OBJECTIVE: This study used the InterVA-4 computerised model to assign probable cause of @entity204 (CoD) to verbal autopsies (VAs) generated from two rural areas, with a difference in health service provision, within the Matlab @entity3337 ( @entity3337 ). This study aimed to compare CoD by gender, as well as discussing possible factors which could influence differences in the distribution of CoD between the two areas. DESIGN: Data for this study came from the Matlab the @entity3337 maintained by the International Centre for @entity66 Research, Bangladesh (icddr,b) since 1966. In late 1977, icddr,b divided @entity3337 and implemented a high-quality maternal, newborn and @entity1 health and family planning (MNCH-FP) services project in one half, called the icddr,b service area (SA), in addition to the usual public and private MNCH-FP services that serve the other half, called the government SA. @entity3337 field workers registered 12,144 deaths during 2003-2010, and trained interviewers obtained VA for 98.9% of them. The probabilistic model InterVA-4 probabilistic model (version 4.02) was used to derive probable CoD from VA symptoms. Cause-specific mortality rates and fractions were compared across gender and areas. Appropriate statistical tests were applied for significance testing. RESULTS: Mortality rates due to neonatal causes and @entity166 ( @entity166 ) were lower in the icddr,b SA than in the government SA, where mortality rates due to @entity166 ( @entity166 ) were lower. Cause-specific mortality fractions (CSMFs) due to @entity166 (23.2% versus 18.8%) and neonatal causes (7.4% versus 6%) were higher in the government SA, whereas CSMFs due to @entity166 were higher (58.2% versus 50.7%) in the icddr,b SA. The rank-order of CSMFs by age group showed marked variations, the largest category being acute @entity1729 / @entity55 in @entity130 in 1-4 and 5-14 years, @entity5 in 15-49 and 50-64 years, and @entity64 in 65+ years. CONCLUSIONS: Automated InterVA-4 coding of VA to determine probable CoD revealed the difference in the structure of CoD between areas with prominence of @entity166 in both areas. Such information can help local planning of health services for prevention and management of disease burden.

Distribution of cause of XXXX in rural Bangladesh during 2003-2010: evidence from two rural areas within Matlab @entity3337 .

@entity1 :: ('9606', 'Species') :: ['child'] @entity64 :: ('MESH:D020521', 'Disease') :: ['stroke'] @entity66 :: ('MESH:D004194', 'Disease') :: ['Diarrhoeal Disease'] @entity55 :: ('MESH:D011014', 'Disease') :: ['pneumonia'] @entity5 :: ('MESH:D009369', 'Disease') :: ['neoplasms'] @entity1729 :: ('MESH:D012141', 'Disease') :: ['respiratory infection'] @entity204 :: ('MESH:D003643', 'Disease') :: ['death'] @entity166 :: ('MESH:D003141', 'Disease') :: ['non-communicable diseases', 'NCDs', 'CDs', 'communicable diseases'] @entity3337 :: ('MESH:D009371', 'Disease') :: ['Health and Demographic Surveillance site', 'HDSS'] @entity130 :: ('MESH:D014947', 'Disease') :: ['infancy, injury']
@entity204 :: (MESH:D003643,Disease) :: ['death']
