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1.     Bonita R, Beaglehole R y Kjellstrom T. (2008). Capítulo 3. Tipos de estudios. En: Bonita R, Beaglehole R y Kjellstrom T. Epidemiología básica. Segunda edición. Organización Panamericana de la Salud.

2.     Gordis L. (2014). Capítulo 9: Estudios de Cohortes. En: Gordis L. Epidemiology. Philadelphia: Elsevier Saunders.

3.     Gordis L. (2014). Capítulo 10: Estudios de casos y controles y otros diseños de estudio: Gordis L. Epidemiology. Philadelphia: Elsevier Saunders.

4.     Hernández-Avila, M., & Garrido-Latorre, F., & López-Moreno, S. (2007). Diseño de estudios epidemiológicos. Revista Cubana de Higiene y Epidemiología, 45 (1), 144-154.

5.     Hernández-Ávila M, Francisco Garrigo MC y Salazar-Martínez E. (2008). Sesgos en estudios epidemiológicos. Revista Cubana de Higiene y Epidemiología, 46 (1).

6.     Lazcano-Ponce et al. (2001). Estudios epidemiológicos de casos y controles. Fundamento teórico, variantes y aplicaciones. Salud pública de México / vol. 43, no. 2.

7.     Lazcano-Ponce et al. (2000). Estudios de cohorte. Metodología, sesgos y aplicación. Salud pública de México / vol. 42, no. 3

8.     Manterola, C. & Otzen, T. (2014). Estudios observacionales. los diseños utilizados con mayor frecuencia en investigación clínica. Int. J. Morphol. 32 (2): 634-645.

9.     Vandenbroucke JP, Von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, et al, for the STROBE Initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med. 2007; 4: e297.

10. Von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2014). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for reporting observational studies. International Journal of Surgery, 12(12), 1495–1499.

11. Mirahmadizadeh, A., Rezaei, F., Jokari, K., Moftakhar, L., Hemmati, A., Dehghani, S. S., ... & Ghelichi-Ghojogh, M. (2021). Correlation between environmental factors and COVID-19 indices: a global level ecological study. Environmental Science and Pollution Research, 1-11.

12. Jiang, M., Mishu, M. M., Lu, D., & Yin, X. (2018). A case control study of risk factors and neonatal outcomes of preterm birth. Taiwanese Journal of Obstetrics and Gynecology, 57(6), 814-818.

13. Oeser, P., Napierala, H., Schuster, A., & Herrmann, W. J. (2023). Risk Factors for Monkeypox Infection-a Cross-Sectional Study. Deutsches Arzteblatt international, 120(5), 65-66.

14. Delgado-Miguel, C., Muñoz-Serrano, A. J., Barrena Delfa, S., Núñez Cerezo, V., Velayos, M., Estefanía, K., ... & Martínez, L. (2020). Influence of overweight and obesity on acute appendicitis in children. A cohort study. Cir Pediatr, 33(1), 20-24.
15.     Abate, S. M., Chekole, Y. A., Minaye, S. Y., & Basu, B. (2020). Global prevalence and reasons for case cancellation on the intended day of surgery: A systematic review and meta-analysis. International Journal of Surgery Open, 26, 55-63.

16.     Meier, P., Hemingway, H., Lansky, A. J., Knapp, G., Pitt, B., & Seiler, C. (2012). The impact of the coronary collateral circulation on mortality: a meta-analysis. European heart journal, 33(5), 614-621.
17.     Molina Arias M, Ochoa Sangrador C. Ensayo clínico (I). Definición. Tipos. Estudios cuasiexperimentales. Evid Pediatr. 2014;10:52.

Grupo 4.
Javier Ramirez,
John Alexander Bustos Molina
Paola A. Galindo Picón
Laura Quintero

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Es un método estadístico que se utiliza para combinar los resultados de varios estudios independientes sobre un mismo tema de investigación con el fin de obtener una estimación más precisa y confiable del efecto de una intervención o variable en particular.

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Utilizando la estrategia PRISMA se aplicaría así:

1. Definición de la pregunta de investigación:
¿Cuál es el impacto de la circulación colateral coronaria en la mortalidad en pacientes con enfermedad coronaria estable o aguda?"

2. Búsqueda de literatura: búsquedas exhaustivas en EMBASE, PubMed, BIOS e ISI Web of Science, desde 1980 hasta el 25 de abril de 2011, utilizando términos de búsqueda relacionados con "circulación colateral coronaria", "supervivencia", "pronóstico" y "mortalidad".

3. Selección de estudios: Se identificaron los estudios potencialmente relevantes que cumplian con los criterios de inclusión.

4. Extracción de datos y evaluación de la calidad: Se utilizo un formulario de extracción de datos estandarizado.

5. Análisis y síntesis de datos: Los datos de los estudios incluidos deben combinarse para estimar el impacto agrupado (riesgo relativo, RR) de una buena circulación colateral en comparación con una baja circulación colateral en la mortalidad.

6. Análisis de subgrupos

7. Sensibilidad y sesgo de publicación

8. Conclusiones y recomendaciones: Se proporcionan recomendaciones basadas en los hallazgos.

John Alexander Bustos Molina

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Se Utilizó la  estrategia PRISMA (http://www.prisma-statement.org/) Esta consiste en seguir una serie de pasos para garantizar que la revisión sistemática esté bien estructurada y se informe adecuadamente. Algunos de los elementos clave que se aplican para este artículo en la estrategia PRISMA son:

1. Pregunta de investigación: La prevalencia global y los factores determinantes de la cancelación de casos en el día previsto de la cirugía.

2. Desarrollo del protocolo de revisión: El resumen menciona que la revisión se realizó según los protocolos PRISMA

3. Búsqueda exhaustiva y sistemática de literatura: El resumen menciona que se realizó una búsqueda exhaustiva en varias bases de datos sin restricción de idioma

4. Selección y evaluación de estudios: El resumen menciona que 33 estudios con más de 300,000 participantes fueron incluidos en la revisión y se menciona el proceso de selección.

5. Extracción y síntesis de datos: El resumen proporciona detalles sobre los datos extraídos de cada estudio incluido, como autor, país, tamaño de muestra, razones de cancelación, etc.

6. Evaluación del riesgo de sesgo: No se menciona

7. Presentación transparente de los resultados y análisis: El resumen presenta claramente los resultados del meta-análisis, incluida la prevalencia global de la cancelación de casos y los factores principales que contribuyen a la cancelación.

8. Conclusiones El resumen concluye que la prevalencia de la cancelación de casos es muy alta, especialmente en países de ingresos bajos y medianos, y sugiere medidas para abordar los factores que contribuyen a la cancelación

John Alexander Bustos Molina

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Global prevalence and reasons for case cancellation on the intended day of surgery- A systematic review and meta-analysis

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Utiliza métodos rigurosos y sistemáticos para recopilar, analizar y sintetizar la evidencia disponible sobre un tema específico. Su objetivo principal es responder a una pregunta de investigación bien definida mediante el análisis crítico y la integración de los resultados de múltiples estudios primarios previamente publicados.

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ESTUDIOS POBLACIONALES: Ejemplo Ver link
El ejemplo cumple con las siguientes Características:
-Se miden dos o mas variables para establecer si están o no correlacionadas
-Mide el grado de relación entre las variables
-La correlación puede ser positiva, inexistente o inversa
-utilizan datos de la población general para comparar frecuencias de enfermedad entre diferentes grupos durante un mismo periodo de tiempo
-su utilidad esta en conocer el comportamiento de una
variable conociendo el de la otra.

Javier Andrés Ramírez

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Este es un estudio de intervención dado que se realizó una intervención a los participantes (Intervención multidisciplinaria).
Es cuasiexperimental ya que no se usa aleatorización para asignar los grupos de estudio.
Se incluyeron en el grupo de intervención ancianos frágiles hospitalizados recientemente (2016) y  el grupo control fue una cohorte de ancianos frágiles realizada en 2013. Por esta misma razón se clasifica como control histórico.

- Paola A. Galindo Picón

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Multidimensional intervention to improve the

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Este es un estudio de intervención ya que se realiza una intervención a los participantes (el uso de iontoforesis con ácido acético y ultrasonido sobre la calcificación)
Es cuasiexperimental ya que no se usó aleatorización para el grupo de pacientes. Se aplicaron criterios de inclusión y exclusión en pacientes con tendinitis calcificante dando un total de 138 pacientes. No hubo grupo control.
Es de tipo antes y después dado que se midió el evento (dolor, tamaño de la calcificación y limitación funcional) antes de realizar la intervención y después de realizarla.
Es longitudinal ya que se realizó seguimiento cada 10 días.

- Paola A. Galindo P.

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Calcific tendinitis of the shoulder risk factors

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El presente articulo se trata de un estudio de intervención de tipo experimental en animales ya que la población de estudio fueron cerdos, se realizó aleatorización para definir los grupos del estudio y se realizó una intervención en cada grupo (Recibir norepinefrina o angiotensina II)

- Paola A. Galindo Picón

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Considero que este es un estudio experimental debido a que se realizó una intervención en la población de estudio (uso de antilarvales de Anopheles sp) para evaluar la efectividad del control de vectores de Malaria.
Es un estudio de campo debido a que la población objeto son personas sanas expuestas a un riesgo (contraer malaria) y la intervención fue realizada en su lugar de vivienda.
Adicionalmente es un estudio prospectivo ya que se tomaron los datos al inicio del estudio y posteriormente se evaluaron los resultados. Es longitudinal ya que se hizo un seguimiento semanal.

- Paola a. Galindo Picón

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Field assay with Bactivec® (Bacillus thuringiensis) and Griselesf® (Bacillus sphaericus) in breeding sites of Anopheles sp. from Luanda, Angola

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Considero este estudio es un ensayo clínico ya que los investigadores asignaron una intervención a uno de los grupos de los pacientes (La intervención es iniciar suplementación con omega 3). Fue aleatorizado ya que inicialmente escogieron un grupo de mujeres con cáncer de mama en tratamiento con quimioterapia y posteriormente se dividieron en 2 grupos de manera aleatoria, al grupo A se les dio la intervención (omega 3) y al  grupo B se les dio placebo.  Es longitudinal ya que se hizo un seguimiento a los 3 meses. y prospectivo ya que los datos de exposición se tomaron cuando inició el estudio y el efecto se midió meses después.

-Paola A. Galindo Picón

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Effects of omega-3 fatty acids supplementation on neoadjuvant chemotherapyinduced toxicity in patients with locally advanced breast cancer

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SERIE DE CASOS: Ejemplo Ver link
El ejemplo cumple con las siguientes Características:
-Descripción cuidadosa de mas de 10 casos clínicos
-Son los estudios mas comúnmente encontrados en las revistas científicas entre el 70 al 80% de la literatura
-No existe un grupo de comparación

Javier Andres Ramirez

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ENCUESTA : Ejemplo Ver link
El ejemplo cumple con las siguientes Características:

Identifica el problema ( modalidad de control de parto).

Puede definir el diseño de investigación.

Especificación de las hipótesis.

Definición de las variables.

Selección de la muestra.

Diseño del cuestionario.

Organización del trabajo de campo.

Obtención y tratamiento de los datos.

Análisis de los datos e interpretación de los resultados.

Javier Andrés Ramírez

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Utilizando la estrategia strobe, puedo concluir que es un estudio de cohortes , en total 403 niños con apendicitis que son llevados apendicectomía, donde hay una  cohorte expuesta (los niños con apendicitis obesos, 97) y una no cohorte no expuesta (lo niños con apendicitis no obesos 306) y se pudo establecer que la cohorte expuesta presentó un mayor tiempo quirúrgico al igual que  mayor tasa de infección de herida quirúrgica al compararla con la cohorte no expuesta. También  una mayor tasa de dehiscencia de herida quirúrgica.
Laura Quintero

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Es un estudio de corte transversal porque se estudió una población de 1840 participantes de una encuesta para evaluar los factores de riesgo de la viruela símica pudiendo establecer los siguientes factores de riesgo: muchas parejas sexuales, VIH +, vivir en un ambiente urbano con más 100 000 y bajo nivel educativo

Laura Quintero

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In May 2022, an increase in monkeypox infections was observed worldwide. In Germany alone, 3656 cases had been registered by 18 October 2022 ( 1). Transmission shifted from travel-related to autochthonous ( 2), and early publications on confirmed monkeypox infections showed that men who have sex with men (MSM) are disproportionally affected compared with other segments of the population ( 3, 4). Sexual transmission seems to play a major role, as concomitant sexually transmitted infections (STI), multiple sex partners, and attendance of sex-on-site events have been reported in the infected population ( 5).

To date, research on monkeypox infections has mostly been limited to case series. In this report, we describe specific risk factors for monkeypox infection in a population of M*SM* (a wider definition of MSM to embrace trans and non-binary persons) resident in Germany by comparing data between an infected and a non-infected M*SM* group.

Methods

From 13 August to 31 August 2022, we conducted an anonymous online survey on monkeypox infection, risk factors, attitudes to and experience of vaccination, and treatment of monkeypox infections in Germany. By means of snowball sampling we shared the survey with approximately 60 organizations representing the interests of lesbian, gay, bisexual, trans, queer, intersexual, and asexual (LGBTQIA) persons. A total of 3338 persons participated in the survey. For this analysis, we excluded participants who did not provide any responses beyond the initial questions on monkeypox (n = 306). We included all participants who did not identify as a woman and who had sex with persons not identifying as a woman (M*SM*). We used the R statistical software for analysis. Crude and adjusted odds ratios were calculated by logistic regression, and we conducted a sensitivity analysis with multiple imputation using the MICE package with five data sets.

Results

A total of 1840 participants were included in the analysis, 62 of whom reported previous monkeypox infection (Table 1). Unadjusted odds ratios showed an elevated risk of monkeypox infection for M*SM* who reported having anal sex during their last sexual intercourse, sex without a condom during their last sexual intercourse, positive HIV status, regular intake of pre-exposure prophylaxis (PrEP), or living in an urban environment (Table 2). Moreover, the chance of infection increased with the number of sex partners. Multivariate regression showed that having anal sex during the last sexual intercourse, a higher number of sex partners in the previous 12 months, positive HIV status, regular intake of PrEP, a lower education level and living in an urban environment raise the risk of monkeypox infection (Table 2). Sensitivity analysis showed no relevant differences in the pooled imputed regression.

Discussion

The description of risk factors for monkeypox infection in previous research has so far largely been restricted to case reports. A strength of our analysis was the ability to compare persons with monkeypox infection with a relatively large group of non-infected persons. We succeeded in generating a large sample by distributing the survey to LGBTQIA organizations.

Most of the participants in our cross-sectional survey identified as homosexual, in accordance with the previously published case reports ( 3, 4, 5). Our analysis showed that characteristics described repeatedly in the case studies, such as number of sex partners, positive HIV status, and taking PrEP, are risk factors for monkeypox infection. Further-newly identified-risk factors are residence in an urban environment with more than 100 000 inhabitants and lower education level.

Our study has limitations. Because it was conceived as a survey, the data are purely cross-sectional, which restricts causal inferences. Disease status is self-reported, so there is potential for misclassification. The risk factors were also self-reported and therefore prone to recall bias. Furthermore, the case reports describe a high proportion of concomitant or recent STIs in individuals with monkeypox ( 3, 5), which was not part of our survey. The survey allowed participants to specify their sexual identity, which in turn enabled the use of the broader definition of M*SM* in our analysis. However, the case numbers in the individual categories were too small for subgroup analyses.

Our results indicate that M*SM* living in urban environments are disproportionately affected by monkeypox infection. This should be considered in future prevention, information, and vaccination campaigns. M*SM* with lower educational status should be specifically targeted in such programs as they are at increased risk of monkeypox infection. We encourage treating physicians to explore the sexual history of all their patients, whatever their sexual identity, and to ask them about potential risk factors in order to support the prevention of monkeypox infections.

Ethical statement
The study was carried out in accordance with the Helsinki Declaration and German Law. The study has been approved by the local ethics committee. All participants gave electronic informed consent.

Funding statement

There was no specific funding for the study.

Conflict of interest statement
Prof. Herrmann is a spokesman of the Sexual Diversity Working Group of the German Society for Social Medicine and Prevention (DGSMP).

The remaining authors declare that no conflict of interest exists.

Philip Oeser, Hendrik Napierala, Angela Schuster, Wolfram J Herrmann

Charité - Universitätsmedizin Berlin, Institute of General Practice and Family Medicine, Berlin (Oeser, Napierala, Schuster, Herrmann)
wolfram.herrmann@charite.de

Cite this as:
Oeser P, Napierala H, Schuster A, Herrmann WJ: Risk factors for monkeypox infection-a cross-sectional study. Dtsch Arztebl Int 2023; 120: 65-66. DOI: 10.3238/arztebl.m2022.0365

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Deutscher Ärzteverlag GmbH, Redaktion Deutsches Ärzteblatt

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Risk Factors for Monkeypox Infection-a Cross-Sectional Study (03.02.2023)

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In May 2022, an increase in monkeypox infections was observed worldwide. In Germany alone, 3656 cases had been registered by 18 October 2022 (1). Transmission shifted from travel-related to autochthonous (2), and early publications on confirmed...

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Utilizando la estrategia strobe, puedo concluir que es un estudio de casos y controles porque el objetivo era determina los factores de riesgo y los desenlaces neonatales en nacimientos pretérmino. Los casos fueron 1328 bebés pretérmino y los controles fueron 1328 bebés a término.

Sirvió para determinar que la edad de la madre gestante, embarazo previo, aborto , cuidado prenatal y complicaciones del embarazo, presentación anormal están asociados a parto pretérmino.
Laura Quintero

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Abstract

Objective

The aim is to examine risk factors and neonatal outcomes of preterm birth and to provide basis in preventing preterm birth.

Materials and methods

we carried out our study on 1328 term controls and 1328 preterm birth cases. By using multivariable logistic regression procedures we estimated odds ratio (OR) of potential preterm birth risk factors. T-test and chi-square test were used to estimate differences between groups.

Results

Maternal age, prior history of pregnancy and abortion, prenatal care, complications of pregnancy (includes hypertension, intrahepatic cholestasis of pregnancy (ICP), fetal growth restriction (FGR), premature rupture of the membranes (PROM), placenta previa, abnormal presentation, abnormal S/D ratio et al.) were significantly associated with preterm birth. Several factors emerged as being statistically significant risk factors for preterm birth, such as prior history of pregnancy, hypertension, ICP, FGR, PROM, placenta previa and abnormal presentation. The time of prenatal care was shown to be a protective factor. Additionally, we observed evidence suggested that male babies are known to have a significant higher risk of preterm birth than female babies.

Conclusion

Prior history of pregnancy, hypertension, ICP, FGR, PROM, placenta previa and abnormal presentation were covariates identified in this study as risk factors for preterm birth. Preterm birth is an important reason of neonatal poor prognosis and death.

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A case control study of risk factors and neonatal outcomes of preterm birth

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The aim is to examine risk factors and neonatal outcomes of preterm birth and to provide basis in preventing preterm birth.we carried out our study on...

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Investigar la relación entre factores ambientales y los índice de COVID 19 . Se usó la información  de todos los países basado en las muertes acumuladas, tasa de letalidad, tasa de recuperación y el número de pruebas de Covid 19 realizadas. Montenegro y Luxemburgo tuvieron la incidencia acumulativa de Covid 19 más alta y por otro lado Vietnam  y Tanzania la más baja.  Se demostró la incidencia acumulada de casos, de muertes y el número de pruebas de COVID 19 tenías una correlación directa con acceso a agua potable y servicios de sanidad y por lo tanto se aumentó los dos indiciadores de tasa de incidicencai de casos y la incidenica acumulada de muertes.

Es un estudio ecológico porque se realizó el estudio teniendo en cuenta todos los países , en una época especifica ( durante la pandemia de COVID 19 ), analizando como la enfermdad se comporta en países desarrollados vs subdesarrollados

Laura Quintero

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Abstract

This global level ecological study aimed to investigate the correlation between environmental factors and the COVID-19 indices. This survey is an ecological study, so all studied variables are aggregate variables. To collect the variables in the study, a data set was provided, which includes the information of each country based on the cumulative deaths, case fatality rate, recovery rate, and the number of performed COVID-19 tests. Scatter plots of environmental factors for the studied countries were drawn based on cumulative incidence rate of cases, cumulative incidence rate of death, tests, recovery rate, and case fatality rate of COVID-19. Furthermore, Spearman correlation coefficient was also used to verify the correlation between environmental factors and indicators related to COVID-19. The results of this ecological study showed that among all countries surveyed, Montenegro (60,310.56 per million) and Luxembourg (54,807.89 per million) had the highest cumulative incidence rates of COVID-19 cases, when Tanzania (8.42 per million) and Vietnam (13.78 per million) had the lowest cumulative incidence rates of COVID-19. In addition, in this study, it was shown that the cumulative incidence rate of cases, the cumulative incidence rate of deaths, and performed COVID-19 tests had significant direct correlations with the access to drinking water and the access to sanitation services ( p  < 0.001). The findings of the present study showed an inverse correlation between the mortality rate due to unhealthy water consumption, poor health status, and a positive correlation between access to drinking water and health services with the cumulative incidence and mortality rates of COVID-19. The differences between our findings and many other studies could be due to the ecological nature of the study. Nevertheless, our findings will help health policymakers to develop timely strategies to reduce the mortality and incidence rate of COVID-19.

Keywords: COVID-19, Coronavirus, Environmental factors, Ecologic study

Introduction

COVID-19 is a potentially fatal respiratory disease named after the coronavirus detected in December 2019 in Wuhan, China (Hamidian Jahromi et al. 2020; Hu et al. 2020; Kalteh et al. 2020). The disease marks the third outbreak of beta-coronaviruses in the twenty-first century, sparking a worldwide public health crisis. The previous outbreaks of this viral family occurred in 2002 and 2012 (Shahriarirad et al. 2020). Although COVID-19 mortality is lower than SARS (severe acute respiratory syndrome), it is a highly contagious disease spreading rapidly throughout the world. Some people with COVID-19 infection remain asymptomatic, while others often show mild to moderate to severe symptoms including fever, cough, and shortness of breath within 2 to 14 days of exposure to the virus (Daneshfar et al. 2020; Gharakhanlou and Hooshangi 2020; McMichael et al. 2020; Struyf et al. 2020). As of December 23rd, 2020, 76,858,506 confirmed cases of COVID-19 had been identified, of which 1,711,498 were deceased. Of these, 33,251,796 were in the USA, 24,379,383 were in Europe, 11,710,770 were in Southeast Asia, and 4,731,359 were in the eastern Mediterranean region (Organization, 2020).

Various factors play important roles in the transmission of this virus, including factors related to human behavior, along with environmental factors such as air quality, the presence of safe water, wastewater collection, and effective hygiene in the COVID-19 epidemic which play essential roles in maintaining human health (Eslami and Jalili 2020). One study conducted in the USA identified air pollutants, particularly NO ₂ as an important factor associated with susceptibility against COVID-19 and its mortality(Sarmadi et al. 2021). Air temperature has also been expressed as an influential environmental factor in the development of COVID-19 (Babu et al., 2020; Tosepu et al. 2020; Zhu et al. 2020). According to the World Health Organization (WHO), in 2017, 71% of the world's population (5.3 billion people) used safe drinking water services, and about 785 million people did not even have access to any basic drinking water services.

According to the studies conducted in 60 countries which have had the highest health risks for COVID-19, one out of every two schools lacked basic water and sewage services (Organization 2017). The results of a study also showed that inequalities in access to the necessities of life, including water, sanitary equipment, and food storage, can neutralize the preventive measures taken against COVID-19 and increase the incidence and spread of the disease (Ekumah et al. 2020). Considering that the studies on the relationship between environmental factors and the rate of coronavirus transmission are more in the field of air and atmosphere (Poirier et al. 2020; Shi et al. 2020; Vantarakis et al. 2020), studies on the impact of water status and safe health services in coronavirus transmission are very rare. Also, hygiene and regular hand washing are two of the main factors in preventing the transmission of coronavirus to a person, which could be achieved only in the context of access to safe water services and safe health conditions. Therefore, the aim of this study was to investigate the correlation between environmental factors (access to drinking water, access to sanitary bathroom facilities, the mortality rate of unsafe water and PM 2.5 pollution) and the epidemiological indices of COVID-19, including new cases, recovery rate, mortality rate, and the number of tests taken.

Methods

Study design and data collection

This survey is an ecological study, so all of the studied parameters are aggregate variables. To collect the variables in the study, a data set was provided which included the information of each country about the cumulative incidence rate of cases, cumulative incidence rate of deaths, case fatality rate, recovery rate, and the number of performed COVID-19 tests per million of the population. Information about COVID-19 for each country was retrieved from https://www.worldometers.info/ for the period of time from the date of the first reported case until November 30th, 2020. The data regarding the cumulative incidence rate of cases, cumulative incidence rate of deaths, and cumulative tests performed per million were available for 164 (Montenegro, Luxembourg, Bahrain, Belgium, Qatar, Czech Republic, Armenia, USA, Panama, Switzerland, Israel, Slovenia, Spain, Georgia, France, Kuwait, Croatia, Austria, Argentina, the Netherlands, Brazil, North Macedonia, Peru, Portugal, Chile, Bosnia and Herzegovina, Costa Rica, Moldova, Italy, Poland, Colombia, Romania, Sweden, Oman, Maldives, UK, Lithuania, Hungary, Malta, Jordan, Bulgaria, Serbia, Slovak Republic, Cabo Verde, the Bahamas, Lebanon, United Arab Emirates, Ukraine, Iceland, Russia, Ireland, Belize, Belarus, Denmark, Albania, Dominican Republic, South Africa, Iraq, Germany, Bolivia, Libya, Azerbaijan, Paraguay, Iran, Kyrgyz Republic, Honduras, Ecuador, Saudi Arabia, Greece, Singapore, Canada, Morocco, Estonia, Latvia, Suriname, Cyprus, Mexico, Tunisia, Nepal, Turkey, Kazakhstan, Guyana, India, Guatemala, Norway, El Salvador, Djibouti, Eswatini, Namibia, Trinidad and Tobago, Finland, Sao Tome and Principe, Botswana, Gabon, the Philippines, Malaysia, Jamaica, Equatorial Guinea, RB Venezuela, Bangladesh, Guinea-Bissau, Uzbekistan, Indonesia, Algeria, Mauritania, Pakistan, Myanmar, Ghana, Uruguay, Kenya, the Gambia, Tajikistan, Afghanistan, Japan, Egypt, Sri Lanka, Australia, Guinea, Lesotho, Barbados, Senegal, Zambia, Ethiopia, Cameroon, Nicaragua, Haiti, Cuba, Comoros, the republic of Korea, Zimbabwe, Madagascar, Bhutan, Mozambique, Angola, Rwanda, Syrian Arab Republic, Uganda, New Zealand, Mauritius, Sudan, Togo, Nigeria, Liberia, Malawi, Sierra Leone, Somalia, South Sudan, Benin, Mongolia, Mali, Burkina Faso, Chad, Republic of Yemen, Papua New Guinea, Democratic Republic of Congo, Niger, China, Thailand, Burundi, Fiji, Timor-Leste, Cambodia, Vietnam, and Tanzania), 160 (all countries that had cumulative incidence rate of cases except Bhutan, Mongolia, Timor-Leste, and Cambodia), and 152 (all countries that had cumulative incidence rate of cases except Comoros, Tajikistan, Burkina Faso, Chad, Somalia, Sierra Leone, Nicaragua, Algeria, Sudan, Syrian Arab Republic, Tanzania, and Democratic Republic of Congo) countries respectively. Recovery rate data were available for 159 (all countries that had cumulative incidence rates of cases except UK, Sweden, the Netherlands, and Democratic Republic of Congo) countries, and case fatality rate data was accessed for 160 (all countries that had cumulative incidence rate of cases except Bhutan, Mongolia, Timor-Leste, and Cambodia) nations. Data on access to drinking water services, access to sanitary bathroom facilities, the mortality rate of unsafe water, and PM 2.5 pollution were taken from the World Bank website (De Angelis et al. 2021a, b; Odih et al. 2020; Rodriguez-Villamizar et al. 2021a, b; Yao et al. 2020).

In this study, accessible drinking water services was defined as the existence of a source of drinking water from an improved resource, provided that the collection time is not more than 30 min for a round trip. This definition encompasses both sources of essential drinking water and those with safely managed drinking water services. Improved water sources included piped water, boreholes, protected dug wells, protected springs, and packaged or delivered water (Contini and Costabile 2020). Also, accessible sanitary services were defined as improved bathroom facilities that are not shared with other households. This definition encompasses both facilities with basic and safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines, and improved ventilated pit latrines, composing of toilets or pit latrines with slabs (Ekumah et al. 2020). The mortality rate of unsafe water was defined as the deaths attributable to consumption of unsafe water, as well as sanitation and hygiene focusing on inadequate wash services, expressed per 100,000 people. Death rates were calculated by dividing the number of deaths by the total population (Nwobodo and Chukwu 2020). Living under PM 2.5 pollution was also defined as living in places where mean annual concentrations of PM 2.5 are greater than 15 μg per cubic meter. The WHO Air Quality Guideline (AQG) recommends 10 μg per cubic meter as the minimum concentration over which adverse health effects due to PM 2.5 exposure have been observed (Anim and Ofori-Asenso 2020).

Statistical analysis

Scatter plots of environmental factors for the studied countries were drawn based on cumulative incidence rate of cases, cumulative incidence rate of deaths, tests performed per million, recovery rate, and case fatality rate of COVID-19. Furthermore, Spearman correlation coefficient was also used to verify the correlation between environmental factors and indices related to COVID-19.

Results

The results of this ecological study showed that among all surveyed countries, Montenegro (60,310.56 per million) and Luxembourg (54,807.89 per million) had the highest cumulative incidence rates of COVID-19, when Tanzania (8.42 per million) and Vietnam (13.78 per million) had the lowest cumulative incidence rates of COVID-19. Also, it revealed that Belgium (1425.15 per million) followed by Spain (1118.96 per million) had the highest cumulative incidence rates of deaths due to COVID-19, when Burundi (0.08 per million) and Tanzania (0.35 per million) had the lowest cumulative incidence rates of deaths due to COVID-19. Luxembourg (2,180,641.18 per million) and United Arab Emirates (1,682,880.81 per million) were the countries with the highest numbers of performed COVID-19 tests per million among the studied countries.

The lowest number of performed COVID-19 tests per million belonged to Yemen (560.05 per million) and Niger (1885.52 per million). The highest recovery rates were in Timor-Leste (100%) and Singapore (99.86%), when the lowest recovery rates were seen in Belgium (6.48%) and France (7.28%). In addition, the highest case fatality rates of COVID-19 were in Yemen (28.34%), followed by Mexico (9.54%), while, the lowest case fatality rates of COVID-19 were in Singapore (0.05%) and Burundi (0.15%). Table ​Table1 1 shows the correlation coefficient between the indicators related to COVID-19 and the environmental health indices. This study showed that the cumulative incidence rate of cases, the cumulative incidence rate of deaths, and the performed COVID-19 tests per million had a significant direct correlation with accessible drinking water services and accessible sanitation services ( p < 0.001). Thus, increasing these two indicators would lead to increased cumulative incidence rate of cases, cumulative incidence rate of deaths, and performed tests per million. However, there were inverse significant correlations between the cumulative incidence rate of cases, the cumulative incidence rate of deaths, and performed tests per million with mortality rate attributed to unsafe water, unsafe sanitation, lack of hygiene, and PM 2.5 pollution ( p  < 0.001).

Table 1

Variable

All countries

Countries with ≥ 10 million population

N

Correlation coefficient

p value

Comment*

N

Correlation coefficient

p value

Comment

Cumulative incidence rate of cases (per million)

Cumulative incidence rate of death (per million)

Tests performed per million

Recovery rate (%)

Case fatality rate (%)

The recovery rate was directly and significantly correlated with the mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene, and PM 2.5 pollution. Case fatality rate had a significant inverse correlation with accessible sanitation services and direct correlations with mortality rate attributed to unsafe water, unsafe sanitation, and lack of hygiene. Figures 1 , ​,2, 2 , ​,3 3 to ​to4 4 show the scatter plots of environmental health indicators by cumulative incidence rate of cases, cumulative incidence rate of deaths, performed tests per million, recovery rate, and case fatality rate in all countries and countries with a population more than 10 million.

Discussion

The present study is an ecological study that examined the correlation between the environmental factors with the incidence of COVID-19, performed COVID-19 tests, lethality, and recovery rates of COVID-19 in 164 countries. The coronavirus is now a serious threat to the whole world (Nwobodo and Chukwu 2020). One of the ways by which the disease is transmitted is by contact of the contaminated hands with eyes and nose. As a result, regular hygiene, hand washing, and personal hygiene are highly recommended to control the spread of the virus (Anim and Ofori-Asenso 2020). This would be possible if sufficient water and the required sanitary resources were available (Nwobodo and Chukwu 2020).

Based on the results of the present study, there is a direct correlation between the level of access to health services and drinking water with the cumulative incidence rate, the cumulative death rate, and the number of tests performed per million of the population. Other studies have shown that the lack of adequate sanitation facilities in rural communities increases the prevalence of COVID-19 (Bourgonje et al. 2020; Stoler et al. 2020). There is a widespread inequality in access to some sanitary equipment as well as sanitary water, which has increased the vulnerability of some people to COVID-19 (Ekumah et al. 2020).

According to the WHO, 663 million people in Africa and Asia (Odih et al. 2020) including 71% of people in Nigeria do not have access to clean and safe water or even water disinfectants (Nwobodo and Chukwu 2020). United Nations (UN) estimates that about 3 billion people do not have the facilities to wash their hands (Lal et al. 2020). A study in sub-Saharan Africa found that 46% of the surveyed households did not have access to basic necessities of life, including sanitation (Ekumah et al. 2020). Some people have been infected with COVID-19 without contact with infected people, indicating other modes of transmission than respiratory route (Odih et al. 2020). The virus can attack the gastrointestinal tract directly through the angiotensin converting enzyme 2, and the virus has also been found in the fecal samples of newly discharged patients (Silva et al. 2020). In areas that do not have adequate sanitation facilities, there are feces around the sewage (Odih et al. 2020), and it enters the surface water and pollutes it, so it is essential to provide people with a sanitary toilet (Purnama and Susanna, 2020). Furthermore, between 60 and 90% of Africans do not have a proper sewage system and defecate outdoors, causing these pollutants to enter surface water (Odih et al. 2020), and about one billion people in the world drink from public tap water and wells or use surface water sources (Stoler et al. 2020).

Betany and colleagues estimated in their study that more than one billion people worldwide go outside to get the water they need and that 9% of the world's population defecates outdoors and 8% (627 million) use a facility shared with at least another one (Caruso & Freeman, 2020). However, Renata et al. stated that the incidence and morbidity of COVID-19 are significantly related to the overall index of safe water supply services, as well as the rate of mortality with wastewater treatment index (Silva et al. 2020). These hypotheses are based on the suspicion of the oral fecal transmission of COVID-19 and indicate that the lack of sanitary water and adequate sanitation facilities increase the rate of mortality and the incidence of COVID-19 cases; we must keep in mind that there are many other factors that contribute to this method of transmission. Transmissions are effective, in the context of rainfall, which causes sewage to overflow and transmit more viruses (Odih et al. 2020). The number of viruses required for oral transmission is also very high. In addition, in areas where people use rationalized water, they have to stand in crowded queues without abiding by social distancing to receive their water ration, and, since they do not wash their hands to save water, the incidence of COVID-19 is higher (Odih et al. 2020). We must keep in mind that the main route of transmission of COVID-19 is by respiration of infective droplets and that the gastrointestinal transmission is a minor route; however, the reason for the difference between the results of our study and many other studies can be due to the fact that our study is an ecological study and only shows correlations and cannot give us an accurate causal relationship. On the other hand, in our study, information about all countries with appropriate and inadequate health levels was piled up and then examined, which could be a reason for erroneous study results.

Additionally, based on the results of our study, there was an inverse and moderate correlation between the amount of PM 2.5 in the air with the cumulative incidence rate of cases and the cumulative incidence rate of deaths. Also, there was a weak inverse correlation between performed tests per million and PM 2.5 in the air. PM 2.5 is one of the most important environmental health hazards that cause the mortality of many people around the world every year (Contini & Costabile, 2020). An ecological study in Colombia found a positive correlation between PM 2.5 and the mortality due to COVID-19 (Laura A Rodriguez-Villamizar et al. 2021a, b). Numerous studies have measured the effects of air quality on the incidence and mortality rate of COVID-19. The results of the studies in Canada, Spain, and Turkey showed that there is a strong positive correlation between PM 2.5 concentration and the number of new daily cases of COVID-19 (Sarwar et al. 2021; Shahzad et al. 2021; K. Shahzad et al. 2020a, b). A study in the UK also showed that with each microgram increase in PM 2.5, the mortality of COVID-19 increased by 12%, and the risk of infection also increased (Travaglio et al. 2021).

Angelis et al. also showed that with each 10-μg increase in PM 2.5, the incidence and mortality due to COVID-19 increased by 8% and 23%, respectively (Elena De Angelis et al. 2021a, b). Another study in China showed that for each 10-μg increase in PM 2.5, the lethality of COVID-19 increased by 24% (Yao et al. 2020). Other studies have shown this direct and positive relationship (Conticini et al. 2020; Yao et al. 2020), while Laura et al. reported that there was no significant association between long-term exposure to PM 2.5 and the mortality due to COVID-19 [37]. Other studies in China and New Jersey, USA have shown a positive and direct correlation between incidence rate of COVID-19 and the concentration of PM 2.5 (Doğan et al. 2020; Fareed et al. 2020). The results of a study in China showed a positive correlation in some states, while in other states a negative correlation was seen (F. Shahzad et al. 2020a, b). The reason for the difference between the results of our study and other studies can be due to various factors, for example, the differences in population density of different regions, the different climatic conditions, and even the existence of different industries and different pollutants in each region that can affect the results. It should be noted that quarantining people and restricting their activities can help reduce environmental pollutants and thus reduce the incidence of COVID-19. (K. Shahzad et al. 2020a, b).

Regarding the reason for the direct relationship between PM 2.5 and the incidence and mortality of COVID-19, various speculations have been expressed, including that these particles cause inflammation in the lungs and other organs of the body through inflammatory and oxidation mechanisms (Contini & Costabile, 2020; Elena De Angelis et al. 2021a, b).

In this way, by increasing the inflammation in the pulmonary cells, they intensify the symptoms of the disease (Comunian et al. 2020). On the other hand, these particles have adverse effects on other (Contini & Costabile, 2020) body systems, including the cardiovascular system, blood, and respiratory system in a way that they weaken the immune system and thus predispose people to COVID-19 (Contini & Costabile, 2020). Another hypothesis has been stated that the angiotensin-converting enzyme 2 facilitates the entry of these particles into the lungs, resulting in inflammation and exacerbation of COVID-19 (Comunian et al. 2020; Daminov et al. 2021; Elena De Angelis et al. 2021a, b; Pezeshgi et al. 2021). Moreover, although this hypothesis is yet to be proven, some researchers have suggested that SARS-CoV-2 may attach to PM 2.5 particles in the air, enter the lungs, and cause the disease (Laura A Rodriguez-Villamizar et al. 2021a, b). Despite all the hypotheses that there should be a direct correlation between PM 2.5 and COVID-19 incidence, an inverse correlation has been found in our study, which may be due to the ecological nature of our research. We should also note that we have studied different countries of the world together in which the age and sex composition, economic and social status and development, and executive control policies are different, all of which can affect the type of correlation seen. On the other hand, it is hypothesized that PM 2.5 only indirectly changes the course and severity of the disease from mild to severe and is effective on the prognosis of the disease and thus increases the mortality rate (Yao et al. 2020). Also, the duration of exposure to these particles, the relative weight of these particles, and other underlying diseases in people can be factors influencing the incidence of this disease.

Limitations

One of the strengths of the present study is that for the first time, the relationship between environmental health factors and COVID-19 indices, including the cumulative incidence rate of cases, the cumulative incidence rate of deaths, cumulative tests performed per million, recovery rate, and the case fatality rate were examined in 164 countries, but the limitation of this study is ecologic fallacy due to its ecological nature.

Conclusion

The findings of the present study showed an inverse correlation between the mortality rate due to unhealthy water consumption, poor health status, and a positive correlation between access to drinking water and health services with the cumulative incidence and mortality rate of COVID-19. The differences between our findings and many other studies could be due to the ecological nature of the study. Nevertheless, our findings will help health policymakers to develop timely strategies to reduce the mortality and the incidence rates of COVID-19. However, it is essential that health policymakers develop strategies to identify rural and high-risk areas that do not have access to these services and provide safe drinking water. Also, facilitating the access to health services in those areas is vital. Furthermore, the necessary training on the importance of hand washing, safe drinking water, and proper and hygienic disposal to residents of these areas should be done with more emphasis.

Abbreviations

SARS

Severe acute respiratory syndrome

WHO

World Health Organization

AQG

Air Quality Guideline

UN

United Nations

Author contribution

All authors contributed to the study conception and design. AMA and MGG participated in the design of the study. KJ, LM, and AH performed data collection. FR and AJ wrote the manuscript. SSD and ML revised the manuscript. FR and MGG helped with statistical analysis and prepared the illustrations. AHH edited the manuscript. All authors read and approved the final manuscript.

Funding

This study was approved and financially supported by Shiraz University of Medical Sciences (Grant number: 99-01-106-19536). The authors would like to thank the vice chancellor for research and technology for financial support.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the research ethics committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.174).

Consent for publication

All the authors agreed this article be published.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

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Articles from Environmental Science and Pollution Research International are provided here courtesy of Nature Publishing Group

# data[].attributes.attachment_link.title
Correlation between environmental factors and COVID-19 indices: a global level ecological study

# data[].attributes.attachment_link.description
This global level ecological study aimed to investigate the correlation between environmental factors and the COVID-19 indices. This survey is an ecological study, so all studied variables are aggregate variables. To collect the variables in the study, ...

# data[].attributes.body
: “De casos y testigos”. Sirve para investigar las causas de enfermedad, en especial de enfermedades infrecuentes.  Se comparan dos grupos de personas, uno que tiene la enfermedad (casos) y un grupo de controles o testigos que no tienen la enfermedad que se quiere estudiar. Los datos usados son anteriores o posteriores en el tiempo.
Laura Quintero

# data[].attributes.body
“De seguimiento o incidencia”. Un grupo de personas ( una cohorte) inicialmente sanas se clasifican en subgrupos según la exposición a una causa potencial de enfermedad o de algún otro efecto. Se especifican y miden la variable de interés  y se sigue la evolución de la totalidad de la cohorte para ver como la aparición posterior de nuevos casos d enfermedad difiere entre los grupos con y sin exposición. Son longitudinales.

Laura Quintero

# data[].attributes.body
“De correlación”. Las unidades de análisis son poblaciones o grupos de personas en vez de individuos. Se pueden comparar poblaciones de distintas regiones en un mismo periodo o puede inclurise en una serie temporal observaciones sucesivas de la misma población en distintos periodos.  Son útiles para generar hipótesis y patrones a nivel poblacional, pero pueden no ser válidas para cada individuo dentro de esa población.
Laura Quintero

# data[].attributes.body
“De prevalencia”. Miden la prevalencia de una enfermedad. Son útiles para evaluar las necesidades de atención de salud de las poblaciones. Estudia la frecuencia de enfermedad o de factores de riesgo que contribuyen a las enfermedades crónica en distintos grupos sociales, en mujeres y varones y distintas edades y grupo étnicos.
Laura Quintero

# data[].attributes.body
-Este tipo de estudio observacional se limita a describir la  frecuencia de una enfermedad de una población
-Es el primer paso de una investigación epidemiológica
-Sugiere ideas para estudios epidemiológicos mas completos

Javier Andrés Ramírez

# data[].attributes.body
REPORTE DE CASOS: Ejemplo Ver link
El ejemplo cumple con las siguientes Características:
Descripción cuidadosa de un hasta 10 casos clínicos
-Son los estudios mas comúnmente encontrados en las revistas científicas.
-No existe un grupo de comparación

Javier Andrés Ramirez

# data[].attributes.headline
                               CLASIFICACIÓN

# data[].attributes.subject
                               CLASIFICACIÓN

# data[].attributes.body
Según el control de asignación de factores del estudio

# data[].attributes.body
Se mide determinada variable, en un mismo grupo de estudio, antes y después de realizar X intervención.
Puede tener grupo control, en el que no se realiza intervención pero también se realizan las 2 mediciones.

# data[].attributes.body
Se escoge un grupo de pacientes para realizar X intervención y se comparan los resultados con un grupo de pacientes que anteriormente recibió la misma intervención o una similar.

# data[].attributes.body
Los participantes del estudio son personas sanas que están expuestas a un riesgo. Se recogen los datos en "el campo", es decir, en la población general en su lugar de residencia o trabajo. Un grupo es expuesto a factores preventivos y  a una comunidad se le expone a factores preventivos la otra no se expondrá a este factor y será el control.

# data[].attributes.body
Son estudios experimentales en donde el investigador asigna una intervención: fármacos, productos cosméticos, agroquímicos, de aseo personal, procedimientos quirúrgicos etc; con el objetivo de ser finalmente usados en humanos.

# data[].attributes.body
- El investigador tiene control sobre la exposición.
- La asignación de los participantes a los diferentes grupos del estudio se realiza de manera aleatoria
- Son longitudinales ya que se realiza seguimiento en el tiempo para evaluar el efecto de determinada intervención
- Son prospectivos

- La unidad de análisis son los individuos.
- Son la mejor herramienta para evaluar relaciones de causalidad.

# data[].attributes.body
No se usa la  aleatorización para definir los grupos de estudio. Pueden o no tener grupo control

# data[].attributes.body
Se usa la aleatorización para definir los grupos de estudio

# data[].attributes.headline
                                    ANALITICOS: 

# data[].attributes.subject
                                    ANALITICOS: 

# data[].attributes.body
Analiza las relaciones entre el estado de salud y otras variables. En estos estudios se observan y recopilan datos en su entorno natural y sin intervención directa

Laura Quintero

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El investigador controla la exposición, es decir, asigna una variable nueva a los participantes

# data[].attributes.body
Dejan que la naturaleza siga su curso, el investigador mide pero no interviene
Laura Quintero

# data[].attributes.headline
         DISEÑO DE ESTUDIOS EPIDEMIOLOGICOS 

# data[].attributes.subject
         DISEÑO DE ESTUDIOS EPIDEMIOLOGICOS 