How Kaduna State overhauled their service delivery architecture using responsive feedback

Author: Dr Hamza Abubakar, Kaduna State Primary Healthcare Board

Country focus: Nigeria

Theme: Service-delivery

In brief:

The 2018 National Demographic and Health Survey Report revealed that Kaduna state had above average mortality rates when compared to other states. Upon investigation, it was revealed that poor-quality data was leading to systemic errors in service delivery planning in the primary healthcare system. By addressing the quality of the data inputs, the team were able to optimize service delivery and create long-term improvements in healthcare for everyone in Kaduna state.

 Problem and context: 

The 2018 National Demographic and Health Survey Reports ranked Kaduna State as first in neonatal mortality rates, forth in infant mortality rates and sixth in under five mortality rates across the country.

Kaduna State Primary Healthcare Board wished to understand the reasons for this poor performance, which healthcare strategies and policies were ineffective and how to improve them.

How did they address this information gap?

The state Commissioner of Health led a bottleneck assessment to assess demand and supply factors responsible for the suboptimal service delivery in the primary healthcare system.

Data collection methods included desk reviews of administrative data; qualitative reviews; and in-depth interviews with health care workers, patients and community members.

 What did the evidence reveal?

The bottleneck assessment revealed several demand- and supply-side gaps in Kaduna’s service delivery architecture. *

One structural issue identified was in relation to the implementation of the REW Microplan. There was poor and inaccurate data for planning that was leading to cost inefficiencies of over 40million NGN ($100,000) a year just within this one activity alone as well as and poor service delivery.

After triangulating data from various sources, it was revealed that there were inaccuracies in the categorization of population settlements. These inaccuracies affected 261 Primary Healthcare Centers (PHC) facilities, implying that each of these served communities residing over two kilometers from the facility and therefore should conduct outreach and mobile consultation sessions. However, upon reexamination, this data was proven false and therefore these 261 PHC facilities could operate on a fixed basis.

How did things improve as a result? 

As a result of this evidence, the overall number of PHC facilities equipped to conduct outreach reduced by 25% from 1,042 in 2019 to 781 in 2020, reducing annual spending reduction of 40million NGN ($100,000) a year.

In response to the overall bottleneck analysis findings, three key decisions were taken by the State to improve coverage of PHC interventions:

  1. A new Integrated PHC micro-plan to reach every ward developed for the year 2020, aiming to improve overall healthcare access and enabled sessions to offer a compendium of services, not one or two as we previously had from a vertical program.
  2. Prioritization of LGAs to tackle mortality rates
  3. Development of the Kaduna State Integrated Demand Creation plan to improve health seeking behaviour of the communities
  4. Complete overhaul of our community engagement strategy, with a linking between the existing Community Volunteers with “Mai’Unguwas” ** as well as new guidelines and tools for community engagement

 Lessons for others on using data for making better decisions:

  • Intra and inter sector collaboration is very key in sourcing, analyzing and taking decisions on data. Kaduna State had brilliant support from Kaduna Bureau of Statistics and the National Population Council. They were able to draw upon census data and settlement data as well as their own data sources.
  • Triangulate data (especially if multiple sources are available) in order to improve the robustness of conclusions drawn. Triangulation helps to quickly identify outliers and know what to depend on and what not to
  • Use several methodologies for analysis. Whilst quantitative data is helpful for scale, qualitative data can provide a brilliant guide to some very important questions on quality and standards of service delivery.
  • Institutionalize routine reviews of data to ensure quality is upheld.

 Where can I find more information?

Dr Hamza Abubakar, Kaduna State Primary Healthcare Board – Nigeria



*Full outline of all demand and supply-side gaps revealed by bottleneck analysis

  1. Inadequate awareness of available services by the community lending to poor health-seeking behaviour. The 2016/17 N/MICS showed that 42% of caregivers lack awareness, while 22% have mistrust and fears about immunization
  2. Poor community linkages inhibiting collaboration between the PHC service and traditional community and the traditional community leadership.
  3. Uncoordinated Demand Creation structures: demand creation interventions were mostly vertical and grossly uncoordinated. It seemed as if every program had its separate community engagement structure.
  4. Poor stock of drugs and commodities which led to serious unmet needs and caused the loss of confidence in the system. g. Zinc/ORS, Amoxicillin DT, FP and HIV commodities/drugs etc.
  5. Inadequate human resource for health in number/quality of healthcare providers, especially in PHC facilities
  6. Service delivery: Poor implementation of the Optimized Integrated Routine Immunization Sessions (OIRIS), which made service delivery unattractive to client
  7. Poor and inaccurate data for planning. Wrong categorization of settlements by distance & population; and not used by state to decide which facility is to conduct outreach

** Traditional leader (Village Head)