Abstract: Logistics systems of public health programs generally employ either a push or pull system to supply commodities to health facilities. The objective of this study was to compare the systems on key performance indicators related to supply methods. One each of push and pull logistics systems were selected, and sample sizes for both systems were determined by assuming that 50.0% facilities would have a “gold-standard situation,” 95% confidence interval, 3.5% variance between groups, and 10.0% total width of the confidence interval. Eleven personnel recruited, trained, and deployed for the exercise collected data using a validated tool. A total of 442 health facilities were visited comprising of 319 and 123 facilities that receive commodities through a push and pull systems respectively. Mann-Whitney tests were conducted using the Statistical Package for the Social Sciences. Findings showed that the pull system is a statistically better option than the push system regarding the frequency of stockouts (p=0.000), days of stock out (p=0.000), the number of products stocked out (p=0.000), products expiry (p=0.004), and lead time (p=0.000). However, the rates of emergency orders between the systems are statistically indifferent (p=0.481).

1.      Introduction

Stockout of essential public health commodities in Nigeria is widespread.[1] This is worse, particularly in Northern Nigeria, where many challenges contribute to the overwhelming shortage of health commodities.[2]^{,[3],[4],[5],[6]} Part of these challenges are inadequate funding, increase in population density, environmental factors that harbor vectors and facilitate transmission of diseases, and inefficient logistics systems that respond poorly to requisitions from health facilities.[7]^,[8]

In studying the effectiveness of logistics systems in their response to public health needs, it has been noted that diseases alone have worse indices in Nigeria. Logistics systems have two principal ways of responding (supplying) to commodity needs of service centers to combat the public health diseases in question; these are the push and pull systems. The push system (allocation system) refers to a method of determining the amount of products to deliver to a facility often by another higher-level facility such as the district level store or a central store.[9] This is not the best form of determining what goes to a facility. However, if commodities are not enough to service all orders by the lower-level facilities, this appears to be the only option. On the other hand, in the pull system (requisition system), the personnel at the Service Delivery Point (SDP) determines (by formula) how much of the products are required.9 This is regarded as the best practice because it factors the average monthly consumption of products at the facility, therefore, stock out of commodities while using this method is less likely.

Katsina State, a northwestern state of Nigeria with a population of 7,831,300 people and 34 Local Governments Areas (LGAs), there are up to eleven supply chains running parallel to each other.[10][11] One robust program from each of push and pull systems was selected for this study. The choices arose as a result of similarity in funding, maximum stock level (6 months each), minimum stock level (3 months each), same emergency order points (2 months) and same lead times (2 weeks). The push system selected was a malaria program that supplies 1,718 health facilities across Katsina State. The pull system chosen was the Tuberculosis and Leprosy Control Program (TBLCP), which supplies 173 Directly Observed Treatment Short Course (DOTS), centers across the state. Table I below provides a summary of the systems.

Table 1: The Push and Pull Systems to be Compared

The choice of malaria and tuberculosis (TB) has coincided with the fact that the two programs deal with the two most threatening public health diseases in Nigeria and both are funded by the same donor. In the whole world, the highest burden of malaria is suffered by Nigeria (about 30% of malaria burden in Africa) which has approximately 51 million cases and deaths up to 207,000 annually; again, nearly 173 million people (97% of the Nigerian population) are at risk of malaria.[12]

About 30% child mortality (mostly children under five years), 11% maternal deaths, and 60% of outpatient visits to health facilities in Nigeria are the result of malaria infection.[13] With this prevalence of malaria in Nigeria, the country’s economic productivity is threatened with a resultant loss of about $700 million (or 132 billion Naira) to costs of treatment, preventive measures and indirect costs.[14]

Nigeria’s National Malaria Control Program (NMCP) in 2008 set a goal to reduce, by 2013, malaria burden by half.[15]There was a significant achievement; for example, households use LLINs by up to 70% by the year 2010 as against the 5% obtained in 2008.[16] Despite these efforts and significant results recorded, studies showed that there is still a high prevalence of malaria throughout Nigeria especially in the northern part.[17]^,[18]

On the other hand, TB in Nigeria has very poor indices too. On the scale of the high TB burden, TB/HIV co-infection, and multidrug resistance, Nigeria is among the top 14 countries worldwide.[19] Across the world, Nigeria is the 7^thhigh-burden TB prevalence nation and second in Africa. Each year, an estimated 407,000 people contract TB who are HIV-negative of whom 115,000 die annually. Among HIV-positive people, there are about 63,000 new TB infections annually in Nigeria, out of which 39,000 die each year.

Funding for both malaria and TB programs is meager. For the malaria program (which is the one whose logistics system is being assessed), the national funding is $18 million for 18 states of which Katsina is a part. For TB, though no known figure at hand, reliably, the government of Nigeria only spends 4% of its supposed 15% commitment to fight TB and Non-Governmental Organizations (NGOs) contribute 60% of the expenditure giving a total of 64% spending for the TB program nationally.[20]

The TB program in Katsina State has 889 adult cases and 524 children cases on treatment which represent a total of 1,413 client burden for the program and has 83% stock availability for all the clients.

The incidence of stockouts was caused mostly by slow lead times, but there were no studies conducted so far to confirm the claim. Moreover, many of the complaints of stockouts have been on the push system even though the pull system does experience stock out. This study sets out to study the lead time, the rate of an emergency order, the frequency of stock-outs, duration of stockouts, and records of expiry between push and pull systems to determine which method is more effective in achieving the relevant indicators.

2.      Methodology

The research, a cross-sectional survey study, had both primary and secondary data. Because the data elements of interest are only obtainable at service delivery points (health facilities), sample sizes were first determined. These were calculated using the population sizes represented by the total number of health facilities supported by the two programs of interest operating push and pull systems of health products allocation. Online sample size calculator was used for determination of sample sizes by assuming that 50.0% facilities would have a “gold-standard situation” on the chosen key performance indicators, 95% confidence interval, 3.5% variance between groups, and 10.0% total width of the confidence interval.[21] Table II below shows the sample sizes calculated. Ethics permission was obtained from the Institutional Review Board of Pôle Universitaire Euclide. Informed consent was obtained from all participants before their participation. No direct patient-related or any other identification data was collected.

Table 2: Sample Sizes Calculated Versus Number of Sites Visited

The Logistics Indicators Assessment Tool (LIAT) developed by USAID|DELIVER Project was adapted to arrive at the 19 data elements of interest for this research.[22]

Considering the vast geography of Katsina State, which has 34 LGAs, 11 data collectors were recruited for the exercise. Ten each covered 3 LGAs while the 11^th collector covered 4 LGAs. The health facilities to be visited are scattered across the entire 34 LGAs and selection of the facilities were across the wards in the local governments and done at random.

Data collectors received three rounds of training. Collection of data was by an interview with the logistics officer at the health facility and verification of indicators of interest using Logistics Management Information System (LMIS) records.

Data reporting in all cases was purely electronic using MS Excel format. The electronic templates have aggregated summary sheets that simplify analysis.

After collation and aggregation of the collated data, qualitative data analyses were done for all the 19 data elements collected per health facility.

Before statistical analyses were done to test the hypotheses put forward, the data collated were coded (cases and variables) for input into SPSS, and all variables were appropriately defined.

Data were run in the first instance for descriptive statistics (mean, median, mode, variance, and standard deviation). To determine the distribution of the data- parametric or non-parametric, Kolmogorov-Smirnov and Shapiro-Wilk tests were conducted on all the coded data. Having confirmed that the p-values (0.000) of the normality tests indicated all data sets are nonparametric, the Mann-Whitney test was conducted for testing all the five hypotheses because in all cases, to groups are being tested for differences.

3.      Results

A total of 442 health facilities were visited across 34 LGAs of the state in August 2018. The total figure is comprised of 319 health facilities receiving antimalarial commodities via a push system of supply and 123 health facilities that receive anti-tuberculosis products via a pull system of supply. Table 3 below gives details of data obtained from the survey.

Table 3: Collated Results (in number and percentage) of 19 Indicators from 442 Health Facilities

Out of the 19 data elements collated for each health facility of the 442 visited, five indicators (lead time, emergency order, the incidence of stock out, duration of stock out, and records of expiry) were tested using appropriate statistical procedures to answer the hypotheses put forward. Table 4 below summarizes all the statistical tests conducted.

Table 4: Summary of Statistical Tests Conducted to Test Hypotheses and Decisions

4.      Discussion

The LIAT is a comprehensive tool that covers all logistics indicators virtually. It was adapted and re-structured to capture only 19 data elements of interest, for this research, which has been categorized into- staffing & capacity building, inventory management and reporting, order and delivery, and stock out and expiry. These are explained using qualitative data analysis.

4.1.  Main Findings of this Study

The findings from both systems revealed somewhat sufficient staffing and capacity building. Across the facilities surveyed, 224 or 70% of facilities of push system and 96 or 78% of facilities of pull systems have dedicated personnel that solely carry out logistics activities of health commodities. However, as per expectation that logistics personnel should preferably be pharmacists, pharmacy technician, or pharmacy assistants, this was difficult to achieve due to the shortage of human resources of the required cadre. On this, only 9 or 3% and 4 or 3% of facilities with push and pull systems respectively are of the needed cadre. What the state resorts to is task shifting which is in the right direction to avoid cessation of these services. But, the officers assigned this vital task were solely deployed for logistics services, and it was found that 313 or 98% and 120 or 98% of all facilities visited for push and pull systems have their logistics personnel formally trained on logistics management information system.

Furthermore, these facilities receive routine monitoring and supervision to provide on-the-job refresher training and address some identified gaps. In the past six months before the survey, 315 or 98% and 106 or 86% of facilities with push and pull systems respectively received monitoring and supportive supervision. During these visits, it was further reported that 315 or 98% and 114 or 93% of facilities with push and pull systems respectively had on-the-job training on commodity management.

Assessment and comparison of both push and pull systems revealed that there is dedicated logistics staff who are sufficiently trained and regularly supervised for continuous quality improvement.

Both push and pull systems maintain stock cards for the commodities managed by the logistics systems. For the push system, 304 or 95% of facilities have stock cards for products, and 287 or 90% of health facilities under this system have their stock cards updated with real-time balances. On the other hand, 119 or 92% of facilities using the pull system have stock cards for the commodities managed, and 110 or 89% of these facilities have the stock cards updated.

Review of the immediate reports sent for re-supply of commodities for the push system (even though these are not eventually used to determine quantities to be delivered to supported facilities) revealed that 306 (96%) of SoH, 276 (87%) rate of consumption, and 160 (50%) losses and adjustments were reported. For the pull system, 113 (92%) SoH, 107 (87%), and 70 (57%) losses and adjustments were reported. While the SoH and rate of consumption reporting are quite good, reporting losses and adjustments are low. However, the under-reporting, from interaction with the facility logisticians, is not a problem because losses and adjustments are only reported when there were such during the reporting period. While losses and adjustments are occasional, SoH and rate of consumption are crucial in any reporting which sufficiently are performing good in both systems.

Assessment of availability of roads has a direct implication on the ability to deliver commodities to the last mile and the lead-time. For facilities operating the push system, 253 (79%) have roads leading to them whereas 108 (88%) of facilities operating the pull system of supply have roads leading to them.

Reliably from the program design, facilities supported by both systems are supposed to receive their commodities through an agent or transporter otherwise referred to as third-party logistics (3PL). However, of the 319 facilities accessed under the push system, only 131 (49%) reported receiving commodities from 3PL while 67 (54%) of facilities under the pull system reported receiving products via 3PL. This likely had a direct effect on the poor lead time seen. Only 73 (23%) facilities out of 319 visited under the push system reported receiving their commodities within two weeks of placing an order, which is the standard lead-time. This might also have affected both the incidence and duration of stock-outs observed in the push system. On the other hand, 86 (70%) of the 123 facilities under the pull system reported receiving their commodities within two weeks.

As per a critical indicator of interest, this is the first difference in terms of efficiency between the two systems. The statistical test conducted (Mann-Whitney) confirmed that there is a significant difference (p=0.000) between lead times of push and pull systems in favor of the pull system.

The second critical indicator measured (rate of emergency order) showed that, in the past three months, 50 facilities (16%) of ‘push facilities’ had placed an emergency order to the central medical store. On the order hand, 16 (13%) of ‘pull facilities’ were reported to have placed emergency orders to the central medical store. To technically say which system is better, Mann-Whitney test was conducted, and the result (p=0.481) showed that, statistically, there is no difference in the rate of placement of emergency orders between the pull and pull systems.

Stock out indicators are principally two- frequency of stock out and duration of a stockout. Besides, other supportive indicators were measured- stock out on the day of visit and the number of products stocked out. The frequency of stock out was measured as the incidence of stock-outs in the last six months before the study. On this, 201 (63%) and 23 (19%) of health facilities operating under push and pull systems respectively reported incidences of a stockout. Even though on the surface, the push system appears to have more occurrences, the Mann-Whitney test was carried out because the sample sizes are different and to statistically indicate which one is better off. At a significance level (p=0.000), the vast difference observed statistically is in favor of a pull system that has a lesser incidence of a stockout.

Since the frequency of stock out was taken historically to arrive at a fixed rate, stock out on the day of the visit was also assessed. It was found out that 184 (58%) of facilities with the push system had recorded stock out of 203 (64%) of products managed by the program. Both the stock out and the number of products are huge and seem to be due to push system of requisition because the pull system recorded only 34 (28%) stock out on the day of visit for 24 (20%) of products stocked out. Moreover, the duration of stock out days for the push system was 222 days while only one day of the stock was recorded for the pull system. This validates the fact that both push and pull systems are not different in terms of placing emergency order, but the pull system is more efficient in serving those orders within a reasonable lead time to avert stock out of more than 24 hours. Whereas the push system also places emergency orders but the lead time is extended, therefore, the frequency of stock out and duration cannot be controlled due to delayed deliveries by the system.

The magnitude of expiries recorded by push and pull systems are widely different as well. Up to 66 (21%) of facilities of the push system have expired products against 11 (9%) of facilities of the pull system. Statistically, the Mann-Whitney test proved that (at p=0.004), the two systems are substantially different in terms of their ability to manage expiries in favor of the pull system.

The rate of expiries can be attributed to a push system because, in this system of requisition, quantities of commodities can be lesser or more than the amount required by the health facility. In instances where the supplies are more than needed and probably above the maximum stock level, there could be a risk of expiry if this allocation happens for more than one cycle. On the other hand, the pull system can be more posed to control expiry because the distribution of quantities of products is according to the request of the health facility. In this way, inventory management is more efficient and within the full control of the health facility.

4.2.  What is Already Known on this Topic

Historically, in Katsina State, the malaria program uses the push system of allocation of commodities to health facilities and has more records of stockout than the corresponding TB program that employs the pull system of a requisition. In the last one and a half years, a comparison of stockout incidence beyond 24 hours and the placement of emergency orders between the two systems has shown contrasting results. From April – June 2017, the malaria program which uses the push systems recorded 35% stockout rate and 29% rate of emergency orders but the counterpart TB program which uses pull systems reported 0% stockout and emergency order rates.[23]

In the subsequent quarter (July – September 2017) published by the same state’s Logistics Management Coordination Unit (LMCU), both the push and pull systems recorded 0% stockout and emergency order rates.[24] However, during the October – December 2017 QSSR, the LMCU reported that the push system filed 33% stockout rate and the facilities receiving commodities via this system had an average of 8.3% emergency order placements.[25] Then on, during the January – March 2018 QSSR published by the LMCU, the push system recorded only 1.7% stockout rate with no incidence of an emergency order; however, the pull system recorded 6.7% rate of emergency order with no stockout.[26] As of the assessment (April – June 2018), both the push and pull systems did not record any incidence of stockout exceeding 24 hours in duration. However, both systems filed emergency orders 8.3% (push) and 20% (pull) respectively.[27]

4.3.  What this Study Adds

This research is the first in Nigeria to compare the performance of push and pull systems concerning performance indicators. It has critically assessed health facilities supplied by both methods and the underlying factors that affect the performance being measured.

Apart from the qualitative data analysis that showed the findings, this study has scientifically (statistically) asserted that the practice of push system of supply is associated with pronounced frequency of stock-outs of longer duration, more incidences of expired products, and extended lead-times. This is the opposite of the corresponding pull system.

This study established that even though the pull system is the best; the incidence of emergency orders of the two methods of supply is not different.

Therefore, this calls for policy recommendation to consider the use of the pull system in supplying health products by primarily increasing funding for health to combat public health threats and contribute to reducing the global burden.

4.4.  Limitations of this Study

This research was conducted of two separate logistics system of the same funder. Furthermore, it was only performed across the entire Katsina State of Nigeria, thus, may not represent similar performances across the whole of the northern region or the country.

5.      The Need for Policy Shift

The primary reason behind the use of a push system is resource constraint. Whereas at times, the use of push systems cannot be avoided, especially locally and with local funding, it is possible to abolish the use of push systems by making policy shifts internationally.

In the fight against deadly diseases such as malaria, tuberculosis, and HIV/AIDS, among others, major funders such as the World Health Organization (WHO), United Nations (UN), Global Fund (GF), United States Agency for International Development (USAID), Centers for Disease Control and Prevention (CDC), United Kingdom’s Department for International Development (DfID), other funders, and governments of countries should look into the possibilities of increasing funding. Especially for infectious diseases that require extended treatment time and at risk of drug resistance, there is an immense need for increased funding to avoid the possibility of practicing push system which has higher chances of stockouts that would lead to the emergence of resistant strains of organisms that cause the diseases.

Along the line of increasing funding, the relevant stakeholders that have been called upon to increase resources should also move for a policy change that will prohibit the use of push systems and direct the use of pull systems entirely. Such policies should be directed at organizations that implement public health programs for putting into action the exclusive use of pull systems for all health service delivery points that have been earmarked to receive commodities.

Local and international organizations that implement public health programs have a crucial role to play in executing the policy of pull system and institutionalizing the practice. This is because they are funded by donors to implement projects meant to curtail diseases. Therefore, as implementers of programs, organizations should also be mandated by their donors to achieve the exclusive use of pull systems in supply of commodities.

6.      Conclusion

Reliably, this study has established that all facilities supplied via push and pull systems have adequate staffing (dedicated logistician) for commodity management who were trained on logistics management of health commodities. Although the staff is not of the recommended cadre, task shifting implemented by the state has solved the problem of the shortage of human resources that was also complemented by proper training. The staff in both systems are also routinely supervised and re-trained on the job on commodity management. Inventory management of both systems is excellent as stock-keeping records are available and are reasonably updated with the most recent transaction to reflect real-time balances. Moreover, the rate of emergency orders between the two systems is not statistically different.

However, the pull system is better, statistically and practically, than the push system regarding the ability to serve order within two weeks, lesser incidences stock out, a minimum duration of stock out, and fewer expiries.

7.      Acknowledgments

It is with profound gratitude that the authors wish to acknowledge the ethics permission of the Institutional Review Board of Pôle Universitaire Euclide. The authors also want to appreciate the efforts of the 11 data collectors and 442 logistics officers at health facilities that gave the consent for interview and assessment of health facility records.

References

1. Louis, N., Solomon, M., Cheryl, L. A., Jackline, O., Elisephan, N., Laura, C., Joseph, M., Bethany, H; Assessment of Essential Medicines Stock-Outs at Health Centers in Burera District in Northern Rwanda. Rwanda Journal Series F: Medicine and Health Sciences; 2015; Vol. 2 No. 1.
2. Kingma, M; Workplace Violence in the Health Sector: A Problem of Epidemic Proportion; International Nursing Review; 2001; 48(3); Pp.: 129-130.
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4. Jeremy, S; Donor Funding Priorities for Communicable Disease Control in the Developing World; The Journal on Health Policy and Systems Research; November 2006; Volume 21, Issue 6; Pp. 411–420.
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6. Pitt C, Grollman C, Martínez-Álvarez M; Countdown to 2015: An Analysis of Donor Funding for Prenatal and Neonatal Health, 2003–2013; British Medical Journal Global Health, 2017; 2: e000205.
7. Carl-Andy, D., Debbie, S; From Staff-Mix to Skill-Mix and Beyond: Towards a Systemic Approach to Health Workforce Management. Human Resource Health; 2009; 7:87.
8. Xiao; Challenges in Data Quality: The Influence of Data Quality Assessments on Data Availability and Completeness in a Voluntary Medical Male Circumcision Programme in Zimbabwe; British Medical JournalOpen 7, no. 1 (January 1, 2017): e013562, accessed January 30, 2019, https://bmjopen.bmj.com/content/7/1/e013562.
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11. DfID/MNCH2; Mapping of Supply Chain Systems in Katsina State, Nigeria, February 1, 2018.
12. WHO; World Malaria Report 2014; Geneva: World Health Organization; 2014.
13. National Malaria Control Programme, Federal Ministry of Health, Nigeria; Strategic Plan 2009 –2013: A Road Map for Malaria Control in Nigeria (abridged version).Abuja: Yaliam Press Ltd; 2009.
14. WHO; Progress and Impact Series: Focus on Nigeria.Geneva: World Health Organization; 2012.
15. Salwa D; (2016). Is Nigeria Winning the Battle against Malaria? Prevalence, Risk Factors, and KAP Assessment among Hausa Communities in Kano State. Malaria Journal 15 (July 8), accessed June 22, 2018, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4938925/.
16. WHO; World Malaria Report 2010. Geneva: World Health Organization; 2010
17. Ibekwe, A. C; Okonkwo, I. O; Onunkwo, A. I; Ogun, A. A; Udeze, A. O; and Ejembi, J; Comparative Prevalence Level of Plasmodiumin Freshmen (First Year Students) of Nnamdi Azikiwe University in Awka, South-Eastern, Nigeria. Malays Journal of Microbiology; 2009; 5; Pp. 51–54.
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[1] Louis, N., Solomon, M., Cheryl, L. A., Jackline, O., Elisephan, N., Laura, C., Joseph, M., Bethany, H; Assessment of Essential Medicines Stock-Outs at Health Centers in Burera District in Northern Rwanda. Rwanda Journal Series F: Medicine and Health Sciences; 2015; Vol. 2 No. 1.

[2] Kingma, M; Workplace Violence in the Health Sector: A Problem of Epidemic Proportion; International Nursing Review; 2001; 48(3); Pp.: 129-130.

[3] Westin, T., Stalfors, J; Tumor Boards/Multidisciplinary Head and Neck Cancer Meetings: Are they of Value to Patients, Treating Staff or a Political Additional Drain on Healthcare Resources? Current Opinion in Otolaryngology & Head and Neck Surgery; April 2008; Volume 16, Issue 2; Pp. 103–107.

[4] Jeremy, S; Donor Funding Priorities for Communicable Disease Control in the Developing World; The Journal on Health Policy and Systems Research; November 2006; Volume 21, Issue 6; Pp. 411–420.

[5] Karen, A. G., Crossley, B. P., Zubin, C. S., Abdul, G; Donor Funding Health Policy and Systems Research in Low- and Middle-Income Countries: How Much, From Where and to Whom; Health Research Policy and Systems; 2017; 15:68.

[6] Pitt C, Grollman C, Martínez-Álvarez M; Countdown to 2015: An Analysis of Donor Funding for Prenatal and Neonatal Health, 2003–2013; British Medical Journal Global Health, 2017; 2: e000205.

[7] Carl-Andy, D., Debbie, S; From Staff-Mix to Skill-Mix and Beyond: Towards a Systemic Approach to Health Workforce Management. Human Resource Health; 2009; 7:87.

[8] Y. Xiao; Challenges in Data Quality: The Influence of Data Quality Assessments on Data Availability and Completeness in a Voluntary Medical Male Circumcision Programme in Zimbabwe; British Medical Journal Open 7, no. 1 (January 1, 2017): e013562, accessed January 30, 2019, https://bmjopen.bmj.com/content/7/1/e013562.

[9] Push vs. Pull in Your Supply Chain…What’s the Difference? (2014). The Network Effect, last modified December 5, accessed October 1, 2018, https://supplychainbeyond.com/push-vs-pull-supply-chain-whats-difference/.

[10] National Population Commission. (2006). PHC Priority Tables, accessed July 8, 2018, http://population.gov.ng/core-activities/surveys/dataset/2006-phc-priority-tables/.

[11]DfID/MNCH2; Mapping of Supply Chain Systems in Katsina State, Nigeria, February 1, 2018.

[12] WHO; World Malaria Report 2014; Geneva: World Health Organization; 2014.

[13] National Malaria Control Programme, Federal Ministry of Health, Nigeria; Strategic Plan 2009 –2013: A Road Map for Malaria Control in Nigeria (abridged version). Abuja: Yaliam Press Ltd; 2009.

[14] WHO; Progress and Impact Series: Focus on Nigeria. Geneva: World Health Organization; 2012.

[15] Salwa D; (2016). Is Nigeria Winning the Battle against Malaria? Prevalence, Risk Factors, and KAP Assessment among Hausa Communities in Kano State. Malaria Journal 15 (July 8), accessed June 22, 2018, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4938925/.

[16] WHO; World Malaria Report 2010. Geneva: World Health Organization; 2010.

[17] Ibekwe, A. C; Okonkwo, I. O; Onunkwo, A. I; Ogun, A. A; Udeze, A. O; and Ejembi, J; Comparative Prevalence Level of Plasmodium in Freshmen (First Year Students) of Nnamdi Azikiwe University in Awka, South-Eastern, Nigeria. Malays Journal of Microbiology; 2009; 5; Pp. 51–54.

[18]December – Malaria Fact Sheet 2 accessed June 22, 2018, https://photos.state.gov/libraries/nigeria/231771/Public/December-MalariaFactSheet2.pdf.

[19] TB in Nigeria – Funding, Children, Diagnosing TB, HIV/TB. TB Facts | TB, Tests, Drugs, Statistics, accessed December 27, 2018, https://www.tbfacts.org/tb-nigeria/.

[20] Grace O; World Tuberculosis Day 2018, on the Search for TB Leaders in Nigeria | ARFH; 2018; accessed December 27, 2018, https://arfh-ng.org/world-tuberculosis-day-2018-on-the-search-for-tb-leaders-in-nigeria/.

[21] Qualtrics; Sample Size Calculator; Qualtrics; last modified May 12, 2018, accessed September 1, 2018, https://www.qualtrics.com/blog/calculating-sample-size/.

[22] USAID | DELIVER PROJECT, Task Order 1; Logistics Indicators Assessment Tool (LIAT). Arlington, Va.; USAID | DELIVER PROJECT, Task Order 1; 2008.

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