Respondents reporting an event in clusters where exactly one event had taken place during the reference period.

<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

Accuracy of reporting of events in the respondents' own clusters and agreement with HDSS records and other respondents in the same cluster.

<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

Results of simulations.

<p>(A) Ratios of observed neighbourhood indices for a comparison area and the reference area (<i>I</i>_comp/<i>I</i>_ref) compared with ratios of true MMRs (MMR_comp/MMR_ref) 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_comp/MMR_ref which could be detected with 80% power at the 5% significance level for a given sample size when MMR_ref = 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

Additional file 2: of Comparing tariff and medical assistant assigned causes of death from verbal autopsy interviews in Matlab, Bangladesh: implications for a health and demographic surveillance system

Cause-specific mortality fractions for medical assistants, Tariff, and reallocated Tariff by age group. Three separate tables that provide cause-specific mortality fractions for medical assistants, Tariff, and reallocated Tariff for adults, children, and neonates. These tables differ from Tables 1, 2, and 3 because these include Tariff cause-specific mortality fractions prior to reallocation of the undetermined cause of death. (XLSX 14 kb

Additional file 3: of Comparing tariff and medical assistant assigned causes of death from verbal autopsy interviews in Matlab, Bangladesh: implications for a health and demographic surveillance system

Cause-specific mortality fractions for medical assistants and reallocated Tariff by age group. Bar graphs that mirror Table 1 by comparing the cause-specific mortality fraction for medical assistants to reallocated Tariff with the addition of indicating statistical significance at the 0.05 significance level. (PNG 72 kb

Additional file 1: of Comparing tariff and medical assistant assigned causes of death from verbal autopsy interviews in Matlab, Bangladesh: implications for a health and demographic surveillance system

ICD-10 codes to text COD mapping. Three separate tables that provide mapping from ICD-10 codes to text causes of death for adults, children, and neonates. (DOCX 14 kb

Additional file 2: of The quality of medical death certification of cause of death in hospitals in rural Bangladesh: impact of introducing the International Form of Medical Certificate of Cause of Death

Changes in death certificate by age group, place of death, and sex. One table proving the percentage of death certificates that required a change in the UCOD due to a change in diagnosis or change in sequence by age group, place of death, and sex. (XLSX 9 kb

Additional file 1: of The quality of medical death certification of cause of death in hospitals in rural Bangladesh: impact of introducing the International Form of Medical Certificate of Cause of Death

ICD-10 codes to text COD mapping. Three separate tables providing mapping from ICD-10 codes to text causes of death for adults, children, and neonates. (DOCX 19 kb

Additional file 1: of Impact of mobile phone-based technology to improve health, population and nutrition services in Rural Bangladesh: a study protocol

Household survey questionnaire. (DOCX 91 kb

Factors influencing patients’ satisfaction at different levels of health facilities in Bangladesh: Results from patient exit interviews

<div><p>There is a paucity in current literature about the level of patients’ satisfaction and factors influencing it in Bangladesh health system. We aimed to measure the level of patients’ satisfaction across different types and levels of healthcare facilities and to determine which factors influence this satisfaction level. A patient exit interview was carried out among 2207 patients attending selected health facilities in two administrative divisions of Bangladesh, namely Rajshahi and Sylhet. Information on healthcare experience and satisfaction with received care was collected through an electronic structured questionnaire. Information about <i>‘overall satisfaction with healthcare’</i> was collected on a 10-point scale and then dichotomized based on the median-split. Binomial logistic regressions, both simple and multivariable, were conducted to identify which factors contribute significantly to patients’ satisfaction. We found that 63.2% of the participants were satisfied with the healthcare service they received. Patients attending the private facilities had the highest level of satisfaction (i.e. 73%) and patients attending the primary care facilities had the lowest level of satisfaction (i.e. 52%). Factors like convenient opening hours, asking related questions to the providers, facility cleanliness and privacy settings were significantly associated with patients’ satisfaction. Being satisfied with facility cleanliness (multivariable OR 4.30; 95% CI: 3.29–5.62) and privacy settings (multivariable OR 1.68; 95% CI: 1.28–2.21) were the strongest predictors of patients’ satisfaction. In conclusion, a significant portion of the patients in Bangladesh are not satisfied with their received care. Patients’ satisfaction can be increased by focusing on improving facility cleanliness, privacy settings and providers’ interpersonal skills.</p></div