Disparities in critical care resources across Pakistan – findings from a national survey

doi:10.21203/rs.3.rs-1306239/v1

Background: In response to the COVID-19 pandemic, concerted efforts were made by provincial and federal governments to invest in critical care infrastructure and medical equipment to bridge the gap of resource-limitation in Intensive Care Units (ICUs) across Pakistan. An initial step in creating a plan towards strengthening Pakistan’s baseline critical care capacity was to carry out a needs-assessment within the country to assess gaps and devise strategies for improving the quality of critical care facilities.

Methods: To assess the baseline critical care capacity of Pakistan, we conducted a series of cross-sectional surveys of hospitals providing COVID-19 care across the country. These hospitals were pre-identified by the Health Services Academy (HSA), Pakistan. Surveys were administered via telephonic and on-site interviews and based on a unique checklist for assessing critical care units which was adapted from the Partners in Health 4S Framework, which is: Space, Staff, Stuff, and Systems. These components were scored, weighted equally, and then ranked into quartiles.

Results: A total of 106 hospitals were surveyed, with the majority being in the public sector (71.7%) and in the metropolitan setting (56.6%). We found infrastructure, staffing, and systems lacking as only 19.8% of hospitals had negative pressure rooms and 44.4% had quarantine facilities for staff. Merely 36.8% of hospitals employed accredited intensivists and 54.8% of hospitals maintained an ideal nurse-to-patient ratio. 31.1% of hospitals did not have a staffing model while 37.7% of hospitals did not have surge policies. On chi-square analysis, statistically significant differences (p<0.05) were noted between public and private sectors along with metropolitan versus rural settings in various elements. Almost all ranks showed significant disparity between public-private and metropolitan-rural settings, with private and metropolitan hospitals having a greater proportion in the 1^st rank, while public and rural hospitals had a greater proportion in the lower ranks.

Conclusion: Pakistan has an underdeveloped critical care network with significant inequity between public-private and metropolitan-rural strata. We hope for future resource allocation and capacity development projects for critical care in order to reduce these disparities.

Critical care is a multidisciplinary specialty which encompasses the comprehensive diagnosis, management, and monitoring of patients who have, or are at risk of having, a life-threatening illness (1). The requirements of an intensive care unit (ICU) are quite vast with the inclusion of an allocated location, interprofessional staff, specialized beds and costly high-tech equipment (2). The latter includes mechanical ventilators, oxygen and air supply ports, access to electricity, and adequate space for staff, patients, and attendees. Analogously, multidisciplinary staff including nurses, intensivists, and allied health professionals are the bastions of a functioning ICU with observational evidence suggesting that an abundance in staffing can be correlated with reduced ICU mortality and length of stay (3,4).

The continued growth in global population and life-expectancy has led to a rise in non-communicable disease, provoking an increase in the demand of intensive care units and resources (5,6). The advent of the COVID-19 pandemic in 2020 acutely increased that burden and revealed the global deficit in ICU capacity, with current estimates suggesting that at least 96 countries and territories have a density of less than 5.0 ICU beds per 100,000 population (7). This gap is most evident in low-middle income countries (LMIC), leading to a potential inability to manage the anticipated influx of critically ill patients in surge situations, like the Covid-19 pandemic (8–10).

The World Health Organization (WHO) advocates for well-defined systems of acute care for the critically ill and injured as an integral part of resilient national health care systems (11). However, in order to successfully develop such systems in diverse LMIC settings, a thorough characterization and quantification of regional and national acute care capacity, human and material resources, and barriers to capacity growth is essential (11). In a recent systematic review, it was found that only 15 of 36 low-income countries had any publishable data regarding their ICU capacity (8). Due to this paucity of data, however, the capacity to care for critically ill patients in LMIC settings remains largely unknown. Where available, ICU beds are minimal (0-2.8 beds per 100,000 population), with most countries at around 2 per 100,000 including Pakistan (7,8,12–16).

To fill this knowledge gap, we evaluated critical care facilities across Pakistan by carrying out a national critical care resource assessment of infrastructure, equipment, and staffing. We aim to evaluate the capacity of Pakistan’s ICUs during the era of Covid-19 to outline shortcomings in care for critically ill patients. Our hope is to provide a comprehensive assessment in order to inform resource allocation and pandemic-planning by policy-makers, public-private partnerships, and other stakeholders in Pakistan’s healthcare system.

Study Objectives and Design:

Our survey assesses key central aspects required to deliver critical care safely and successfully to patients. By landscaping the ICU infrastructure across the country, we aimed to accentuate both the principal strengths and the gaps in our systems and practices, which would subsequently be then targeted with sustainable capacity building interventions. Our secondary objective is to establish a scoring and ranking system by which we can accurately assess critical care resources.

We conducted a nation-wide cross-sectional study to delineate the existing conditions of critical care facilities in Pakistan. The National ICU Preparedness Survey is an integral component of the overarching national scale-up endeavor, the COVID-19 Tele-ICU Project. This project is a key public-private collaboration between the Government of Pakistan and the Aga Khan University that aims to bolster critical care capacity and health care systems across the country.

Study Setting, Population and Definitions:

With over 220 million residents, Pakistan is a developing country that employs two large-scale independent healthcare networks: the public setup that is led by provincial governments and the private one that is administered by autonomous stakeholders. We defined ICU as any hospital unit with the availability of invasive mechanical ventilation for the maximum number of patients within that unit. We also surveyed high-dependency units (HDUs) if hospitals did not have ICUs available for the care of Covid-19 patients, and HDUs were defined as units which offered continuous monitoring and care to patients short of delivering invasive mechanical ventilation. Neonatal and pediatric facilities were excluded.

Study Instrument (Supplement 1):

We deployed a novel 52-point checklist that utilizes the Partners in Health, 4S (Staff, Stuff, Systems and Space) framework to assess and evaluate critical care facilities (Supplement 1). The existing framework was modified to account for contextual applicability after a detailed literature search and expert review.

This foundational assessment included information on essential and recommended facilities in an ICU/HDU. Basic hospital information collected included number of hospital beds, number of ICU beds and type of unit. The components were: Space (infrastructure), Staff (healthcare workers in a critical care unit) Stuff (consumable and non-consumable supplies, both medical and non-medical) and System (policies and protocols).

Each question was given a score of 1 if affirmative and 0 if not present or unknown. Unknown variables were captured but counted as not present for the purpose of analysis. The scores of each 4S component were then tallied. The Space component was scored out of 9, the Staff component out of 8, the Stuff component out of 19, and the System component out of 16.

Data Collection (Figure 1):

Data collection was done in two phases. In the first phase, the Pakistan Ministry of National Health Services Regulations and Coordination provided the core research team an official, authorized list of existing hospital facilities to be surveyed, along with their contact information. The first phase took place from May 2020 to November 2020. In the second phase, a list of hospitals was obtained from each of Pakistan’s respective provincial national ministries. This phase took place from June 2021 to August 2021. A bimodal interview strategy was executed, whereby, telephonic, or on-site interviews were conducted. All collected data from the checklists was entered onto a standardized REDCap survey form.

Quality control:

Prior to data collection, all team members were trained through orientation and practice sessions. To further augment this and minimize inter-operator reporting bias, a guidance document with descriptions of each item on the checklist and a uniform written script were also provided to all interviewers. Pilot testing of the study instrument was done prior to official rollout of the survey. A central communications team was also established to keep track of all outgoing correspondence and conduct real-time troubleshooting.

Statistical Analysis:

Analyses were done using R version 4.1.1. Descriptive statistics have been reported as frequencies and percentages for the categorical variables and mean and standard deviation for the continuous variables. Chi-square analysis and Fischer’s exact test were done to analyze the differences between public against private hospitals, and also metropolitan against rural hospitals.

As the score of each of the 4S components did not add up to same amount, we used a weighted index by dividing the total number of questions by the number of questions in each component so that all the components were scored uniformly out of a denominator of 25. These sections were then also added up to make an overall percentage score out of 100. Analysis of variance (ANOVA) and post-hoc Tukey’s Honestly Significant Difference (HSD) tests were done to observe any statistically significant heterogeneity between component scores.

The hospitals were then ranked using the quartiles from the percentage scores. Ranks were also clustered within the individual components of Space, Staff, Stuff, and Systems, within which we observed the proportionate breakup of hospitals along the lines of hospital setting, sector, and size.

Ethical Considerations and Data Management:

This study was approved by the Ethical Review Committee, the institutional review board at the Aga Khan University. Verbal consent was obtained before every interview and all data were kept confidential. Access to data was password-protected and limited to the core analysts only, to ensure due data privacy and security protocols.

A total of 135 hospitals were approached, of which 106 (78.5%) responded, and their characteristics are displayed in Table 1. All these hospitals accepted care of adult COVID-19 patients. There were regional disparities in the distribution of critical care facilities, with almost 90% of ICUs/HDUs concentrated in Punjab, Sindh, and Khyber Pakhtunkhwa (KPK) respectively and fewer facilities in Gilgit-Baltistan (5.7%), Azad Jammu and Kashmir (AJK) (4.7%), Baluchistan (0.9%) (Table 2). 76 hospitals (71.7%) were in the public sector, 26 (24.4%) were private hospitals, and 4 (3.77%) were administrated by philanthropy-based foundations. 60 hospitals (56.6%) were located in the metropolitan setting while 46 (43.4%) were located in the rural setting.

Table 1: Hospital Characteristics

Variables

Number of Responses (n=106)

Percentage

Provinces of Pakistan

Punjab

Sindh

KPK

AJK

Baluchistan

Gilgit-Baltistan

33

30

31

5

1

6

31.13%

28.30%

29.25%

4.72%

0.94%

5.66%

Cities

Metropolitan

Non-Metropolitan

60

46

56.60%

43.40%

Type of Hospital

Public

Private

Philanthropy-based

76

26

4

71.70%

24.43%

3.77%

Hospital size [Number of Beds]

≤100

100-499

500-999

≥1000

Not reported (Not Known)

Median

9

56

26

10

5

326

8.49%

52.83%

24.53%

9.43%

4.72%

IQR = 200, 560

Stratification of Critical Care Units Included in the Survey

ICU

HDU

No Critical Care Unit

85

12

9

80.19%

11.32%

8.49%

Average number of ICU beds †

Cumulative number of ICU beds † (n=86)

Average number of HDU beds †

Cumulative number of HDU beds† (n=39)

12

1560

9

1105

IQR = 8, 20

IQR = 6,40

Type of respondent

Consultant Physician

Trainee/Medical Officer

ICU Physician Director

Head Nurse

Administrative Staff

Staff Nurse

Others

47

19

10

7

11

4

8

44.34%

17.92%

9.43%

6.60%

10.38%

3.77%

7.55%

Table 2: Hospital Capacity Across Pakistan

	Number of Beds	Number of ICU Beds	Number of HDU Beds	Ventilators	Population (17)
Total	51592 (100%)	1560 (100%)	1105 (100%)	1185 (100%)	207,684,626
Punjab (n=33)	23100 (44.77%)	684 (43.85%)	152 (13.76%)	560 (47.26%)	112,019,014
Sindh (n=30)	12463 (24.16%)	515 (33.01%)	431 (39.00%)	273 (23.04%)	30,439,893
KPK (n=31)	13157 (25.50%)	262 (16.79%)	492 (44.52%)	287 (24.22%)	30,523,371
Baluchistan (n=1)	1062 (2.06%)	10 (6.41%)	20 (1.81%)	3 (0.25%)	12,344,408
Gilgit-Baltistan (n=6)	410 (0.80%)	31 (1.99%)	0 (0.00%)	26 (2.19%)	1,249,000 (est.)
AJK (n=5)	1400 (2.71%)	58 (3.71%)	10 (0.91%)	36 (3.04%)	4,045,366
Healthcare Sector
Public (n=76)	43495 (84.31%)	1145 (73.40%)	1075 (97.29%)	903 (76.20%)
Private (n=26)	6247 (12.11%)	333 (21.35%)	30 (2.71%)	253 (21.35%)
Philanthropy-based (n=4)	1850 (3.58%)	82 (5.26%)	0 (0.00%)	29 (2.45%)
Healthcare Setting
Metro (n=60)	36378 (70.51%)	1136 (72.82%)	518 (46.88%)	834 (70.38%)
Rural (n=46)	15214 (29.49%)	424 (27.20%)	587 (53.12%)	351 (29.62%)

97 hospitals (91.5%) had either an ICU or HDU critical care facility. Of these 97 hospitals, 85 hospitals (80.2%) cared for Covid-19 patients in an ICU while 12 (11.3%) only offered HDUs as the highest level of critical care support, while 9 (8.49%) hospitals did not have any Covid-19 critical care unit despite being listed as such in government registries. We included 86 ICUs and 39 HDUs overall as part of our survey which reported exact bed numbers, regardless of whether they cared for Covid-19 patients or not. The median number of total beds per facility was 326 (IQR= 360), ICU beds was 12 (IQR=12) and HDU beds was 9 (IQR=31). Survey respondents were traditionally consultant physicians (44.3%), followed by trainee medical officers (17.9%), and hospital administrative staff (10.4%).

Type of healthcare setup and geographical location were the main categories for comparison. Philanthropy-based facilities were included as private hospitals for the purpose of analysis. The number of ICU beds per hospital in the public sector is 15.1 beds while in the private sector it is 13.8. In the metropolitan setting, it is 18.9 while in the rural setting, it is 9.2. There are 11.9 ventilators per hospital in public hospitals, 9.4 in private ones, 13.9 in metropolitan ones, and 7.6 in rural hospitals. Our 4S components were also analyzed along these lines.

Space

The majority of units had gaps in their infrastructure and were not adequately equipped. Only 21 (19.8%) contained negative pressure rooms, with greater scarcity in public sector hospitals compared to private ones (p=0.004). 59 facilities (55.6%) had no quarantine and lodging facility for the staff members and isolation rooms were present in 74 facilities (69.8%). Significant difference was noted in the availability of medical air, vacuum, adequate gas, and adequate power outlets at the beds in public sector hospitals and rural areas as compared to private or metropolitan hospitals. Notably, rural areas are comparatively lacking in a centralized manifold for oxygen delivery (p=0.048), with oxygen being delivered to patients via individual bedside cylinder. The mean score for the Space components was 5.91 out of a total of 9.

Detailed characteristics of the Space component can be seen in Table 3.

Table 3: Space component characteristics

Space	Total (n=106)	Type of hospital			Healthcare setting
Space		Public (n=76)	Private (n=30)	P-value	Metropolitan (n=60)	Rural (n=46)	P-value
Negative Pressure Rooms	21 (19.81%)	9 (11.84%)	12 (40.00%)	0.004*	16 (26.67%)	5 (10.87%)	0.036
Isolation Rooms/ Areas	74 (69.81%)	50 (66.67%)	24 (80.00%)	0.32	44 (73.33%)	30 (65.22%)	0.67
Adequate Power Outlets	81 (76.42%)	53 (70.67%)	28 (93.33%)	0.042	54 (90.00%)	27 (58.70%)	0.001
Adequate Gas Outlets	83 (78.30%)	55 (73.33%)	28 (93.33%)	0.007	54 (90.00%)	29 (63.04%)	0.003
Oxygen	86 (81.13%)	59 (78.67%)	27 (90.00%)	0.066	53 (88.33%)	33 (71.74%)	0.048
Medical air	75 (70.75%)	47 (62.67%)	28 (93.33%)	0.002	52 (86.67%)	22 (73.33%)	<0.001
Vacuum	76 (71.70%)	48 (64.00%)	28 (93.33%)	0.003	52 (86.67%)	24 (52.17%)	<0.001
Donning and doffing area	72 (67.92%)	51 (68.00%)	21 (70.00%)	0.77	41 (68.33%)	31 (67.39%)	0.87
Quarantine and lodging facility for staff members	59 (55.66%)	39 (52.00%)	20 (66.67%)	0.72	39 (65.00%)	20 (43.48%)	0.40

Staff

Most hospitals were well equipped with trainee doctors (95.2%) and ICU nurses (94.3%). However, 39 hospitals (36.8%) employed board certified intensivists, with significantly less prevalence in public (p=0.001) and rural (p=0.011) settings. Care of critical care patients was predominantly handled by anesthesiologists and pulmonologists. Similarly, despite the presence of ICU nurses, only 58 hospitals (54.7%) contained the optimal nurse-to-patient ratio. The public sector and rural areas suffered from a significant dearth of sufficient nursing coverage (p<0001 for both). Access to pharmacists (68.9%), physical therapists (68.9%) and dedicated housekeeping staff (88.7%) was reasonable in all facilities. Very few hospitals had access to a dietician (35.9%), with significant decrease in availability in public (p<0.001) and rural (p<0.001) settings. The mean score for the Staff components was 5.43 out of a total of 8.

Detailed characteristics of the Staff component can be seen in Table 4.

Table 4: Staff component characteristics

Staff	Total (n=106)	Type of hospital			Healthcare setting
Staff		Public (n=76)	Private (n=30)	P-value	Metropolitan (n=60)	Rural (n=46)	P-value
Availability of Qualified Intensivists	39 (36.79%)	21 (27.63%)	18 (60.00%)	0.001	29 (48.33%)	10 (21.74%)	0.011
Availability of Trainee doctors / Medical officers	101 (95.28%)	72 (94.74%)	29 (96.67%)	0.81	57 (95.00%)	44 (95.65%)	0.40
ICU nurses
Presence of ICU Nurses	100 (94.34%)	70 (92.11%)	30 (100.00%)	0.11	58 (96.67%)	42 (91.30%)	0.24
Availability of optimal nurse ratio (1:2/1:3)	58 (54.72%)	31 (40.79%)	27 (90.00%)	<0.001	43 (71.67%)	15 (32.61%)	<0.001
Ancillary staff / services
Access to Pharmacist	73 (68.87%)	51 (67.11%)	22 (73.33%)	0.53	44 (73.33%)	29 (63.04%)	0.26
Physical therapist	73 (68.87%)	48 (63.16%)	25 (83.33%)	0.12	44 (73.33%)	29 (63.04%)	0.32
Dietician	38 (35.85%)	18 (23.68%)	20 (66.67%)	<0.001	30 (50.00%)	8 (17.39%)	<0.001
Dedicated housekeeping / cleaning staff	94 (88.70%)	65 (85.52%)	29 (96.67%)	0.25	57 (95.00%)	37 (80.43%)	0.047

Stuff

Equipment was present in most facilities including ventilators (94.3%, mean=11.97±1.47) and BiPap machines (81.1%, mean=9.48±1.68), with a relative lack of high-flow nasal cannulas (56.6%, mean=7±1.52). However, there was significantly less ventilator availability in the rural setting (p=0.004) and BiPap machine availability in public sector hospitals (p=0.016). Rural areas were also underserved in terms of the availability of intubation equipment (p=0.004), vascular access devices (p=0.003), medication pumps (p=0.005), and suction apparatus (p=0.045). Both public healthcare setups and rural facilities demonstrated a significant lack of information tools such as phones and computers, with public sector hospitals having additional limited internet availability. The mean score for the Stuff component was 16.00 out of a total of 19.

Detailed characteristics of the Stuff component can be seen in Table 5.

Table 5: Stuff component characteristics

Stuff	Total	Type of hospital (n=106)			Healthcare setting (n=106)
Stuff	n=106	Public (n=76)	Private (n=30)	P-value	Metropolitan (n=60)	Rural (n=46)	P-value
Personal Protective Equipment	99 (93.40%)	71 (93.42%)	28 (93.33%)	0.99	57 (95.00%)	42 (91.30%)	0.45
In-house Laboratory Testing Facility	100 (94.34%)	70 (92.11%)	30 (100.00%)	0.11	57 (95.00%)	43 (93.48%)	0.74
Critical Care drugs	98 (92.45%)	69 (90.79%)	29 (96.15%)	0.30	58 (96.67%)	40 (86.96%)	0.061
Vascular Access Devices	87 (82.08%)	58 (76.32%)	29 (96.67%)	0.014	55 (91.67%)	32 (69.57%)	0.003
Ventilators	100 (94.34%)	70 (92.11%)	30 (100.00%)	0.11	60 (100.00%)	40 (86.96%)	0.004
BiPap Machine	86 (81.13%)	58 (76.32%)	28 (93.33%)	0.016	52 (86.67%)	34 (73.91%)	0.178
High-Flow Nasal Cannula	60 (56.60%)	38 (50.00%)	22 (73.33%)	0.085	36 (60.00%)	24 (52.17%)	0.449
Integrated Physiologic Monitors	97 (91.51%)	68 (89.47%)	29 (96.67%)	0.23	59 (98.33%)	38 (82.61%)	0.004
Specialized beds for ICU/HDU patients	88 (83.02%)	61 (80.26%)	27 (90.00%)	0.45	53 (88.33%)	35 (76.09%)	0.11
Intubation Equipment	100 (94.34%)	70 (92.11%)	30 (100.00%)	0.11	60 (100.00%)	40 (86.96%)	0.004
Medication Pumps (for IVs, tube feed, etc.)	91 (85.85%)	61 (80.26%)	30 (100.00%)	0.032	57 (95.00%)	34 (73.91%)	0.005
Suction Apparatus	103 (97.17%)	62 (81.58%)	30 (100.00%)	0.27	60 (100.00%)	43 (93.48%)	0.045
Crash Cart with Defibrillator	92 (86.19%)	54 (71.05%)	30 (100.00%)	0.041	55 (91.67%)	37 (80.43%)	0.18
X-Ray Machine	79 (75.96%)	69 (90.79%)	25 (83.33%)	0.22	47 (78.33%)	32 (69.56%)	0.79
Decontamination / cleaning materials and chemicals	97 (91.51%)	69 (90.79%)	28 (93.33%)	0.67	55 (91.67%)	42 (91.30%)	0.95
ICU patient information record / flow sheets	91 (85.85%)	63 (82.89%)	28 (93.33%)	0.16	51 (85.00%)	40 (86.96%)	0.77
Telephones	83 (78.30%)	55 (72.37%)	28 (93.33%)	0.018	53 (88.33%)	30 (65.22%)	0.004
Computers	71 (66.98%)	44 (57.89%)	27 (90.00%)	0.002	45 (75.00%)	26 (56.52%)	0.045
Internet connection	73 (68.87%)	46 (60.53%)	27 (90.00%)	0.003	45 (75.00%)	28 (60.87%)	0.12

System

84 hospitals (79.3%) had specific COVID-19 protocols in place. More than 80% of hospitals also had protocols in place for resuscitation, biomedical support, information technology (IT) support and transport. Less had them for patient surge (62.3%), risk mitigation (51.9%) and environmental control (56.6%). Significantly fewer rural and public hospitals had support access via biomedical, IT and infrastructure support policies. 73 hospitals (68.87%) had staffing models for doctors and nurses, but ICU workflow policies with staffing models for doctors (p=0.009) and nurses (p=0.028) were reported to be significantly less in rural hospitals. Public sector hospitals showed gaps in emphasizing infrastructure failure (p=0.013) and cardiopulmonary resuscitation (CPR) policy (p=0.05), with rural hospitals also being less likely to implement CPR policies (p=0.011) as well. The mean score for the System component was 11.68 out of a total of 16.

Detailed characteristics of the System component can be seen in Table 6.

Table 6: System component characteristics

System	Total (n=106)	Type of hospital			Healthcare setting
System		Public (n=76)	Private (n=30)	P-value	Metropolitan (n=60)	Rural (n=46)	P-value
COVID protocol	84 (79.25%)	58 (76.32%)	26 (86.67%)	0.46	50 (83.33%)	34 (73.91%)	0.26
Staffing models for Doctors	73 (68.87%)	47 (61.84%)	26 (86.67%)	0.037	47 (78.33%)	26 (56.52%)	0.009
Staffing models for Nurses	73 (68.87%)	46 (60.53%)	27 (90.00%)	0.028	48 (80.00%)	25 (54.35%)	0.013
Admission Policy	73 (68.87%)	49 (64.47%)	24 (80.00%)	0.24	46 (76.67%)	27 (58.70%)	0.11
Referral/discharge policy	75 (70.75%)	52 (68.42%)	23 (76.67%)	0.68	45 (75.00%)	30 (65.22%)	0.54
Surge policy	66 (62.26%)	45 (59.21%)	21 (70.00%)	0.29	40 (66.67%)	26 (56.52%)	0.32
Personal Protective Equipment policy	84 (79.25%)	57 (75.00%)	27 (90.00%)	0.076	50 (83.33%)	34 (73.91%)	0.48
CPR/Resuscitation policy	85 (80.19%)	56 (73.68%)	29 (96.67%)	0.028	54 (90.00%)	31 (67.39%)	0.011
Airway Management protocol	82 (77.36%)	55 (72.37%)	27 (90.00%)	0.12	53 (88.33%)	29 (63.04%)	0.008
Infrastructure failure policy	75 (70.75%)	48 (63.16%)	27 (90.00%)	0.013	49 (81.67%)	26 (56.52%)	0.006
Risk mitigation policy	55 (51.89%)	37 (48.68%)	18 (60.00%)	0.53	34 (56.67%)	21 (45.65%)	0.42
Environmental control policy	60 (56.60%)	39 (51.32%)	21 (70.00%)	0.14	38 (56.67%)	22 (47.83%)	0.13
Supply chain	80 (75.47%)	55 (72.37%)	25 (83.33%)	0.55	49 (81.67%)	31 (67.39%)	0.091
Biomedical support	95 (89.62%)	65 (85.52%)	30 (100.00%)	0.028	59 (98.33%)	36 (78.26%)	<0.001
IT support	89 (83.96%)	60 (78.95%)	29 (96.67%)	0.007	56 (93.33%)	33 (71.74%)	0.005
Transport facility	89 (83.96%)	61 (80.26%)	28 (93.33%)	0.099	51 (85.00%)	38 (82.61%)	0.1106

4S Scoring

We had hypothesized that private hospitals were better-resourced as compared to public ones, and also that metropolitan hospitals more well-equipped than rural ones. We performed a cluster analysis where we made 4 quartiles of ranks in each of the 4S components, and also in overall scoring. We then observed the breakdown of each rank in the components according to hospital setting, hospital sector, and hospital size in terms of bed numbers, and this breakdown is seen in Table 7, which shows statistically significant disparity between these strata.

Table 7: Proportionate Ranks of the Component Scores. Percentages are calculated column-wise to calculate the proportion of hospitals in each rank.

Space

Staff

Stuff

System

Overall

Rank 1

Rank 2

Rank 3

Rank 4

Rank 1

Rank 2

Rank 3

Rank 4

Rank 1

Rank 2

Rank 3

Rank 4

Rank 1

Rank 2

Rank 3

Rank 4

Rank 1

Rank 2

Rank 3

Rank 4

Hospital Sector

Public

28
63.6%

10
62.5%

17
73.9%

21
91.3%

6

33.3%

4

33.3%

37

88.1%

29

85.3%

26
54.2%

0

0.0%

24

77.4%

26

96.3%

0

0.0%

40

65.6%

15

68.2%

21

91.3%

12

44.4%

20

76.9%

19

73.1%

25

92.6%

76

Private

16
36.4%

6
37.5%

6

26.1%

2
8.7%

12

66.7%

8

66.7%

5

11.9%

5

14.7%

22

45.8%

0

0.0%

7

22.6%

1

3.7%

0

0.0%

21

34.4%

7

31.8%

2

8.7%

15

55.6%

6

23.1%

7

26.9%

2

7.4%

30

p-value

0.090

<0.001

0.059

<0.001

Hospital Setting

Metropo-litan

31
70.5%

10
62.5%

14
60.9%

5
21.7%

16

88.9%

10

83.3%

22

52.4%

12

35.3%

38

79.2%

0

0.0%

12

38.7%

10

37.%

0

0.0%

41

67.2%

11

50.0%

8

34.8%

23

85.2%

16

61.5%

13

50.0%

8

29.6%

60

Rural

13
29.5%

6
37.5%

9
39.1%

18
78.3%

2

11.1%

2

16.7%

20

47.6%

22

64.7%

10

20.8%

0

0.0%

19

61.3%

17

63.0%

0

0.0%

20

32.8%

11

50.0%

15

65.2%

4

14.8%

10

38.5%

13

50.0%

19

70.4%

46

p-value

0.002

<0.001

0.001

0.025

<0.001

Hospital Size

<100

0

0.0%

3

18.8%

2

8.7%

4

17.4%

0

0.0%

0

0.0%

1
2.4%

8
23.5%

0

0.0%

0

0.0%

4

12.9%

5

18.5%

0

0.0%

3

4.9%

2

9.1%

4

17.4%

0

0.0%

0

0.0%

3

11.5%

6

22.2%

9

100-499

26

59.1%

6

37.5%

10

43.5%

14

60.9%

11
61.1%

9

75.0%

21

50.0%

15

44.1%

25

52.1%

0

0.0%

16

51.6%

15

55.6%

0

0.0%

32

52.5%

11

50.0%

13

56.5%

16

59.3%

10

38.5%

18

69.2%

12

44.4%

56

500-999

14

31.8%

3

18.8%

8

34.8%

1

4.3%

5

27.8%

3

25.0%

11

26.2%

7

20.6%

17

35.4%

0

0.0%

6

19.4%

3

11.1%

0

0.0%

19

31.1%

5

22.7%

2

8.7%

8

29.6%

11

42.3%

3

11.5%

4

14.8%

26

>1000

4

9.1%

3

18.8%

2

8.7%

1

4.3%

2

11.1%

0

0.0%

8

19.0%

0

0.0%

5

10.4%

0

0.0%

4

12.9%

1

3.7%

0

0.0%

7

11.5%

1

4.5%

2

8.7%

3

11.1%

4

15.4%

2

7.7%

1

3.7%

10

Unknown

0

0.0%

1

6.2%

1

4.3%

3

13.0%

0

0.0%

0

0.0%

1

2.4%

4

11.8%

1

2.1%

0

0.0%

1

3.2%

3

11.1%

0

0.0%

0

0.0%

3

13.6%

2

8.7%

0

0.0%

1

3.8%

0

0.0%

4

14.8%

5

p-value

0.004

0.008

0.014

0.033

0.002

Total

44
100%

16

100%

23

100%

23

100%

48

100%

0

100%

31

100%

27

100%

18

100%

12

100%

42

100%

34

100%

0

100%

61

100%

22

100%

23

100%

27

100%

26

100%

26

100%

27

100%

106

ANOVA testing on the mean scores of each component yielded significant variation between the scores, F(3, 424)=11.2, p<0.01. Tukey’s HSD post hoc comparisons were done between pairs of the 4 components and statistically significant differences were seen between stuff-staff (p<0.001), stuff-space (p<0.001), and system-stuff (p=0.008). Staff-space (p=0.921), system-space (p=0.157), and system-staff (p=0.463) did not show a statistically significant difference. The results of this are presented in Figure 2.

There were no hospitals in the 1^st rank of the System component. In each component, and also overall, the majority of private hospitals scored in the 1^st rank, with the exception of the System component where there were no hospitals in the 1^st rank. A majority of metropolitan hospitals also scored in the 1^st rank, except for in the Staff component, where a majority was seen in the 3^rd rank, and in the System component where they were in 2^nd rank. With the exception of the System component, hospitals in the 100-499 bed number range were consistently ranking 1^st.

We found significant disparities between public/private and urban/rural hospitals with public and rural hospitals being significantly under-resourced. Overall, we found a deficiency in negative pressure rooms, qualified intensivists, nurses, and institutional policies across Pakistan. We also found that public sector hospitals and rural hospitals were significantly under-resourced in a number of areas. Our scoring system is potentially valid assess healthcare capacity to care for critically ill patients.

Across the board, there is also a shortage of accredited intensivists and nurses in Pakistan’s critical care units. Only 36.79% of hospitals had even 1 qualified intensivist as the consultant physician in their ICU. While almost all hospitals employed nurses, only 54.72% of hospitals had an optimal nurse-to-patient ratio of 1:2, with a significant dip in their availability in both public and rural hospitals. The literature shows that higher nurse-to-patient ratios result in increased incidence of morbidity, mortality, and increased ventilator time for patients, so underqualification and understaffing could lead to compromised patient outcomes (3,18–20). Research to assess barriers towards critical care training is required to inform the advancement of accredited critical care training programs.

There were no hospitals at all that ranked 1^st in our System component, showing that Pakistan’s ICUs require more well-defined organizational policies across the board. Several hospitals were lacking in protocols for admissions, surge situations, PPE, airway management, and infrastructure failure, which could compromise patient care. Pakistan’s rural hospitals were also significantly less likely to make use of staffing models for doctors and nurses. This could potentially leave critical care doctors and nurses more susceptible to burnout. Healthcare systems abroad employ tiered staffing models to circumvent shortages of healthcare workers by repurposing staff from other specialties for specific critical care procedures, and this is recommended in managing ICU surge capacity (21,22). More work should be done in introducing policies and strategizing around the current constraints in critical care human resources.

We observed substantial variation in the overall healthcare delivery of critical care units throughout Pakistan. Pakistan’s decentralized healthcare setup meant that we anticipated the differences in resources across provinces, as each province is responsible for the budgeting and upkeep of their own respective public hospitals. The lack of any robust healthcare coverage system means that substantial swathes of society are dependent on the subsidized public setup for healthcare. Therefore, the lack of adequate resources at these hospitals renders the less fortunate to inequitable critical care and possible morbidity or mortality (23,24). As of yet, there are no studies on the effect of public and private critical care on Covid-19 outcomes. However, there is literature from Brazil, a high-middle income country with a similar dichotomy in its public-private healthcare system as Pakistan, which showed that being treated at a public hospital ICU is an independent risk factor of mortality in sepsis patients (25). They reported that these hospitals featured an “unfavorable patient-healthcare professional ratio, non-optimized processes, and a lack of adequate infrastructure”; these findings are also present in our setting.

Rural areas are more lacking in important consumable resources and infrastructural components, which is alarming because 63.56% of Pakistan’s population is based in rural areas, a sizeable majority (17). They are lagging behind in several key characteristics in each section of our 4S checklist. While developed countries like the United States also experience disparity in critical care delivery between rural and metropolitan areas, the gap that we have found in our setting is more stark (26,27). The shortcomings in critical care delivery to these areas makes its populace susceptible to the worst complications of critical Covid-19.

The current literature that we found on ICU capacity assessment only includes descriptive data. Our checklist scoring and clustering system represents a novel and potentially useful method of assessing hospital resources. ANOVA testing of the means of our component scores reveals that there is a significant difference between components, and we can provisionally say that our system of scoring and ranking hospitals is valid. We did not capture any patient data in our study, but comparing clinical outcomes between hospital ranks would help assess our ranking system’s applicability.

There are some limitations to our study. The list of hospitals was obtained from government registries which meant that we did not have access to hospitals that were not featured on such registries. Baluchistan was underrepresented, with only 1 hospital in our survey. More partnerships between the federal government and hospitals in Baluchistan are needed, as there was an overall lack of hospitals and limited accessibility to them. We were logistically unable to conduct a field visit at each hospital, which meant data collection was left to the knowledge of the telephonic respondents who may or may not have had an adequate inventory of their hospitals.

However, this is still the first national level cross-sectional survey conducted during the Covid-19 era; it employs and adapts the 4S data collection instrument to holistically assess and rank infrastructure, inventory, human resources, and policy at each critical care unit. It can also be utilized for other capacity strengthening initiatives in Pakistan and worldwide (21,28). We have observed the disparities in Pakistan’s critical care delivery, between government and private hospitals and also between the metropolitan and rural settings. We hope that our study will encourage stakeholders to find targeted solutions to better critical care delivery across Pakistan such as training programs, broader investment, and creative thinking.

The study has highlighted how Pakistan has an underdeveloped critical care network with significant inequity between across population densities and healthcare structure. The nature of Pakistan’s decentralized healthcare system and lacking infrastructure represent key areas for policy development and resource allocation by decisionmakers to overcome the disparities in critical care. Our survey model may be replicated in other countries to assess the adequacy of healthcare delivery, in critical care and beyond.

ICU: Intensive care unit

WHO: World Health Organization

LMIC: Low-middle income country

HDU: High-dependency unit

ANOVA: Analysis of variance

Tukey’s HSD test: Tukey’s Honestly Significant Difference test

IT: Information technology

CPR: Cardiopulmonary resuscitation

PPE: Personal protective equipment

Ethical Approval and Consent to Participate

Not applicable

Consent for Publication

Not applicable

Availability of Data and Materials

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing Interests

The authors declare that they have no competing interests.

Funding

This study is part of the Aga Khan University Covid-19 Tele-ICU project and was funded by the Bill and Melinda Gates Foundation.

Author’s Contributions

HS, HA, AHH, ZS, and AL were responsible for the conception and design of this paper. MAK, HS, AAN, ANE, and MZ were major contributors to writing the manuscript. HS, AAN, ANE, FA, MMH, MHA, MS, AAA, MFK, AS, MA, MHK, and SKA were responsible for data acquisition and collection. MAK and TM were responsible for data analysis. MAK, AAN, and TM were responsible for the figures. MAK, ZS, AHH, and AL drafted and substantively revised the work.

All authors approved the final version of the manuscript and agree both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

Acknowledgements

Not applicable

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No competing interests reported.

Supplement1.docx

Disparities in critical care resources across Pakistan – findings from a national survey

Status:

Version 1

Abstract

Figures

Background

Methodology

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1