There have been few studies on the impact of the COVID-19 pandemic in low- and middle-income countries (LMICs). Giebel et al. studied the impact of COVID-19 public health restrictions on older people in Uganda by adopting a qualitative methodology (Giebel et al., Reference Giebel, Ivan, Burger and Ddumba2022). They found that there were significant impacts on five aspects: (1) economic impacts; (2) lack of access to basic necessities; (3) impact on healthcare utilization; (4) social impacts; and (5) violent reinforcement of public health restrictions. They concluded that government measures to restrict the virus spread need to be coupled with enough support for people to get the necessities they need to live as normal a life as possible without being harmed by public health restrictions. The situation in LMICs is very different from that of developed countries. Therefore, the real-life impacts of the COVID-19 pandemic on LMICs and developed countries may differ. This commentary aims to use Giebel et al.’s work as an example to discuss the values and limitations of qualitative methodology and how qualitative and quantitative methods can complement each other to reveal the whole picture of the target of an investigation.

The values of qualitative studies on the impact of COVID-19 in older people aged 60 or above, especially in vulnerable populations, may at least include the following: (1) to allow us to know the details of how older people live, feel, and think about COVID-19, (2) to experience the voices of poor, marginalized people, (3) to understand the loneliness and frailty of older people in a more profound manner, (4) to comprehend the complex or ambivalent thinking and processes of the older people’s caregivers in socioeconomically disadvantaged circumstance, (5) to provide novel insights that are beyond quantitative statistical analysis such as to capture unobserved intentions, inner meaning, options considered but not taken, and (6) to contribute to theory development or deepen our understanding of theory. All these may go beyond quantitative statistical methods that have tried to control different variables in the analysis (Phoenix, Reference Phoenix2018; Warren-Findlow, Reference Warren-Findlow2013).

The GDP per capita of Uganda in 2019 is only $794 USD (The World Bank, 2021), making it one of the world’s poorest countries. Giebel et al.’s study provides information on some of these distinct features that may not occur in developed countries such as (1) Lack of access to food: Some people did not receive the government’s food aid and some could not tolerate the food as it made them unwell. Furthermore, because certain food ingredients were only sold in the evening, the 7 pm curfew made these ingredients unavailable. (2) Lack of access to education: Many participants said they could not afford children’s school fees due to restricted income, which needed to be spent buying the limited foods available to them. (3) Lack of access to transportation: Almost all participants said restricted transportation affected their lives significantly, partly due to raised transportation fees and the difficulty in getting or bringing foods to relatives, leading to hunger. Lack of transportation also affected accessibility to medicines or health care, further aggravating their health conditions. (4) Cancelation of religious ceremonies: In addition to decreased well-being due to the cancelation of religious gatherings, participants felt pain after their loved ones died of the COVID-19 infection, but they could not bid farewell to their loved ones because of lockdown. These features are not easy to reveal themselves through quantitative research alone, which provides mainly numbers and percentages. The two quantitative studies related to COVID-19 from Uganda can be good examples of contrast (Kitara and Ikoona, Reference Kitara and Ikoona2020; Olum et al., Reference Olum2020). In the two studies, quantitative figures showed relatively good outcomes of government measures and sufficient knowledge of COVID-19 in medical students.

There are methodological limitations in qualitative research too. Inherently, there are rare statistical numbers (such as the percentage of families suffering from hunger or harmed by security forces) that can be used for comparison with those in other countries. The in-depth interview method used in qualitative research often limits its sample size and generalizability. For example, in Giebel et al.’s study, the sample size was 30, a number not insignificant in qualitative research but smaller than usual countrywide quantitative studies. Since participants were a convenience sample drawn from a phone contact list, those without a household phone apparently would be excluded from the study. Moreover, those with poor health conditions might be unable to receive an interview call. These factors limit the generalizability too.

In contrast to the quantitative data of COVID-19-related infection rates, morbidity, and mortality, qualitative research sheds light on how COVID-19 influences people’s viewpoints (such as value, perspective, and stigma) and behaviors (such as change of lifestyle and social interactions), especially in the vulnerable or marginalized populations, and populations living in LMICs. Although their sufferings are likely to be more severe, these populations’ voices are less likely to be heard. Since qualitative and quantitative research methods complement each other, we need both in-depth qualitative research and well-designed quantitative studies to have a comprehensive picture of the actual impacts of the COVID-19 pandemic.