Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics

Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.

Achieving optimal heath data impact in rural African healthcare settings: measures to barriers in Bukomansimbi District, Central Uganda

BACKGROUND

Health data is one of the most valuable assets in health service delivery yet one of the most underutilized in especially low-income countries. Health data is postulated to improve health service delivery through availing avenues for optimal patient management, facility management, and public health surveillance and management. Advancements in information technology (IT) will further increase the value of data, but will also call for capacity readiness especially in rural health facilities. We aimed to understand the current knowledge, attitudes and practices of health workers towards health data management and utilization.

METHODS

We conducted key informant interviews (KII) for health workers and data staff, and focus group discussions (FGD) for the village health teams (VHTs). We used both purposive and convenience sampling to recruit key informants, and convenience sampling to recruit village health teams. Interviews and discussions were audiotaped and transcribed verbatim. We manually generated the codes and we used thematic analysis to identify the themes. We also developed a reflexivity journal.

RESULTS

We conducted a total of 6 key informant interviews and 3 focus group discussions of 29 participants. Our analysis identified 7 themes: One theme underscored the health workers' enthusiasm towards an optimal health data management setting. The rest of the six themes resonated around working remedies to the systemic challenges that grapple health data management and utilization at facilities in rural areas. These include: Building human resource capacity; Equipping the facilities; Improved coordination with partners; Improved data quality assurance; Promotion of a pull supply system and Reducing information relay time.

CONCLUSION

Our findings reveal a plethora of systematic challenges that have persistently undercut optimal routine health data management and utilization in rural areas and suggest possible working remedies. Health care workers express enthusiasm towards an optimal health management system but this isn't matched by their technical capacity, facility readiness, systems and policy willingness. There is an urgent need to build rural lower facilities' capacity in health data management and utilization which will also lay a foundation for exploitation of information technology in health.

Impact of seasonal malaria chemoprevention on prevalence of malaria infection in malaria indicator surveys in Burkina Faso and Nigeria

BACKGROUND

In 2012, the WHO issued a policy recommendation for the use of seasonal malaria chemoprevention (SMC) to children 3-59 months in areas of highly seasonal malaria transmission. Clinical trials have found SMC to prevent around 75% of clinical malaria. Impact under routine programmatic conditions has been assessed during research studies but there is a need to identify sustainable methods to monitor impact using routinely collected data.

METHODS

Data from Demographic Health Surveys were merged with rainfall, geographical and programme data in Burkina Faso (2010, 2014, 2017) and Nigeria (2010, 2015, 2018) to assess impact of SMC. We conducted mixed-effects logistic regression to predict presence of malaria infection in children aged 6-59 months (rapid diagnostic test (RDT) and microscopy, separately).

RESULTS

We found strong evidence that SMC administration decreases odds of malaria measured by RDT during SMC programmes, after controlling for seasonal factors, age, sex, net use and other variables (Burkina Faso OR 0.28, 95% CI 0.21 to 0.37, p<0.001; Nigeria OR 0.40, 95% CI 0.30 to 0.55, p<0.001). The odds of malaria were lower up to 2 months post-SMC in Burkina Faso (1-month post-SMC: OR 0.29, 95% CI 0.12 to 0.72, p=0.01; 2 months post-SMC: OR: 0.33, 95% CI 0.17 to 0.64, p<0.001). The odds of malaria were lower up to 1 month post-SMC in Nigeria but was not statistically significant (1-month post-SMC 0.49, 95% CI 0.23 to 1.05, p=0.07). A similar but weaker effect was seen for microscopy (Burkina Faso OR 0.38, 95% CI 0.29 to 0.52, p<0.001; Nigeria OR 0.53, 95% CI 0.38 to 0.76, p<0.001).

CONCLUSIONS

Impact of SMC can be detected in reduced prevalence of malaria from data collected through household surveys if conducted during SMC administration or within 2 months afterwards. Such evidence could contribute to broader evaluation of impact of SMC programmes.

Can routine health facility data be used to monitor subnational coverage of maternal, newborn and child health services in Uganda?

Background

Routine health facility data are a critical source of local monitoring of progress and performance at the subnational level. Uganda has been using district health statistics from facility data for many years. We aimed to systematically assess data quality and examine different methods to obtain plausible subnational estimates of coverage for maternal, newborn and child health interventions.

Methods

Annual data from the Uganda routine health facility information system 2015–2019 for all 135 districts were used, as well as national surveys for external comparison and the identification of near-universal coverage interventions. The quality of reported data on antenatal and delivery care and child immunization was assessed through completeness of facility reporting, presence of extreme outliers and internal data consistencies. Adjustments were made when necessary. The denominators for the coverage indicators were derived from population projections and health facility data on near-universal coverage interventions. The coverage results with different denominators were compared with the results from household surveys.

Results

Uganda’s completeness of reporting by facilities was near 100% and extreme outliers were rare. Inconsistencies in reported events, measured by annual fluctuations and between intervention consistency, were common and more among the 135 districts than the 15 subregions. The reported numbers of vaccinations were improbably high compared to the projected population of births or first antenatal visits – and especially so in 2015–2016. There were also inconsistencies between the population projections and the expected target population based on reported numbers of antenatal visits or immunizations. An alternative approach with denominators derived from facility data gave results that were more plausible and more consistent with survey results than based on population projections, although inconsistent results remained for substantive number of subregions and districts.

Conclusion

Our systematic assessment of the quality of routine reports of key events and denominators shows that computation of district health statistics is possible with transparent adjustments and methods, providing a general idea of levels and trends for most districts and subregions, but that improvements in data quality are essential to obtain more accurate monitoring.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06554-6.

Using routine health information data for research in low- and middle-income countries: a systematic review

BACKGROUND

Routine health information systems (RHISs) support resource allocation and management decisions at all levels of the health system, as well as strategy development and policy-making in many low- and middle-income countries (LMICs). Although RHIS data represent a rich source of information, such data are currently underused for research purposes, largely due to concerns over data quality. Given that substantial investments have been made in strengthening RHISs in LMICs in recent years, and that there is a growing demand for more real-time data from researchers, this systematic review builds upon the existing literature to summarize the extent to which RHIS data have been used in peer-reviewed research publications.

METHODS

Using terms 'routine health information system', 'health information system', or 'health management information system' and a list of LMICs, four electronic peer-review literature databases were searched from inception to February 202,019: PubMed, Scopus, EMBASE, and EconLit. Articles were assessed for inclusion based on pre-determined eligibility criteria and study characteristics were extracted from included articles using a piloted data extraction form.

RESULTS

We identified 132 studies that met our inclusion criteria, originating in 37 different countries. Overall, the majority of the studies identified were from Sub-Saharan Africa and were published within the last 5 years. Malaria and maternal health were the most commonly studied health conditions, although a number of other health conditions and health services were also explored.

CONCLUSIONS

Our study identified an increasing use of RHIS data for research purposes, with many studies applying rigorous study designs and analytic methods to advance program evaluation, monitoring and assessing services, and epidemiological studies in LMICs. RHIS data represent an underused source of data and should be made more available and further embraced by the research community in LMIC health systems.

Methodological Considerations for Use of Routine Health Information System Data to Evaluate Malaria Program Impact in an Era of Declining Malaria Transmission

Coverage of malaria control interventions is increasing dramatically across endemic countries. Evaluating the impact of malaria control programs and specific interventions on health indicators is essential to enable countries to select the most effective and appropriate combination of tools to accelerate progress or proceed toward malaria elimination. When key malaria interventions have been proven effective under controlled settings, further evaluations of the impact of the intervention using randomized approaches may not be appropriate or ethical. Alternatives to randomized controlled trials are therefore required for rigorous evaluation under conditions of routine program delivery. Routine health management information system (HMIS) data are a potentially rich source of data for impact evaluation, but have been underused in impact evaluation due to concerns over internal validity, completeness, and potential bias in estimates of program or intervention impact. A range of methodologies were identified that have been used for impact evaluations with malaria outcome indicators generated from HMIS data. Methods used to maximize internal validity of HMIS data are presented, together with recommendations on reducing bias in impact estimates. Interrupted time series and dose-response analyses are proposed as the strongest quasi-experimental impact evaluation designs for analysis of malaria outcome indicators from routine HMIS data. Interrupted time series analysis compares the outcome trend and level before and after the introduction of an intervention, set of interventions or program. The dose-response national platform approach explores associations between intervention coverage or program intensity and the outcome at a subnational (district or health facility catchment) level.

Using health-facility data to assess subnational coverage of maternal and child health indicators, Kenya

Objective

To develop a systematic approach to obtain the best possible national and subnational statistics for maternal and child health coverage indicators from routine health-facility data.

Methods

Our approach aimed to obtain improved numerators and denominators for calculating coverage at the subnational level from health-facility data. This involved assessing data quality and determining adjustment factors for incomplete reporting by facilities, then estimating local target populations based on interventions with near-universal coverage (first antenatal visit and first dose of pentavalent vaccine). We applied the method to Kenya at the county level, where routine electronic reporting by facilities is in place via the district health information software system.

Findings

Reporting completeness for facility data were well above 80% in all 47 counties and the consistency of data over time was good. Coverage of the first dose of pentavalent vaccine, adjusted for facility reporting completeness, was used to obtain estimates of the county target populations for maternal and child health indicators. The country and national statistics for the four-year period 2012/13 to 2015/16 showed good consistency with results of the 2014 Kenya demographic and health survey. Our results indicated a stagnation of immunization coverage in almost all counties, a rapid increase of facility-based deliveries and caesarean sections and limited progress in antenatal care coverage.

Conclusion

While surveys will continue to be necessary to provide population-based data, web-based information systems for health facility reporting provide an opportunity for more frequent, local monitoring of progress, in maternal and child health.

Perspectives on utilization of community based health information systems in Western Kenya

INTRODUCTION

Health information systems (HIS) are considered fundamental for the efficient delivery of high quality health care. However, a large number of legal and practical constraints influence the design and introduction of such systems. The inability to quantify and analyse situations with credible data and to use data in planning and managing service delivery plagues Africa. Establishing effective information systems and using this data for planning efficient health service delivery is essential to district health systems' performance improvement. Community Health Units in Kenya are central points for community data collection, analysis, dissemination and use. In Kenya, data tend to be collected for reporting purposes and not for decision-making at the point of collection. This paper describes the perspectives of local users on information use in various socio-economic contexts in Kenya.

METHODS

Information for this study was gathered through semi-structured interviews. The interviewees were purposefully selected from various community health units and public health facilities in the study area. The data were organized and analysed manually, grouping them into themes and categories.

RESULTS

Information needs of the community included service utilization and health status information. Dialogue was the main way of information utilization in the community. However, health systems and personal challenges impeded proper collection and use of information.

CONCLUSION

The challenges experienced in health information utilization may be overcome by linkages and coordination between the community and the health facilities. The personal challenges can be remedied using a motivational package that includes training of the Community Health Workers.

Pattern of diarrheal diseases in Atwima Nwabiagya District-Ghana, 2009- 2013

Introduction

Diarrheal diseases remain one of the most important public health challenges worldwide. In 2011, Ghana recorded average annual diarrheal cases of 2,218 per 100,000 populations for children under-five with Ashanti region recording the third highest. In the Atwima Nwabiagya District, summary statistics are done without detailed analysis. We analyzed diarrheal surveillance data to determine its pattern and to develop threshold levels for the disease in Atwima Nwabiagya District in the Ashanti Region of Ghana.

Methods

District level diarrheal morbidity data from January 2009 to December 2013 was extracted from District Health Information Management System II, cleaned and analyzed. Descriptive analysis was done and expressed as frequencies and relative frequencies. Description of the data was done in time, place and person. We calculated diarrhea threshold using the C2 method.

Results

Overall, 51,131 cases were reported with 55.2% being females over the five year period. The highest episode of diarrhea by age-group occurred in children under-five during the study period. Changes in disease occurrence did not conform to a seasonal pattern. District analysis showed one outbreak whilst sub-district analysis revealed more than one outbreak.

Conclusion

Diarrheal disease pattern did not show a seasonal trend. Only one outbreak was observed at district level but each sub-district, showed more than one outbreak. The highest number of episodes of diarrhea per year occurred in Children under- five. Data analysis should be done at lower levels to inform interventions. Interventions should be targeted towards children under-five years.

The application of a biometric identification technique for linking community and hospital data in rural Ghana

BACKGROUND

The reliability of counts for estimating population dynamics and disease burdens in communities depends on the availability of a common unique identifier for matching general population data with health facility data. Biometric data has been explored as a feasible common identifier between the health data and sociocultural data of resident members in rural communities within the Kintampo Health and Demographic Surveillance System located in the central part of Ghana.

OBJECTIVE

Our goal was to assess the feasibility of using fingerprint identification to link community data and hospital data in a rural African setting.

DESIGN

A combination of biometrics and other personal identification techniques were used to identify individual's resident within a surveillance population seeking care in two district hospitals. Visits from resident individuals were successfully recorded and categorized by the success of the techniques applied during identification. The successes of visits that involved identification by fingerprint were further examined by age.

RESULTS

A total of 27,662 hospital visits were linked to resident individuals. Over 85% of those visits were successfully identified using at least one identification method. Over 65% were successfully identified and linked using their fingerprints. Supervisory support from the hospital administration was critical in integrating this identification system into its routine activities. No concerns were expressed by community members about the fingerprint registration and identification processes.

CONCLUSIONS

Fingerprint identification should be combined with other methods to be feasible in identifying community members in African rural settings. This can be enhanced in communities with some basic Demographic Surveillance System or census information.

Bayesian imperfect information analysis for clinical recurrent data

In medical research, clinical practice must often be undertaken with imperfect information from limited resources. This study applied Bayesian imperfect information-value analysis to realistic situations to produce likelihood functions and posterior distributions, to a clinical decision-making problem for recurrent events. In this study, three kinds of failure models are considered, and our methods illustrated with an analysis of imperfect information from a trial of immunotherapy in the treatment of chronic granulomatous disease. In addition, we present evidence toward a better understanding of the differing behaviors along with concomitant variables. Based on the results of simulations, the imperfect information value of the concomitant variables was evaluated and different realistic situations were compared to see which could yield more accurate results for medical decision-making.

Spatiotemporal approaches to analyzing pedestrian fatalities: the case of Cali, Colombia

OBJECTIVE

Injuries among pedestrians are a major public health concern in Colombian cities such as Cali. This is one of the first studies in Latin America to apply Bayesian maximum entropy (BME) methods to visualize and produce fine-scale, highly accurate estimates of citywide pedestrian fatalities. The purpose of this study is to determine the BME method that best estimates pedestrian mortality rates and reduces statistical noise. We further utilized BME methods to identify and differentiate spatial patterns and persistent versus transient pedestrian mortality hotspots.

METHODS

In this multiyear study, geocoded pedestrian mortality data from the Cali Injury Surveillance System (2008 to 2010) and census data were utilized to accurately visualize and estimate pedestrian fatalities. We investigated the effects of temporal and spatial scales, addressing issues arising from the rarity of pedestrian fatality events using 3 BME methods (simple kriging, Poisson kriging, and uniform model Bayesian maximum entropy). To reduce statistical noise while retaining a fine spatial and temporal scale, data were aggregated over 9-month incidence periods and censal sectors. Based on a cross-validation of BME methods, Poisson kriging was selected as the best BME method. Finally, the spatiotemporal and urban built environment characteristics of Cali pedestrian mortality hotspots were linked to intervention measures provided in Mead et al.'s (2014) pedestrian mortality review.

RESULTS

The BME space-time analysis in Cali resulted in maps displaying hotspots of high pedestrian fatalities extending over small areas with radii of 0.25 to 1.1 km and temporal durations of 1 month to 3 years. Mapping the spatiotemporal distribution of pedestrian mortality rates identified high-priority areas for prevention strategies. The BME results allow us to identify possible intervention strategies according to the persistence and built environment of the hotspot; for example, through enforcement or long-term environmental modifications.

CONCLUSIONS

BME methods provide useful information on the time and place of injuries and can inform policy strategies by isolating priority areas for interventions, contributing to intervention evaluation, and helping to generate hypotheses and identify the preventative strategies that may be suitable to those areas (e.g., street-level methods: pedestrian crossings, enforcement interventions; or citywide approaches: limiting vehicle speeds). This specific information is highly relevant for public health interventions because it provides the ability to target precise locations.

A methodological framework for the improved use of routine health system data to evaluate national malaria control programs: evidence from Zambia

Background

Due to challenges in laboratory confirmation, reporting completeness, timeliness, and health access, routine incidence data from health management information systems (HMIS) have rarely been used for the rigorous evaluation of malaria control program scale-up in Africa.

Methods

We used data from the Zambia HMIS for 2009–2011, a period of rapid diagnostic and reporting scale-up, to evaluate the association between insecticide-treated net (ITN) program intensity and district-level monthly confirmed outpatient malaria incidence using a dose–response national platform approach with district-time units as the unit of analysis. A Bayesian geostatistical model was employed to estimate longitudinal district-level ITN coverage from household survey and programmatic data, and a conditional autoregressive model (CAR) was used to impute missing HMIS data. The association between confirmed malaria case incidence and ITN program intensity was modeled while controlling for known confounding factors, including climate variability, reporting, testing, treatment-seeking, and access to health care, and additionally accounting for spatial and temporal autocorrelation.

Results

An increase in district level ITN coverage of one ITN per household was associated with an estimated 27% reduction in confirmed case incidence overall (incidence rate ratio (IRR): 0 · 73, 95% Bayesian Credible Interval (BCI): 0 · 65–0 · 81), and a 41% reduction in areas of lower malaria burden.

Conclusions

When improved through comprehensive parasitologically confirmed case reporting, HMIS data can become a valuable tool for evaluating malaria program scale-up. Using this approach we provide further evidence that increased ITN coverage is associated with decreased malaria morbidity and use of health services for malaria illness in Zambia. These methods and results are broadly relevant for malaria program evaluations currently ongoing in sub-Saharan Africa, especially as routine confirmed case data improve.

Electronic supplementary material

The online version of this article (doi:10.1186/s12963-014-0030-0) contains supplementary material, which is available to authorized users.

Prevalence and burden of diseases presenting to a general pediatrics ward in Gondar, Ethiopia

BACKGROUND

Little is known about pediatric hospital admissions in Ethiopia.

METHODS

This cross-sectional study analyzed all data entered into the Gondar University Hospital pediatric ward's admission registration books over 1 year. Patient age, sex, origin, length of stay, diagnosis and discharge condition were transcribed into an electronic database for all observations. Missing data were retrieved by chart and death certificate review. Primary outcome measures included death and death in the first 24 h of admission.

RESULTS

In all, 1927 patients were admitted to our facility during the year of study. Of these, 64.5% improved, 4.6% were discharged unchanged, 6.5% disappeared and 7.5% died; the remaining 17.0% of outcome data were registered as 'non-death' but could not be specified further. The median age of admission was 2.2 years (interquartile range 1-7 years), with more admissions for children younger than 5 years (70.3%) and more male subjects admitted than female subjects (59.6% male). The median length of stay was 4.0 days (interquartile range 2-10 days). Eighty-one percent of admissions originated from Gondar or its neighboring districts. Most admissions carried a respiratory, nutritional or infectious diagnosis (47.5, 46.8 and 36.5%, respectively). Conditions diagnosed most commonly (>200 cases) included community-acquired pneumonia (812 cases), severe acute malnutrition (381), anemia (274) and acute gastroenteritis (219). Seven diagnoses were associated with mortality after adjusting for demographic covariates: severe acute malnutrition (odds ratio (OR) 2.5, P < 0.001), coma (OR 4.2, P < 0.001), meningitis (OR 2.3, P = 0.018), congestive heart failure (OR 2.4, P = 0.001), severe dehydration (OR 2.5, P = 0.004), aspiration pneumonia (OR 5.4, P < 0.001) and sepsis (OR 3.2, P < 0.001). Thirty-three percent of deaths occurred in the first 24 h of admission, with four diagnoses associated with first-24-h mortality after adjusting for demographic covariates: coma (OR 7.0, P < 0.001), meningitis (OR 3.2, P = 0.008), congestive heart failure (OR 3.1, P = 0.008) and aspiration pneumonia (OR 12.1, P < 0.001).

CONCLUSIONS

This study demonstrates a mortality pattern at our hospital that differs considerably from Ethiopia as a whole, and may differ from other hospitals in sub-Saharan Africa. Hospitals must look beyond national and regional agenda when identifying mortality reduction targets.

Embedding health policy and systems research into decision-making processes in low- and middle-income countries

Attention is increasingly directed to bridging the gap between the production of knowledge and its use for health decision-making in low- and middle-income countries (LMICs). An important and underdeveloped area of health policy and systems research (HPSR) is the organization of this process. Drawing from an interdisciplinary conception of embeddedness, a literature review was conducted to identify examples of embedded HPSR used to inform decision-making in LMICs. The results of the literature review were organized according to the World Health Organization’s Building Blocks Framework. Next, a conceptual model was created to illustrate the arrangement of organizations that produce embedded HPSR and the characteristics that facilitate its uptake into the arena of decision-making. We found that multiple forces converge to create context-specific pathways through which evidence enters into decision-making. Depending on the decision under consideration, the literature indicates that decision-makers may call upon an intricate combination of actors for sourcing HPSR. While proximity to decision-making does have advantages, it is not the position of the organization within the network, but rather the qualities the organization possesses, that enable it to be embedded. Our findings suggest that four qualities influence embeddedness: reputation, capacity, quality of connections to decision-makers, and quantity of connections to decision-makers and others. In addition to this, the policy environment (e.g. the presence of legislation governing the use of HPSR, presence of strong civil society, etc.) strongly influences uptake. Through this conceptual model, we can understand which conditions are likely to enhance uptake of HPSR in LMIC health systems. This raises several important considerations for decision-makers and researchers about the arrangement and interaction of evidence-generating organizations in health systems.

Monitoring child survival in 'real time' using routine health facility records: results from Malawi

Objectives

Few developing countries have the accurate civil registration systems needed to track progress in child survival. However, the health information systems in most of these countries do record facility births and deaths, at least in principle. We used data from two districts of Malawi to test a method for monitoring child mortality based on adjusting health facility records for incomplete coverage.

Methods

Trained researchers collected reports of monthly births and deaths among children younger than 5 years from all health facilities in Balaka and Salima districts of Malawi in 2010–2011. We estimated the proportion of births and deaths occurring in health facilities, respectively, from the 2010 Demographic and Health Survey and a household mortality survey conducted between October 2011 and February 2012. We used these proportions to adjust the health facility data to estimate the actual numbers of births and deaths. The survey also provided ‘gold-standard’ measures of under-five mortality.

Results

Annual under-five mortality rates generated by adjusting health facility data were between 35% and 65% of those estimated by the gold-standard survey in Balaka, and 46% and 50% in Salima for four overlapping 12-month periods in 2010–2011. The ratios of adjusted health facility rates to gold-standard rates increased sharply over the four periods in Balaka, but remained relatively stable in Salima.

Conclusions

Even in Malawi, where high proportions of births and deaths occur in health facilities compared with other countries in sub-Saharan Africa, routine Health Management Information Systems data on births and deaths cannot be used at present to estimate annual trends in under-five mortality.

Local use of geographic information systems to improve data utilisation and health services: mapping caesarean section coverage in rural Rwanda

OBJECTIVES

To show the utility of combining routinely collected data with geographic location using a Geographic Information System (GIS) in order to facilitate a data-driven approach to identifying potential gaps in access to emergency obstetric care within a rural Rwandan health district.

METHODS

Total expected births in 2009 at sub-district levels were estimated using community health worker collected population data. Clinical data were extracted from birth registries at eight health centres (HCs) and the district hospital (DH). C-section rates as a proportion of total expected births were mapped by cell. Peri-partum foetal mortality rates per facility-based births, as well as the rate of uterine rupture as an indication for C-section, were compared between areas of low and high C-section rates.

RESULTS

The lowest C-section rates were found in the more remote part of the hospital catchment area. The sector with significantly lower C-section rates had significantly higher facility-based peri-partum foetal mortality and incidence of uterine rupture than the sector with the highest C-section rates (P < 0.034).

CONCLUSIONS

This simple approach for geographic monitoring and evaluation leveraging existing health service and GIS data facilitated evidence-based decision making and represents a feasible approach to further strengthen local data-driven decisions for resource allocation and quality improvement.

Development of paediatric quality of inpatient care indicators for low-income countries - A Delphi study

Background

Indicators of quality of care for children in hospitals in low-income countries have been proposed, but information on their perceived validity and acceptability is lacking.

Methods

Potential indicators representing structural and process aspects of care for six common conditions were selected from existing, largely qualitative WHO assessment tools and guidelines. We employed the Delphi technique, which combines expert opinion and existing scientific information, to assess their perceived validity and acceptability. Panels of experts, one representing an international panel and one a national (Kenyan) panel, were asked to rate the indicators over 3 rounds and 2 rounds respectively according to a variety of attributes.

Results

Based on a pre-specified consensus criteria most of the indicators presented to the experts were accepted: 112/137(82%) and 94/133(71%) for the international and local panels respectively. For the other indicators there was no consensus; none were rejected. Most indicators were rated highly on link to outcomes, reliability, relevance, actionability and priority but rated more poorly on feasibility of data collection under routine conditions. There was moderate to substantial agreement between the two panels of experts.

Conclusions

This Delphi study provided evidence for the perceived usefulness of most of a set of measures of quality of hospital care for children proposed for use in low-income countries. However, both international and local experts expressed concerns that data for many process-based indicators may not currently be available. The feasibility of widespread quality assessment and responsiveness of indicators to intervention should be examined as part of continued efforts to improve approaches to informative hospital quality assessment.

The epidemiology of hospitalization with diarrhea in rural Kenya: the utility of existing health facility data in developing countries

OBJECTIVES

In developing countries where prospective surveillance is resource-intensive, existing hospital data can define incidence, mortality, and risk factors that can help target interventions and track trends in disease burden.

METHODS

We reviewed hospitalizations from 2001 to 2003 at all inpatient facilities in Bondo District, Kenya.

RESULTS

Diarrhea was responsible for 11.2% (n=2158) of hospitalizations. The annual incidence was 550 and 216 per 100,000 persons aged <5 and > or =5 years, respectively. The incidence was highest in infants (1138 per 100,000 persons), decreased in older children, peaked again among 20-29-year-olds (341 per 100,000), and declined among those > or =65 years (157 per 100,000). Female adults had higher incidence than males (rate ratio=1.84, 95% CI 1.61-2.10). Incidence decreased with distance from the district referral hospital (4.5% per kilometer, p<0.0001) and from the nearest inpatient facility (6.6% per kilometer, p=0.012). Case-fatality was high (8.0%), and was higher among adults than young children. Co-diagnosis with malaria, pneumonia, HIV, and tuberculosis was common. Peak diarrhea incidence fell one to two months after heavy rains.

CONCLUSIONS

The trends revealed here provide useful data for public health priority setting and planning, including preventative interventions. The utility of such data justifies renewed efforts to establish and strengthen health management information systems in developing countries.

A world malaria map: Plasmodium falciparum endemicity in 2007

BACKGROUND

Efficient allocation of resources to intervene against malaria requires a detailed understanding of the contemporary spatial distribution of malaria risk. It is exactly 40 y since the last global map of malaria endemicity was published. This paper describes the generation of a new world map of Plasmodium falciparum malaria endemicity for the year 2007.

METHODS AND FINDINGS

A total of 8,938 P. falciparum parasite rate (PfPR) surveys were identified using a variety of exhaustive search strategies. Of these, 7,953 passed strict data fidelity tests for inclusion into a global database of PfPR data, age-standardized to 2-10 y for endemicity mapping. A model-based geostatistical procedure was used to create a continuous surface of malaria endemicity within previously defined stable spatial limits of P. falciparum transmission. These procedures were implemented within a Bayesian statistical framework so that the uncertainty of these predictions could be evaluated robustly. The uncertainty was expressed as the probability of predicting correctly one of three endemicity classes; previously stratified to be an informative guide for malaria control. Population at risk estimates, adjusted for the transmission modifying effects of urbanization in Africa, were then derived with reference to human population surfaces in 2007. Of the 1.38 billion people at risk of stable P. falciparum malaria, 0.69 billion were found in Central and South East Asia (CSE Asia), 0.66 billion in Africa, Yemen, and Saudi Arabia (Africa+), and 0.04 billion in the Americas. All those exposed to stable risk in the Americas were in the lowest endemicity class (PfPR2-10 < or = 5%). The vast majority (88%) of those living under stable risk in CSE Asia were also in this low endemicity class; a small remainder (11%) were in the intermediate endemicity class (PfPR2-10 > 5 to < 40%); and the remaining fraction (1%) in high endemicity (PfPR2-10 > or = 40%) areas. High endemicity was widespread in the Africa+ region, where 0.35 billion people are at this level of risk. Most of the rest live at intermediate risk (0.20 billion), with a smaller number (0.11 billion) at low stable risk.

CONCLUSIONS

High levels of P. falciparum malaria endemicity are common in Africa. Uniformly low endemic levels are found in the Americas. Low endemicity is also widespread in CSE Asia, but pockets of intermediate and very rarely high transmission remain. There are therefore significant opportunities for malaria control in Africa and for malaria elimination elsewhere. This 2007 global P. falciparum malaria endemicity map is the first of a series with which it will be possible to monitor and evaluate the progress of this intervention process.

A spatial national health facility database for public health sector planning in Kenya in 2008

BACKGROUND

Efforts to tackle the enormous burden of ill-health in low-income countries are hampered by weak health information infrastructures that do not support appropriate planning and resource allocation. For health information systems to function well, a reliable inventory of health service providers is critical. The spatial referencing of service providers to allow their representation in a geographic information system is vital if the full planning potential of such data is to be realized.

METHODS

A disparate series of contemporary lists of health service providers were used to update a public health facility database of Kenya last compiled in 2003. These new lists were derived primarily through the national distribution of antimalarial and antiretroviral commodities since 2006. A combination of methods, including global positioning systems, was used to map service providers. These spatially-referenced data were combined with high-resolution population maps to analyze disparity in geographic access to public health care.

FINDINGS

The updated 2008 database contained 5,334 public health facilities (67% ministry of health; 28% mission and nongovernmental organizations; 2% local authorities; and 3% employers and other ministries). This represented an overall increase of 1,862 facilities compared to 2003. Most of the additional facilities belonged to the ministry of health (79%) and the majority were dispensaries (91%). 93% of the health facilities were spatially referenced, 38% using global positioning systems compared to 21% in 2003. 89% of the population was within 5 km Euclidean distance to a public health facility in 2008 compared to 71% in 2003. Over 80% of the population outside 5 km of public health service providers was in the sparsely settled pastoralist areas of the country.

CONCLUSION

We have shown that, with concerted effort, a relatively complete inventory of mapped health services is possible with enormous potential for improving planning. Expansion in public health care in Kenya has resulted in significant increases in geographic access although several areas of the country need further improvements. This information is key to future planning and with this paper we have released the digital spatial database in the public domain to assist the Kenyan Government and its partners in the health sector.