Routine distribution of ITNs targeting children under 5 years old and pregnant women started in 2003 in 20 pilot districts and was scaled up nationwide during 2004–2010. During 2006–2009, >4.5 million ITNs were distributed through antenatal care (ANC), Child Health weeks, voucher schemes, and commercial sources. However, the strategy was replaced with a long-lasting insecticidal net (LLIN) mass campaign, starting at the end of 2010 with the goal of achieving universal coverage in all ages at a ratio of one net per two persons. During 2010–2012, >12 million LLINs were delivered, and another 12.3 million LLINs during 2013–2015. In 2012, the AngloGold Ashanti Malaria Control Programme (AGAMal) implemented indoor residual spraying (IRS) in seven districts, expanding to 22 districts in five regions (Upper West, Upper East, Ashanti, Western, Central) in 2015. PMI supported five districts for IRS in the Northern region. In total, in 2015, 4.7% of the entire population in the country benefited from IRS. This population also received LLINs.
Parasitological testing of patients of age 5 years and above was primarily limited to hospitals. Since 2010, confirmation of all ages with either microscopy or rapid diagnostic tests (RDTs) has been introduced. Health centres and health posts use RDTs as primary diagnostic tool while higher facilities use microscopy, reserving RDTs for emergency cases, and for situations where microscopy is not functional or caseloads exceed microscopy diagnostic capacity. Therefore, only records of microscopic testing from the hospitals were used for this study.
The country changed its first-line anti-malarial treatment from chloroquine to artesunate–amodiaquine (AS–AQ) in 2004 and added artemether–lumefantrine (AL) and dihydro-artemisinin–piperaquine (DHAP) as alternatives in 2009. Quinine was the principal medicine for severe malaria until 2010, when injectable artesunate was adopted as the drug of choice. The Affordable Medicines facility for Malaria (AMFm), launched in 2011, increased access to ACT in the private sector from 31% in 2010 to 83% in 2011 .
For community-based intervention, between 2005 and 2012, nearly 2218 CHPS have been established in the country. The health staff in the CHIPS diagnose with RDTs and treat malaria and assist delivery at the community. However, presumptive treatment of malaria may occur for logistic reasons, when RDTs stock-out.
Prior to 2007, patients of all ages were charged for malaria diagnosis and treatment, which was a major barrier to accessing basic health services for majority of the population. To address the problem of access, the NHIS was initiated in 2003 and became operational in 2005 to subsidize maternal and child health services, which became free in 2007. In the NHIS, urban and rural populations pay a premium of about US$17 and US$11 per year, respectively, and when the cost is beyond what NHIS cover, patients cover the difference. For people whose inability to pay is certified by local authorities, the Government covers all. Despite these provisions, the coverage of the NHIS remains at 38% owing to financial challenges to sustain the scheme, identification of the poor and vulnerable, identification (ID) card management, quality of care, and slow information system .
Data for intervention coverage
Long-lasting insecticidal nets and IRS data were obtained from district records. The proportion of the population potentially protected by LLINs was calculated for each year, assuming each LLIN covered 1.8 persons and lasted 3 years. Prospective district population was derived from the 2010 Ghanaian census, using the United Nations growth rate for Ghana [5, 6]. To measure ACT availability, number of months-ACT stock-out was obtained from records of hospital medical stores. Stock-out was defined when a hospital had no ACT in stock for more than 7 days in a month.
Data for malaria morbidity and mortality
A WHO standardized protocol (unpublished) was used for hospital data collection. A total of 88 hospitals in three epidemiological zones were randomly sampled (28 in savannah, 30 in forest, 30 in coastal). Although the design was to select 30 representative hospitals from each zone, the savannah had only 28 hospitals and all were sampled. Data abstraction teams consisting of two persons visited each hospital for 2 days.
Monthly summary reports were used as the main source of records for: (i) outpatient all-cause consultations and malaria cases; (ii) inpatient all-cause and malaria admissions, all-cause deaths, malaria deaths, anaemia admissions, and anaemia deaths; and, (iii) laboratory records for microscopically tested and positive cases. Where monthly summary records were missing, register books were used as primary source. Data were collected for two age groups: all ages and under 5 years of age.
Analysis of microscopic tests and confirmed cases was possible only for all ages as records of microscopic results could not be disaggregated by age, outpatient or inpatients. Microscopically confirmed malaria cases, malaria admissions and malaria deaths were the main interest of the analysis. A confirmed malaria case for this study was defined as a blood slide positive for malaria. Malaria admission was defined as a microscopically confirmed inpatient case, diagnosed at discharge as severe malaria. However, some malaria admissions were admitted based on clinical signs suggestive of severe malaria. A malaria death was defined as death attributed to malaria among the admitted cases. All anaemia admissions were assumed to be severe anaemia (<7 mg/dl). The test positivity rate (TPR) was computed by dividing number of positive slides by the total number of slides examined.
To control for other factors that might influence the observed trends, the following additional indicators were computed: (i) non-malaria outpatient consultations (all-cause outpatient cases minus outpatient malaria cases); (ii) non-malaria admissions (all-cause admissions minus malaria admissions); and, (iii) non-malaria deaths (all-cause deaths minus malaria deaths). In addition, data on admitted patients with or without health insurance were extracted from the DHMIS-2 to assess the difference in disease trends among those insured and non-insured. These allowed the assessment of whether changes in malaria cases and deaths were truly attributable to interventions or to changes in healthcare-seeking behaviours or other factors that affect all diseases in a similar way. In addition, the trends of child mortality from population-based surveys for Ghana were also reviewed and triangulated with the trends observed in the hospitals.
To control for the potential effect of climatic changes in trends of malaria during pre- or post-scale-up periods, data on monthly rainfall in mm, minimum and maximum temperature in °C by region, were obtained from the Ghanaian Meteorology Department.
Statistical analysis of time trends
For filling missing monthly data, either of the following approaches were applied: (i) if the missing data were in the 1st year, then average the consecutive 2 years of the same month, (ii) if the missing data were in the last year, then average the two preceding years of the same month; and, (iii) if the missing data were in between 2 years, running averages of the preceding and consecutive year for the same month. Because the data points end in June 2015, monthly data were aggregated biannually (two periods for each year) making a total of 21 periods covering January 2005–June 2015.
Assuming all other interventions constant, the effect of an LLIN mass campaign since 2011 was evaluated by comparing the disease trends of the pre-scale up years (2005–2010), i.e., period 1–11 with that of period 12–21 (post-scale up years 2011–2015). Changes on trends were estimated as relative per cent change by comparing the observed value and predicted value at period 21 assuming a continuation of the pre-scale up time trend throughout period 21 if there were no interventions (counterfactual trend). The interpretation of the predicted relative change in percentage at period 21 therefore takes into account the trends (slop) of all the values during 2011 and 2015. This was done using a segmented regression model of an interrupted time series with breakpoint of period 11 , an approach that has increasingly become useful to evaluate impact of public health interventions [8, 9] including malaria [10, 11]. The 95% confidence intervals (CI) around effect estimates were computed using the CI around the regression coefficient. A percentage difference with a CI that does not include zero in the range was considered a significant change (P < 0.05). This model corrects for autocorrelation [12, 13] and adjusts the change estimate allowing for: (1) possible time trend of the indicator during the pre-scale-up period, (2) a possible immediate drop or rise of the indicator following the start of the campaign; and, (3) an effect of the campaign on the post-scale up at a given period. In addition, a stratified analysis using segmented regression was applied to compare changes in disease trends in IRS districts (Northern region) and non-IRS districts.