Morbidity Patterns in Rural Uganda: Using Machine Learning Methods

1. THE 7TH EAST AFRICAN HEALTH AND SCIENTIFIC CONFERENCE27th – 29th March 2019 Nyerere International Convention Centre (NICC)Dar es Salaam, TanzaniaMakerere University Centre for Health and Population research (MUCHAP) Machine learning methods can be used to demonstrate morbidity patterns and trends in rural Uganda. Nareeba Tryphena, Kajungu .D (PhD), Natukwatsa .D

2. Introduction • Makerere University Centre for Health and Population research (MUCHAP) manages Iganga-Mayuge Health and demographic surveillance system (IMHDSS) carries out data collection that primarily measures the fertility, mortality, and other self-reported health information of an entire population. However, such self-reports usually lack detail and accuracy about the clinical events and services received, and their retrospective nature means they quickly become dated. • To enable individuals and communities take action to protect and improve their individual and community health, Iganga-Mayuge Health and Demographic Surveillance System (IMHDSS) provides a platform for morbidity surveillance through an electronic health observatory system that links IMHDSS individual data to community health facility data in the Demographic Surveillance Area (DSA). • Linking an HDSS database to data from a health facility that serves the HDSS population produces a research infrastructure for generating directly observed data on access to and utilization of health facility services (Sankoh O, INDEPTH Network: CHESS: an innovative concept for a new generation of population surveillance, 2015). • Iganga-Mayuge Health Demographic (IMHDSS) in partnership with Iganga District started to pilot the CHESS at the Busowobi Health facility in July, 2017.

3. Iganga Mayuge HDSS Demographic surveillance Area (DSA)

4. Study site and population • A rural government-run health centre (BUSOWOBI HC) is situated in the Iganga-Mayuge HDSS surveillance area. • This health facility acts as a record linkage site which carries out several health care services such as: the HIV care and treatment centre (CTC), the HIV testing and counselling clinic (HTC), and the antenatal clinic (ANC) which includes prevention of mother-to-child transmission services; all of which operate according to national guidelines and protocols. • The Health Centre has five departments that is OPD, Maternity, Clinician, Laboratory and Pharmacy. • So far, 7290 patient visits have been recorded in the system and of these, 1,447 patient visit data used in this analysis were linked to the HDSS data using the HDSS personal unique identifiers for the months of January to July 2018.

5. Study Objective To study the trends and patterns of diseases using machine learning methods in rural Uganda.

6. Data collection procedure • The data clerks at the health facility regularly update and run data checks on these data using electronic health records system (eHR) . The system is electronic, which utilizes power in order to be able to operate the computers and the server. • Using this system, there is timely production of daily, weekly, monthly, quarterly and annually reports on all the data that is from immunization, vaccination, diagnosis, maternity, TB, ART, NCD, and others, captured at the health centre Data Linkage system

7. Methods • A data mining method called Tree Net of Salford Predictive Modeller (SPM) was used to determine morbidity among different age groups of 0-4 years, 5-12 years, 13-24 years and above 25 years in the months of January to July 2018. • TreeNet Gradient Boosting is Salford Predictive Modeler’s most flexible and powerful data mining tool, capable of consistently generating extremely accurate models. The TreeNet methodology is not sensitive to data errors and needs no time-consuming data preparation, pre-processing or imputation of missing values.

8. Results • Malaria and Pneumonia were the mostly diagnosed diseases at the health facility in the months of January to July 2018. • Amongst the months used in analysis, May had the majority of patients diagnosed with malaria. • Pneumonia was majorly diagnosed among the younger age groups and in the months of April and March

9. Contd….

10. Results • Most malaria diagnoses were among people living in households with the social economic status of the less and least poor and Patients of the lower age group between 0 to 12 years most especially the age group of 5 to 12 years. • More Chicken pox cases were reported in the early months of the year January to April. • These chicken pox cases were more pronounced in the poorest households.

11. Conclusions and recommendation • The analysis of data using a nonparametric data-mining technique Tree Net that is a powerful tool and which generates easy to interpret results helps to rapidly detect unusual events, epidemics or other changes in the health outcomes and hence helpful to plan and set priorities for health policies and programmes at sub national level.

12. Acknowledgement • Government and people of Sweden • Makerere University • Districts leadership –Technical and political • Local community and opinion leaders • Community members in Iganga Mayuge HDSS • Iganga Mayuge community scouts and field assistants • MUCHAP staff