Mapping the Characteristics of Critical Care Facilities: Assessment, Distribution, and Level of Critical Care Facilities from Central India.

BACKGROUND

In low- and middle-income countries such as India, where health systems are weak, the number of available Critical Care Unit (Intensive Care Unit [ICU]) beds is expected to be low. There is no study from the Indian subcontinent that has reported the characteristics and distribution of existing ICUs. We performed this study to understand the characteristics and distribution of ICUs in Madhya Pradesh (MP) state of Central India. We also aimed to develop a consensus scoring system and internally validate it to define levels of care and to improve health system planning and to strengthen referral networks in the state.

METHODS

We obtained a list of potential ICU facilities from various sources and then performed a cross-sectional survey by visiting each facility and determining characteristics for each facility. We collected variables with respect to infrastructure, human resources, equipment, support services, procedures performed, training courses conducted, and in-place policies or standard operating procedure documents.

RESULTS

We identified a total of 123 ICUs in MP. Of 123 ICUs, 35 were level 1 facilities, 74 were level 2 facilities, and only 14 were level 3 facilities. Overall, there were 0.17 facilities per 100,000 population (95* confidence interval [CI] 0.14-0.20 per 100,000 populations). There were a total of 1816 ICU beds in the state, with an average of 2.5 beds per 100,000 population (95* CI 2.4-2.6 per 100,000 population). Of the total number of ICU beds, 250 are in level 1, 1141 are in level 2, and 425 are in level 3 facilities. This amounts to 0.34, 1.57, and 0.59 ICU beds per 100,000 population for levels 1, 2, and 3, respectively.

CONCLUSION

This study could just be an eye opener for our healthcare authorities at both state and national levels to estimate the proportion of ICU beds per lac population. Similar mapping of intensive care services from other States will generate national data that is hitherto unknown.