Impact of a decision-support tool on decision making at the district level in Kenya

Effect of the data-informed platform for health intervention on the culture of data use for decision-making among district health office staff in North Shewa Zone, Ethiopia: a cluster-randomised controlled trial

BACKGROUND

Similar to other low and middle-income countries, Ethiopia faces limitations in using local health data for decision-making.We aimed to assess the effect of an intervention, namely the data-informed platform for health, on the culture of data-based decision making as perceived by district health office staff in Ethiopia's North Shewa Zone.

METHODS

By designating district health offices as 'clusters', a cluster-randomised controlled trial was implemented. Out of a total of 24 districts in the zone, 12 districts were allocated to intervention arm and the other 12 in the control group arms. In the intervention arm district health office teams were supported in four-monthly cycles of data-driven decision-making over 20 months. This support included: (a) defining problems using a health system framework; (b) reviewing data; (c) considering possible solutions; (d) value-based prioritizing; and (e) a consultative process to develop, commit to, and follow up on action plans. To measure the culture of data use for decision-making in both intervention and control arms, we interviewed 120 health management staff (5 per district office). Using a Likert scale based standard Performance of Routine Information System Management tool, the information is categorized into six domains:- evidence-based decision making, emphasis on data quality, use of information, problem solving, responsibility and motivation. After converting the Likert scale responses into percentiles, difference-in-difference methods were applied to estimate the net effect of the intervention. In intervention districts, analysis of variance was used to summarize variation by staff designation.

RESULTS

The overall decision-making culture in health management staff showed a net improvement of 13% points (95% C.I:9, 18) in intervention districts. The net effect of each of the six domains in turn was an 11% point increase (95% C.I:7, 15) on culture of evidence based decision making, a 16% point increase (95% C.I:8, 24) on emphasis on data quality, a 20% point increase (95% C.I:12, 28) on use of information, a 21% point increase (95% C.I:13, 29) on problem solving, and a 10% point increase (95% C.I:4, 16) on responsibility and motivation. In terms of variation by staff designation within intervention districts, statistically significant differences were observed only for problem solving and responsibility.

CONCLUSION

The data-informed platform for health strategy resulted in a measurable improvement in data use and structured decision-making culture by using existing systems, namely the Performance Monitoring Team meetings. The intervention supported district health offices in identifying and solving problems through a structured process. After further research, DIPH intervention could also be applied to other health administration and facility levels.

TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT05310682, Dated 25/03/ 2022.

Priority setting in cardiovascular research in Iran using standard indigenous methods

Background

Determining cardiovascular disease (CVD) research priorities is essential given the high burden of these diseases, limited financial resources, and competing priorities. This study aimed to determine the research priorities in CVD field in Iran using standard indigenous methods.

Materials and Methods

An extensive search was done in relevant international and national studies. Then, an indigenous standard multistage approach based on multicriteria decision analysis steps was adapted to local situation and implemented. This process included forming a working group of experts in priority setting methodology, identifying the context and prioritization framework, discussing the methodology with the National Network of CVD Research (NCVDR) members who ultimately determined the priority research topics, weighted topics criteria, ranked topics, and reviewed all determined research priorities for final report.

Results

Thirteen cardiovascular research priorities were determined by the NCVDR members. The first five priorities based on their scores include studies in hypertension, prevention and control of ischemic heart disease (IHD) and its risk factors, burden of IHD, Registration of CVDs, and COVID-19 and CVDs.

Conclusion

Cardiovascular research priorities were determined using a standard indigenous approach by national experts who are the NCVDR members. These priorities can be used by researchers and health decision makers.

Understanding reasons for and strategic responses to administrative health data misreporting in an Indian state

The misreporting of administrative health data creates an inequitable distribution of scarce health resources and weakens transparency and accountability within health systems. In the mid-2010s, an Indian state introduced a district ranking system to monitor the monthly performance of health programmes alongside a set of data quality initiatives. However, questions remain about the role of data manipulation in compromising the accuracy of data available for decision-making. We used qualitative approaches to examine the opportunities, pressures and rationalization of potential data manipulation. Using purposive sampling, we interviewed 48 district-level respondents from high-, middle- and low-ranked districts and 35 division- and state-level officials, all of whom had data-related or programme monitoring responsibilities. Additionally, we observed 14 district-level meetings where administrative data were reviewed. District respondents reported that the quality of administrative data was sometimes compromised to achieve top district rankings. The pressure to exaggerate progress was a symptom of the broader system for assessing health performance that was often viewed as punitive and where district- and state-level superiors were viewed as having limited ability to ensure accountability for data quality. However, district respondents described being held accountable for results despite lacking the adequate capacity to deliver on them. Many rationalized data manipulation to cope with high pressures, to safeguard their jobs and, in some cases, for personal financial gain. Moreover, because data manipulation was viewed as a socially acceptable practice, ethical arguments against it were less effective. Potential entry points to mitigate data manipulation include (1) changing the incentive structures to place equal emphasis on the quality of data informing the performance data (e.g. district rankings), (2) strengthening checks and balances to reinforce the integrity of data-related processes within districts and (3) implementing policies to make data manipulation an unacceptable anomaly rather than a norm.

The Effectiveness of the Capacity Building and Mentorship Program in Improving Evidence-Based Decision-making in the Amhara Region, Northwest Ethiopia: Difference-in-Differences Study

Background

Weak health information systems (HISs) hobble countries’ abilities to effectively manage and distribute their resources to match the burden of disease. The Capacity Building and Mentorship Program (CBMP) was implemented in select districts of the Amhara region of Ethiopia to improve HIS performance; however, evidence about the effectiveness of the intervention was meager.

Objective

This study aimed to determine the effectiveness of routine health information use for evidence-based decision-making among health facility and department heads in the Amhara region, Northwest Ethiopia.

Methods

The study was conducted in 10 districts of the Amhara region: five were in the intervention group and five were in the comparison group. We employed a quasi-experimental study design in the form of a pretest-posttest comparison group. Data were collected from June to July 2020 from the heads of departments and facilities in 36 intervention and 43 comparison facilities. The sample size was calculated using the double population formula, and we recruited 172 participants from each group. We applied a difference-in-differences analysis approach to determine the effectiveness of the intervention. Heterogeneity of program effect among subgroups was assessed using a triple differences method (ie, difference-in-difference-in-differences [DIDID] method). Thus, the β coefficients, 95% CIs, and P values were calculated for each parameter, and we determined that the program was effective if the interaction term was significant at P<.05.

Results

Data were collected using the endpoint survey from 155 out of 172 (90.1%) participants in the intervention group and 166 out of 172 (96.5%) participants in the comparison group. The average level of information use for the comparison group was 37.3% (95% CI 31.1%-43.6%) at baseline and 43.7% (95% CI 37.9%-49.5%) at study endpoint. The average level of information use for the intervention group was 52.2% (95% CI 46.2%-58.3%) at baseline and 75.8% (95% CI 71.6%-80.0%) at study endpoint. The study indicated that the net program change over time was 17% (95% CI 5%-28%; P=.003). The subgroup analysis also indicated that location showed significant program effect heterogeneity, with a DIDID estimate equal to 0.16 (95% CI 0.026-0.29; P=.02). However, sex, age, educational level, salary, and experience did not show significant heterogeneity in program effect, with DIDID estimates of 0.046 (95% CI –0.089 to 0.182), –0.002 (95% CI –0.015 to 0.009), –0.055 (95% CI –0.190 to 0.079), –1.63 (95% CI –5.22 to 1.95), and –0.006 (95% CI –0.017 to 0.005), respectively.

Conclusions

The CBMP was effective at enhancing the capacity of study participants in using the routine HIS for decision-making. We noted that urban facilities had benefited more than their counterparts. The intervention has been shown to produce positive outcomes and should be scaled up to be used in other districts. Moreover, the mentorship modalities for rural facilities should be redesigned to maximize the benefits.

Trial Registration

Pan African Clinical Trials Registry PACTR202001559723931; https://tinyurl.com/3j7e5ka5

A Novel Approach to Promote Evidence-Based Development of District Maternal and Newborn Health Plans in Two States in India

Background:

Maternal and child health implementation plan development in districts of India lacks systematic process and capacity resulting in suboptimal health improvements. There is ineffective and limited participation and lack of autonomy to effect changes in district priorities.

Objectives:

Primary objective was to demonstrate a systematic planning approach to develop evidence-based district implementation plans for mothers and children.

Methods:

A planning tool named RAASTA (RMNCH + A Action Agenda using Strategic Approach for evidence-based district work plans) adapted from WHO (World Health Organization) program review tools was used in the states of Uttarakhand and Jharkhand. The tool was implemented in the two states for the development of implementation plans in a 6-step process by prioritizing district health goals; reviewing maternal, neonatal, child, and family planning intervention coverage; and linking them with activity implementation status; assessing strengths, and weaknesses of previous implementation plans and developing solutions based on current gaps in intervention coverage's.

Results:

Tool was used for capacity building of 59 participants and also identification of prioritized activities based on their available data. Several newer activities were identified. The districts mainstreamed them as action plans, many of which were incorporated in the state Program Implementation Plan for budgetary provisions under state NHM (National Health Mission) funds.

Conclusion:

The use of a tool facilitated the systematic development of evidence-based district implementation plans.

A realist systematic review of evidence from low- and middle-income countries of interventions to improve immunization data use

BACKGROUND

The use of routine immunization data by health care professionals in low- and middle-income countries remains an underutilized resource in decision-making. Despite the significant resources invested in developing national health information systems, systematic reviews of the effectiveness of data use interventions are lacking. Applying a realist review methodology, this study synthesized evidence of effective interventions for improving data use in decision-making.

METHODS

We searched PubMed, POPLINE, Centre for Agriculture and Biosciences International Global Health, and African Journals Online for published literature. Grey literature was obtained from conference, implementer, and technical agency websites and requested from implementing organizations. Articles were included if they reported on an intervention designed to improve routine data use or reported outcomes related to data use, and targeted health care professionals as the principal data users. We developed a theory of change a priori for how we expect data use interventions to influence data use. Evidence was then synthesized according to data use intervention type and level of the health system targeted by the intervention.

RESULTS

The searches yielded 549 articles, of which 102 met our inclusion criteria, including 49 from peer-reviewed journals and 53 from grey literature. A total of 66 articles reported on immunization data use interventions and 36 articles reported on data use interventions for other health sectors. We categorized 68 articles as research evidence and 34 articles as promising strategies. We identified ten primary intervention categories, including electronic immunization registries, which were the most reported intervention type (n = 14). Among the research evidence from the immunization sector, 32 articles reported intermediate outcomes related to data quality and availability, data analysis, synthesis, interpretation, and review. Seventeen articles reported data-informed decision-making as an intervention outcome, which could be explained by the lack of consensus around how to define and measure data use.

CONCLUSIONS

Few immunization data use interventions have been rigorously studied or evaluated. The review highlights gaps in the evidence base, which future research and better measures for assessing data use should attempt to address.

Routine health information utilization and associated factors among health care workers in Ethiopia: A systematic review and meta-analysis

Background

Utilization of routine health information plays a vital role for the effectiveness of routine and programed decisions. A proper utilization of routine health information helps to make decisions based on evidence. Considerable studies have been done on the utilization of routine health information among health workers in Ethiopia, but inconsistent findings were reported. Thus, this study was conducted to determine the pooled utilization of routine health information and to identify associated factors among health workers in Ethiopia.

Methods

Search of PubMed, HINARI, Global Health, Scopus, EMBASE, web of science, and Google Scholar was conducted to identify relevant studies from October 24, 2020 to November 18, 2020. The Newcastle-Ottawa scale tool was used to assess the quality of included studies. Two reviewers extracted the data independently using a standardized data extraction format and exported to STATA software version 11 for meta-analysis. Heterogeneity among studies was checked using Cochrane Q and I² test statistics. The pooled estimate of utilization of routine health information was executed using a random effect model.

Results

After reviewing 22924 studies, 10 studies involving 4054 health workers were included for this review and meta-analysis. The pooled estimate of routine health information utilization among health workers in Ethiopia was 57.42% (95% CI: 41.48, 73.36). Supportive supervision (AOR = 2.25; 95% CI: 1.80, 2.82), regular feedback (AOR = 2.86; 95% CI: 1.60, 5.12), availability of standard guideline (AOR = 2.53; 95% CI: 1.80, 3.58), data management knowledge (AOR = 3.04; 95% CI: 1.75, 5.29) and training on health information (AOR = 3.45; 95% CI: 1.96, 6.07) were identified factors associated with utilization of routine health information.

Conclusion

This systematic review and meta-analysis found that more than two-fifth of health workers did not use their routine health information. This study suggests the need to conduct regular supportive supervision, provision of training and capacity building, mentoring on competence of routine health information tasks, and strengthening regular feedback at all health facilities. In addition, improving the accessibility and availability of standard set of indicators is important to scale-up information use.

Medical Documentation in Low- and Middle-income Countries: Lessons Learned from Implementing Specialized Charting Software

Background:

The implementation of electronic health record (EHR) software at healthcare facilities in low- and middle-income countries (LMICs) is limited by financial and technological constraints. Smile Train, the world’s largest cleft charity, developed a cleft treatment EHR system, Smile Train Express (STX), and distributed it to their partnered institutions. The purpose of this study was to investigate trends in medical documentation practices amongst Smile Train-partner institutions to characterize the impact that specialized EHR software has on medical documentation practices at healthcare facilities in LMICs.

Methods:

Surveys were administered electronically to 843 Smile Train-partnered institutions across 68 LMICs. The survey inquired about institutions’ internet connection, documentation methods used during patient encounters, rationale for using said methods, and documentation methods for cloud-based storage of healthcare data. Institutions were grouped by economic and geographic subgroups for analysis.

Results:

A total of 162 institutions (19.2%) responded to the survey. Most institutions employed paper charting (64.2%) or institutional EHR software (25.9%) for data entry during a patient encounter with the latter’s use varying significantly across geographical subgroups (P = 0.01). STX was used by 18 institutions (11.1%) during a patient encounter. Workflow was the most frequently cited reason for institutions to employ their entry method during a patient encounter (51.4%).

Conclusions:

The provision of STX to partnered institutions influenced medical documentation practices at several institutions; however, regulations and guidelines have likely limited its complete integration into clinical workflows. Further studies are needed to characterize trends in medical documentation in LMICs at a more granular level.

Improving quality and use of routine health information system data in low- and middle-income countries: A scoping review

BACKGROUND

A routine health information system is one of the essential components of a health system. Interventions to improve routine health information system data quality and use for decision-making in low- and middle-income countries differ in design, methods, and scope. There have been limited efforts to synthesise the knowledge across the currently available intervention studies. Thus, this scoping review synthesised published results from interventions that aimed at improving data quality and use in routine health information systems in low- and middle-income countries.

METHOD

We included articles on intervention studies that aimed to improve data quality and use within routine health information systems in low- and middle-income countries, published in English from January 2008 to February 2020. We searched the literature in the databases Medline/PubMed, Web of Science, Embase, and Global Health. After a meticulous screening, we identified 20 articles on data quality and 16 on data use. We prepared and presented the results as a narrative.

RESULTS

Most of the studies were from Sub-Saharan Africa and designed as case studies. Interventions enhancing the quality of data targeted health facilities and staff within districts, and district health managers for improved data use. Combinations of technology enhancement along with capacity building activities, and data quality assessment and feedback system were found useful in improving data quality. Interventions facilitating data availability combined with technology enhancement increased the use of data for planning.

CONCLUSION

The studies in this scoping review showed that a combination of interventions, addressing both behavioural and technical factors, improved data quality and use. Interventions addressing organisational factors were non-existent, but these factors were reported to pose challenges to the implementation and performance of reported interventions.

Factors That Influence Data Use to Improve Health Service Delivery in Low- and Middle-Income Countries

BACKGROUND

Health service delivery indicators are designed to reveal how well health services meet a community's needs. Effective use of the data can enable targeted improvements in health service delivery. We conducted a systematic review to identify the factors that influence the use of health service delivery indicators to improve delivery of primary health care services in low- and middle-income settings.

METHODS

We reviewed empirical studies published in 2005 or later that provided evidence on the use of health service delivery data at the primary care level in low- and middle-income countries. We searched Scopus, Medline, the Cochrane Library, and citations of included studies. We also searched the gray literature, using a separate strategy. We extracted information on study design, setting, study population, study objective, key findings, and any identified lessons learned.

RESULTS

Twelve studies met the inclusion criteria. This small number of studies suggests there is insufficient evidence to draw reliable conclusions. However, a content analysis identified the following potentially influential factors, which we classified into 3 categories: governance (leadership, participatory monitoring, regular review of data); production of information (presentation of findings, data quality, qualitative data); and health information system resources (electronic health management information systems, organizational structure, training). Contextual factors and performance-based financing were also each found to have a role; however, discussing these as mediating factors may not be practical in terms of promoting data use.

CONCLUSION

Scant evidence exists regarding factors that influence the use of health service delivery indicators to improve delivery of primary health care services in low- and middle-income countries. However, the existing evidence highlights some factors that may have a role in improving data use. Further research may benefit from comparing data use factors across different types of program indicators or using our classification as a framework for field experiments.

Implementing electronic decision-support tools to strengthen healthcare network data-driven decision-making

BACKGROUND

Ministries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative.

METHODS

Three electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making.

RESULTS

Data from 300 health facilities in four health districts was collected and analyzed (November 2014-June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7-100% of all health facilities achieved all the required items for each indicator.

CONCLUSIONS

Our experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology.

Data-driven quality improvement in low-and middle-income country health systems: lessons from seven years of implementation experience across Mozambique, Rwanda, and Zambia

BACKGROUND

Well-functioning health systems need to utilize data at all levels, from the provider, to local and national-level decision makers, in order to make evidence-based and needed adjustments to improve the quality of care provided. Over the last 7 years, the Doris Duke Charitable Foundation's African Health Initiative funded health systems strengthening projects at the facility, district, and/or provincial level to improve population health. Increasing data-driven decision making was a common strategy in Mozambique, Rwanda and Zambia. This paper describes the similar and divergent approaches to increase data-driven quality of care improvements (QI) and implementation challenge and opportunities encountered in these three countries.

METHODS

Eight semi-structured in-depth interviews (IDIs) were administered to program staff working in each country. IDIs for this paper included principal investigators of each project, key program implementers (medically-trained support staff, data managers and statisticians, and country directors), as well as Ministry of Health counterparts. IDI data were collected through field notes; interviews were not audio recorded. Data were analyzed using thematic analysis but no systematic coding was conducted. IDIs were supplemented through donor report abstractions, a structured questionnaire, one-on-one phone calls, and email exchanges with country program leaders to clarify and expand on key themes emerging from IDIs.

RESULTS

Project successes ranged from over 450 collaborative action-plans developed, implemented, and evaluated in Mozambique, to an increase from <10% to >80% of basic clinical protocols followed in intervention facilities in rural Zambia, and a shift from a lack of awareness of health data among health system staff to collaborative ownership of data and using data to drive change in Rwanda.

CONCLUSION

Based on common successes across the country experiences, we recommend future data-driven QI interventions begin with data quality assessments to promote that rapid health system improvement is possible, ensure confidence in available data, serve as the first step in data-driven targeted improvements, and improve staff data analysis and visualization skills. Explicit Ministry of Health collaborative engagement can ensure performance review is collaborative and internally-driven rather than viewed as an external "audit."

Causes of and contributors to infant mortality in a rural community of North India: evidence from verbal and social autopsy

OBJECTIVE

To identify the medical causes of death and contribution of non-biological factors towards infant mortality by a retrospective analysis of routinely collected data using verbal and social autopsy tools.

SETTING

The study site was Health and Demographic Surveillance System (HDSS), Ballabgarh, North India PARTICIPANTS: All infant deaths during the years 2008-2012 were included for verbal autopsy and infant deaths from July 2012 to December 2012 were included for social autopsy.

OUTCOME MEASURES

Cause of death ascertained by a validated verbal autopsy tool and level of delay based on a three-delay model using the INDEPTH social autopsy tool were the main outcome measures. The level of delay was defined as follows: level 1, delay in identification of danger signs and decision making to seek care; level 2, delay in reaching a health facility from home; level 3, delay in getting healthcare at the health facility.

RESULTS

The infant mortality rate during the study period was 46.5/1000 live births. Neonatal deaths contributed to 54.3% of infant deaths and 39% occurred on the first day of life. Birth asphyxia (31.5%) followed by low birth weight (LBW)/prematurity (26.5%) were the most common causes of neonatal death, while infection (57.8%) was the most common cause of post-neonatal death. Care-seeking was delayed among 50% of neonatal deaths and 41.2% of post-neonatal deaths. Delay at level 1 was most common and occurred in 32.4% of neonatal deaths and 29.4% of post-neonatal deaths. Deaths due to LBW/prematurity were mostly followed by delay at level 1.

CONCLUSION

A high proportion of preventable infant mortality still exists in an area which is under continuous health and demographic surveillance. There is a need to enhance home-based preventive care to enable the mother to identify and respond to danger signs. Verbal autopsy and social autopsy could be routinely done to guide policy interventions aimed at reduction of infant mortality.

District decision-making for health in low-income settings: a systematic literature review

Health management information systems (HMIS) produce large amounts of data about health service provision and population health, and provide opportunities for data-based decision-making in decentralized health systems. Yet the data are little-used locally. A well-defined approach to district-level decision-making using health data would help better meet the needs of the local population. In this second of four papers on district decision-making for health in low-income settings, our aim was to explore ways in which district administrators and health managers in low- and lower-middle-income countries use health data to make decisions, to describe the decision-making tools they used and identify challenges encountered when using these tools. A systematic literature review, following PRISMA guidelines, was undertaken. Experts were consulted about key sources of information. A search strategy was developed for 14 online databases of peer reviewed and grey literature. The resources were screened independently by two reviewers using pre-defined inclusion criteria. The 14 papers included were assessed for the quality of reported evidence and a descriptive evidence synthesis of the review findings was undertaken. We found 12 examples of tools to assist district-level decision-making, all of which included two key stages—identification of priorities, and development of an action plan to address them. Of those tools with more steps, four included steps to review or monitor the action plan agreed, suggesting the use of HMIS data. In eight papers HMIS data were used for prioritization. Challenges to decision-making processes fell into three main categories: the availability and quality of health and health facility data; human dynamics and financial constraints. Our findings suggest that evidence is available about a limited range of processes that include the use of data for decision-making at district level. Standardization and pre-testing in diverse settings would increase the potential that these tools could be used more widely.

District decision-making for health in low-income settings: a feasibility study of a data-informed platform for health in India, Nigeria and Ethiopia

Low-resource settings often have limited use of local data for health system planning and decision-making. To promote local data use for decision-making and priority setting, we propose an adapted framework: a data-informed platform for health (DIPH) aimed at guiding coordination, bringing together key data from the public and private sectors on inputs and processes. In working to transform this framework from a concept to a health systems initiative, we undertook a series of implementation research activities including background assessment, testing and scaling up of the intervention. This first paper of four reports the feasibility of the approach in a district health systems context in five districts of India, Nigeria and Ethiopia. We selected five districts using predefined criteria and in collaboration with governments. After scoping visits, an in-depth field visit included interviews with key health stakeholders, focus group discussions with service-delivery staff and record review. For analysis, we used five dimensions of feasibility research based on the TELOS framework: technology and systems, economic, legal and political, operational and scheduling feasibility. We found no standardized process for data-based district level decision-making, and substantial obstacles in all three countries. Compared with study areas in Ethiopia and Nigeria, the health system in Uttar Pradesh is relatively amenable to the DIPH, having relative strengths in infrastructure, technological and technical expertise, and financial resources, as well as a district-level stakeholder forum. However, a key challenge is the absence of an effective legal framework for engagement with India’s extensive private health sector. While priority-setting may depend on factors beyond better use of local data, we conclude that a formative phase of intervention development and pilot-testing is warranted as a next step.

Building Capacity for Data-Driven Decision Making in African HIV Testing Programs: Field Perspectives on Data Use Workshops

Strategic, high quality HIV testing services (HTS) delivery is an essential step towards reaching the end of AIDS by 2030. We conducted HTS Data Use workshops in five African countries to increase data use for strategic program decision-making. Feedback was collected on the extent to which workshop skills and tools were applied in practice and to identify future capacity-building needs. We later conducted six semistructured phone interviews with workshop planning teams and sent a web-based survey to 92 past participants. The HTS Data Use workshops provided accessible tools that were readily learned by most respondents. While most respondents reported increased confidence in interpreting data and frequency of using such tools over time, planning team representatives indicated ongoing needs for more automated tools that can function across data systems. To achieve ambitious global HIV/AIDS targets, national decision makers may continue to seek tools and skill-building opportunities to monitor programs and identify opportunities to refine strategies.

Verbal/Social Autopsy in Niger 2012-2013: A new tool for a better understanding of the neonatal and child mortality situation

Niger, one of the poorest countries in the world, recently used for the first time the integrated verbal and social autopsy (VASA) tool to assess the biological causes and social and health system determinants of neonatal and child deaths. These notes summarize the Nigerien experience in the use of this new tool, the steps taken for high level engagement of the Niger government and stakeholders for the wide dissemination of the study results and their use to support policy development and maternal, neonatal and child health programming in the country. The experience in Niger reflects lessons learned by other developing countries in strengthening the use of data for evidence–based decision making, and highlights the need for the global health community to provide continued support to country data initiatives, including the collection, analysis, interpretation and utilization of high quality data for the development of targeted, highly effective interventions. In Niger, this is supporting the country’s progress toward achieving Millennium Development Goal 4. A follow–up VASA study is being planned and the tool is being integrated into the National Health Management Information System. VASA studies have now been completed or are under way in additional sub–Saharan African countries, in each through the same collaborative process used in Niger to bring together health policy makers, program planners and development partners.

Barriers and facilitators to health information exchange in low- and middle-income country settings: a systematic review

The exchange and use of health information can help healthcare professionals and policymakers make informed decisions on ways of improving patient and population health. Many low- and middle-income countries (LMICs) have however failed to embrace the approaches and technologies to facilitate health information exchange (HIE). We sought to understand the barriers and facilitators to the implementation and adoption of HIE in LMICs. Two reviewers independently searched 11 academic databases for published and on-going qualitative, quantitative and mixed-method studies and searched for unpublished work through the Google search engine. The searches covered the period from January 1990 to July 2014 and were not restricted by language. Eligible studies were independently, critically appraised and then thematically analysed. The searches yielded 5461 citations after de-duplication of results. Of these, 56 articles, three conference abstracts and four technical reports met the inclusion criteria. The lack of importance given to data in decision making, corruption and insecurity, lack of training and poor infrastructure were considered to be major challenges to implementing HIE, but strong leadership and clear policy direction coupled with the financial support to acquire essential technology, improve the communication network, and provide training for staff all helped to promote implementation. The body of work also highlighted how implementers of HIE needed to take into account local needs to ensure that stakeholders saw HIE as relevant and advantageous. HIE interventions implemented through leapfrog technologies such as telehealth/telemedicine and mHealth in Brazil, Kenya, and South Africa, provided successful examples of exchanging health information in LMICs despite limited resources and capability. It is important that implementation of HIE is aligned with national priorities and local needs.