Biological attributes of age and gender variations in Indian COVID-19 cases: A retrospective data analysis

Kushwaha, Savitesh; Khanna, Poonam; Rajagopal, Vineeth; Kiran, Tanvi

doi:10.1016/j.cegh.2021.100788

Cited by 39 publications

(44 citation statements)

References 28 publications

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“…Overall COVID-19 numbers were higher in males, however the numbers were similar for those who were between 31 and 60 years. This was different to other findings which indicated that females below age 35 years had higher chances of getting infected than males in the same age category ( 20 ). Over the review period, the prevalence rates for positive COVID-19 tests at admission for males and females were 7.2%(639/8837) and 3.6%(322/8837) respectively.…”

Section: Discussioncontrasting

confidence: 99%

Preadmission testing for COVID-19 as a screening strategy: a retrospective chart review from a tertiary hospital in Kenya

Odada

Ndai

Kimeu

et al. 2022

Infection Prevention in Practice

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Section: Discussioncontrasting

confidence: 99%

Preadmission testing for COVID-19 as a screening strategy: a retrospective chart review from a tertiary hospital in Kenya

Odada

Ndai

Kimeu

et al. 2022

Infection Prevention in Practice

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“…Among the COVID-19-positive patients, male was predominant. This was consistent with other nearby countries such as India, where researcher reported that male COVID-19 cases (65.39%) were more than females (34.61%) 23. This might be due to the male-dominant societies’ unique health-seeking behaviour like Bangladesh, where women do not seek healthcare unless severe 24.…”

Section: Discussionsupporting

confidence: 87%

“…This was consistent with other nearby countries such as India, where researcher reported that male COVID-19 cases (65.39%) were more than females (34.61%). 23 This might be due to the maledominant societies' unique health-seeking behaviour like Bangladesh, where women do not seek healthcare unless severe. 24 This finding was similar (68% male) during March-April, the early phase of novel coronavirus detection in Bangladesh.…”

Section: Openmentioning

confidence: 99%

Establishing a sentinel surveillance system for the novel COVID-19 in a resource-limited country: methods, system attributes and early findings

et al. 2021

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ObjectivesTo establish a hospital-based platform to explore the epidemiological and clinical characteristics of patients screened for COVID-19.DesignHospital-based surveillance.SettingThis study was conducted in four selected hospitals in Bangladesh during 10 June–31 August 2020.ParticipantsIn total, 2345 patients of all age (68% male) attending the outpatient and inpatient departments of surveillance hospitals with any one or more of the following symptoms within last 7 days: fever, cough, sore throat and respiratory distress.Outcome measuresThe outcome measures were COVID-19 positivity and mortality rate among enrolled patients. Pearson’s χ2 test was used to compare the categorical variables (sign/symptoms, comorbidities, admission status and COVID-19 test results). Regression analysis was performed to determine the association between potential risk factors and death.ResultsCOVID-19 was detected among 922 (39%) enrolled patients. It was more common in outpatients with a peak positivity in second week of July (112, 54%). The median age of the confirmed COVID-19 cases was 38 years (IQR: 30–50), 654 (71%) were male and 83 (9%) were healthcare workers. Cough (615, 67%) was the most common symptom, followed by fever (493, 53%). Patients with diabetes were more likely to get COVID-19 than patients without diabetes (48% vs 38%; OR: 1.5; 95% CI: 1.2 to 1.9). The death rate among COVID-19 positive was 2.3%, n=21. Death was associated with age ≥60 years (adjusted OR (AOR): 13.9; 95% CI: 5.5 to 34), shortness of breath (AOR: 9.7; 95% CI: 3.0 to 30), comorbidity (AOR: 4.8; 95% CI: 1.1 to 21.7), smoking history (AOR: 2.2, 95% CI: 0.7 to 7.1), attending the hospital in <2 days of symptom onset due to critical illness (AOR: 4.7; 95% CI: 1.2 to 17.8) and hospital admission (AOR: 3.4; 95% CI: 1.2 to 9.8).ConclusionsCOVID-19 positivity was observed in more than one-third of patients with suspected COVID-19 attending selected hospitals. While managing such patients, the risk factors identified for higher death rates should be considered.

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“…The major reasons for the poor performances of these states, specifically in terms of health outcomes and high mortality rates, include poverty, overpopulation, low literacy levels, gender discrimination, and poor health infrastructure [12]. Though biological differences exist between the genders however [13]; the health outcome differences and inequalities are more evident in the case of rural Indian women. Women face diverse socio-economic and cultural issues attributed in terms of gender discrimination, such as female infanticide, child marriage, dowry, domestic violence, lack of education, and unavailability of proper sanitation and healthcare facilities [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Though biological differences exist between the genders however [13]; the health outcome differences and inequalities are more evident in the case of rural Indian women. Women face diverse socio-economic and cultural issues attributed in terms of gender discrimination, such as female infanticide, child marriage, dowry, domestic violence, lack of education, and unavailability of proper sanitation and healthcare facilities [14–16].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive assessment of Age-Specific Mortality Rate and its incremental changes using a composite measure: A sub-national analysis of rural Indian women

Sharma

Kiran

Junaid

et al. 2022

Preprint

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BackgroundDiverse socio-economic and cultural issues contribute to adverse health outcomes and increased mortality rates among rural Indian women across different age categories. The present study aims to comprehensively assess age-specific mortality rates and their temporal trends using a composite measure at the sub-national level for rural Indian females to capture cross-state differences.MethodsA total of 19 states were included in the study to construct a composite age-specific mortality index for 2011 (base year) and 2018 (reference year) and examine the incremental changes in the index values across these years at the sub-national level in India. Sub-index values were calculated for each component age group and were subsequently used to compute the composite ASMR index using the geometric mean method. Based on the incremental changes, the performance of states was categorized into four different typologies.ResultsImprovement in mortality index scores in the 0-4 years age group was documented for all states. The mortality rates for the 60+ age group were recorded to be high for all states. Kerela emerged as the overall top performer in terms of mortality index scores, while Bihar and Jharkhand were at the bottom of the mortality index table. The overall mortality composite score has shown minor improvement from base year to reference year at all India level.ConclusionsAn overall reduction in the mortality rates of rural Indian women has been observed over the years in India. The success of public health interventions to reduce the under-five mortality rate is evident as the female rural mortality rates have reduced sizably for all states. Nevertheless, there is still sizable scope for reducing mortality rates for other component age groups. Additionally, there is a need to divert attention toward the female geriatric (60+ years) population as the mortality rates are still high.

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Biological attributes of age and gender variations in Indian COVID-19 cases: A retrospective data analysis

Cited by 39 publications

References 28 publications

Preadmission testing for COVID-19 as a screening strategy: a retrospective chart review from a tertiary hospital in Kenya

Preadmission testing for COVID-19 as a screening strategy: a retrospective chart review from a tertiary hospital in Kenya

Establishing a sentinel surveillance system for the novel COVID-19 in a resource-limited country: methods, system attributes and early findings

Comprehensive assessment of Age-Specific Mortality Rate and its incremental changes using a composite measure: A sub-national analysis of rural Indian women

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