Four antenatal care visits by four months of pregnancy and four vital tests for pregnant mothers: impact of a community-facility health systems strengthening intervention in Migori County, Kenya.

BACKGROUND: Early attendance at antenatal care (ANC), coupled with good-quality care, is essential for improving maternal and child health outcomes. However, achieving these outcomes in sub-Saharan Africa remains a challenge. This study examines the effects of a community-facility health system strengthening model (known as 4byFour) on early ANC attendance, testing for four conditions by four months of pregnancy, and four ANC clinic visits in Migori county, western Kenya. METHODS: We conducted a mixed methods quasi-experimental study with a before-after interventional design to assess the impact of the 4byFour model on ANC attendance. Data were collected between August 2019 and December 2020 from two ANC hospitals. Using quantitative data obtained from facility ANC registers, we analysed 707 baseline and 894 endline unique ANC numbers (attendances) based on negative binomial regression. Logistic regression models were used to determine the impact of patient factors on outcomes with Akaike Information Criterion (AIC) and likelihood ratio testing used to compare models. Regular facility stock checks were undertaken at the study sites to assess the availability of ANC profile tests. Analysis of the quantitative data was conducted in R v4.1.1 software. Additionally, qualitative in-depth interviews were conducted with 37 purposively sampled participants, including pregnant mothers, community health volunteers, facility staff, and senior county health officials to explore outcomes of the intervention. The interview data were audio-recorded, transcribed, and coded; and thematic analysis was conducted in NVivo. RESULTS: There was a significant 26% increase in overall ANC uptake in both facilities following the intervention. Early ANC attendance improved for all age groups, including adolescents, from 22% (baseline) to 33% (endline, p = 0.002). Logistic regression models predicting early booking were a better fit to data when patient factors were included (age, parity, and distance to clinic, p = 0.004 on likelihood ratio testing), suggesting that patient factors were associated with early booking.The proportion of women receiving all four tests by four months increased to 3% (27/894), with haemoglobin and malaria testing rates rising to 8% and 4%, respectively. Despite statistical significance (p < 0.001), the rates of testing remained low. Testing uptake in ANC was hampered by frequent shortage of profile commodities not covered by buffer stock and low ANC attendance during the first trimester. Qualitative data highlighted how community health volunteer-enhanced health education improved understanding and motivated early ANC-seeking. Community pregnancy testing facilitated early detection and referral, particularly for adolescent mothers. Challenges to optimal ANC attendance included insufficient knowledge about the ideal timing for ANC initiation, financial constraints, and long distances to facilities. CONCLUSION: The 4byFour model of community-facility health system strengthening has the potential to improve early uptake of ANC and testing in pregnancy. Sustained improvement in ANC attendance requires concerted efforts to improve care quality, consistent availability of ANC commodities, understand motivating factors, and addressing barriers to ANC. Research involving randomised control trials is needed to strengthen the evidence on the model's effectiveness and inform potential scale up.

A rapid evidence review on the effectiveness of institutional health partnerships.

BACKGROUND: Institutional Health Partnerships are long-term, institution to institution partnerships between high income and low and middle income countries which seek to build capacity and strengthen health institutions in order to improve health service delivery and outcomes. Funding for Institutional Health Partnerships has increased in recent years. This paper outlines a rapid evidence review on the effectiveness of this modality. METHODS: A rapid evidence review of published and grey literature was conducted. Content relating to the effectiveness of working in partnership and methods and frameworks used were extracted and analysed. The results of this analysis were used to structure a discussion regarding the next steps to strengthen the evidence base for the effectiveness of institutional health partnerships. RESULTS: The evidence review, including citation mapping, returned 27 published papers and 17 grey literature documents that met all of the inclusion criteria. Most of the literature did not meet the high standards of formal academic rigour and there was no original research amongst this literature that specifically addressed the effectiveness of institutional health partnerships. This was not surprising given institutional health partnerships do not lend themselves easily to case control studies and randomised control trials due to their high level of diversity and operation in complex social systems. There was, however, a body of practice based knowledge and experience. CONCLUSIONS: Evidence for the effectiveness of Institutional Health Partnerships is thin both in terms of quantity and academic rigour. There is a need to better define and differentiate Institutional Health Partnerships in order to measure and compare effectiveness across such a diverse group. Effectiveness needs to be measured at the level of individual partnerships, the bodies that facilitate partnership programmes and the level of health service delivery. There is a need to develop indicators and frameworks that specifically address the benefits and values of partnership working and how these relate to effectiveness. These indicators need to be content neutral of specific interventions which are already measured through routine project monitoring and evaluation. This will allow the development of methodological pathways to assess the effectiveness of institutional health partnerships. Until more primary research is conducted or published there is little benefit in further systematic reviews.

Accuracy and quality of immunization information systems in forty-one low income countries.

OBJECTIVES: To measure the accuracy and quality of immunization information systems in a range of low-income countries eligible to receive GAVI support. METHODS: The Data Quality Audit (DQA) uses a WHO validated, standard methodology to compare data collected from health unit (HU) records of immunizations administered with reports of immunizations at central level and to collect quality indicators of the reporting system. The verification factor (VF), as a measure of accuracy, expresses the proportion of immunizations reported at national level that can be tracked down to the HU. A VF of 80% or above entitles countries to receive additional GAVI financial support. Quality indicators are assigned points which were summed to obtain quality scores (QS) at national, district and HU levels. DQAs included here were conducted between 2002 and 2005 in 41 countries, encompassing 1082 primary healthcare units in 188 randomly selected districts. RESULTS: Almost half of countries obtained a VF below 80% and only nine showed consistently high VF and QS scores. The most frequent weaknesses in the information systems were inconsistency of denominators used to estimate coverage, poor availability of guidelines (e.g. for late reporting), incorrect estimations of vaccine wastage and lack of feedback on immunization performance. In all six countries that failed a first DQA and undertook a second DQA, the VF and all QSs improved, not all of them statistically significantly. CONCLUSIONS: The DQA is a diagnostic tool to reveal a number of crucial problems that affect the quality of immunization data in all tiers of the health system. It identifies good performance at HU and district levels which can be used as examples of best practices. The DQA methodology brings data quality issues to the top of the agenda to improve the monitoring of immunization coverage.