Epidemiology of cholera in the Philippines.

Despite being a cholera-endemic country, data on cholera in the Philippines remain sparse. Knowing the areas where cholera is known to occur and the factors that lead to its occurrence will assist in planning preventive measures and disaster mitigation.Using sentinel surveillance data, PubMed and ProMED searches covering information from 2008-2013 and event-based surveillance reports from 2010-2013, we assessed the epidemiology of cholera in the Philippines. Using spatial log regression, we assessed the role of water, sanitation and population density on the incidence of cholera.We identified 12 articles from ProMED and none from PubMed that reported on cholera in the Philippines from 2008 to 2013. Data from ProMed and surveillance revealed 42,071 suspected and confirmed cholera cases reported from 2008 to 2013, among which only 5,006 were confirmed. 38 (47%) of 81 provinces and metropolitan regions reported at least one confirmed case of cholera and 32 (40%) reported at least one suspected case. The overall case fatality ratio in sentinel sites was 0.62%, but was 2% in outbreaks. All age groups were affected. Using both confirmed and suspected cholera cases, the average annual incidence in 2010-2013 was 9.1 per 100,000 population. Poor access to improved sanitation was consistently associated with higher cholera incidence. Paradoxically, access to improved water sources was associated with higher cholera incidence using both suspected and confirmed cholera data sources. This finding may have been due to the breakdown in the infrastructure and non-chlorination of water supplies, emphasizing the need to maintain public water systems.Our findings confirm that cholera affects a large proportion of the provinces in the country. Identifying areas most at risk for cholera will support the development and implementation of policies to minimize the morbidity and mortality due to this disease

Risk for incidence of cholera among municipalities in the Philippines, 2010–2013, <i>using confirmed and suspected cases</i>.

<p>Risk for incidence of cholera among municipalities in the Philippines, 2010–2013, <i>using confirmed and suspected cases</i>.</p

Seasonality of suspected and confirmed cholera cases in PIDSR, by month and year.

<p>Seasonality of suspected and confirmed cholera cases in PIDSR, by month and year.</p

Age and sex distribution of suspected and confirmed cholera cases from PIDSR, 2008–2012.

<p>*Out of 29,144 cases with data on age and 30,282 with data on sex.</p>†<p>Out of 598 cases with data on age and 602 with data on sex.</p>‡<p>Out of 182 cases with data on age and 189 with data on sex.</p>§<p>Out of 182 cases with data on age and 187 cases with data on sex.</p><p>Age and sex distribution of suspected and confirmed cholera cases from PIDSR, 2008–2012.</p

Cholera confirmed outbreaks reported, cases and deaths identified and ages affected, from ESR 2010–2013.

<p>Cholera confirmed outbreaks reported, cases and deaths identified and ages affected, from ESR 2010–2013.</p

Suspected and confirmed cholera cases, estimated annual incidence from 2008 to 2013.

<p>*Per cent culture confirmed among cases.</p>†<p>NA – Not available.</p>‡<p>Average incidence calculation includes data from 2010 to 2013 since ESR began in 2010.</p><p>Suspected and confirmed cholera cases, estimated annual incidence from 2008 to 2013.</p

Risk for incidence of cholera among municipalities in Philippines, 2010–2013, <i>using confirmed cases only</i>.

<p>Risk for incidence of cholera among municipalities in Philippines, 2010–2013, <i>using confirmed cases only</i>.</p

Average annual cholera incidence (per 100,000 population), by municipality of confirmed (1A) and both suspected and confirmed cholera cases (1B) from all sources.

<p>Average annual cholera incidence (per 100,000 population), by municipality of confirmed (1A) and both suspected and confirmed cholera cases (1B) from all sources.</p

Prediction of high incidence of dengue in the Philippines.

BACKGROUND: Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines. METHODS: Model inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data. PRINCIPAL FINDINGS: Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation. CONCLUSIONS: This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and Specificity