ALL Metrics
-
Views
-
Downloads
Get PDF
Get XML
Cite
Export
Track
Research Article

Changes in the readiness of healthcare systems to provide diabetes- and cardiovascular disease-related services: A comparison of indices using data from the 2014 and 2017 Bangladesh Health Facility Surveys

[version 1; peer review: 2 approved with reservations]
PUBLISHED 28 Jul 2023
Author details Author details
OPEN PEER REVIEW
REVIEWER STATUS

This article is included in the Health Services gateway.

This article is included in the Global Public Health gateway.

Abstract

Background: The increasing prevalence of non-communicable diseases (NCDs) in Bangladesh is a significant obstacle for the government's already under-resourced healthcare centers and healthcare management. This study aimed to determine whether healthcare services are prepared to handle cardiovascular disease (CVD) and diabetes in the future.
Methods: This cross-sectional study used the Bangladesh Health Facilities Survey (BHFS) 2014 and 2017 data. The BHFS 2014 completed assessment of 317 facilities providing diabetes care and 407 facilities providing CVD care, while the 2017 BHFS included 305 and 368 facilities providing diabetes and CVD care, respectively.
Results: A slight increase in facility readiness status was observed in 2017 compared with 2014, though it was not statistically significant. District hospitals (DHs) and Upazila health complexes (UHCs) showed improvement in staff and guidelines, basic equipment, diagnostic capabilities, and essential drugs, as their Readiness Index (RI) value increased in 2017 from 2014. The RI values of non-governmental organizations (NGOs) clinics were 48.65% in 2014, whereas the value was slightly increased to 55.28% in 2017. For private clinics, the RI value diminished in 2017 (56.11%), which was lower than the 2014 survey (60.62%). There was a slightly mixed trend for public and private facilities regarding managing CVDs. In DHs and UHCs, the RI value decreased to 58.5% and 53.06% in 2017 from 64.04% and 53.02% in 2014. NGO clinics were valued at 48.65% in 2014, which dropped to 44.53%. For private clinics, the value showed a decreasing trend as the value in 2017 was 61.58%, lower than the value of 2014 (64.15%).
Conclusions: In Bangladesh, public and private healthcare facilities lack readiness for healthcare towards DM and CVD maintenance. It is noteworthy that this improvement has been insignificant over the years in this regard. Healthcare policy reform is urgently required to strengthen NCD healthcare, particularly in public healthcare facilities.

Keywords

Health system, NCDs, Diabetes, CVD, Facility readiness; Bangladesh 

Introduction

The incidence of non-communicable diseases (NCDs) has been resolutely increasing worldwide over the last few decades.1 Diabetes mellitus and cardiovascular diseases (CVDs), including coronary heart disease, peripheral arterial disease, and rheumatic heart disease, are among the NCDs that are prevalent and impact individuals of different ages, geographical areas, and nations.2 The World Health Organization (WHO) estimates that more than 15 million people between the ages of 30 and 69 years die every year from an NCD worldwide, with low-to-middle-income countries (LMICs) accounting for three-fourths (77%) of all early fatalities. According to the WHO, CVDs cause the death of 17.9 million people every year, whereas diabetes claims the lives of 1.5 million individuals each and every year.

Bangladesh is a prosperous nation that is undergoing both epidemiologic and demographic transitions at the same time. This is due to the fact that the disease burden is moving from communicable illnesses to NCDs.2 WHO reported in 2011 that NCDs represent 61% of the overall illness burden in Bangladesh and preliminary research estimates that these diseases are responsible for 51% of the country’s yearly deaths.3 The number of people living with diabetes in Bangladesh is always increasing. According to the International Center for Diarrheal Disease Research in Bangladesh (ICDDR, B), the number of individuals living with diabetes in 2015 was around 7.1 million, and the disease was responsible for approximately 129,000 fatalities. Studies also showed that diabetes raises the chance of debilitating CVDs,4 and patients with diabetes have a significantly increased risk of dying from CVDs.4

An analysis of a case study set within the parameters of Bangladesh found a variety of healthcare system preparedness challenges; the CVD rate is increasing, making it difficult for Bangladesh to treat these diseases.5 In particular, this previous study found that a lack of comprehensive cooperation among many partners hindered the preparedness of the healthcare systems.5 With the changing epidemiological and demographic conditions, the healthcare system faces new challenges such as increasing mortality due to CVDs, diabetes and other NCDs in Bangladesh.6,7 Preventing and managing NCDs differs from addressing acute conditions, in which healthcare system readiness can play an important role.6 Without long-term medical support, early identification of NCDs, mental health promotion, identification of risk factors, self-management, behavior modification, palliative care, and adherence to treatments and medications, NCDs are unlikely to be prevented or managed.8 The lack of healthcare facilities, inadequate infrastructure, access to healthcare professionals, lack of health literacy, and unavailability of medications negatively impact patients with NCDs.2

However, the readiness of a country’s healthcare system is one of the significant aspects that show a country’s readiness to adopt essential and timely steps to minimize any disease, including NCDs. The idea of healthcare system readiness emphasizes the degree to which healthcare systems are ready to handle any particular sickness as well as any and all general disorders.9 Because the readiness identifies possible obstacles that might stand in the way of achievement, it is essential to assess readiness in healthcare systems (U. S. Department of Health and Human Services Health Resources and Services Administration). Diabetes and CVDs are responsible for exponential increases in medical expenses and a diminished capacity for activity.4 In addition to this, the enormous expenditures associated with medical treatment have an impact on the financial situation of a family.4 Research has shown that diabetes significantly increases the risk of developing severe CVDs.7 Heart conditions may create disruptions in the normal flow of fluid out from the lungs, which can result in a variety of abnormalities.10 All of these diseases are linked to one another and are all accountable for the devastation of human lives and the economy.

The healthcare system in Bangladesh is facing an increasingly difficult task as a result of the prevalence of NCDs, notably CVDs and diabetes.11 Previous research focused on determining whether or not the healthcare system in Bangladesh was prepared to provide treatment for NCDs.5,12 It would be more beneficial to identify the illnesses specific readiness (for example, readiness for individuals with diabetes and CVDs, etc.) of the healthcare system to offer services for a given condition if we focused on each specific NCD on its own.13

Based on the information from the Bangladesh Health Facilities Survey (BHFS) in 2014, there has been only one study done so far that investigates whether or not healthcare institutions are prepared for increasing cases of diabetes and CVDS.13 According to what was discovered, just 0.4% to 0.9% of facilities satisfied all four preparedness characteristics (guidelines, trained staff, equipment, and medicine).13 Despite the fact that it is vital to understand the preparedness of the healthcare system in Bangladesh, particularly for services connected to diabetes and CVDs. According to our understanding, the preparedness of the healthcare system to deliver services related to diabetes and CVDs utilizing the most recent BHFS 2017 data has not yet been quantified or analyzed. We studied the readiness of the healthcare system in Bangladesh, especially for diabetes and CVD-related services, by comparing the BHFS from 2014 to 2017 and basing our findings on this constraint.

Methods

Ethical approval

This study arose from the first author’s master’s thesis and when the first author submitted a proposal for their thesis, the study protocol was approved by the Institutional Review Board (IRB) of North South University in Bangladesh (Ref-2020/OR-NSU/IRB/206). However, as this study used secondary data analyses, ethical approval was not required.

Study design and settings

The 2014 and 2017 BHFS surveys were designed as cross-sectional studies and used a stratified random sampling technique of 1,596 and 1,600 healthcare facilities, respectively, to represent all formal healthcare facilities in Bangladesh. Both the 2014 and 2017 BHFS surveys contained questions on the healthcare facilities of Bangladesh’s administrative divisions. The BHFS 2014 survey was fielded between May 22nd and July 20th 2014, whereas the BHFS 2017 survey was fielded during the months of July and October 2017. The goal of the survey was to determine the availability and preparedness of healthcare institutions to provide services in the areas of maternity and child health, family planning, selected NCDs (diabetes and CVDs), and tuberculosis. The study also examined the availability of human resources, basic services, and logistics in healthcare institutions, including equipment, essential pharmaceuticals, laboratory services, and infection control measures that followed standard standards. We extracted the data from the dataset (BHFS 2014 and 2017) between January and March 2020.

Data source

This study mainly consists of two waves (2014 and 2017) of the BHFS dataset. This survey makes use of a standardized questionnaire of service provision assessment from the United States Agency for International Development’s (USAID) demography and health survey program. Together, the National Institute of Population Research and Training (NIPORT) and the Ministry of Health and Family Welfare (MOHFW) were responsible for conducting this survey with funding support from the Government of Bangladesh and USAID, the ICDDR, B aided NIPORT with field monitoring and quality assurance.

Sample size and sampling technique

The number of active healthcare facilities that served as the sample frames for the BHFS in 2014 and 2017 was 19,184 and 19,811 correspondingly. Following a stratified random sample, a total of 1,596 and 1,600 healthcare institutions throughout the nation were chosen for the 2014 BHFS and 2017 BHFS, accordingly. The 2014 and 2017 BHFS samples were planned to contain facilities from the country’s seven administrative divisions (Barisal, Chittagong, Dhaka, Khulna, Rajshahi, Rangpur, and Sylhet). District hospitals (DHs), maternal and child welfare centers, Upazila health complexes (UHCs), upgraded union healthcare and family welfare centers, union subcenters/rural dispensaries, and community clinics (CCs), as well as private hospitals with at least 20 beds and non-governmental organization (NGO) static clinics/hospitals, were all included. In addition, the number of records that were available for the BHFS in 2014 was 1,548, whereas in 2017 it was 1,524. The research examined the infrastructures that provide services for CVDs in order to evaluate how well they are prepared for CVDs. Additionally, the research also examined the facilities that provide services for diabetes in order to evaluate how well they are prepared for diabetes. The Bangladesh healthcare system provides services for NCDs up to the UHC. The study, therefore, excluded facilities like MCWC, UHFWC and CC and also those with missing values. Based on the exclusion criteria, a total of 407 diabetes facilities and a total of 386 CVDs facilities were included in our study (Figures 1 and 2).

b99ee0ac-2746-41c2-b844-939159574080_figure1.gif

Figure 1. Sample size calculation for diabetes and CVD in 2014.

CVD, cardiovascular disease.

b99ee0ac-2746-41c2-b844-939159574080_figure2.gif

Figure 2. Sample size calculation for diabetes and CVD in 2017.

CVD, cardiovascular disease.

Data collection procedures

The data collection process consisted of using the facility inventory questionnaire. In order to gather data about general and specialized service preparedness, the facility inventory questionnaire was employed. Within the framework of Bangladesh, the questionnaires were modified, verified, and field-tested in advance. Earlier, the comprehensive 2014 and 2017 BHFS were made available to the public.

The use of a methodical electronic questionnaire allowed for the collection of data. Following a training period of 15 days, a total of 40 data collecting teams, each consisting of two interviewers, were organized. The Associates for Community and Population Research (ACPR) and the NIPORT were responsible for supervising data collection. Each one of the seven field monitoring groups was provided with a trained data processing specialist and a medical doctor, the former of whom also acted as a position of master trainer. The data collecting teams had frequent visits from the field supervision teams so their performance could be evaluated and data quality could be monitored. Participants consented to the questionnaire before they provided their answers.

Data management and analysis

Following extraction of the relevant information from the BHFS 2014 and 2017 datasets, the dataset required for this research was compiled. Then, in accordance with The Service Availability and Readiness Assessment tool by WHO (WHO-SARA), the analyses for the objectives that were unique to each service (diabetes and cardiovascular diseases) were performed. The service area was taken into consideration as the binary variable, with the value “1” being assigned for availability and “0” being assigned for non-availability. The WHO-SARA score criteria for healthcare system preparation for NCDs was used in the evaluation, which resulted in the assessment. WHO-SARA guidelines for the analyses of the objectives that were unique to each service (diabetes and CVDs) are presented in Table 1. Comparison of healthcare facility characteristics and healthcare system readiness for diabetes and CVDs from 2014 and 2017 survey were performed by using descriptive statistics, statistical significance tests (paired t-test) by using Stata 14 (RRID:SCR_012763) and graphical presentation are provided.

Table 1. WHO-SARA tool for analyses of the objectives that were unique to each service (diabetes and CVDs).

DomainsTracer itemsMeasurementScore
Diabetics indicator and score
1. Staff and trainingGuideline for diabetes diagnosis and treatmentYes1
No0
Staff trained in diabetes diagnosis and treatmentYes1
No0
2. EquipmentBlood Pressure ApparatusYes1
No0
Height BoardYes1
No0
Adult Weight ScaleYes1
No0
3. Diagnostic facilityBlood glucoseYes1
No0
Urine protein testYes1
No0
Urine glucose testYes1
No0
4. MedicineMetforminYes1
No0
GlibenclamideYes1
No0
InsulinYes1
No0
Glucose 50% injectableYes1
No0
Gliclazide tabletYes1
No0
CVD indicator and score
1. Staff and trainingGuidelines for diagnosis and treatment of chronic cardiovascular conditionsYes1
No0
Trained staff to manage CVD patientsYes1
No0
2. EquipmentBlood Pressure ApparatusYes1
No0
StethoscopeYes1
No0
OxygenYes1
No0
Adult Weight ScaleYes1
No0
3. MedicineACE inhibitorYes1
No0
Hydrochlorothiazide tablet or another thiazide diuretic tabletYes1
No0
Beta blockerYes1
No0
Calcium Cannels blockersYes1
No0
Aspirin capsule/tabletYes1
No0

Results

Distribution of facilities provides services for diabetes and CVDs

A comparative distribution among two surveys (2014 and 2017) in diabetes health care facilities is shown in Table 2. The 2017 survey showed an increase in facility coverage at the private clinics but a decrease at NGO clinics compared with the 2014 study.

Table 2. Number of facilities providing services for diabetes.

Facilities for diabetesFacilities that were included in the 2014 surveyFacilities managing diabetes in 2014Facilities that were included in the 2017 surveyFacilities managing diabetes in 2017
District hospitals62596262
Upazila health complex140119141137
NGO clinic16468132105
Private clinic10371130103
Total469317465407

Table 3 shows the distribution of healthcare facilities for patients with CVDs between 2014 and 2017. We found that survey coverage of the facility at private clinics increased in 2017 compared with 2014 but declined at NGO clinics.

Table 3. Number of facilities providing services for CVDs.

Facilities for CVDFacilities that were included in the 2014 surveyFacilities managing CVD in 2014Facilities that were included in the 2017 surveyFacilities managing CVD in 2017
District hospitals62596262
Upazila health complex140119141135
NGO clinic1645813269
Private clinic10369130102
Total469305465368

Readiness scores for facilities of the domains for diabetes

Table 4 presents the readiness scores for facilities of each domain of diabetes in 2014 and 2017. As compared with 2014, the district hospital readiness score for diabetes increased from 60.62 to 59.04, the UHC readiness score increased from 45.01 to 52.29, and the NGO clinic readiness score increased from 48.65 to 55.28 in 2017. By contrast, at the private clinic, the score decreased from 63.12 to 56.11.

Table 4. Readiness scores for facilities of the domains for diabetes.

FacilitiesDistrict hospitalsUpazila health complexNGO clinicsPrivate clinic
Years (n)2014 (n=59)2017 (n=62)2014 (n=119)2017 (n=137)2014 (n=68)2017 (n=105)2014 (n=71)2017 (n=103)
Trained staff and guideline domain
Guideline on diabetes (%)72.8833.8758.8227.0142.6534.2932.399.71
Mean readiness score for guidelines on diabetes72.8833.8758.8227.0142.6534.2932.399.71
Staff trained in diabetes (%)62.7162.957.9877.3738.2441.949.333.98
Mean readiness score for trained staff62.7162.957.9877.3738.2441.949.333.98
Domain mean readiness score (SD)67.79 (7.19)48.38 (20.52)58.4 (0.59)52.19 (35.61)40.44 (3.12)38.1 (5.38)40.84 (11.95)21.85 (17.16)
p-value0.16710.41410.32230.1638
Equipment domain
Blood pressure apparatus (%)94.9298.3995.899.2710099.0598.59100
Height board (%)74.5882.2663.8783.2166.1882.8656.3451.46
Adult weight scale (%)88.1490.3287.3994.8989.7110085.9298.06
Domain mean readiness score (SD)85.88 (10.35)90.32 (8.06)82.35 (16.55)92.46 (8.3)85.29 (17.3)93.97 (9.63)80.28 (21.68)83.17 (27.48)
p-value0.2950.1990.2450.447
Diagnostic facility domain
Blood glucose (%)98.3110067.2375.9182.3590.4891.5588.35
Urine protein test (%)49.1561.2931.9335.0461.7671.4376.0668.93
Urine glucose test (%)49.1558.0631.9331.3957.3567.6271.8372.82
Domain mean readiness score (SD)65.53 (28.38)73.12 (23.33)43.69 (20.38)47.45 (24.71)67.15 (13.4)76.51 (12.24)79.81 (10.38)76.7 (10.27)
p-value0.3700.4250.2110.365
Medicine domain
Metformin (%)38.9864.5214.2940.8820.5937.1459.1563.11
Glibenclamide (%)25.429.6818.497.35.883.8135.2117.48
Insulin regular injectable (%)33.917.7410.9213.8713.248.5747.8947.57
Glucose 50% injectable (%)20.3412.91.682.195.882.8653.5236.89
Gliclazide tab or glipizide tab (%)NA48.39NA21.17NA10.48NA48.54
Domain mean readiness score (SD)29.66 (8.36)28.6 (25.75)11.34 (7.14)17.1 (17.22)11.39 (7.04)12.57 (16.22)48.94 (10.24)42.72 (19.16)
p-value0.3020.1630.3230.395
All domain total mean readiness score (SD)59.04 (26.93)60.62 (32.58)45.01 (31.06)52.29 (35.09)48.65 (32.8)55.28 (36.69)63.12 (21.59)56.11 (29.84)
p-value0.4210.3440.3590.442

Compared with 2014, in 2017, the readiness score of trained staff and guideline domain dropped in the district from 67.79 to 48.38, UHCs from 58.4 to 52.19, NGO clinics from 40.44 to 38.1, and private clinics from 40.84 to 21.85. On the contrary, the readiness score of the equipment domain increased at the district hospital in 2017 from 90.32 to 85.88, at the UPHC from 92.46 to 82.35, at the NGO clinic from 93.97 to 85.29 and at the private clinics from 83.17 to 80.28. Similarly, the district hospital had a domain readiness score of 73.12 compared with 65.53, UHC scored 47.45 with 43.69, and NGO clinics scored 76.51 with 67.15. At the same time, the private clinics’ scores declined from 79.81 to 76.7 in 2017. In 2017, the medicine domain’s score increased at the UHC from 17.1 to 11.34 and at NGO clinics from 12.57 to 11.39, while it decreased at the district hospital from 28.6 to 29.66 and at private clinics from 42.72 to 48.94.

Readiness scores for facilities of the domains for CVD

The readiness scores for all CVD facilities are shown in Table 5. Table 5 lists the readiness scores for all CVD facilities. There was a decrease in the total mean readiness scores of CVDs at the district hospitals from 64.04 to 58.50, UHC from 53.06 to 53.0, NGO clinics from 44.53 to 48.65, and private clinics from 61.58 to 64.15 in 2017 compared with 2014. Similarly, compared with 2014, in 2017, readiness scores for trained staff and guidelines decreased at district hospitals from 52.42 to 58.48, UHC from 48.52 to 57.14, NGO clinics from 26.09 to 42.25, and private clinics from 18.63 to 40.58. The readiness score of the equipment domain increased at UHC (88.02 vs. 91.67) and private clinics from 92.75 to 95.34; however, at district hospitals, it dropped from 89.11 to 92.37 in 2017 compared with 2014. In 2017, the readiness score of the medicine domain went up at private clinics from 51.76 to 50.69; instead, the score rose at district hospitals from 36.45 to 44.07, UHC from 23.99 to 24.2, and NGO clinics from 18.84 to 20.34.

Table 5. Readiness scores for facilities of the domains for CVD.

FacilitiesDistrict hospitalsUpazila health complexNGO clinicPrivate clinic
Years (n)2014 (n=59)2017 (n=62)2014 (n=119)2017 (n=135)2014 (n=58)2017 (n=69)2014 (n=69)2017 (n=102)
Trained staff and guideline domain
Guideline on CVD (%)61.0233.8748.742044.8327.5440.587.84
Mean readiness score for guidelines on CVD61.0233.8748.742044.8327.5440.587.84
Staff trained in CVD (%)55.9370.9765.5577.0439.6624.6440.5829.41
Mean readiness score for trained staff on CVD55.9370.9765.5577.0439.6624.6440.5829.41
Domain mean readiness score (SD)58.48 (3.59)52.42 (26.23)57.14 (11.88)48.52 (40.33)42.25 (3.66)26.09 (2.05)40.58 (0.00)18.63 (15.2)
p-value0.32240.39950.0160.0894
Equipment domain
Blood pressure apparatus (%)94.9298.3995.899.2610098.5598.55100
Stethoscope (%)10010099.1610010098.55100100
Adult weight scale (%)88.1490.3287.3994.0789.6610086.9698.04
Oxygen (%)86.4467.7469.7573.3353.4546.3885.5183.33
Domain mean readiness score (SD)92.37 (6.26)89.11 (14.86)88.02 (13.15)91.67 (12.5)85.78 (22.09)85.87 (26.33)92.75 (7.575)95.34 (8.06)
p-value0.34990.35110.49790.3282
Medicine domain
ACE inhibitor (Enalapril) (%)33.917.7410.9214.0707.2517.2448.04
Thiazide (%)18.646.455.042.2217.244.3540.5830.39
Beta blocker (Atenolol) (%)71.1946.7758.8233.3336.2130.4363.7757.84
Calcium channel blockers (Amlodipine) (%)54.2456.4528.574034.4830.4375.3666.67
Aspirin (%)42.3754.8417.6530.3713.7921.7456.5255.88
Domain mean readiness score (SD)44.07 (19.95)36.45 (22.88)24.2 (21.23)23.99 (15.46)20.34 (15.15)18.84 (12.46)50.694 (22.55)51.76 (13.66)
p-value0.2450.49340.43410.465
All domain total mean readiness score (SD)64.04 (28.32)58.5 (31.14)53.03 (36.69)53.06 (36.43)48.65 (37.79)44.53 (36.77)64.15 (28.12)61.58(31.47)
p-value0.32280.49120.40810.4202

Region-wise readiness index (RI) scores specific to services for diabetes and CVD

Figure 3 depicts the region’s preparedness score. The bar chart shows the readiness score for both diabetes and CVD between 2014 and 2017 and informs us that the readiness score for diabetes and CVD management by region has not exceeded the 70% cutoff limit.14 In terms of diabetes treatment, the RI score dropped in urban regions, and increased in rural areas. The difference between urban and rural regions was significant (P=0.0001), however it did not hold over time (P=0.4443). However, the availability of CVD services in healthcare institutions is declining in both urban and rural areas. For CVD, the difference between urban and rural areas was substantial (P=0.0001), however it was not significant over time (P=0.3665).

b99ee0ac-2746-41c2-b844-939159574080_figure3.gif

Figure 3. Distribution of facility readiness of diabetes and CVD in 2014 and 2017 by location.

CVD, cardiovascular disease.

Division-wise readiness index scores specific to services for diabetes and CVD

Figure 4 depicts the readiness score for diabetes and CVD services by division. There is no division in which the readiness score has a high RI value. The difference in preparedness scores throughout the division was not statistically significant. Except for the Rajshahi division, the RI score for diabetes management indicated a downward trend. In 2017, all CVD divisions had lower RI scores than in 2014.

b99ee0ac-2746-41c2-b844-939159574080_figure4.gif

Figure 4. Readiness score by division.

CVD, cardiovascular disease.

Discussion

This research was effective in describing a thorough scenario of the availability of services and preparedness of healthcare systems in Bangladesh to deliver care associated with diabetes and CVDs. We have also identified the positive or negative changes that occurred from 2014 to 2017, identified the gaps in facilities regarding readiness towards diabetes and CVD services and compared the findings of previous research.13 The public health facilities in Bangladesh consist of CCs, Union health and family welfare centers (UHFWCs), UHCs, and DHs. The UHCs are the focal points for providing NCD services in Bangladesh. Moreover, recently MOHFW published an NCD management protocol and is trying to establish an NCD management model through establishment of a rereferral system between CCs and UHC.14

Historically, the healthcare system of Bangladesh has focused on infectious diseases, immunization, and maternal, child and reproductive health through public health facilities.11 Although, Bangladesh is among the top countries having large proportion of patients with diabetes, there is still a lack of information on it. The scenario is worse for CVD and it is associated with a large number of mortalities in Bangladesh.11 The overall improvement of healthcare facilities during 2014 to 2017 regarding readiness to provide NCD (DM, CVD) services is not quite satisfactory. In terms of services towards diabetes, the mean readiness score increased in 2017 for district hospitals, UHC, and the NGO clinic from 2014, although this improvement was not statistically significant. However, at the private clinics, the facility readiness score decreased. The MOHFW and owners of the private centers need to take effective actions for significant improvement.

There has always been a lack of trained human resources in healthcare facilities of Bangladesh.15 It a matter of concern that the readiness score of trained staff and guideline domain dropped in all four types of facilities we assessed. The government should revise their training modules and guidelines for the overall improvement of human resources. The MOHFW and DGHS of the Government of Bangladesh need to come forward to take effective action to reduce the human resource gap. Recent studies published in Bangladesh and other countries have observed low number of trained healthcare providers on diabetes management.15,16 While there is a lack of trained human resources in facilities, the readiness score of the equipment domain is quite satisfactory and increased at all types of facilities during the targeted three years. The effective use of equipment requires trained human resources, which is also very much essential for better service delivery. For diagnostic facility domain, the status increased in DHs, UHCs and NGO clinics, however it decreased for private facilities. A large proportion of our population are dependent on private healthcare facilities.17 Therefore, the diagnostic facility needs to be improved in private clinics.

According to our data, the score in medicine domain was found to be quite low and the situation was worse in district hospitals and private clinics during 2014 to 2017. The medicine domain’s score increased at the UHC and NGO clinics. These results are consistent with the findings of a large number of studies conducted in LMICs, which suggest that healthcare providers are not yet totally equipped to deliver comprehensive diabetes care services.18,19 Our results are in line with those of prior research conducted in Bangladesh, which found that basic healthcare facilities had a major deficiency in the availability of relevant and important drugs for diabetes as well as an insufficient or restricted supply of such medicines.15

The prevalence of CVDs in Bangladesh is around 5%, and this percentage is consistent across all forms of CVD, sex and geographic locations, and for this reason, CVDs are the leading cause of death in Bangladesh.20 There was a decrease in the mean readiness scores of CVDs at the district hospitals, NGO clinics, and private clinics from 2014 to 2017. Similar to diabetes, readiness scores for trained staff and guidelines decreased in all types of facilities. The score of the equipment domain was decreased at district hospitals. The availability of skilled employees to deliver CVD–related healthcare services is of the utmost important. As a result, establishing specialist training for CVDs might provide optimal treatment.21 The readiness score of the medicine domain also reduced at district hospitals from UHC and NGO clinics. According to the findings of a number of studies conducted in Bangladesh, one of the primary difficulties faced by public healthcare facilities is a shortage of medications for CVDs.22 According to the findings of a study that was carried out recently, it was found that one of the primary reasons for patients’ disappointment with the government healthcare facilities is that there are not enough drugs or that they are of low quality.23 Disagreement has also been found in research conducted in India, on the availability of the sorts of pharmaceuticals suggested for the treatment of CVDs.24

In addition, despite the existence of a national standard for the treatment of diabetes, it is not followed appropriately in the vast majority of healthcare institutions at both the primary and secondary levels. Moreover, CVD management is not part of the national NCD management model in Bangladesh. Therefore, the respective authorities must incorporate CVD management guidelines into the national NCD management policy.

This study has identified the changes that took place in terms of facility readiness regarding NCD care from 2014 to 2017. The samples are typical of the nation as a whole and include data from all of Bangladeshi administrative units. On the other hand, this research does have a few restrictions when it comes to the method and the time frame. The BHFS 2017 was completed in 2017 and only covered information about DHs, UHCs, NGO clinics and private facilities. After 2017, no BHFS has been conducted until 2022, therefore most of the data are outdated. The tools used in these surveys are also outdated, which may also require modification for future surveys. These issues make it challenging for our study to reach a specific conclusion. The authors recommend collecting primary data with updated tools for a better understanding of facility readiness towards NCDs. Some further in-depth research and the outcomes of that research might point policymakers in the right path when it comes to taking the required actions.

Conclusions

In summary, there are significant deficiencies in essential categories of facility preparation, such as recommendations on diagnosing and treating diabetes and CVDs. These deficiencies need to be addressed as soon as possible. There is also a lack of skilled workers for services that are diabetes and CVD specific. The supply of medicines for diabetes and CVD is also inadequate. This study has compared the readiness score for diabetes and CVD over two separate survey data in 2014 and 2017. Our analysis concluded that no statistically significant development occurred during the targeted years regarding facility readiness. Since there has been no rise in the number of qualified workers throughout this period, services for diabetes and CVD have been ignored. Therefore, the crucial actions that need to be made are to ensure guidelines on the diagnosis and treatment of illnesses, the capacity to conduct diagnostics, and the capability to have sufficient medication and pharmaceuticals. These steps will help facilities be ready and help provide health coverage to the population. The information presented in this research might assist in the generation of scientific evidence that could be used by policymakers and other relevant stakeholders in establishing policies and programs that would benefit both the institutions and the population.

Comments on this article Comments (0)

Version 1
VERSION 1 PUBLISHED 28 Jul 2023
Comment
Author details Author details
Competing interests
Grant information
Copyright
Download
 
Export To
metrics
Views Downloads
F1000Research - -
PubMed Central
Data from PMC are received and updated monthly.
- -
Citations
CITE
how to cite this article
Jahan F, Ahmed A, Mohsin FM et al. Changes in the readiness of healthcare systems to provide diabetes- and cardiovascular disease-related services: A comparison of indices using data from the 2014 and 2017 Bangladesh Health Facility Surveys [version 1; peer review: 2 approved with reservations]. F1000Research 2023, 12:898 (https://doi.org/10.12688/f1000research.138772.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
track
receive updates on this article
Track an article to receive email alerts on any updates to this article.

Open Peer Review

Current Reviewer Status: ?
Key to Reviewer Statuses VIEW
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 1
VERSION 1
PUBLISHED 28 Jul 2023
Views
7
Cite
Reviewer Report 30 Nov 2023
Rashmi Maharjan, Epidemiology, University of Washington, Seattle, Washington, USA 
Approved with Reservations
VIEWS 7
Introduction: I am not sure about the guideline of this journal for introduction but the introduction section for this paper is too long, over 800 words. Typically journals allow introduction for 250-350 word count. Authors should reduce the word count ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Maharjan R. Reviewer Report For: Changes in the readiness of healthcare systems to provide diabetes- and cardiovascular disease-related services: A comparison of indices using data from the 2014 and 2017 Bangladesh Health Facility Surveys [version 1; peer review: 2 approved with reservations]. F1000Research 2023, 12:898 (https://doi.org/10.5256/f1000research.151998.r209886)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Views
8
Cite
Reviewer Report 26 Oct 2023
Dian Sidik Arsyad, Department of Epidemiology, Hasanuddin University, Makassar, Indonesia;  Cardiovascular Research, University Medical Center Utrecht, Utrecht, Utrecht, The Netherlands 
Approved with Reservations
VIEWS 8
In overall, the authors present an important study which demonstrate trends for the availability and readiness of the health care facility to deliver CVD and diabetes care in Bangladesh between 2014 and 2017. However, there are some areas that warrant ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Sidik Arsyad D. Reviewer Report For: Changes in the readiness of healthcare systems to provide diabetes- and cardiovascular disease-related services: A comparison of indices using data from the 2014 and 2017 Bangladesh Health Facility Surveys [version 1; peer review: 2 approved with reservations]. F1000Research 2023, 12:898 (https://doi.org/10.5256/f1000research.151998.r206265)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.

Comments on this article Comments (0)

Version 1
VERSION 1 PUBLISHED 28 Jul 2023
Comment
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
Sign In
If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password.

The email address should be the one you originally registered with F1000.

Email address not valid, please try again

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.

Code not correct, please try again
Email us for further assistance.
Server error, please try again.