IODINE STATUS AND SOURCES OF DIETARY IODINE INTAKE IN KENYAN WOMEN AND CHILDREN.
INTRODUCTIONIodine is required for biosynthesis of thyroid hormones in the thyroid gland. Since thyroid hormones regulate the metabolism and functions of proteins, carbohydrates, and lipids during all stages of life, insufficient dietary iodine can lead to a variety of serious consequences. Due to rapid growth and specialization of the brain during its early development, the most critical consequence of hypothyroidism due to iodine deficiency is brain damage in the fetus and young infant, which has serious consequences for learning and education of children and their prospects of a future productive adult life [1]. Even mild iodine deficiency may be causally involved in cognitive impairment among children [2]. From data available up to 2011, an estimated 2 billion people worldwide had insufficient dietary iodine intake to meet their physiologic needs. This estimate represents a reduction of 6.4% from 2007 but still encompasses more than one-quarter of the world population [3].
Frequent large goiter occurrence was reported in Kenya as early as the 1920s. The first survey in 1964 reported goiter prevalence ranging between 15% and 72%, with the highest burden in the highlands of Rift Valley, Nyanza and Western regions [4]. Subsequently, salt iodization was started in 1970 on a voluntary basis, first benchmarked at 20mg iodine/kg, which was then revised to 30mg iodine/kg in 1973. Subsequent studies reported improved urinary iodine levels but this was not accompanied by corresponding goiter prevalence reductions [5]. Consequently, in 1978, iodization of all edible salt was made mandatory with an iodine standard set at 100mg iodine/kg (equivalent to 168.5mg potassium iodate/kg). A micronutrient survey of 8-10 year-old school children in 1994 showed a significant reduction of goiter prevalence throughout Kenya, but residual goiter at [greater than or equal to]20% prevalence was still found in the historically most affected highland areas. During the next period of [+ or -]15 years, the strategy efforts became focused on methods and procedures for the improved performance of production and supply of iodized salt. After elevated iodine status findings were reported among school-age children in the Midlands area, the Government of Kenya in 2009 lowered the mandatory iodine standard for salt to 30 - 50 mg iodine/kg salt, while maintaining potassium iodate as an exclusive fortificant [6, 7].
The iodization of salt for human consumption aims to raise the dietary iodine provision of a population through the salt used in preparation and seasoning of meals in the household, and through the salt used in the recipes of foods processed outside the home. The two additional iodized salt sources add dietary iodine on top of the native iodine (that is, iodine naturally present in foods and drinking water) [8]. In combination, the three dietary sources of iodine from household salt, processed food salt, and native iodine content are aimed at ensuring a total iodine intake from the diet that meets the biological needs of a population [9].
Setting an appropriate salt iodine standard depends on information about the native dietary iodine content and the consumption of salt from processed food and salt added in the household. In case these data are not available, international guidelines recommend a mean salt iodine standard content ranging between 20 and 40 mg iodine per kg, with the lower level of 20 mg/kg considered advisable when the salt used in processed food is also iodized [10]. For monitoring a population's iodine status and, if needed, adjusting the salt iodine standard, it is important to assess the fractions of total iodine intake that can be attributed to the native dietary iodine supply and the additional iodine supplies from salt in processed foods and from household salt. Ideally, such estimates would rest on dietary measurements of iodine and salt consumption.
Derived from the iodine content in soil and groundwater, naturally occurring dietary iodine varies considerably by geography [11], which makes an accurate estimate of native iodine consumption difficult. Seasonal variations in the iodine content of some foods and the limited available iodine data in food composition tables add to the difficulty of assessing the consumption of iodine by dietary methods [12,13,14]. In contrast, it is relatively simple to determine the typical iodine intake from iodine concentration data in casual urines of a large, representative population sample [13]. An accurate measurement of salt consumption is also not easy [14]. Dietary methods, such as duplicate diets, food frequency questionnaires and 24- hour dietary recalls, face limitations due to participant memory, and conversion with sodium values in food composition tables [15, 16]. Data of sodium content in food tables are often not up-to-date and, moreover, the amount of salt in the recipes of commercially manufactured foods can vary significantly by brand and even by company. Finally, the amount of sodium consumed from salt used in households, restaurants, and canteens cannot be measured accurately with the common existing dietary methods [17]. In contrast, sodium in excreted urine captures the discretionary salt consumption and does not depend on respondent recall or up-to-date detailed food composition data to calculate a sodium or salt intake estimate [18].
The Kenya National Micronutrient Survey 2011 (KNMS) was instigated by the Government of Kenya under the leadership of the Ministry of Health (formerly Ministry of Public Health and Sanitation) with the overall objective to obtain population-representative data of nutrition and micronutrient status. Data collection from various population groups covered a range of micronutrients (including iodine), dietary intake and food product use patterns, child growth and development measurements, and information of infection patterns and common diseases. For comparison with previous findings in Kenya and in accordance with international guidelines school-age children (SAC) and non-pregnant, non-lactating women of reproductive age (NPW) were selected for collections of salt supply and use, and iodine status data [19]. A key underlying reason for the iodine component was to examine whether the new salt iodine standard was adequate to achieve a dietary iodine supply that provides optimal population iodine status. Because iodized food-grade salt is the principal means for delivering additional dietary iodine as well as the major dietary source of sodium, the KNMS protocol also included sodium concentration measurements in the same urine samples collected for iodine assessment [20].
Primarily, this report sets out to describe the findings of iodized salt supply and iodine status in SAC and NPW, based on the 2011 survey data. Second, the paper introduces an approach to apportion the principal sources of dietary iodine intake in the SAC and NPW of Kenya, taking advantage of the fact that both iodine and sodium concentrations were measured in the same casual urine samples. The objective of this paper, therefore, is to report the methods and findings of the iodine nutrition status of SAC and NPW, assessed by their urinary iodine concentrations (UIC), and partitioned for each group into the three principal sources of iodine intake from the diet, namely, native iodine, processed food salt and household salt.
MATERIALS AND METHODS
Selection of participants
To obtain representative estimates nationally and separately for urban and rural areas, the design of the KNMS consisted of two-stage cluster sampling, stratified by urban and rural areas. The National Sample Survey and Evaluation Program IV master frame, maintained at the Kenya National Bureau of Statistics (KNBS), was used to systematically draw 296 clusters with equal probability from urban and rural areas. Urban areas were defined as cities, municipalities, town councils, urban councils, and all district headquarters, while the remaining areas of the country were classified as rural areas where people's main economic activity was farming. This first stage selection resulted in 123 urban and 173 rural clusters. At the second stage, 10 households were selected randomly from each cluster for general data collection. All eligible SAC (5-14y old) and NPW (aged 15-49y) residing in a random sub-sample of four out of the 10 selected households were invited for urine sampling and collection of salt supply and use data. The KNMS protocol was reviewed and approved by the Ethical Review Committee of the Kenya Medical Research Institutes (KEMRI) and written informed consent was obtained from all individuals who agreed to be enrolled. The survey participants did not receive a monetary incentive.
Sample collection and processing
The KNMS fieldwork took place from September to December 2011. Survey workers collected a single, [+ or -] 20ml "on-the-spot" urine sample from each SAC and NPW. Due to the nature of the survey, it was not possible to consistently collect the urine samples at a specified time of the day. At the same time also, a salt sample of [+ or -] 10g was obtained from the household of each enrolled subject. One senior laboratory technician analyzed the urine samples in duplicate for urinary iodine concentration (UIC) and urinary sodium concentration (UNaC) in the Center for Public Health Research (CPHR) of KEMRI by a manual Sandell-Kolthoff method and atomic absorption spectrophotometry, respectively [21, 22]. Salt samples were measured by one laboratory expert by single manual titration in the National Public Health Laboratory (NPHL) for potassium iodate content and converted to salt iodine (SI) content [23]. To keep analytical error within preset limits, the CPHR lab applied acceptance rules with UIC control limits set at values obtained from prior runs of left-over urine samples, and the NPHL lab used control limits for SI content similarly set from previous runs. From bench control pools, the total analytical error for UIC ranged between 5.2% and 7.9%. The titration method achieved a total analytical variation of 5.0%. The accuracy of UIC and SI data was assessed in a blinded three-way inter-laboratory comparison with two separate accredited iodine laboratories in Tanzania and Kazakhstan. Comparisons of reported data showed no significant difference in UIC results between laboratories, but the SI data reported by the NPHL were consistently higher by 6.9 mg/kg compared with the mean SI results of the two external laboratories [24]. For the regression analyses reported in this paper, the SI data of the NPHL was therefore reduced by 6.9 mg/kg to ensure internal consistency of the dietary iodine source estimations. During the period of KNMS urine sample processing, the CPHR laboratory earned a certificate of successful performance from the Ensuring the Quality of Urinary Iodine Procedures program at the Centers of Disease Control and Prevention, Atlanta, USA [25].
Statistical Analysis
Descriptive findings for UIC, UNaC and SI were obtained with SPSS on weighted data to account for refusal, absence from the household and missing data. For UIC, findings were reported as median values and interquartile ranges (IQR). For UNaC, means and 95% confidence intervals (95% CI) and for SI, means and standard deviations (SD) were reported. Findings were categorized by age, gender, residence type, region and household wealth index, as applicable. Pearson's [chi square] test was used to test associations between UIC levels and co-factors and Fisher's F ratio was used for testing associations of co-factors with UNaC levels.
Combined data processing of the UIC, UNaC and SI data was conducted by generalized linear regression (GLR) as described by Heeringa et al. [26], after transformation of the UIC data to their natural logarithms. The regression technique aimed to examine those variables that were making important contributions in explaining the variation of UIC values in each survey group. In the data processing with GLR, the UIC data (a biomarker of iodine intake) of SAC and NPW were positioned as the outcome variable and the UNaC (salt use indicator) and SI (iodine quality of household salt) data as explanatory variables. Residence type (a dichotomous variable) of the household was also added as an explanatory factor to assess the influence of a household's location on the UIC while taking the UNaC and SI effects into account. Weighted regression analyses of the logUIC data were performed in Stata 14 and R software (StataCorp LP, College Station, Texas) with settings for the two-stage stratified cluster survey design. The aptness of models was assessed graphically, homoscedasticity was examined by plotting of the standardized residuals against predicted logUIC values and the normality of residuals was checked by probability plots. Calculation of 95% confidence intervals did not include a finite population correction as the population size of Kenya is large compared to the sample sizes of SAC and NPW.
Mean estimates for UIC portions that correspond with the three sources of iodine intake (native dietary content, processed food salt, and household salt) were derived with the intercept and slope estimates obtained from GLR analyses as follows. First, the logUIC intercept estimate was back-transformed to its corresponding UIC value, which is the geometric mean UIC value that does not depend on either the UNaC or the SI value when both are zero. The resulting finding is interpreted as the UIC part that corresponds with the native dietary iodine content. Second, the back-transformed UIC value was calculated with the intercept and slope while inputting the respective group's average UNaC value and keeping the SI value at zero. The resulting finding corresponds to the mean UIC of the group due to the mean dietary sodium content without iodine contained in household salt. The difference between this UIC finding and the former UIC value derived from the intercept alone is interpreted as the UIC part that corresponds with dietary iodine intake from salt contained in processed food. Finally, the difference between the group's total UIC finding and the UIC estimate arising from sodium content in the diet, that is, the result obtained in step two, was interpreted as the UIC part that corresponds with the iodine intake obtained from household salt.
RESULTS AND DISCUSSION
Participant Response rates
For iodine status assessment, the KNMS set out to enroll all the SAC and NPW in 740 households (the number of households includes 10% refusal). Based on the master frame, 1,072 SAC and 1,708 NPW were projected to reside in the 740 targeted households. SI data were obtained from 625 households (84.5% response rate); UIC was measured in 951 urine samples of SAC (88.7% response) and 623 urine samples of NPW (36.5% response), and UNaC data were obtained from 863 SAC (80.5% response) and 579 NPW (33.9% response). Complete sets of indicator variables for multiple GLR analysis were available from 563 SAC (53% of expected) and 382 NPW (22%).
Iodized salt supply
The minimum SI content in the 625 household samples collected in this survey was 1.6 mg/kg and the maximum 150.2 mg/kg. The mean SI content of all salt samples (Table 1) was 40.3 mg/kg (SD 19.4 mg/kg). The SI contents in 94.9% of salt samples were [greater than or equal to]15 mg/kg and in 70.1% of households, the SI values ranged between 20 and 50 mg/kg. Variations in SI content across regions were significant (P = 0.018), with Coast region (mean [+ or -] SD: 46.7 [+ or -] 24.1 mg/kg) having highest and Eastern region (36.0 [+ or -] 15.7 mg/kg) having lowest SI levels. Urban households had higher SI contents in their salt (41.9 [+ or -] 20.3 mg/kg) than rural households (39.0 [+ or -] 18.5 mg/kg), but this difference did not attain statistical significance (P=0.063). Also, while the SI content in Kenya's leading salt brand (39.8 [+ or -] 19.3 mg/kg) was lower than in all other brands combined (41.4 [+ or -] 19.5 mg/kg), the difference was not statistically significant (P=0.350).
The KNMS findings of household SI content demonstrate high dedication by the salt industries in meeting mandated standards in iodizing edible salt at 30-50 mg iodine/kg. The mean household SI content of 40.3 mg/kg, a small proportion of SI values below 15 mg/kg and the substantial majority of SI findings in the range preferred at the level of households shows that the USI strategy in Kenya performed successfully [13]. Nevertheless, the fraction of iodized salt sold for direct household use represents only a partial picture of the total edible salt supply. Salt for use in food manufacturing, such as in food processing industries, restaurants, catering, food aid, and street-side food vending constitutes a separate, meaningful source of salt intake for many people [9]. Therefore, to ascertain that the USI strategy is really loyally executed, it is important to also have information about the iodine intake from the use of iodized salt in these forms of processed foods.
Population iodine status
Urinary Iodine Concentration (UIC) findings in SAC and NPW are reported in Table 2 and Table 3, respectively. The median UIC in SAC was 208 [micro]g/L (IQR: 108 - 333) and in NPW 167[micro]g/L (IQR: 98 - 299), that is, both within the range considered internationally as indicative of adequate iodine nutrition. The median UIC in male SAC (231, IQR: 119 - 368 [micro]g/L) was higher (P =0.002) than in females (190, IQR 99 - 295 [micro]g/L). Higher median UIC values were also found in urban, compared to rural households, both in SAC (231, IQR 153 - 341 [micro]g/L vs. 188, IQR 99 - 327 [micro]g/L, respectively, P <0.001) and NPW (180, IQR 125 - 321 [micro]g/L vs. 163, IQR 90 - 279 [micro]g/L, P =0.008). Median UIC levels in SAC as well as NPW varied across regions and household wealth categories (all P <0.001). Thus, highest median UIC in SAC and NPW were from North Eastern region, followed by Nairobi, Central, Coast and Eastern regions and lowest UIC levels were found in Nyanza, Rift Valley, and Western regions. Classified by household wealth levels, the SAC and NPW in the wealthiest households had highest median UIC, while UIC levels in SAC and NPW from poorer homes were lowest.
A key purpose of the UIC measurements was to assess the iodine status in the population of Kenya after the sizeable downward revision of the national salt iodine standard in 2009. Both the median UIC findings in SAC (median 208; IQR 108 - 333 [micro]g/L) and NPW (median 167; IQR 98 - 299 [micro]g/L) were within the range of optimal iodine nutrition and, despite the clear variations of UIC levels by various categories, virtually all median UIC findings fell within the adequacy range of 100-300 [micro]g/L, with a single exception of North-Eastern region, where the median UIC levels, particularly in SAC, were starkly elevated [13, 27]. High iodine concentrations have been reported previously in drinking water samples taken from spring, well and river sources along the Rift Valley of Ethiopia and a more recent national iodine survey in Somalia similarly found high iodine content in drinking water from boreholes [[28, 29]. Hence, the elevated UIC levels in Kenya's North-Eastern region, which borders on Ethiopia and Somalia, may be related to high iodine content in drinking water and were likely not caused by excessive salt intake or high SI content in salt. A revision of the salt iodine standard would have little effect in such a case. It is also worth noting that the median UIC levels in the historically most affected highland areas of Nyanza and Rift Valley regions were only slightly above the minimum threshold for iodine deficiency. Therefore, reducing the current salt iodine standard would conceivably put the population in the historically most affected areas at risk of recurrent iodine deficiency.
Urinary sodium concentrations
The UNaC findings for the SAC and NPW groups are presented in Table 4 and Table 5, respectively. The mean UNaC in SAC was 192 mmol/L (95% CI: 185, 199) and in NPW 186 mmol/L (95% CI: 178, 193). Male SAC had higher mean UNaC (P <0.001) values than females. Higher UNaC levels were also found for both groups in urban, compared to rural households (P <0.001). As was the case for the UIC findings, UNaC levels in both SAC and NPW were strongly associated with the region and household wealth category (all P <0.001). In each survey group, the UNaC findings were higher in Nairobi, Coast, and Eastern regions, while they were lower in Nyanza, Rift Valley and Western regions. SAC and NPW who lived in the wealthiest households had the highest mean UNaC findings, while lowest UNaC values were found in the poorer households.
As is the case for iodine, sodium consumed with the diet also ends up in urine, but unlike iodine, the amount of excreted sodium is regulated by hormonal actions on the kidneys for maintenance of water and sodium homeostasis [18]. Population salt intake estimates derived from casual urine sample collections are therefore deemed less robust than 24-h urine sampling and a recent taxonomy by the World Hypertension League (WHL) for classifying population salt intakes does not include criteria for spot UNaC [[30, 31]. In the absence of any other authoritative guidance, this report nonetheless used the WHL cut-off values to describe the UNaC distributions and for comparing the UNaC levels by different categories.
The existing difference between physiological control of the excretions of iodine and sodium in urine would suggest that the iodine and sodium amount in casual urine samples of individuals do not necessarily vary in tandem, even when the habitual consumption of iodine and sodium with the diet are both largely supplied with iodized salt. However, because the volume of urine and, therefore, the concentrations of iodine and sodium in urine also depend on the amount of water intake the kidney's control of the amount of urine volume gives reason to expect that the UIC and UNaC values in the groups of SAC and NPW may be co-varying. This is consistent with global experience [31, 32-34] that the urinary excretions of iodine and sodium are closely related to the consumption of iodized salt in countries, such as Kenya, with a well-established USI strategy. That covariation of UIC and UNaC values indeed existed in the KNMS survey groups was evident from the very similar fluctuation patterns of UIC and UNaC findings, classified by residence, region and household wealth index, as illustrated in Figure 1. Both urinary indicators were higher in urban areas, Coast region, and the wealthiest households, while they were lowest in rural households, Rift Valley and Western regions, and in poorer households.
Figure 1 illustrates that the high UIC values of SAC and NPW in North-Eastern region diverged from the pattern of close covariation of group-wise UIC and UNaC findings. As suggested before, a likely high iodine content of drinking water in the North-Eastern region may have been the reason for this discrepancy. These strong overall associations between the UIC and the UNaC levels indicate that the variations in UIC levels of both survey groups were largely explained by the variations of their UNaC data. This supports the idea that the amount of (iodized) salt consumption is a key determinant of the iodine intake and, thus, iodine status of the population of Kenya.
Partitioning of dietary iodine sources
The first GLR model aimed to examine the dependency of the UIC outcome variable within each group on the explanatory variables UNaC, SI and household residence [26]. Results reported in Table 6 show that in each group, the UIC showed highly significant associations with variations of UNaC (P <0.001) and household SI content, which attained statistical significance in NPW (P <0.05). The rural residence did not contribute significantly in explaining UIC levels within each group when the UNaC and SI variables were considered.
Next, the regression estimates reported in Table 6 were used to calculate the UIC portions that correspond with the key sources of dietary iodine (Table 7). The UIC fractions due to native iodine in the diet were below the 100 [micro]g/L threshold for iodine deficiency (60.5 [micro]g/L and 66.8 [micro]g/L in SAC and NPW, respectively). The UIC fractions corresponding with iodine in processed food were larger (86.7 [micro]g/L in SAC and 83.7ug/L in NPW), while the UIC levels attributable to iodine from household salt were smaller (47.0 [micro]g/L and 29.8 [micro]g/L in SAC and NPW, respectively). These findings for apportioned UIC levels suggest that in SAC, 31% of the total iodine intake was sourced from the native iodine content, and 45% and 24%, respectively, from iodized salt in processed foods and from iodized household salt. The respective UIC portion estimates in NPW were: native dietary iodine 37%, food salt iodine 47% and household salt iodine 17%.
To our knowledge, the use of regression to apportion the UIC findings from a population survey has not been described previously. Because the findings reported in Table 6 may not be familiar, Figure 2 illustrates an example of the calculation of UIC portions in urban NPW. The rising line in Figure 2 portrays the main association between the UIC and UNaC values in urban NPW, adjusted for SI content and residence. Marker symbols on the ordinate (Y-axis) indicate UIC portions obtained from back-transformed logUIC findings, that is, UIC = [e.sup.z], in which the z values are calculated in three steps with the GLR parameters reported in Table 6. In sequence, these steps proceed as follows:
(1) The start-out z value equals the intercept estimate for NPW, that is, z = 4.158. Then, the back-transformed UIC value is [e.sup.4158] = 63.9 [micro]g/L (indicated by the round symbol on the ordinate). This finding is interpreted as the UIC part in urban NPW that corresponds with native dietary iodine intake
(2) In the 2nd step, z is calculated with the regression equation using the average UNaC finding in urban NPW (mean UNaC = 194.6mmol/L). Then, z = 4.158 + 0.0042 x 194.6 = 4.982 and [e.sup.4982] = 145.8 [micro]g/L (the triangular symbol). The UIC fraction in urban NPW from iodine intake in processed foods is obtained as the increment above the step (1) result, that is, UIC = 145.8 - 63.9 = 81.9 [micro]g/L
(3) The 3rd step uses the total geometric mean UIC in urban NPW (square symbol = 174.9 [micro]g/L) and obtains the increment above the result of step (2): UIC = 174.9 - 145.8 = 29.1 [micro]g/L. This finding is interpreted as the UIC part in urban NPW that corresponds with iodine intake from household salt.
Two findings from the technique to apportion the UIC values to the dietary sources of iodine intake (Table 7) should be noted. First, in both survey groups, the estimated UIC parts attributed to native dietary iodine content fell below the 100 [micro]g/L threshold for iodine deficiency. This implies that iodine deficiency can be expected to reoccur in the population should the USI strategy in Kenya be suspended. Second, the finding of very similar GLR parameter estimates for the UNaC variable (Table 6) suggests that in both groups (SAC and NPW) the UIC levels are impacted in the same way by the salt consumed from their diets. The different findings for total UIC between SAC and NPW would, therefore, mainly be caused by a difference in dietary consumption between children and adults, and less so by a different composition of their diets.
It is noteworthy also, that the estimates of UIC portions are imprecise. A first reason for the wide CIs around the UIC point estimates is that the numbers of SAC and NPW with a complete dataset was small, which made for relatively high imprecision of UIC portion findings in each group. The second reason for imprecision is inherent in the technique of generalized regression [26]. To account for the uncertainty related to finite sample selection in the survey, the statistical analysis applied design-based (generalized) linear regression, which is superior to simple regression because it yields more accurate regression parameter estimates. However, the higher accuracy of the GLR technique comes at the cost of less certainty and, consequently, wider CIs for the UIC part estimates. Figure 3 shows findings of the UIC parts corresponding with the key dietary iodine sources in each survey group and illustrates their wide CIs.
Finally, an extended GLR model to analyze differences between survey groups (Table 8) found that the adjusted effects from variations in the UNaC and the SI values on the UIC did not differ significantly (P=0.82 for UNaC and P =0.56 for SI) between the groups of SAC and NPW. Also, no significant difference was found for the different effect between survey groups on the UIC when adjusted for all the other explanatory variables of interest (P =0.68). The lack of significance for the difference between survey groups and for the different UNaC and SI associations within survey groups buttresses the previous inference that the UIC levels in SAC, as well as NPW, are primarily and in a similar way explained by their UNaC and SI values, and that the different median UIC findings for the SAC and NPW groups are likely, not due to a difference in composition of the diet between the children and adults.
Among the limitations of the present study is that the native dietary sodium content was not considered when deriving the UNaC-related UIC portions. Studies of typical diets in the UK and the US, and a recent analysis of iodine intakes in Swiss adults reported that12- 18% of the dietary sodium in these industrialized countries comes from the sodium "naturally" contained in foods[35, 36, 37]. An estimate for native-source dietary sodium in less industrialized countries could not be found. The native dietary sodium content might be somewhat lower in Kenya where cereals, beans, and horticultural products are important items in the common food basket [37]. Nevertheless, an approach that estimates the UIC parts with the UNaC set at the level of native-source sodium would have allowed more accurate estimates of the native dietary and processed food sources of iodine intake.
The strength of this study would be the illustration of a practical approach to approximate the sources of dietary iodine in a population from data of a national house-to-house survey, without placing an additional burden on the respondents. The approach adds only a small expense for sodium analysis in the urine samples that are already collected for iodine assessments. Practical advantages in large surveys of spot urine sampling over 24 hour collections have been described and there is also growing recognition that the mean UNaC from a large number of spot urine samples can approximate the mean daily sodium excretion well enough for the purposes of comparing and tracking population sodium intake over time [[38, 39, 40]. Similarly, the current UIC partitioning approach by way of the UNaC levels in spot urine may approximate the proportions of iodine intake sources well enough to be of use for monitoring future changes in iodine intakes from processed food salt and household salt.
For improving the iodine intake apportioning technique, priority would be to correct the UIC and UNaC data for their typical within-person variation [30, 31]. As suggested for spot UNaC data of US adults [41] and shown for spot UIC data of SAC a repeat collection of urine samples from a subset of survey participants can be used to adjust the UIC and UNaC data to more closely resemble their habitual distributions[42]. Use of adjusted UNaC data for UIC partitioning would increase the accuracy of the UNaC regression coefficient and thereby yield more accurate point estimates for the native dietary and processed food source iodine intakes. And the use of adjusted UIC data would reduce the uncertainties of all three UIC portion estimates [43].
In view of an anticipated increase in hypertension prevalence worldwide, the World Health Organization (WHO) is promoting initiatives in each country toward a reduction of 30% in mean population salt consumption by 2025 [44]. On preciously little and dated information, the Global Burden of Diseases study in 2010 classified Kenya's sodium intake among the East Sub-Sahara African countries with the lowest salt intakes of all regions in the world [45,46]. The purpose in the KNMS of including UNaC data collection was not to obtain equivalent salt intake estimates but to assist in a more refined analysis of the iodine status assessment. Nevertheless, as noted in a recent review of the use of UNaC from casual (spot) urine sampling, several countries have elected casual urine sampling for population surveillance and tracking of change in salt intakes [47]. There is increased acceptance that spot urine measurements may be sufficiently robust and be the preferred method in large population surveys [39, 40]. Hence, the current mean UNaC findings in SAC and NPW could be a useful reference point for future efforts to reduce population salt intake in Kenya.
CONCLUSION
Over the past decades, major progress has been made in Kenya towards the national objective of ensuring optimal iodine nutrition of the population. The Government of Kenya has enacted regulations that compel universal supplies of adequately iodized salt for household use. Kenya's salt industries have stepped up the assurance of adequately iodized salt deliveries, while the Kenya Bureau of Standards is keeping close oversight of iodization performance in salt factories, with support from partner Ministries. The benefits of this collaboration are clear from the KNMS findings of high-quality iodized salt supply, adequate iodine intake and optimal population iodine status in Kenya. These findings do not suggest a need to change the salt iodization strategy or to adjust the current salt iodine standard. The major remaining challenge in Kenya, then, is to make sure that the success of preventing iodine deficiency disorders with the current USI strategy will be sustained.
Acknowledgements
Technical support in data processing was provided by the Statistical Service Center, University of Reading, UK (Alessandro Leidi, Cathy Garlick). We are grateful for assistance from Laird Ruth of the Centers for Disease Control and Prevention, Atlanta, GA, USA in adding the urinary sodium measurements to the survey protocol
Funding sources
Nutrition International (formerly Micronutrient Initiative), Iodine Global Network, Bill and Melinda Gates Foundation
Authors' contributions
ZB, FH, LM and JN designed the iodine survey component; ZB, MM, GM, JM, LK, PN and YK conducted field research; PN performed laboratory analyses; MM and FH conducted statistical analyses; ZB, FH and MM wrote the manuscript with inputs from all the co-authors; ZB had final responsibility for content. All authors read and approved the final manuscript
Conflict of interest statement
The authors declare no conflict of interest
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(*) Corresponding author email: fvander@emory.edu
(1) Center for Public Health Research, Kenya Medical Research Institute, P.O. Box 54840, Nairobi, Kenya
(2) Rollins School of Public Health, Emory University, 1518 Clifton Rd N.E., Atlanta, GA 30322, USA and Iodine Global Network, Ottawa K1E 3E6, Canada
(3) Nutrition and Dietetics Unit, Ministry of Health, P.O. Box 43319, Nairobi, Kenya
(4) Nutrition International (formerly Micronutrient Initiative), P.O. Box 22296, Nairobi, Kenya
(5) Kenya National Bureau of Statistics, P.O. Box 30266, Nairobi, Kenya
DOI: 10.18697/ajfand.85.17255
Table 1: Iodine content (mg/kg) in household salt, Kenya 2011
Number of Fisher's F
samples Mean SD P-value
Residence 3.47
Rural 343 39.0 18.5 0.063
Urban 280 41.9 20.3
Region 5.63
Nairobi 58 38.0 19.6 0.018
Central 74 41.9 23.9
Coastal 63 46.7 24.1
Eastern 94 36.0 15.7
N/Eastern 23 38.4 14.1
Nyanza 104 40.8 19.2
Rift Valley 137 41.0 15.2
Western 71 37.8 17.6
Kenya 625 40.3 19.4
Table 2: Urinary Iodine Concentration ([micro]g/L) in School-Age
Children, Kenya 2011
UIC distribution shares (%)
Category n Median IQR <100 100-299 300-499
Age
5 to 8y 433 200 109 to 310 21 51 18
9 to 14y 518 217 107 to 349 23 45 17
Gender
Male 478 231 119 to 368 19 45 21
Female 473 190 99 to 295 25 50 14
Residence
Rural 699 188 99 to 327 25 46 15
Urban 252 231 153 to 341 14 51 25
Region
Nairobi 49 209 169 to 313 6 55 38
Central 97 244 130 to 348 18 40 28
Coast 78 294 170 to 481 8 46 23
Eastern 139 240 178 to 457 9 50 19
N/Eastern 77 846 303 to 1157 3 19 13
Nyanza 138 122 88 to 222 30 61 8
Rift Valley 258 151 81 to 275 36 46 16
Western 115 164 72 to 245 32 53 12
HH wealth
Poorest 241 221 106 to 349 23 46 14
HHW=2 270 142 83 to 243 32 48 14
Middle 206 233 72 to 393 20 45 14
HHW=4 135 220 155 to 346 11 61 22
Wealthiest 99 294 192 to 405 11 40 33
All SAC 951 208 108 to 333 22 47 18
UIC distribution shares (%) [chi square] and
Category [greater than or equal to]500 P-value
Age 5.99
5 to 8y 10 0.112
9 to 14y 15
Gender 14.85
Male 15 0.002
Female 11
Residence 24.15
Rural 14 <0.001
Urban 10
Region 344.8
Nairobi 2 <0.001
Central 13
Coast 23
Eastern 22
N/Eastern 65
Nyanza 1
Rift Valley 2
Western 3
HH wealth 80.9
Poorest 16 <0.001
HHW=2 6
Middle 21
HHW=4 7
Wealthiest 16
All SAC 13
Table 3: Urinary Iodine Concentration ([micro]g/L) in Non-Pregnant
Women, Kenya 2011
UIC
distribution
shares (%)
Category n Median IQR <100 100-299
Age
<20y 119 165 104 to 287 20 61
[greater than or equal to]20y 504 164 97 to 305 27 47
Residence
Rural 389 163 90 to 279 30 47
Urban 234 180 125 to 321 18 55
Region
Nairobi 65 252 175 to 402 9 47
Central 76 222 117 to 367 15 51
Coast 54 220 133 to 368 10 51
Eastern 91 203 111 to 310 21 49
N/Eastern 30 372 231 to 609 0 36
Nyanza 85 148 86 to 244 32 51
Rift Valley 159 138 80 to 165 36 56
Western 65 98 64 to 172 52 41
HH wealth
Poorest 114 163 98 to 384 27 39
Next 130 131 67 to 226 42 46
Middle 120 140 98 to 225 25 58
Next 111 154 105 to 298 23 52
Wealthiest 148 202 134 to 359 12 53
All NPW 623 167 98 to 299 26 50
UIC distribution
shares (%)
Category 300-499 [greater than or equal to]500
Age
<20y 13 6
[greater than or equal to]20y 17 9
Residence
Rural 16 8
Urban 17 10
Region
Nairobi 30 14
Central 19 16
Coast 18 21
Eastern 21 9
N/Eastern 37 27
Nyanza 13 4
Rift Valley 7 1
Western 7 0
HH wealth
Poorest 22 12
Next 9 4
Middle 13 5
Next 16 8
Wealthiest 22 13
All NPW 16 9
[chi square] and
Category P-value
Age 7.19
<20y 0.066
[greater than or equal to]20y
Residence 11.85
Rural 0.008
Urban
Region 127.9
Nairobi <0.001
Central
Coast
Eastern
N/Eastern
Nyanza
Rift Valley
Western
HH wealth 51.4
Poorest <0.001
Next
Middle
Next
Wealthiest
All NPW
Table 4: Urinary Sodium Concentration (mmol/L) in School-Age Children,
Kenya 2011
UNaC distribution
shares (%)
Category n Mean 95%CI <87 87-174 174-261
Age
5 to 8y 386 178 167 to 188 22 32 22
9 to 14y 477 203 194 to 213 18 21 30
Gender
Male 424 209 199 to 219 14 23 28
Female 439 176 165 to 186 25 28 26
Residence
Rural 635 182 174 to 190 23 25 29
Urban 228 221 206 to 235 10 28 21
Region
Nairobi 45 224 202 to 245 3 31 26
Central 88 194 174 to 214 13 35 23
Coast 71 241 210 to 272 15 18 21
Eastern 126 226 208 to 244 10 20 32
N/Eastern 70 210 182 to 238 19 12 38
Nyanza 125 184 168 to 200 17 28 33
Rift Valley 233 171 157 to 185 26 30 22
Western 104 146 128 to 165 36 25 23
HH wealth
Poorest 224 180 167 to 193 19 27 36
HHW=2 234 164 150 to 178 32 25 19
Middle 189 203 189 to 218 19 21 27
HHW=4 125 217 199 to 236 7 35 25
Wealthiest 90 236 213 to 260 8 23 25
All SAC 863 192 185 to 199 20 26 27
UNaC distribution Fisher's F
shares (%)
Category [greater than or equal to]261 P-value
Age 12.3
5 to 8y 24 <0.001
9 to 14y 31
Gender 21.2
Male 35 <0.001
Female 21
Residence 22.5
Rural 23 <0.001
Urban 41
Region 9.55
Nairobi 40 <0.01
Central 29
Coast 46
Eastern 38
N/Eastern 31
Nyanza 21
Rift Valley 22
Western 16
HH wealth 11.4
Poorest 17 <0.001
HHW=2 25
Middle 33
HHW=4 32
Wealthiest 45
All SAC 28
Table 5: Urinary Sodium Concentration (mmol/L) in Non-Pregnant Women,
Kenya 2011
UNaC
distribution
shares (%)
Category n Mean 95% CI <87 87-174
Age
<20y 108 218 201 to 236 11 19
[greater than or equal to]20y 470 178 169 to 187 19 29
Residence
Rural 361 175 164 to 185 22 30
Urban 218 204 192 to 215 11 24
Region
Nairobi 61 231 213 to 249 5 8
Central 71 197 177 to 217 7 37
Coast 47 218 187 to 249 16 23
Eastern 84 221 203 to 239 6 18
N/Eastern 28 198 157 to 240 4 43
Nyanza 79 193 169 to 218 19 29
Rift Valley 148 149 135 to 163 28 33
Western 61 125 102 to 149 40 27
HH wealth
Poorest 99 186 166 to 206 17 29
Next 131 149 132 to 167 31 30
Middle 109 177 160 to 194 20 27
Next 110 189 171 to 208 16 35
Wealthiest 130 226 212 to 240 4 18
All NPW 579 186 178 to 193 18 27
UNaC distribution
shares (%)
Category 174-261 [greater than or equal to]261
Age
<20y 35 35
[greater than or equal to]20y 31 21
Residence
Rural 29 20
Urban 37 29
Region
Nairobi 53 34
Central 33 23
Coast 24 36
Eastern 44 32
N/Eastern 19 34
Nyanza 23 28
Rift Valley 29 10
Western 22 11
HH wealth
Poorest 31 23
Next 22 17
Middle 37 16
Next 22 27
Wealthiest 45 33
All NPW 32 23
Fisher's F
Category P-value
Age 15.6
<20y <0.001
[greater than or equal to]20y
Residence 12.5
Rural <0.001
Urban
Region 12.5
Nairobi <0.001
Central
Coast
Eastern
N/Eastern
Nyanza
Rift Valley
Western
HH wealth 11.3
Poorest <0.001
Next
Middle
Next
Wealthiest
All NPW
Table 6: Associations of UIC with UNaC, SI and Household Residence by
survey group, Kenya 2011
School-age Non-pregnant
Children (n = 563) Women
GLR Parameters Estimate (*) 95% CI P-value (n = 382)
Estimate (*)
Intercept 4.112 3.477, 4.747 <0.001 4.158
UNaC (mmol/L) 0.0044 0.0031, 0.0057 <0.001 0.0042
SI (mg/kg) 0.0086 -0.0030, 0.0202 0.14 0.0056
Residence
Rural area -0.0093 -0.2889, 0.2703 0.95 0.0436
Urban area Reference - Reference
Non-pregnant
Women (n = 382)
GLR Parameters 95% CI P-value
Intercept 3.848, 4.468 <0.001
UNaC (mmol/L) 0.0034, 0.0050 <0.001
SI (mg/kg) 0.0002, 0.0109 <0.05
Residence
Rural area -0.0171, 0.2585 0.69
Urban area -
(*) Weighted estimates are the [beta]-coefficients from GLR with
natural log-transformed UIC as the dependent variable
Table 7: UIC portion estimates corresponding with the principle sources
of iodine intake by residence in SAC and NPW, Kenya 2011
School-Age Non-Pregnant
Children Women
Residence type and Geomean Geomean
source of intake UIC ([micro]g/L) (*) 95% CI UIC
([micro]g/L) (*)
Rural areas
native iodine 60.5 34.2, 86.8 66.8
food salt 86.3 48.8, 123.8 85.5
household salt 46.8 -7.9, 101.5 30.4
Urban areas
native iodine 61.1 22.5, 99.6 63.9
food salt 87.1 32.2, 132.1 81.9
household salt 47.2 -0.4, 94.7 29.1
All areas
native iodine 60.8 29.0, 92.6 65.3
food salt 86.7 41.3, 132.1 83.7
household salt 47.0 -3.9, 97.8 29.8
All Kenya 194.5 145.3, 243.6 178.8
Non-Pregnant
Women
Residence type and Geomean
source of intake UIC
([micro]g/L) (*)
Rural areas 95% CI
native iodine 51.1, 82.5
food salt 65.1, 105.9
household salt 3.1, 57.7
Urban areas
native iodine 44.2, 83.6
food salt 57.8, 105.9
household salt 3.5, 54.7
All areas
native iodine 48.8, 81.8
food salt 63.1, 102.2
household salt 3.5, 56.0
All Kenya 160.1, 197.4
(*) UIC values are geometric means, obtained by back-transformation of
the natural logUIC values
Table 8: Associations of UIC with UNaC, SI, Household Residence and
survey group, Kenya 2011
GLR Parameters Estimate (*) 95% CI P-value
Intercept 4.183 3.875, 4.491 <0.001
UNaC (mmol/L) 0.0042 0.0034, 0.0050 <0.001
SI (mg/kg) 0.0054 -0.0001,0.0110 0.054
Domain
Rural area 0.0144 -0.1916, 0.2204 0.89
Urban area Reference - -
Survey group
SAC -0.0927 -0.5375, 0.3521 0.68
NPW Reference - -
Interaction terms
SAC # UNaC 0.0002 -0.0012, 0.0016 0.82
SAC # SI 0.0032 -0.0076, 0.0141 0.56
(*) Weighted estimates are the [beta]-coefficients from GLR with
natural log-transformed UIC as the dependent variable
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| Author: | Bukania, Zipporah; Van der Haar, F.; Mwangi, M.; Mugambi, G.; Murage, L.; Mwai, J.; Ng'ang'a, J.; Ka |
|---|---|
| Publication: | African Journal of Food, Agriculture, Nutrition and Development |
| Geographic Code: | 4EUUK |
| Date: | Mar 1, 2019 |
| Words: | 9069 |
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