Socio-professional characteristics of the participants
Respondents’ age range was 24 to 60 years with an average of 38.3±9.3 years; 58 (52.3%) male and 53(47.7%) female. Their median years of experience was 8(4-16) years. Nurses and nurse assistants made up 59.5%, while medical doctors were 10.8% (Table 1). With respect to post of responsibility of the participants, heads of HFs represented 45.0%, statistician/data managers (21.6%), Ward Charge (18.0%), and General supervisor (15.4%).
Table 1: Socio-professional characteristics of respondents.
Variable
|
Count
|
Percentage (%)
|
|
Age (years)
|
|
|
|
< 30
|
25
|
22,5
|
|
31 – 40
|
46
|
41,5
|
|
41 – 50
|
27
|
24,3
|
|
> 50
|
13
|
11,7
|
|
Sex
|
|
|
|
Female
|
53
|
47,7
|
|
Male
|
58
|
52,3
|
|
Professional Qualification
|
|
Medical Doctor
|
12
|
10,8
|
|
Nurse and Nurse assistant
|
66
|
59,5
|
|
Midwife and assistant
|
7
|
6,3
|
|
Lab technician
|
9
|
8,1
|
|
Health administrator
|
4
|
3,6
|
|
Specialised nurse
|
6
|
5,4
|
|
Others
|
7
|
6,3
|
|
Function
|
Head of HF
|
50
|
45.0
|
General Supervisor
|
17
|
15.4
|
Ward Charge
|
20
|
18.0
|
Statistician/Data manager
|
24
|
21.6
|
Characteristics of health facilities
There were 16 (14.4%) public and 95 (85.6%) private HFs. These HFs were distributed per district as follows: Biyem-Assi (21, 18.9%), Cité-Verte (6, 5.4%), Djoungolo (20, 18.0%), Efoulan (18, 16.2), Nkolbisson (9, 8.1%), and Nkolndongo (9, 8.1%).
Health facility and community information system standards
Overall, between 15% and 22% of the participants stated that the four domains of the RHIS functioned adequately, i.e., needed no strengthening action (Figure 1). The proportions of respondents who proposed strengthening action for the various domains were: 51% for Data Collection and Processing, 48% for Data Analysis, Dissemination and Use, 43% for Data Decision and Support Needs, and 41% for Management and Governance.
All the twelve subdomains of the RHIS were stated to function adequately by varying proportions of respondents ranging between 7% and 30% (Figure 2). These included Human Resources (7%), Data Analysis (10%), Information and Communication Technology (11%), Standards and System Design (15%), Policies and Planning (15%), Information Dissemination (16%), Data Demand and Use (16%), Management (18%), Data Needs (18%), Data Quality Assurance (20%), Collection and Management of Individual Client Data (26%), Collection, Management, and Reporting of Aggregated Facility Data (30%).
Subdomains that were most solicited for some or a lot of strengthening actions were: Collection, Management and Reporting of Aggregated Facility Data (59%), Data Demand and Use (57%), Collection and Management of Individual Client Data (54%), and Data Quality Assurance (50%).
The maximum proportion of respondents who stated that no strengthening action was needed was 30%. This proportion corresponded to the domain Data Collection and Processing, in particular the subdomain collection, management and reporting of aggregate facility data (Figure 3).
The proportions of respondents that mostly solicited strengthening actions also differed in the six districts from one domain to the other (Figure 4). E.g., in Cite Verte and Biyem-Assi, 56% and 53% of the respondents respectively stated that Data Analysis, Dissemination, and Use should be strengthened; while 54% and 59% respectively stated that Data Collection and Processing should be strengthened. With respect to the domain Data and Decision Support Needs, 55% of the respondents in Biyem-assi and 47% in Nkolbisson stated that this domain needed strengthening. Lastly, in the districts of Cite-Verte and Efoulan, 67% and 62% of participants respectively stated that Management and Governance should be strengthened.
The strengthening measures proposed by key informants at the regional health office were summarised and presented in the Tables 2-5.
Table 2: Proposed strengthening actions for Management and Governance.
Management and Governance
|
Subdomains
|
Proposed strengthening measures
|
Policies and Planning
|
ü Review the legislation and regulation.
ü Define clearly the roles and responsibilities of stakeholders at all pyramidal levels and disseminate to all HFs, especially private HFs during their creation.
ü Develop and disseminate a procedure manual and appropriate data management guidelines.
ü Ensure coordination between stakeholders at the district level.
ü Include stakeholders in the data validation process.
|
Management
|
ü Harmonize the various Standard Operating Procedures (SOP) between stakeholders.
ü Train and post the personal in charge of monitoring and evaluation (M&E).
ü Introduce performance-based financing (PBF) in M&E activities.
ü Produce and disseminate supervision guidelines to all stakeholders.
ü Enforce the implementation of the already existing supervision action plans.
ü Update the Master facility list (MFL) to include service domains and unique identifier codes for all HFs.
ü Consider regular trimestral update by the districts and a general census every 5 years to update the MFL.
|
Human resources
|
ü Define clearly in the procedure manual the various positions and the competencies of staff required at every level.
ü Identify the various required staffs and then post according to their competencies.
ü Develop and validate a costed work force training plan for pre- and in-service training.
ü Standardise the training curriculum and modules among training institutions in the health sector.
ü Harmonise staff training data bases between the Regional authorities and partners.
ü Use staff management software to manage pre- and in-service training of staff.
|
Table 3: Proposed Data and Decision Support Needs strengthening interventions.
Data and Decision Support Needs
|
Subdomains
|
Proposed strengthening measures
|
Data needs
|
ü Develop a regional data dictionary aligning with international standards.
ü Standardise data on mortality to be collected by all HFs.
ü Introduce the use of the international certificate of cause of death by all facilities to collect data on causes of death.
ü Train health professionals on the use of the international certificate of cause of death.
ü Introduce coding of cause of death (DHIS 2 start-up mortality list) into DHIS 2, and train staff on the coding of cause of death.
ü Introduce the use of Verbal Autopsy (VA) to investigate community deaths of unknown causes.
ü Train targeted HF and community staff to conduct VA.
ü Put in place review committees to analyse the cases of deaths of unknown causes.
ü Sign contracts with community workers and compensate them accordingly.
ü Enforce the sensitisation of stakeholders on the surveillance of epidemic prone diseases.
ü Equip the national laboratory to confirm the diagnosis of detected cases at the regional level.
|
Data standards
|
ü Widely disseminate community-based information guideline to all HFs and community agents.
ü Intensify efforts to harmonise indicators between partners
ü Integrate all national classifications and data collection forms into DHIS 2.
ü Ensure participation of all stakeholders (end users inclusive) in the evaluation and update of the HF and community HIS.
ü Enforce regular monthly meetings between stakeholders to discuss ways to render routine data more relevant
|
Table 4: Proposed strengthening measures for the domain Data Collection and Processing.
Data Collection and Processing
|
Subdomains
|
Proposed strengthening measures
|
Collection and Management of Individual Client Data
|
ü Gradually and steadily introduce patient electronic files into DHIS 2 to standardise the collection of individual client data across all implementing partners.
ü Train district staffs in the use of DHIS 2, and ensure that the district staff trains their respective staff.
ü Put suitable video training tutorials at the disposal of HFs.
ü Produce and disseminate data management guidelines according to DHIS 2 indicators.
|
Collection, Management and Reporting of Aggregated Facility Data
|
ü Harmonise data compilation among implementing partners.
ü Ensure regular follow-up of reporting of activities to improve on completeness and timeliness
ü Train staff on the techniques of physical and electronic records archiving.
ü Develop a plan to update, produce and distribute data management tools (registers, compilation forms and DHIS 2).
ü Collect data from personal computers of staffs and store them according to national data storage policies.
|
Data quality assurance
|
ü Develop and disseminate a standardised data quality assurance plan to all actors.
ü Enforce the implementation of data quality norms, especially at the HF level;
ü Ensure that findings from data quality assessments are published.
ü Hold regular data quality validation and review meetings with all stakeholders before forwarding the data.
ü Introduce data quality checks into DHIS2 at all levels.
|
Information and communication Technology
|
ü Update ICT framework and define needs of HFs at all the levels.
ü Improve on the stability and simplicity of the android version of DHIS 2 for remote areas.
ü Ensure better internet and electricity coverage to remote areas to facilitate aggregated facility data reporting.
|
Table 5: Proposed strengthening actions for Data Analysis, Dissemination and Use.
Data Analysis, Dissemination, and Use
|
Subdomains
|
Proposed strengthening measures
|
Analysis
|
ü Collaborate with local research and academic institutions to conduct analytical reviews of HF and community-based data.
ü Standardise and diffuse SOPs on data analysis, dissemination and use.
|
Dissemination
|
ü Produce summaries of key finding (bulletins) every 3 to 6 months and distribute through mass media to all stakeholders.
ü Make use of dashboards and summary charts to convey information to target populations accordingly.
|
Data demand and Use
|
ü Sensitise and train clinical staff, facility managers and local level decision-makers on the use of information for monitoring their activities.
ü Ensure that HF and community-based information is used in health sector planning.
ü Render managers of RHI autonomous in defining their interventions and data needs and implement them.
|