Institutional mortality rate and cause of death at health facilities in Ghana between 2014 and 2018

Background The epidemiological transition, touted as occurring in Ghana, requires research that tracks the changing patterns of diseases in order to capture the trend and improve healthcare delivery. This study examines national trends in mortality rate and cause of death at health facilities in Ghana between 2014 and 2018. Methods Institutional mortality data and cause of death from 2014–2018 were sourced from the Ghana Health Service’s District Health Information Management System. The latter collates healthcare service data routinely from government and non-governmental health institutions in Ghana yearly. The institutional mortality rate was estimated using guidelines from the Ghana Health Service. Percent change in mortality was examined for 2014 and 2018. In addition, cause of death data were available for 2017 and 2018. The World Health Organisation’s 11th International Classification for Diseases (ICD-11) was used to group the cause of death. Results Institutional mortality decreased by 7% nationally over the study period. However, four out of ten regions (Greater Accra, Volta, Upper East, and Upper West) recorded increases in institutional mortality. The Upper East (17%) and Volta regions (13%) recorded the highest increase. Chronic non-communicable diseases (NCDs) were the leading cause of death in 2017 (25%) and 2018 (20%). This was followed by certain infectious and parasitic diseases (15% for both years) and respiratory infections (10% in 2017 and 13% in 2018). Among the NCDs, hypertension was the leading cause of death with 2,243 and 2,472 cases in 2017 and 2018. Other (non-ischemic) heart diseases and diabetes were the second and third leading NCDs. Septicaemia, tuberculosis and pneumonia were the predominant infectious diseases. Regional variations existed in the cause of death. NCDs showed more urban-region bias while infectious diseases presented more rural-region bias. Conclusions This study examined national trends in mortality rate and cause of death at health facilities in Ghana. Ghana recorded a decrease in institutional mortality throughout the study. NCDs and infections were the leading causes of death, giving a double-burden of diseases. There is a need to enhance efforts towards healthcare and health promotion programmes for NCDs and infectious diseases at facility and community levels as outlined in the 2020 National Health Policy of Ghana.

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Assessment of neonatal mortalities and stillbirths data quality in Offinso North District of Ghana

International Journal of Scientific Research and Management ◽

10.18535/ijsrm/v10i01.mp01 ◽

2022 ◽

Vol 10 (01) ◽

pp. 508-518

Author(s):

Richmond Nsiah ◽

Wisdom Takramah ◽

Solomon Anum-Doku ◽

Richard Avagu ◽

Dominic Nyarko

Keyword(s):

Data Quality ◽

Neonatal Mortality ◽

Assessment Tool ◽

Information Management System ◽

Health Facilities ◽

Primary Data ◽

Mortality Data ◽

Neonatal Deaths ◽

Discrepancy Rate

Background: Stillbirths and neonatal deaths when poorly documented or collated, negatively affect the quality of decision and interventions. This study sought to assess the quality of routine neonatal mortalities and stillbirth records in health facilities and propose interventions to improve the data quality gaps. Method: Descriptive cross-sectional study was employed. This study was carried out at three (3) purposively selected health facilities in Offinso North district. Stillbirths and neonatal deaths recorded in registers from 2015 to 2017, were recounted and compared with monthly aggregated data and District Health Information Management System 2 (DHIMS 2) data using a self-developed Excel Data Quality Assessment Tool (DQS). An observational checklist was used to collect primary data on completeness and availability. Accuracy ratio (verification factor), discrepancy rate, percentage availability and completeness of stillbirths and neonatal mortality data were computed using the DQS tool. Findings: The results showed high discrepancy rate of stillbirth data recorded in registers compared with monthly aggregated reports (12.5%), and monthly aggregated reports compared with DHIMS 2 (13.5%). Neonatal mortalities data were under-reported in monthly aggregated reports, but over-reported in DHIMS 2. Overall data completeness was about 84.6%, but only 68.5% of submitted reports were supervised by facility in-charges. Delivery and admission registers availability were 100% and 83.3% respectively. Conclusion: Quality of stillbirths and neonatal mortality data in the district is generally encouraging, but are not reliable for decision-making. Routine data quality audit is needed to reduce high discrepancies in stillbirth and neonatal mortality data in the district.

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Improving quality of medical certification of causes of death in health facilities in Tanzania 2014–2019

BMC Health Services Research ◽

10.1186/s12913-021-06189-7 ◽

2021 ◽

Vol 21 (S1) ◽

Author(s):

Trust Nyondo ◽

Gisbert Msigwa ◽

Daniel Cobos ◽

Gregory Kabadi ◽

Tumaniel Macha ◽

...

Keyword(s):

Quality Improvement ◽

Health Policy ◽

Cause Of Death ◽

Causes Of Death ◽

Health Facilities ◽

Mortality Data ◽

Medical Certification ◽

Specific Mortality ◽

Icd 10

Abstract Background Monitoring medically certified causes of death is essential to shape national health policies, track progress to Sustainable Development Goals, and gauge responses to epidemic and pandemic disease. The combination of electronic health information systems with new methods for data quality monitoring can facilitate quality assessments and help target quality improvement. Since 2015, Tanzania has been upgrading its Civil Registration and Vital Statistics system including efforts to improve the availability and quality of mortality data. Methods We used a computer application (ANACONDA v4.01) to assess the quality of medical certification of cause of death (MCCD) and ICD-10 coding for the underlying cause of death for 155,461 deaths from health facilities from 2014 to 2018. From 2018 to 2019, we continued quality analysis for 2690 deaths in one large administrative region 9 months before, and 9 months following MCCD quality improvement interventions. Interventions addressed governance, training, process, and practice. We assessed changes in the levels, distributions, and nature of unusable and insufficiently specified codes, and how these influenced estimates of the leading causes of death. Results 9.7% of expected annual deaths in Tanzania obtained a medically certified cause of death. Of these, 52% of MCCD ICD-10 codes were usable for health policy and planning, with no significant improvement over 5 years. Of certified deaths, 25% had unusable codes, 17% had insufficiently specified codes, and 6% were undetermined causes. Comparing the before and after intervention periods in one Region, codes usable for public health policy purposes improved from 48 to 65% within 1 year and the resulting distortions in the top twenty cause-specific mortality fractions due to unusable causes reduced from 27.4 to 13.5%. Conclusion Data from less than 5% of annual deaths in Tanzania are usable for informing policy. For deaths with medical certification, errors were prevalent in almost half. This constrains capacity to monitor the 15 SDG indicators that require cause-specific mortality. Sustainable quality assurance mechanisms and interventions can result in rapid improvements in the quality of medically certified causes of death. ANACONDA provides an effective means for evaluation of such changes and helps target interventions to remaining weaknesses.

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Mortality among free-ranging California condors (Gymnogyps californianus) during 2010–2014 with determination of last meal and toxicant exposure

Journal of Veterinary Forensic Sciences ◽

10.32473/jvfs.v1i2.128637 ◽

2021 ◽

Vol 1 (2) ◽

Author(s):

Tabitha Viner ◽

Rebecca Kagan ◽

Bruce Rideout ◽

Ilse Stalis ◽

Rebecca Papendick ◽

...

Keyword(s):

Mortality Rate ◽

Cause Of Death ◽

Time Frame ◽

Mortality Data ◽

Toxicant Exposure ◽

Crude Mortality Rate ◽

In The Wild ◽

Proportional Mortality ◽

Gymnogyps Californianus ◽

The Impact

Over the past 30 years, the California condor (Gymnogyps californianus) population has rebounded from 22 individuals to over 200 birds living in the wild. Historical impacts to the population have been largely anthropogenic. In this study, we explore mortality and cause of death data from condors that died during the years 2010-2014 and compare these to mortality data described by Rideout et al. in 2012, covering the years 1992-2009. In addition, morphologic and genetic analysis of the contents of the upper gastrointestinal (GI) tract was performed on the 2010-2014 condor mortalities to determine animal origins of the last meal eaten. The maximum population at risk within this time frame was 329 birds. During this time, 88 condors died and underwent post-mortem examination, and 41 birds were lost to tracking efforts and presumed dead (crude mortality rate of 39%; 129/329). A cause of death was determined for 66 of the 88 necropsied birds. Lead toxicosis remained a significant negative factor in condor population recovery, being related to the deaths of 37 adult and juvenile condors (proportional mortality rate 56%). Compared to condors succumbing to other causes of death, cattle were less often part of the last meal of lead-intoxicated condors. Based on these data, continued efforts to mitigate the impact of lead on California condors should be pursued.

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Evaluation of neonatal mortality data completeness and accuracy in Ghana

PLoS ONE ◽

10.1371/journal.pone.0239049 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0239049

Author(s):

Dora Dadzie ◽

Richard Okyere Boadu ◽

Cyril Mark Engmann ◽

Nana Amma Yeboaa Twum-Danso

Keyword(s):

Public Sector ◽

Data Quality ◽

Neonatal Mortality ◽

Cause Of Death ◽

Health Facilities ◽

Mortality Data ◽

Death Certificates ◽

Neonatal Deaths ◽

Specific Mortality ◽

Brong Ahafo

Background Cause-specific mortality data are required to set interventions to reduce neonatal mortality. However, in many developing countries, these data are either lacking or of low quality. We assessed the completeness and accuracy of cause of death (COD) data for neonates in Ghana to assess their usability for monitoring the effectiveness of health system interventions aimed at improving neonatal survival. Methods A lot quality assurance sampling survey was conducted in 20 hospitals in the public sector across four regions of Ghana. Institutional neonatal deaths (IND) occurring from 2014 through 2017 were divided into lots, defined as neonatal deaths occurring in a selected facility in a calendar year. A total of 52 eligible lots were selected: 10 from Ashanti region, and 14 each from Brong Ahafo, Eastern and Volta region. Nine lots were from 2014, 11 from 2015 and 16 each were from 2016 and 2017. The cause of death (COD) of 20 IND per lot were abstracted from admission and discharge (A&D) registers and validated against the COD recorded in death certificates, clinician’s notes or neonatal death audit reports for consistency. With the error threshold set at 5%, ≥ 17 correctly matched diagnoses in a sample of 20 deaths would make the lot accurate for COD diagnosis. Completeness of COD data was measured by calculating the proportion of IND that had death certificates completed. Results Nineteen out of 52 eligible (36.5%) lots had accurate COD diagnoses recorded in their A&D registers. The regional distribution of lots with accurate COD data is as follows: Ashanti (4, 21.2%), Brong Ahafo (7, 36.8%), Eastern (4, 21.1%) and Volta (4, 21.1%). Majority (9, 47.4%) of lots with accurate data were from 2016, followed by 2015 and 2017 with four (21.1%) lots. Two (10.5%) lots had accurate COD data in 2014. Only 22% (239/1040) of sampled IND had completed death certificates. Conclusion Death certificates were not reliably completed for IND in a sample of health facilities in Ghana from 2014 through 2017. The accuracy of cause-specific mortality data recorded in A&D registers was also below the desired target. Thus, recorded IND data in public sector health facilities in Ghana are not valid enough for decision-making or planning. Periodic data quality assessments can determine the magnitude of the data quality concerns and guide site-specific improvements in mortality data management.

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Overview and Development of the Child Health and Mortality Prevention Surveillance Determination of Cause of Death (DeCoDe) Process and DeCoDe Diagnosis Standards

Clinical Infectious Diseases ◽

10.1093/cid/ciz572 ◽

2019 ◽

Vol 69 (Supplement_4) ◽

pp. S333-S341 ◽

Cited By ~ 5

Author(s):

Dianna M Blau ◽

J Patrick Caneer ◽

Rebecca P Philipsborn ◽

Shabir A Madhi ◽

Quique Bassat ◽

...

Keyword(s):

Child Health ◽

Cause Of Death ◽

Sub Saharan Africa ◽

World Health ◽

Mortality Data ◽

Sequence Of Events ◽

Decode Process ◽

Clinical Records ◽

Health And Mortality

Abstract Mortality surveillance and cause of death data are instrumental in improving health, identifying diseases and conditions that cause a high burden of preventable deaths, and allocating resources to prevent these deaths. The Child Health and Mortality Prevention Surveillance (CHAMPS) network uses a standardized process to define, assign, and code causes of stillbirth and child death (<5 years of age) across the CHAMPS network. A Determination of Cause of Death (DeCoDe) panel composed of experts from a local CHAMPS site analyzes all available individual information, including laboratory, histopathology, abstracted clinical records, and verbal autopsy findings for each case and, if applicable, also for the mother. Using this information, the site panel ascertains the underlying cause (event that precipitated the fatal sequence of events) and other antecedent, immediate, and maternal causes of death in accordance with the International Classification of Diseases, Tenth Revision and the World Health Organization death certificate. Development and use of the CHAMPS diagnosis standards—a framework of required evidence to support cause of death determination—assures a homogenized procedure leading to a more consistent interpretation of complex data across the CHAMPS network. This and other standardizations ensures future comparability with other sources of mortality data produced externally to this project. Early lessons learned from implementation of DeCoDe in 5 CHAMPS sites in sub-Saharan Africa and Bangladesh have been incorporated into the DeCoDe process, and the implementation of DeCoDe has the potential to spur health systems improvements and local public health action.

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Latent Trajectories of Cross-National Homicide Trends: Structural Characteristics of Underlying Groups

Homicide Studies ◽

10.1177/1088767918774083 ◽

2018 ◽

Vol 22 (4) ◽

pp. 343-369 ◽

Cited By ~ 10

Author(s):

James Tuttle ◽

Patricia L. McCall ◽

Kenneth C. Land

Keyword(s):

Latin American ◽

Structural Characteristics ◽

World Health ◽

Mortality Data ◽

Structural Factors ◽

Latin American Region ◽

Health Organization ◽

National Trends ◽

Cross National ◽

Latent Trajectories

Relative to studies of U.S. homicide trends, few have investigated cross-national trends. We explore hidden heterogeneity across a sample of 82 nations between 1980 and 2010, and examine (a) what distinct latent trajectories are represented among these nations? and (b) what structural factors characterize these latent trajectory groups? World Health Organization mortality data were used for the trajectory analyses wherein three distinct groups were identified. Structural characteristics of each group are compared to determine which factors account for their trajectories. Characteristics that predicted group placement include a development index, divorced males, female labor force participation, and Latin American region.

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923Quantifying multiple causes of death: Observed patterns in Australia, 2006–2017

International Journal of Epidemiology ◽

10.1093/ije/dyab168.084 ◽

2021 ◽

Vol 50 (Supplement_1) ◽

Author(s):

Karen Bishop ◽

Saliu Balogun ◽

James Eynston-Hinkins ◽

Lauren Moran ◽

Margarita Moreno-Betancur ◽

...

Keyword(s):

Cause Of Death ◽

Causes Of Death ◽

International Classification Of Diseases ◽

World Health ◽

Mortality Data ◽

Multiple Causes Of Death ◽

Underlying Causes ◽

Health Organization ◽

The Impact ◽

Multiple Causes

Abstract Background Four fifths of deaths in Australia involve multiple causes, but statistics typically use a single underlying cause of death (UC). The UC approach alone is insufficient for understanding the impact of non-underlying causes and identifying comorbid disease associations at death. Analysis of multiple causes of death (MC) is needed to measure the impact of all causes. We described MC patterns, considering cause-of-death coding and certification practices in Australia. Methods Using deaths registered in Australia from 2006 to 2017 (n = 1773525) coded to the International Classification of Diseases (ICD) and an extended classification (n = 136 causes) based on a World Health Organization short list, we described MCoD data by cause. Age-standardised rates based on UC and MC were compared using the standardised ratio of multiple to underlying causes (SRMU) to estimate the contribution of the cause to mortality compared to using the UC approach. Comorbidity was explored using the cause of death association indicator (CDAI) to compare the observed joint frequency of a contributory-underlying cause combined with expected frequency of the contributory cause (with any UC). Results On average 3.4 conditions caused each death and 24.4% of deaths had 5 plus causes. Largest SRMUs were for genitourinary diseases (8.0), blood diseases (7.8) and musculoskeletal conditions (6.7). CDAIs showed high associations between, for example, accidental alcohol and opioid poisoning, septicaemia and skin infections, and traumatic brain injury and falls. Conclusions MC indicators enhance measures of mortality and reassess the role of causes of death for descriptive and analytical epidemiology. Key messages This research demonstrates the value of MC analysis for Australian mortality data.

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Lessons Learned from Data Mining of WHO Mortality Database

Methods of Information in Medicine ◽

10.3414/me10-02-0019 ◽

2011 ◽

Vol 50 (04) ◽

pp. 380-385 ◽

Cited By ~ 7

Author(s):

W. Paoin

Keyword(s):

Data Mining ◽

Cause Of Death ◽

Association Analysis ◽

Causes Of Death ◽

World Health ◽

Mortality Data ◽

Common Causes ◽

The World ◽

C4.5 Decision Tree ◽

Health Organization

SummaryObjectives: The objectives of this research were to test the ability of classification algorithms to predict the cause of death in the mortality data with unknown causes, to find association between common causes of death, to identify groups of countries based on their common causes of death, and to extract knowledge gained from data mining of the World Health Organization mortality database.Methods: The WEKA software version 3.5.3 was used for classification, clustering and association analysis of the World Health Organization mortality database which contained 1,109,537 records. Three major steps were performed: Step 1 – preprocessing of data to convert all records into suitable formats for each type of analysis algorithm; Step 2 – analyzing data using the C4.5 decision tree and Naïve Bayes classification algorithm, K-means clustering algorithm and Apriori association analysis algorithm; Step 3 – interpretation of results and hypothesis testing after clustering analysis.Results: Using a C4.5 decision tree classifier to predict cause of death, we obtained 440 leaf nodes that correctly classify death instances with an accuracy of 40.06%. Naïve Bayes classification algorithm calculated probability of death from each disease that correctly classify death instances with an accuracy of 28.13%. K means clustering divided the data into four clusters with 189, 59, 65, 144 country-years in each cluster. A Chi-square was used to test discriminate disease differences found in each cluster which had different diseases as predominant causes of death. Apriori association analysis produced association rules of linkage among cancer of the lung, hypertension and cerebrovascular diseases. These were found in the top five leading causes of death with 99–100% confidence level.Conclusion: Classification tools produced the poorest results in predicting cause of death. Given the inadequacy of variables in the WHO database, creation of a classification model to predict specific cause of death was impossible. Clustering and association tools yielded interesting results that could be used to identify new areas of interest in mortality data analysis. This can be used in data mining analysis to help solve some quality problems in mortality data.

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Perbandingan Linieritas Dalam Pendeteksian Faktor-Faktor Yang Mempengaruhi Angka Kematian Bayi Di Provinsi Aceh

Talenta Conference Series Science and Technology (ST) ◽

10.32734/st.v2i2.460 ◽

2019 ◽

Vol 2 (2) ◽

Author(s):

Desfira Ahya ◽

Inas Salsabila ◽

Miftahuddin

Keyword(s):

Mortality Rate ◽

Infant Mortality ◽

Linear Model ◽

Health Workers ◽

Additive Model ◽

Infant Mortality Rate ◽

Mortality Data ◽

Important Indicator ◽

Penalized Spline ◽

Aceh Province

Angka Kematian Bayi/ Infant Mortality Rate (IMR) merupakan indikator penting dalam mengukur keberhasilan pengembangan kesehatan. Nilai IMR juga dapat digunakan untuk mengetahui tingkat kesehatan ibu, kondisi kesehatan lingkungan dan secara umum, tingkat pengembangan sosio-ekonomi masyarakat. Penelitian ini bertujuan untuk memperoleh model IMR terbaik menggunakan tiga pendekatan: Model Linear, Model Linear Tergeneralisir dan Model Aditif Tergeneralisir dengan basis P-spline. Sebagai tambahan, berdasarkan model tersebut akan terlihat variabel yang mempengaruhi tingkat kematian bayi di provinsi Aceh. Penelitian ini menggunakan data jumlah kematian bayi di tahun 2013-2015. Data dalam penelitian ini diperoleh dari Profil Kesehatan Aceh. Hasil menunjukkan bahwa model terbaik dalam menjelaskan angka kematian bayi di provinsi Aceh tahun 2013-2015 ialah Model Linear Tergeneralisir dengan basis P-spline menggunakan parameter penghalusan 100 dan titik knots 8. Faktor yang sangat mempengaruhi angka kematian ialah jumlah pekerja yang sehat. Infant mortality rate (IMR) is an important indicator in measuring the success of health development. IMR also can be used to knowing the level of maternal health, environmental health conditions and generally the level of socio-economic development in community. This research aims to get the best model of infant mortality data using three approaches: Linear Model, Generalized Linear Model and Generalized Additive Model with Penalized Spline (P-spline) base. In addition, based on the model can be seen the variables that affect to infant mortality in Aceh Province. This research uses data number of infant mortality in Aceh Province period 2013-2015. The data in this research were obtained from Aceh’s Health Profile. The results show that the best model can be explain infant mortality rate in Aceh Province period 2013-2015 is GAM model with P-spline base using smoothing parameter 100 and knots 8. Factor that high effect to infant mortality is number of health workers.

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Treatment of ebola and other infectious diseases: melatonin “goes viral”

Melatonin Research ◽

10.32794/mr11250047 ◽

2020 ◽

Vol 3 (1) ◽

pp. 43-57 ◽

Cited By ~ 21

Author(s):

Russel J Reiter ◽

Qiang Ma ◽

Ramaswamy Sharma

Keyword(s):

Mortality Rate ◽

Infectious Diseases ◽

Hemorrhagic Shock ◽

Safety Profile ◽

Ebola Virus ◽

Viral Infections ◽

Virus Disease ◽

Attractive Potential ◽

Potential Treatment

This review summarizes published reports on the utility of melatonin as a treatment for virus-mediated diseases. Of special note are the data related to the role of melatonin in influencing Ebola virus disease. This infection and deadly condition has no effective treatment and the published works documenting the ability of melatonin to attenuate the severity of viral infections generally and Ebola infection specifically are considered. The capacity of melatonin to prevent one of the major complications of an Ebola infection, i.e., the hemorrhagic shock syndrome, which often contributes to the high mortality rate, is noteworthy. Considering the high safety profile of melatonin, the fact that it is easily produced, inexpensive and can be self-administered makes it an attractive potential treatment for Ebola virus pathology.

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