An update of malaria infection and anaemia in adults in Buea, Cameroon

<p>Abstract</p> <p>Background</p> <p>Anaemia is caused by many factors in developing countries including malaria. We compared anaemia rates in patients with malaria parasitaemia to that of patients without malaria parasitaemia.</p> <p>Findings</p> <p>A cross-sectional study was carried out from November 2007 to July 2008 in health units in Buea, Cameroon. Adult patients with fever or history of fever were included in the study. Information on socio-demographic variables and other variables was collected using a questionnaire. Malaria parasitaemia status was determined by microscopy using Giemsa stained thick blood smears. Haemoglobin levels were determined by the microhaematocrit technique.</p> <p>The study population consisted of 250 adult patients with a mean age of 29.31 years (SD = 10.63) and 59.44% were females. 25.60% of the patients had malaria parasitaemia while 14.80% had anaemia (haemoglobin < 11 g/dl). Logistic regression revealed that those with malaria parasitaemia had more anaemia compared to those without malaria parasitaemia(OR = 4.33, 95%CI = 1.21-15.43, p = 0.02) after adjusting for age, sex, rural residence, socioeconomic status, use of antimalarials, use of insecticide treated nets(ITN) and white blood cell count.</p> <p>Conclusions</p> <p>In adult patients with fever in this setting, malaria parasitaemia contributes to anaemia and is of public health impact. Our results also provide a baseline prevalence for malaria parasitaemia in febrile adults in health units in this setting.</p

Similar efficacy and safety of artemether-lumefantrine (Coartem®) in African infants and children with uncomplicated falciparum malaria across different body weight ranges

<p>Abstract</p> <p>Background</p> <p>Artemisinin-based combination therapy, including artemether-lumefantrine (AL), is currently recommended for the treatment of uncomplicated <it>Plasmodium falciparum </it>malaria. The objectives of the current analysis were to compare the efficacy and safety of AL across different body weight ranges in African children, and to examine the age and body weight relationship in this population.</p> <p>Methods</p> <p>Efficacy, safety and pharmacokinetic data from a randomized, investigator-blinded, multicentre trial of AL for treatment of acute uncomplicated <it>P. falciparum </it>malaria in infants and children in Africa were analysed according to body weight group.</p> <p>Results</p> <p>The trial included 899 patients (intent-to-treat population 886). The modified intent-to-treat (ITT) population (n = 812) comprised 143 children 5 to < 10 kg, 334 children 10 to < 15 kg, 277 children 15 to < 25 kg, and 58 children 25 to < 35 kg. The 28-day PCR cure rate, the primary endpoint, was comparable across all four body weight groups (97.2%, 98.9%, 97.8% and 98.3%, respectively). There were no clinically relevant differences in safety or tolerability between body weight groups. In the three AL body weight dosing groups (5 to < 15 kg, 15 to < 25 kg and 25 to < 35 kg), 80% of patients were aged 10-50 months, 46-100 months and 90-147 months, respectively.</p> <p>Conclusion</p> <p>Efficacy of AL in uncomplicated falciparum malaria is similar across body weight dosing groups as currently recommended in the label with no clinically relevant differences in safety or tolerability. AL dosing based on body weight remains advisable.</p

Antibody responses to <i>P. falciparum</i> blood stage antigens and incidence of clinical malaria in children living in endemic area in Burkina Faso

Abstract Background High parasite-specific antibody levels are generally associated with low susceptibility to Plasmodium falciparum malaria. This has been supported by several studies in which clinical malaria cases of P. falciparum malaria were reported to be associated with low antibody avidities. This study was conducted to evaluate the role of age, malaria transmission intensity and incidence of clinical malaria in the induction of protective humoral immune response against P. falciparum malaria in children living in Burkina Faso. Methods We combined levels of IgG and IgG subclasses responses to P. falciparum antigens: Merozoite Surface Protein 3 (MSP3), Merozoite Surface Protein 2a (MSP2a), Merozoite Surface Protein 2b (MSP2b), Glutamate Rich Protein R0 (GLURP R0) and Glutamate Rich Protein R2 (GLURP R2) in plasma samples from 325 children under five (05) years with age, malaria transmission season and malaria incidence. Results We notice higher prevalence of P. falciparum infection in low transmission season compared to high malaria transmission season. While, parasite density was lower in low transmission than high transmission season. IgG against all antigens investigated increased with age. High levels of IgG and IgG subclasses to all tested antigens except for GLURP R2 were associated with the intensity of malaria transmission. IgG to MSP3, MSP2b, GLURP R2 and GLURP R0 were associated with low incidence of malaria. All IgG subclasses were associated with low incidence of P. falciparum malaria, but these associations were stronger for cytophilic IgGs. Conclusions On the basis of the data presented in this study, we conclude that the induction of humoral immune response to tested malaria antigens is related to age, transmission season level and incidence of clinical malaria

Advancing the science behind human resources for health: highlights from the Health Policy and Systems Research Reader on Human Resources for Health

Health workers are central to people-centred health systems, resilient economies and sustainable development. Given the rising importance of the health workforce, changing human resource for health (HRH) policy and practice and recent health policy and systems research (HPSR) advances, it is critical to reassess and reinvigorate the science behind HRH as part of health systems strengthening and social development more broadly. Building on the recently published Health Policy and Systems Research Reader on Human Resources for Health (the Reader), this commentary reflects on the added value of HPSR underpinning HRH. HPSR does so by strengthening the multi-disciplinary base and rigour of HRH research by (1) valuing diverse research inferences and (2) deepening research enquiry and quality. It also anchors the relevance of HRH research for HRH policy and practice by (3) broadening conceptual boundaries and (4) strengthening policy engagement. Most importantly, HPSR enables us to transform HRH from being faceless numbers or units of health producers to the heart and soul of health systems and vital change agents in our communities and societies. Health workers’ identities and motivation, daily routines and negotiations, and training and working environments are at the centre of successes and failures of health interventions, health system functioning and broader social development. Further, in an increasingly complex globalised economy, the expansion of the health sector as an arena for employment and the liberalisation of labour markets has contributed to the unprecedented movement of health workers, many or most of whom are women, not only between public and private health sectors, but also across borders. Yet, these political, human development and labour market realities are often set aside or elided altogether. Health workers’ lives and livelihoods, their contributions and commitments, and their individual and collective agency are ignored. The science of HRH, offering new discoveries and deeper understanding of how universal health coverage and the Sustainable Development Goals are dependent on millions of health workers globally, has the potential to overcome this outdated and ineffective orthodoxy

Assessing the hospital surge capacity of the Kenyan health system in the face of the COVID-19 pandemic

Introduction The COVID-19 pandemic will test the capacity of health systems worldwide and especially so in low- and middle-income countries. The objective of this study was to assess the surge capacity of the Kenyan of the Kenyan health system in terms of general hospital and ICU beds in the face of the COVID-19 pandemic. Methods We assumed that 2% of the Kenyan population get symptomatic infection by SARS-Cov-2 based on modelled estimates for Kenya and determined the health system surge capacity for COVID-19 under three transmission curve scenarios, 6, 12, and 18 months. We estimated four measures of hospital surge capacity namely: 1) hospital bed surge capacity 2) ICU bed surge capacity 3) Hospital bed tipping point, and 5) ICU bed tipping point. We computed this nationally and for all the 47 county governments. Results The capacity of Kenyan hospitals to absorb increases in caseload due to COVID-19 is constrained by the availability of oxygen, with only 58% of hospital beds in hospitals with oxygen supply. There is substantial variation in hospital bed surge capacity across counties. For example, under the 6 months transmission scenario, the percentage of available general hospital beds that would be taken up by COVID-19 cases varied from 12% Tharaka Nithi county, to 145% in Trans Nzoia county. Kenya faces substantial gaps in ICU beds and ventilator capacity. Only 22 out of the 47 counties have at least 1 ICU unit. Kenya will need an additional 1,511 ICU beds and 1,609 ventilators (6 months transmission curve) to 374 ICU beds and 472 ventilators (18 months transmission curve) to absorb caseloads due to COVID-19. Conclusion Significant gaps exist in Kenya’s capacity for hospitals to accommodate a potential surge in caseload due to COVID-19. Alongside efforts to slow and supress the transmission of the infection, the Kenyan government will need to implement adaptive measures and additional investments to expand the hospital surge capacity for COVID-19. Additional investments will however need to be strategically prioritized to focus on strengthening essential services first, such as oxygen availability before higher cost investments such as ICU beds and ventilators

Sub-Saharan Public Hospitals Geo-coded database

Timely access to emergency care can significantly reduce mortality. International benchmarks for access to emergency hospital care have been established to guide ambitions for universal health care by 2030. However, there is no complete geo-coded inventory of hospital services in Africa in relation to how populations might access these services. We assembled a geocoded inventory of public hospitals across 48 countries and islands of sub-Saharan Africa from 100 different sources. A cost distance algorithm based on the location of 4908 public hospitals, population distributions and road networks were used to compute the proportion of populations living within a combined walking and motorised travel time of 2 hours to emergency hospital services. We estimate that 286 million (29%) people and 64 million (28%) women of child bearing age are located more than 2 hours from the nearest hospital. Marked differences were observed within and between countries. Only 17 countries reached the international benchmark of more than 80% of their populations living within a 2-hour travel time of the nearest hospital. </p

Coverage of routine reporting on malaria parasitological testing in Kenya, 2015–2016

Following the launch of District Health Information System 2 across facilities in Kenya, more health facilities are now capable of carrying out malaria parasitological testing and reporting data as part of routine health information systems, improving the potential value of routine data for accurate and timely tracking of rapidly changing disease epidemiology at fine spatial resolutions.This study evaluates the current coverage and completeness of reported malaria parasitological testing data in DHIS2 specifically looking at patterns in geographic coverage of public health facilities in Kenya.Monthly facility level data on malaria parasitological testing were extracted from Kenya DHIS2 between November 2015 and October 2016. DHIS2 public facilities were matched to a geo-coded master facility list to obtain coordinates. Coverage was defined as the geographic distribution of facilities reporting any data by region. Completeness of reporting was defined as the percentage of facilities reporting any data for the whole 12-month period or for 3, 6 and 9 months.Public health facilities were 5,933 (59%) of 10,090 extracted. Fifty-nine per Cent of the public facilities did not report any data while 36, 29 and 22% facilities had data reported at least 3, 6 and 9 months, respectively. Only 8% of public facilities had data reported for every month. There were proportionately more hospitals (86%) than health centres (76%) and dispensaries/clinics (30%) reporting. There were significant geographic variations in reporting rates. Counties along the malaria endemic coast had the lowest reporting rate with only 1% of facilities reporting consistently for 12 months.Current coverage and completeness of reporting of malaria parasitological diagnosis across Kenya's public health system remains poor. The usefulness of routine data to improve our understanding of sub-national heterogeneity across Kenya would require significant improvements to the consistency and coverage of data captured by DHIS2

Coverage of routine reporting on malaria parasitological testing in Kenya, 2015–2016

Following the launch of District Health Information System 2 across facilities in Kenya, more health facilities are now capable of carrying out malaria parasitological testing and reporting data as part of routine health information systems, improving the potential value of routine data for accurate and timely tracking of rapidly changing disease epidemiology at fine spatial resolutions.This study evaluates the current coverage and completeness of reported malaria parasitological testing data in DHIS2 specifically looking at patterns in geographic coverage of public health facilities in Kenya.Monthly facility level data on malaria parasitological testing were extracted from Kenya DHIS2 between November 2015 and October 2016. DHIS2 public facilities were matched to a geo-coded master facility list to obtain coordinates. Coverage was defined as the geographic distribution of facilities reporting any data by region. Completeness of reporting was defined as the percentage of facilities reporting any data for the whole 12-month period or for 3, 6 and 9 months.Public health facilities were 5,933 (59%) of 10,090 extracted. Fifty-nine per Cent of the public facilities did not report any data while 36, 29 and 22% facilities had data reported at least 3, 6 and 9 months, respectively. Only 8% of public facilities had data reported for every month. There were proportionately more hospitals (86%) than health centres (76%) and dispensaries/clinics (30%) reporting. There were significant geographic variations in reporting rates. Counties along the malaria endemic coast had the lowest reporting rate with only 1% of facilities reporting consistently for 12 months.Current coverage and completeness of reporting of malaria parasitological diagnosis across Kenya's public health system remains poor. The usefulness of routine data to improve our understanding of sub-national heterogeneity across Kenya would require significant improvements to the consistency and coverage of data captured by DHIS2