Climate change, according to the International Union for Conservation of Nature, is a long-term alteration in the climate of a specific location, region, or the planet.1 When the impact directly affects individuals, it becomes a health problem, and the first point of contact with the health care system for such issues is often hospital Emergency Departments (EDs). For this reason, the policy statement published in 2018 by the American College of Emergency Physicians (ACEP) emphasized the importance of fostering collaboration between emergency medicine and public health in the field of climate change research. It also laid the foundation for strategies to combat the effects of climate change.2 For a comprehensive evaluation of patients presenting to the ED in the context of climate change, it may be more appropriate to assess both the patients and the risks they are exposed to in their environment. Therefore, categorizing the pathologies caused by climate change as direct effects on patients, while referring to environmental factors as indirect effects, can provide a clearer depiction of the etiology. The impact of climate change on patients primarily manifests through thermoregulation, resulting in clinical presentations such as hyperthermia, normothermia, and hypothermia. Its indirect effects include environmental factors such as chemical, biological, radiological, and nuclear (CBRN) hazards. Although this study focuses on biological impacts, understanding such frameworks is important for structuring ED preparedness strategies in response to evolving climate-related health risks.
As global warming continues, it is likely that we will observe an increasing impact on the trends of infectious diseases. Climate change affects numerous aspects of the environment, human society, and public health, including the distribution and activity of infectious diseases. Of the 375 diseases considered to have a measurable impact on humanity, evidence suggests that 58% have experienced a significant increase in severity attributable to climate change.Reference Mora, McKenzie and Gaw3 The key parameter to understand the impact of air temperature on human metabolism is the heat index.4 The heat index, also known as apparent temperature, reflects how air temperature combined with relative humidity feels to the human body.Reference Back, Bach and Jasper-Tönnies5 Thermoreceptors stimulated by the heat index activate certain mechanisms in the body based on temperature levels.Reference Osilla, Marsidi and Shumway6 Similarly, in nature, microorganisms and their pathogenicity are also thought to be influenced by the heat index, making it an effective parameter linked to climate change.
Amoebiasis is 1 of the 3 most common causes of death from parasitic diseases.7 The World Health Organization (WHO) reports that amoebiasis causes approximately 50 million cases and 100 000 deaths annually.Reference Walsh8 Free-living amoebae are widely distributed in environmental settings such as water, air, and soil. When transmitted to humans, clinical manifestations can range from asymptomatic infection to acute fulminant colitis. Clinical presentations include amoebic dysentery and extraintestinal disease.Reference Shirley, Farr, Watanabe and Moonah9 Symptoms vary from mild diarrhea to severe dysentery, abdominal pain (12% to 80%), diarrhea (94% to 100%), bloody stools (94% to 100%), and fulminant amoebic colitis.10 With the rising temperatures associated with climate change, an increase in amoeba transmission to humans via water and food is expected; however, evidence-based studies on this subject remain limited. Reference Gharpure, Gleason and Salah11(pp1978-2018)
The aim of this study is to examine the impact of the heat index over a 1-month period in the region where patients reside on those presenting to the ED with acute intestinal complaints and diagnosed with amoebic diarrhea based on their test results.
Material and Methods
Study Design
This retrospective cross-sectional study was conducted on patients presenting to the ED of a tertiary hospital with symptomatic diarrhea complaints over the course of 1 year. The study was carried out in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Ethical approval was obtained from the Yeni Yüzyıl University, Ethics Committee for Non-Interventional Health Sciences Research (Approval Meeting Number: 2025/01-1447, Approval Date: January 7th, 2025).
This study was conducted as a retrospective analysis, and no direct participant interaction was involved. The Ethics Committee of [Yeni Yüzyıl University, Ethics Committee for Non-Interventional Health Sciences Research] approved the study and waived the requirement for informed consent due to the use of anonymized, pre-existing medical records.
Data collection
Patients diagnosed with acute symptomatic diarrhea were identified by reviewing medical records and patient files from the hospital’s electronic information system. Amoebic infection was confirmed through stool microscopy. Demographic data included age, gender, and residency information, while clinical parameters consisted of heart rate, systolic and diastolic blood pressure, body temperature, stool frequency, and the presence of dehydration. Laboratory parameters included Alanine Aminotransferase (ALT), C-Reactive Protein (CRP), and complete blood count (leukocytes, platelets, lymphocytes, and neutrophils).
The data collected were compiled and organized using Microsoft Excel. Data entry was performed by 2 emergency medicine specialists with a minimum of 3 years of clinical experience. To ensure inter-observer reliability, all data entries were cross verified. Any discrepancies in the data were reviewed and resolved by the principal investigator.
Environmental data collection. The study was conducted in Istanbul, Türkiye’s most densely populated city, with a population exceeding 16 million. Istanbul is located at 41°N latitude and 28°E longitude, at an average elevation of 39 meters above sea level. Due to its low altitude, the city is more sensitive to temperature and humidity fluctuations. The annual average temperature in Istanbul is approximately 14.4°C, with high relative humidity levels ranging between 70% and 80%. The city also receives an annual average rainfall of around 870 mm.
Environmental data for the study region were obtained from official publicly available records provided by the Republic of Türkiye Ministry of Environment, Urbanization, and Climate Change, General Directorate of Meteorology. The heat index, a measurement combining air temperature and relative humidity, was calculated.12 A calculator was used for the heat index calculation,13 and the calculated values were cross-referenced with the hazard levels associated with different heat index ranges as defined by the US National Weather Service (NWS).14 Notably, the 93°C heat index threshold used in the study was not set by the NWS but was proposed by Lu and Romps in 2022.Reference Romps15 During the study period, no threshold values were reached. For each patient, the 7-day average heat index prior to their presentation to the ED was calculated and recorded.
Study Population
The study included patients aged 18 years and older who were presented with acute diarrhea and underwent stool microscopy and antigen testing. Acute diarrhea was defined, in line with the WHO, as the passage of 3 or more watery stools within 24 hours and lasting less than 14 days.16 Patients with chronic diarrhea, immunodeficiency, antibiotic use within the past 30 days, simultaneous infections with multiple pathogens, a history of extraintestinal infections at the time of presentation, or insufficient stool samples were excluded from the study.
Laboratory Analyses
Fresh stool samples were collected in sterile containers and analyzed within 30 minutes. Amoebic trophozoites and cysts were examined microscopically using standard concentration techniques. Additionally, the presence of mucus, blood, and leukocytes in the stool was assessed. Negative cases were confirmed by the absence of pathogenic findings in stool microscopy.
Analysis
All statistical analyses were carried out using SPSS version 30.0 (IBM Corp, Armonk, NY, USA). Descriptive statistics were used to summarize demographic, clinical, and laboratory parameters. Continuous variables were expressed as means with standard deviations (mean ± SD) for normally distributed data, or medians with interquartile ranges (IQR) for non-normally distributed data. Categorical variables were presented as frequencies with percentages (n, %). Comparisons between 2 independent groups were performed using the Student’s t test for normally distributed continuous variables, the Mann-Whitney U test for non-normally distributed variables, and the chi-square test or Fisher’s exact test for categorical variables, as appropriate. Multivariate logistic regression analysis was used to identify independent predictors of amebiasis. Variables with a P value < 0.20 in univariate analysis were included in the multivariate model. Results were presented as odds ratios (OR) with 95% confidence intervals (CI). The model fit was assessed using the Hosmer-Lemeshow test, and the explanatory power of the model was evaluated using Cox & Snell R2 and Nagelkerke R2. Correlation analyses were performed to assess the relationships. Spearman’s rank correlation coefficient (r) was calculated for non-parametric data. Lagged correlation analysis was conducted to explore the temporal relationship between the heat index and the occurrence of amebiasis and gastroenteritis, examining lags from 0-21 days. A P value of less than 0.05 was considered statistically significant for all tests.
Results
A total of 1406 patients were included in the analysis, of whom 251 (17.9%) were with laboratory-confirmed amebiasis, while 1155 (82.1%) were classified as non-amebiasis. The median age of the cohort was 31.1 ± 5.2 years, with no statistically significant difference between the amebiasis and non-amebiasis groups (31.4 ± 5.5 vs. 31.1 ± 5.3, P = 0.415). Similarly, the proportion of female patients was comparable between the groups (55.4% vs. 52.6%, P = 0.237).
The 7-day heat index average was statistically significantly higher in the amebiasis group compared to the non-amebiasis group [median 25 (IQR 18-28.8) vs. 11.3 (IQR 2.7-24.2), P < 0.001]. No significant differences were observed in systolic blood pressure (120.1 ± 14.7 vs. 120.7 ± 15.1, P = 0.479), diastolic blood pressure (80.4 ± 9.5 vs. 80.1 ± 9.7, P = 0.630), or body temperature [37.1 (IQR 36.3-37.7) vs. 37 (IQR 36.6-37.5), P = 0.518] between the groups. However, the median pulse rate was statistically significantly higher in the amebiasis group compared to the non-amebiasis group [median 82.7 (IQR 76.6-88.4) vs. 80.2 (IQR 73.1-93.6, P = 0.002) (Table 1).
Demographics, environmental, and vital parameters
Environmental factors revealed that 21.3% of the patients consumed non-packaged or unbottled water, with no statistically significant difference between the groups (17.9% vs. 22%, P = 0.089]. Similarly, the proportion of patients consuming street food (27.9% vs. 29.9%, P = 0.294) and those reporting household transmission (12.7% vs. 14.1%, P = 0.325) were comparable. Hospital admission rates were also similar between the groups (1.2% vs. 1.6%, P = 0.603).
Laboratory parameters showed no statistically significant differences in median C-reactive protein [10.1 (IQR 7-13.2) vs. 10.2 (IQR 6.8-13.5), P = 0.481], white blood cells [7.9 (IQR 6.8-9.6) vs. 8 (IQR 6.7-9.3), P = 0.824], neutrophils [5.2 (IQR 4-6.2) vs. 5.1 (IQR 4-6.1), P = 0.694], lymphocytes [2.4 (IQR 1.9-2.9) vs. 2.5 (IQR 2-3), P = 0.701], or platelets [255 (IQR 217-284) vs. 249 (IQR 216 - 282), P = 0.295]. However, ALT levels were significantly lower in the amebiasis group compared to the non-amebiasis group [24.3 (IQR 17.5-31.8) vs. 30 (IQR 20.5-40.2), P < 0.001].
Stool analysis revealed statistically significantly higher stool frequency in the amebiasis group compared to the non-amebiasis group [median 3.4 (IQR 2.4-4.7) vs. 3 (IQR 2.4-3.6), P < 0.001]. Mucus in stool was observed in 41.4% of amebiasis cases compared to 39% in non-amebiasis, with no statistically significant difference (P = 0.255). Similarly, the presence of stool leukocytes (32.7% vs. 29.5%, P = 0.181) and stool blood (20.7% vs. 19.8%, P = 0.404) were not significantly different between the groups (Table 2).
Laboratory, stool analysis, and clinical outcomes
Multivariate logistic regression analysis identified higher pulse rate (OR: 1.025, 95% CI: 1.009-1.042, P = 0.003), lower ALT levels (OR: 0.967, 95% CI: 0.956-0.978, P < 0.001), increased 7-day heat index average (OR: 1.12, 95% CI: 1.099-1.141, P < 0.001), and higher stool frequency (OR: 1.735, 95% CI: 1.562-1.927, P < 0.001) as independent predictors of amebiasis. Non-packaged or unbottled water use (OR: 0.899, 95% CI: 0.564-1.433, P = 0.653) and stool leukocytes presence (OR: 1.075, 95% CI: 0.768-1.505, P = 0.674) were not significantly associated with amebiasis (Table 3).
Multivariate analysis results
ALT: Alanine Aminotransferase.
Model fit was evaluated using the Hosmer-Lemeshow test, which demonstrated a good fit (χ2 = 15.516, P = 0.304). Cox & Snell R2 and Nagelkerke R2 values were 0.209 and 0.343, respectively, indicating moderate explanatory power for the model.
Figure 1 demonstrates the temporal variation in total cases, amebiasis cases, and their proportion on a daily basis, highlighting significant trends across the study period. Correlation analysis revealed a moderate positive correlation between the heat index and daily amebiasis cases (r 0.55, P < 0.001), indicating that higher heat index values were associated with an increase in amebiasis cases. Conversely, a weak negative correlation was observed between the heat index and daily non-amebiasis cases (r = -0.27, P = 0.034), suggesting a slight decrease in non-amebiasis cases with rising heat index values. Additionally, a very weak negative correlation was found between the heat index and the total daily cases (r = -0.01, P = 0.840), indicating no meaningful association.
Daily trends of total cases, amebiasis cases, and proportion of amebiasis cases over time.
Lagged correlation analysis revealed a moderate positive correlation between the heat index and daily amebiasis cases at lag 0 (r = 0.55, P < 0.001), which peaked at lag 5 (r = 0.60, P < 0.001). The correlation strength decreased progressively, reaching a weaker but still statistically significant level by lag 21 (r = 0.40, P < 0.001). In contrast, the correlation between the heat index and daily gastroenteritis cases was weak and negative at lag 0 (r = -0.27, P = 0.002) and weakened further with increasing lag times, becoming statistically insignificant by lag 7 (r = -0.15, P = 0.073).
The proportion of amebiasis cases demonstrated a moderate positive correlation with the heat index at lag 0 (r = 0.55, P < 0.001), peaking at lag 4 (r = 0.57, P < 0.001). This correlation remained statistically significant up to lag 14 (r = 0.46, P = 0.013) but weakened substantially at longer lags. These findings suggest that the heat index is positively associated with amebiasis cases and their proportion in a time-dependent manner, while the association with gastroenteritis cases is weaker and transient (Table 4).
Lagged correlation analysis of heat index with amebiasis and diarrhea cases
Discussion
In this study, we evaluated the impact of climate change on patients presenting to ED, focusing on those with diarrhea and using amoebic pathogen cases as an example. Our research identified independent predictors of amoebic infection in patients presenting with diarrhea, including high heart rate, low ALT levels, increased stool frequency, and the average heat index increase over the week prior to presentation. The average heat index demonstrated a significant, time-varying positive effect on the daily total number of amoebic cases (lag 0, lag 5, lag 21) and the proportion of amoebic cases among total cases (lag 0, lag 4, lag 14). This effect was most pronounced after a delay of a few days, weakened over longer durations, and contrasted with the weak and short-lived effect observed on gastroenteritis cases.
According to the Institute Pasteur, 10%-20% of amoeba-infected individuals develop severe symptoms of amoebiasis, with 40 000-100 000 deaths annually, particularly in hot-climate regions such as Southeast Asia, Africa, and Central and South America, where infection rates can reach up to 20% in areas like India, Mexico, Bangladesh, and South Africa.17 Although the frequent occurrence of this infection in hot regions has led to various hypotheses and strategies regarding transmission pathways, rising temperatures due to climate change suggest that amoebic infections could exhibit epidemiological variations beyond these regions. Some studies have reported a significant increase in the frequency of symptomatic intestinal amoebiasis with warming temperatures, while observing an inverse relationship between amoebiasis and factors such as humidity and atmospheric pressure.Reference Erdem, Kiliç, Cinar and Pahsa18 In our study, we utilized the heat index—a combination of air temperature and relative humidity—because it is not only a critical determinant of human body temperature but also a key factor influencing amoebic pathogenicity and transmission. The heat index, due to its composite parameters, serves as an effective indicator impacting both human physiology and the pathogenic behavior of amoebae.Reference Guillén19 To evaluate the effects of temperature changes and provide a comprehensive analysis, we employed the weekly average heat index and observed a clear association between this parameter and amoebiasis cases. These findings underscore the persistent role of the heat index in influencing the incidence of amoebic infections over time. Furthermore, they highlight the importance of this parameter as a crucial data source for understanding the health impacts of climate change, emphasizing its value in disease prediction and the development of prevention strategies.
Studies have shown that compared to hotter cities, cooler cities experience greater changes in deaths and hospitalizations due to rising temperatures.20 Thus, climate-related mortality should not be viewed solely as a result of heat index-induced body temperature changes but also as a consequence of inadequate adaptation mechanisms and shifts in human interactions with the microorganisms in their environment.Reference Navas-Martín, Cuerdo-Vilches and López-Bueno21 An additional key finding of our study is that there was no significant difference in body temperature between diarrhea patients with and without amoebic infection. The heat index is a fundamental factor influencing both human and environmental temperatures. Even if physiological mechanisms regulate the human body temperature, alterations in the surrounding microbial flora can act as a secondary factor, leading to disease in humans.
Studies indicate that clinical features of amoebic infection often include blood and mucus in stool as well as abdominal cramps. However, stool tests may not always reveal leukocytes, as these white blood cells can be destroyed by the organisms.22 Similarly, in our study, we found no significant difference between patients with and without amoebae detected in stool microscopy in terms of the presence of mucus, stool leukocytes, or stool blood.
A comprehensive evaluation of the impact of climate change on patients presenting to EDs requires assessing both the patients and the risks they are exposed to in their environment. It may be more accurate to describe the pathologies caused by climate change as direct effects on patients, while referring to the environmental factors influencing patients as indirect effects. The direct effects of climate change on patients primarily manifest through thermoregulation, resulting in clinical presentations such as hyperthermia, normothermia, and hypothermia. Indirect effects, however, can be categorized as environmental factors such as CBRN-E events. From this perspective, climate change itself represents a risk factor that could result in CBRN-E events. In this context, amoebic infections, influenced by climate change, can pose a biological risk, potentially affecting ED operations. Changes in the heat index driven by climate change have become situations requiring preparedness and planning for EDs. In our study, we observed a 5-day lag in the effect of weekly changes in the heat index on amoebic infections, with this effect lasting up to 2 weeks. This suggests that during periods of high heat index, preparations for diseases caused by amoebic pathogens may require a 2-week fluctuation period. Our findings provide scientific observations and examples for developing strategies to manage fluctuations in ED operations, demonstrating the importance of heat index monitoring and climate-informed health care planning in mitigating the impact of climate change on emergency services.
This study has several limitations that should be acknowledged. First, city-level meteorological data were used as a proxy for patient exposure, as individual-level residential information was not available due to the retrospective design and anonymization of records. Although this could introduce some degree of exposure misclassification, the hospital primarily serves the Istanbul metropolitan area, which minimizes geographic variability. Second, certain environmental and behavioral risk factors, such as individual hygiene practices and access to clean water, could not be assessed. Finally, the retrospective observational design precludes causal inference. Nevertheless, the large sample size, consistent diagnostic methodology, and robust statistical analyses contribute to the strength and validity of our findings.
Conclusion
This study examined the impact of climate change on patients presenting to the ED by analyzing the effect of the weekly average heat index on the detection of amoebic pathogens in patients with symptomatic diarrhea. The findings revealed that an increased weekly average heat index prior to presentation significantly influenced the detection of amoebic pathogens in these patients, with the effect being strongest 5 days after the heat index increase and persisting for up to 14 days. These results suggest that rising heat index levels following temperature changes may lead to fluctuations in symptomatic diarrhea infections caused by amoebic pathogens in EDs. To address these fluctuations, it is recommended to develop strategies that account for the impact of high heat index periods and extend preparedness measures for up to 14 days to effectively manage the potential burden on ED operations.
Author contribution
NES, ÖFA, and SY conceived the study, designed the trial. NES, ÖFA, and SY supervised the conduct of the trial and data collection. NES, ÖFA, and SY undertook recruitment of participating centers and patients and managed the data, including quality control. ÖFA and SY provided statistical advice on study design and analyzed the data; ÖFA and SY chaired the data oversight committee. NES and SY drafted the manuscript, and all authors contributed substantially to its revision. NES, ÖFA, and SY take responsibility for the whole paper.
Competing interests
The authors declare that there is no conflict of interest regarding the publication of this article.
