Testing the validity and feasibility of using a mobile phone-based method to assess the strength of implementation of family planning programs in Malawi.

BACKGROUND: To effectively deliver on proposed objectives, it is vital that practitioners, policymakers, and other stakeholders are able to clearly understand how strongly their large-scale program is being implemented. This study sought to test the feasibility, cost-effectiveness, and validity of a phone-based method as an innovative and cost-efficient approach to assessing program implementation strength (through an Implementation Strength Assessment - ISA), alternative to the traditional in-person field methods. METHODS: We conducted 701 mobile phone and 356 in-person interviews with facility in-Charges and two types of community health workers who provide family planning services in the Dowa and Ntcheu districts in Malawi. Responses received via the phone interview were validated through in-person review of records and inspections. Sensitivity and specificity were calculated to determine validity. RESULTS: Most indicators at the health facility and community health worker levels were above a 70% threshold for sensitivity. However, there were fewer indicators that met this threshold for specificity. The primary reason for lower specificity was due to poor recordkeeping. Collecting data via mobile phone was found to be feasible and twice as cost-efficient as collecting the same data via in-person inspections. CONCLUSIONS: The rapid increase in mobile phone ownership and network availability in lower income countries could offer an alternative, cost-effective avenue to collect data for a better understanding of program implementation. Through rigorous assessment, this study found that using mobile phones could be a low-cost alternative to collect data on health system delivery of services, especially in places where routine data quality is poor and traditional, in-person methods are costly.

National Assessment of Data Quality and Associated Systems-Level Factors in Malawi.

BACKGROUND: Routine health data can guide health systems improvements, but poor quality of these data hinders use. To address concerns about data quality in Malawi, the Ministry of Health and National Statistical Office conducted a data quality assessment (DQA) in July 2016 to identify systems-level factors that could be improved. METHODS: We used 2-stage stratified random sampling methods to select health centers and hospitals under Ministry of Health auspices, included those managed by faith-based entities, for this DQA. Dispensaries, village clinics, police and military facilities, tertiary-level hospitals, and private facilities were excluded. We reviewed client registers and monthly reports to verify availability, completeness, and accuracy of data in 4 service areas: antenatal care (ANC), family planning, HIV testing and counseling, and acute respiratory infection (ARI). We also conducted interviews with facility and district personnel to assess health management information system (HMIS) functioning and systems-level factors that may be associated with data quality. We compared systems and quality factors by facility characteristics using 2-sample t tests with Welch's approximation, and calculated verification ratios comparing total entries in registers to totals from summarized reports. RESULTS: We selected 16 hospitals (of 113 total in Malawi), 90 health centers (of 466), and 16 district health offices (of 28) in 16 of Malawi's 28 districts. Nearly all registers were available and complete in health centers and district hospitals, but data quality varied across service areas; median verification ratios comparing register and report totals at health centers ranged from 0.78 (interquartile range [IQR]: 0.25, 1.07) for ARI and 0.99 (IQR: 0.82, 1.36) for family planning to 1.00 (IQR: 0.96, 1.00) for HIV testing and counseling and 1.00 (IQR: 0.80, 1.23) for ANC. More than half (60%) of facilities reported receiving a documented supervisory visit for HMIS in the prior 6 months. A recent supervision visit was associated with better availability of data (P=.05), but regular district- or central-level supervision was not. Use of data by the facility to track performance toward targets was associated with both improved availability (P=.04) and completeness of data (P=.02). Half of facilities had a full-time statistical clerk, but their presence did not improve the availability or completeness of data (P=.39 and P=.69, respectively). CONCLUSION: Findings indicate both strengths and weaknesses in Malawi's HMIS performance, with key weaknesses including infrequent data quality checks and unreliable supervision. Efforts to strengthen HMIS in low- and middle-income countries should be informed by similar assessments.