Bangladesh is going through a shift in the epidemiology of non-communicable diseases, with chronic kidney disease (CKD) becoming more common. CKD is a worldwide public health issue that is becoming more common in many nations, including Bangladesh (Kar, 2023). Banik and Ghosh, (2021) found that Bangladesh has a higher overall prevalence of chronic kidney disease (22.48%) than the global rate (13.4%). 

The COVID-19 pandemic harms non-communicable disease (NCD) patients both from demand and supply side health care provisions. Chan et al. (2020) found 11% of the NCD patients reported difficulties in managing care during the first 2 months of the pandemic and 10% reported having less than one weeks supply of medication. Yang et al. (2020) have found that NCDs with comorbidities may be a risk factor for severe patients compared with non-severe patients. The outbreak of COVID-19 severely affected the health service utilization of CKD patients.  Prasad et al. (2020) surveyed dialysis patients and reported that during the lockdown period, 28.2% patients missed one or more dialysis sessions, 2.74% required emergency dialysis sessions, 4.13% stopped reporting for dialysis, and 0.36% died. Natale et al. (2023, p.405) in a systematic review pointed out that uncertainty in accessing health care exacerbated vulnerability, emotional distress, and burden; and led to reduced capacity to self-manage among patients with CKD and their caregivers. In Bangladesh, after the lockdown at the end of March-June 2020, patients with CKD missed their regular treatment and experienced more complicated treatment and increased morbidity and mortality rates. Begum et al. (2021) examined the impact of COVID-19 on dialysis patients in Bangladesh and found a higher mortality rate among the patients during the pandemic compared to the previous year. They reported that financial difficulties, missed appointments due to fear of infection, and lack of transport have influenced this high mortality (Akter et al., 2025).

The aim of this paper is to evaluate how COVID-19 has affected the ongoing care of kidney disease in selected hospitals in Bangladesh. Determining supply-side obstacles, calculating CKD patients costs for curative care, examining variations in NCD provision amongst healthcare facilities and making policy recommendations are some of the specific goals.

There are no conflicts of interest.

Study Design and Site Selection

Both primary and secondary data were used. The secondary analysis was conducted through desk research and relevant documents were reviewed. The primary data was collected using a mixed method of qualitative and quantitative techniques. The quantitative part assessed the utilization of CKD patients care and identified the barriers to utilization during COVID-19 through patients in-depth interviews (IDI). The qualitative technique was used to find out the supply-side challenges of the care provision of CKD patients during COVID-19 through Key Informant Interviews (KII) and In-depth Interviews. KIIs/IDIs were conducted with the experts of kidney disease, policymakers, health managers, and technicians of the dialysis unit. 

Four categories of the sample hospitals were selected dividing them into the public general hospital that provides hemodialysis for CKD patients and also provides service to COVID-19 patients; a kidney-specific public hospital that provides hemodialysis for CKD patients but did not provide services to COVID-19 patients; private general hospital that provides hemodialysis for CKD patients and also provided service to COVID-19 patients; kidney-specific private hospital that provides hemodialysis for CKD patients but did not provide services to covid-19 patients. Four hospitals were selected as shown in Table 1. 

Table 1: Selected facilities and number of patients.

Sample size

For patient interview, the sample size is calculated using the standard Cochrans formula for sample size 465 kidney patients were surveyed. Multi-stage cluster random sampling approaches for data collection were adopted. Nurses, matrons, resident medical officers, or physicians were interviewed from each selected hospital. In addition, five experts for kidney disease were interviewed on their perspective on the overall condition of treatment. The key variables on the demand side were- demographic characteristics of the patients, years of taking kidney dialysis, sessions needed per week, choice of place of taking public/private treatment, cost of treatment. On the supply side, the major variables included were the supply of necessary equipment, availability of staff, and volume of patients. 

Data Analysis 

Quantitative data was analyzed using STATA. Univariate, bivariate, and multivariate regression methods were used to analyze the quantitative part of the data. We applied the Probit model for a better fit. 

In a probit model, the value of β_k y_ki is taken to be the z-value of a normal distribution, meaning higher values of β_k y_ki mean that the event is more likely to happen. To further analyze the impact of the variables on the choice between specialized and general hospitals for dialysis, we conducted Marginal Effects estimation in Probit models to determine the specific influence of each significant regressor on this decision.

To understand the determinants and magnitude in the choice of healthcare chosen by the respondents in our study, the following equation was used:

Where, Type of Facility is two separate dummy variables, public and spclzd, and a regression analysis is run for each one of the dummies. public is 1 if the facility is a public or governmental facility and 0 if its a private facility. spclzd is 1 if the facility is an NCD-specific or specialized facility and 0 if its a general hospital. Treatment Costit is cost of the treatment for each patient i and at time periods before and after COVID-19, hence two separate varibles BerCost and AftrCost. Agei is the age of each patient; Educationi is the educational status of each patient; Genderi is the Gender of each patient; Religioni is the religion of each patient; Marrital Statusi is the marital status of each patient; Occupationi is the main occupation of each patient; Monthly Incomei is the monthly income of each patient; Regioni is the place of residence of each patient; Duration of receving Treatmenti is the duration since which each patient each receiving the treatment; Weekly Frequency of Treatmenti is the number of treatment session each patient receives. The equation (2) is run for diseases, end-stage kidney disease, for both costs and other scenarios both before and after the COVID-19 pandemic.

Characteristics and disease profile of the Patients 

Most of the patients were interviewed while they were receiving treatment at the hospitals and some of the patients were reached using telephonic interviews. Male patients seeking dialysis constitute a majority (54%) compared to females. 33% of the patients have at least a primary school education followed by secondary school graduates (20%). Most of the patients were between the ages of 35 to 60 years. Most patients income ranged between BDT10,000 to BDT50,000. A considerable proportion of respondents were engaged in housework or faced unemployment due to mobility limitations.

A significant number of patients (50%) have been receiving dialysis for the last 4 to 5 years (Table 2). Moreover, 66% of the patients received 2 dialysis sessions per week, although the proportion of patients receiving 3 sessions per week is 30%. It indicates that a good number of patients have been receiving dialysis for some years, with a significant proportion starting within the last 5 years. Most patients undergo 2 to 3 dialysis sessions per week. 

Table 2: Disease History of End-Stage Kidney Disease Patients.

Effects of COVID-19 on the Patients Treatment and Physiological Conditions

Bangladesh was not an exception to the global impact of the COVID-19 epidemic on NCD therapies. After the COVID-19 pandemic, over 12% of the patients had missed one or more dialysis appointments; this represented an increase from the 10% rate of patients who had missed scheduled sessions before to the pandemic (January 2019–December 2019). More than 40% of patients reported that finance is the major cause for missing scheduled dialysis appointments. More than 15% of patients who missed dialysis sessions said they were unable to pay because of an increase in related costs, such as travel, food, and other opportunity costs, while over 28% of patients said that COVID-19 had worsened their familys financial situation. The patients suffered grave physiological effects because of these entry obstacles. The renal function of almost 26% of the patients who skipped their planned dialysis sessions indicated a decline. Of the patients, 11% required hospitalization due to multiple problems, and the same proportion experienced severe discomfort. Over 7% of them had trouble breathing because of water buildup in their lungs. The additional difficulties raise the patients overall healthcare costs. Therefore, compared to previous COVID-19 outbreaks, financial limitations appear to have played a more substantial role due to physiological repercussions.

Determinants of Costs of Treatments

To examine the determinants of dialysis treatment costs, a series of regression analyses were conducted using total treatment costs as the dependent variable and a set of socioeconomic and demographic factors as explanatory variables. Among these, the variable representing geographic region emerged as highly statistically significant. Patients in the sample traveled to Dhaka from 20 different districts, with those from 13 of these districts reporting increased treatment costs post-COVID-19 relative to patients residing in Dhaka. Furthermore, patients from 8 districts exhibited statistically significant positive effects on treatment costs. These geographical disparities likely reflect differences in local availability of dialysis services and access to healthcare infrastructure. An intriguing finding pertains to the regression coefficients for dialysis costs at the time of receiving care. The results suggest that the longer patients continue treatment, the lower their per-session costs become, with these cost reductions being statistically significant. This may reflect learning effects, adaptation in treatment-seeking behavior, or changes in provider practices over time. Prior to the COVID-19 pandemic, treatment costs were significantly lower in public facilities. Patients receiving dialysis in public hospitals incurred approximately BDT 1,100 less per session compared to those treated in private facilities. However, this difference diminished following the pandemic, and the coefficient for public facility use lost statistical significance in the post-COVID period.

Table 3: Effect on Dialysis Costs by Facility Type.

To further assess the impact of COVID-19 on treatment costs across different facility types, we considered all relevant cost components - including direct treatment costs, travel expenses, and temporary accommodation costs - before and after the pandemic. These costs were regressed on key facility-type dummy variables: public versus private and specialized versus general hospitals (see Table 3). The results indicate that receiving treatment in a general hospital, as opposed to a specialized one, was associated with a statistically significant increase of approximately BDT 1,400 in total per-session treatment costs post-pandemic. Conversely, patients treated in private hospitals experienced a reduction in costs of about BDT 1,670 per session after the pandemic compared to before. These findings suggest substantial cost variability across facility types, likely influenced by differing operational policies, levels of preparedness, and resource allocation during the pandemic period.

Effect of the Type of Facility on the Possibility of Transmission of COVID-19

Patients with chronic conditions like kidney disease are already at higher risk of transmittable serious infections like COVID-19, given their existing health challenges. Thats why it becomes especially important to understand whether the type of facility where they receive treatment-public or private, general or specialized - had any impact on their chances of getting infected during the pandemic. If the choice of hospital really does play a role in infection risk, its something patients and their families should be aware of for future decision-making

Table 4: Effects of the type of Facility on the Possibility of Getting Infected with Coronavirus for Kidney Patients: Marginal Effects Analysis.

Note: t statistics in parentheses; *** p<0.001, ** p<0.01, * p<0.05

We employed Probit regression models to examine whether patients tested positive for COVID-19 during dialysis treatment, and whether they or their families believed the infection was acquired at the dialysis center. The analysis also explored whether the facility was public or private, and whether it was a general or specialized hospital. In addition, we included demographic variables such as the patients age and level of education to assess their potential influence. To enhance interpretability, we computed marginal effects, which indicate the extent to which each factor increases or decreases the probability of infection. Detailed results are presented in Table 4.

Marginal Effects of Facility Type on COVID-19 Infection and Perceived Source of Infection

Table 4 presents the results of the marginal effects analysis assessing the relationship between facility type and the probability of COVID-19 infection among dialysis patients. In the first model, which examines whether a patient undergoing regular dialysis was ever infected with the Coronavirus, the coefficient for the dummy variable public (equal to 1 if the facility is public or government-owned, 0 otherwise) is 0.777, and it is highly statistically significant. This suggests that the probability of a patient having received dialysis from a public facility is 77 percentage points higher among those who contracted COVID-19, compared to those who did not contract the virus and were treated at a private facility. The coefficient for the dummy variable spclzd (equal to 1 if the facility is specialized, 0 otherwise) is -0.540, also statistically significant. This indicates that the likelihood of a patient receiving dialysis from a specialized facility is 54 percentage points lower among those who tested positive for the virus, relative to those who did not contract the virus and received care from a general hospital. Additionally, the education variable is positive and statistically significant. The marginal effect implies that with each one-level increase in education (e.g., from primary to secondary), the probability of contracting the coronavirus increases by approximately 14 percentage points.

The second model explores whether the patient or their family suspected the dialysis center to be the source of infection. In this case, the coefficient for the public is 0.812, again highly statistically significant. This indicates that patients treated at public facilities are 81 percent more likely to suspect that the dialysis center was the source of their infection, compared to those treated at private facilities. In contrast, the coefficient for spclzd is -0.720, suggesting that the likelihood of the facility being specialized is 72 percentage points lower among those who suspected the dialysis center as the source of infection. This result is relative to patients treated at general hospitals who did not suspect the center.

Effects on Patients Ability to Seek Healthcare from Abroad

In recent years, Bangladesh has emerged as one of the key sources of medical tourists for its neighboring countries, especially India, Thailand, Singapore etc. According to annual figures released by Indias Ministry of Tourism, in 2017, a total of 221,751 Bangladeshis travelled to India for medical treatment. This ever-rising number has been a concern for the country, as not only is this a financial burden on the patients who are entitled to quality healthcare in their own country, this also develops a worrying dependency of the countrys population on foreign countries over something fundamental like their healthcare. This dependence on essential healthcare on a foreign country surely became a source of much concern when the pandemic hit and borders were closed for travel. 

18% of the kidney disease patients have travelled abroad at some point in the last five years for their treatment while over have done so. 3.9% who went abroad for treatment went to India. A few patients each also went to Thailand and Singapore for kidney disease treatments. Naturally, the number of patients travelling abroad for treatment dwindled during the pandemic, with only 5 kidney disease patients being able to make the trips for their required healthcare (Annex Table 1). 

When asked about the cost differences between the countries, the patients have travelled for healthcare and the same treatment in Bangladesh, 87% of the kidney disease patients were affirmative. While most patients who sought treatments abroad felt the difference in cost ran up to BDT 1000-5000. More interestingly, when asked about why they went abroad to seek treatment, 48% of the patients cited their distrust in the medical system of the country as a reason. When asked to elaborate, they attributed their distrust to alleged malpractice and unethical practices in hospitals and diagnostic centers, they have either experienced or read about in newspapers. Kidney disease patients also cited the absence of ease of availability of advance treatment, i.e., transplants, as their reason for venturing abroad for treatment. 

Most of the patients (almost 17%) stressed on ensuring better quality of diagnostic services will restore some of the goodwill of Bangladeshs medical system. Reduction of the direct and associated medical costs were mentioned by patients (more than 15%) as a factor that would improve the state of the countrys healthcare system.  

Supply side Barriers: Findings from Key Personnel from kidney hospitals

During the COVID-19 pandemic, most key personnel reported no significant change in the flow of kidney disease patients. On the one hand there was an increase in dialysis patients due to interrupted medical tourism and on the other financial constraints led some patients to reduce dialysis sessions. In a public facility, disrupted dialysis schedules reportedly led to the deaths of 10-15 patients.

Providers faced shortages due to roster duty, isolation, and COVID-19 infections, leaving jobs out of fear. The lack of treatment guidelines exacerbated challenges, and patients struggled to reach hospitals due to vehicle shortages during lockdowns. Respondents emphasized the need for standardized guidelines and comprehensive training for healthcare workers, along with timely dissemination of pandemic-related information. Recognition and appreciation of frontline healthcare workers were also recommended. On average there were about 21 dedicated nurses in these facilities, 3 dedicated doctors, and 3 technicians. 

Moreover, all facilities reported to have dedicated beds for kidney patients. Only 3 out of 16 providers reported that the supply of various consumables needed for the unit was disrupted at the time of the COVID-19 pandemic. 75% of facilities conduct 4 dialysis sessions daily. DMCH reduced sessions by up to 33% due to COVID-19. Only KF and NIKDU reported patients stopped once-a-week dialysis treatments. DMCH saw the highest drop (30) in patients undergoing dialysis twice or thrice a week. On average beds per facility was 76.5, unchanged for social distancing. Daily patient numbers decreased by around 21 in government facilities and increased by over 53 in specialized hospitals after COVID-19. All providers, except NIKDU, had dedicated COVID-19 beds. Lockdown travel restrictions were cited as the main reason for treatment discontinuation, followed by financial constraints. Fear of infection at hospitals was a major factor. Providers faced difficulties due to positive cases among staff and restricted movement. Quarantine of doctors/nurses was also a significant challenge.

This study sheds light on the realities of dialysis patients during the COVID-19 pandemic. Over 12% of patients in our sample missed scheduled dialysis sessions during the pandemic. The reasons were varied - lockdowns, personal illness, caring for family members, or simply being unable to navigate the many logistical hurdles that stood in their way. These missed sessions werent just disruptions in routine; they had real consequences, contributing to worsening health, greater complications, and higher out-of-pocket expenses. Trivedi et al. (2020) supports the findings and noted that nearly a third of dialysis patients globally missed treatments during the pandemic due to similar systemic and social barriers. One particularly revealing insight from our data was how a patients location shaped their financial burden. Those traveling from outside Dhaka faced steeper costs - expenses that went well beyond medical care to include transportation, lodging, and food. For these individuals, every dialysis session meant hidden costs and logistical stress, often while managing the toll of chronic illness (Hennig et al., 2021).

We also observed that time spent on dialysis made a difference. Patients who had been receiving dialysis for longer seemed to learn how to stretch their limited resources - where to find cheaper medicines, how to avoid unnecessary costs, and how to schedule their treatments more efficiently. Even so, those who required more frequent sessions couldnt avoid higher overall spending, no matter how resourceful they were. The type of hospital patients visited mattered, too. General hospitals tended to be more expensive than specialized facilities - not just because of medical fees, but because they were under enormous strain during the pandemic. The costs of keeping staff safe, rotating shifts, maintaining physical distancing, and acquiring protective gear were all passed down to patients in one way or another. Over time, even the “small” things - extra transport costs, higher drug prices, mandatory COVID-19 tests, and daily meals - quietly piled up (Henry, 2020; Shathi et al., 2023).

Surprisingly, some private hospitals proved to be more affordable than public ones during the post-pandemic period. A few even waved fees or introduced subsidies to support patients who were financially struggling. These quiet gestures of support may have made a real difference for many people struggling to afford essential care. From the perspective of hospital staff, the challenges were immense. Public facilities had to operate with fewer staff and smaller patient volumes, making it harder to maintain service standards (Siam et al., 2021; Udod et al., 2024; Mohammadinia et al., 2023). Meanwhile, our findings show clear differences in COVID-19 infection rates based on facility type. Patients in public hospitals were more likely to contract the virus- possibly a result of crowding, limited infrastructure, and the sheer volume of patients (WHO, 2020). In contrast, many private hospitals that werent designated as COVID-19 centers offered a calmer, more controlled environment that may have reduced the risk of infection (Islam et al., 2020). Specialized hospitals offered even greater protection. These facilities served smaller, more targeted patient groups and were able to implement focused protocols to keep people safe. In contrast, larger institutions had to juggle emergency services, general care, and COVID-19 response all at once - conditions that likely increased exposure risk (Parveen et al., 2024).

One unexpected finding was that more educated patients were more likely to contract COVID-19. This could be linked to their work environments, social exposure, or even less physically active lifestyles during lockdowns (Liu, 2023; Cho et al., 2021). Patient perception also played a role. Those receiving care at public hospitals were more likely to believe they contracted COVID-19 during dialysis visits. Their concerns were understandably crowded waiting areas, stretched staff, and overwhelmed facilities all contributed to a sense of vulnerability. Patients at specialized centers, on the other hand, expressed fewer concerns, perhaps because those facilities didnt double as COVID-19 treatment centers. For example, when Dhaka Medical College was converted into a COVID-19 hospital, its dialysis unit was temporarily closed to make space for isolation wards (DGHS, 2020).

In summary, the pandemic didnt just introduce new challenges; it deepened existing flaws in the system. For patients with chronic conditions like kidney disease, regular and safe access to care isnt just a need; its a lifeline. Where care is given, how its delivered, and who can afford it all play a role in shaping whether patients experience stability - or a health crisis. As we move forward, these lessons point to a clear need: resilient, inclusive health systems that can protect the most vulnerable, even in times of crisis.

Finding ways to reduce these cost disparities, such as improving service efficiency and resource allocation, could help alleviate the financial burden on dialysis patients. The pandemic has led to decreased patient numbers in public facilities and staffing shortages, exposing underlying issues with healthcare management and resource distribution. There is evidence linking COVID-19 to increased death rates in CKD patients, which emphasizes the importance of implementing infection management strategies to stop the virus from spreading among this susceptible group (CDC, 2020; Guan et al., 2020; Huang et al., 2020; Zhou et al., 2020). Our findings lead to the following recommendations: Implement targeted financial assistance programs to alleviate the economic burden on dialysis patients. This could include subsidies for transportation costs, medication expenses, and accommodation for patients traveling from distant regions. Develop strategies to improve access to affordable dialysis services in underserved regions. Invest in strengthening healthcare infrastructure and workforce capacity to enhance resilience and preparedness for future public health emergencies.

This study was approved by the Institutional Review Board (IRB) (Ref. No. IHE/IRB/DU/29/2021/final) on 10/16/2021. All the participants provided written informed consent.

Study conception and design: N.S., S.M.B., F. I. Data collection: N.S., S.M.B., F.I. Analysis: F.I. Interpretation of results: N.S., S.M.B. Manuscript preparation: S.M.B.

We are grateful to the Centennial Research Grant, the University of Dhaka, for granting us the fund to conduct this study. The Centennial Research Grant was offered to commemorate the hundred-year anniversary of the University of Dhaka, Bangladesh.

The author(s) disclose receipt of financial support for the research of this article: the Centennial Research Grant, the University of Dhaka [Ref no.reg/admin-3/47974 dt: 3/6/2021].