Background: Iraq has endured several conflicts and socio-political tensions that have disrupted its public health system. Nowadays, because health data are not collected on a routine basis, the country still lacks proper statistics and, consequently, response plans to meet present and future health needs of its population. An international partnership is developing in the Iraqi Kurdistan a Health Monitoring System with the aim of supporting evidence-based health policy decisions. Methods: The pilot phase for assessing the feasibility of the programme was launched in 2015. In 2018 the implementation phase began. The first step was to choose the software platform and the coding system, as well as to identify the public hospitals (PH) and Public Health Centers (PHC) to be included in the e-health system. The technical infrastructure of each PHC or PH was updated. The staff of each center was trained in the use of the e-health system and in disease coding. Several seminars introduced regional and district health managers to the basic concepts of data-driven decision making. A local team of experts was trained to create a highly specialized staff with the objective of "training the trainers" and ensuring the future self-sufficiency of the system. Results: By September 2019, 59 PHC and PH were entering data in the Health Monitoring System, while 258 health operators (medical doctors, administrative staff, nurses, statisticians, IT and public health specialists, pharmacists) have been already trained. Currently, more than 600,000 disease events have been collected. Additionally, further 734 medical doctors, statisticians, and health managers have been trained on the basics of public health practice. The goal during the next 3 years is to reach 120 operative centers within the region, envisaging a subsequent expansion of the system to all Iraq. Emberti Gialloreti et al. Health Monitoring System in Iraq Conclusions: The creation of a functioning health monitoring system is feasible also in regions characterized by socio-political tensions. However, multiple stakeholder partnerships are essential. The provision of an e-health information system, coupled with the establishment of a team of local experts, allows the routinely and timely collection of health information, facilitating prompt responses to present and emerging needs, while guiding the formulation and evaluation of health policies.

The availability of models for predicting future events is essential for enhancing the efficiency of systems. This paper attempts to predict demographic variation by employing multi-layer perceptron network. Here we present the implementation of a system for predicting the number and causes of deaths, for a future 2-year period. The system was built using predictive models and data that is as accurate as possible under the current conditions of the northern Region of Iraq (the Autonomous Region of Kurdistan). Our predictive model is based on quarterly periods, with the intention of providing predictions on the number of deaths, classified by gender, cause of death, age at death, administrative district (governorate), and hospital where the death occurred. The data was collected from birth and death registry bureaus and forensic medicine departments for the years 2009–2020. The python programming language was used to test the designed multi-layer perceptron network with backpropagation training algorithm. With learning rate 0.01 and 500 epochs we were able to obtain good results, as the neural network was able to represent the string, and predict future values well, with a mean squared error of 0.43, and we found that number of deaths is quite stable, with a slight increase.

Gross Domestic Product (GDP) is the total pecuniary or mart value of all final commodity and services that are produced within country's borders in a given time. We choose GDP to predict in Iraq since 2000 to 2018.The state and governments rely on GDP to help shape policy or decide how much public spending is affordable. Combining grey regression is a modern statistical technique of modeling, using this type of model is related to its highly accuracy therefor, in this study we used combined grey regression model to predict the gross domestic production of Iraq because it gives less Mse than grey or regression models alone which is equal to 1.3165 and estimated parameter of grey C1, regression C2 are equals to (2.9 and 0.205) respectively and the intercept C3 is equal to 1.8569 the outcome showed that the new model could attain preferable predicting result contrasted with different predicting methods.

Background: Mortality and causes of death are among the most important statistics used in assessing the effectiveness of a country’s health system. Several countries do not have information systems for collecting these data, and they must therefore be estimated from surveys. Objective: This study analyzes mortality data retrieved from official government databases in Iraqi Kurdistan to describe ten-year trends in natural causes of death. Methods: Data for natural causes of death, reported from 2009 to 2018, were extracted from the databases of the Registration Bureau of Births and Deaths and of the Forensic Medicine of the Province of Sulaymaniyah. A sample of 16,433 causes of death was analyzed. Results: Causes of death were coded according to the ICD-10 classification. Overall, cardiovascular diseases were the leading cause of mortality (52.6%), followed by neoplasms (17.7%), infectious and parasitic diseases (8.9%), and genitourinary diseases (6.3%). Neonatal conditions, congenital anomalies, and neurological conditions each accounted for less than 1% each. Numbers of natural deaths by cause and cause-specific mortality rates have been estimated for the entire Region of Iraqi Kurdistan. Comparisons with other sources suggest that there is a substantial amount of underreporting, especially in relation to deaths of infants and under-five children. Conclusion: Our findings confirm that the region is facing a burden of non-communicable diseases, coupled with high proportions of infectious diseases. However, the lack of effective vital statistics with combined under-reported data collection highlights the need for implementation of health monitoring systems. Advancements in generating high-quality data are essential in improving health and reducing preventable deaths. The establishment of a novel Health Information System is discussed.