before the survey).</p><p>n =  number of respondents correctly reporting the event.</p><p>N =  number of clusters with exactly one event of this type during the reference interval (x 2 respondents).</p><p>% =  % of potential reports included in responses.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Social epidemiology", "Spatial epidemiology", "Population metrics", "Death rate", "Clinical research design", "Survey research", "Epidemiological methods", "Survey methods", "Global health", "Non-clinical medicine", "Health care policy", "Child and adolescent health policy", "Health risk analysis", "Health statistics", "Obstetrics and gynecology", "Labor and delivery", "Public health", "Behavioral and social aspects of health", "Child health", "Health screening", "women's health", "Research assessment", "Sociology", "demography", "Social research", "clusters", "had", "taken"], "article_id"=>893143, "categories"=>["Biological Sciences", "Medicine", "Science Policy", "Sociology"], "users"=>["Nurul Alam", "John Townend"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0083590.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Respondents_reporting_an_event_in_clusters_where_exactly_one_event_had_taken_place_during_the_reference_period_/893143", "title"=>"Respondents reporting an event in clusters where exactly one event had taken place during the reference period.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-02 03:50:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1337435", "https://ndownloader.figshare.com/files/1337436"], "description"=>"<div><p>In the absence of reliable systems for registering rare types of vital events large surveys are required to measure changes in their rates. However some events such as maternal deaths are widely known about in the community. This study examined the utility of asking respondents about events in their neighbourhood as an efficient method for measuring relative rates of rare health events such as maternal and infant deaths. A survey was conducted in the health and demographic surveillance system (HDSS) in Matlab, Bangladesh, which includes two areas with different health care regimes. Adult women were asked about any maternal deaths; multiple births; infant deaths, live births and some other events they knew of in a small specified area around their home. Agreement between HDSS records and survey responses was moderate or better (kappa≥0.44) for all the events and greatest for maternal deaths (kappa = 0.77) with 84% being reported. Most events were more likely to be reported if they were recent (p<0.05). Infant mortality rate in one area was 0.56 times that in the other which was well reflected by the ratio of survey results (0.53). Simulations were used to study the ability of the method to detect differences in maternal mortality ratio. These suggested that a sample size around 5000 would give 80% power to detect a 50% decrease from a baseline of 183 which compared well with an estimated sample size around 10 times larger using the direct sisterhood method. The findings suggest that the Neighbourhood Method has potential for monitoring relative differences between areas or changes over time in the rates of rare demographic events, requiring considerably smaller sample sizes than traditional methods. This raises the possibility for interventions to demonstrate real effects on outcomes such as maternal deaths where previously this was only feasible by indirect methods.</p></div>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Social epidemiology", "Spatial epidemiology", "Population metrics", "Death rate", "Clinical research design", "Survey research", "Epidemiological methods", "Survey methods", "Global health", "Non-clinical medicine", "Health care policy", "Child and adolescent health policy", "Health risk analysis", "Health statistics", "Obstetrics and gynecology", "Labor and delivery", "Public health", "Behavioral and social aspects of health", "Child health", "Health screening", "women's health", "Research assessment", "Sociology", "demography", "Social research", "neighbourhood", "measuring", "differences", "maternal"], "article_id"=>893144, "categories"=>["Biological Sciences", "Medicine", "Science Policy", "Sociology"], "users"=>["Nurul Alam", "John Townend"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0083590.s001", "https://dx.doi.org/10.1371/journal.pone.0083590.s002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Neighbourhood_Method_for_Measuring_Differences_in_Maternal_Mortality_Infant_Mortality_and_Other_Rare_Demographic_Events_/893144", "title"=>"The Neighbourhood Method for Measuring Differences in Maternal Mortality, Infant Mortality and Other Rare Demographic Events", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-01-02 03:50:11"}

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Relative Metric

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