Comparison of the Causes of Death Identified Using Automated Verbal Autopsy and Complete Autopsy among Brought-in-Dead Cases at a Tertiary Hospital in Sub-Sahara Africa.

BACKGROUND: Over one-third of deaths recorded at health facilities in Zambia are brought in dead (BID) and the causes of death (CODs) are not fully analyzed. The use of automated verbal autopsy (VA) has reportedly determined the CODs of more BID cases than the death notification form issued by the hospital. However, the validity of automated VA is yet to be fully investigated. OBJECTIVES: To compare the CODs identified by automated VA with those by complete autopsy to examine the validity of a VA tool. METHODS: The study site was the tertiary hospital in the capital city of Zambia. From September 2019 to January 2020, all BID cases aged 13 years and older brought to the hospital during the daytime on weekdays were enrolled in this study. External COD cases were excluded. The deceased's relatives were interviewed using the 2016 World Health Organization VA questionnaire. The data were analyzed using InterVA, an automated VA tool, to determine the CODs, which were compared with the results of complete autopsies. RESULTS: A total of 63 cases were included. The CODs of 50 BID cases were determined by both InterVA and complete autopsies. The positive predictive value of InterVA was 22%. InterVA determined the CODs correctly in 100% cases of maternal CODs, 27.5% cases of noncommunicable disease CODs, and 5.3% cases of communicable disease CODs. Using the three broader disease groups, 56.0% cases were classified in the same groups by both methods. CONCLUSION: While the positive predictive value was low, more than half of the cases were categorized into the same broader categories. However, there are several limitations in this study, including small sample size. More research is required to investigate the factors leading to discrepancies between the CODs determined by both methods to optimize the use of automated VA in Zambia.

Analysis of causes of death among brought-in-dead cases in a third-level Hospital in Lusaka, Republic of Zambia, using the tariff method 2.0 for verbal autopsy: a cross-sectional study.

BACKGROUND: Over one third of deaths in Zambian health facilities involve someone who has already died before arrival (i.e., Brough in Dead), and in most BiD cases, the CoD have not been fully analyzed. Therefore, this study was designed to evaluate the function of automated VA based on the Tariff Method 2.0 to identify the CoD among the BiD cases and the usefulness by comparing the data on the death notification form. METHODS: The target site was one third-level hospital in the Republic of Zambia's capital city. All BiD cases who reached the target health facility from January to August 2017 were included. The deceased's closest relatives were interviewed using a structured VA questionnaire and the data were analyzed using the SmartVA to determine the CoD at the individual and population level. The CoD were compared with description on the death notification forms by using t-test and Cohen's kappa coefficient. RESULTS: One thousand three hundred seventy-eight and 209 cases were included for persons aged 13 years and older (Adult) and those aged 1 month to 13 years old (Child), respectively. The top CoD for Adults were infectious diseases followed by non-communicable diseases and that for Child were infectious diseases, followed by accidents. The proportion of cases with a determined CoD was significantly higher when using the SmartVA (75% for Adult and 67% for Child) than the death notification form (61%). A proportion (42.7% for Adult and 46% for Child) of the CoD-determined cases matched in both sources, with a low concordance rate for Adult (kappa coefficient = 0.1385) and a good for Child(kappa coefficient = 0.635). CONCLUSIONS: The CoD of the BiD cases were successfully analyzed using the SmartVA for the first time in Zambia. While there many erroneous descriptions on the death notification form, the SmartVA could determine the CoD among more BiD cases. Since the information on the death notification form is reflected in the national vital statistics, more accurate and complete CoD data are required. In order to strengthen the death registration system with accurate CoD, it will be useful to embed the SmartVA in Zambia's health information system.

Influenza surveillance in Zambia, 2008-2009.

BACKGROUND: Limited information exists about influenza viruses in Africa. We used data from a new sentinel surveillance system to investigate the seasonality and characteristics of influenza, including pandemic (pdm) influenza A H1N1, in Zambia. METHODS: In June 2008, we established sentinel surveillance for influenza-like illness (ILI) and severe acute respiratory illness (SARI) at 4 healthcare facilities in Zambia. Nasopharyngeal and oropharyngeal swabs and structured questionnaires were collected from eligible patients and samples were tested by real-time reverse-transcription polymerase chain reaction for influenza virus types and subtypes. RESULTS: From June 2008 to December 2009, we collected 1234 specimens, of which 334 (27%) were ILI, and 900 (63%) were SARI. Overall, 4% (57) of specimens were positive for influenza. The influenza detection rate in ILI and SARI cases was 5% (17/334) and 4% (40/900), respectively. Among all influenza cases, 54 (95%) were influenza A and 3 (5%) were influenza B. Of the influenza A viruses, 16 (30%) were A(H1N1)pdm09, 29 (54%) were seasonal A(H1N1), 6 (11%) were A(H3N2), and 4 (7%) were unsubtyped. The detection rate for A(H1N1)pdm09 cases was highest in persons aged 5-24 years (5/98; 5%), 25-44 years (4/78; 5%), and 45-64 years (1/17; 6%). Conversely, for seasonal influenza the detection rate was highest in children aged 1-4 years (18/294; 6%). Influenza virus circulation peaked during June-August in both years and A(H1N1)pdm09 occurred at the end of the influenza season in 2009. CONCLUSIONS: Seasonal influenza virus infection was found to be associated with both mild and severe respiratory illness in Zambia. Future years of surveillance are necessary to better define the seasonality and epidemiology of influenza in the country.

Diagnosis of latent tuberculosis infection among HIV discordant partners using interferon gamma release assays.

BACKGROUND: There is limited data on the effect of HIV status and CD4 counts on performance of Interferon-g Release assays (IGRAs) for diagnosis of latent tuberculosis infection (LTBI). METHODS: A cross sectional study was conducted to assess the prevalence of and risk factors for a positive diagnostic test for LTBI, using tuberculin skin test (TST) and IGRAs among HIV-discordant couples in Zambia. RESULTS: A total of 596 subjects (298 couples) were enrolled. Median CD4 count among HIV positive persons was 388 cells/µl, (range 51-1330). HIV negative persons were more likely than their HIV positive partner, to have a positive diagnostic test for LTBI with TST (203 vs 128), QFT (171 vs 109) and TSPOT (156 vs. 109). On multivariate analysis, HIV negative status was an independent predictor for a positive QFT (OR = 2.22, 95% CI 1.42- 3.46) and TSPOT (OR = 1.79, 95% CI 1.16-2.77). Among HIV positive subjects a CD4 count ≥ 388 cells/µl was associated with a positive TST (OR = 1.76 95% CI 1.10-2.82) and QFT (OR = 1.71 95% CI 1.06-2.77) but not TSPOT (OR = 1.20 95% CI 0.74-1.94). CONCLUSIONS: Persons with HIV had significantly fewer positive diagnostic tests for LTBI with TST, QFT and TSPOT. Persons with a CD4 count < 388 cells/µl were less likely to have a positive TST or QFT, but not less likely to have a positive TSPOT. TSPOT may perform better than TST or QFT in HIV positive individuals.