Reviewing the literature on access to prompt and effective malaria treatment in Kenya: implications for meeting the Abuja targets

<p>Abstract</p> <p>Background</p> <p>Effective case management is central to reducing malaria mortality and morbidity worldwide, but only a minority of those affected by malaria, have access to prompt effective treatment.</p> <p>In Kenya, the Division of Malaria Control is committed to ensuring that 80 percent of childhood fevers are treated with effective anti-malarial medicines within 24 hours of fever onset, but this target is largely unmet. This review aimed to document evidence on access to effective malaria treatment in Kenya, identify factors that influence access, and make recommendations on how to improve prompt access to effective malaria treatment. Since treatment-seeking patterns for malaria are similar in many settings in sub-Saharan Africa, the findings presented in this review have important lessons for other malaria endemic countries.</p> <p>Methods</p> <p>Internet searches were conducted in PUBMED (MEDLINE) and HINARI databases using specific search terms and strategies. Grey literature was obtained by soliciting reports from individual researchers working in the treatment-seeking field, from websites of major organizations involved in malaria control and from international reports.</p> <p>Results</p> <p>The review indicated that malaria treatment-seeking occurs mostly in the informal sector; that most fevers are treated, but treatment is often ineffective. Irrational drug use was identified as a problem in most studies, but determinants of this behaviour were not documented. Availability of non-recommended medicines over-the-counter and the presence of substandard anti-malarials in the market are well documented. Demand side determinants of access include perception of illness causes, severity and timing of treatment, perceptions of treatment efficacy, simplicity of regimens and ability to pay. Supply side determinants include distance to health facilities, availability of medicines, prescribing and dispensing practices and quality of medicines. Policy level factors are around the complexity and unclear messages regarding drug policy changes.</p> <p>Conclusion</p> <p>Kenya, like many other African countries, is still far from achieving the Abuja targets. The government, with support from donors, should invest adequately in mechanisms that promote access to effective treatment. Such approaches should focus on factors influencing multiple dimensions of access and will require the cooperation of all stakeholders working in malaria control.</p

Malaria drug shortages in Kenya: a major failure to provide access to effective treatment.

A key bench mark of successful therapeutic policy implementation, and thus effectiveness, is that the recommended drugs are available at the point of care. Two years after artemether-lumefathrine (AL) was introduced for the management of uncomplicated malaria in Kenya, we carried out a cross-sectional survey to investigate AL availability in government facilities in seven malaria-endemic districts. One of four of the surveyed facilities had none of the four AL weight-specific treatment packs in stock; three of four facilities were out of stock of at least one weight-specific AL pack, leading health workers to prescribe a range of inappropriate alternatives. The shortage was in large part caused by a delayed procurement process. National ministries of health and the international community must address the current shortcomings facing antimalarial drug supply to the public sector

Reducing stock-outs of life saving malaria commodities using mobile phone text-messaging: SMS for life study in Kenya.

Health facility stock-outs of life saving malaria medicines are common across Africa. Innovative ways of addressing this problem are urgently required. We evaluated whether SMS based reporting of stocks of artemether-lumefantrine (AL) and rapid diagnostic tests (RDT) can result in reduction of stock-outs at peripheral facilities in Kenya.All 87 public health facilities in five Kenyan districts were included in a 26 week project. Weekly facility stock counts of four AL packs and RDTs were sent via structured incentivized SMS communication process from health workers' personal mobile phones to a web-based system accessed by district managers. The mean health facility response rate was 97% with a mean formatting error rate of 3%. Accuracy of stock count reports was 79% while accuracy of stock-out reports was 93%. District managers accessed the system 1,037 times at an average of eight times per week. The system was accessed in 82% of the study weeks. Comparing weeks 1 and 26, stock-out of one or more AL packs declined by 38 percentage-points. Total AL stock-out declined by 5 percentage-points and was eliminated by the end of the project. Stock-out declines of individual AL packs ranged from 14 to 32 percentage-points while decline in RDT stock-outs was 24 percentage-points. District managers responded to 44% of AL and 73% of RDT stock-out signals by redistributing commodities between facilities. In comparison with national trends, stock-out declines in study areas were greater, sharper and more sustained.Use of simple SMS technology ensured high reporting rates of reasonably accurate, real-time facility stock data that were used by district managers to undertake corrective actions to reduce stock-outs. Future work on stock monitoring via SMS should focus on assessing response rates without use of incentives and demonstrating effectiveness of such interventions on a larger scale

A mobile phone displaying the stock request message sent to registered health workers.

<p>A mobile phone displaying the stock request message sent to registered health workers.</p

AL stock-out trends in study districts and stock-out comparisons with national trends.

<p>(A) Proportion of health facilities stocked out of all four AL packs and at least one AL pack over 26 weeks. Legend. Blue bars show stock-outs of at least one AL pack; red bars show stock-outs of all four AL packs. (B) Proportion of health facilities stocked out of AL 6, AL 12, AL 18 and AL 24 packs over 26 weeks. Legend. Yellow bars show stock-out of AL 6; blue bars stock-out of AL 12; red bars stock-out of AL 18 and green bars stock-out of AL 24 (C) Stock-out trends of all four AL packs and at least one AL pack in study districts compared to a nationally representative sample. Legend. The 7 consecutive bars show stock-outs in August, September, October, November, December, January and February. (D) Stock-out trends of AL 6, AL 12, AL 18 and AL 24 packs in study districts compared to a nationally representative sample. Legend. The 7 consecutive bars show stock-outs in August, September, October, November, December, January and February.</p

Weekly proportion of health facilities that responded to stock request messages and SMS formatting errors.

<p>Legend. Black bars show responses within 0–24 hrs; dark grey bars responses within 24–27 hrs (after reminder but within incentive period); light grey bars responses within 27 hrs-7 days (after the incentive period); white bars shows no responses, and black line shows SMS formatting errors.</p

RDT stock-out trend over 26 weeks monitoring period.

<p>RDT stock-out trend over 26 weeks monitoring period.</p