Progress towards Every Newborn Action Plan (ENAP) implementation in Iran: obstacles and bottlenecks.

BACKGROUND: Neonatal mortality accounts for more than 47% of deaths among children under five globally but proper care at and around the time of birth could prevent about two-thirds of these deaths. The Every Newborn Action Plan (ENAP) offers a plan and vision to improve and achieve equitable and high-quality care for mothers and newborns. We applied the bottleneck analysis tool offered by ENAP to identify obstacles and bottlenecks hindering the scale-up of newborn care across seven health system building blocks. METHODS: We applied the every newborn bottleneck analysis tool to identify obstacles hindering the scale-up of newborn care across seven health system building blocks. We used qualitative methods to collect data from five medical universities and their corresponding hospitals in three provinces. We also interviewed other national experts, key informants, and stakeholders in neonatal care. In addition, we reviewed and qualitatively analyzed the performance report of neonatal care and services from 16 medical universities around the country. RESULTS: We identified many challenges and bottlenecks in the scale-up of newborn care in Iran. The major obstacles included but were not limited to the lack of a single leading and governing entity for newborn care, insufficient financial resources for neonatal care services, insufficient number of skilled health professionals, and inadequate patient transfer. CONCLUSIONS: To address identified bottlenecks in neonatal health care in Iran, some of our recommendations were as follows: establishing a single national authorizing and leading entity, allocating specific budget to newborn care, matching high-quality neonatal health care providers to the needs of all urban and rural areas, maintaining clear policies on the distribution of NICUs to minimize the need for patient transfer, and using the available and reliable private sector NICU ambulances for safe patient transfer.

Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study.

OBJECTIVE: The current study was undertaken for use of the decision tree (DT) method for development of different prediction models for incidence of type 2 diabetes (T2D) and for exploring interactions between predictor variables in those models. DESIGN: Prospective cohort study. SETTING: Tehran Lipid and Glucose Study (TLGS). METHODS: A total of 6647 participants (43.4% men) aged >20 years, without T2D at baselines ((1999-2001) and (2002-2005)), were followed until 2012. 2 series of models (with and without 2-hour postchallenge plasma glucose (2h-PCPG)) were developed using 3 types of DT algorithms. The performances of the models were assessed using sensitivity, specificity, area under the ROC curve (AUC), geometric mean (G-Mean) and F-Measure. PRIMARY OUTCOME MEASURE: T2D was primary outcome which defined if fasting plasma glucose (FPG) was ≥7 mmol/L or if the 2h-PCPG was ≥11.1 mmol/L or if the participant was taking antidiabetic medication. RESULTS: During a median follow-up of 9.5 years, 729 new cases of T2D were identified. The Quick Unbiased Efficient Statistical Tree (QUEST) algorithm had the highest sensitivity and G-Mean among all the models for men and women. The models that included 2h-PCPG had sensitivity and G-Mean of (78% and 0.75%) and (78% and 0.78%) for men and women, respectively. Both models achieved good discrimination power with AUC above 0.78. FPG, 2h-PCPG, waist-to-height ratio (WHtR) and mean arterial blood pressure (MAP) were the most important factors to incidence of T2D in both genders. Among men, those with an FPG≤4.9 mmol/L and 2h-PCPG≤7.7 mmol/L had the lowest risk, and those with an FPG>5.3 mmol/L and 2h-PCPG>4.4 mmol/L had the highest risk for T2D incidence. In women, those with an FPG≤5.2 mmol/L and WHtR≤0.55 had the lowest risk, and those with an FPG>5.2 mmol/L and WHtR>0.56 had the highest risk for T2D incidence. CONCLUSIONS: Our study emphasises the utility of DT for exploring interactions between predictor variables.

Determinants of Self-Rated Health in Tehran, from Individual Characteristics towards Community-Level Attributes.

INTRODUCTION: It is generally believed that the attributes of shared environment affect health of residents beyond the individual risk factors. This study investigates some individual and neighborhood characteristics that may affect self-rated health (SRH) in Iran. METHODS: Questions were asked about the social capital, economic status and SRH of 1,982 citizens from 200 randomly selected locations in Tehran. The neighborhood characteristics were assessed by an observational checklist. A multilevel model was designed. RESULTS: SRH was significantly different between neighborhoods (P-value < 0.001) and between economic groups (P-value < 0.001). At the individual level, social capital (SC) and being married had a positive association with SRH, while age, being female and bad economic statuses were negatively associated with SRH. At the neighborhood level, neighborhoods with higher average education were positively association with SRH, and living in neighborhoods under construction had a negative association with SRH. CONCLUSIONS: These findings highlight the importance of shared social and physical environment, as well as individual characteristics on health, although the mechanisms may still be controversial.

Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study.

AIMS: The aim of this study was to create a prediction model using data mining approach to identify low risk individuals for incidence of type 2 diabetes, using the Tehran Lipid and Glucose Study (TLGS) database. METHODS: For a 6647 population without diabetes, aged ≥20 years, followed for 12 years, a prediction model was developed using classification by the decision tree technique. Seven hundred and twenty-nine (11%) diabetes cases occurred during the follow-up. Predictor variables were selected from demographic characteristics, smoking status, medical and drug history and laboratory measures. RESULTS: We developed the predictive models by decision tree using 60 input variables and one output variable. The overall classification accuracy was 90.5%, with 31.1% sensitivity, 97.9% specificity; and for the subjects without diabetes, precision and f-measure were 92% and 0.95, respectively. The identified variables included fasting plasma glucose, body mass index, triglycerides, mean arterial blood pressure, family history of diabetes, educational level and job status. CONCLUSIONS: In conclusion, decision tree analysis, using routine demographic, clinical, anthropometric and laboratory measurements, created a simple tool to predict individuals at low risk for type 2 diabetes.

Temporal variations of health indicators in Iran comparing with other Eastern Mediterranean Region countries in the last two decades.

BACKGROUND: The recent significant improvement in most health indicators in Iran has not been explored deeply particularly in comparison with other countries in Eastern Mediterranean Region (EMR). We aimed to explore the temporal variations of five main indicators in Iran and compare their variations in EMR countries. METHODS: Data on DPT vaccination and birth weight were obtained from EMR office reports, and total fertility rate, under 5 mortality rate (U5MR) and adult literacy rate (ALR) were obtained from WHO sources for the time period 1995-2005. Using linear regression, we modeled the temporal variations in Iran and other EMR countries classified by their human development index (HDI) levels. RESULTS: The estimated annual decline rate of U5MR in Iran as a middle HDI country was 2.5 per 1000 live birth which was much greater than the corresponding number in countries with medium HDI (1.85) and very close to countries with high HDI (2.67). The WHO data showed that Iran was very successful in increasing ALR. CONCLUSION: It seems that most health indicators in Iran have improved more rapidly compared with countries with low and medium HDI in EMR. The improvement rates were also very close to countries with high HDI in the region.