Modeled Impact of Seasonal Malaria Chemoprevention on District-Level Suspected and Confirmed Malaria Cases in Chad Based on Routine Clinical Data (2013–2018)

Sulfadoxine-pyrimethamine plus amodiaquine to children aged 3–59 months is delivered as seasonal malaria chemoprevention (SMC) in areas where transmission is highly seasonal such as Chad and other Sahelian countries. Although clinical trials show a 75% reduction in malaria cases, evidence of SMC’s impact at scale remains limited. Using data from the Chadian National Health Management Information System, we analyzed associations between SMC implementation during July–October and monthly district-level malaria incidence (suspected and confirmed outpatient cases) among children aged 0–59 months at health facilities in 23 health districts with SMC implementation during 2013–2018. Generalized additive models were fitted with separate cyclic cubic spline terms for each district to adjust for seasonality in cases. SMC implementation in Chad was associated, compared with no implementation, with lower monthly counts of both suspected (rate ratio [RR]: 0.82, 95% CI: 0.72–0.94. P = 0.006) and confirmed malaria cases (RR: 0.81, 95% CI: 0.71–0.93, P = 0.003), representing around 20% reduction in malaria incidence. Sensitivity analyses showed effect sizes of up to 28% after modifying model assumptions. Caution should be exercised in interpreting our findings, which may not be comparable with other studies, and may over- or underestimate impact of SMC; not all malaria cases present at health facilities, not all suspected cases are tested, and not all facilities report cases consistently. This study’s approach presents a solution for employing readily available routine data to evaluate the impact of health interventions at scale without extensive covariate data. Further efforts are needed to improve the quality of routine data in Chad and elsewhere.

Download Full-text

Estimating malaria incidence from routine health facility-based surveillance data in Uganda

Malaria Journal ◽

10.1186/s12936-020-03514-z ◽

2020 ◽

Vol 19 (1) ◽

Author(s):

Adrienne Epstein ◽

Jane Frances Namuganga ◽

Emmanuel Victor Kamya ◽

Joaniter I. Nankabirwa ◽

Samir Bhatt ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Health Management ◽

Malaria Incidence ◽

Additive Models ◽

Surveillance Data ◽

Health Management Information System ◽

Care Seeking ◽

Catchment Areas ◽

Over Time

Abstract Background Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility. Methods Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011–2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts. Results A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the 3 years in Kihihi (0.5 cases per person-year (PPY) vs 1.7 cases PPY) and Nagongera (0.3 cases PPY vs 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera). Conclusions Although malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

Download Full-text

Estimating Malaria Incidence From Routine Health Facility-Based Surveillance Data in Uganda

10.21203/rs.3.rs-70803/v1 ◽

2020 ◽

Author(s):

Adrienne Epstein ◽

Jane Frances Namuganga ◽

Emmanuel Victor Kamya ◽

Joaniter I Nankabirwa ◽

Samir Bhatt ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Health Management ◽

Malaria Incidence ◽

Additive Models ◽

Surveillance Data ◽

Health Management Information System ◽

Care Seeking ◽

Catchment Areas ◽

Over Time

Abstract Background. Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviors are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility.Methods. Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a three year period (2011-2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts. Results. A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the three years in Kihihi (0.5 cases per person-year [PPY] versus 1.7 cases PPY) and Nagongera (0.3 cases PPY versus 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera).Conclusions. Although malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

Download Full-text

Estimating malaria incidence from routine health facility-based surveillance data in Uganda

10.21203/rs.3.rs-70803/v2 ◽

2020 ◽

Author(s):

Adrienne Epstein ◽

Jane Frances Namuganga ◽

Emmanuel Victor Kamya ◽

Joaniter I Nankabirwa ◽

Samir Bhatt ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Health Management ◽

Malaria Incidence ◽

Additive Models ◽

Surveillance Data ◽

Health Management Information System ◽

Care Seeking ◽

Catchment Areas ◽

Over Time

Abstract Background: Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviors are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility. Methods: Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a three year period (2011-2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts. Results: A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the three years in Kihihi (0.5 cases per person-year [PPY] versus 1.7 cases PPY) and Nagongera (0.3 cases PPY versus 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera). Conclusions: Although malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

Download Full-text

Estimating malaria incidence from routine health facility-based surveillance data in Uganda

10.21203/rs.3.rs-70803/v3 ◽

2020 ◽

Author(s):

Adrienne Epstein ◽

Jane Frances Namuganga ◽

Emmanuel Victor Kamya ◽

Joaniter I Nankabirwa ◽

Samir Bhatt ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Health Management ◽

Malaria Incidence ◽

Additive Models ◽

Surveillance Data ◽

Health Management Information System ◽

Care Seeking ◽

Catchment Areas ◽

Over Time

Abstract Background Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility. Methods Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011-2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts. Results A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi=0.64, p <0.001; correlation in Nagongera=0.34, p=0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the 3 years in Kihihi (0.5 cases per person-year (PPY) vs 1.7 cases PPY) and Nagongera (0.3 cases PPY vs 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera). Conclusions Although malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

Download Full-text

Looking at the bigger picture: effect of performance-based contracting of district health services on equity of access to maternal health services in Zambia

Health Policy and Planning ◽

10.1093/heapol/czz130 ◽

2019 ◽

Author(s):

Collins Chansa ◽

Mulenga Mary Mukanu ◽

Chitalu Miriam Chama-Chiliba ◽

Mpuma Kamanga ◽

Nicholas Chikwenya ◽

...

Keyword(s):

Maternal Health ◽

Health Services ◽

Health System ◽

Health Expenditure ◽

Maternal Health Services ◽

Health Facilities ◽

District Level ◽

Health Service Provision ◽

Equity Of Access ◽

The Impact

Abstract Zambia has been using output-based approaches for over two decades to finance whole or part of the public health system. Between 1996 and 2006, performance-based contracting (PBC) was implemented countrywide with the Central Board of Health (CBoH) as the provider of health services. This study reviews the association between PBC and equity of access to maternal health services in Zambia between 1996 and 2006. A comprehensive document review was undertaken to evaluate the implementation process, followed by a trend analysis of health expenditure at district level, and a segmented regression analysis of data on antenatal care (ANC) and deliveries at health facilities that was obtained from five demographic and health survey datasets (1992, 1996, 2002, 2007 and 2014). The results show that PBC was anchored by high-level political support, an overarching policy and legal framework, and collective planning and implementation with all key stakeholders. Decentralization of health service provision was also an enabling factor. ANC coverage increased in both the lower and upper wealth quintiles during the PBC era, followed by a declining trend after the PBC era in both quintiles. Further, the percentage of women delivering at health facilities increased during the PBC era, particularly in rural areas and among the poor. The positive trend continued after the PBC era with similar patterns in both lower and upper wealth quintiles. Despite these gains, per capita health expenditure at district level declined during the PBC era, with the situation worsening after the PBC era. The study concludes that a nationwide PBC approach can contribute to improved equity of access to maternal health services and that PBC is a cost-efficient and sustainable policy reform. The study calls for policymakers to comprehensively evaluate the impact of health system reforms before terminating them.

Download Full-text

Assessment of implementation of the health management information system at the district level in southern Malawi

Malawi Medical Journal ◽

10.4314/mmj.v29i3.3 ◽

2017 ◽

Vol 29 (3) ◽

pp. 240 ◽

Cited By ~ 5

Author(s):

Ansley Kasambara ◽

Save Kumwenda ◽

Khumbo Kalulu ◽

Kingsley Lungu ◽

Tara Beattie ◽

...

Keyword(s):

Information System ◽

Management Information System ◽

Health Management ◽

District Level ◽

Health Management Information System ◽

Management Information

Download Full-text

Malaria incidence and mortality in Zimbabwe during the COVID-19 pandemic: analysis of routine surveillance data

Malaria Journal ◽

10.1186/s12936-021-03770-7 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Samuel Gavi ◽

Oscar Tapera ◽

Joseph Mberikunashe ◽

Mufaro Kanyangarara

Keyword(s):

Health Services ◽

Malaria Incidence ◽

Indoor Residual Spraying ◽

Population Data ◽

District Level ◽

Mitigation Measures ◽

Spatial And Temporal Variation ◽

Health Management Information System ◽

Unique Challenge ◽

Incidence And Mortality

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic has posed a unique challenge to health care systems globally. To curb COVID-19 transmission, mitigation measures such as travel restrictions, border closures, curfews, lockdowns, and social distancing have been implemented. However, these measures may directly and indirectly affect the delivery and utilization of essential health services, including malaria services. The suspension of indoor residual spraying (IRS) and insecticide-treated net (ITN) distribution, shortages of malaria commodities, and reduced demand for health services have hindered the continued delivery of malaria services. The overall goal of this analysis was to describe the trends in malaria incidence and mortality in Zimbabwe prior to and during the pandemic to understand the consequences of COVID-19-related changes in the delivery and utilization of malaria services. Methods Monthly data on the number of malaria cases and deaths by district for the period January 2017 to June 2020 were obtained from the national health management information system (HMIS). District-level population data were obtained from the 2012 Census. Malaria incidence per 1000 population and malaria deaths per 100,000 population were calculated for 2017, 2018, 2019, and 2020 and mapped to describe the spatial and temporal variation of malaria at the district level. Results Compared to the same period in 2017, 2018 and 2019, there was an excess of over 30,000 malaria cases from January to June 2020. The number of malaria deaths recorded in January to June 2020 exceeded the annual totals for 2018 and 2019. District level maps indicated that areas outside high malaria burden provinces experienced higher than expected malaria incidence and mortality, suggesting potential outbreaks. Conclusions The observed surge in malaria cases and deaths in January to June 2020 coincided with the onset of COVID-19 in Zimbabwe. While further research is needed to explore possible explanations for the observed trends, prioritizing the continuity of essential malaria services amid the COVID-19 pandemic remains crucial.

Download Full-text

Level of data quality from Health Management Information Systems in a resources limited setting and its associated factors, eastern Ethiopia

SA Journal of Information Management ◽

10.4102/sajim.v18i1.612 ◽

2016 ◽

Vol 18 (1) ◽

Cited By ~ 2

Author(s):

Kidist Teklegiorgis ◽

Kidane Tadesse ◽

Gebremeskel Mirutse ◽

Wondwossen Terefe

Keyword(s):

Information System ◽

Data Quality ◽

Health Management ◽

Health Facilities ◽

Health Management Information System ◽

Department Heads ◽

Management Information ◽

Cross Sectional ◽

Eastern Ethiopia ◽

Limited Setting

Background: A Health Information System (HIS) is a system that integrates data collection, processing, reporting, and use of the information necessary for improving health service effectiveness and efficiency through better management at all levels of health services. Despite the credible use of HIS for evidence-based decision-making, countries with the highest burden of ill health and the most in need of accurate and timely data have the weakest HIS in the vast majority of world’s poorest countries. Although a Health Management Information System (HMIS) forms a backbone for strong health systems, most developing countries still face a challenge in strengthening routine HIS. The main focus of this study was to assess the current HIS performance and identify factors affecting data quality in a resource-limited setting, such as Ethiopian health facilities.Methods: A cross-sectional study was conducted by using structured questionnaires in Dire Dawa Administration health facilities. All unit and/or department heads from all government health facilities were selected. The data was analysed using STATA version 11. Frequency and percentages were computed to present the descriptive findings. Association between variables was computed using binary logistic regression.Results: Over all data quality was found to be 75.3% in unit and/or departments. Trained staff to fill format, decision based on supervisor directives and department heads seek feedback were significantly associated with data quality and their magnitudes were (AOR = 2.253, 95% CI [1.082, 4.692]), (AOR = 2.131, 95% CI [1.073, 4.233]) and (AOR = 2.481, 95% CI [1.262, 4.876]), respectively.Conclusion: Overall data quality was found to be below the national expectation level. Low data quality was found at health posts compared to health centres and hospitals. There was also a shortage of assigned HIS personnel, separate HIS offices, and assigned budgets for HIS across all units and/or departments.

Download Full-text

Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors

Malaria Journal ◽

10.1186/s12936-019-3097-z ◽

2020 ◽

Vol 19 (1) ◽

Cited By ~ 3

Author(s):

James Chirombo ◽

Pietro Ceccato ◽

Rachel Lowe ◽

Dianne J Terlouw ◽

Madeleine C Thomson ◽

...

Keyword(s):

Risk Factors ◽

Environmental Factors ◽

Malaria Case ◽

Significant Relationship ◽

Malaria Incidence ◽

Information Source ◽

Clinical Malaria ◽

District Level ◽

Spatio Temporal ◽

The Impact

Abstract Background Malaria transmission is influenced by a complex interplay of factors including climate, socio-economic, environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions. Methods Routine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socio-economic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with district-level variation in clinical malaria rates in Malawi, and visualized using maps. Results Between 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi. Conclusion The modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

Download Full-text

Data Utilisation and Factors Influencing the Performance of the Health Management Information System in Tanzania

10.21203/rs.3.rs-150624/v1 ◽

2021 ◽

Author(s):

Leonard Mboera ◽

Susan Rumisha ◽

Doris Mbata ◽

Irene Mremi ◽

Emanuel Lyimo ◽

...

Keyword(s):

Health Care ◽

Information System ◽

Management Information System ◽

Health Management ◽

Health Care Facilities ◽

Health Facilities ◽

Health Management Information System ◽

Management Information ◽

Factors Influencing ◽

Care Facilities

Abstract Background: Health Management Information System (HMIS) is a set of data regularly collected at health care facilities, aimed to meet the needs of statistics on health services. This study aimed to determine the utilisation of HMIS data and factors influencing the performance of health system at the district and primary health care facility levels in Tanzania. Methods: This cross-sectional study was carried out in 11 districts and involved 115 health care facilities in Tanzania. Data were collected using a standard questionnaire and an observational checklist. The collected data was cleaned, summarized into proportions and graphical presentation using STATA version 13 software. Results: This study involved 115 health facilities in 11 districts. A total of 93 health facility workers and 13 district officials were interviewed. About two-thirds (60%) of the facility respondents reported to use the HMIS data they collect. Data were mainly used for comparing performance in terms of services coverage (53%), monitoring of disease trends over time (50%), and providing evidence for community health education and promotion programme (55%). The majority (41.4%) of the facility’s personnel had not received any training on data management related to HMIS in the past 12 months. Only five out of 13 district respondents reported to routinely analyse HMIS data. Patient load was described to frequently affect staff performance on data collection and management. Less than half (42%) of the health facilities (HFs) had received supervisory visits from the district office. Nine district respondents reported to systematically receive feedback on the quality of their reports on monthly and quarterly bases from higher authorities. More than half (n=7) of district respondents reported that those responsible for HMIS activities are also responsible for other equally important activities. Conclusion: Poor data utilisation was common in most of the districts and health facilities in Tanzania. Inadequate human and financial resources, inadequate training, lack of supervision, and lack of standard operating procedures were the major challenges affecting the HMIS performance in Tanzania.

Download Full-text