Chhattisgarh is one of the poorest states in India. The state had a population of around 29 million and 77% of it lived in rural areas in 2020. The vulnerable indigenous communities called Scheduled Tribes constitute 31% of its population. In 2018–2019, the state had a density of 2.9 MOs per 10 000 population, which was poorer than the national average of 7.6 per 10 000 .
The state has 837 primary health centres, one per 30 000 population. The state had 5206 sub-centres, each covering around 5000 population and providing reproductive and child health services. By September 2020, the state had converted 1895 of its sub-centres into HWCs .
Selection of providers and HWCs
The study was aimed at assessing the in-service competence of CHOs, RMAs and MOs working at the primary care level. Therefore, individuals in the above cadres working at primary health centres or HWCs for 6 months or longer were included in the study. There were 1110 CHOs, 1212 RMAs and 396 MOs fulfilling the above criteria in September 2020, when the data collection was started . The minimum sample size was calculated to detect a 15% difference in mean competence scores between the groups with 90% power and confidence level of 95%. According to the above calculation, a minimum of 50 providers of each type were necessary to be covered. It was decided that around 10% of the eligible individuals in each cadre will be covered while ensuring the above minimum required sample size. The above sample size for each cadre was increased by 25% to account for non-response. Thus, a sample of 139 CHOs, 152 RMAs and 63 MOs was to be selected. The list of all 1110 CHOs working in the state was arranged district-wise from north to south. From the above list, 139 CHOs were selected using systematic random sampling. The required sample of 152 RMAs and 63 MOs was also selected through a similar procedure. The study was able to assess 132 CHOs, 129 RMAs and 50 MOs. The response rate for CHOs, RMAs and MOs was 95%, 85% and 79%, respectively.
Provider competence was assessed in terms of clinical knowledge for specific primary care services by using clinical vignettes. Clinical vignettes are a form of simulated clinical case structure, used primarily to measure knowledge and clinical reasoning of a healthcare provider [11, 25,26,27]. A key advantage of using clinical vignettes is that the case-mix is same for all the providers being assessed. This allows a valid comparison of their scores.
Similar to earlier studies, each clinical vignette was structured in the following stages—history taking, examination and investigations, diagnosis, treatment (prescription) and follow-up [11, 27]. Under each of the above stages, a set of relevant elements was added based on standard treatment guidelines and inputs from clinical experts.
The form of clinical vignettes used in this study, one of the interviewers played the part of patient and started by describing the main complaint (e.g. I am a 30-year-old woman with fever) and the provider was requested to proceed with the simulated consultation by subsequently asking questions related to history, examination and investigations. The provider was aware that the patient was imaginary and it was a simulated conversation with the interviewer. Whenever the provider asked any relevant question related to history, examination or investigation, the surveyor gave a standard response. After the history, examination and investigation sections, the provider was asked to state the diagnosis, treatment and follow-up [11, 27].
Each part of the vignette was standardized: (a) the elements expected to covered by the provider in history, examination and investigation; (b) the responses to be given by the interviewer to any relevant question by provider and c) the correct diagnosis, treatment (prescription) and follow-up care against which the providers’ responses are to be judged. The standardization was done using standard guidelines and advice of experts from the All India Institute of Medical Sciences, Raipur. The vignettes were pretested with a few CHOs, RMAs and MOs before being finalized.
The clinical vignettes were developed for 10 tracer conditions that cover the illnesses commonly seen at primary care level in Chhattisgarh. They were selected based on consultations with experts and practising clinicians. The clinical vignettes were on the following conditions: diarrhoea with severe dehydration, pneumonia, malaria, hypertension, diabetes, vulvo-vaginal candidiasis, pre-eclampsia, scabies, poisoning and sickle cell disease.
Some of the other important diseases in the state, like tuberculosis and leprosy, were not included as they are not expected to be diagnosed or treated at HWCs. Though HWCs do refer the presumptive cases of above diseases to higher facilities, the role does not involve clinical care.
Scoring of clinical vignettes
The maximum score for each vignette was of 100 marks. The 100 marks for each vignette were divided across relevant elements in proportion to the relative importance of that element in ensuring the best clinical care. The element-wise distribution of marks for each vignette was decided by a set of experts. It was validated by another set of clinical experts. Other studies have also used a similar approach for deciding the marks for different parts of a clinical vignette [27,28,29,30,31].
Data collection and analysis
The data collection was managed by the State Health Resource Centre, an autonomous body providing technical support to the department of health in Chhattisgarh. Each interviewer deployed for the data collection had an undergraduate degree in health sciences and a master’s degree in public health. Data collection for the study was done from October 2020 to February 2021. Apart from the vignettes, data were collected on the number of persons provided treatment by the concerned provider (CHO/RMA/MO) for various kinds of ailments. Mean and median with 95% confidence intervals were calculated for scores in different sections and vignettes. For statistical significance, one-way ANOVA was used to compare the difference in mean score of all three providers.
A multi-variate linear regression model was applied to confirm the difference in clinical scores of the three cadres. The outcome variable was the competence score achieved by the providers. We did not expect any extreme values for this variable. Five of the six independent variables included in the adjusted model were based on an existing study in Chhattisgarh . The above variables were—cadre, age, sex, distance of posting place from district headquarter and type of area (tribal/non-tribal). We expected the years of experience to be a relevant variable and therefore included it in the model.
Data analysis was done using IBM-SPSS 20.0 version software.
Ethics approval for the current study was provided by the Institutional Ethics Committee of State Health Resource Centre, Chhattisgarh, India. Informed written consent was obtained from each participant.