{"type":"survey","doc_desc":{"title":"NGA_2014_QICGI_v01_M","idno":"DDI_NGA_2014_QICGI_v01_M_WB","producers":[{"name":"Development Data Group","abbreviation":"DECDG","affiliation":"World Bank","role":"Documentation of the study"}],"prod_date":"2023-07-07","version_statement":{"version":"Version 01 (2023-07-07)"}},"study_desc":{"title_statement":{"idno":"NGA_2014_QICGI_v01_M","title":"Quality Improvement and Clinical Governance Initiative - Piloting Quality Improvement Packages in Primary Care Centers Impact Evaluation 2014-2016","sub_title":"Baseline & Follow-up Surveys","alt_title":"QICGI 2014-2016"},"authoring_entity":[{"name":"David Evans","affiliation":"World Bank"},{"name":"Mario Macis","affiliation":"John Hopkins University"},{"name":"Felipe Dunsch","affiliation":"World Bank"}],"production_statement":{"producers":[{"name":"Ezinne Eze-Ajoku","affiliation":"Harvard University","role":"Advisor, Quality Measurement and Evaluation"},{"name":"Qiao Wang","affiliation":"World Bank","role":"Research Assistant"}],"funding_agencies":[{"name":"Bill and Melinda Gates Foundation","abbreviation":"Gates Foundation","role":""}]},"distribution_statement":{"depositor":[{"name":"","abbreviation":"","affiliation":""}]},"series_statement":{"series_name":"Impact Evaluation Study","series_info":"While maternal, neonatal, and child health (MNCH) outcomes are improving in Nigeria, the rate of improvement is not sufficient to meet the MDGs related to child and maternal health. Nigeria\u2019s under-5 mortality rate, estimated to be 124 deaths per 1000 live births in 2012 is one of the highest in the World. In fact, UNICEF (2012) ranked Nigeria as the country with the 12th highest under 5 mortality rate in the world. The Nigerian Federal Ministry of Health (FMOH) is addressing these challenges by introducing important reforms and is committed to learning which of these are working and worth scaling up. Evidence on the causal impact of past and ongoing quality improvement programs is, however, lacking, and so the scope for using previous experience to reliably guide future policy and program design is limited. Assessing and improving the quality of healthcare delivery in developing countries has been recognized as a priority by the WHO and other health agencies (WHO 2006; Institute of Medicine, 2001).\n\nIn this context, the Nigerian Federal Ministry of Health proposes to experimentally evaluate variants of a healthcare management consulting intervention to enhance the quality of health care, especially maternal, newborn, and child health care. The consulting program studied in this IE aims at improving service quality and patient safety at primary healthcare centers (PHCs) by relaying information to providers and through mentoring and tutelage. Service quality and patient safeties are systemic healthcare challenges throughout Nigeria and the broader region. The goal is to impact provider knowledge and behavior, improving effort, and ultimately health outcomes, patient safety and patient satisfaction. This program is implemented jointly by the Ministry and the National Primary Healthcare Development Agency (NPHCDA).\n\nAs data sources to measure and quantify the impact, the IE will use a combination of PHC administrative data, facility-level survey data, the tools developed by the healthcare consulting firm, the SDI and SURE-P surveys, as well as additional instruments to assess the quality of care.\n\n-  The Service Delivery Indicators (SDI) is an initiative by the World Bank, in partnership with the African Economic Research Consortium and the African Development Bank, that collects data on service delivery in schools and health facilities across Africa.\n\n-  The SURE-P MCH Program is an ambitious initiative to tackle key supply and demand-side constraints to the effective delivery of maternal and child health services in order to improve Nigeria\u2019s MCH outcomes. SURE-P MCH will incorporate a set of four IEs looking at various pre-identified supply and demand-side challenges. The SURE-P baseline data collection was carried out in September and October of 2013. All 80 clinics of the QE project were featured in this data collection. From this data, information on facility characteristics and staffing details are being reflected in this report.\n\n-  As part of the quality improvement program, the healthcare management consulting firm Pharm Access Safe Care conducted a baseline quality of care assessment in the 48 clinics that comprise treatment groups A and B. The baseline assessment included 829 indicators (of which not all are applicable to the QE context). For the 24 clinics of Treatment Group A (\u201cfull treatment\u201d), the firm also created \u201cQuality Improvement Plans\u201d (QIPs).\n\nIn order to track progress over time and in order to increase the statistical power of the study, the research team decided to conduct a monthly high-frequency data collection (HFDC) as a follow up instrument"},"version_statement":{"version":"- v0.1: Edited, anonymous dataset for public distribution."},"study_info":{"abstract":"The Nigerian Government has prioritized improving the quality of healthcare delivery throughout its care facilities. There are multiple facets to implementing successful quality improvement processes, including providing a transparent system with quantifiable outcome measures and ensuring workforce engagement for healthcare providers. \n\nThe government project team contracted a healthcare management consulting firm to provide support to 80 primary healthcare facilities in six Nigerian states to meet international healthcare standards. The IE was designed to measure the effectiveness of two different levels of consulting services on healthcare quality outcomes: \n\n-  Treatment A consisted of the \"full package\" of consulting services, including an initial extensive quality assessment, action plans, and continuous feedback and support. \n-  Treatment B was \"information only\". The consulting firm conducted the baseline quality assessment and provided initial feedback in the form of a report on these indicators, which was presented to center staff. Treatment B did not provide hands-on tutelage throughout the quality improvement process.","coll_dates":[{"start":"2014-07-07","end":"2014-07-31","cycle":"Round 1"},{"start":"2014-08-15","end":"2014-09-04","cycle":"Round 2"},{"start":"2014-09-19","end":"2014-10-16","cycle":"Round 3"},{"start":"2014-10-23","end":"2014-11-13","cycle":"Round 4"},{"start":"2014-11-27","end":"2014-12-18","cycle":"Round 5"},{"start":"2015-01-14","end":"2015-02-11","cycle":"Round 6"},{"start":"2016-03-16","end":"2016-05-20","cycle":"Round 7"}],"nation":[{"name":"Nigeria","abbreviation":"NGA"}],"geog_coverage":"Six Nigerian states: Anambra, Bauchi, Cross River, Ekiti, Kebbi and Niger.","analysis_unit":"-  Primary Health Care Facilities\n-  Patients\n-  Primary Health Care Facility Health Workers.","notes":"The scope of the study includes:\n\n-  Facility: General facility information, infrastructure, availability of equipment, materials, drugs, and supplies \n-  Staff roster: List of all health workers by cadre type\n-  Clinical knowledge assessment: Clinical knowledge using 5 medical vignettes + 2 vignettes for maternal & newborn complications \n-  Public expenditure: Collects receipts and spending (monetary and in-kind) by health facilities \n-  Exit module: User satisfaction, socio-demographic characteristics & payments."},"method":{"data_collection":{"sampling_procedure":"The sampling frame for this impact evaluation consists of all 80 PHCs in the 6 states that are being covered by the project.\n\nRANDOMIZATION\nRandomization of PHCs into Treatment A, Treatment B, and control followed these steps:\n1. We assigned a random number to each of the 80 PHCs in our population.\n2. These numbers were ranked in ascending order.\n3. We ranked these numbers within each cluster (of 4 PHCs around a referral hospital).\n4. The PHC with the highest random number in each was assigned to Treatment A, the second highest number was assigned to Treatment B, and the third highest number was assigned to the control group. This created groups of 20 for each treatment arm.\n5. Lastly, the 20 PHCs with the fourth highest numbers were ranked again. Then, the 4 highest numbers were allocated to Treatment A, numbers 5-8 went to Treatment B, and the rest was assigned to the control group. \n\nThis resulted in the following group sizes:\n-  Treatment A: 24\n-  Treatment B: 24\n-  Control group: 32","coll_mode":["Face-to-face [f2f]"],"research_instrument":"As data sources, the IE will use a combination of PHC administrative data, facility level survey data, the tools developed by the healthcare consulting firm, the SDI and SURE-P surveys, as well as additional instruments to assess the quality of care."},"analysis_info":{"response_rate":"Out of 80 primary healthcare facilities, the response rates for:\n-  round 1: 100%\n-  round 2: 100%\n-  round 3: 100%\n-  round 4: 100%\n-  round 5: 89%\n-  round 6: 100%\n-  round 7: 100%"}},"data_access":{"dataset_use":{"conf_dec":[{"txt":"","required":"","form_no":"","uri":""}],"cit_req":"Use of the dataset must be acknowledged using a citation which would include: \n - the Identification of the Primary Investigator\n - the title of the survey (including country, acronym and year of implementation)\n - the survey reference number\n - the source and date of download\n\n Example:\n David Evans (World Bank), Mario Macis (John Hopkins University), Felipe Dunsch (World Bank). Nigeria - Quality Improvement and Clinical Governance Initiative - Piloting Quality Improvement Packages in Primary Care Centers Impact Evaluation 2014-2016, Baseline & Follow-up Surveys (QICGI 2014-2016). Ref: NGA_2014_QICGI_v01_M. 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