Large-scale data reporting of paediatric morbidity and mortality in developing countries: it can be done

Abstract

Although the WHO recommends all countries use International Classification of Diseases (ICD)-10 coding for reporting health data, accurate health facility data are rarely available in developing or low and middle income countries. Compliance with ICD-10 is extremely resource intensive, and the lack of real data seriously undermines evidence-based approaches to improving quality of care and to clinical and public health programme management. We developed a simple tool for the collection of accurate admission and outcome data and implemented it in 16 provincial hospitals in Papua New Guinea over 6 years. The programme was low cost and easy to use by ward clerks and nurses. Over 6 years, it gathered data on the causes of 96 998 admissions of children and 7128 deaths. National reports on child morbidity and mortality were produced each year summarising the incidence and mortality rates for 21 common conditions of children and newborns, and the lessons learned for policy and practice. These data informed the National Policy and Plan for Child Health, triggered the implementation of a process of clinical quality improvement and other interventions to reduce mortality in the neediest areas, focusing on diseases with the highest burdens. It is possible to collect large-scale data on paediatric morbidity and mortality, to be used locally by health workers who gather it, and nationally for improving policy and practice, even in very resource-limited settings where ICD-10 coding systems such as those that exist in some high-income countries are not feasible or affordable.

Cause‐specific neonatal morbidity and mortality in the Solomon Islands: An assessment of data from four hospitals over a three‐year period

“…Our findings emphasise the need for improved data collection for mortality rates and causes of death through routine collection systems, standardised with International Classification of Diseases (ICD) codes . Facility based data systems for monitoring newborn outcomes have been recommended to address data gaps where vital registration systems are not in place, and these are feasible at large scale in low and middle income countries . Emphasis is placed on determining the causes of death in the newborn period, to inform programme design and monitoring, however policy and funding interventions may be limited by gaps in data and subsequent interpretation.…”

Continuous positive airway pressure in children with severe pneumonia and hypoxaemia in Papua New Guinea: an evaluation of implementation

“…In Papua New Guinea (PNG) as in many other low‐ and middle‐income countries pneumonia and other acute lower respiratory infections (ALRI) are the most common causes of serious illness and death. In PNG, these illnesses account for 25–30% of all hospitalisation in children, and 15% of all deaths, with case fatality rates for ALRI of all severity being 5% . Hypoxaemia is the strongest risk factor for deaths from pneumonia, and detection of hypoxaemia with pulse oximetry and provision of oxygen therapy can substantially reduce deaths from pneumonia .…”

“…Hypoxaemia is the strongest risk factor for deaths from pneumonia, and detection of hypoxaemia with pulse oximetry and provision of oxygen therapy can substantially reduce deaths from pneumonia . However despite this, mortality rates for severe pneumonia in PNG hospitals average 10% and this is similar in many low‐income countries . In addition, nearly 30% of all admissions of children to hospitals in PNG are in the neonatal period and neonatal deaths account for just over one‐third of all childhood deaths .…”

Methodological Challenges and Lessons Learned from Assessing the Routine Health Information Management Data Quality: Experience from Tanzania

“…The analysis was simpler after the initial transcription of the records, just tabulation of missing data and comparison of the registers counts with the monthly reports. The simplified version of the data quality assessment is counting from the summary tables, the proportion of reporting health facilities and the total reports submitted against the expected [12][13][14][15][16]. However, these methods leave out the recording process, which is an essential step toward quality assessment.…”

“…Harmonization of classification would be the first step towards assessment RHIS data quality performance [9]. Other studies as shown that where the classification of diseases is harmonized the quality of reporting morbidity data improved [12,13].…”