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Title and abstract

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The Incidence and Risk Characteristics of SARS-CoV-2 Infection Among Migrant and Thai Children Attending Care at a Hospital in Samut Sakhon, Thailand: A Retrospective Cohort Study

Introduction

Background/rationale

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Thailand responded quickly to the initial COVID-19 outbreak, which was caused by events such as boxing matches and nightclubs in Bangkok. Their response included comprehensive public health and social measures and was supported by a strong health infrastructure and community involvement, including numerous surveillance teams and health volunteers. As a result, the first wave of the outbreak was effectively contained with a relatively low number of cases and fatalities. However, the second wave of COVID-19 in Thailand was primarily due to neglect in the migrant labor sector, which resulted in a substantial increase in cases among undocumented migrants not covered by the quarantine system. This factor led to a significant outbreak among migrant workers in Samut Sakhon, a province neighboring Bangkok.[1][2] [3]

The migrant workers in Samut Sakhon province worked in manufacturing facilities, factories, and seafood markets. [1] These migrant workers were at a high risk of infection due to their poor living conditions.[4]In December 2020, a major outbreak occurred in the wholesale seafood market in Samut Sakhon, which led to a surge in infections. These infections among the laborers in this fish market—the nation's biggest supplier—caused the local spread to over 50% of the provinces. This unfortunate event worsened the situation, causing stigma and anxiety among migrant workers. Over 40,000 migrant workers were quarantined in their cramped dormitories, which resulted in a shortage of essential supplies and medication. [1] [5]

The migrant population in Thailand includes a significant number of children, estimated between 300,000 to 400,000 as of 2018. Many of these children are not registered, particularly in healthcare. [6] Although most children with COVID-19 develop mild illnesses, [7][8] the disease could be severe and highly contagious among migrant children due to their living conditions, financial hardship, and the poor literacy of their parents. Moreover, the illegal status of children led to their parents being hesitant to bring them to the hospital for fear of being reported. [9][10] [11]

Since Thai government permits undocumented migrant children to utilize public health services[12] , this presents an opportunity to address health equity and disparities during the COVID-19 outbreak. The impact of this ongoing pandemic on the health of migrant children remains largely unexplored.

This research was conducted at a hospital in Samut Sakhon province, a key location for treating SAR-CoV-2 infections, particularly in migrant populations. The objective of this research is to establish the relative risk and associated risk factors of SAR-CoV-2 infection between migrant and local Thai children.

Objectives

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To identify relative risk and associated risk factors of SARS-CoV-2 infection comparing between foreign migrant and local Thai children aged under 15 undertaking diagnostic test for SARS-CoV-2 at Samut Sakhon Hospital during December 3, 2020, to October 31, 2021.

Methods

Study design

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a retrospective cohort study.

Setting

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The research was carried out at a government hospital in Samut Sakhon Province, Thailand, which was central during the COVID-19 outbreak among migrant populations. The study spanned from December 3, 2020, to October 31, 2021. The onset of the first SARS-CoV-2 case in Samut Sakhon Province on December 17, 2020, with a retrospective 14-day incubation period, sets the study's start date to December 3, 2020. The study concluded on October 31, 2021, the day before Thailand commenced reopening its borders to international travelers on November 1, 2021, marking a pivotal shift in the nation's approach to pandemic control.

Participants

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This study included all children under 15 years of age, both migrant and Thai, who were deemed at risk of SARS-CoV-2 infection and underwent diagnostic testing at a hospital in Samut Sakhon during the study period. Participants were excluded if essential data, such as nationality or the result of the diagnostic testing ,were missing from the hospital records.

Variables

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Exposure Factors

· Sex: Male or female.

· Age: Measured in years.

· Nationality: Categories include Thai, Burmese, Cambodian, Laotian, Kuwaiti, Indian, and unspecified.

· Province of Residence: Categorized as Samut Sakhon, Bangkok, and others.

· Type of Health Coverage: For Thai nationals – Universal Coverage, Social Security Scheme (SSS), Civil Servant Medical Benefit Scheme (CSMBS), other insurance, and out-of-pocket. For non-Thai nationals – Interim public subsidy, other insurance, and out-of-pocket.

· Focused Medical Conditions*: Including cardiovascular diseases, cerebrovascular disease, diabetes mellitus, immunodeficiencies, asthma, and history of preterm birth.

Outcome Measure

· SARS-CoV-2 Infection: Laboratory results of SARS-CoV-2 infection categorized as detected or not detected.

· The diagnostic testing for SARS-CoV-2 employed included:

Molecular Tests: such as RT-PCR, GeneXpert, and Liat

Antigen Tests: the SARS-CoV-2 Antigen Test

* Focused Medical Conditions: specific medical conditions identified by the Department of Medical Sciences in the "Thai Guideline for COVID-19 Management and Control in Hospital" (updated on November 2, 2021) as risk factors for severe COVID-19. [13]

* Data extracted from electronic health records (EHRs), filled out in both Thai and English including abbreviations, occasionally do not specify particular diseases. For instance, entries might broadly categorize a condition as heart disease without further detail.

For research purposes, the following entries have been categorized and included as variables:

· Cardiovascular Diseases: This includes heart disease, cardiomegaly, congenital heart disease, coronary heart disease, coronary artery anomaly, valvular regurgitation, atrial septal defect, ventricular septal defect, atrioventricular septal defect, arrhythmia, pulmonary valve stenosis, aortic valve stenosis, cardiac tumor, and double outlet right ventricle.

Data sources/ measurement

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Data for this retrospective cohort study were sourced from the electronic health records (EHRs) of a hospital in Samut Sakhon, Thailand. These records were used to identify pediatric patients who met the inclusion criteria and to extract relevant demographic, clinical, and laboratory information.

Bias

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Data Collection

· When analyzing the medical condition data from the "underlying disease" section in records, which consists of typewritten entries that do not always specify diseases by their full names and sometimes note conditions such as shortness of breath, seizures, or elevated white blood cell count without additional specifics, a '.' (period) is assigned to those variables where the information is missing or unspecified.

· The study encountered challenges in accurately collecting data, particularly with variables such as weight, height, and vaccination history. This was especially evident during the initial phase of the pandemic when children were not eligible for vaccination, and vaccination records were not centrally organized. These challenges highlighted the crucial need for advancing health record systems, particularly in the context of a pandemic.

Study size

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In calculating the sample size for research on SARS-CoV-2 infections, due to the absence of previous studies on the infection rates among Thai and foreign children and given that infection reports do not provide a breakdown by the child population, instead offering combined figures for both foreigners and Thais, the study will rely on general data for both Thai and foreign individuals, not specifically targeting the child population. This calculation will utilize data from three sources:

1) the number of residents according to the population registry in Samut Sakhon province as of December 31, 2021[14]; 2) the number of foreign workers in Samut Sakhon authorized by the Office of Foreign Workers Administration in October 2021[15]; and 3) the number of Thai and foreign individuals diagnosed with SARS-CoV-2 in Samut Sakhon, according to the COVID-19 situation report from the Samut Sakhon Public Health Office (covering the period from December 17, 2020, to November 1, 2021). [16]

Two-sided confidence level = 95%

Power = 80%

Number of registered residents in Samut Sakhon province as of December 31, 2021, according to the Central Registration Office = 586,789 people

Number of foreign workers permitted to work in Samut Sakhon province in October 2021, according to the Office of Foreign Workers Administration = 213,642 people

Ratio of unexposed to exposed group (population ratio of Thai nationals to foreigners, when exposure is defined as nationality) = 2.75

Number of individuals found to be infected with SARS-CoV-2, including both Thai nationals and foreigners in Samut Sakhon province, according to COVID-19 situation reports from the Samut Sakhon Provincial Public Health Office (data from December 17, 2020, to November 1, 2021) = 72,913 and 37,479 individuals, respectively

Percent of unexposed with outcome (percentage of Thai nationals infected with SARS-CoV-2) = (72,913/586,789) x 100 = 12.4%

Percent of exposed with outcome (percentage of foreigners infected with SARS-CoV-2) = (37,479/213,642) x 100 = 17.5%

So sample size = 1,896 (Fleiss)

Quantitative variables

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Age: This variable was analyzed as both a continuous variable, providing an overview of age distribution, and in categorized form for detailed descriptive analysis.

The initial categorization for descriptive purposes included:

· Newborn (<28 days)

· Infant (>28 days & <1 year)

· Toddler (>1 year & < 2 years)

· Preschool (>2 years & <6 years)

· School-age (>6 years & <12 years)

· Adolescent (>12 years & <15 years)

For the univariable and multivariable analyses, the age variable was consolidated into broader categories based on developmental stages and statistical robustness. This approach mitigated model complexity and interpretability issues, with finer age divisions leading to sample size fragmentation. Grouping ages into:

· Less than 2 years (< 2 years)

· Preschool Age (>2 years & <6 years)

· School Age (>6 years & <12 years)

· Adolescent (>12 years & <15 years)

It was essential to maintain sufficient sample sizes within each group, ensuring the statistical power and model stability necessary for valid conclusions. These categories reflect distinct stages of childhood development and social exposure, aligning with the varying risks of SARS-CoV-2 transmission and immune response.

Statistical methods

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Descriptive Statistics

Categorical Variables: Frequencies and percentages were calculated for variables such as sex, nationality, province of residence, type of health insurance, and the result of diagnostic testing for SARS-CoV-2.

Continuous Variables: For variables like age, which were not normally distributed, medians and interquartile ranges (IQR) were used to summarize the data.

Fisher's exact test and chi-square test was used to compare the significant differences of categorical data about incidences of Thai and non-Thai patients with SARS-CoV-2 infection versus those without infection in various factors and also the outcomes. 

Univariable and Multivariable Poisson Regression

Risk Ratio Estimation: Both crude and adjusted risk ratios (RR) were estimated using Poisson regression with robust standard errors. This approach was used to identify factors associated with SARS-CoV-2 infection, taking into account the potential confounding effects in the multivariable model.

Results

Participants

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Data Collection:

This retrospective cohort study used electronic health records (EHRs) from a hospital in Samut Sakhon, Thailand.

The hospital's IT staff extracted data for children aged 0-15 years (although the study focused on children under 15 years) who underwent COVID-19 testing at the hospital. This included various testing methods such as RT-PCR, GeneXpert, Liat, and antigen tests. The data was collected in two Excel files: one for outpatient (10,813 records) and the other for inpatient records (2,975 records).

Inclusion and Exclusion Criteria:

The data initially included children aged 15 years and above. These records were removed from both the outpatient and inpatient files. In the outpatient dataset, from 10,813 records, 833 records of children aged 15 years and above were removed, leaving 9,980 records. Additionally, 2 records were excluded due to pending COVID-19 test results.

In the inpatient dataset, from 2,975 records, 153 records of children aged 15 years and above were removed, leaving 2,822 records .

Data Consolidation for Individual Patient Distinction:

The outpatient and inpatient files were merged, totaling 12,802 records. (9,980 outpatient records + 2,822 inpatient records).

In cases of multiple test results, the 'detected' results were prioritized, followed by 'inconclusive' and then 'non-detected'.

For example, If both 'detected' and 'inconclusive' results were present, 'detected' was prioritized.

if only 'inconclusive' and 'non-detected' results were available, 'inconclusive' was prioritized.

If all results were 'non-detected', the earliest visit was considered.From the 12,802 records, duplicates and multiple visit records were removed, prioritizing the most significant test results as per the criteria above, reducing the number to 9,122 records.

Final Dataset Composition:

For the analysis of infection risk, only records with outcomes marked as 'detected' and 'non-detected' were included. 25 records with 'inconclusive' results were removed. The final dataset used for analysis consisted of 9,097 records. This dataset represented children under the age of 15 who underwent diagnostic testing for SARS-CoV-2 with either a 'detected' or 'non-detected' outcome.

- Extracted data for children aged 0-15 years (focus <15 years) tested for COVID-19.

- Testing methods: RT-PCR, GeneXpert, Liat, antigen tests.

- Data in two Excel files: Outpatient (n=10,813), Inpatient (n=2,975).

Outpatient (n=10,813)

Inpatient (n=2,975)

removed 833 aged 15 and above, 2 pending results (n=9,980)

(removed 833 aged 15 and above, 2 pending results).

removed 153 older children

(n=2,822)

Merged outpatient and inpatient files, (n=12,802)

Prioritized 'detected' test results, then 'inconclusive', then 'non-detected'.

Eliminated duplicates and multiple visit records (n=9122 )

Excluded 25 records of inconclusive results

(n=9,097)

Descriptive data

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(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders

(b) Indicate number of participants with missing data for each variable of interest

(c) Cohort study—Summarise follow-up time (eg, average and total amount)=

Outcome data

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Cohort study—Report numbers of outcome events or summary measures over time

Case-control study—Report numbers in each exposure category, or summary measures of exposure

Cross-sectional study—Report numbers of outcome events or summary measures

Main results

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( a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were included

( b) Report category boundaries when continuous variables were categorized

( c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period

Other analyses

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Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses

Discussion

Key results

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Limitations

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Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias

Interpretation

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Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence

Generalisability

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Discuss the generalisability (external validity) of the study results

Other information

Funding

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Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based