Keywords
COVID-19, Social isolation, Health-related quality of life
This article is included in the Emerging Diseases and Outbreaks gateway.
This article is included in the Coronavirus (COVID-19) collection.
COVID-19, Social isolation, Health-related quality of life
A detailed review and update has been carried out based on the recommendations of the reviewers. The tables and figures have been modified to show the statistics used. The grammatical revision has proceeded through several filters, as well as the bibliographic references of the manuscript.
To read any peer review reports and author responses for this article, follow the "read" links in the Open Peer Review table.
Coronavirus disease 2019 (COVID-19) is an infectious pathology caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 12 December 2019, the first case was detected in Wuhan, China,1 and the disease spread so rapidly globally that on 11 March 2020, the World Health Organization (WHO) declared it a pandemic.2 This pandemic has changed the lives of millions of people and has 6.3 million deaths around the world.3
Governments applied various public policies to reduce its spread, one of them being social isolation. This measure was applied in several countries around the world, Peru being one of them and probably with the longest duration.4
Trials to test the efficacy of the vaccine were reported at the end of 2020, and vaccination officially began around that time in several countries.5 Vaccination in Peru began on 9 February 2021 and continues in various age groups around the country.6 Currently, 72% of the population is of Metropolitan Lima immunized with two doses of the vaccine 73.92% have received the third dose and 25% have received the fourth dose.7
The conditions of social confinement have had an impact on people's health, including a decrease in physical activity levels and an increase in sedentary behavior.8 According to a study in Chile, the increase in physical inactivity could have important metabolic implications for the health of the population.9
On the other hand, the emergence of pandemic-related psychological reactions; cases in people without a pre-existing mental health problem have increased and pre-existing cases were accentuated by confinement.10 A study in China reported moderate to severe levels of stress in the population. The most emotionally affected age group according to this study was young people aged 18-30 years.11
The human response to a pandemic is not uniform and may vary between countries and regions. Likewise, given that lethality differs by gender and age, it is possible that coping may also differ. Subjects with co-morbidities, males and older people have had higher case fatality rates.12,13 Peru is one of the countries with the highest case fatality and currently has COVID-19 mortality of 202, 424 people.14
In Lima, a study by the Consortium of Universities revealed that the mental health of students has deteriorated due to the situation of confinement. Academic factors had a greater impact on mental health, which may lead to a decrease in their performance. Higher levels of anxiety, depression, and stress symptoms were also found. Other factors such as fear of contagion, hours of sleep, and demographic conditions were also assessed as influencing mental health; however, they did not present statistical results that allow further analysis.15
It is therefore important to have instruments that allow us to assess health-related quality of life (HRQoL) in pandemic circumstances. For this purpose, there are several HRQoL questionnaires,16–18 which have been successfully applied in different parts of the world in situations other than the COVID-19 pandemic, and which can be applied to the general population or certain pathologies. They can be used to monitor both diseases19,20 and interventions.21 It would therefore be of utmost importance to use an instrument that assesses HRQOL and to be able to compare it in subjects who have suffered from COVID-19 with those who have not.
The best known and most validated HRQoL questionnaire for the Spanish-speaking population is the “short form-36” (SF-36). The SF-36, which contains 36 questions, assesses the personal perception of each individual's health in three components: physical, mental and general health component. Its application has been optimal in different age groups.22 This instrument, developed in the United States, has subsequently been translated and applied in Spain.23
In Peru, a variant of this HRQoL questionnaire called SF-20, containing 20 questions, has been validated for both sea level and high-altitude populations.21,24 Other groups have further reduced the questionnaire and developed the SF-12.25
This was a cross-sectional analytical study. Participants were recruited by a convenience sample of males and females over 18 years of age and residents of Metropolitan Lima, capital of Peru for more than 1 year before the time of participation in the survey. Access to an electronic device was required. Participants completed the survey after the established date and those who did not agree to participate in the informed consent were excluded.
This is a cross-sectional analytical study conducted during the months of October and December 2021. For data collection we worked with Google Forms, which contained the survey and informed consent form. The sampling was carried out by convenience, and additionally with the dissemination in social networks, allowing a greater number of surveys to be carried out.
The initial sample size was calculated using a random sampling technique for a confidence level of 95% and a statistical power of 80% and was n=600 participants. Finally, a population of 638 participants was studied, consisting of men and women aged 18 years or older who had been residents of Metropolitan Lima for more than 1 year before participation. Only 638 participants were evaluated since participants who completed the form after the established date and those who did not agree to participate in the informed consent were excluded.
The city of metropolitan Lima is the capital of Peru and is located on the central coast of the country (150 meters above sea level), next to the Pacific Ocean. Lima is made up of 43 districts, which cover 2,672.28 km2 of land and can be divided into five zones: North Lima, Central Lima, East Lima, West Lima and South Lima. Lima has an estimated population of 10,004,141 inhabitants,26 of which 64.9% are between 15 and 59 years old.
A virtual questionnaire was administered to each participant, which covered the following aspects:
The socio-demographic characteristics obtained through the survey were age, sex, level of education, place of birth, and current district of residence. Age (years) was analyzed as a continuous variable. The variables were categorized as follows:
Anthropometric measurements obtained in the survey were self-reported, collected and reported by the participants. Measurements included height (meters), weight (kg), abdominal circumference (cm), and neck circumference (cm) of each participant, the latter two being optional. Tutorials and instructions for performing each measurement were included in the survey to make the reported measurements as accurate as possible. Body mass index (BMI) was calculated using the formula: weight (kg)/height (m2) and was classified into the following categories according to the World Health Organization (WHO): underweight BMI < 24.9 kg/m2), overweight (25 ≤ BMI < 29.9 kg/m2) and obese (BMI ≥ 30 kg/m2.27 Waist to height ratio (WHtR) and neck to height ratio (NHtR) were also calculated.
The data obtained from the questionnaire were: being sick in the last 12 months other than COVID-19, hospitalizations in the last 12 months, surgeries, or medical treatments in the last 12 months before completing the survey. In addition, respondents were asked whether they currently have a chronic illness, whether they are receiving treatment for a chronic illness, and whether they are seeing a psychologist.
Data were obtained on the presence of illness or symptoms related to COVID-19 and the time from the occurrence until the date the survey was completed; Initial symptoms of COVID-1 infection are fever, headache, cough, fatigue, absence of smell and taste; if the illness worsens, dyspnea is manifested.28
It was also recorded whether the participant underwent compulsory social isolation as a preventive measure or quarantine as a measure to avoid new infections after contracting COVID-19.
In addition, the type of test used to diagnose COVID-19 (antigen test, PCR test and serologic test) was recorded. Information on COVID-19 hospitalization (NON – ICU hospitalization, ICU hospitalization and no hospitalization), vaccination, perceived weight gain, stress, decreased physical activity due to social isolation, and participation in clinical trials of COVID-19 vaccines was included.
Compulsory social isolation was assessed as a dichotomous variable and as a continuous variable by asking how long the respondent engaged in compulsory social isolation.
The SF-20 questionnaire contains 20 questions related to 7 dimensions: general health, physical function, physical role, emotional role, bodily pain, vitality, and mental health. This survey was translated into Spanish and validated for the Spanish-speaking population,29 determining a Cronbach's alpha greater than 0.7 in all dimensions (0.71-0.94), which indicates a good correlation. In Peru, it was validated for application in populations at sea level and high altitudes.21,24
The seven health dimensions of the SF-20 questionnaire are as follows:
1. General health: Perceived assessment of the individual’s health status, including current status, future status, and resistance to disease.
2. Physical function: Level at which poor health impedes daily physical activities of moderate intensity (walking, climbing stairs, personal hygiene and household activities) or activities of vigorous intensity (running, heavy lifting or strenuous activities).
3. Physical role: degree to which poor health limits the performance of work and daily activities causing lower than expected performance.
4. Emotional role: degree to which emotional problems limit the development of people’s work and domestic activities causing a decrease in time and care in their performance.
5. Body pain: Measurement of the pain presented by the person and how it affects their performance in their work and daily activities.
6. Vitality: Measures the energy level of people in the face of fatigue and discouragement.
7. Mental health: Measures the frequency of nervousness, sadness, tiredness, happiness and tranquility in people and how it influences the state of health.30
The 20 questions of the questionnaire can be divided into 3 main components. The first is the physical component, which includes 8 questions corresponding to the dimensions of physical function, physical role and bodily pain. The second is the mental component, which includes 7 questions from the dimensions: emotional role, vitality and mental health. Finally, the third component is general health, which includes 5 remaining questions.31
Each question in the questionnaire has between 2 to 4 response options and the score ranges from 0 to 100. For example, a question with 2 answers will score 0 or 100; with 3 answers, 0 - 50 - 100; with 4 answers, 0 - 33.3 - 66.6 - 100. The scores obtained from the questions are summed to give a total score from 0 to 2000, where scores close to 2000 indicate a better health-related quality of life and worse scores are close to 0.30
The analysis was performed with the statistical package STATA version 16 (STATACorp, Texas USA, RRID:SCR_012763).
For descriptive analysis, continuous variables such as HRQoL score and its physical and mental components, anthropometric measures, and age were expressed as averages and standard deviations. HRQoL variables were evaluated using the Mann-Whitney U test. Spearman’s correlation analyzed the relationship between continuous variables. Likewise, socio-demographic characteristics were evaluated with the Chi-square test or Fisher's exact test, also by sex and COVID-19 status. A Generalized Linear Model (GLM) of the Gaussian family was used to determine the relationship between HRQoL with BMI, COVID-19 status and obligatory social isolation. Predicted values and anscombe residual values of the final models were evaluated. A value of p<0.05 was considered statistically significant.
The methodology of using virtual surveys limits the information to a group with the availability of electronic equipment; therefore, we tried to disseminate the survey to population groups of different ages and within the same socio-economic level. The survey and data entry was performed by the same person, considering memory bias and accuracy of the measurement.
Based on the inclusion criteria, data from 638 study participants (256 men, and 382 women) were analyzed.50
The characteristics of the population are shown in Table 1. In total, 40% of participants were men and the remaining 60% were women. Of the study population 56.9% (95% CI 52-59.9%) resided in Lima downtown, 20.60% (17.4-23.8%) in eastern Lima, 14.1% (11.3-16.8%) in northern Lima, and 3.3% (1.9-4.7%) in western Lima.
Variables | Participants with COVID-19 (n=128) | Participants without COVID-19 (n=510) | p* |
---|---|---|---|
mean±SD (95% CI) | mean±SD (95% CI) | ||
Male/Female | 50/78 (64.1%) | 206/304 (67.7%) | 0.784 |
Age (years) | 37.9±14.04 (35.4–40.4) | 37.3±16.92 (35.8–38.8) | 0.710 |
Height (m) | 1.65±0.09 (1.63–1.67) | 1.64±0.09 (1.63–1.65) | 0.261 |
Weight (kg) | 69.9±14.6 (67.3–72.3) | 69.1±14.1 (67.9–70.3) | 0.569 |
BMI (kg/m2) | 25.8±4.04 (25.1–26.5) | 25.2±3.9 (24.9–25.5) | 0.122 |
WC | 86.4±13.5 n=87 (83.5–89.3) | 88.2±14.9 n=331 (86.6–89.8) | 0.213 |
NC | 36.07±6.6 n=71 (34.5–37.6) | 35.8±5.7 n= 285 (35.1–36.5) | 0.643 |
WHtR | 53.9±8.6 n= 86 (52.05–55.7) | 52.1±8.6 n= 331 (51.2–53) | 0.034 |
NHtR | 22.04±4 n= 71 (21.1–23) | 21.5±3.6 n= 285 (21.1–21.9) | 0.138 |
Total HRQoL Score, SF-20 | 1471.8±311.4 (1417–1526) | 1473.5±261.9 (1450–1496) | 0.460a |
SF-20 physical component | 683.4±151.4 (656.9–709.9) | 693.0±115.9 (682.9–703.1) | 0.657a |
SF-20 Mental Component | 459.1±118.4 (438.4–479.8) | 447.1±124.4 (436.3–457.9) | 0.460a |
In total, 85.9% (95% CI 83.2-88.6%) of respondents performed compulsory social isolation. Quarantine, for cases where COVID-19 was detected, was performed by 96.9% (95% CI 92.1-99.1%) of those diagnosed with the disease. In the case of vaccines, 77.1% (95% CI 73.9-80.4%) of the study population had completed both doses of the vaccine.
Overall, 19.5% of male respondents studied had COVID-19 while 20.4% of women had COVID-19 (p>0.05). No significant differences were found in anthropometric measurements between the groups with or without COVID-19. No significant difference was found concerning the final HRQoL score or any of its components in people with COVID-19 compared to people without COVID-19 (p>0.05).
Age correlated with BMI, waist circumference (WC), neck circumference (NC), HRQoL - mental component (MC) and time after vaccination (TAV); BMI with WC, NC, HRQoL - general health (GH), and time post-vaccination; WC correlated with NC, general health, and TAV (Figure 1).
Spearman correlation *p<0.05, **p<0.01, BMI: body mass index, WC: Waist circumference, NC: neck circumference, HRQoL: health-related quality of life, PC: physical component, MC: mental component, GH: general health, Time of CSC: time of compulsory social confinement, TVA: time after vaccination.
The results related to COVID-19 are presented in Table 2. Among subjects who had COVID-19 82.4% performed compulsory social isolation while in those who did not have COVID-19 86.3% performed compulsory social isolation (p>0.05). Of the subjects who had COVID-19, 96.9% (95% CI 92.1-99.1%) reported having quarantined themselves after diagnosis of the disease.
Variables | Participants with COVID-19 (n=128) | Participants without COVID-19 (n=510) | p* |
---|---|---|---|
Frequency (%) [95% CI] | Frequency (%) [95% CI] | ||
COVID-19 related data | |||
Social isolation: Yes No | 108 (84.4) [76.9–90.2] 128 (15.6) [9.8–23.1] | 440 (86.3) [83–89.1] 70 (13.7) [10.8–17.0] | 0.879 |
COVID-19 symptoms Yes No | 112 (87.5) [80.5–92.7] 16 (12.5) [7.31–19.5] | 81 (15.9) [12.8–19.3] 429 (84.1) [80.6–87.2] | 0.001 |
Hospitalization for COVID-19 No Hospitalized no ICU Hospitalized in ICU | 121 (94.5) [89.1–97.8] 4 (3.1) [0.85–7.8] 3 (2.34) [0.5–6.69] | 510 (100.0) | 0.001a |
COVID-19 vaccine: Yes No | 125 (97.7) [93.3– 99.5] 3 (2.3) [0.50–6.7) | 492 (96.5) [94.5–97.9] 18 (3.5) [2.10–5.52] | 0.931a |
COVID-19 vaccine doses No Incomplete doses Complete doses Additional doses | 3 (2.3) [0.5–6.7] 12 (9.4) [4.9– 15.8] 97 (75.8) [67.4–82.9] 16 (12.5) [7.3–19.5] | 16 (3.1) [1.8–5.0] 63 (12.4) [9.6–15.5] 395 (77.5) [73.6–81.0] 36 (7.1) [5–9.6] | 0.214a |
Gained weight Yes No | 75 (58.6) [50.0–67.2] 53 (41.4) [32.8–50.4] | 287 (56.3) [51.8–60.6] 223 (43.7) [39.4–48.1] | 0.804 |
Physical activity has decreased Yes No | 88 (68.8) [60–76.7] 40 (31.2) [23.3–40.0] | 344 (67.5) [63.2–71.5] 166 (32.59) [28.5–36.8] | 0.902 |
Anxiety or depression Yes No | 71 (55.4) [46.4–64.3] 57 (44.59) [35.7–53.5] | 290 (56.9) [52.4–61.2] 220 (43.1) [38.8–47.5] | 0.881 |
Participation in clinical trial for COVID-19 vaccine Yes No | 5 (3.9) [1.3–8.8] 123 (96.1) [91.1–98.7] | 22 (4.3) [2.7–6.5] 488 (95.7) [93.5–97.3] | 0.992 |
Of the total subjects diagnosed with COVID-19, only 87.5% (95% CI 80.5-92.7%) had disease symptomatology. Of the subjects who were not diagnosed with COVID-19, 15.9% (95% CI 12.8-19.3%) reported having COVID-19 symptoms. The initial symptoms of COVID-19 infection are fever, headache, cough tiredness, absence of smell and taste; if they aggravate the disease, shortness of breath manifests.28
Regarding weight gain, 58.6% of COVID-19 subjects and 56.3% of non-COVID-19 subjects felt that they had gained weight from COVID-19 (p>0.05 between groups). Similarly, 68.85% of subjects with COVID-19 and 67.5% of subjects without COVID-19 felt that they had decreased physical activity during compulsory social isolation (p>0.05 between groups). The presence of anxiety or depression during compulsory social isolation was observed in more than half of the subjects evaluated, with no difference between the groups with or without COVID-19 (p>0.05) (Table 2).
Of the population studied, 3.9% of subjects with COVID-19 and 4.3% of subjects without COVID-19 reported having participated in a clinical trial of the COVID-19 vaccine (p>0.05 between groups) (Table 2).
Table 3 presents the results of the multivariate analysis to associate the HRQoL questionnaire total score controlling for the variables BMI, sex, age in years, whether or not hospitalized for COVID-19, time in compulsory social confinement, and history of chronic disease.
In the crude model, obesity, female sex, time in compulsory social confinement, and the presence of chronic disease are associated with lower total scores on the HRQoL questionnaire. These same variables remain significantly associated in the adjusted model.
Table 4 presents the results of the multivariate analysis to associate the physical component of the HRQoL questionnaire. Obesity, female sex, older age, hospitalization in ICU, longer time in compulsory social confinement, and pre-existence of chronic disease are associated with low values for the physical component of the HRQoL questionnaire. In the adjusted model it is observed that obesity, female sex, longer time in compulsory social confinement, and the existence of chronic disease are associated with a low score on the physical component of the HRQoL questionnaire.
Table 4 also shows the analysis for the association with the mental component of the HRQoL questionnaire. In the crude model, female sex, younger age, and time in compulsory social confinement were the variables associated with lower scores on the mental component of the HRQoL questionnaire. In the adjusted model, obesity, female sex, younger age, time in compulsory social confinement, and pre-existence of chronic disease were associated with lower scores on the metal component of the HRQoL questionnaire.
The present study aimed to determine the perception of HRQoL in the population of Metropolitan Lima according to whether or not they had COVID-19. Health-related quality of life (HRQoL) is an indicator that helps us to measure people's self-perception of their health. This can be measured with the questionnaire validated in Peru, the SF-20,21,24,30 used in this study. Cronbach's alpha, which is the internal consistency reliability coefficient, shows a value of 0.71, which is considered acceptable.
The HRQoL score can also be disaggregated into its physical, mental, and general health components. These can be affected by one or all of them together by different factors.
According to the results found in the present study, the main factor associated with lower HRQoL is the time of compulsory social confinement.
This variable social isolation in the crude and adjusted models showed a statistical significance where the longer the time of confinement, the lower the total HRQoL score and the lower the score in the mental and physical components of HRQoL.
Associated with this, other factors affecting HRQoL were also observed, such as obesity, female sex, and a history of chronic illness. In itself, having or not having COVID-19 did not affect the HRQoL score. This is an important finding as it is assumed that COVID-19 can lead to health impairment.32 There are likely ethnic or idiosyncratic differences in the response to COVID-19 following an illness. A recent study in Peru shows that patients with COVID-19 do not have a higher rate of postoperative complications than patients without COVID-19.33 Further studies will be needed to follow patients with COVID-19 for longer-term follow-up and to have a clearer conclusion on post-COVID-19 effects.
Interestingly, the health perception of the study participants is affected more by the compulsory social confinement than by the disease itself. A recent study on psychiatric teleconsultations in Peru shows that the third leading cause of psychiatric consultation was related to compulsory social isolation (19.7%), the first two being related to control, follow-up, or worsening of mental health problems before the pandemic (41.9%) and related to the appearance or increase of intrafamily conflicts (21.4%).34
In similar situations, as in the case of Middle East respiratory syndrome (MERS) in 2015 in South Korea, mandatory social confinement for two weeks showed negative effects on mental health even 4-6 months after the end of social isolation.35 Likewise, a study of 1008 young adult (18-35 years old) residents of the USA reported that higher the level of isolation and lack of social interaction during the context of the COVID-19 pandemic, higher mental disturbances and poorer the performance.36
Several factors influence the impact that disease outbreaks can have on the mental state of the population, such as lack of knowledge of the possible means of virus transmission, uncertainty about the future, misinformation, and quarantine. These stressful events negatively affected various behaviors, such as eating habits, sleep, physical activity, and sedentary lifestyles.37 They also cause an increase in anxiety and depression.38
During the pandemic, an increase in sleep disturbance and insomnia has been reported, as stress and anxiety affect the quality of sleep during the night and even alter the state of energy during the day.37,38 In addition, although our study has not focused on sleep disorders associated with the pandemic, this has been reported in several studies.39,40 Social confinement times have been shorter than those observed in Peru.
During social isolation, the adoption of bad habits increased, such as higher consumption of caloric and unhealthy foods; lower consumption of fresh fruit and vegetables; and a move away from the Mediterranean diet, which is considered to be healthy.37
In the case of physical activity, the total blockade, the closure of sports facilities, social restriction, and the increase in hours in front of an electronic device, due to work and study, among others, has caused a decrease in physical activity, and in turn an increase in sedentary lifestyles.37
Thus, although COVID-19 disease has been described as a fatal disease, it was not the main cause of the damage to the quality of life of the population, but during compulsory social confinement, health risk behaviors increased.
Indeed, the percentage of subjects who underwent compulsory social confinement was similar in those subjects who presented COVID-19 than in those who did not. This is since compulsory social confinement was prolonged, but there was access to massive exposure, such as in the case of markets, banks (government bonds), and public transport, which allowed many people to become infected, who may have been asymptomatic and eventually carried the infection home, infecting other family members with varying degrees of severity.
In Metropolitan Lima, a greater number of food markets was associated with higher incidence and mortality of COVID-19 (p<0.01 for both); these associations persisted when cases (r=0.49; p<0.01) and deaths (r=0.58; p<0.01) were adjusted for population density.41
In our study, obesity is a risk factor for lower HRQoL. This corroborates findings from other studies.42 The results of our study show that 57% of the surveyed population perceived weight gain and 67% had a decrease in physical activity during social confinement. According to the Peruvian College of Nutritionists, during the pandemic Peruvians gained an average of 7.7 kg, the main causes being increased caloric food intake and a sedentary lifestyle.43
The relationship between obesity and HRQOL is negative. sedentary lifestyle and weight gain in the population, which is a risk condition that predisposes to many comorbidities such as predisposes to many comorbidities such as metabolic alterations, dyslipidemias, and even cardiovascular diseases, which affect and cardiovascular diseases, which affect and deteriorate the quality of human life.44,45
In other pathologies, despite having the same care, women show lower HRQoL scores.46 Our results show that women have lower total scores on the HRQoL questionnaire, as well as on its physical and mental components, confirming what has been observed in other pathologies.
A history of chronic illness is also associated with lower scores on the HRQoL questionnaire.47,48 The same has been confirmed in our study.
Age has shown different results in our study. For the total score and the mental component, younger age is associated with lower HRQoL scores, while for the physical component older age is associated with lower HRQoL scores.
The young population in compulsory social confinement has been subjected to a high degree of stress and anxiety due to the suspension of the face-to-face university and non-university classes and the use of tele-education involving many hours of the day in front of the computer. In our study more than half of the population studied showed cases of anxiety and/or depression during the period of compulsory social confinement. This would be one of the probable conditions for lower scores on the HRQoL questionnaire.
Having been vaccinated shows a trend towards better HRQoL scores with the second and third doses of the vaccine. The non-significance may be since the number of subjects vaccinated with two or three doses is still insufficient to show statistical significance. Further studies will show whether or not this trend becomes significant.
For healthy people in mandatory social confinement, lifestyle changes, fear of contracting COVID-19 disease, young age, female sex, history of mental illness and lower coping capacity for stress appear to be risk factors for insomnia.49
In Peru, the impact of social isolation may have been greater because of the time in which it has occurred. This confinement has not only been inefficient as events with crowds of people occurred simultaneously while in other situations, such as the suspension of classes for school and university students, but it has also significantly affected our educational level. In addition, the long-standing compulsory confinement has not only failed to reduce infections and deaths, but Peru has one of the highest mortality rates in the world, with more than 200,000 Peruvians having died to date.
The strength of this study was the methodology, which shows within the context of the pandemic the use of technological tools is valuable for an understanding of society and its relationship with health. The instrument used included diverse sections of information, including a validated questionnaire (SF-20) to evaluate de HRQoL, which allowed a wider perspective to analyze causes for the results obtained. This study also shows how the application of health policies or measures should be based on the evaluation and social context.
This study presents certain limitations as well. First, our database was recollected by convenience; therefore, the results present in this paper cannot be generalized for the entire Peruvian population. However, our results show tendencies consistent with other studies. Also, due to social distancing and COVID-19 regulations, the anthropometric measurements were not able to be taken by health workers but were self-reported by the participants. This is a limitation since the margin of error increases; nevertheless, the correlation between measurements was high. Another limitation present was the research tool, the electronic questionnaire, which was more often completed by respondents with higher education and residents of high-income districts, probably also due to the better quality of Internet connection. Therefore, a follow-up is recommended according to the ethnicity and culture of each country or city.
In conclusion, although it is not a study that can be extrapolated to a large population, due to the type of design, this study allows us to have prior knowledge about health-related quality of life and how it has been affected to a large or medium extent in the context of the COVID-19 pandemic. The research team proposes longitudinal follow-ups of the population to avoid adverse outcomes in later life.
Figshare: The social isolation enforced by the COVID-19 pandemic reduces the Health-Related Quality of Life score in the adult population of Metropolitan Lima. https://doi.org/10.6084/m9.figshare.19248635.v2.50
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Views | Downloads | |
---|---|---|
F1000Research | - | - |
PubMed Central
Data from PMC are received and updated monthly.
|
- | - |
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Aging, drug consumption, health systems.
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Epidemiology. Public Health. Pediatric Dentistry. Quality of Life. Social Determinants of Health. Iniquities.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
No
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
Yes
References
1. Cuschieri S: The STROBE guidelines.Saudi J Anaesth. 2019; 13 (Suppl 1): S31-S34 PubMed Abstract | Publisher Full TextCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: Epidemiology. Public Health. Pediatric Dentistry. Quality of Life. Social Determinants of Health. Iniquities.
Is the work clearly and accurately presented and does it cite the current literature?
No
Is the study design appropriate and is the work technically sound?
Yes
Are sufficient details of methods and analysis provided to allow replication by others?
No
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Epidemiology, Public health, antibiotic resistance, Infectious disease, vaccines
Alongside their report, reviewers assign a status to the article:
Invited Reviewers | |||
---|---|---|---|
1 | 2 | 3 | |
Version 2 (revision) 30 Jan 23 |
read | read | |
Version 1 12 Apr 22 |
read | read |
Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list:
Sign up for content alerts and receive a weekly or monthly email with all newly published articles
Already registered? Sign in
The email address should be the one you originally registered with F1000.
You registered with F1000 via Google, so we cannot reset your password.
To sign in, please click here.
If you still need help with your Google account password, please click here.
You registered with F1000 via Facebook, so we cannot reset your password.
To sign in, please click here.
If you still need help with your Facebook account password, please click here.
If your email address is registered with us, we will email you instructions to reset your password.
If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance.
Comments on this article Comments (0)