Exploring routine data collection systems in Iran, focussing on maternal mortality and using the city of Bam as a case study
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Introduction: Health information systems provide information obtained from data for decision making in order to improve the performance of a health system. Although health information systems can be very influential, it can not be exit on its own. It is discussed that the flaw and inefficiency of health information system is rooted to the powerlessness of health system and lack of incorporation in the overall health system . The benefits of using data in planning and implementation go beyond the normal everyday functions of a heath system and include catastrophic situations. Iran is a developing country which experiences a large number of natural disasters each year with a significant number of casualties. Owing to the importance of data for planning, implementation and evaluation, the necessity for sound data is even more pronounced in a country with such conditions. The main aim of this project is to use the city of Bam as a case study to explore the routinely collected data systems in Iran. This investigated the collection of mortality data from all causes, and maternal mortality specifically, in order to determine the usefulness and application of these data systems to monitor the immediate and ongoing health effects of a natural disaster, and to plan for future disasters. Methods: A mixed qualitative and quantitative method used to provide better understanding of the problem at two main data sources, the Medical University and the Civil Registry. This research has commenced with numeric results of maternal ratios and then has employed a qualitative method to gain better understanding of data collection system. The sampling methods are purposive and probability sampling. Interviews, review of documents, and personal observation are the main data collection methods. The data are analysed using qualitative and quantitative methods. They are presented in four sub –chapters , three sub-chapters for non numeric results and one for numeric results. Results: The results show that there are dramatic differences on data collection and data processing between the Civil Registry and the Medical Sciences University. Also it is found that there are some sorts of shortcomings in different stages of data collection system in each organisation. This includes incomplete data coverage, shortcoming in academic staff, insufficient technology infrastructures, lack of training for staff, inadequate data quality checking. Moreover, there are many limitations affecting data collection after the earthquake. These limitations are rooted in basic problems within the existing data collection system and a lack of co-ordination between the groups collecting the data, including national and international aid groups that provided help after the earthquake. Regarding maternal mortality data collection it is found that there was no consistent definition of maternal deaths among interviewees. All data sources are not aware of urgently reporting of maternal deaths as it should be. The results of the estimation of maternal mortality ratios from different sources present inconsistent pictures. This inconsistency is found in both of the denominators and nominators. Also, the results of case matching show that the data collected from two different sources authorised commonly by the Medical Sciences University are not consistent. Additional exploring on the mortality data in disaster and non disaster cities reveal that the inconsistency is not limited to the maternal mortality data. Indeed, there is considerable difference on the total mortality data reported by these two organisations in target cities. Discussion: There are some requirements before setting the systems including introducing appropriate rules and regulation to oblige different data sources to send the data. Also allocating enough resources including human resources and providing appropriate training before commencing the job are of important factor to improve the system. Having good and strong enough communication infrastructures can increase the speed and accuracy of data. In addition, some supervisory activities should be in placed to ensure that the data collection procedures is on the right track and data checking is undertaken by related stuff. Using consistent software in different organisations provides not only more complete data by data transferring they can also improve the quality of data through data cross checking. Finally the data usage culture should be encouraged by the government in all levels including national, provincial and districts levels. This can be achieved through introducing a system of incentives for use the data in decision making and allocating budget via the data. Regarding disaster and data collection it is very important to have the collaboration of international organisation to send the data to the host country. Low collaboration might be due to this fact that there is little awareness about the importance of having the flow of data collection after a disaster for planning for disaster stricken country. Therefore appropriate strategies might be needed to increase this awareness in the national and global level. This can be achieved through international organisations such as World Health Organisations or Red Cross Organisations. Conclusion: The main aim of data collection is to use the data in planning and evaluation. Incomplete and inaccurate data must be misleading and useless. in order to strengthen the data collection system it should be established based on certain standards to ensure that the data is complete and accurate. This would be of importance in non disaster and disaster situation.