The Impact of Extreme Environmental and Security Conditions on Prematurity and Low Birth Weight Rates

Low Birth Weight, Prematurity

low birth weight, prematurity, extreme environmental and security-related situations

Numerous reports indicate that various extreme environmental and security-related situations during pregnancy may increase the risk of prematurity, LBW, and miscarriage, as these events increase both the incidence and intensity of maternal stress. Study Objective: To map and validate this phenomenon within the catchment area of the Hillel Yaffe Medical Center.

Background Prematurity accounts for approximately 10% of all births. A preterm infant is defined as a baby born before 37 completed weeks of gestation, calculated from the first day of the last menstrual period (LMP). Low birth weight (LBW) is defined as a birth weight below 2,500 grams. Both prematurity and LBW are associated with various early and late complications, the frequency and severity of which increase as gestational age and birth weight decrease. Numerous reports indicate that various extreme environmental and security-related situations during pregnancy may increase the risk of prematurity, LBW, and miscarriage, as these events increase both the incidence and intensity of maternal stress. Examples of such conditions include pandemics, wars, economic collapses, and environmental disasters, such as earthquakes or storms. In instances of economic collapse, the impact manifests more frequently as fetal growth restriction (FGR) than as prematurity. Pandemics are more closely associated with prematurity, although some reports indicated a decrease in prematurity rates during the COVID-19 pandemic - likely due to a reduction in the diagnosis of pregnancy complications -with a concurrent increase in miscarriage rates. These situations are characterized by unexpected changes in residence, decreased food security, shifts in disease vectors, socioeconomic instability, and alterations in air quality, temperature, and precipitation, all of which can impact expectant mothers. An additional factor is the diversion of medical resources toward emergency needs during these events, often at the expense of routine prenatal care. Study Objective: To map and validate this phenomenon within the catchment area of the Hillel Yaffe Medical Center. This study aims to examine the correlation between the occurrence of extreme environmental and significant security events in Israel and the increase in the rate of preterm and LBW infants at our center. Israel experiences natural extreme environmental events, economic crises, and pandemics, similar to the rest of the world. Additionally, security-related emergencies (wars and military operations) occur in Israel with high frequency. Annual birth data is regularly published. Furthermore, the Israel Neonatal Association has maintained a database of all infants born weighing 1,500 grams or less since 1995. Comprehensive birth data from Hillel Yaffe Medical Center is fully available to the research team. By cross-referencing this data, we can investigate the link between maternal exposure to extreme conditions and neonatal outcomes. This is a preliminary study focusing on our center's birth population; if significant correlations are identified, a larger-scale national study may follow. \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Methods Initially, the researchers will precisely define the events categorized as "extreme environmental and security situations" and establish the specific timeframes for these periods. Data Collection: Data will be collected regarding: * Total number of births. * Incidence of prematurity and infants Small for Gestational Age (SGA). * Relevant demographic data. Statistical Analysis: The study will compare data from "event periods" against "routine/calm periods." As this is a population-based study rather than a sample, standard sampling discussions are not applicable. * Normal Distribution: If data are normally distributed, the Z-test will be used. * Non-Normal Distribution: Non-parametric tests (Wilcoxon, Mann-Whitney, etc.) will be employed. * Categorical Data: The Chi-square (χ2) test will be used for comparisons. * Significance: Statistical significance will be defined as p \< 0.05.

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