Tracking health commodity inventory and notifying stock levels via mobile devices: a mixed methods systematic review

A systematic scoping review of medicine availability and affordability in Africa

BACKGROUND

The most recent World Medicines Situation Report published in 2011 found substantial medicine availability and affordability challenges across WHO regions, including Africa. Since publication of the 2011 report, medicine availability and affordability has risen on the international agenda and was included in the Sustainable Development Goals as Target 3.8. While numerous medicine availability and affordability studies have been conducted in Africa since the last World Medicines Situation Report, there has not been a systematic analysis of the methods used in these studies, measures of medicine availability and affordability, categories of medicines studied, or geographic distribution. Filling this knowledge gap can help inform future medicine availability and affordability studies, design systems to monitor progress toward Sustainable Development Goal Target 3.8 in Africa and beyond, and inform policy and program decisions to improve medicine availability and affordability.

METHODS

We conducted a systematic scoping review of studies assessing medicine availability or affordability conducted in the WHO Africa region published from 2009-2021.

RESULTS

Two hundred forty one articles met our eligibility criteria. 88% of the articles (213/241) reported descriptive studies, while 12% (28/241) reported interventional studies. Of the 198 studies measuring medicine availability, the most commonly used measure of medicine availability was whether a medicine was in stock on the date of a survey (124/198, 63%). We also identified multiple other availability methods and measures, including retrospective stock record reviews and self-reported medicine availability surveys. Of the 59 articles that included affordability measures, 32 (54%) compared the price of the medicine to the daily wage of the lowest paid government worker. Other affordability measures were patient self-reported affordability, capacity to pay measures, and comparing medicines prices with a population-level income standard (such as minimum wage, poverty line, or per capita income). The most commonly studied medicines were antiparasitic and anti-bacterial medicines. We did not identify studies in 22 out of 48 (46%) countries in the WHO Africa Region and more than half of the studies identified were conducted in Ethiopia, Kenya, Tanzania, and/or Uganda.

CONCLUSION

Our results revealed a wide range of medicine availability and affordability assessment methodologies and measures, including cross-sectional facility surveys, population surveys, and retrospective data analyses. Our review also indicated a need for greater focus on medicines for certain non-communicable diseases, greater geographic diversity of studies, and the need for more intervention studies to identify approaches to improve access to medicines in the region.

Assessing unConventional Evidence (ACE) tool: development and content of a tool to assess the strengths and limitations of 'unconventional' source materials

BACKGROUND

When deciding whether to implement an intervention, decision-makers typically have questions on feasibility and acceptability and on factors affecting implementation. Descriptions of programme implementation and of policies and systems are rich sources of information for these questions. However, this information is often not based on empirical data collected using explicit methods. To use the information in unconventional source materials in syntheses or other decision support products, we need methods of assessing their strengths and limitations. This paper describes the development and content of the Assessing unConventional Evidence (ACE) tool, a new tool to assess the strengths and limitations of these sources.

METHODS

We developed the ACE tool in four stages: first, we examined existing tools to identify potentially relevant assessment criteria. Second, we drew on these criteria and team discussions to create a first draft of the tool. Third, we obtained feedback on the draft from potential users and methodologists, and through piloting the tool in evidence syntheses. Finally, we used this feedback to iteratively refine the assessment criteria and to improve our guidance for undertaking the assessment.

RESULTS

The tool is made up of 11 criteria including the purpose and context of the source; the completeness of the information presented; and the extent to which evidence is provided to support the findings made. Users are asked to indicate whether each of the criteria have been addressed. On the basis of their judgements for each criterion, users then make an overall assessment of the limitations of the source, ranging from no or very minor concerns to serious concerns. These assessments can then facilitate appropriate use of the evidence in decision support products.

CONCLUSIONS

Through focussing on unconventional source materials, the ACE tool fills an important gap in the range of tools for assessing the strengths and limitations of policy-relevant evidence and supporting evidence-informed decision-making.

Factors Affecting the Successful Implementation of a Digital Intervention for Health Financing in a Low-Resource Setting at Scale: Semistructured Interview Study With Health Care Workers and Management Staff

BACKGROUND

Digital interventions for health financing, if implemented at scale, have the potential to improve health system performance by reducing transaction costs and improving data-driven decision-making. However, many interventions never reach sustainability, and evidence on success factors for scale is scarce. The Insurance Management Information System (IMIS) is a digital intervention for health financing, designed to manage an insurance scheme and already implemented on a national scale in Tanzania. A previous study found that the IMIS claim function was poorly adopted by health care workers (HCWs), questioning its potential to enable strategic purchasing and succeed at scale.

OBJECTIVE

This study aimed to understand why the adoption of the IMIS claim function by HCWs remained low in Tanzania and to assess implications for use at scale.

METHODS

We conducted 21 semistructured interviews with HCWs and management staff in 4 districts where IMIS was first implemented. We sampled respondents by using a maximum variation strategy. We used the framework method for data analysis, applying a combination of inductive and deductive coding to organize codes in a socioecological model. Finally, we related emerging themes to a framework for digital health interventions for scale.

RESULTS

Respondents appreciated IMIS's intrinsic software characteristics and technical factors and acknowledged IMIS as a valuable tool to simplify claim management. Human factors, extrinsic ecosystem, and health care ecosystem were considered as barriers to widespread adoption.

CONCLUSIONS

Digital interventions for health financing, such as IMIS, may have the potential for scale if careful consideration is given to the environment in which they are placed. Without a sustainable health financing environment, sufficient infrastructure, and human capacity, they cannot unfold their full potential to improve health financing functions and ultimately contribute to universal health coverage.

Galvanizing Action on Primary Health Care: Analyzing Bottlenecks and Strategies to Strengthen Community Health Systems in West and Central Africa

INTRODUCTION

The renewed commitment to primary health care (PHC) presents an opportunity to strengthen health systems in West and Central Africa (WCA). Though evidence-based cost-effective interventions that are predicted to prevent up to one-third of maternal, newborn, and child health complications and deaths with universal coverage have been identified, more than 50% of people living in rural areas or from poor families still do not have access to these interventions in resource-constrained settings.

METHODS

We conducted a multicountry systematic analysis of bottlenecks and proposed solutions to strengthen community health systems through a series of collaborative workshops in 22 countries in WCA. Countries were categorized by their under-5 mortality rate (U5MR) to assess specificities related to reported challenges. We also reviewed existing data on selected health system tracer interventions to analyze country profiles.

RESULTS

The bottlenecks identified as severe or very severe were related to health financing (19 countries, 86%), essential medical technology and products (16 countries, 73%), integrated health service delivery (14 countries, 64%), and community ownership and partnerships (self-reported by 14 countries, 64%). Only the integrated service delivery was self-reported as a severe challenge by countries with high U5MR. The issue of human resources for community health was one of the least reported challenges.

CONCLUSION

In WCA, strengthening community health systems as part of PHC revitalization efforts should focus on increasing health financing and innovative investments, strengthening the logistics management system, and fostering community ownership and partnerships. Countries with high U5MR should also reinforce integrated service delivery approaches through innovation. Government actions galvanized by global and regional ongoing initiatives should be sustained to ensure that no one is left behind.

Routine Health Information System (RHIS) improvements for strengthened health system management

BACKGROUND

A well-functioning routine health information system (RHIS) can provide the information needed for health system management, for governance, accountability, planning, policy making, surveillance and quality improvement, but poor information support has been identified as a major obstacle for improving health system management.

OBJECTIVES

To assess the effects of interventions to improve routine health information systems in terms of RHIS performance, and also, in terms of improved health system management performance, and improved patient and population health outcomes.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library, MEDLINE Ovid and Embase Ovid in May 2019. We searched Global Health, Ovid and PsycInfo in April 2016. In January 2020 we searched for grey literature in the Grey Literature Report and in OpenGrey, and for ongoing trials using the International Clinical Trials Registry Platform (ICTRP) and ClinicalTrials.gov. In October 2019 we also did a cited reference search using Web of Science, and a 'similar articles' search in PubMed.

SELECTION CRITERIA

Randomised and non-randomised trials, controlled before-after studies and time-series studies comparing routine health information system interventions, with controls, in primary, hospital or community health care settings. Participants included clinical staff and management, district management and community health workers using routine information systems.

DATA COLLECTION AND ANALYSIS

Two authors independently reviewed records to identify studies for inclusion, extracted data from the included studies and assessed the risk of bias. Interventions and outcomes were too varied across studies to allow for pooled risk analysis. We present a 'Summary of findings' table for each intervention comparisons broadly categorised into Technical and Organisational (or a combination), and report outcomes on data quality and service quality. We used the GRADE approach to assess the certainty of the evidence.

MAIN RESULTS

We included six studies: four cluster randomised trials and two controlled before-after studies, from Africa and South America. Three studies evaluated technical interventions, one study evaluated an organisational intervention, and two studies evaluated a combination of technical and organisational interventions. Four studies reported on data quality and six studies reported on service quality. In terms of data quality, a web-based electronic TB laboratory information system probably reduces the length of time to reporting of TB test results, and probably reduces the overall rate of recording errors of TB test results, compared to a paper-based system (moderate certainty evidence). We are uncertain about the effect of the electronic laboratory information system on the recording rate of serious (misidentification) errors for TB test results compared to a paper-based system (very low certainty evidence). Misidentification errors are inaccuracies in transferring test results between an electronic register and patients' clinical charts. We are also uncertain about the effect of the intervention on service quality (timeliness of starting or changing a patient's TB treatment) (very low certainty evidence). A hand-held electronic device probably improves the length of time to report TB test results, and probably reduces the total frequency of recording errors in TB test results between the laboratory notebook and the electronic information record system, compared to a paper-based system (moderate-certainty evidence). We are, however, uncertain about the effect of the intervention on the frequency of serious (misidentification) errors in recording between the laboratory notebook and the electronic information record, compared to a paper-based system (very low certainty evidence). We are uncertain about the effect of a hospital electronic health information system on service quality (length of time outpatients spend at hospital, length of hospital stay, and hospital revenue collection), compared to a paper-based system (very low certainty evidence). High-intensity brief text messaging (SMS) may make little or no difference to data quality (in terms of completeness of documentation of pregnancy outcomes), compared to low-intensity brief text messaging (low-certainty evidence). We are uncertain about the effect of electronic drug stock notification (with either data management support or product transfer support) on service quality (in terms of transporting stock and stock levels), compared to paper-based stock notification (very low certainty evidence). We are uncertain about the effect of health information strengthening (where it is part of comprehensive service quality improvement intervention) on service quality (health worker motivation, receipt of training by health workers, health information index scores, quality of clinical observation of children and adults) (very low certainty evidence).

AUTHORS' CONCLUSIONS

The review indicates mixed effects of mainly technical interventions to improve data quality, with gaps in evidence on interventions aimed at enhancing data-informed health system management. There is a gap in interventions studying information support beyond clinical management, such as for human resources, finances, drug supply and governance. We need to have a better understanding of the causal mechanisms by which information support may affect change in management decision-making, to inform robust intervention design and evaluation methods.

Mobile reporting of vaccine stock-levels in primary health care facilities in the Eastern Cape Province of South Africa: perceptions and experiences of health care workers

The use of mobile and wireless digital technologies - mobile health (mhealth)- is increasingly been adopted in low- and middle-income countries (LMICs) to improve data visibility, improve decision-making, and consequently help ensure availability of health commodities in health facilities. In a bid to improve availability of medicines in primary health care facilities, the South African department of Health launched the Stock Visibility Solution (SVS), a mobile application developed for the purpose of capturing and monitoring stock levels of medicines including vaccines using mobile phones. The stock levels of medicines in facilities are usually uploaded to the central stock management system so that managers can act promptly to address stock-out situations. Pilot studies show that the SVS has the potential to reduce stock-outs from occurring. This study aimed to explore the perceptions and experiences of the SVS system amongst healthcare workers (HCWs) who are involved with managing stock levels of medicines in primary health care facilities in the Eastern Cape Province. This will help identify potential barriers and facilitators to implementation of the system and contribute to the development of strategies to improve its efficiency and effectiveness. A qualitative research design was employed, including semi-structured interviews with 64 HCWs working in primary health care facilities in the OR Tambo district, Eastern Cape Province in South Africa. Data was transcribed verbatim and analyzed using thematic analysis. Most HCWs understood the SVS as a system for reporting stock levels to managers and conveyed commitment to ensuring the system works. However, they highlighted a number of factors that demotivated efficient usage of the system: inadequate training, staff shortages and high staff turnover, lack of responses from the managers, the extra workload that comes with the system, amongst others. HCWs made various suggestions for how the system might be improved, most pertinently the need for more pharmacists and pharmacy assistants and for these cadres to be primarily in-charge of stock management and the use of the SVS. While HCWs are committed to addressing vaccine stock-outs, they face various barriers to an effective and efficient implementation of the SVS system. We make various recommendations for how these barriers might be addressed.

Health workers' perceptions and experiences of using mHealth technologies to deliver primary healthcare services: a qualitative evidence synthesis

BACKGROUND

Mobile health (mHealth), refers to healthcare practices supported by mobile devices, such as mobile phones and tablets. Within primary care, health workers often use mobile devices to register clients, track their health, and make decisions about care, as well as to communicate with clients and other health workers. An understanding of how health workers relate to, and experience mHealth, can help in its implementation.

OBJECTIVES

To synthesise qualitative research evidence on health workers' perceptions and experiences of using mHealth technologies to deliver primary healthcare services, and to develop hypotheses about why some technologies are more effective than others.

SEARCH METHODS

We searched MEDLINE, Embase, CINAHL, Science Citation Index and Social Sciences Citation Index in January 2018. We searched Global Health in December 2015. We screened the reference lists of included studies and key references and searched seven sources for grey literature (16 February to 5 March 2018). We re-ran the search strategies in February 2020. We screened these records and any studies that we identified as potentially relevant are awaiting classification.

SELECTION CRITERIA

We included studies that used qualitative data collection and analysis methods. We included studies of mHealth programmes that were part of primary healthcare services. These services could be implemented in public or private primary healthcare facilities, community and workplace, or the homes of clients. We included all categories of health workers, as well as those persons who supported the delivery and management of the mHealth programmes. We excluded participants identified as technical staff who developed and maintained the mHealth technology, without otherwise being involved in the programme delivery. We included studies conducted in any country.

DATA COLLECTION AND ANALYSIS

We assessed abstracts, titles and full-text papers according to the inclusion criteria. We found 53 studies that met the inclusion criteria and sampled 43 of these for our analysis. For the 43 sampled studies, we extracted information, such as country, health worker category, and the mHealth technology. We used a thematic analysis process. We used GRADE-CERQual to assess our confidence in the findings.

MAIN RESULTS

Most of the 43 included sample studies were from low- or middle-income countries. In many of the studies, the mobile devices had decision support software loaded onto them, which showed the steps the health workers had to follow when they provided health care. Other uses included in-person and/or text message communication, and recording clients' health information. Almost half of the studies looked at health workers' use of mobile devices for mother, child, and newborn health. We have moderate or high confidence in the following findings. mHealth changed how health workers worked with each other: health workers appreciated being more connected to colleagues, and thought that this improved co-ordination and quality of care. However, some described problems when senior colleagues did not respond or responded in anger. Some preferred face-to-face connection with colleagues. Some believed that mHealth improved their reporting, while others compared it to "big brother watching". mHealth changed how health workers delivered care: health workers appreciated how mHealth let them take on new tasks, work flexibly, and reach clients in difficult-to-reach areas. They appreciated mHealth when it improved feedback, speed and workflow, but not when it was slow or time consuming. Some health workers found decision support software useful; others thought it threatened their clinical skills. Most health workers saw mHealth as better than paper, but some preferred paper. Some health workers saw mHealth as creating more work. mHealth led to new forms of engagement and relationships with clients and communities: health workers felt that communicating with clients by mobile phone improved care and their relationships with clients, but felt that some clients needed face-to-face contact. Health workers were aware of the importance of protecting confidential client information when using mobile devices. Some health workers did not mind being contacted by clients outside working hours, while others wanted boundaries. Health workers described how some community members trusted health workers that used mHealth while others were sceptical. Health workers pointed to problems when clients needed to own their own phones. Health workers' use and perceptions of mHealth could be influenced by factors tied to costs, the health worker, the technology, the health system and society, poor network access, and poor access to electricity: some health workers did not mind covering extra costs. Others complained that phone credit was not delivered on time. Health workers who were accustomed to using mobile phones were sometimes more positive towards mHealth. Others with less experience, were sometimes embarrassed about making mistakes in front of clients or worried about job security. Health workers wanted training, technical support, user-friendly devices, and systems that were integrated into existing electronic health systems. The main challenges health workers experienced were poor network connections, access to electricity, and the cost of recharging phones. Other problems included damaged phones. Factors outside the health system also influenced how health workers experienced mHealth, including language, gender, and poverty issues. Health workers felt that their commitment to clients helped them cope with these challenges.

AUTHORS' CONCLUSIONS

Our findings propose a nuanced view about mHealth programmes. The complexities of healthcare delivery and human interactions defy simplistic conclusions on how health workers will perceive and experience their use of mHealth. Perceptions reflect the interplay between the technology, contexts, and human attributes. Detailed descriptions of the programme, implementation processes and contexts, alongside effectiveness studies, will help to unravel this interplay to formulate hypotheses regarding the effectiveness of mHealth.