Hospital mortality statistics in Tanzania: availability, accessibility, and quality 2006–2015

Abstract Background Accurate and reliable hospital information on the pattern and causes of death is important to monitor and evaluate the effectiveness of health policies and programs. The objective of this study was to assess the availability, accessibility, and quality of hospital mortality data in Tanzania. Methods This cross-sectional study involved selected hospitals of Tanzania and was carried out from July to October 2016. Review of hospital death registers and forms was carried out to cover a period of 10 years (2006–2015). Interviews with hospital staff were conducted to seek information as regards to tools used to record mortality data, staff involved in recording and availability of data storage and archiving facilities. Results A total of 247,976 death records were reviewed. The death register was the most (92.3%) common source of mortality data. Other sources included the International Classification of Diseases (ICD) report forms, Inpatient registers, and hospital administrative reports. Death registers were available throughout the 10-year period while ICD-10 forms were available for the period of 2013–2015. In the years between 2006 and 2010 and 2011–2015, the use of death register increased from 82 to 94.9%. Three years after the introduction of ICD-10 procedure, the forms were available and used in 28% (11/39) hospitals. The level of acceptable data increased from 69% in 2006 to 97% in 2015. Inconsistency in the language used, use of non-standard nomenclature for causes of death, use of abbreviations, poorly and unreadable handwriting, and missing variables were common data quality challenges. About 6.3% (n = 15,719) of the records had no patient age, 3.5% (n = 8790) had no cause of death and ~ 1% had no sex indicated. The frequency of missing sex variable was most common among under-5 children. Data storage and archiving in most hospitals was generally poor. Registers and forms were stored in several different locations, making accessibility difficult. Conclusion Overall, this study demonstrates gaps in hospital mortality data availability, accessibility, and quality, and highlights the need for capacity strengthening in data management and periodic record reviews. Policy guidelines on the data management including archiving are necessary to improve data

Cause-specific mortality patterns among hospital deaths in Tanzania, 2006-2015.

BACKGROUND:Understanding the causes of inpatient mortality in hospitals is important for monitoring the population health and evidence-based planning for curative and public health care. Dearth of information on causes and trends of hospital mortality in most countries of Sub-Saharan Africa has resulted to wide use of model-based estimation methods which are characterized by estimation errors. This retrospective analysis used primary data to determine the cause-specific mortality patterns among inpatient hospital deaths in Tanzania from 2006-2015. MATERIALS AND METHODS:The analysis was carried out from July to December 2016 and involved 39 hospitals in Tanzania. A review of hospital in-patient death registers and report forms was done to cover a period of 10 years. Information collected included demographic characteristics of the deceased and immediate underlying cause of death. Causes of death were coded using international classification of diseases (ICD)-10. Data were analysed to provide information on cause-specific, trends and distribution of death by demographic and geographical characteristics. PRINCIPAL FINDINGS:A total of 247,976 deaths were captured over a 10-year period. The median age at death was 30 years, interquartile range (IQR) 1, 50. The five leading causes of death were malaria (12.75%), respiratory diseases (10.08%), HIV/AIDS (8.04%), anaemia (7.78%) and cardio-circulatory diseases (6.31%). From 2006 to 2015, there was a noted decline in the number of deaths due to malaria (by 47%), HIV/AIDS (28%) and tuberculosis (26%). However, there was an increase in number of deaths due to neonatal disorders by 128%. Malaria and anaemia killed more infants and children under 5 years while HIV/AIDS and Tuberculosis accounted for most of the deaths among adults. CONCLUSION:The leading causes of inpatient hospital death were malaria, respiratory diseases, HIV/AIDS, anaemia and cardio-circulatory diseases. Death among children under 5 years has shown an increasing trend. The observed trends in mortality indicates that the country is lagging behind towards attaining the global and national goals for sustainable development. The increasing pattern of respiratory diseases, cancers and septicaemia requires immediate attention of the health system

Effects of Prednisolone on Disease Progression in Antiretroviral-Untreated HIV Infection: A 2-Year Randomized, Double-Blind Placebo-Controlled Clinical Trial

Background HIV-disease progression correlates with immune activation. Here we investigated whether corticosteroid treatment can attenuate HIV disease progression in antiretroviral-untreated patients. Methods Double-blind, placebo-controlled randomized clinical trial including 326 HIV-patients in a resource-limited setting in Tanzania (clinicaltrials.gov NCT01299948). Inclusion criteria were a CD4 count above 300 cells/μl, the absence of AIDS-defining symptoms and an ART-naïve therapy status. Study participants received 5 mg prednisolone per day or placebo for 2 years. Primary endpoint was time to progression to an AIDS-defining condition or to a CD4-count below 200 cells/μl. Results No significant change in progression towards the primary endpoint was observed in the intent-to-treat (ITT) analysis (19 cases with prednisolone versus 28 cases with placebo, p = 0.1407). In a per-protocol (PP)-analysis, 13 versus 24 study participants progressed to the primary study endpoint (p = 0.0741). Secondary endpoints: Prednisolone-treatment decreased immune activation (sCD14, suPAR, CD38/HLA-DR/CD8+) and increased CD4-counts (+77.42 ± 5.70 cells/μl compared to -37.42 ± 10.77 cells/μl under placebo, p < 0.0001). Treatment with prednisolone was associated with a 3.2-fold increase in HIV viral load (p < 0.0001). In a post-hoc analysis stratifying for sex, females treated with prednisolone progressed significantly slower to the primary study endpoint than females treated with placebo (ITT-analysis: 11 versus 21 cases, p = 0.0567; PP-analysis: 5 versus 18 cases, p = 0.0051): No changes in disease progression were observed in men. Conclusions This study could not detect any significant effects of prednisolone on disease progression in antiretroviral-untreated HIV infection within the intent-to-treat population. However, significant effects were observed on CD4 counts, immune activation and HIV viral load. This study contributes to a better understanding of the role of immune activation in the pathogenesis of HIV infection

Study drug adherence and loss to follow-up.

<p><b>A, B</b>: Study drug adherence calculated from pill counts of returned, unused study medication in female (A) and male (B) study participants. P-values were calculated by 2way ANOVA <b>C</b>: Loss to follow-up during the two-year study. P value was calculated by log-rank (Mantel-Cox) analysis.</p

Progression to HAART as treated.

<p>Kaplan-Meyer estimate of progression to HAART as treated within the intent-to-treat (ITT) population (<b>A</b>) or the per protocol (PP) population (<b>B</b>). Separate analyses for female study participants (B, E) and for male study participants (C, F) for progression to HART as treated. P values were calculated by log-rank (Mantel-Cox) analysis.</p

Effects of prednisolone on HIV disease progression: Primary Study Endpoint.

<p>Kaplan-Meyer estimate of progression to combined study endpoint (onset of CDC stage-C-condition or drop of CD4 counts below 200) within the intent-to-treat (ITT) population (<b>A</b>) or the per protocol (PP) population (<b>B</b>). <b>C</b>: Progression to AIDS-defining condition. All study participants who received HAART, but did not reach the study endpoint were censored for the KM analysis. <b>D-F</b> and <b>G-I</b>: Post-hoc analyses for female study participants (D-F) and for male study participants (H-I) for progression to combined endpoint within the ITT population (D, G), progression to combined endpoint within the PP population (E, H), and progression to AIDS-defining condition (F, I). A-I: P values were calculated by log-rank (Mantel-Cox) analysis.</p

Study participants at risk.

<p>Number of study participants at risk within the intent-to-treat (ITT) population (<b>A</b>) or the per protocol (PP) population (<b>B</b>). Separate analyses for female study participants (C, D) and for male study participants (E, F).</p

CONSORT statement 2010 flow diagram.

<p>The number of participants enrolled, randomized, allocated to study medication, followed-up and analyzed is shown. Study participants who progressed to the endpoint of the study (CD4 < 200 or CDC stage-C disease) received HAART. In some cases, study participants also received HAART when they progressed to CD4 < 350 in combination with WHO stage 3-disease. This was in accordance to the National Tanzanian treatment recommendations Update in 2008 (1 case in the placebo arm and 3 cases in the prednisolone arm). In addition, one patient in the placebo arm and 2 study participants in the prednisolone arm received HAART without fulfilling either the study endpoint or the criteria listed in the National Treatment recommendation update.</p

CD4 counts of study participants with loss of follow-up.

<p><b>A:</b> Baseline (BL) CD4 counts from study participants who were later lost to follow up (lost) and who fulfilled (fulfilled) the study. P value was calculated by Mann-Whitney test. <b>B:</b> CD4 counts prior to loss to follow up (loss) or to progression to the primary endpoint (HAART). Red bars (placebo) and blue bars (prednisolone) represent medians. P values were calculated by Kruskal-Wallis test with multiple comparisons.</p

Effects of prednisolone on immune activation and HIV viral load.

<p><b>A, E, I:</b> Concentration of sCD14 was determined by ELISA from n = 134 (placebo) and n = 136 (prednisolone) available plasma samples collected at baseline, 3, 6 and 12 months. A: all sexes, E: females (n_Plac = 106, n_Pred = 110), I: males (n_Plac = 28, n_Pred = 26). <b>B, F, J:</b> Concentration of sUPAR was determined by ELISA from n = 122 (placebo) and n = 124 (prednisolone) available plasma samples collected at baseline, 3, 6 and 12 months. B: all sexes, F: females (n_Plac = 95, n_Pred = 102), J: males (n_Plac = 27, n_Pred = 22). A, B, E, F, I, J: Data as means ± S.D. P-values were determined by 2-way ANOVA (difference between the two treatments over the whole time period) or by Wilcoxon matched-pairs signed test (changes between Baseline and month 12). <b>C, G, K:</b> CD38/HLA-DR expression was determined by flow cytometry from n = 22 (placebo) and n = 30 (prednisolone) available frozen PBMC samples collected at baseline and 12 months. C: all sexes, G: females (n_Plac = 19, n_Pred = 27), K: males (n_Plac = 3, n_Pred = 3). P-values were determined by Wilcoxon matched-pairs signed test (changes between Baseline and month 12). <b>D, H, L:</b> HIV viral load was determined from n = 86 (placebo) and n = 80 (prednisolone) available plasma pairs at baseline and month 12. D: all sexes, H: females (n_Plac = 70, n_Pred = 66), L: males (n_Plac = 16, n_Pred = 14). P-values were determined by Wilcoxon matched-pairs signed test (changes between Baseline and month 12) and by Mann-Whitney test (comparison of month 12 placebo versus month 12 prednisolone).</p