Correlation between Seasons and the Prevalence 3of Preeclampsia at Tertiary Hospital, Indonesia
Abstract and Figures Background: Preeclampsia is the second most common cause of maternal mortality following postpartum haemorrhage. In East Java alone the maternal mortality rate due to preeclampsia was 31%. Some studies explain that the incidence of preeclampsia can be caused by seasonal variati...
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| Main Authors: | , , , , , , |
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| Format: | Article PeerReviewed |
| Language: | English Indonesian English |
| Published: |
Institute of Medico-legal Publications
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| Subjects: | |
| Online Access: | https://repository.unair.ac.id/127251/1/6%20correlation%20betwen%20season.pdf https://repository.unair.ac.id/127251/2/6.pdf https://repository.unair.ac.id/127251/3/6.%20correlation%20betwen%20season.pdf https://repository.unair.ac.id/127251/ https://www.researchgate.net/publication/347517384_Correlation_between_Seasons_and_the_Prevalence_of_Preeclampsia_at_Tertiary_Hospital_Indonesia http://dx.doi.org/10.37506/ijfmt.v14i4.12133 |
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| Institution: | Universitas Airlangga |
| Language: | English Indonesian English |
| Summary: | Abstract and Figures
Background: Preeclampsia is the second most common cause of maternal mortality following postpartum haemorrhage. In East Java alone the maternal mortality rate due to preeclampsia was 31%. Some studies explain that the incidence of preeclampsia can be caused by seasonal variations, whereas other studies say that seasonal factors can be a protective factor for preeclampsia. Objective: To find out how seasons relate to the prevalence of preeclampsia. Method: A descriptive-analytic study using medical record data. The sample meets the inclusion criteria, namely pregnant women with preeclampsia, severe preeclampsia, chronic hypertension with superimposed severe preeclampsia, or eclampsia. The exclusion criteria were for pregnant women with chronic hypertension or gestational hypertension. The sampling technique used was total sampling and cross-sectional design with an observation method using secondary data from medical records. Data was processed using IBM SPSS Statistics and presented narratively. Results: Respondents who experienced preeclampsia in the dry season were 558 people or 19.3% and those who did not experience preeclampsia were 844 people or 29.2% The results of data analysis with statistical tests using Chi-Square obtained a significance value or p-value of 0.091 (p> 0.05). Conclusion: No correlation between seasonality and the prevalence of preeclampsia. |
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